kinetica user manual

Kinetica 5.0

Kinetica User Manual

© 2005–2008 Thermo Fisher Scientific Inc. All rights reserved.

The information in this document is subject to change without notice and should not be construed as a commitment by Thermo Fisher Scientific. Thermo Fisher Scientific assumes no responsibility for any errors that may appear in this document.

No part of this document may be reprinted, reproduced or utilized in any form by any electronic, mechanical or other means, now known or hereafter invented, including photocopying and recording, or in any information storage and retrieval system without permission in writing from Thermo Fisher Scientific.

All trademarks recognized.

Thermo Fisher Scientific Kinetica User Manual i

Your Comments

Thermo Fisher Scientific is interested in receiving any comments you have regarding this manual.

Contact details can be found at:

http://www.thermo.com/informaticscontacts

Part Number KIN-UM-5.0

Revision Number 1.00

Software Version Kinetica 5.0



ii Kinetica User Manual Thermo Fisher Scientific

Notes

Thermo Fisher Scientific Kinetica User Manual iii

Contents

1. Introduction and Configuration .............................................. 1 Kinetica Documentation..................................................... 2 Understanding Kinetica Basic Concepts.............................. 3 Kinetica Templates ............................................................. 4 Kinetica Methods................................................................ 6 Configuring Kinetica .......................................................... 7 Setting up Reports .............................................................. 8 Configuring Print Setup.................................................... 12

2. Starting Kinetica.................................................................... 13 Using the Kinetica Workspace .......................................... 14 Kinetica Main Menu......................................................... 16 Kinetica Toolbar ............................................................... 41 Kinetica Views .................................................................. 45 Kinetica Status Bar............................................................ 51 Kinetica Spreadsheet Interface........................................... 52 Kinetica Default Data Structure........................................ 61

3. Opening and Saving Data Files ............................................ 63 Files in Kinetica ................................................................ 64 Opening an Existing Kinetica File..................................... 65 Customizing the Normal Kinetica Template..................... 66 Opening an Existing Kinetica Template............................ 68 Saving Kinetica Files ......................................................... 70

4. Unit Configuration in Kinetica .............................................. 73 Configuring Units............................................................. 74 Specifying Units................................................................ 75 Specifying Concentration Units ........................................ 76 Specifying Column Units.................................................. 80 Specifying Rate Units........................................................ 82 Getting Units.................................................................... 85 Multiplying Units ............................................................. 87 Specifying Molar Units for the AUC* Method.................. 93

Contents

iv Kinetica User Manual Thermo Fisher Scientific

5. Importing and Exporting Data ............................................... 97 Intelligent Spreadsheet and Intelligent Import...................98 Importing Files into Kinetica with Import Assistant ........104 Using the Import Assistant Wizard..................................105 Importing Data from Excel Spreadsheets .........................107 Importing Data from ASCII Files....................................128 Importing Data from Databases ......................................142 Importing Data in Proprietary Formats ...........................156 Importing Data from Watson LIMS................................162 Importing Miscellaneous Data.........................................165 Exporting Data to External Databases .............................168 Report Log Files ..............................................................172

6. Graphs in Kinetica ............................................................... 175 Working with Graphs......................................................176 Starting the Chart Wizard ...............................................177 Hot Graphs .....................................................................191 Dataset Graph .................................................................192 Spaghetti Plot ..................................................................196 LZ Graph ........................................................................198 Mean Curve ....................................................................199 Plotting a Graph Manually ..............................................210 Plotting a Graph Automatically .......................................212 Inserting a Graph Method...............................................214 Modifying Graphs ...........................................................218 Displaying Outlier Data ..................................................220 Displaying Non-Detectable Data Points..........................223 Mean Curve by Group Method.......................................226 Creating Graph Templates ..............................................230 Working with the Graph Gallery.....................................233 Exporting Graphs ............................................................243 Linear Regression ............................................................245 Linear Regression with CI ...............................................248 Mean Curve Statistics ......................................................249 Histogram Statistics.........................................................250 Scattered XY Plot (Numeric X-Axis) Plotting ..................254 Scattered XY Plot (Textual X-Axis) Plotting ....................255

Contents

Thermo Fisher Scientific Kinetica User Manual v

7. Methods and Models .......................................................... 257 Working with Methods and Models in Kinetica ............. 258 Area Under the Curve (AUC) Calculation Methods ....... 264 AUC Steady State* Methods ........................................... 284 Sparse AUC Method....................................................... 286 Superposition–Variable Dosage Method ......................... 287 Convolution Method ...................................................... 290 Deconvolution Method................................................... 293 Derivation Method ......................................................... 296 Linear Regression by Zero Method ................................. 298 Macro to Micro Method ................................................. 299 Micro to Macro Method ................................................. 302 tN% Method .................................................................. 305 tN% of Cmax Method.................................................... 306 Kinetica Method Editor .................................................. 307 Opening Methods/Models .............................................. 308 Kinetica Designer............................................................ 311 Designer Dialog .............................................................. 312 Link Types...................................................................... 317 Available Tool Operations............................................... 319 Example of a Designer Method ....................................... 324 Using Designer to Generate a Differential Equation ....... 325

8. Non-Compartmental Analysis ............................................ 327 Performing Non-Compartmental Analysis in Kinetica .... 328 The AUC* Method......................................................... 329 Built-in templates for NCA............................................. 336 IV Bolus Template.......................................................... 337 IV Infusion Template...................................................... 350 Extravascular Route Template......................................... 360 Summary of First Dose PK Parameters for each Administration Route ..................................................... 367 Steady State Template..................................................... 369 The AUC steady-state with Lz* and AUC steady-state* Methods.......................................................................... 383 The Sparse AUC* Method .............................................. 390 The Superposition – Variable Dosage Method................ 397 Working with the NCA Assistant.................................... 402 Flagging Data as BLQ (Undetectable)............................. 410

Contents

vi Kinetica User Manual Thermo Fisher Scientific

9. Performing Compartmental Analysis ................................. 419 Fitting Data with Kinetica - Single Dose .........................420 Fitting Data with Kinetica - Multiple Dose .....................424 Available Models .............................................................429 Selecting a Method Model...............................................439 Initial Parameter Estimates ..............................................440 Stripping Algorithm ........................................................441 Weighting Schemes .........................................................442 Minimization Algorithm .................................................443 Differential Equation Solver ............................................444 Statistics and Goodness of Fit ..........................................445 PK Template Examples ...................................................449 Single Dose Zero Order Input Macro Constants Template........................................................................................451 Single Dose IV Bolus Macro Constants Template ...........460 Single Dose IV Infusion Macro Constants Template.......464 Single Dose Zero Order Input Micro Constants Template........................................................................................467 Single Dose Extravascular Micro Constants Template .....470 Single Dose IV Bolus Micro Constants Template............474 Single Dose IV Infusion Micro Constants Template .......477 Multiple Dose Extravascular Micro Constants Template .481 Multiple Dose IV Bolus Micro Constants Template........485 Multiple Dose IV Infusion Micro Constants Template ...490 Multiple Dose Multi Route Template .............................496 Performing PD Analysis ..................................................502 Performing PK/PD Analysis ............................................513 PK/PD Extravascular Template .......................................517 PK/PD IV Bolus Template..............................................529 PK/PD IV Infusion Template .........................................539

10. Creating Tables and Scripts ............................................... 549 Creating Tables and Scripts using the Table Assistant......550 Table Assistant Wizard ....................................................551 Regenerating Embedded Tables from Script Files ............573 Deleting a Table Script....................................................574

Contents

Thermo Fisher Scientific Kinetica User Manual vii

11. Population Pharmacokinetics (PK) .................................... 575 Introduction to Population PK Analysis in Kinetica........ 576 Features of Population PK Analysis in Kinetica ............... 578 Kinetica Population PK/PD Methods ............................. 582 Inserting a Population PK/PD Method........................... 586 Modifying Global Options.............................................. 589 Routes of Administration ................................................ 591 Kinetica Population Output Columns ............................ 597 Kinetica Population Templates ....................................... 598 Performing Advanced Fitting .......................................... 625 Working with Kinetica Population Graphs ..................... 627 Population Method Validation........................................ 636 Working with Population Designer................................. 652 Exporting Data to Microsoft Word................................. 654

12. Performing Statistical Analysis.......................................... 657 Statistical Analysis in Kinetica ......................................... 658 ANOVA ......................................................................... 659 Latin Square.................................................................... 673 Incomplete Block ............................................................ 697 Kruskall-Wallis Test........................................................ 703 Friedman Test................................................................. 707 Descriptive Statistics ....................................................... 710 The Paired and Unpaired t Test ...................................... 715

A. Population Methodology..................................................... 719 Models and Notation ...................................................... 720 Population Parameter Estimates - Sparse Data Situation . 722 Initial Parameter Estimates.............................................. 728 Minimization Algorithm ................................................. 733 Termination Criteria of EM Algorithm........................... 735 Differential Equation Solver............................................ 736 Stepwise Method............................................................. 737 Population Hard-Coded Equations ................................. 739 References ....................................................................... 740

B. Kinetica Population Method Writing ................................. 743 OSMacro2compBasic ..................................................... 744 Multiple Dose Example................................................... 746 IV RungeKuttaMultidose................................................ 748

Index..................................................................................... 751

viii Kinetica User Manual Thermo Fisher Scientific

Notes

Thermo Fisher Scientific Kinetica User Manual 1

1. Introduction and Configuration

Welcome to Kinetica, a powerful industry-standard pharmacokinetic-pharmacodynamic (PK/PD) template and method-driven system that enables you to standardize your laboratory analyses. Kinetica has an intuitive point-and-click graphical interface that facilitates data analysis and reporting in a structured yet flexible, easy-to-automate environment.

Each Kinetica analysis is comprised of results computed using either the default validated methods or customized methodologies. Once specified, the Kinetica spreadsheet containing the columns of methods can be saved as a template and automatically applied to any other study. This offers an efficient means of providing standardization across your organization.

From non-compartmental to population PK/PD analyses, Kinetica facilitates data analysis and reporting in a flexible environment. Kinetica offers fast, high-throughput data analysis for discovery, preclinical, clinical, drug metabolism and drug delivery settings. Kinetica streamlines your data analysis process by reducing the need for multiple software packages and associated training.

Introduction and Configuration

2 Kinetica User Manual Thermo Fisher Scientific

In addition to this user guide, your Kinetica package also contains the following documentation:

• The Kinetica Installation Guide instructs administrators how to install, configure, and maintain Kinetica.

• The Kinetica Basic Reference Guide, a self-contained manual for the Kinetica-specific Visual Basic-based scripting language you may use to write your own methods.

This user guide is also available in WinHelp format; access it by selecting Help on the Kinetica toolbar.

Kinetica Documentation



Kinetica is a template-driven system that provides standardized pharmacokinetic-pharmacodynamic analysis for drug development across your organization. The template design allows you to reuse analysis settings, enabling you to maintain consistency among analysts and analyses.

The organization of Kinetica templates and methods is made through a single document interface consisting of a series of panes, spreadsheets, graphical and web views. In addition, Kinetica offers numerous statistical tests for bioequivalence studies that are compliant with FDA, EMEA and MHW regulatory guidelines.

Understanding Kinetica Basic

Concepts



A template is a collection of methods created and saved as an empty shell for future analysis. Kinetica is delivered with over 50 different analytical template samples. In addition, you can create as many templates as you need by modifying the templates provided with the application.

Kinetica templates offer several advantages:

• High consistency between analyses and between users.

• Time savings by not having to validate each user’s analysis.

• Version control between analyses of the same study.

Kinetica is installed with nine subdirectories containing validated templates that provide unique non-compartmental assistant technology and a multitude of different standard and advanced PK, PD and PK/PD models that offer the best in high-throughput data analysis Templates are available for use with the following types of kinetic analyses:

• Absorption Kinetics

• Compartmental Fitting

• Convolution/Deconvolution

• Enzyme Kinematics

• In Vivo/In Vitro Correlation

• Non-Compartmental Analyses

• Population Pharmacokinetics

• Protein Binding

• Urine Pharmacokinetics

Some of the templates that were previously accessed from the New Analysis dialog are now located in the subdirectories of ‘Program Files\Kinetica\Example,’ as follows:

Kinetica Templates



• Fitting Simulation

IVInfusion.kdb

• In Vitro Dissolution

Dissolution Basic.kdb Dissolution with Volume Correction.kdb Dissolution without Volume Correction.kdb

• In Vitro/In Vivo Correlation

In Vitro/In Vivo.kdb

• Michaelis-Menton

MM Extravascular 1 Comp.kdb MM IVBolus 1 Comp.kdb

• Statistics

Anova.kdb Descriptive Statistics.kdb Group Table.kdb Kruskall Wallis.kdb Latin Square 2 Formulations.kdb Latin Square Multiple Formulations.kdb Latin Square N Formulations.kdb Linear Regression.kdb Unbalanced Block.kdb

• Transdermal

Transdermal Bioavailability.kdb Transdermal Feasibility.kdb



A method is a default or user-defined calculation, which computes values from existing columns of information. Calculations are performed regardless of whether the existing columns contain raw data or data computed using other methods. If you save the dataset, Kinetica saves the results and the method(s) in its proprietary database called KDB, in a file with a .kdb extension. If you save the dataset as a template, Kinetica deletes all data in all columns but embeds the method(s) in a database as a template file with a .ktp extension. This template file is then available for future use with other experimental data. Method output columns have headings in blue.

Kinetica provides a library of pre-defined methods and models covering the most appropriate functions. For more information, see the chapter, “Working with Methods and Models.” Other methods and models can be created with the macro language provided called Kinetica Basic. This language is a subset of Visual Basic for Applications (VBA) which is the same language used in Microsoft Excel, Word and Access. For more information, see the chapter, “Kinetica Method Editor,” and Appendix C.

Kinetica Methods



Once you have successfully installed Kinetica, some settings must be configured in order for the program to run efficiently. You are encouraged to follow these instructions to ensure that Kinetica performs as you expect.

Configuring Kinetica



During analysis various information is calculated, messages are displayed, and errors are generated. In order to help Kinetica handle this information, you need to dictate how the application should operate. The Report Setup utility allows you to specify the default destination for report-type output files. You only need to do this once after you have installed Kinetica

Report Setup is accessed from Kinetica’s File menu. The Report Setup dialog contains two areas: Select Output and Double Click to Set Destination.

Figure 1-1. Report Setup Dialog, Sample Configuration

The different report types available in Kinetica (as shown in Select Output) are summarized in the table below.

Output Use in Kinetica

Report Automatically-generated text summary of methods used during analysis, options chosen for the methods, and the results computed during the analysis, i.e., initial parameters, statistics on goodness of fit, transformation of AUC calculations, variance-covariance matrix, etc.

Setting up Reports

Report Setup



Output Use in Kinetica

Error Short text output altering you to errors encountered during program execution.

Message Messages are generally warnings generated during normal program use.

Table Table output is only generated via user interaction. The user must select Kinetica information stored in tables (arrays or grids) and can choose to send it to Excel or delimited text files. It can include any Kinetica information.

For each output option, you have several choices for the output destination. Click once on the appropriate output, e.g. Report. Double-click on the Destination you require, or select it once with the mouse and click Set Destination. Destinations are described in the table below.

Destination Destination Action

Text file Writes the selected output to a text file. You are prompted to save the file as Repo.txt in the \Kinetica\odriver sub-directory. You can name the file as required, and store it in any directory on the local or network drive. If you store the file on a network drive, ensure that Kinetica has access to the file at all times. This option is recommended for Report output but not for Error or Message outputs.

Message Box

Displays the selected output in a message box. This option is recommended for the Error or Message outputs but not for the Report output.

Nothing Does not display or write any information. Available as an option but is not recommended for any output because you will not see any information during an analysis.




Word 6/95 document

Links the Report output type to Microsoft Word. You are prompted to save the file as Report.doc (version 6.0 or 95 only) in the \Kinetica\odriver directory. You can name the file as required and store it in any directory on the local or network drive. If you store the file on a network drive, ensure that Kinetica has access to the file at all times. This option does not automatically load Microsoft Word when you run an analysis or click on the export icon within Kinetica. Open Word before executing an analysis if you want to see your report automatically written to Word.

Word 97 document

Links the Report output type to Microsoft Word version 97. You are prompted to save the file as Report.doc (version 97 only) in the \Kinetica\odriver sub-directory. You can name the file as required and store it in any directory on the local or network drive. If you store the file on a network drive, ensure that Kinetica has access to the file at all times. This option automatically finds, and then loads Microsoft Word when you run an analysis or click on the Word export icon within Kinetica. The report is then automatically written to Word.




Graph for Word 97 document

Links all Graph output to Microsoft Word version 97. You are prompted to save the file as Repo.doc (version 97 only) in the \Kinetica\odriver sub-directory. You can name the file as required and store it in any directory on the local or network drive. If you store the file on a network drive, ensure that Kinetica has access to the file at all times. This option automatically finds, and then loads Microsoft Word when you run an analysis or click on the Word export icon within Kinetica

and sends all plotted graphs to Word. No other text information is sent to Word with these graphs.

Excel Links the Table Assistant or Data export output type to Microsoft Excel. You are prompted to save the file as Tabl.xls in the \Kinetica\odriver sub-directory. You can name the file as required and store it in any directory on the local or network drive. If you store the file on a network drive, ensure that Kinetica has access to the file at all times.

This option automatically finds and loads Microsoft Excel when you create structured tables using Table Assistant.

Note This option is only active when the output is set to “table.”



The appearance of the Print Setup dialog differs according to the system and printer you are using. Microsoft Windows controls the dialog displayed for your particular system. Please refer to your Microsoft Windows documentation for assistance with printer setup.

Configuring Print Setup


2. Starting Kinetica

This chapter provides information on starting the Kinetica application. Also included is information on the various Kinetica commands, views, toolbar options, and spreadsheet interfaces, as well as the default Kinetica data structure.

Starting Kinetica


When you first open Kinetica, the Welcome dialog appears. Click Cancel and the Workspace appears. The Workspace comprises a main menu, toolbar, left pane, and spreadsheet interface.

The Workspace is organized as a series of spreadsheets, graphical and web views. These views, in conjunction with the main menu, toolbar and left pane, allow you to apply methods, run analyses, produce tables, and create and organize graphs.

Figure 2-1. Kinetica Workspace, Dataset pane

Kinetica has a structure similar to that of a database; it has various interrelated tables, unlike a flat spreadsheet. Due to this design, there is hierarchy in the Kinetica data file. Worksheet columns in the Dataset pane (see figure above) are at the bottom of the hierarchy. They are placeholders for non-static time-series data, such as individual time-concentration data. Datasets can be viewed one at a time. To view another dataset, use the icon on

Using the Kinetica Workspace

Starting Kinetica


the toolbar to move forward one dataset or the icon to move backward.

The second level in the Kinetica structure contains the dataset variables under the Study pane. Dataset variables are unique to the individual datasets, i.e., each cell takes only one value for that individual. For example, the pharmacokinetic parameter Cmax is a unique value since for each analysis run only one Cmax value for that individual is generated. The dataset variable often contains the pharmacokinetic results generated from a non-compartmental analysis method.

The highest level in the hierarchy is the global variables level, also under the Study pane. Population mean and Variance from a population pharmacokinetic analysis are both global variables. This level is not often used as most analyses deal with individuals rather than an entire study.

Starting Kinetica


The Kinetica main menu bar is located at the top of the Kinetica workspace. The menu commands are described in the table below.

Option Description

File Access options for file manipulation, printing and default program setup parameters, open and save galleries, export data to Microsoft Word or Excel, or exit Kinetica.

Edit Access options for editing and/or removing fields and methods from the current dataset or template. You can also search and replace specified text.

View Access options for customizing toolbars and commands, displaying toolbars, properties, headers and footers, zoom in or out.

Insert Access options for inserting fields and/or methods into the current dataset or template.

Format Access options for changing the way the program displays spreadsheets.

Dataset Access options for scrolling through the available datasets (subjects) in the Dataset pane. You can also calculate a single subject (or dataset), selected subjects or all subjects.

Population Access the population pharmacokinetic analysis module to set-up initial parameter estimates, run analysis, add covariables and model validation.

Graph Access built-in graphical tools to create and organize graphs.

Statistics Access a series of statistical tests for use in bioequivalence.

Kinetica Main Menu

Starting Kinetica


Option Description

Tools Access options for password protection, Method Editor, Designers, Assistants, Options (parameters) and Macros.

Help Access the online Help system, tutorials, and license information.

You can insert several raw data text and/or numeric fields in a Kinetica spreadsheet. All study and dataset text and numeric fields are alphanumeric. You can define a text or numeric field by entering text, numbers, or a combination of both. The new field name is visible in the selected worksheet in the Dataset pane. You do not need to work in a specific view when you insert a field. Text fields will appear before numeric fields in the dataset and global variables worksheets.

Insert commands are accessed from the Kinetica main menu. The commands are described in the following table.

Option Description

Dataset Inserts a new dataset.

Worksheet Inserts a new worksheet for each defined dataset.

Column Inserts a new column for all datasets in a selected worksheet; text and numeric entries are accepted.

Numeric Field Global – Inserts a numeric cell in the Global Variables worksheet

Dataset – Inserts a numeric cell for each dataset defined in the Dataset Variables worksheet.

Insert Commands

Starting Kinetica


Option Description

Text Field Global – Inserts a text cell in the Global Variables worksheet.

Dataset – Inserts a text cell for each dataset defined in the Dataset Variables worksheet.

Method Inserts a method in the Methods pane.

Pop Method Inserts a Population PK/PD method in the Methods pane.

To insert a new dataset:

1. Select Dataset from the Insert menu. The Insert a Dataset dialog appears.

Figure 2-2. Insert a Dataset Dialog

2. Enter an identifier for the new dataset in the Dataset Name field.

Note The dataset name must be unique or it will not be inserted.

3. Click OK to insert the dataset and exit the dialog or click Insert if you would like to add another dataset.

A new dataset is created with the same number of columns, names of columns and methods as those existing in the pane unless it is the first in an empty dataset. The new dataset is

Inserting a New Dataset

Starting Kinetica


visible in the Plasma worksheet of the Dataset pane and the All Variables worksheet of the Study pane.

To insert a column into a worksheet:

1. Select Column from the Insert menu. The Insert a Column dialog appears.

Figure 2-3. Insert a Column Dialog

2. Select the appropriate worksheet name from the Worksheet List. The Worksheet List loads all worksheets found in the Dataset pane.

3. Enter the name of the column you want to insert in the Column Name field.

4. Click OK to insert the column and exit the dialog or click Insert if you will add another column.

5. To add another column, repeat Steps 2-4. The column is inserted to the right of the last column in the worksheet.

Inserting a Column into a Worksheet

Starting Kinetica


To insert a Dataset Numeric Field:

1. Select Dataset Numeric Field from the Insert menu. The Insert a Dataset Numeric Field dialog appears.

Figure 2-4. Insert a Dataset Numeric Field dialog

2. Type in an identifier for the new dataset numeric field in the Field Name field.

3. Click OK to exit the dialog or click Insert if you will add another numeric field.

4. To add another numeric field, repeat Steps 2-3. The new field is added after the last field present in the All Variables view.

To insert a Dataset Text Field:

1. Select Dataset Text Field from the Insert menu. The Insert a Dataset Text Field dialog appears.

Figure 2-5. Insert a Dataset Text Field Dialog

2. Type in the name for the new dataset text field in the Field Name field.

Inserting a Dataset Numeric Field

Inserting a Dataset Text Field

Starting Kinetica


3. Click OK to exit the dialog or click Insert if you will add another text field.

4. To add another text field, repeat Steps 2-3. The new field will be added after the last field defined in the All Variables view.

To insert a new Worksheet:

1. Select Worksheet from the Insert menu. The Insert a Worksheet dialog appears.

Figure 2-6. Insert a Worksheet Dialog

2. Enter the name for the worksheet in the Worksheet Name field.

3. Click OK to exit the dialog or click Insert if you will add another worksheet.

4. To add another worksheet, repeat Steps 2 and 3.

Inserting a New Worksheet

Starting Kinetica


To insert a Global Numeric Field:

1. Select Global Numeric Field from the Insert menu. The Insert a Study Numeric Field dialog appears.

Figure 2-7. Insert a Study Numeric Field Dialog

2. Type in the name of the new global numeric field. For example, type in Weight.

3. Click OK to exit the dialog or click Insert if you will add another numeric field.

4. To add another numeric field, repeat Steps 2-3. The new field will be added after the last field present in the Study Variables view.

To insert a Global Text Field:

1. Select Global Text Field from the Insert menu. The Insert a Study Text Field dialog appears.

Figure 2-8. Insert a Study Text Field Dialog

Inserting a Global Numeric Field

Inserting a Global Text Field

Starting Kinetica


2. Type in the name of the new global text field in the Field Name field.

3. Click OK to exit the dialog or click Insert if you will add additional text fields.

4. To add another text field, repeat Steps 2-3. The new field is added after the last text field but before the first defined numeric field.

To insert a Method:

1. Launch Kinetica and use the Normal template.

2. Select Method from the Insert menu. The Method Selection dialog appears.

Figure 2-9. Method Selection Dialog

3. Choose a hard-coded model method or a soft-coded method from the available lists.

Inserting a Method

Starting Kinetica


4. Make the selections under the User names and Worksheets columns corresponding to the Input Cols&Vars and Output Cols columns, by clicking on the associated drop down lists.

5. Click Insert, and then click OK to exit the Method Selection dialog.

To insert a population PK/PD method:

1. Launch Kinetica and open the appropriate file.

2. Select Pop Method from the Insert menu. The Method Selection dialog appears.


3. Select a population PK/PD method from the available list (e.g. PopFitMicroIVBolus1comp) to activate Kinetica Population.

4. Map the input columns (e.g. Time for X and Concentration for Y) Make selections under the User names and Parameter

Inserting a Population PK/PD Method

Starting Kinetica


columns corresponding to the Input Cols&Vars, Output Cols and In/Out Vars columns, by clicking on the associated drop down lists.

Parameters have a Yes or No option. If you select Yes, you are prompting Kinetica to calculate the parameter. If you select No, you are specifying that there is an existing value for the parameter and thus the parameter will not be recalculated.

5. Click Datasets. The Select Dataset dialog appears.

Figure 2-11. Select Dataset Dialog

6. Use the Ctrl key and mouse to select a particular range of datasets or click Select All to include all datasets.

7. Click OK to exit the Select Dataset dialog.

8. Click Insert, and then click OK to exit the Method Selection dialog.

For more information, see the chapter, “Population Pharmacokinetics.”

Starting Kinetica


Once you have mastered building your own data structures, you may need to delete some of the fields you created. Kinetica offers most of the standard commands for editing the data structure. These options are found in the Edit item in the main menu and are described in the following table.

Option Description

Dataset Deletes the specified dataset.

Worksheet Deletes the specified sample for each defined dataset.

Column Deletes the specified column for all datasets in a selected sample.

Global Numeric Field Deletes the specified numeric cell for each dataset found in the Global Variables view.

Global Text Field Deletes the specified text cell for each dataset found in the Global Variables view.

Dataset Numeric Field

Deletes the specified numeric cell for each dataset found in the All Variables and Study Variables views.

Dataset Text Field Deletes the specified text cell for each dataset found in the All Variables and Study Variables views.

All Dataset Info Deletes all dataset input.

Study Info Deletes all study information.

Rename Dataset Renames the specified dataset.

Remove Last Method Deletes the last method in the list of methods along with all output columns and variables associated with the last method.

Using the Delete Commands

Starting Kinetica


Option Description

Remove All Methods Deletes all methods in the list of methods along with all output columns and variables associated with the methods.

Remove Last Pop Method

Deletes the last method in the list of population methods. You can manually remove study and dataset output columns and variables, and any information appearing in the Study and Dataset views.

Remove All Pop Methods

Deletes all methods in the list of population methods. You can manually remove study and dataset output columns and variables, and any information appearing in the Study and Dataset views.

Starting Kinetica


To delete a dataset:

1. From the Edit menu select Delete Study Object, and then Dataset. The Delete a Dataset dialog appears.

Figure 2-12. Delete a Dataset Dialog

2. Select the dataset you want to remove from the available list.

3. Click Delete. The dataset is deleted from the Dataset pane.

4. Click OK to exit the dialog.

Once you delete a dataset, you notice that all data pertaining to the deleted dataset has also disappeared from the Study pane (Dataset Variables and Global Variables views).

Deleting a Dataset

Starting Kinetica


To delete a worksheet (matrix):

1. Select Delete Study Object, and then Dataset from the Edit menu. The Delete a Study Worksheet dialog appears.

Figure 2-13. Delete a Study Worksheet Dialog

2. Select the name of the worksheet you want to remove from the available list.

3. Click Delete. The worksheet is deleted from the Dataset pane.


Deleting a Worksheet (Matrix)

Starting Kinetica


To delete a column:

1. Select Delete Study Object, and then Column from the Edit menu. The Delete a Column dialog appears.

Figure 2-14. Delete a Column Dialog

2. Select the name of the column you want to remove from the available list.

Note If only one worksheet is defined, the column names are displayed in the Columns List. If multiple worksheets are defined, the column names are displayed in the Columns List along with the worksheet name, for example PLASMA.X where PLASMA is the worksheet and X is the column name.

3. Click Delete. The column is deleted from the Dataset pane.


Delete a Column

Starting Kinetica


To delete a Global Numerical Field:

1. Select Delete Study Object, and then Global Numeric Field from the Edit menu. The Delete a Study Numerical Field dialog appears.

Figure 2-15. Delete a Study Numerical Field Dialog

2. Select the name of the study numeric field you want to remove from the available list.

3. Click Delete. The study numeric field is deleted.


Deleting a Global Numeric Field

Starting Kinetica


To delete a Global Text Field:

1. Select Delete Study Object, and then Global Text Field from the Edit menu. The Delete a Study Text Field dialog appears.

Figure 2-16. Delete a Study Text Field Dialog

2. Select the name of the Study text field from the available list.

3. Click Delete. The study text field is deleted.


Deleting a Global Text Field

Starting Kinetica


To delete a Dataset Numerical Field:

1. Select Delete Study Object, and then Dataset Numeric Field from the Edit menu. The Delete a Dataset Numerical Field dialog appears.

Figure 2-17. Delete a Dataset Numerical Field Dialog

2. Select the name of the dataset numeric field from the available list.

3. Click Delete. The dataset numeric field is deleted.


To delete the last method:

1. Select Remove Last Method from the Edit menu. The following prompt appears: “Are you sure you want to delete the last method?”

Warning All data stored with the last method will also be deleted.

Deleting a Dataset Numerical Field

Deleting the Last Method

Starting Kinetica


2. Click Yes to delete the method. Click No if you do not want to delete the method.

To delete all methods:

1. Select Remove All Methods from the Edit menu. The following prompt appears: "Are you sure you want to delete all methods?"

Warning All data stored with the methods will also be deleted. If you open a template and select this option, all Method columns will be deleted.

2. Click Yes to delete the methods. Click No if you do not want to delete the methods.

To delete any method:

1. From the Edit menu select Remove Any Methods. The Delete Method (Step 1 of 2) dialog will appear.

Deleting All Methods

Deleting Any Method

Starting Kinetica


Figure 2-18. Delete Method (Step 1 of 2) dialog

2. Select the method to be removed and click Next.

3. In Step 2, choose the output columns and variables of the selected method to be deleted. Click Finish to complete removal of the method.

Starting Kinetica


Figure 2-19. Delete Method (Step 2 of 2) dialog

When you delete a population method, you manually remove study and dataset output columns and variables. You can also remove any information appearing in the Study and Dataset views.

1. Select Remove Last Pop Method from the Edit menu. The following prompt appears: “Are you sure you want to delete the last pop method?”

2. Click No if you do not want to delete the method. Click Yes to delete the method. The Select Columns and Variables to Remove dialog is displayed.

Deleting the Last Population Method

Starting Kinetica


Figure 2-20. Select Columns and Variables to Remove Dialog

3. Highlight the appropriate columns, dataset, and study variables to remove by clicking on each item.

4. To remove information from the Study Info view, select the Clear Study Info check box.

5. To remove information from the Dataset Info View, select the Clear All Dataset Info check box.

6. Click OK to save your selections and exit the dialog.

Starting Kinetica


When you delete all population methods, you manually remove study and dataset output columns and variables. You can also remove any information appearing in the Study and Dataset views.

1. Select Remove All Pop Methods from the Edit menu. The following prompt appears: “Are you sure you want to delete all pop methods?”

2. Click No if you do not want to delete the population methods. Click Yes to delete the population methods. The Select Columns and Variables to Remove dialog appears.

3. Highlight the appropriate columns, dataset variables, and study variables to remove by clicking on each item.

4. To remove information from the Study Info View, select the Clear Study Info check box.

5. To remove information from the Dataset Info view, select the Clear All Dataset Info check box.


Creating your own data structure is a good idea when you first begin using Kinetica because it ensures you understand how the program functions. However, to avoid undue process steps, a feature in the application allows you to extract parts of a study to create a new study.

Extracting fields from studies to create new data structures is quite simple. Before you can extract any fields you must open an existing dataset or template using the Extract Study dialog.

Deleting All Population Methods

Using the Extract Study Command

Starting Kinetica


This dialog is accessed by selecting Extract Study from the Edit menu. The contents of the dialog vary depending on the structure of the dataset or template you open.

The dialog is divided into two sections: Datasets and Data. The Datasets area, on the left side of the dialog, displays all datasets found in the current structure. The Data area is split into two components: Columns and Variables. The Columns component, located in the middle of the dialog, displays all columns found in the structure (remember, the identification convention for columns is Worksheet.ColumnName i.e. Plasma.X). The Variables component, on the right side of the dialog, displays all study/dataset numeric/text fields found in the structure.

To extract fields from a study:

1. In Kinetica, open the appropriate dataset or template.

2. From the Edit menu select Extract Study. The Extract Study dialog appears.

Figure 2-21. Extract Study Dialog

3. Select the datasets you want to extract by clicking once on each dataset identifier.

Extract Study Dialog

Extracting Fields from a Study

Starting Kinetica


4. Select the columns you want to extract by clicking once on each column identifier. The worksheet containing the column is created automatically.

5. Select the variables you want to extract by clicking once on each variable identifier.

6. Click OK.

The new structure is created in the workspace. You can continue to add/edit fields and save the structure as a dataset or template.

Note The extracted structure appearing on your screen is named Extract.kdb, by default. You can rename the structure using the Save As dialog, accessed by selecting Save As from the File menu.

Starting Kinetica


The Kinetica toolbar provides an alternate method for accessing various workspace and viewing options. The availability of the items depends on whether you open a dataset or a template. The various toolbar buttons are described in the following table.

Button Action

Compile macro

Insert or remove a macro break point (Stop toggle)

Evaluate the expression (Quick watch)

Step through macro code line by line

Step over macro lines

Run macro

Stop macro

Import PCNonlin

Import Phast

Import Siphar dos

Increase the spreadsheet magnification

Reduce the spreadsheet magnification

Restore the spreadsheet view to default dimensions

Left justify the contents of a cell

Kinetica Toolbar

Starting Kinetica


Button Action

Center the contents of a cell

Right justify the contents of a cell

Bold the contents of a cell

Italicize the contents of a cell

Underline the contents of a cell

Invert columns and rows

Execute Table Assistant

Execute Import Assistant

Execute NCA Assistant

Execute Chart Wizard

Update or insert user license

Move to the previous or next screen

Open a new default data structure

Open an existing dataset

Save the current dataset

Cut the current selection to the clipboard

Starting Kinetica


Button Action

Copy the current selection to the clipboard

Paste the contents of the clipboard into selected cells

Print the current selection of cells

Launch Report Setup

Export to Microsoft Word

Export to Microsoft Excel

Display the Kinetica About dialog box

Insert a method

Insert a population PK/PD method

Run a population method analysis without covariables

Set initial parameter estimates for the EM algorithm

Calculate the current dataset

Calculate a range of the dataset

Calculate all datasets

Plot the selected columns

Starting Kinetica


Button Action

Display all graphs

Move to the first dataset

Move to the previous dataset

Display dataset

Move to the next dataset

Move to the last dataset

Starting Kinetica


The Kinetica views are located in panes on the left side of the workspace.

The Kinetica Study pane displays worksheets containing all global information related to the study. Data shown in the study spreadsheet views are not time-dependent in nature (for example, Tmax, Cmax, etc). This enables you to quickly view information across all subjects in the study.

The views available in this pane are listed in the following table.

Study Icon Study View Description

Dataset Variables Non time-dependent values unique to a dataset computed during an analysis.

Global Variables Values that are global to a study, for example, study name, study notes, population parameter mean and variance.

Study Info Text area where computed information global to the study is stored, e.g., statistics calculated from mean curves.

Study Objects Displays the study structure, the objects in the structure and the nature of those objects.

Macro Editor A VBA editor where input/output routines for creating internal structures can be created, compiled, and modified.

Spaghetti Plot By default, when a study is opened, this plot uses the two left-most columns for all subjects, found in the first sample matrix to create a graph.

Kinetica Views

Study Pane

Starting Kinetica


Study Icon Study View Description

Mean Curve Kinetica offers the generation of mean curves, with two different menu options. The first option plots an overlay graph with up to three separate mean curves. The second option is a mean curve by group, with the ability to choose any groups within the available Kinetica structure.

Intelligent Spreadsheet

The intelligent spreadsheet allows user to paste dataset-column-type (similar to the WinNonlin format) data and use the intelligent import under the Tools menu to bring data to the Kinetica structure.

The Kinetica Dataset pane displays views containing specific information relating to the subjects in the study. Data shown in the Dataset spreadsheet views are time-dependent (for example, AUC, Cumulative AUC, etc.). The term dataset is used because Kinetica allows multiple subjects to have the same identifier. This is practical when dealing with bioequivalence. There are three default views in the Dataset pane plus an additional view for each sample you specify, for example, plasma, urine, etc.

Dataset Pane

Starting Kinetica


The views available in the Dataset pane are listed in the following table.

Dataset Icon Dataset View

Description

Sample Matrix

Time-dependent values that are specific to the subject sample matrix, e.g., Plasma.

Dataset Info

Text area where information is recorded or specific transformations made when analyzing the selected subject.

Dataset Graph

Displays a graphical plot of the first two left-most columns of the sample view.

Starting Kinetica


The Kinetica Methods pane displays views containing information corresponding to the methods inserted in the study. The views available in this pane are listed in the following table.

Methods Icon Methods View

Description

Methods Displays the list of methods inserted into the study and the available options selected for each.

LZ Graph

Launches an LZ Graph based on the AUC* Method inserted

Method Editor

A VBA editor enabling you to create and modify methods and integrated or differential models.

The Kinetica Tables pane displays the Table Assistant view.

Table Icon Table View

Description

Table Info

Tables are listed as views within the Kinetica Tables pane in order of creation. A single click on a table view will re-generate the table inside Kinetica, using the most recently computed data.

Methods Pane

Tables Pane

Starting Kinetica


The Reports pane displays the current report defined by selecting Report Setup from the File menu.

Reports Icon Reports View

Description

Report Info

Displays the list of methods inserted into the study and the available options selected for each.

My Report

Displays the report created using the Report Setup option.

The Gallery pane displays all graphs generated during analysis, or graphs sent to the Gallery.

Gallery Icon Gallery View

Description

Gallery Displays all computed and/or selected study graphs. You can view or modify single or all available graphs.

Current Graph

Magnifies the current graph to full screen mode.

Reports Pane

Gallery Pane

Starting Kinetica


The Exchange pane displays a list of active web views enabling you to share data with colleagues or to view research data on the World Wide Web.

Exchange Icon Exchange View

Description

My Exchange

Integrated set of web pages including FDA news items, the latest industry news and internet links. You can customize this page, as required.

Thermo Fisher Scientific

Thermo Fisher Scientific home page with links to services, training resources, products and support.

Resources List of pharmacokinetic resources. You can also request links to be added to this page.

Intranet Enables you to create your own centralized intranet and share objects such as PK/PD reports with your colleagues.

Exchange Pane

Starting Kinetica


The Status Bar is found along the bottom edge of the workspace. It is an information line noting the application status (Ready or Done). It also displays the actual numerical value of an individual cell when the cursor is placed in that cell, the window that is currently active, and license information.

Kinetica Status Bar

Starting Kinetica


When you launch Kinetica and open a template or dataset, the spreadsheet interface appears. Whether you have data present or an empty template, any changes you make will only be applied to the pane that is currently active. The spreadsheet view contains the raw and computed column data used in the analysis.

Figure 2-22. Kinetica Spreadsheet Interface

The default view in both the Study and Dataset panes is organized in columns. You can organize rows of data by clicking the Invert Columns and Rows button on the main toolbar. You can also zoom in/out or select the full-page view option from the menu bar or main toolbar.

Note Units of measurement are always stored in the first row of a Kinetica spreadsheet, labeled Unit (see figure above).

Kinetica offers numerous formatting options for dataset spreadsheets. You can format the cells in a spreadsheet, format adjust individual columns and rows, or adjust all columns and rows in a spreadsheet.

In Kinetica, numerical values are displayed and printed in f format. The f format takes the form [-]dddd.dddd, where dddd is one or more decimal digits. The number of digits before the decimal point depends on the magnitude of the number, and the number of digits after the decimal point depends on the number of decimal places specified. Trailing zeros are truncated, and the decimal point appears only if one or more digits follow it.

Kinetica Spreadsheet Interface

Formatting Kinetica Spreadsheets

Starting Kinetica


Note If, for example, a value is 34.565457. In Kinetica, it will be displayed as 34.5655, by default. You can change the display by specifying the required number of decimal places in the Format tab of the Cells dialog.

You can format cells using the Cells dialog, which provides numerous options for formatting spreadsheet cells.

Cells Dialog

This dialog is accessed by selecting Cells from the Format menu. There are five formatting options available: Format, Font, Color, Borders and Align. The options are included on separate tabs described in the following table.

Tab Description

Format Specify the number of decimal places for data. You can specify a maximum of 6 decimal places.

Font Specify font effects such as bold, italic, color, underlining, outlining, etc.

Color Color the foreground and background of dataset cells, shade cells with a pattern or give cells dimension.

Borders Add a specific type of border and/or border color to cells.

Align Adjust data horizontally and vertically within cells. You can also select the Wrap Text, Auto Size and Allow Enter options by selecting the appropriate check boxes.

Note Changes are applied only to the cells you select in the spreadsheet.

Formatting Cells

Starting Kinetica


Formatting Cells in a Spreadsheet

To format cells in a spreadsheet:

1. Select Cells from the Format menu. The Cells dialog appears.

Figure 2-23. Cells Dialog

2. Specify or modify font, font effects, color, border properties, etc., as required.


Starting Kinetica


All Kinetica spreadsheet columns are set to a standard width. Although row height is set to a default, it automatically adjusts to accommodate the largest font entered into the row. Column width does not adjust unless you modify it. You have the option to change the standard column width and row heights for individual worksheets in a group.

Manually drag the sides of a cell up and/or down with your mouse to adjust the standard column width and row heights for individual worksheets in a group. The information is stored and the adjusted size is used until you close the file.

To adjust all columns in a spreadsheet:

1. Click the top left cell of the worksheet.

2. Drag the border below any selected row heading until the row is the appropriate height.

3. Release the mouse button. All the selected rows are resized.

Adjusting Individual Columns and Rows in a Spreadsheet

A

Adjust All Columns and Rows in a Spreadsheet

Starting Kinetica


You can Move or Sort columns by selecting Edit>Move Columns:

Figure 2-24. Move/Sort Columns Example — Selecting Move Columns from the Edit menu

A dialog box appears. Select the column to move and use the up

or down arrow to move the specific column. User can

use the icon to order the column in alphabetical order.

Move Columns

Starting Kinetica


Figure 2-25. Move/Sort Columns Example — Choosing which columns to move/sort

Note You can only Sort columns if they are the same type (Numeric or Text).

Starting Kinetica


In Kinetica you may transpose columns or rows to change how your data are displayed. This option is accessible from the main menu by selecting Invert columns and rows from the View menu, or by clicking the Invert columns and rows button (illustrated below). This button is available to invert columns and rows in both the Study and Dataset levels.

Figure 2-26. Invert Columns and Rows Button

Kinetica gives you the ability to protect datasets using password protection. You can lock a dataset completely or make the data read-only. To apply a password:

1. Select Protection then Password or Read Only Password from the Tools menu. The Open Password dialog appears.

2. Enter your password and click OK.

Changing the Dataset View

Protecting Kinetica Datasets

Starting Kinetica


Kinetica allows you to remove previously set open and read-only passwords. To remove password protection:

1. Select Embedded Objects then select either Remove Read-Only Password or Remove Open Password from the Tools menu. When a read-only password or an open password is present, this option will be enabled.

Figure 2-27. Read Only Password dialog

2. Enter the current password in the dialog box and click OK.

Kinetica provides the ability to protect macros using password protection. A user may lock a macro and make the macro script accessible only by password. To protect a macro script:

1. Select Macro then Macro from the Tools Menu. The macro dialog appears.

2. Select a macro from the list (if macros were previously created in the file), and then click the Password button. The Set Macro Password dialog appears.

Removing Password Protection

Protecting Kinetica Macros and Removing

Password Protection

Starting Kinetica


Figure 2-28. Set Macro Password dialog

3. Enter your password in both the New Password and Confirm Password fields, and click OK.

To remove macro password protection:

1. Select Macro, and then Macro from the Tools Menu. The macro dialog appears.

2. Select a macro with existing password protection, then click the Remove Password button (The Remove Password button will be enabled if password protection was previously set). The Open Password dialog appears.

Figure 2-29. Open Password dialog

3. Enter the password and click OK. A message box confirming removal of the password appears. Click OK again.

Removing Macro Password Protection

Starting Kinetica


Kinetica uses a dynamic data structure enabling you to create and edit almost any organization.

The default data structure contains default panes and views that cannot be deleted or omitted from the structure.

When you select New from the File menu, you are opening the default data structure “Plasma.” You can then add fields to this basic structure. Although you are given the flexibility to add whatever fields required, it is much quicker to open, edit and resave the structure of one of the existing templates or datasets included with Kinetica. However, when you are new to Kinetica, it is a good idea to try one of your own in order to fully understand the data structure.

After you open the default data structure you can start building your own fields. These fields are empty and prepared for raw data entry. It is important to note that Kinetica does not automatically fill in the fields you create after an analysis. The only fields Kinetica fills in after an analysis are those created when you add a method, or if you open a template with existing methods.

Kinetica Default Data Structure

Starting Kinetica


Notes


3. Opening and Saving Data Files

This chapter provides information on opening and saving data files within Kinetica.

Opening and Saving Data Files


Kinetica files have a kdb (Kinetica DataBase) extension. Kinetica templates are files with a ktp (Kinetica TemPlate) extension.

The Kinetica kdb file format holds Kinetica data and datasets, including methods, settings, table scripts, and appended macros. The Kinetica ktp file format is a template file that contains methodologies, pre-defined settings for the methods, table scripts, macros and appended graphs.

When you open a ktp file, it is automatically renamed with a kdb file extension and is formatted for use with data entry or import. Both the kdb and ktp file types are private formats; neither format can be edited outside of Kinetica.

Note Kinetica 5.0 is not backward-compatible. Files created or saved using Kinetica 5.0 will not be able to be opened with previous versions of Kinetica.

Files in Kinetica



To open an existing Kinetica file:

1. Within Kinetica, select Open from the File menu. The Open a Kinetica File dialog appears. By default the program displays the sub-directories of the Data directory (see the figure below).

Figure 3-1. Open a Kinetica File Dialog

2. Double-click on the directory corresponding to the type of analysis you want to run, e.g. Convolution_Deconvolution. The available datasets of the selected directory are displayed.

3. Double-click on the appropriate dataset (e.g. Deconvolution.kdb) or right-click it with the mouse and select Open. The selected file displays in the Kinetica workspace.

Note You can open any valid Kinetica file by dragging and dropping it onto the Kinetica workspace. You can also drag any valid file onto the Kinetica icon. Kinetica will launch and open the file.

Opening an Existing Kinetica File



Kinetica uses technology similar to Microsoft’s Word and Excel programs, allowing you to create default file structures based on a “normal” template. When you create a new file, Kinetica uses a default structure called Normal.kdb that is stored in Kinetica’s Data directory. By default, Normal.kdb contains the most basic structure: one dataset containing an X-column and a Y-column inside a plasma matrix. To customize Kinetica’s Normal template, follow the steps below.

Note Make sure that you do not overwrite Normal.kdb.

1. In Kinetica select New from the File menu. The New Analysis dialog appears.

Figure 3-2. New Analysis Dialog

2. Select the Normal button located on the General tab.

3. Click OK. The Plasma dataset group appears:

Customizing the Normal Kinetica

Template



Figure 3-3. Dataset View


5. Enter Test in the Column Name field, and then click OK.

A column called Test appears in the Plasma view adjacent to the Y-column. You may continue to add as many columns as you like to Normal.kdb.

6. When you are finished adding columns select Save As from the File menu to save your modified template as a new template.



To open an existing Kinetica template:

1. In Kinetica select New from the File menu. The New Analysis dialog appears. By default the program displays all the subdirectories of the Template directory.

2. Select the tab corresponding to the type of analysis you want to run, e.g. Non Compartmental. The available templates of the selected directory are displayed:

Figure 3-4. New Analysis dialog open to Non Compartmental tab, Extravascular template selected.

3. Click on the template you want to open, e.g. Extravascular (see figure above).

4. Do one of the following:

• To open the selected template with example data, select the Open with Data check box. If you do not check this box, an empty template opens, ready for your own data.

• To view step-by-step help instructions related to the selected template, check the Open with Help box.

Opening an Existing Kinetica Template



5. Click OK. The selected template is displayed in the Dataset pane.



When you save a Kinetica file you have three options:

• Save the file containing the raw data, analyzed data, graphs, table scripts with all information.

• Save the file with all information under a different name.

• Save the file as an empty template.

These options are discussed in detail below.

To save a Kinetica file with its original file name:

1. Launch Kinetica.

2. Open a valid dataset or template, enter data and/or compute values, as required.

3. Select Save from the File menu or click the Save button on the toolbar. The file is saved under its original name.

To save a Kinetica file with a new name:

1. Launch Kinetica.

2. Open a valid dataset or template, enter data and/or compute values, as required.

3. Select Save As from the File menu. The Save As dialog appears. By default the program displays the sub-directories of the Data directory.

Saving Kinetica Files

Saving a Kinetica File Using Its Original File

Name

Saving a Kinetica File with a New Name



Figure 3-5. Save As Dialog

Note Make sure that you do not overwrite Normal.kdb.

4. Select the appropriate directory where you want to save the new file, e.g. Absorption. The existing datasets of the selected directory are displayed.

5. Enter the new name for the file, e.g. myfile.kdb, and then click Save. The selected dataset is saved under the new name. The previous version of the file is saved under the original name.

When you open or create a dataset (or another template), you can enter data and add methods. When the analysis is complete, you will have a collection of fields and methods full of data. You can save all your data in a .kdb file or you can save the file as a template. Saving the file as a template preserves only the file structure with the embedded methods; all data in the file are deleted.

1. In Kinetica open a valid dataset or template, enter data and/or compute values, as needed.

Saving a File as an Empty Kinetica Template



2. Select Save As Template from the File menu. The Save As Template dialog appears.

Figure 3-6. Save As Template Dialog

3. Navigate through the Kinetica directories until you find the directory where you want to save the new template.

4. Enter the new name for the template file, e.g., template.ktp and then click Save.

Note By default Kinetica generates a default filename for your template composed of the name of the current dataset and a .ktp suffix.


4. Unit Configuration in Kinetica

This chapter provides information on configuring units of measurement within Kinetica.

Unit Configuration in Kinetica


This section is specifically for Kinetica templates that do not have a built-in unit management tool. These templates, such as those for performing compartmental analyses, have units specified by unit-handling methods. You may, however, want to create your own methods/templates to understand how to set up your own units.

You can specify the following types of units:

• Concentration

• Column

• Rate

• Molar

Note Non-compartmental analysis methods such as AUC*, AUC steady-state*, AUC inter*, AUC steady-state with Lz*, and sparse AUC* already have unit management that can be pre-defined.

Configuring Units



Kinetica does not provide units for output variables, however, it does provide the units for output columns. You must insert the basic units for time, quantity (or amount), and volume. Do this by inserting dataset and study numeric or text fields with the relevant names typed on the fields provided.

Three hard-coded methods allow you to specify units:

• Column Unit

• Get Column Unit

• Set Column Unit

Three soft-coded methods allow you to specify units:

• MakeConcUnit

• MakeRateUnit

• Xyunit

Specifying Units



You cannot enter concentration units; however, you can generate the unit using the units for the amount and the volume (for example, mg for the amount (dose) and µg/L for the concentration). This is performed by inserting the soft-coded method “MakeConcUnit.”

To insert and use the MakeConcUnit method:

1. Select New from the Kinetica File menu. The New Analysis dialog appears:

Figure 4-1. New Analysis dialog

2. Click on the General tab, select the Normal icon, and then click OK.

3. Select the Global Variables worksheet in the Study pane. You are now ready to insert text fields that can hold units of measurement definitions.

Specifying Concentration Units

Inserting and Using the MakeConcUnit Method



To insert text fields:

1. From the Insert menu select Global Text Field. The Insert a Global Text Field dialog appears.

2. For this example, enter the text QteUnit,VolumeUnit in the Field Name field.

Note The comma in the text string tells Kinetica that we are creating two separate fields.

3. Click Insert.

4. The global variables you’ve added now appear as the column headings QteUnit and Volume Unit in the Global Variables view of the Study pane.

To assign units to the text fields you’ve inserted:

1. Enter the value mg (for milligrams) in the Unit row under the QteUnit field and press the return key.

2. Enter the value L (for liters) in the VolumeUnit field and press the return key.

3. From the Insert menu select Method (or use the Insert

Method button on the toolbar).

4. In the Method Selection pop-up window scroll down the list of methods to the Soft Coded Methods.

5. Expand the list of soft-coded methods. Expand General and select MakeConcUnit.bas.

Inserting Text Fields

Setting Units for Text Fields



Figure 4-2. Method Selection dialog – MakeConcUnit.bas selected for insertion

6. Before you can insert the method you must apply User names to the Input Cols&Vars fields found at the top right of the Method Selection dialog (see figure above).

7. Click inside the white area below User names to activate the drop-down lists populated with your study variables. Select Study.QteUnit for QteUnit and Study.VolumeUnit for Volume Unit (refer to figure below).

Figure 4-3. Detail of Method Selection dialog: assigning Input Cols&Vars for the MakeConcUnit method.

8. Click Insert to insert the method. When the method is inserted click OK to exit the Method Selection dialog.

9. Select the Dataset Variables view in the Study pane to see the new field, ConcUnit, added by the MakeConcUnit method.



10. Click the Calculate All button (or use the Dataset menu and select Calculate All) to execute the method. The method reads the unit definitions from the columns selected in Input Cols&Vars and uses them to generate the unit for ConcUnit; the value mg/L is inserted in the ConcUnit field.

Figure 4-4. Unit mg/L as generated by MakeConcUnit method



Column units are entered by using the “Set Column Unit” hard-coded method. Kinetica computes values using the time unit specified and places the value in the appropriate column. This method must be inserted into each column in which you need to specify units.

If a template is open, the Set Column Unit method may already be included. You can verify this by clicking Methods in the Methods pane and viewing the list of methods in the spreadsheet.

To insert and use the Set Column Unit method:

1. In Kinetica select New from the File menu. The New Analysis dialog appears.


3. Select the Global Variables worksheet in the Study pane. You are now ready to insert text fields. (Refer to the Inserting Text Fields section.)

To view the inserted units:

1. Highlight the Plasma worksheet in the Dataset pane and click the Calculate One button. The units are visible in the spreadsheet.

2. Click the Insert Method button. The Method Selection dialog appears.

3. Select the Set Column Unit method from the hard-coded Methods list.

4. Select Plasma.Y from the Col User names drop down list in the upper right area of the dialog (under User names).

Specifying Column Units

Inserting and Using the Set Column Unit Method

Viewing the Inserted Units



5. Select ConcUnit from the Unit User names drop down list in the upper right area of the dialog (under User names).

6. Click Insert. The method is inserted. This time Kinetica uses the result of the MakeConcUnit method for the Y column units.

Note You must have already inserted the MakeConcUnit method in order to see the ConcUnit field in the list box.

7. Click the Calculate All button. Kinetica inserts the value h in the first row of the X column and mg/L in the first row of the Y column.

8. Repeat this procedure to configure units for as many columns as required.

Note The first row of sample worksheets is always reserved for column units.



Rate units can be inserted using the "MakeRateUnit" soft-coded method. Kinetica then computes values using the quantity (or amount) and time unit specified and places the value in the selected column. This method must be inserted for column(s) where you want to specify the rate units.

If a template or dataset is open, the MakeRateUnit method may already be included. You can verify this by clicking Methods in the Methods pane and viewing the list of methods in the spreadsheet.

To insert and use the MakeRateUnit method:

1. Launch Kinetica.

2. Select New from the File menu or click the New button. The New Analysis dialog appears.


4. Highlight the Global Variables worksheet in the Study pane. You are now ready to insert text fields.

To insert text fields:

1. Select Global Text Field from the Insert menu. The Insert a Global Text Field dialog appears.

2. Enter the text QteUnit,VolumeUnit,TimeUnit in the Field Name field and click Insert.


4. Select Global Variables in the Study pane to view the inserted global text fields.

Specifying Rate Units

Inserting and Using the MakeRateUnit Method

Inserting Text Fields



5. Enter the value mg (for milligrams) in the QteUnit field and press the return key.

6. Enter the value L (for liters) in the VolumeUnit field and press the return key.

7. Enter the value h (for hours) in the TimeUnit field and press the return key.

8. Select the Method pane and click on Basic Editor. The Basic Editor dialog appears.

9. Click Open from the File menu. The Open dialog appears.

10. Select the soft-coded method called MakeRateUnit.bas from the Kinetica\models\general directory.

11. Click Open. The following basic code appears in the Basic Editor dialog:

Dim AmountUnit as InputText Dim TimeUnit as InputText Dim RateUnit as OutputText Sub MakeRateUnit() RateUnit = AmountUnit + "/" + TimeUnit End Sub

12. Click the Insert Method button. The Method complies and the following message appears: “Method successfully compiled.”

13. Click OK. The Method Selection dialog appears. The MakeConcUnit soft-coded method is included in the list of soft-coded methods.



Note If the method does not compile properly, a message appears informing you of the error and indicates the line of code in which the error occurred.

14. Select this method from the Methods list.

15. Select Study.QteUnit from the AmountUnit User names drop down list in the upper right area of the dialog (under User names).

16. Select Study.TimeUnit from the TimeUnit User names drop down list in the upper right area of the dialog (under User names). Click Insert. The method is inserted.

To view the RateUnit output field:

1. Highlight the All Variables worksheet in the Study pane.

2. Click the Calculate All button. Kinetica inserts the value mg/h in the RateUnit field (in blue).

Viewing the RateUnit Output Field



Sometimes you may want to get the units inserted from one column for use in a new column. You can do this using the “Get Column Unit” hard-coded method. Kinetica takes a copy of a selected column unit that has already been specified and applies it to another column.

If a template is open, the Get Column Unit method may already be included. You can verify this by clicking Methods in the Methods pane and viewing the list of methods in the spreadsheet.

To insert and use the Get Column Unit method:

1. Complete the steps included in the procedure for calculating observed concentration units (see the section Specifying Concentration Units). ConcUnit is computed in the example using the Get Column Unit method.


3. Enter the text MyTest in the Column Name field.

4. Click Insert. You can view the new column “MyTest” in the Dataset pane.


6. Select the Set Column Unit method from the hard-coded Methods list.

Note Since it is a hard-coded method, you cannot see the Basic source code.

7. Select Plasma.MyTest from the Col User names drop down list in the upper right area of the dialog.

Getting Units

Inserting and Using the Get Column Unit method



8. Click Insert. The method is inserted.


10. Select the Set Column Unit method from the Methods list.

11. Select Plasma.MyTest (assuming we are using a Plasma worksheet) from the Col User names drop down list in the upper right area of the dialog (under User names).

12. Select ConcUnit from the Unit User names drop down list in the upper right area of the dialog (under User names).


14. Highlight the Plasma worksheet in the Dataset pane.

15. Click Calculate One.

Kinetica inserts the value mg/L in the first row of the MyTest column. This unit is derived from whatever value is inserted and output to the ConcUnit field.

The first row of the Plasma worksheet appears as follows:

X Y MyTest h mg/L mg/L

Repeat the above procedure for as many columns as required.

Viewing the Units Inserted



In the case where the values of one column are multiplied by another column, Kinetica supplies the soft-coded method “XY Unit” to insert the correct unit labels. The method creates a unit label using the units specified from any two different columns or fields.

If a template is open, the XY Unit Method may already be included. You can verify this by clicking Methods in the Methods pane and viewing the list of methods in the spreadsheet.

To insert and use the XY Unit Method we will multiply the ConcUnit by the RateUnit using the XY Unit method:

1. Complete the steps included in the procedure for calculating observed concentration units (see the section Specifying Concentration Units).

2. Complete the steps included in the procedure for calculating rate units (see the section, Specifying Rate Units).


4. Enter the text UnitTest in the Column Name field.

5. Click Insert, and then click OK to exit the dialog. You can view the new column “UnitTest” in the Dataset pane.

6. Select the Method pane and click on Basic Editor. The Basic Editor dialog appears.

7. Click Open from the File menu. The Open dialog appears.

8. Select the soft-coded method called XYunit.bas from the Kinetica\models\general sub-directory.

Multiplying Units

Inserting and Using the XY Unit Method



9. Click Open. The following Basic source code appears in the Method Editor dialog:

'XY unit Dim XYunit as OutputText Dim xunit as InputText Dim yunit as InputText Sub calc_XYunit() XYunit = "(" + yunit + ")" + "." + "(" + xunit + ")" End Sub

10. Click OK.

11. Click the Insert Method button. The method compiles and the following message appears: "Method successfully compiled."

12. Click OK. The Method Selection dialog appears. The XYunit method is included in the list of soft-coded methods.

Note If the method does not compile properly, a message appears informing you of the error and indicates in which line of code the error occurred.

13. Select this method from the soft-coded Methods list.

14. Select ConcUnit from the xunit User names drop down list in the upper right area of the dialog (under User names).

15. Select RateUnit from the yunit User names drop down list in the upper right area of the dialog (under User names).


17. Highlight the All Variables worksheet in the Study pane.



18. Click the Calculate All button. The value (mg/h) (mg/L) is inserted in the XYunit field.

Now we will assign this XYunit label to the UnitTest column we inserted using the Set Column Unit method.


2. Select the Set Column Unit method from the Methods list.

3. Select Plasma.UnitTest from the Col User names drop down list in the upper right area of the dialog.

4. Select XY Unit from the Unit User names drop down list in the upper right area of the dialog.



1. Highlight the Plasma worksheet in the Dataset pane.

2. Click the Calculate One button. The value (mg/h).(mg/L) is inserted in the first row of the UnitTest column. This unit is taken from whatever value is inserted and output to the XYunit field. The first row of the Plasma worksheet appears as follows:

X Y UnitTest h mg/L (mg/h).(mg/L)

3. Repeat this procedure for as many columns as required.

To view the XYunit output field




Generally, units are inserted by the method used in the output columns. However, Kinetica does not simplify the units when possible. In some templates you can find methods that direct the program to put simplified units in a particular column. For example, in the Deconvolution.ktp template there are two columns that do not have units in the output columns:

• dAdt

– the rate of change of the amount of drug absorbed with

time.

• A(t) – the amount of absorbed drug.

To see an example of a template containing units handling methodologies, open Deconvolution.ktp (with data) which provides dA/dt and A(t). You can also refer to the following three methods:

• MakeRateUnit – inserts the unit for the rate by associating the Study.AmountUnit field with the Study.TimeUnit field.

• Set Column Unit – inserts the unit for the rate by associating the os.dA/dt column with the RateUnit field (assuming we are using an os worksheet.)

• Set Column Unit – inserts the unit for the rate by associating the os.A(t) column with the Study.AmountUnit field.

You can use the Unit Management feature to specify the input units for the AUC methods. AUC methods include:

• AUC*

• AUCinter*

• AUC Steady State*

• AUC Steady State with Lz*

• Sparse AUC

Obtaining Simplified Units

Unit Management for AUC Methods

http://www.os.dA/dt



Chapter 8, Non-Compartmental Analysis discusses the AUC methods in detail.

To use the Unit Management feature, you need to specify the input units and select the type of units for the output variables. Unit Management will then convert the units to your specifications. We provide an example of this procedure below.

1. In Kinetica select the Methods pane.

2. Using Insert > Method… from the Menu bar or the Insert Method button on the toolbar, select and insert one of the AUC methods. After inserting the method the Methods view will appear as follows:

Figure 4-5. Methods view after insertion of the AUC Steady State* method

3. Click Set under Global Options. The AUC* Method Global Options dialog appears.



Figure 4-7. AUC Method Global Options Dialog – Units Tab

4. Select the Units tab.

5. Under the Selected Unit column, specify Time, Concentration, and Dose units from the drop down list according to your input data. You can change the display of the units using the Display Label column (see figure above).

6. Select the units for the output variables.

7. Click OK.



You can use the Unit Management feature to specify molar units for the AUC* method. You can define a different molecular weight for each dataset in a selected study, or, if you select the global option, all datasets in the study.

To use this feature, you must specify:

• Input units for quantity (such as dose) and concentration

• Molecular weight

• Type of units for the output variables.

The units are then converted according to your specifications.

Note Ensure that you define molar units for each AUC* method. Each AUC* method is related to a particular defined molecular weight.

Use the 2WayCrossover.kdb for this exercise. To specify molar units:

1. In Kinetica select the Methods pane.

2. Select the AUC* method.


• Click Set under Global Options (defines the molecular weight for all datasets in the study).

• Click Set under Local Options (defines the molecular weight for an individual dataset in the study). The AUC* Options dialog appears.

Specifying Molar Units for the AUC*

Method

Specifying Molar Units



Figure 4-8. AUC* Method Global Options Dialog – Units Tab


5. Under the Selected Unit column, specify Time (e.g. h), Concentration (e.g. mg/mL), a Dose (e.g. mg), AUC (e.g. M*h) and any other required units from the drop down list according to your input data. You can change the display of the units under the Display label column.



• Select Variable Name and select the appropriate variable name for the molecular weight from the available list. Select this option if the variable already exists in the



dataset numerical field (All Variables view in the Study pane).

• Select Value and enter a numerical value for the molecular weight in the adjacent field.

8. Click Apply and/or click OK to save the selections and exit the dialog.


1. Highlight the Plasma worksheet in the Dataset pane (assuming we are using a Plasma worksheet).

2. Click the Calculate One button.

The first row of the Plasma worksheet appears as follows:

X Y AUC h mMolar mM.h




Notes


5. Importing and Exporting Data

The following chapter describes the different ways to import and export data in Kinetica.

Importing and Exporting Data


Kinetica provides a rapid and easy-to-use method to import data from Excel or WinNonlin into Kinetica. Note that the data in the Intelligent Spreadsheet is not saved when the file is closed; use it for quick import into the Kinetica structure. Intelligent Import recognizes two types of data layout: Dataset Column and XYj. These are described below.

The Dataset Column layout option indicates that the selected range of Excel data is stored in column format with Time and Concentration columns. These columns are stacked ‘vertically’ per dataset, and the subject ID repeats. See the table below for an example:

Subj Time Conc

Subj101 x y

Subj101 x y

Subj101 x y

Subj101 x y

Subj101 x y

Subj102 x y

Subj102 x y

Subj102 x y

Subj102 x y

Subj102 x y

Intelligent Spreadsheet and Intelligent Import

Dataset Column



In this example we use Intelligent Import to transfer data from an Excel worksheet into Kinetica’s Intelligent Spreadsheet in the Dataset Column layout. We use the file DatasetColumn Import.xls found in the Kinetica/Data folder.

1. In Kinetica, select Intelligent Spreadsheet in the Study pane.

2. With DatasetColumn Import.xls open in Excel, copy the spreadsheet content and then go to Kinetica and paste it into the Intelligent Spreadsheet. The column headings should appear in the first row (see figure below).

Figure 5-1. Intelligent Spreadsheet after pasting in data copied from Excel

3. Select Intelligent Import from the Tools menu. The Intelligent Import pop-up window appears:

Example



Figure 5-2. Intelligent Import window

The table below describes the different fields in the Intelligent Import window.

Selection Description

Variables Strings from the first row of cells of the Intelligent Spreadsheet, as read by Intelligent Import.

Sort Variables (Dataset Name)

Entries or selections in this box are used to identify the Dataset name of the Kinetica file. Entries in this box will also be populated as dataset fields in the Dataset Variables level of the Kinetica data structure.

Carry Alongs Entries in this box will be populated as dataset fields in the Dataset Variables level of the Kinetica data structure. The data in this box should be unique to the dataset name such that there is only one value for each individual dataset name.

Time Series Data Entries in this box will be populated as new columns in the first available worksheet. The columns handle both numeric and text entries. For example, comments related to a specific time-concentration data can be brought into the Kinetica data structure.

Units in Second Row box

If your spreadsheet has units in the second row, check the Units in Second Row box. Intelligent Import will bring the second row to the unit rows.




XYj Data Type box

Indicates that data are stored in column format with one Time column at the first column that is the same for each subject but with different Concentration columns.

4. Intelligent Import reads the first row of the Intelligent Spreadsheet and adds the cell values to the Variables list.

5. Drag and drop variable names from the Variables list to the other text boxes of the Intelligent Import window as follows:

• DrugName and SbjName to Sort Variables (Dataset Name)

• Dose and Sequence to Carry Alongs

• SampleTime, SampleValue, and SampleStatus to Time Series Data

6. When you are done, the Intelligent Import window will look similar to the figure below:

Figure 5-3. Intelligent Import after organizing variables into categories.



Note If your spreadsheet has units in its second row, check the box Units in Second Row.

7. Click Import. Kinetica displays a message when the import is complete. Once the data are imported, you can associate methods to compute pharmacokinetic parameters (see Chapter 8, “Non-Compartmental Analysis”).

The XYj layout option indicates that the selected range of Excel data is stored in column format with one time column as the first column that is the same for each subject but with different Concentration columns. Note that there could be more than one concentration column associated with a subject. These columns are arranged ‘horizontally’ per dataset. See the example below:

Time Subj101 Subj102 Subj103

x y Y y

x y Y y

x y Y y

x y Y y

x y Y y

In this example we use Intelligent Import to transfer data from an Excel worksheet into Kinetica’s Intelligent Spreadsheet in the XYj layout. We use the file XnY Import.xls found in the Kinetica/Data folder.

1. In Kinetica, select Intelligent Spreadsheet in the Study pane.

2. With XnY Import.xls open in Excel, copy the spreadsheet content, then go to Kinetica and paste the data into the Intelligent Spreadsheet. The column headings should appear in the first row.

XYj

Example



3. Select Intelligent Import from the Tools menu. The Intelligent Import pop-up window appears; the first row of the Intelligent Spreadsheet is shown as list of variables.

4. Check the box XYj Data Type. Notice that when the box is checked, the values in the Variables section of the Intelligent Import window disappear. Intelligent Import assumes that the first column is the time column and that the remaining columns are individual dataset data. The first row starting from the second column is used as the dataset name for the Kinetica study.

5. Click Import. Kinetica displays a message when the import is complete.



Kinetica includes an easy-to-use utility called Import Assistant. With Import Assistant you can import Microsoft Excel, ASCII, Watson LIMS data, and the proprietary formats of P-Pharm and Siphar data.

The Import Assistant allows you to append new data to existing study data, insert data into empty study templates, or create a new study structure.

Importing Files into Kinetica with Import

Assistant



The Import Assistant Wizard is a series of dialogs that simplifies importing data and guides you through the process step-by-step. The Import Assistant Wizard can be accessed from the Tools menu on the Kinetica toolbar; select Assistants, then Import.

Figure 5-4. Import Assistant Wizard

When using the Import Assistant Wizard, follow the instructions given in the wizard screens to guide you. You can move back and forth between the dialogs and change information as needed until you complete the wizard. You can exit without saving at any point before completing the wizard.

The first dialog is used to select an import format/source type. You can select one of the options described in the following table.

Input Format Description

Excel Import data from an Excel spreadsheet. Supports Excel 97 and later.

ASCII Import data from an ASCII file.

Using the Import Assistant Wizard



Input Format Description

Database Import data from a database through Microsoft ODBC (ODBC) technology. Only database vendors that provide ODBC drivers for their systems can be imported.

Proprietary format

Import data that has been entered in a proprietary format (such as Siphar or P-Pharm).

Watson Import data from the Watson LIMS system. Select studies from a list and see the data automatically loaded into Kinetica.



This option enables you to import Excel files into the database to create new Kinetica studies. This section illustrates how to import the spreadsheet “DatasetColumn Import Filter.xls” into Kinetica.

1. In Kinetica, from the Tools menu, select Assistants, then

Import…, or click the Import Assistant icon on the toolbar.

2. When the Import Assistant Wizard starts up you will notice that the default setting source type is Excel. Keep this default, and then click Next. The “Import Assistant – Step 1 of 5” dialog appears. For this example, we will use the default data layout, Dataset Column.

Figure 5-5. Import Assistant – Step 1 of 5

The selections in this dialog are described in the following table.


Workbook Displays the selected import file name and path. The Excel file is selected via the Browse button.

Importing Data from Excel Spreadsheets




Worksheet Identifies the name of the Excel worksheet containing the incoming data.

Range Identifies the range of the incoming data within the selected Excel worksheet.

Data Layout

This section specifies the organization of the incoming data so Kinetica understands the incoming structure. The options are described in the following table:

The table below lists the different types of data layouts recognized by the Import Assistant application.


Dataset Column

Indicates that the selected range of Excel data is stored in column format with unique Time and Concentration columns for each subject.

XYj Indicates that the selected range of Excel data is stored in column format with one Time column at the first column that is the same for each subject but with different Concentration columns for each subject

XiYj Indicates that the selected range of Excel data is stored in column format with time and various analyte concentration columns for each subject. This selection allows the user to choose the number of y columns per individual profile.

TXiYj Indicates that the selected range of Excel data is stored in column format with nominal and actual times and multiple concentration columns for each subject. This selection allows the user to choose the number of y columns per individual profile.




X Allows the user to choose the number of independent variable columns.

Y Allows the user to choose the number of dependent variable columns.

Column Header

Indicates that the selected range of Excel columns include a header row. While headers are not necessary, if they are present, the first header must be the first cell selected for export. In addition, the header must be one line only and there may not be any blank lines or cells between the header and the data.

3. Click Browse and locate the Excel file called DatasetColumn Import Filter.xls. This file is located in the Kinetica\Data folder.

4. The selected file opens in Excel and you are prompted to select the range of cells you wish to import.

Figure 5-6. Example – Selecting a Range of Cells to import from Excel



5. Using your mouse, select the range of cells from A4 to H244. Include all displayed values with the column headers. As illustrated in the figure, do not include the information “Plasma profiles (mg/l),” “tablet A = 75 mg and tablet B = 75 mg” in your selection.

6. Verify the selected range of cells within the dialog box is correct and click OK.

7. Excel closes and you the Import Assistant Step 1 of 5 dialog is now populated with the Excel workbook name, worksheet name and data range as you selected. Keep the Dataset Column layout option selected and click Next.

Figure 5-7. Import Assistant Step 1 of 5, with Excel Workbook, Worksheet and Data Range shown.



8. In the “Import Assistant Step 2 of 5” window, drag the DrugName field from the top Merge Results edit box to the second edit box.

9. Next, drag the SbjName field from the Source Columns to the top Merge Results edit box. You will notice that the Dataset Name is now equal to “Subj01-A,” a concatenation of the subject and drug name identifiers in the import file. This dialog box allows you to set unique identifier for each subject, period and/or treatment that the subject has undergone. Click Next.

Figure 5-8. Import Assistant Step 2 of 5

10. The Step 3 of 5 dialog allows you to set up worksheet(s) for the subject profile. From the Source Columns list, drag and drop SampleTime and SampleValue to the Source Column column in the rows X and Y, respectively.

11. Select SampleType for the Import Filter box.

12. Select the Plasma filter for the Plasma worksheet. Click Next.




13. In the Step 4 of 5 dialog, map the numeric and text datasets to either numeric or text fields. Drag Dose to the source column for a numeric field and SbjName, DrugName, and Sequence to the source column for text fields. This step allows you to set-up demographics, covariables and other data pertinent to the subjects for the analysis.




14. In the Step 5 of 5 dialog, you may set flags for undetectable, outlier, missing and error data in the analysis. There are no identifiers that require filtering for this exercise. The entry of symbols does not need to be entered under the Dictionary table. However, you may change the symbols to whatever status code is set in the original dataset. For example, you may change the symbol “<” to “BLQ”, “!” to “outlier”, “#ERR” to “error” depending on the SampleStatus code in the original Excel file and then choose SampleValue in the Data column box and SampleStatus in the Status column box.

The Manage Existing Data box allows you to manage the new import dataset over the existing datasets by mapping the unique identifiers corresponding to the two datasets. There are three options:

• The Append to end box allows you to append the new import data after the last row of data in the current worksheet.

• The Append after last value box allows you to append the new import data after the last value in the corresponding columns in the current dataset.



• The Delete Existing Data box allows you to overwrite the new import dataset over the existing datasets by mapping the unique identifiers corresponding to the two datasets

Figure 5-11. Import Assistant, Step 5 of 5

Note If your source file contains any of the above-mentioned identifiers (Undetectable, Error, etc.), enter the symbol(s) in the Dictionary list under the corresponding identifier. If you choose to replace one of the identifiers provided with a user-defined symbol, ensure that you copy the remaining system-provided markers into the row as well then press Enter. This action prompts Kinetica to insert the automatic data status flag before the indicated data point inside the Kinetica study.



Object Description

Undetectable Specifies the import code to look for during the import process to signify a data point is undetectable. The corresponding internal Kinetica flag for undetectable data (<) will be inserted before the data point. During an analysis, Kinetica will adjust calculations for the profile according to this status flag. You can also create your own graphic for this flag.

Outlier Specifies the import code to look for during the import process to signify that a data point is an outlier. The corresponding internal Kinetica flag for outlier data (!) will be inserted before the data point inside the Kinetica study. No data is lost or transformed in this process. During an analysis, Kinetica will adjust calculations for the profile according to this status flag. You can also create your own graphic for this flag

Missing Specifies the import code to look for during the import process to signify a data point is missing. The corresponding internal Kinetica flag for missing data (blank cell) will be inserted inside the Kinetica study. During an analysis, Kinetica will adjust calculations for the profile according to this status flag. You can also create your own graphic for this flag.

Error An internal code used to avoid floating point errors in Kinetica.

Data Column This is linked to a column specified in the Status Column list.



Object Description

Status Column

Identifies the incoming column that contains a data flag. This is linked to a column specified in the Data Column list (e.g. Code). You can also enter a user-defined status code. This code can be a numerical value or one of the symbols provided in the Dictionary area of the dialog. If there are no symbols, the data is imported as usual. If there are symbols associated with the data, the symbol is imported into its own status column.

15. Click Finish and the Import Assistant will start processing the import file for entry into the Kinetica kdb file.

Figure 5-12. Importing Data Dialog

For the second example, we will use the XYj data layout import method.

1. In Kinetica, from the Tools menu, select Assistants, then

Import…, or click the Import Assistant icon on the toolbar.

2. Select XYj in the Data layout area of the dialog (refer to figure below). The organization of these data is XYj because we have a single Time column that is identical for all subjects, and subsequent Concentration columns that are different for every subject. We have to inform the Import Assistant of this organization.

XYj Import Method




3. Click Browse to locate the XnY Import.xls file, which can be found under the Data subdirectory. Using the mouse, select the range of cells from A4 to Y14. This dictates to import the time and concentration values for all subjects. Notice the data layout. All datasets contain the same time column but different concentration columns for each subject. Click OK.

Figure 5-14. Example – Selecting a Range of Cells

4. Click Next to the dialog Step 4 of 6. The next Import Assistant appears, enabling you to drag and drop column



names from the import file to the appropriate worksheet/column destination inside the Kinetica study.


We did not select a Kinetica template before starting the Import Assistant. Therefore, the Kinetica study structure in this dialog displays the objects found in the Normal.kdb structure (i.e., plasma worksheet with an X and Y column). If we had selected an existing template or file containing data, Import Assistant would display the objects of the selected structure.

5. Drag the Source column called X and Y into the Source Column of the Dataset columns worksheet corresponding to X and Y, respectively.



Figure 5-16. Import Assistant Step 4 of 6

You have now specified that all imported time (X) data should be placed in the Kinetica column called X, and all concentration (Y) data should be placed in the Kinetica column called Y during the import process (both will be found in the Kinetica sample Plasma worksheet). We call this process Column Mapping. The dialog should appear as follows:

6. Select the column that contains the data. If a separate column exists with flags (e.g. codes), specify the column as the status column, and define the codes in the Dictionary.



Figure 5-17. Import Assistant – Step 5

7. Click Finish. The Import Assistant now processes the incoming data. The following dialog appears during this process:


Once the process is complete, the Import Assistant terminates and the imported data are displayed in Kinetica.



To use the XiYj Import method for importing Excel data:

1. Click the Import Assistant icon located on the toolbar. The default setting source type is Excel. Click Next.


2. Select XiYj in the Data layout area of the dialog. Specify the number of analyte or dependent columns for each subject by entering the number in Y.

The organization of this data is XiYj because we have a single Time column that is identical for all subjects and multiple concentration columns for single subject. We have to inform the Import Assistant of this organization.

Using the XiYj Import Method




3. Select the XiYj layout and enter 2 for Y. Click Next to the dialog Step 2 of 6.


4. Drag the Source column called X, Y1, and Y2 into the Source Column of the Dataset columns worksheet corresponding to X, Y, and the following row respectively.



Figure 5-22. Import Assistant - Step 4







Once the process is complete, the Import Assistant terminates and the imported data is displayed in Kinetica.

To use the TXiYj import method:

1. Click the Import Assistant icon located on the toolbar. The default setting source type is Excel. Click Next.


Using the TXiYj Import Method



2. Select TXiYj in the Data layout area of the dialog. Specify the number of analyte or dependent columns for each subject by entering the number for X and Y.

Figure 5-26. Import Assistant Step 1 of 3 – TxiYj data layout

The organization of these data is TXiYj because in this example we have a single Time column that is identical for all subjects, another subject-specific time column, and multiple concentration columns for single subject. We have to inform the Import Assistant of this organization.

3. Click Browse to locate the TXiYj Import.xls file, which can be found under the Data subdirectory. Using the mouse, select the range of cells from A4 to J14. This dictates to import the time and concentration values for all subjects. Notice the data layout. All datasets contain a same time column, another subject-specific time column and two concentration columns for each subject. Click OK.






Once the process is complete, the Import Assistant terminates and the imported data is displayed in Kinetica.



This option provides the import of data from ASCII files into the database to create new Kinetica studies.

1. Load Kinetica.

2. Select Assistants then Import Assistant from the Tools menu or click the Import Assistant icon on the toolbar. The Import Assistant wizard appears.

Figure 5-31. Import Assistant Dialog

3. Select ASCII from the Source type list and click Next. The next Import Assistant dialog appears.

Importing Data from ASCII Files



Figure 5-32. ASCII Import Assistant – Selecting Source Files Dialog

The objects in this dialog are described in the following table.

Object Description

Data Layout Start line: Identifies the first line number within the incoming structure. The Import Assistant uses an algorithm that attempts to locate the logical start line of numeric information. This is always 1 by default before opening a file and must be checked carefully once the incoming file is opened. The value can be increased or decreased, using the up and down arrow control, depending on which line you want to start the import process.



Object Description

End line: Identifies the last line number within the incoming structure. The Import Assistant uses an algorithm that attempts to locate the logical end line of numeric information. This is always one by default before opening a file, and must be checked carefully once the incoming file is opened. The value can be increased or decreased, using the up and down arrow control, depending on the line you select to end the import process.

Fields: Identifies the number of fields found in the lines of data to be imported within the incoming structure (within the line range specified via the Start line and End line identifiers). The Import Assistant uses an algorithm that reads each line to locate the field separator identifier. This is always one by default before opening a file and must be checked carefully once the incoming file is opened. The value can be increased or decreased using the up and down arrow control, depending on the line you select to start the import process.

Header: Specifies the presence ( ) of header lines in the import file. Many import files contain header information that is not part of the main body of analytical data. The Import Assistant automatically detects the presence of the header lines and, if found, selects the Header check box. If there is a header, there must be no blank lines between the header and the data, and the header must be the first cell selected for import.

Data Format Delimited: Specifies the field separator characters, e.g. commas, tabs, etc. used in the data file.



Object Description

Fixed Length: Specifies the field length used in the data file. You are requested to define the length of each field in the grid below the fixed length option.

To select a source file to import into Kinetica:

1. Click Browse and locate the MyAsciiData.txt ASCII file. This file is found in the Kinetica\data subdirectory. After you open this standardized ASCII file, information related to the incoming data structure appears.

Figure 5-33. Example – Selecting an ASCII File to Import

We can now visualize the incoming data structure in the Data View frame. The Start and End lines have been updated to reflect the numeric start line as 1 and the numeric end line as 241. In addition, the Import Assistant has found 4 fields in the numeric lines to import. You can scroll through the Data View to see the entire contents of the incoming file.

Selecting the Source File to Import



2. Click Next. The next Import Assistant dialog appears. You can drag and drop column names from the import file to the merge fields required to create Kinetica dataset names as follows:

Figure 5-34. ASCII Import Assistant – Creating Dataset Names


Object Description

Source Columns

Contains a list of the column headers found in the import file. If no column headers are found, Import Assistant uses automatic labels and increments them for the number of fields found in the structure i.e. Col1, Col2, etc. The ordering of these columns in the list box is organized by the order they appear in the import file. Then, the first field found (called DrugName in our case) is automatically placed in the first Merge Result field. This is because most import structures are ordered with the first column as the subject identifier, and is designed to help quickly in the merge process.



Object Description

Merge Results

Contains a maximum of five ordered fields where source columns can be dragged to create the final merged dataset name. The sixth field contains the resulting label created from a concatenation of the data contents found in each merge field.

3. Using your mouse, drag the DrugName field from the first Merge Results field to the second field (if you are using the example file).

4. Drag the SbjName field from the Source Columns list box to the first Merge Results field that should be empty (if you are using the example file). Notice that the Dataset Name is now equal to "Sbj01-A" which represents the first row of data and is a concatenation of the subject and drug name identifiers in the import file. You are encouraged to select as many dataset identifiers as possible to ensure data integrity. The dialog appears as follows:

Figure 5-35. ASCII Import Assistant – Selecting Dataset Identifiers



Note Import Assistant places a hyphen (-) between the two field values for easier understanding once the data is opened inside Kinetica.

5. Click Next. The next Import Assistant dialog appears, enabling you to drag and drop column names from the import file to the appropriate worksheet/column destination inside the Kinetica study.

Figure 5-36. Import Assistant – Selecting Worksheet Columns

We did not select a Kinetica template before calling the Import Assistant; therefore, the Kinetica study structure in this screen displays the objects found in the Normal.kdb structure (i.e. Plasma worksheet with an X and Y column). If we had selected an existing template or file containing data, Import Assistant would display the objects of the selected structure.

The objects in this dialog are described in the table below.



Object Description

Source Columns

Contains a list of the column headers found in the import file. If no column headers are found, Import Assistant uses automatic labels and increments them for the number of fields found in the structure i.e. Col1, Col2, etc. The ordering of these columns in the list box is organized by the order they appear in the import file. Then, the first field found (called DrugName in our case) is automatically placed in the first Merge Result field. This is because most import structures are ordered with the first column as the subject identifier and is designed to help speed the merge process.

Dataset Columns

Typically a series of time-dependent data values that are either imported as raw data or computed output during a Kinetica analysis. Examples are Time, Concentration, AUC, AUMC, etc.

The Dataset Columns spreadsheet contains a destination column where the import column headers can be dragged to (a) map incoming column data into pre-defined Kinetica template structure, or (b) map incoming column data into a user-defined Kinetica template structure.

6. Drag the Source column called SampleTime into the empty destination Source Column cell that is adjacent to the X column.

7. Drag the Source column called SampleValue into the empty destination Source Column cell that is adjacent to the Kinetica Y column.

You have just specified that all imported time data should be placed in the column called X and all concentration data should be placed in the column called Y during the import process (both will be found in the Kinetica Plasma worksheet). We call this process Column Mapping. The dialog appears as follows:



Figure 5-37. ASCII Import Assistant – Creating Kinetica worksheet Columns

8. Click Next. The next Import Assistant appears, enabling you to drag and drop column names from the import file to their study/field destination inside the Kinetica template.

Figure 5-38. Import Assistant – Creating Kinetica Import File Names




Object Description

Source Columns

Source Columns contain a list of the column headers found in the import file. If no column headers are found, Import Assistant uses automatic labels and increments them for the number of fields found in the structure i.e. Col1, Col2, etc. The ordering of these columns in the list box is organized by the order they appear in the import file. Then, the first field found (called DrugName in our case) is automatically placed in the first Merge Result field. This is because most import structures are ordered with the first column as the subject identifier and is designed to help quickly in the merge process.

Dataset Columns

Typically a series of time-dependent data values that are either imported as raw data or computed output during a Kinetica analysis. Examples are Time, Concentration, AUC, AUMC, etc.

Dataset Fields

Dataset fields are typically a series of non-time dependent data values, which are either imported as raw data or computed output during a Kinetica analysis. Examples are AGE, WEIGHT, T1/2, Cmax, etc.

The Dataset Fields spreadsheet contains destination numeric and text fields where the import column headers can be dragged to (a) map incoming column data into pre-defined Kinetica template structure or (b) map incoming column data into a user-defined Kinetica template structure.

9. In our example import file, the SbjName and DrugName fields are both text fields. Drag the Source column called SbjName into an empty destination Source Column cell that is adjacent to the first empty Kinetica Text field column.



10. Drag the Source column called DrugName into the next successive empty destination Source Column cell.

You have specified to the Import Assistant that all imported treatment and subject identifier data should be placed in the Kinetica dataset text fields called SbjName and DrugName, both found in the Kinetica Dataset pane. We call this process Field Mapping. The dialog appears as follows:

Figure 5-39. ASCII Import Assistant – Field Mapping



Figure 5-40. ASCII Import Assistant – Specifying Import Filter Identifiers


Object Description

Undetectable

Specifies the import code to look for during the import process that signifies a data point is undetectable. The corresponding internal Kinetica flag for undetectable data (<) will be inserted before the data point inside the Kinetica study. No data is lost or transformed in this process. During an analysis, Kinetica will adjust calculations for the profile according to this status flag.

Outlier Specifies the import code to look for during the import process that signifies a data point is an outlier. The corresponding internal Kinetica flag for outlier data (!) will be inserted before the data point inside the Kinetica study. No data is lost, or transformed in this process. During an analysis, Kinetica will adjust calculations for the profile according to this status flag.



Object Description

Missing Specifies the import code to look for during the import process that signifies a data point is missing. The corresponding internal Kinetica flag for missing data (blank cell) will be inserted inside the Kinetica study. During an analysis, Kinetica will adjust calculations for the profile according to this status flag.


Data Column Identifies the incoming column that contains data that is flagged. This is linked to a column specified in the Status Column list.

Status Column

Identifies the incoming column that contains a data flag. This is linked to a column specified in the Data Column list. You can also enter a user-defined status code. This code can be a numerical value or one of the symbols provided in the Dictionary area of the dialog. If there are no symbols, the data is imported as usual. If there are symbols associated with the data, the symbol is imported into its own status column.

Note If your source file contain any of the above-mentioned identifiers (Undetectable, Error, etc.), enter the symbol(s) in the Dictionary list under the corresponding identifier. If you choose to replace one of the identifiers provided with a user-defined symbol, ensure that you copy the remaining system-provided markers into the row as well then press Enter. This action prompts Kinetica to insert the automatic data status flag before the indicated data point inside the Kinetica study.

11. Do not identify any flags for this example. Click Finish and the Import Assistant processes the incoming data. Once the



process is complete, the Import Assistant terminates and Kinetica displays the imported data.



The Import Data from a Database option enables you to import data from external databases to create new Kinetica studies. Only databases that support the Microsoft ODBC technology can be imported.

1. Load Kinetica.


Figure 5-41. Import Assistant – Importing from a Database

Importing Data from Databases



Figure 5-42. Import Assistant – Selecting a Source Database


Object Description

Available Views

Provides a visualization of the selected database views. This is useful for quickly understanding the incoming database structure.

Data Preview A check box that, if selected, displays a preview of the data contained in the currently selected database view.



To select a database to import data into Kinetica:

1. Click Select to call the Microsoft ODBC engine. The Select Data Source dialog appears.

Figure 5-43. Select Data Source – Machine Data Source Tab

2. Click the Machine Data Source tab. If ODBC has been installed on your PC and some Machine Data Sources have been created, you should see a list of drivers. Double-click on the Microsoft Access (Access) driver. If you do not see a list of drivers, or Access is not in your list, and ODBC has been installed, you can create one by clicking New and following the on-screen instructions.

Note At this point you will ascertain if ODBC has been installed with Microsoft Windows 95 or NT 4.0. If you can not proceed further than this point in the exercise, please consult your IT personnel or Microsoft Windows documentation for further information related to installing ODBC, and then try the exercise again.

Selecting a Database for Data Import



3. Once you have double-clicked on the Access machine data source the Login dialog appears if your database has security enabled. Databases are usually protected by passwords; however, for this exercise we did not protect the database. The Select Database dialog appears.

Figure 5-44. Select Database Dialog

4. Locate the Access database file called ImportAccess.mdb. This file is in the Kinetica\data subdirectory. The Import Assistant screen is updated with the database views found in the selected file. Some of these database views contain the time/concentration information required for import into a Kinetica study.



Figure 5-45. Import Assistant – Selecting a Database View

Figure 5-46. Import Assistant – Selecting a Source Database

5. Click Next. The next Import Assistant dialog appears, enabling you to drag and drop column names from the import file to the merge fields required to create Kinetica dataset names.



Figure 5-47. Import Assistant – Creating a Dataset Name


Object Description

Source Columns

Contains a list of the table names found in the selected database view. The table names in the Source Columns list box are organized in the order they appear in the database view. The first table name found (called DrugName in our example) is automatically placed in the first Merge Result field.

Merge Results Contains a maximum of five ordered fields where source columns can be dragged to create the final merged dataset name. The sixth field contains the label created as a result of a concatenation of the data contents found in each merge field.

6. Using your mouse, drag the DrugName field from the first Merge Results field to the second Merge Results field.

7. Drag the SbjName field from the Source Columns list to the first Merge Results field, which should be empty. Notice that the Dataset Name is now equal to "Sbj01-A" which



represents the first row of data. It is a concatenation of the subject and drug name identifiers in the import file. The dialog appears as follows:

Figure 5-48. Import Assistant – Creating a Dataset Name

Note Import Assistant places a hyphen (-) between the two field values for easier understanding once the data is opened inside Kinetica.

8. Click Next. The next Import Assistant dialog appears, enabling you to drag and drop table names from the import file to their worksheet/column destination inside the Kinetica study.



Figure 5-49. Import Assistant – Creating Worksheet Columns

We did not select a Kinetica template before calling the Import Assistant; therefore, the Kinetica study structure in this screen displays the objects found in the Normal.kdb structure (i.e. plasma worksheet with an X and Y column). If we had selected an existing template or file containing data, Import Assistant would display the objects of the selected structure.


Object Description

Source Columns

Contains a list of the table names found in the database view.

Dataset Columns

The Dataset Columns spreadsheet contains a destination column where the import column headers can be dragged to: (a) map incoming column data into pre-defined Kinetica template structure, or (b) map incoming column data into a user-defined Kinetica template structure.



9. Drag the Source column called SampleValue into the empty destination Source Column cell that is adjacent to the Y column.

You have specified to the Import Assistant that all imported time data should be placed in the X column, and all concentration data should be placed in the Y column during the import process (both will be found in the Kinetica sample Plasma worksheet). We call this process Column Mapping. The dialog appears as follows:

Figure 5-50. Import Assistant – Column Mapping Dialog



Figure 5-51. Import Assistant – Creating Kinetica Dataset Numeric and/or Text Fields


Object Description

Source Columns

Contains a list of the table names found in the database view.

Dataset Fields

The Dataset Fields spreadsheet contains destination numeric and text fields. The import column headers can be dragged to:

map incoming column data into pre-defined Kinetica template structure, or

map incoming column data into a user-defined Kinetica template structure.

A dataset field is typically a series of non-time dependent data values that are either imported as raw data or computed output during a Kinetica analysis. Examples of dataset fields include: AGE, WEIGHT, T1/2, Cmax, etc. In our example import file, the SbjName and DrugName fields are both text fields.



10. Drag the SbjName Source column into the empty destination Source Column cell in the Dataset fields area of the dialog, adjacent to the first empty Kinetica Text field column.

11. Drag the DrugName Source column into the next empty destination Source Column cell.

You have specified that all imported treatment and subject identifier data should be placed in the Kinetica study text fields called SbjName and DrugName, both found in the Kinetica Study pane. We call this process Field Mapping. The dialog appears as follows:

Figure 5-52. Import Assistant – Field Mapping



Figure 5-53. Import Assistant – Specifying Import Filter Identifiers


Object Description

Undetectable Specifies the import code to look for during the import process that signifies a data point is undetectable. The corresponding internal Kinetica flag for undetectable data (<) will be inserted before the data point inside the Kinetica study. No data is lost, or transformed in this process. During an analysis, Kinetica will adjust calculations for the profile according to this status flag.




Object Description



Data Column Identifies the incoming table name that contains flagged data. This is linked to a table name specified in the Status Column field.

Status Column


Note If your source file contain any of the above-mentioned identifiers (Undetectable, Error, etc.), enter the symbol(s) in the Dictionary list under the corresponding identifier. If you choose to replace one of the identifiers provided with a user-defined symbol, ensure that you copy the remaining system-provided markers into the row as well then press Enter. This action prompts Kinetica to insert the automatic data status flag before the indicated data point inside the Kinetica study.

12. Do not identify any flags for this example. Click Finish. The Import Assistant processes the incoming data. Once the



process is complete, the Import Assistant terminates and Kinetica displays the imported data.



To import data in a proprietary format into Kinetica:

1. Load Kinetica.


3. Select Proprietary format from the Source type list and click Next. The next Import Assistant dialog appears.

Figure 5-54. Import Assistant – Proprietary Format Import

4. Select the file you want to import and click Next.


• If you selected an .xpd file, select the file type from the Select XPD Type dialog and click OK.

• If you selected a .kdb file, proceed to Step 6.

The next Import Assistant dialog appears.

Importing Data in Proprietary Formats




6. Drag and drop column names from the available columns list to create the identifiers that will make up your dataset.

7. Click Next. The next Import Assistant dialog appears.

Note If you are importing Kinetica or P-Pharm data, Kinetica recognizes this structure so it is not necessary to drag the column names. The Subject name followed by the Drug Name should appear under the Merge Results list.

8. Further define your dataset by dragging and dropping names from the source columns list.

9. Click Next. The next Import Assistant dialog appears.

10. For this example, drag and drop Extravascular.T and Extravascular.C from the Source Columns list to the x and y columns in the Dataset columns area of the dialog.



Figure 5-56. Import Assistant – Creating Kinetica Worksheet Columns

11. Further define the dataset fields by creating the appropriate numeric and text fields.

Figure 5-57. Import Assistant – Defining the Dataset Fields



12. Click Next. The next Import Assistant dialog appears, enabling you to specify import filter identifiers to set automatic data status flags inside the Kinetica template.

Figure 5-58. Import Assistant – Specifying Import Filter Identifiers


Object Description

Undetectable Specifies the import code to look for during the import process that signifies a data point is undetectable. The corresponding internal Kinetica flag for undetectable data (<) will be inserted before the data point inside the Kinetica study. No data is lost, or transformed in this process. During an analysis, Kinetica will adjust calculations for the profile according to this status flag.



Object Description




Data Column Identifies the incoming table name that contains flagged data. This is linked to a table name specified in the Status Column field.

Status Column


Note If your source file contain any of the above-mentioned identifiers (Undetectable, Error, etc.), enter the symbol(s) in the Dictionary list under the corresponding identifier. If you choose to replace one of the identifiers



provided with a user-defined symbol, ensure that you copy the remaining system-provided markers into the row as well then press Enter. This action prompts Kinetica to insert the automatic data status flag before the indicated data point inside the Kinetica study.

13. Do not identify any flags for this example. Click Finish. The Import Assistant processes the incoming data. Once the process is complete, the Import Assistant terminates and Kinetica displays the imported data.



To import data from Watson LIMS:

1. Load Kinetica.


3. Select Watson from the Source type list and click Next. The Import Assistant dialog appears.


4. Select the Watson study you want to import from the available list and click Next. Kinetica will display the following screen through which you will select the data source.

Importing Data from Watson LIMS



Figure 5-60. Select Data Source Dialog

5. Select the appropriate data source from the screen and click OK. You will be prompted to logon to the Oracle database for Watson.

Figure 5-61. Oracle Logon Dialog

6. Select the Server Name using the drop down menu.

7. Enter your User Name and Password for the Oracle database and click OK. Kinetica will display the Import Assistant Window again, through which you simply select information you wish to import and click Finish.



Figure 5-62. Import Assistant Study Selection Window



Kinetica supports the import of data files from P-Pharm, Pharm-ABS, Pharm-NCA, SIPHAR (DOS); SIPHAR (Win), PCNonlin and WinNonlin.

Kinetica uses a private four-dimensional database for data storage that ensures security and data integrity. The database can handle simple and complex study structures. Many other software programs use an ASCII-based file structure that can be read by any text editor. This enables the successful import of data files into Kinetica.

To import xpd files:

1. Load Kinetica.

2. Select Open from the File menu. The Open File dialog appears. By default the Data subdirectory is displayed.

3. Navigate through the directory structure to find the directory where your .xpd files are stored.

Note Kinetica will not display any xpd files until you enter *.xpd in the File Name field.

4. Click Open. The window refreshes and displays the xpd files found in the selected directory.

5. Select the xpd file you want to import and click Open. The selected file is opened, renamed as a kdb file and displayed in the Kinetica workspace.

The original xpd file you opened is not altered or deleted. Kinetica makes a copy of the file and translates it into a kdb file.

When you are finished reviewing and/or editing the imported file, you can close it. At this point you are prompted to save the file. You must save the file in order to keep the imported copy of your data; otherwise Kinetica discards the file contents.

Importing Miscellaneous Data

Importing xpd Files



To import SIPHAR files (DOS or Win):

1. Load Kinetica.

2. Select the Macro Editor from the Study pane.

3. Select Open from the File menu to load the Import Siphar Dos.kmd.

4. Select Macro Editor from the Tools menu to execute the macro.

5. Navigate through the directory structure until you find the directory where your SIPHAR/DOS files (*.dB) are stored.

Note Kinetica will not display any of these files until you enter the file extension in the File Name field and click Open. The window refreshes and displays any Cipher/DOS files found in the selected directory.

6. Select the appropriate file and click Open. The selected file is opened, renamed as a kdb file and displayed in the Kinetica workspace. If a worksheet is not selected, a deletion will not occur.

7. When you are finished reviewing and/or editing the imported file, you can close it. At this point you are prompted to save the file. You must save the file now in order to keep the imported copy of your data; otherwise Kinetica discards the file and its contents.

Importing SIPHAR Files



Kinetica automatically translates PCNonlin data and model data (which is stored in the file) into the Kinetica structure, when the files are imported.

1. Launch Kinetica.

2. Select Macro from the Tools menu. The Macros dialog appears.

3. Click the Import PCNonlin button on the toolbar.

4. Click Run. The File Open dialog appears.

Note Kinetica will not display any *.CMD files until you enter the file extension in the File Name field and click Open. The window refreshes and displays any PCNonlin (*.cmd) files found in the selected directory.

5. Select the appropriate file and click Open. The selected file is opened, renamed as a kdb file and displayed in the Kinetica workspace. The original file you opened is not altered or deleted. Kinetica makes a copy of the file and translates it into a kdb file.

When you are finished reviewing and/or editing the imported file, you can close it. At this point you are prompted to save the file. You must save the file now in order to keep the imported copy of your data; otherwise Kinetica discards the file and its contents.

Importing PCNonlin Files



Use this option to export your data to external databases. Only data supporting Microsoft ODBC technology can be exported.

1. In Kinetica, from the Tools menu, select Assistants then Export. The Export to Database dialog appears.

Figure 5-63. Export Dialog to select the type of file

2. Select the file type for the export then click next.

3. Select the fields to export. The left hand box shows the study-level fields while the right hand box lists the worksheet column fields. Check the Append Worksheet Name box to include the worksheet name in the column header in the final exported file.

Exporting Data to External Databases



Figure 5-64. Select Field Dialog

4. Click Export.

5. Type the name of the outgoing file in the File name field.

Figure 5-65. Set Data file name Dialog



6. Click Save.

Kinetica offers two methods for automatically exporting data to Microsoft Word (Word) and Microsoft Excel (Excel). These options can be found on the menu bar as follows:

Figure 5-66. Export Options

Kinetica uses the Normal.dot that is loaded by default in your version of Word. The format of the exported results depends on how styles are defined in your Normal.dot file. The tabulation may look skewed when you export to Word. Adjust the tab stops in Word to reorganize the data, as required.

Note In order to export data to Excel successfully, ensure that you specify one symbol for decimals (e.g. “,”), and a different symbol for digit grouping (e.g. “.”). Symbols are defined in the Numbers tab of the Region Options dialog, located in your Windows Control Panel.

To export data to Word or Excel:

1. Load Microsoft Word or Excel.

2. Load Kinetica.

3. Run an analysis or open a dataset with raw data values.

4. Using the mouse, highlight the columns or rows you want to export from the sample view in the Dataset pane.

5. Choose Report Setup from the File menu. The Report Setup dialog appears.

Exporting Data to Microsoft Word and

Microsoft Excel



Figure 5-67. Report Setup Dialog

6. Click Set Destination or double click on the appropriate destination in the lower part of the dialog (in this case Word 97). The Open dialog appears.

7. Name the file and path and click OK to exit the dialog and return to the Report Setup dialog. The information is displayed in the Select Output area of the Report Setup dialog.

8. Highlight the required columns and variables and select Export to Word or Export to Excel from the File menu. If you are exporting to Word, you can also select one of the following Calculate buttons to generate a full report:

Figure 5-68. Calculate Buttons

9. Switch to Microsoft Word or Excel. The information you exported is displayed.



Kinetica writes specific information on the transformation of data into a log file. This information is copied to the Info view for each dataset (subject) after you run each analysis. If you modify specific data values and/or method options, Kinetica will automatically update the necessary parts of the information/log file.

The transformation of data into a log file is very useful because it gives you access (at all times) to the program during calculations. This facility is not a full audit trail but it does help you to understand the way the data was analyzed and details the revisions that have been made.

In addition to writing this information to the Info view, Kinetica also creates a log file of all the collective information on the study including the transformations of the data. This file is called report.txt and is automatically created as a text file in the Kinetica\Reports subdirectory.

To change the name of this file and the directory where it is stored:

1. Load Kinetica. Do not open any files.

2. Select Report Setup from the File menu. The Report Setup dialog appears.

Report Log Files



Figure 5-69. Report Setup Dialog

3. Click on the Report item in the Select Output area of the dialog.

4. Click Set Destination. The Open dialog appears displaying the Kinetica\Reports directory.

5. Navigate to the appropriate directory and enter the name of the file in the File Name field.



Notes


6. Graphs in Kinetica

Kinetica offers a variety of ways to examine and present your data graphically. All graphs can be incorporated into Microsoft Office applications.

Graphs in Kinetica


Kinetica graphs can be created manually or automatically. You can generate standard graphs or insert a graph method. This inserted method type of graph will be plotted each time you run an analysis. Many graph attributes can be modified; outlier and non-detectable data points can be flagged. You can create your own libraries of standardized graph templates and quickly apply them to single or multiple graphs.

Kinetica includes a powerful graph gallery which enables you to collect, edit and save multiple sets of graphs simultaneously. Graphs can be reorganized inside the gallery, previewed before printing, saved to the Graph Gallery, and/or exported to Word.

Working with Graphs

Graphs in Kinetica


With a .kdb file is open in Kinetica, you can start the Chart Wizard by selecting Tools from the menu bar, then Chart Wizard:

Figure 6-1. Opening Chart Wizard from the Kinetica Tools menu

Once the option is selected, you will be prompted that the study is being opened and that the Study Tree is being created.

The first step in the Chart Wizard is to select the columns to be plotted. From the directory tree in the left pane of the Chart Wizard, drag and drop the column names for the data to be plotted along the x-axis and y-axis into the X Variable and Y Variable boxes, respectively.

You may also use Chart Wizard to plot the standard deviation of your data columns, displayed as error bars on your final chart. To include standard deviation data, each Y-variable MUST have a corresponding Up and Down value defined in the dataset. Drag the column representing the upward error to the Up box and drag the column for the downward error to the Down box.

Starting the Chart Wizard

Chart Wizard Step 1: Select X and Y Axis,

Mean Curve and Overlay Plot

Graphs in Kinetica


Figure 6-2. Chart Wizard Step 1 – Selecting data columns for the X and Y axes, standard deviation in Y.

If you check the Create Mean Curve checkbox, you will be presented with an option to group the data by dragging and dropping any variable presented on the left window.

Graphs in Kinetica


Figure 6-3. Chart Wizard Step 1 – Create a Mean Curve

Chart wizard allows you to have up to five sets of X-Y data overlaid on a single plot. Your plot can contain two separate Y-scales; one on the left side of the plot and one on the right.

Graphs in Kinetica


Figure 6-4. Chart Wizard Step 1– Create Overlay Plot

Note If you check the box to make an overlay plot you must provide the X and Y variable names in order for the plot to be generated.

The X-variable often has discontinuities or large gaps. For example, a data set could only have sampling data from the first few minutes after each dose that is given once a day. In such a case, when there are large gaps in the time values, creating a line-graph that joins all of the data points would be undesirable. The “Threshold for discontinued X-axis” option enables you to not join Y-variable data points that have relatively large gaps in their corresponding X-axis data. You can choose to provide a value for the threshold at which the plot will display a discontinuity (by placing a point in the X-axis) or let Chart Wizard determine the same.

Graphs in Kinetica


Figure 6-5. Chart Wizard Step 1 – Threshold for Discontinued X-Axis

Note The Chart Wizard does not check the validity of what you are trying to graph. You need to make sure that you select the correct variables and the correct settings to allow the Chart Wizard to produce the expected results.

Chart Wizard allows you to select the option to Stagger Plots on a Graph. When Y variable data for the different datasets are very close to each other, the resulting plot may be difficult to read. The Stagger Plots option separates the plots so that they are easier to distinguish from one another. You may choose to let Chart Wizard to determine the spacing for the staggered graph for successive datasets, or you may select the Manual Override option. When using the Manual Override option, you must provide a value in the available text box that will be added to each X-value for the staggered graph (see figure below).

Graphs in Kinetica


Figure 6-6. Chart Wizard Step 1– Stagger Plots on a Graph

Once you have finished setting the appropriate parameters for your graph, click Next to proceed to Step 2 of the Chart Wizard.

Graphs in Kinetica


The second step of the Chart Wizard asks you to select which datasets you wish to see on your graph. You may select all datasets with the Select All checkbox, or choose individual datasets by clicking on the dataset name. Multiple datasets may be selected by pressing the CTRL key while clicking on the dataset names.

Figure 6-7. Chart Wizard Step 2 – Selecting a Dataset

Once you have selected the appropriate datasets, click Next to proceed to Step 3 of the Chart Wizard.

Chart Wizard Step 2: Selecting Datasets of

Interest

Graphs in Kinetica


The third step of the Chart Wizard allows you to set the sorting criteria. Select the sort criteria from the left pane using the drag and drop function.

Figure 6-8. Chart Wizard Step 3 – Selecting Grouping Criteria

Variables can be removed by selecting the variable and clicking the Remove Selected button. The button is not active unless a variable has been selected from the existing list.

Chart Wizard Step 3: Selecting Sort Criteria

Graphs in Kinetica


The fourth step of the Chart Wizard allows you to select filters to be applied to the results by constructing logical AND/OR conditions. Below is the default view of Step 4 (without having selected an Overlay option in Step 1).

Figure 6-9. Chart Wizard Step 4 – Selecting Filters

1. Select the appropriate variables by using the drag and drop function.

2. Select the appropriate operator using the drop down menu provided.

3. Select a value from the drop down menu following the selected operator or, if none exist, enter a value into the text box provided.

Chart Wizard – Step 4 Selecting Filters

Graphs in Kinetica


Note This menu is populated based on the Variable chosen. Not all Variables will have information to populate this menu.

4. Select the AND of OR button to add the condition to the text field below.

5. Repeat the steps above until all of your conditions have been entered.

6. Click Next to proceed to Step 5 of the Chart Wizard.

Note You can remove any Conditions by selecting the Condition and then clicking on the Remove button.

If you had chosen the Overlay option at the Step 1, Step 4 will appear as follows:

Graphs in Kinetica


Figure 6-10. Chart Wizard Step 4 – Selecting Filters - Overlay Example

Follow the same steps as stated above when creating an Overlay Plot filter. The application does not require that you enter filter information in the Overlay Plot section included on this screen. When filter information has been entered successfully, click Next to proceed to Step 5 of the Chart Wizard.

Graphs in Kinetica


The fifth and final step of the Chart Wizard sets the chart title and/or subtitle for the graph you are creating. Enter the title into the text box provided and click the Chart button.

Figure 6-11. Chart Wizard Step 5 – Setting Chart Properties

Clicking the Chart button completes the chart as designed through the Chart Wizard. The following image is an example of a completed chart. The vertical lines in the graph represent the Standard Deviation.

Chart Wizard Step 5: Setting Chart Properties

Graphs in Kinetica


Figure 6-12. Example 1– A finished chart.

From the left side of the chart window you can toggle specific plots on and off by selecting the check box next to the dataset name or show all plots by checking the Show All checkbox.

The Error Bars check box allows you turn the Standard Deviation error bars on or off.

The Reset button returns the chart view to the original settings.

The Finish button closes Chart Wizard and gives you the option to save your settings prior to closing. If you choose to save the settings, the chart will automatically reopen when you select Tools | Chart Wizard.

Graphs in Kinetica


You may send the graph to the Kinetica Gallery by right-clicking in the graph area of the Chart Wizard and selecting Send Graph to Kinetica Gallery.

Figure 6-13. Chart Wizard Pop-up Menu

You may choose to send all graphs in Chart Wizard to the Kinetica Gallery by selecting Send All Graphs to Kinetica Gallery from the pop-up menu.

Note Not all of the formatting features in the Chart Wizard plot are transferred to the graph in the Kinetica gallery, because of inherent differences between the Kinetica graphs and Chart Wizard plots.

Sending the Graph to Kinetica Gallery

Graphs in Kinetica


A hot graph is a graph built within Kinetica. These graphs are linked to data present in your study and appear in Views within the Kinetica panes. Each time your data changes, these graphs are automatically updated. Kinetica includes three hot graphs:

• Dataset Graph

• Spaghetti Plot

• Mean Curve

The Dataset Graph is found in the Dataset pane; the Spaghetti Plot and Mean Curve are located in the Study pane. To access the graph(s), click on the corresponding icon and Kinetica will display the graph. Below is an example of the Dataset Graph.

Figure 6-14. Hot Graph Example - Dataset Graph

Hot Graphs

Graphs in Kinetica


The Dataset graph is hot-linked to time-series data. By default, when a study file is opened, the plot uses the left two columns for the first subject, found in the first sample matrix, to create the graph. The view appears as follows:

Figure 6-15. Example - Dataset Graph

A right-click anywhere on the Dataset Graph displays the following menu options:

Figure 6-16. Dataset Graph Menu Options

The options are described in the following table.

Dataset Graph

Dataset Graph Options

Graphs in Kinetica


Option Description

Select X,Y Displays the Select Data dialog that enables you to over-ride the columns used to display the Dataset Graph All Variables computed during an analysis.

The Column X and Column Y list boxes display all available matrices and columns within the study. Select a Matrix.ColumnName for both the X and Y columns, click OK, and the Dataset Graph is updated.

Note: X and Y must be from the same worksheet.

Graph Properties

Displays a cascade menu.

Show Sets Legend Show/hide the legend associated with the plot

Show Points Legend

Show/hide the points legends associated with the description of the markers

Add free comment You can add a text comment to your graph.

Profile Properties You can modify a datapoint symbol, status, line, AUC and error bars. Once the Profile Properties dialog is open, modifications can be applied to the selected dataset and adjustments can be made to the remaining datasets by scrolling through the graphs. You can select Properties to access more settings. Ensure that you click Apply then OK to save your selections.

Graphs in Kinetica


Option Description

Note The Common properties check box is used to set the marker and line color to black instead of blue and red.

Send to Gallery Once Send to Gallery option is selected, you can choose to send the current dataset graph or all dataset graphs to the Gallery.

Save As Graph Template

Saves the current style to a graph template file (filename extension of a graph template file is *.kgr).

Load Graph Template

Applies the style in an existing graph template file to the selected

Save Graph As Saves the graph in Bitmap (*.bmp), Metafile (*.wmf) or JPEG (*.jpg) formats.

Apply Axis Scale to All Dataset Graphs

Selecting this feature allows the axis scale options selected for one dataset graph applied to all existing dataset graphs in a study.

Note The axis scale options have to be accessed first before selecting this feature. Right click on either axis to see the list of options. Once the adjustments are made, right click again anywhere on the graph and select Apply Axis Scale to All Dataset Graphs.

If there are multiple subjects in your study, you can use the dataset scrollbar in conjunction with this graph view to browse the dataset graphs within the current study. The dataset scroll bar can be found on the Kinetica toolbar.

Graphs in Kinetica


Figure 6-17. Dataset Scrollbar

Graphs in Kinetica


The Spaghetti Plot is hot-linked to time-series data. By default, when a study file is opened this plot uses the left two columns for all subjects, found in the first sample matrix, to create the graph. A legend is plotted to the right of the graph to identify the individual subject data. The view appears as follows:

Figure 6-18. Example - Spaghetti Plot

Spaghetti Plot

Graphs in Kinetica


A right-click anywhere on the Spaghetti Plot displays the following menu options:

Figure 6-19. Spaghetti Plot Menu Options

For a description of these options, see this chapter, section, “Dataset Graph Options.”

Spaghetti Plot Options

Graphs in Kinetica


You can now access the LZ Graph from Methods | LZ Graph:

Figure 6-20. LZ Graph

LZ Graph

Graphs in Kinetica


The Mean Curve is hot-linked to time-series data. By default, when a study file is opened this plot uses the left two columns for all subjects, found in the first sample matrix, to compute the mean graph.

Kinetica includes an option for plotting Mean Curves and at the same time outputs the relevant computed statistics and summary table. The Mean Curve dialog enables you to plot three different mean curves on the same plot (overlay graph) or a mean curve by Group. A Mean Curve graphical view linked to the data in real-time is also provided in the Study pane. The view appears as follows:

Figure 6-21. Mean Curve Chart

Mean Curve

Graphs in Kinetica


A right-click anywhere on this graph displays a series of menu options:

Figure 6-22. Mean Curve Menu Options

The options are explained in the following table.

Option Description

Mean Curve

Standard: Displays the Mean Curve dialog.

This dialog is used to override the number of overlaid graphs (maximum is three), the columns used to display the mean curve, the mean type, the error type, and the title.

By Group: Displays the Mean Curve by Group dialog.

This dialog is used to override the columns used to display the mean curve, the group identifier, the mean type, the error type, and the title.

For a description of the remaining options, see this chapter, section “Dataset Graph Options.”

Mean Curve Options

Graphs in Kinetica


Kinetica has a “hot graph” mean curve that is linked to the data in real-time. As the data changes, Kinetica amends the mean curve within this view. You can plot up to three different mean curves on an overlay and create a mean curve by group.

No statistics report is available with this hot view.

To plot mean curves:

1. Launch Kinetica.

2. Click Open from the File menu.

3. Select the Group Table.kdb file, found in the Kinetica\example\statistics subdirectory.

4. Click OK. Notice that the file contains data for 24 subjects across two treatments (two-way crossover).

5. Select the Study pane.

6. Click the Mean Curve item. The mean curve appears in the view.

Plotting Mean Curves without Statistics

Graphs in Kinetica


Figure 6-23. Example – Mean Curve Display

Try changing some of the data values in the Dataset pane and return to the mean curve to see the updated graph.

To plot mean curves by group without statistics:

1. Complete the procedure for plotting mean curves without statistics (see this chapter, section, “Plotting Mean Curves without Statistics”).

2. Right-click on the graph and select Mean Curve then By Group from the popup menu. The Mean Graph by Group dialog appears.

3. You can select the group and sort variables by clicking on them. You can select multiple group and sort variables by pressing the Ctrl key, while you click on each desired variable.

Plotting Mean Curves by Group without Statistics

Graphs in Kinetica


4. You can insert variables into the Group Label or Graph Title by using ‘&’:

5. The Mean type for the graph can be set to Mean (arithmetic mean), Geometric Mean, or Harmonic Mean.

6. The Error type can also be specified to be SD, SEM, or None.

7. You can type in a Graph Title, leave it blank, or select the Kinetica default title.

8. The created plot can then be saved to a gallery, by setting the Write to Saved Gallery option.

Figure 6-24. Mean Curve by Group Dialog

9. Specify the various options, as required and click OK. The plot appears.

Graphs in Kinetica


Figure 6-25. Example – Mean Curve by Group

Note This graph is hot and will therefore change as your data changes.

Kinetica can also create mean curves using a menu option. The generated graphs are sent to the Graph Gallery. The graphs are created with full statistics that can be sent to Word if the correct Report Setup option has been specified. These are not hot graphs.

Plotting Mean Curves with Statistics

Graphs in Kinetica


In the example below we will plot a standard mean curve using 24 datasets via the menu option that creates an output report on the computed statistics. This option allows you to plot up to three different mean curves on an overlay graph.

1. Launch Kinetica.

2. Click Open from the File menu.

3. Select the Group Table.kdb file, found in the \kinetica\example\statistics subdirectory.

4. Click OK. Note that the file contains data for 24 subjects across two treatments (two-way crossover).

5. Select the Study pane.

6. Select Mean Curve and then Standard from the Statistics menu. The Mean Curve dialog appears.

Figure 6-26. Mean Curve Dialog

Plotting Standard or Overlay Mean Curves

with Statistics

Graphs in Kinetica


7. Select Plasma.Time in the X = list box located in the First Graph area of the dialog.

8. Select Plasma.Conc from the Y = list box located in the First Graph area of the dialog.

9. Select Mean in the Mean Type area of the dialog.

10. Select SD in the Error Type area of the dialog.

11. Enter a title in the Graph Title field and click OK.

The mean curve is displayed with error bars in the Graph Gallery. The respective mean statistics (as a summary) are written in the Info worksheet of the Study pane. The mean table results and table will output to Word automatically if you selected Word in the Report Setup option.

Graphs in Kinetica


You can access the Mean Curve By Group function through Statistics>Mean Curve>By Group:

Figure 6-27. Mean Curve by Group Dialog

1. You can select the group and sort variables by clicking on them. You can select multiple group and sort variables by pressing the Ctrl key, while you click on each desired variable.


3. The Mean type for the graph can be set to Mean (arithmetic mean), Geometric Mean, or Harmonic Mean.

4. The Error type can also be specified to be SD, SEM, or None.

5. You can type in a Graph Title, leave it blank, or select the Kinetica default title.

Plotting Mean Curves by Group with Statistics

Graphs in Kinetica


6. The created plot can then be saved to a gallery, by setting the Write to Saved Gallery option.


Figure 6-28. Example – Inserting Variables into the Group Label

Graphs in Kinetica


Figure 6-29. Example – Inserting Variables into the Graph Title

Figure 6-30. Example of Variables Inserted into Group Label and Graph Title

Graphs in Kinetica


You must define the basic attributes for a manual graph before it can be plotted. This is more time consuming than using the automated options for a graph display, however, it gives you the flexibility to easily select the data. You can select a maximum of three axes. The first axis is always the X-axis. If you choose two axes, the second axis is the Y-axis. If you choose three axes, the second axis is Y1 and the third axis is Y3.

This dialog is accessed by selecting Select Dataset Graphs from the View menu. A description of the dialog is provided in the following table.

Column Description

Datasets Displays a list of all the available datasets in the study

Column X Displays a list of all the available columns found across all sample worksheets in the study

Column Y Displays a list of all the available columns found across all sample worksheets in the study

To plot a scatter graph in log scale with customized titles for two datasets in a study (example):

1. Launch Kinetica.

2. Click New from the File menu.

3. Enter the following data using the default columns on the default Plasma worksheet in the Dataset pane:

Note In Kinetica, units always appear in the first cell of each column.

X Y

h ng/mL

Plotting a Graph Manually

Dataset Graph Dialog

Graphs in Kinetica


X Y

0 0

0.5 04.2356

1 10.2419

1.5 24.0354

2 31.9365

3 37.0409

6 32.4406

9 21.3285

12 15.0597

18 826494

24 2.01253

4. Select Select Dataset Graphs from the View menu. The Dataset Graph dialog appears; displaying the study datasets and respective columns (we only have one dataset in this study).

5. Select Dataset 1, Plasma.X, and Plasma.Y.

6. Click OK. The graph is displayed.

Now try to create a graph with multiple datasets on the same plot by inserting more data into the study. You can continue exploring the available editing options by right clicking anywhere on the graph and selecting Graph Properties.

Graphs in Kinetica


There are two ways to plot a graph automatically:

• Use the graph buttons found on the toolbar

• By inserting a Graph Method.

The toolbar contains the following buttons for plotting graphs:

Show one graph: Plots the selected column for the current dataset

Show multiple graphs: Plots the selected columns for all datasets in the study

These buttons allow you to view different plots very quickly. They are only activated when you highlight specific columns found in certain views. There are pre-defined rules the program uses when a graph is plotted, depending on how many columns are selected, and the column contents. The rules are explained in the following table.

Situation Result

Data columns empty and highlighted

If you highlight an empty column and select the either Show One Graph or Show Multiple Graph buttons on the toolbar, the graph view displays “Graph Not Available.”

One data column highlighted

If you highlight one column with data, the only button activated is the Show One Graph button. By default, the plot generated is a histogram for the current dataset.

Plotting a Graph Automatically

Graph Buttons

Graphs in Kinetica


Situation Result

Two data columns highlighted

If you highlight two columns with data, both graph buttons are activated. By default, the plot generated is a scatter plot with the points joined by a line. If you click on the Show One Graph button, a plot for the current dataset only is displayed. If you click on the Show all graphs button, a plot for all datasets found in the study is displayed.

Three data columns highlighted

If you highlight three columns with data, only the Show One Graph button is activated. By default, an overlay plot is displayed with a left, bottom and right axis. This applies to the current dataset only and the result depends on the data you selected.

You can change the attributes of any graph in the Profile Properties dialog.

Graphs in Kinetica


You can insert a Graph Method so that when you run an analysis, the graph(s) are automatically displayed. This can be performed in both batch and individual modes.

Kinetica includes several predefined graph methods, which are described in the following table.

Method Graph Plotted

Mean Curve Plot the average of Y versus X and estimates the distribution of the Y value

Mean Curve by Group

Plot the average of Y versus X and estimates the distribution of the Y value. Grouping and sorting are enabled to group individuals based on a parameter within a graph or to separate them to different graphs based on a common parameter, respectively.

XY_Graph Plots a graph of selected X and Y columns

XYY_Graph Plots a graph of selected X, Y and Y1 columns as an overlay

X1Y1_X2Y2_Graph Plots a graph of two simultaneous X and Y columns

XYError Graph Plots an error graph using pre-selected X Y columns and the ‘up and down’ Error bars.

Curve Extrapolation Plots a graph showing the slope of the terminal phase of the selected X and Y columns

To insert a graph method:

1. Select Method from the Insert menu. The Method Selection dialog appears.

Inserting a Graph Method

Graphs in Kinetica


Figure 6-31. Insert > Method Menu item

2. Select one of the graph methods from the available list of hard-coded methods.


3. Enter the input columns under the “User names” column on the right hand side of the Method dialog. To do so, click in the corresponding text field to display the drop down menu as shown in the image below.

Graphs in Kinetica


Figure 6-33. Input Columns Selection

4. Click Insert and click OK to exit the dialog. The graph method is inserted into the study.

5. Click Calculate one from the Dataset menu on the toolbar or

click the icon to run the analysis. The results are calculated and displayed in the dataset view. Kinetica will also automatically display the graph generated by the method in the Graph Gallery. If you are running in batch mode, a graph for each dataset is inserted into the Graph Gallery.

Graphs in Kinetica


Figure 6-34. Gallery View

To modify graph attributes:

1. Right click the graph and select All Properties from the popup menu. The Profile Properties dialog appears.

Figure 6-35. Profile Properties Dialog

2. Modify the selections, as necessary. You can then apply your changes to all graphs in the gallery.

Modifying graph attributes

Graphs in Kinetica


You can modify X and Y axes, ticks, font numbers, spacing, labels/titles and grid by right-clicking on either graph axis and selecting “All properties” from the popup menu.

Figure 6-36. All Properties Dialog

The All properties options are described in the following table.

Option Description

Ticks Modify tick length and tick position.

Axis Activate or deactivate the log scale, specify the minimum, maximum, increment and sub-ticks.

Numbers Modify font sizes and/or position numbers at a 45-degree angle.

Space Between

Adjust the distance between the axis and plot, ticks and numbers, numbers and label.

Label Enter a label/name for the selected axis and adjust the font size.

Note You can modify the title of a graph, font, and/or alignment by right clicking on the graph title.

Modifying Graphs

Graphs in Kinetica


Option Description

Grid Modify the background of a plot.

Note A few of the options, including Log scale, Labels and Tick, can be accessed without using All Properties. Simply right click on the appropriate axis and select the required item.

Graphs in Kinetica


Kinetica has a built-in feature for flagging outlier data. The program ignores any flagged outlier values found in the dataset and uses an interpolation rule to join the preceding and succeeding points. Outlier data points are identified in Kinetica with an exclamation mark (!) before the suspected cell value in the spreadsheet.

To display outlier data points:

1. Click Open from the Kinetica File menu.

2. Select the Extravascular.kdb file, found in the Kinetica\data\non compartmental subdirectory.

3. Click OK.

4. Enter an exclamation mark (!) before the C value at time 32.17 hours.

Figure 6-37. Example – Identified Outlier Data Point

5. Press the Enter key.

Displaying Outlier Data

Graphs in Kinetica


6. Switch to the Dataset Graph view found in the Dataset pane. Notice the outlier point (the last data point) we specified is marked.

Figure 6-38. Example – Dataset Graph with Outlier Point Specified

Note The outlier data point is marked with an empty circle in our example but this symbol can be changed in the Profile Properties dialog. Notice that the line does not pass through the outlier point.

7. Run the analysis for this subject. The calculations are executed and a graph of the terminal phase regression is sent to the Gallery.

Graphs in Kinetica


Figure 6-39. Example – Terminal Phase Regression Graph

Note Our flagged outlier point remains visible. You can re-include this point in the analysis by returning to the Dataset group and deleting the exclamation mark.

Graphs in Kinetica


Kinetica has a built-in feature for flagging BLQ (below limit of quantification) data points. BLQ data points are identified in Kinetica with a less-than sign (<) before the suspected cell value in the spreadsheet. Kinetica does not ignore non-detectable values found in the dataset when displaying a graph and does not use an interpolation rule to join the preceding and succeeding points. Instead Kinetica displays the true line through all points and highlights the data point with a circle.

To plot a graph with a non-detectable point displayed:


2. Select the Extravascular.kdb file, found in the \kinetica\data\non compartmental subdirectory.

3. Click OK.

4. Enter a less-than sign (<) before the last C value which is at time 32.17 hours.

5. Press the Enter key.

6. Switch to the Dataset Graph view found in the Dataset pane.

Displaying Non-Detectable Data

Points

Graphs in Kinetica


Figure 6-40. Dataset Graph View

Note The non-detectable data point is marked with an X-marked circle in our example. This symbol can be changed using the Profile Properties dialog. If your non-detectable symbol is different, check the Markers tab in the Profile Properties dialog.

7. Run the analysis for this subject. The calculations are executed and a graph of the terminal phase regression is sent to the Gallery.

Graphs in Kinetica


Figure 6-41. Terminal Phase Regression Graph

Note Our flagged data point remains visible. You can re-include this point in the analysis by returning to the Dataset pane and deleting the less-than sign.

Graphs in Kinetica


The graph method plots the average of Y versus X and estimates the distribution of the Y value. Grouping and sorting are enabled, respectively, to group individuals based on a parameter within a graph, or to separate them into different graphs based on a common parameter. The mean curve by group method is designed to be incorporated into other methodologies as a template. Global options allow you to set graphical properties to standardize your graphical reports. To create a graph template:

1. Open the 2WayCrossOver.kdb file, found in the \kinetica\data subdirectory (include the sample data).

2. Insert the Mean Curve by Group method by clicking on the insert method icon.

3. Set the method after the available method. Map X to Extravascular.T and Y to Extravascular.C

Figure 6-42. Method Selection dialog

4. Click Insert then OK.

Mean Curve by Group Method

Graphs in Kinetica


5. Select the Method pane on the left navigation bar and select the Global Option for Mean Curve by Group.

6. In the Mean Curve by Group dialog, under the Mean Curve by Group tab, select DrugName for Group variables, Mean for Mean Type and SD for Error Type. Type “Summary Statistical Profile” for Graph Title.

Figure 6-43. Mean Curve by Group dialog

7. Select the Graph Properties tab and uncheck the Use Kinetica Defaults box.

8. Select your preferred symbol, pattern, symbol color, line color and line size for the first five graphs. Check the Show Error Bars box. Set both the Symbol size and the Error Bar Size to 4.

Graphs in Kinetica


Figure 6-44. Mean Curve by Group dialog, Graph Properties tab selected

9. Click OK.

10. Click the Calculate All button, found on the Kinetica toolbar and examine the mean curve in the gallery.

Graphs in Kinetica


Figure 6-45. Mean curve generated from the mean curve by group method

Graphs in Kinetica


Graph templates offer standardization for the way graphs appear each time they are plotted. You can create as many graph templates as you require in Kinetica.

To create a graph template:

1. Select the Extravascular.kdb file, found in the \kinetica\data\non compartmental subdirectory (include the sample data).

2. Click the Calculate All button, found on the Kinetica toolbar and the Lz plot appears. By default, it is always plotted using the log scale inside the Graph Gallery.

3. Right-click the X-axis on the Dataset Graph (under Dataset pane).

4. Deactivate the log scale by deselecting the Log option.

5. Repeat Steps 4 and 5 for the Y-axis.

6. Click OK. The graph is updated.

7. Right-click on the plot and select Save As Graph Template from the popup menu. The Save As dialog box appears. By default Kinetica prompts you to save graph templates in the Kinetica\graph subdirectory with the *.kgr suffix. You can enter the file name Test.kgr for your template in a different folder other than the default subdirectory.

8. Click Save.

Creating Graph Templates

Graphs in Kinetica


To apply a custom graph template to another graph of the same type:

1. Right click and select Load Graph Template. The Apply Graph Properties dialog appears.

Figure 6-46. Apply Graph Properties Dialog

2. Select the changes you would like to apply to other graphs and click OK to exit the dialog.

Note The Save As Graph Template and Load Graph Template can only be accessed from the Lz plot if the graph is viewed in its minimum size. Double click to switch between minimize and maximize view.

Applying a Custom Template to Other Graphs

of the Same Type

Graphs in Kinetica


To apply a graph template:

1. Select any graph in the Graph gallery.

2. Right click and select Load Graph Template from the popup menu. The Open dialog appears. By default Kinetica displays all graph templates present in the Kinetica\graph subdirectory with the .kgr suffix.

Note This procedure can be accessed only if the graph is viewed in its minimum size. Double-click to switch from minimum to maximum view.

3. Select a .kgr template and click Open. Kinetica loads the template and applies it to the selected graph. The following message appears: “Apply current properties to all graphs with the same type?”

Figure 6-47. Apply Template Dialog


• Select Yes. Kinetica applies the styles found within your template to every graph in the gallery that has the same type e.g. Scatter.

• Select No. Kinetica applies the styles found within your template to the selected graph only.

Applying Graph Templates

Graphs in Kinetica


The Kinetica Graph Gallery is a powerful feature for batch graph viewing, editing and processing. This utility enables you to visualize many graphs as thumbnails or a range of different zoom sizes. Graph properties can be changed on specific plots and applied to every graph with the same type in the gallery. This feature enhances productivity and removes tedious graph management problems.

Graphs generated automatically during an analysis via methods are sent to the Gallery by default allowing batch processing of their graphical properties. Any other graph created manually in Kinetica or any of the graph views can be sent to the gallery by right clicking a graph and selecting Send to Gallery from the popup menu. When graphs are present in the gallery, the view appears as follows:

Figure 6-48. Graph Presentation in the Gallery

Working with the Graph Gallery

Gallery Interface

Graphs in Kinetica


Horizontal and Vertical scrollbars allow you to navigate through the gallery. These scrollbars appear automatically depending on the number of graphs within the current window size. You can control graph size by using the Zoom In, Zoom Out, and 100% Kinetica toolbar buttons. The graphs appear in columns that can be resized using the mouse.

You can access the Gallery options via a right-click menu. The options available when the plot viewed is minimized are described in the following table.

Option Description

Graph Properties

Displays the Profile Properties dialog. You can set graphical attributes, such as:

X-axis: access and adjust the X-axis.

Y-axis: access and adjust the Y-axis.

Profile Properties: Modify a data point symbol, status and line, AUC and error bars. Once the Profile Properties dialog box is open, modifications are applied by selecting the Common properties check box. Click Properties to access more options. After changes are made, a message appears asking if you want to apply the same changes to the remaining datasets in a study.

Show Sets Legend and Show Points Legend: Display or hide dataset graph legends by selecting (a check mark should be present) or deselecting this option.

Maximize/ Minimize

Maximize the selected graph to fill the complete gallery window

Save As Graph Template

Save a graph template for standardizing the way graphs appear each time they are plotted

Graphs in Kinetica


Option Description

Load Graph Template

Apply a saved template to the current graph. When a template is selected, you are prompted to apply your selected styles to all graphs with the same type in the Gallery.

View Selected Gallery

Highlight a column or selection of graphs and creates a separate Gallery.

Number of Columns…

Specify the number of columns in the Gallery

Save Graph As Save the graph in Bitmap (*.bmp), Metafile (*.wmf) or JPEG (*.jpg) format.

The options available when the plot viewed is maximized are described in the following table.

Option Description

Show sets legend

Show or hide the dataset legend by selecting or deselecting this option.

Show status legend

Show or hide dataset status legend by selecting or deselecting this option.

Add free comment

Enter text information on the graph.

All properties

Modify a data point symbol, status and line, AUC and error bars for a particular dataset or for all datasets in a study.

You can save a gallery to a stand-alone file with the .kgg suffix.

1. From Kinetica, open the Group Table.kdb file found in the Kinetica\example\statistics subdirectory. This file contains the data for 24 datasets.

2. Click Methods in the Methods pane.

Saving a Gallery to File

Graphs in Kinetica


3. Locate AUC∗ method.

4. Click Set under the Global Options column. The AUC * Method Global Options dialog appears.

Figure 6-49. AUC * Method Options Dialog

5. Select the Show Lz Plot check box and click OK. We have directed Kinetica to plot a graph of the terminal phase regression for each dataset in the study.

6. Click the Calculate All button on the Kinetica toolbar. A plot appears inside the Graph Gallery for each dataset in the study. You now have 24 graphs.

7. Select Save Gallery then To KGG from the File menu. The Save As dialog appears. By default, Kinetica prompts you to

Graphs in Kinetica


save your gallery in the \kinetica\graph subdirectory with the *.kgg suffix.

8. Enter the filename Gallery1.kgg and click Save. If the graph is saved to the current KDB file, you must save the KDB file as well.

Note This procedure can only be performed if the graph is viewed in minimum size. Double click to switch between minimize and maximize view.

You can save a gallery inside a Kinetica study (KDB).


2. Open the Group Table.kdb file, found in the Kinetica\example\statistics subdirectory. Notice that the file contains data for 24 subjects across two treatments (two way cross-over).

3. Click Methods in the Methods pane.

4. Locate AUC* method.

5. Click Set under the Global Options column. The AUC* Method Global Options dialog appears.

Saving a Gallery Inside a KDB File

Graphs in Kinetica


Figure 6-50. AUC* Method Global Options Dialog

6. Select the Show Lz Plot checkbox and click OK. We have directed Kinetica to plot a graph of the terminal phase regression for each dataset in the study.

7. Click the Calculate All button on the Kinetica toolbar. A plot appears inside the Graph Gallery for each dataset in the study. You now have 24 graphs.

8. Select Save Gallery then To Current KDB File from the File menu. The Save Gallery to Kinetica File dialog appears.

Graphs in Kinetica


Figure 6-51. Save Gallery to Kinetica File Dialog

9. Enter the name My Gallery in the Gallery Name field and click OK. The gallery and graph is now embedded inside the Group Table.kdb file.

Note The Gallery list in the Save Gallery to Kinetica File dialog contains all embedded gallery names. You can save as many galleries as you like inside a KDB file. If you want to delete a gallery from a KDB file, select Embedded Objects then Graph Galleries from the Tools menu.

Once a gallery has been saved to a stand-alone file with the .kgg suffix, it can be re-opened inside Kinetica at a later date.

1. Load Kinetica.

2. Click New from the File menu.

3. Select Open Gallery then From KGG File from the File menu. The Open dialog appears. By default Kinetica prompts you to open your gallery from the \kinetica\graph subdirectory with the .kgg suffix.

Opening a Gallery from a KGG File

Graphs in Kinetica


4. Select a gallery file and click Open.

Note This procedure can only be performed if the graph is viewed in minimum size. Double click to switch between minimize and maximize view.

Once a gallery has been embedded inside the active Kinetica study it appears automatically inside the KDB file until it is deleted at a later date. Simply open a KDB file and switch to the Gallery pane to see any embedded galleries.

Galleries saved inside Kinetica database files (KDB) can be deleted.

1. From Kinetica open the Group Table.kdb file, found in the Kinetica\example\statistics subdirectory. This file contains the data for 24 datasets and an embedded gallery file called “My Gallery” - if you completed the example procedure for saving a gallery inside a KDB file (see this chapter, section, “Saving a Gallery Inside a KDB File”).

2. Select Embedded Objects then Graph Galleries from the Tools menu. The Embedded Galleries dialog appears.

Figure 6-52. Embedded Galleries Dialog

3. Select the gallery called My Gallery from the Gallery List and click Remove.

Opening a Gallery from Inside a KDB File

Deleting an Embedded Gallery from a KDB File

Graphs in Kinetica



5. Save the KDB file. The gallery is deleted from the KDB file.

Note This procedure can only be performed if the graph is viewed in minimum size. Double-click to switch between minimize and maximize view.

If you made changes to the Gallery and you did not save them, when you attempt to exit Kinetica, the Gallery Not Saved dialog is displayed.

Figure 6-53. Gallery Not Saved dialog

You must select the appropriate option to proceed. The options are:

• Cancel - cancels the quit operation all together

• Don’t Save Gallery - saves the Gallery and then you will be prompted to save the KDB file separately from the gallery.

• Save Gallery to file – achieves the same result as choosing Save Gallery to KGG file from the File menu.

• Save Gallery to KDB – achieves the same result as choosing Save Gallery to KDB from the File menu.

Save Gallery Prompt

Graphs in Kinetica


To save a graph:

1. Select a gallery.

2. Right click and select Save Graph As from the popup menu. The Save As dialog appears.

3. Enter a path and filename for the graph.

4. Specify one of the following file suffixes:

• BMP = Windows bitmap format

• JPEG = JPEG format

• WMF = Windows metafile format

5. Click Save. The graph is saved in the format that you selected.

Saving a Graph

Graphs in Kinetica


There are two ways you can export graphs in Kinetica. The basic method is to simply copy and paste the active graph to the application of your choice. An advanced option enables you to export multiple graphs that may be generated in a batch analysis.

Generally, when dealing with large studies, you will want to export your graphs to a word processor in batch mode without all the associated statistics generated by the analysis. Kinetica provides a driver for exporting all graphs created during a batch analysis. This option is limited to Word 97 and later versions only, because Microsoft does not support all OLE automation in previous versions.

To export an active graph file to another application (basic method):

1. Select a graph by selecting a gallery, a graph or multiple graphs in a gallery.

2. Select Copy from the Edit menu.

3. Switch to the destination application.

4. Select the Paste command. The graph is displayed.

You can also highlight a graph and select Export to Word or Export To Excel from the File menu (this executes a copy/paste operation but sends the graph in metafile format instead of bitmap). In order to perform this function, you must have Microsoft Word or Excel defined in the Report Setup dialog before exporting graphs.

To export batch graphs (advanced method):

1. From within Kinetica, open the Group Table.kdb file, found in the Kinetica\example\statistics subdirectory. This file contains the data for 12-subject two-way crossovers study (24 datasets).

Exporting Graphs

Graphs in Kinetica


2. Select Report Setup from the File menu. The Report Setup dialog appears.

3. Click on Report in the Select Output area of the dialog.

4. Double-click on Graph for Word 97 document in the Double Click to Set Destination area of the dialog. The Open dialog appears.

5. You must select a file from this dialog that will be the destination for your Word output or you can enter a document name and Kinetica will create a new Word file for you. For the purposes of this example create a new file called MyGraphs.doc in the \kinetica\odriver subdirectory (you can choose a different folder when creating a new file).

6. Click Open to create the file and click OK to exit the Report Setup dialog. Word 97 Graph export has now been activated.

Note No statistics will be sent to Word when you select this option.

7. Select Methods in the Methods pane.

8. Locate AUC* method.

9. Click Set under the Global Options column. The AUC* Method Global Options dialog appears.

10. Select the Show Lz Plot check box.

11. Click OK.

12. Click the Calculate all button on the toolbar. The graphs are exported into the report along with the data, in a tabular format.

Graphs in Kinetica


Linear regression is a simple test allowing you to analyze linear regression problems (the relationship between a dependent random variable and an independent variable) or correlation problems (the relationship between two random variables). Kinetica offers you the option to compute the regression using log or no log transformation of the Y values.

The Linear Regression is calculated using Y = A + B X

where:

A is the intercept

B is the slope.

The correlation coefficient (R), is calculated using the equation

( )( )

( ) ( )22∑∑

∑−−

−−=

ii

ii

iii

yyxx

yyxxR

In this example, we have two data columns: X and Y. We will perform a linear regression analysis on these two data columns without a log transformation of the Y values.

To complete an analysis for linear regression:

1. Select Open from the Kinetica File menu. The Open dialog appears. By default, the Data subdirectory is displayed.

2. Select the LinearRegression.kdb file, found in the Program Files\Kinetica\Example\Statistics directory, and click Open.

3. Select the Study pane. The Study group appears as follows, containing only the raw data we entered before sending you the program:

Linear Regression

Example of Linear Regression

Graphs in Kinetica


Figure 6-54. Study Group – Raw Data Display

4. Select Linear Regression from the Statistics menu. The Linear Regression dialog appears.

Figure 6-55. Linear Regression Dialog

5. Select X from the X axis Data column list and Y from the Y axis Data column list. The dialog appears as follows:

Figure 6-56. Populated Linear Regression Dialog

Graphs in Kinetica


6. Click OK to exit the dialog and generate the report.

You can view the results of the Linear Regression in the Study Info view of the Study pane.

Graphs in Kinetica


To run Linear Regression with CI, see Linear Regression.

The upper and lower interval limit is based on 95% confidence. The confidence limit is based on the equation:

xx

2_

02n,2/0

^

S)xx(

n1st)x(y

−+± −α

Where ^y is the expected value at x0, t is the t-statistics at

probability level of α/2 and n-2 degrees of freedom, s is the standard error, Sxx is the corrected sum of squares for the x’s.

Linear Regression with CI

Graphs in Kinetica


See the sections, “Mean Curve”, “Plotting Mean Curves without Statistics”, “Plotting Mean Curves with Statistics”, “Plotting Standard or Overlay Mean Curves with Statistics”, and “Plotting Mean Curves by Group with Statistics” in this chapter.

Mean Curve Statistics

Graphs in Kinetica


The Histogram plot provides information on the evaluation of parameter distribution frequency. With this plotting, you can analyze the data and determine which pattern best describes the data behavior.

For information related to histogram plots for Kinetica Population Pharmacokinetics, see the section, “Working with Kinetica Population Graphs” in the chapter, “Population Pharmacokinetics (PK).”

This dialog is accessed by selecting Histogram from the Graph menu. This dialog is used to define the information to include in the plot. A description of the items contained in the dialog is contained in the following table.

Item Description

Data Used to select a parameter or a variable (derived from the All Variables worksheet). The distribution of the selected parameter will be analyzed

Distribution Step

Used to indicate how the specified parameter or variable is displayed on the plot (width of bar step)

Log Transformation

If you select this option, the distribution is plotted using log transformation

Select Dataset Used to select the datasets to include in the distribution plot

By Group Used to group the datasets in the distribution plot by selecting a dataset input variable

To generate a parameter distribution plot:

1. Launch Kinetica and open the appropriate .kdb file.

Histogram Statistics

Histogram Dialog

Graphs in Kinetica


2. Select the All Variables worksheet in the Study pane and examine the displayed results.

3. Select Distribution from the Statistics menu. The Distribution dialog appears.

Figure 6-57. Distribution Dialog

4. Select the parameter or variable for analysis from the Data list. For example, choose Cmax.

5. Specify the Distribution Step. By default, the distribution step is 0.5.

6. Specifying datasets to include in the plot

7. Click Select Dataset. The Calculate Range dialog appears.

Graphs in Kinetica


Figure 6-58. Calculate Range Dialog

8. Use your mouse and the CTRL key to select datasets individually. Click Select All to include all datasets.

9. Click OK to exit the dialog and return to the Distribution dialog.

10. Select the Log Transformation check box, if required.

To group datasets:

1. Select the By Group check box and select the appropriate dataset input variable from the available list.

2. Click OK to exit the dialog and display the graph.

Grouping datasets (optional)

Graphs in Kinetica


Figure 6-59. Example – Parameter Distribution Probability Curve

Graphs in Kinetica


Scattered XY Plot (Numeric X-Axis) is found under the Graph menu. Formerly known as Parameter plotting, the scattered XY Plot with numeric X-axis is similar to a scatter plot in order to view relationship between different numeric parameters related to the subject.

To examine the relationship of two parameters:

1. Select Graph | Scattered XY Plot (Numeric X-Axis).

2. Select the numeric parameters for X and Y. To select specific dataset to perform the analysis, click Select Dataset and choose the subjects/datasets by CTRL-click. Click OK.

Figure 6-60. X Y Plot Dialog

3. The graph of the relationship is exported to the gallery pane.

Scattered XY Plot (Numeric X-Axis)

Plotting

Graphs in Kinetica


The scattered XY Plot (Textual X-Axis) is found under the Graph menu. Formerly known as Categorical plotting, the scattered XY plot with textual X-axis provides visualization of categorical variable versus numerical variable, as well as paired (spaghetti) plots related to a particular subject.

To plot categorical parameters against numeric parameters:

1. Select Graph | scattered XY Plot (Textual X-Axis).

2. Select a Categorical (non-numeric) variable for X and a numeric variable for Y. Click By Pair box and select a unique identifier, which could be a subject that had undergone different treatment in X in the drop-down menu. Click OK.

Figure 6-61. Categorical Plot

3. The result is plotted in the gallery as a spaghetti plot.

Scattered XY Plot (Textual X-Axis)

Plotting

Graphs in Kinetica


A BDrugName

3.9

4.2

4.5

4.8

5.1

Cm

ax()

Cmax v s DrugName

Sbj01Sbj02Sbj03Sbj04Sbj05Sbj06Sbj07Sbj08Sbj09Sbj10Sbj11Sbj12

Figure 6-62. Example Graph – Categorical Plotting


7. Methods and Models

This chapter provides information on working with both methods and models in Kinetica.

Methods and Models


There are two types of method (models) in Kinetica:

• Hard-coded

• Soft-coded

Equations for both hard-coded and soft-coded methods are provided in this chapter for your reference. The hard-coded methods are written in C++. You cannot access the code for these methods. You can access the corresponding soft-coded methods that are written in Kinetica Basic (see the Kinetica Basic Reference Guide for more on Kinetica Basic). You can access and manipulate the code for these methods.

Note If an output column is left blank, the variable is not calculated.

The hard-coded methods included in Kinetica are listed in the following table.

Kinetica Hard-Coded Methods

Adjust Time Curve extrapolation

AUC * Convolution

AUCinter * Deconvolution

AUC Steady State * Derivation

AUC Steady State * with Lz

Exp (Col)

Sparse AUC Linear Regression

Superposition – Variable Dosage

Linear Regression by Zero

Run Macro Log(Col)

Col - Value Macro To Micro

Working with Methods and Models

in Kinetica

Methods and Models


Kinetica Hard-Coded Methods

Col * Value Micro To Macro

Col / Value Sqrt (Col)

Col + Value T50%

Col a − Col b tN%

Col a * Col b tN% of Cmax

Col a / Col b Value - Col

Col a + Col b X1Y1_X2Y2_Graph

Col Sum XYerror_Graph

Value + Value XYGraph

Value − Value XYYGraph

Value * Value Mean Curve

Value / Value Mean Curve by Group

Value^ Value

Col%

Col^Value

You can also refer to the on-line help system for additional information.

Units are described by the following hard-coded methods:

• Column units

• Get Column unit

• Set column unit.

Methods and Models


For more information regarding these methods, see the chapter, “Configuring Units.”

The following hard-coded methods describe compartmental analysis:

• FitDynamic

• FitMacro0orderinput

• FitMacroExtravascular

• FitMacroIVBolus

• FitMacroIVInf

• FitMicro0orderinput

• FitMicroExtravascular

• FitMicroIVBolus

• FitMicroIVInf

• FitMultiMicroExtravascular

• FitMultiMicroIVBolus

• FitMultiMicroIVInf

• FitMultiMicro

• FitPKPD_Extravascular

• FitPKPD_IVB

• FitPKPD_IVInf

• Micro to Macro

• Macro to Micro

• Simple Hard-Coded Methods

Methods and Models


The following table summarizes Kinetica calculations for most of the simple hard-coded methods.

Method Kinetica Calculation

Run Macro Runs a specific kdb-appended macro once

Col + Value Adds a user-defined constant value to the cell values in the selected column.

Col − Value Subtracts a user-defined constant value from the cell values in the selected column.

Col * Value Multiplies a user-defined constant value with the cell values in the selected column.

Col/Value Divides the cell values in the selected column by a user-defined constant value.

Col a + Col b Adds cell values from two different selected columns.

Col a − Col b Subtracts cell values from two different selected columns.

Col a * Col b Multiplies cell values from two different selected columns.

Col a / Col b Divides cell values from two different selected columns.

Col Sum Adds all cell values together from the selected column.

Col % Divides the cell value by a user-specified value and then multiplies the result by 100.

Methods and Models



Col ^ Value Raises the cell values in the selected column to a power of a user-defined constant value.

exp(Col) Computes the exponent of the cell values in the column (user-selected).

log(Col) Computes the logarithm of the cell values in the column (user-selected).

Sqrt (Col) Computes the square root of the cell values in the column (user-selected).

Col Sum Adds all cell values together from the selected column.

Col % Divides the cell value by your specified value and then multiplies the result by 100.

Col ^ Value Raises the cell values in the selected column to a power of a user-defined constant value.

exp(Col) Computes the exponent of the cell values in the column (user-selected).

log(Col) Computes the logarithm of the cell values in the column (user-selected).

Sqrt (Col) Computes the square root of the cell values in the column (user-selected).

t 50% Computes the time when the cell value in the column reaches 50% of the final cumulative values:

t50% = time where Y = Ylast/2

t50% is computed using linear extrapolation.

Methods and Models



Value − Col Subtracts a user-defined constant value from the cell values in the specified column.

Mean Curve Plots mean graph of Y1=f(X1) across individuals and computes the standard deviation for the whole group.

Mean Curve by Group

Plots mean graph of Y1=f(X1) across individuals and computes the standard deviation with the ability to group by specific parameter.

X1Y1_X2Y2_Graph Plots a graph of Y1 = f(X1) and Y2 = f(X2).

XYerror_Graph Plots a graph of Y = f(X) with error bars.

XYGraph Plots a graph of Y = f(X).

XYYGraph Plots a graph of Y = f(X) and a Y(Overlay) = f(X).

Methods and Models


Kinetica’s Area Under the Curve (AUC) calculation methods include:

• AUC*

• AUCinter*

• AUCsteady-state*

• AUCsteady-state with Lz*

• Sparse AUC*

The pharmacokinetic parameters computed by these methods are described in the table below.

Method Column and Variable Outputs

AUC∗

AUC inter*

AUC steady state*

AUC steady-state* with Lz

Sparse AUC*

Column Output:

AUC: AUC-ss: compAUC: partial area under the curve

AUCcum: AUCcum-ss: compAUCcum: accumulated AUC

AUMC: AUMC-ss: compAUMC: partial area under the moment curve

AUMCcum: AUMCcum-ss: compAUMCcum: accumulated AUMC

R: R-ss: correlation coefficient of linear regression on log transformation

G: G-ss: criteria used to estimate Lz

Area Under the Curve (AUC) Calculation

Methods

Methods and Models



Variable Output:

Cmax: maximum concentration

Tmax: time to reach maximum concentration

Tlag: lag time

HVD: Half-value duration. HVD describes the time coverage of drug concentration in the plasma between half of Cmax and Cmax. This parameter is estimated as the last time the concentration time curve falls below half-Cmax subtracted by the first time that it climbs above half-Cmax.

AUClast: AUC from t=0 to tlast (last sampling time)

AUCextra: extrapolated AUC

AUCtot: AUC total (=AUClast+AUCextra)

%AUCextra: percentage of AUC extra with respect to AUC total

Lz

AUMClast: AUMC from t=0 to tlast.

AUMCextra: extrapolated AUMC

AUMCtot: total AUMC (=AUClast+AUMCextra)

AUCall:

If the last sample is at normal, missing or outlier status, AUCall = AUClast

If the last sample is BLQ or zero, or the last several data points are BLQ or zero, AUCall is calculated as:

AUCall = AUClast + AUCtriangle, where, AUClast is from t=0 to the last normal status data, and AUCtriangle is:

)tt(2

CeAUCtriangl last1last

last −= +

Methods and Models



Clast can be the predicted or observed value, depending on which alternative has been specified by the user in the method options. tlast+1 is the first BLQ or zero data sampling time.

There are two outputs from the two options in AUCall calculation:

To use Clast predicted value

To use Clast observed value

R: linear regression coefficient

G: Criteria used for C0 extrapolation and Lz calculation

Rstart: First point used for Lz calculation

Rend: Last point used for Lz calculation

Rnbpoint: Number of points used in Lz calculation

Rsmooth: Ratio between AUC below the curve and AUC above the curve. The output value is always between 0 and 1. A small difference between AUC below the curve and AUC above the curve reveals a good estimation of the real AUC and a good sampling time

Raccurate: Raccurate =1−AUCpartial/AUC, where AUCpartial is the value that has the largest difference from AUC (AUCpartial was calculated using n-1 data points.)

C0: Concentration at t=0

T1/2: Half-life of elimination

MRT: Mean residence time

Clearance: Total clearance

Vz: the apparent volume of distribution during the terminal phase

Vss: the apparent volume of the plasma compartment

Methods and Models



ComputedCLast: Last concentration point estimated using the linear equation obtained from linear regression on log transformed data

Clast(Obs): last observed concentration data.

TLast: Last time point

A: Intercept of the linear equation on log transformed data

B: Slope of the linear equation on log transformed data

R2: Coefficient of determination

AUCinter* AUCi: Total intermediary area under the curve between t start and t end

AUMCi: Total intermediary area under the moment curve between t start and t end

Cmax: Maximum concentration in the interval

Tmax: Time required to reach Cmax in the interval

Tstart: Enter the time corresponding to the beginning of Tau. If you do not enter a value, Kinetica uses the time corresponding to the first data point.

Tend: Enter the time corresponding to the end of Tau. If you do not enter a value, Kinetica uses the time corresponding to the last data point.

AUC steady state* Tau: Dosage interval time (Tau = t end - t start)

Cmax: Maximum steady state drug concentration during a dosing interval

Cmin: Minimum steady state drug concentration during a dosing interval

Tmax: Maximum time corresponding to Cmax

AUCss: Area under the curve during a dosing interval at steady state

AUMCss: Area under the moment curve during a dosing interval at steady state

Methods and Models



Caverage: Mean or average steady state drug concentration expressed as:

TauAUCssCav =

%ptf: Peak-through-fluctuation expressed as:

average

minmax

CCC

100%ptf−

⋅=

%AUCdf: Percentage-area-fluctuation expressed as:

SS

SS

belowabove

AUCCav and C(t) between AUC

AUCAUCAUC

%AUCdf

⋅=

+⋅=

100

100

%swing: Degree of fluctuation expressed as:

min

minmax

CCC

100%swing−

⋅=

Tstart: Dosing interval start time

Tend: Dosing interval end time

Methods and Models



AUC steady state* with Lz

Accumulation: computes accumulation index based on the following equation:

τLzeRac −−

=1

1 where τ is Tstart−Tend

CLss: the steady state apparent clearance

Vss: the steady state apparent volume of distribution

Sparse AUC* Composite Tmax: time to reach the maximum average concentration

Composite Cmax: maximum average concentration

SD of Cmax: standard deviation of the maximum average concentration

Composite AUC: area under the average concentration curve

SE of Composite AUC: standard error of the estimate of the area under the average concentration curve

Approximate df: approximate degree of freedom

Tstart: start time of the sparse area under the curve computation

Tend: end time of the sparse area under the curve computation

You may specify whether to use the first, last or median Tmax value. By default, Kinetica uses the smallest value for Tmax for which C = Cmax. This is applicable in cases where the concentration curve reaches Cmax several times (during peaks or plateaus). It is the last value that will indicate the beginning of the elimination phase.

The soft-coded method CalcAmaxTmax defines the time to reach the first Cmax as Tmax. You can also enter your own Tmax. If you indicate a negative Tmax, Kinetica will automatically calculate a value for Tmax.

Tmax

Methods and Models


In the subsections below we describe the options available for AUC* computation.

The mixed log linear rule is performed in the following way: the linear rule is applied to the range where concentration is ascending and the log linear rule is applied to the range where concentration is descending.

When the concentration increases, the following linear rule is used:

2)CC(

)tt(AUC i1i1iii

+−= −

−

2)CtCt()tt(AUMC ii1i1i

1iii+

−= −−−

When the concentration decreases, the log-linear rule is used:

)C/Cln()CC()(AUC

1

i11

ii

iiii tt

−

−−

−−=

The log linear rule is used in the following way when the concentration is descending:

)C/Cln()CC()(AUC

1

i11

ii

iiii tt

−

−−

−−=

212

1

1-i

1

i111 )(

)C/Cln()CC(

)C/Cln()CC()(AUMC −

−−

−−− −

−−

−−= ii

ii

i

ii

iiiiii tttttt

The trapezoidal (linear rule) is performed in the following way when concentration is ascending:

AUC* Options

Mixed log linear

Log-Linear

Trapezoidal

Methods and Models


2)CC()tt(AUC i1i

1iii+

−= −−


1iii+

−= −−−

Note When the linear rule is applied to AUC calculation, BLQ, or zero data points are not included in AUClast (AUC from t=0 to last sampling time) calculation if there are no normal status data following the BLQ or zero data points.

Methods and Models


You have the option to flag data that are below the limit of quantization (BLQ). In Kinetica, there are three ways to flag data as BLQ. These options are described in the following table.

Option Description

Default All concentration values that are BLQ will be labeled with the original numerical value in the Dataset spreadsheet. These original values will be used in all AUC calculations.

Set as 0 All concentration values that are BLQ will be labeled with the original numerical value in the Dataset spreadsheet. These original values will be replaced with “0” for all AUC calculations.

Set as missing All concentration values that are BLQ will be labeled with the original value in the Dataset spreadsheet. These original values will be omitted from all AUC calculations.

User-defined LLOQ

All concentration values that are BLQ will be labeled with a factor of the entry for LLOQ column. The options include LLOQ, LLOQ/2, LLOQ/3 and LLOQ/4.

Note The BLQ option you select will be applied to ALL undetectable data points in all datasets.

In order to flag data as BLQ, you must first run the analysis, then identify each undetectable data point with a less-than sign (<) before selecting a BLQ Data indicator in the AUC Method Global Options dialog. For more information, see the chapter, “Working with Graphs,” section “Displaying Non-Detectable Data Points.”

If you do not specify a BLQ Data indicator, the Default option will be used for all AUC computations.

BLQ Data

Methods and Models


If the first (data) point has a time value of 0, no calculation will be carried out. In all other cases, Kinetica must know the concentration value at time 0 to calculate AUC between t0 and t1 (AUC0). The possible options are:

C0 = C1

2Ct

tAUMC ,CtAUC 1110110 ⋅==

This is the default option. It represents a bias on C0 for all administration routes. It is used when automatic extrapolation fails.

C0 = 0

2⋅=

2⋅= 11

101

10CttAUMC ,CtAUC

This option is adequate to treat extravascular kinetics and intravenous infusion.

Extrapolated C0

2⋅=

2+

⋅= 1110

1010

CttAUMC ,

CCtAUC extra

This option permits a correct estimation of the AUC0 for IV bolus administration. C0 extrapolated is obtained by extrapolating the concentration profile to t=0 using the linear regression equation on the logarithmic transformation. This regression equation is obtained using the first several data points on the concentration profile. The number of data points to be included in the calculation is obtained following the same G criteria (below) as for Lz estimation:

2n)R1)(1(n1G

2

−−−

−=

where:

AUC0 Calculation

Methods and Models


R2= coefficient of determination

n = number of points used to determine Lz

Note Lz is also referred to as Kel or LambdaZ.

Between the last data point and infinity, the kinetics fall back on a simple exponential decrease, C = Aexp(−αt), which is a good approximation when the last samples have been taken sufficiently “late.”

Kinetica carries out linear regression on the logarithmic transformation of the last (data) points of the curve. The slope of this straight line is equal to -Lz (constant rate). In order to determine the number of (data)points to use in the calculation, Kinetica attempts the regression with 3, 4, …n points. The best regression line is the one that maximizes G.

Note AUC Infinity is also called AUCextra.

2n)R1)(1(n1G

2

−−−

−=

where:


n = number of points used to determine Lz

Note Lz is also referrred to as Kel or LambdaZ.

The options are:

ComputedClast/Lz

The following formula is used:

AUC Infinity = computedClast/Lz, where the estimated value of the latest data point is used

AUC Infinity

Methods and Models


Observed Clast/Lz

The following formula is used:

AUC Infinity = observed Clast/Lz, where the observed value of the latest data point is used.

Note If observation option was selected, AUCinf and AUMCinf calculation will only use the normal status flag with data. For example, AUCextra=Clast(observed)/Lz, where Clast(observed) is a normal status data.

This option is for handling missing values.

• No interpolation – No value is computed on the line containing a missing value.

• Interpolation – There is a value computed on the line containing a missing value.

Note If concentration at ti is missing, called Ci, a linear or log interpolation for Ci can be selected.

The interpolated Ci is used to calculate AUCi (AUCcum from t=0 to ti can be calculated based on AUCi).

How AUC is calculated in the range of C(i-1) and C(i+1) will determine whether linear or loglinear interpolation is used.

If Ci is missing and within Ci-1 to Ci+1 , AUC is calculated using the linear rule, then Ci is interpolated using linear method:

)t(t)C(C)t(tCC

1i1i

1i1i1ii1ii

−+

−+−− −

−−+=

If Ci is missing, and within Ci-1 to Ci+1 AUC is calculated using the log linear rule, then Ci is interpolated using the log linear method:

Ci Missing and AUC Accumulated

Methods and Models


)t(t)lnC(lnC)t(tlnClnC

1i1i

1i1i1ii1ii

−+

−+−− −

−−+=

Once Ci is obtained, AUC from i-1 to i and i to i+1 are calculated using this interpolated value.

By default, Kinetica uses Tmax as the start time to use logarithmic method to calculate AUC. You can, however, specify the start time under this option to use logarithmic method to calculate AUC.

The default method to select the number of points for Lz estimation is based on the criteria of G.

2n)R1)(1(n1G

2

−−−

−=

where:


n = number of points used to calculate Lz

You can also select the starting data point for Lz estimation under this option. In addition, the calculation of the regression error is included in the number of points in the termination phase.

This option allows the setting and specification of an “end” data point for estimating the Lz.

The default method for reporting the Cmax value is the to use the last value. Users can specify if the program should report either the first or the median value instead.

AUC Log Start Time

Lz Start Time

Lz End Time

Multiple Cmax

Methods and Models


This option lets you specify if the Tmax value should be excluded in the Lz calculations. If this is checked, the program will use all time points but the Tmax value.

This option lets you specify if BLQ values before the first non-zero normal data in the dataset need to be set to zero for the calculations. By default this option is not checked.

This option allows you to calculate the area between the last measurable concentration and the following BLQ, depending on the BLQ option set.

• If BLQ is set as missing, the area will not be calculated.

• If BLQ is set as default, partial AUC will be calculated as the area between the last measurable concentration and the value after the < sign.

• If BLQ handling is set as 0, the partial AUC will be the triangle between the last measurable concentration and 0.

This option lets users specify if all outlying values in the datasets should be used for the AUC calculations. The default is the option not checked, i.e. the outliers will be ignored.

C0 Start Time (for IV Bolus administration)

C0 extrapolated is obtained by extrapolating the concentration profile to t=0 using the linear regression equation on the logarithmic transformation. The regression equation is obtained using the first several data points on the concentration profile. The number of data points to be included in the calculation is obtained following the same G criteria as for the Lz estimation.

Exclude Tmax in Lz calculation

BLQ Before First Non-Zero Normal Data = 0

Partial AUC

Use outlier in AUC calculation

Methods and Models


There are two choices for the Show Lz Plot option:

• Yes: Graph appears after running analysis.

• No: Graph does not appear after running analysis.

You can assign one of three status flags to any data point. In addition, the error flag is output to a spreadsheet cell when the value for that cell cannot be computed due to an error. The flags available for selection are listed in the following table:

Status Flag Example

Missing No flag symbol Leave spreadsheet cell empty

Outlier ! ! 999.99

Undetectable < < 0.0001

Error #ERR #ERR

Note The missing, outlier, or error status given to data points means that the points will be systematically ignored during calculations and will therefore have no effect on the results, unless the use Outliers options is checked, as described above.

Show Lz Plot

Special Cases: Missing, Outlier, Undetectable and

Error Flags

Methods and Models


The AUCinter* method is identical to the AUC* method except that it enables you to choose a time interval in which to calculate the AUC. The result is a partial AUC at this interval.

The pharmacokinetic parameters computed are described in the following table.

Parameter Kinetica Calculation

AUC Partial area under the curve between t start and t end

AUCcum Accumulated area under the curve between t start and t end

AUMC Partial area under the moment curve between t start and t end

AUMCcum Accumulated area under the moment curve between t start and t end

AUCi Total intermediary area under the curve between t start and t end

AUMCi Total intermediary area under the moment curve between t start and t end

Cmax Maximum concentration in the interval

Tmax Time required to reach Cmax in the interval

Tstart Enter the time corresponding to the beginning of Tau. If you do not enter a value, Kinetica uses the time corresponding to the first data point.

Tend Enter the time corresponding to the end of Tau. If you do not enter a value, Kinetica uses the time corresponding to the last data point.

AUCinter* Method

Methods and Models


AUCinter* method contains the same set of options as in AUC* method for computing AUC. For more information, see the Linear, Log Linear, Mixed Log Linear information in the AUC* Method section of this chapter.

The mixed log linear rule is performed in the following way: the linear rule is applied to the range where concentration is ascending and the log linear rule is applied to the range where concentration is descending.

When the concentration increases, the following linear rule is used:

2)CC()tt(AUC i1i

1iii+

−= −−


1iii+

−= −−−

When the concentration decreases, the log-linear rule is used:

)C/Cln()CC()(AUC

1

i11

ii

iiii tt

−

−−

−−=

The log linear rule is performed in the following way when the concentration is descending:

)C/Cln()CC()(AUC

1

i11

ii

iiii tt

−

−−

−−=

212

1

1-i

1

i111 )(

)C/Cln()CC(

)C/Cln()CC()(AUMC −

−−

−−− −

−−

−−= ii

ii

i

ii

iiiiii tttttt

AUCinter* Options

Mixed log linear

Log-Linear

Methods and Models


The trapezoidal (linear rule) is performed in the following way when concentration is ascending:

2)CC()tt(AUC i1i

1iii+

−= −−


1iii+

−= −−−

Note When the linear rule is applied to AUC calculation, BLQ, or zero data points are not included in AUClast (AUC from t=0 to last sampling time) calculation if there are no normal status data following the BLQ or zero data points.

You have the option to flag data that are BLQ (below the limit of quantization). In Kinetica, there are three ways to flag data as BLQ. These options are described in the following table.

Option Description

Default All concentration values that are BLQ will be labeled with the original numerical value in the Dataset spreadsheet. These original values will be used in all AUC calculations.

Set as 0 All concentration values that are BLQ will be labeled with the original numerical value in the Dataset spreadsheet. These original values will be replaced with “0” for all AUC calculations.

Set as missing All concentration values that are BLQ will be labeled with the original value in the Dataset spreadsheet. These original values will be omitted from all AUC calculations.

Note The BLQ option you select will be applied to ALL undetectable data points in all datasets.

In order to flag data as BLQ, you must first run the analysis, then identify each undetectable data point with a "less than" sign (<) before selecting a BLQ Data indicator in the AUC Method Global Option dialog. For more information, see the sections

Trapezoidal

BLQ Data

Methods and Models


titled “Working with Graphs” and “Displaying Non-Detectable Data Points”.

If you do not specify a BLQ Data indicator, the "Default" option will be used for all AUC computations, by default.

This option allows you to specify if BLQ values before the first non-zero normal data in the dataset need to be set to zero for the calculations. By default, this option is not checked.

If the concentration within Tstart or Tend is missing, (e.g. at Tstart, Ci is missing) a linear or log linear interpolation can be selected. The interpolated concentration is then used to calculate the AUC at that missing point. The interpolation follows the same rule as the AUC ∗ method.

If Tstart or Tend is missing then the actual range for AUCinter ∗ will be generated.

A time profile ranging from .13333 h to .33333 h. Since the timepoint for C=204 (last Concentration datapoint) is missing, then the actual range (.13333 h to .33333 h ) for AUCinter* will be generated. Please see Example 1 shown here.

Figure 7-1. Example 1

BLQ before first non-zero normal data = 0

AUC Accumulated

Example

Methods and Models


By default, Kinetica uses Tmax as the start time to use logarithmic method to calculate AUC. You can, however, specify the start time under this option to use logarithmic method to calculate AUC.

The default method to select the number of points for Lz estimation is based on the criteria of G.

2n)R1)(1(n1G

2

−−−

−=

where:


n = number of points used to calculate Lz

You can also select the starting data point for Lz estimation under this option. In addition, the calculation of the regression error is included in the number of points in the termination phase.

This option allows the setting and specification of an “end” data point for estimating the Lz.

The default method for reporting the Cmax value is to use the last value. Users can specify if the program should report either the first or the median value instead.

This option lets users specify if all outlying values in the datasets should be used for the AUC calculations. The default is the option not checked, i.e. the outliers will be ignored.

AUC Log Start Time

T Start

T End

Multiple Cmax

Use Outlier in AUC Calculation

Methods and Models


The AUC Steady State* method enables you to calculate the AUC during steady state on only one dosage interval time (Tau). For a given administration, steady-state parameters are different from single dose parameters. The pharmacokinetic parameters computed are listed in the following table.


AUC Partial area under the curve between t start and t end

AUCcum Accumulated area under the curve between t start and t end

AUMC Partial area under the moment curve between t start and t end

AUMCcum Accumulated area under the moment curve between t start and t end

Tau Dosage interval time (Tau = t end - t start)

Cmax Maximum steady state drug concentration during a dosing interval

Cmin Minimum steady state drug concentration during a dosing interval

Tmax Maximum time corresponding to Cmax

AUCss Area under the curve during a dosing interval at steady state

AUMCss Area under the moment curve during a dosing interval at steady state

Caverage Mean or average steady state drug concentration expressed as:

AUCssTau

%ptf Peak-through-fluctuation expressed as:

%ptf 100C C

Cmax min

average= ⋅

−

AUC Steady State* Methods

Methods and Models



%AUCdf Percentage-area-fluctuation expressed as:

%AUCdfAUC AUC

AUCAUC between C(t) and Caverage

AUCabove below

SS SS= ⋅

+= ⋅100 100

%swing Degree of fluctuation expressed as:

%swing 100C C

Cmax min

min= ⋅

−

Tstart Dosing interval start time

Tend Dosing interval end time

Note The options available for the AUC Steady State* method and the AUCinter* method are the same. For more information, see the section “AUCinter* Method” of this chapter.

Methods and Models


The Sparse AUC method allows you to stack repeated data over various time points, take the average over each time point and perform non-compartmental analysis on the averaged data. This method, based on the article published by Nedelman and Jia, is also known as the Bailer-Satterthwaite’s method.

Refer to the following articles: Nedelman JR, Jia XW. An Extension of Satterthwaite's Approximation Applied to Pharmacokinetics. Journal of Biopharmaceutical Statistics 8(2), 317 - 328 (1998). Also see comments by Holder on the method, appearing in Holder DJ. Comments on Nedelman and Jia's Extension of Satterthwaite's Approximation Applied to Pharmacokinetics. J. of Biopharmaceitical Statistics 11(1&2), 75-79 (2001).

This calculation can be performed for the design that has:

• more than a single subject measured at one time.

• a subject that can be measured at both ti and tj.

The algorithm computes the standard error of the AUC values and is based on the trapezoidal (or linear) rule from sparse sampled data. The input variables are sampling times and individual drug concentration from plasma or tissue.

Note The options for the Sparse AUC* method and the AUCinter* method are similar. For more information, see the section “AUCinter* Method” in this chapter.

Sparse AUC Method

Methods and Models


The superposition–variable dosage method or the overlay technique allows you to predict concentration data after multiple dosing based on single dose data. Using the original time and concentration data, the method utilizes the Curve Extrapolation method to get a value for the intercept and slope, using the “stripping” and regression algorithm. The method can either select the number of points to fit the terminal descending phase or allow you to select the start and end time of the terminal regression phase.

The method assumes linearity and proportionality in the data to allow a proportional increase in the dose, if a different dose is given at any interval. The method requires four input columns: X, Y, Admin Time and Admin Dose, and the initial dose as an input dataset numeric field. The initial dose in the study pane is assumed to be the original dose for the single dose data. The AdminTime and AdminDose columns are for predicting the time-concentration data for multiple dosages.

You need to tell the method the total number of points to simulate. The method takes the last simulated time point and divides it by the total number of points minus 1 to increment the time points from the first time point all the way to the last simulated time point. Therefore, you will need to provide n+1 points to include zero time point. For example, if you were to simulate every 0.125 hour interval up to 24 for the independent variable, then the value for the total number of output data points will be 24 × 23 + 1 = 193.

The pharmacokinetic parameters computed are described in the following table.

Variable Explanation

A Y-intercept of the linear equation of log transformed data.

B Slope of the linear equation on log-transformed data.

R2 Coefficient of determination.

R Correlation or regression coefficient.

Superposition–Variable Dosage

Method

Methods and Models


Variable Explanation

G Criterion used for selecting number of points for regression.

Rstart First point used for Lz calculation.

Rend Last point used for Lz calculation.

Rnbpoint Number of points used in Lz calculation.

Rsmooth Ratio between AUC below the curve and AUC above the curve. The output value is always between 0 and 1. A small difference between AUC below the curve and AUC above the curve reveals a good estimation of the real AUC and a good sampling time.

Raccurate Raccurate =1−AUCpartial/AUC

where:

AUCpartial is the value that has the largest difference from AUC (AUCpartial was calculated using n−1 data points).

Thalf Half-life of elimination.

Lz Elimination rate constant derived from the slope of the linear equation of log-transformed data.

Methods and Models


The options available in the superposition–variable dose method are explained in the following table.

Option Description

Last Simulated Time point

The time point for the last value of the superposition time

Number of Output Data Points

The total number of points to simulate. You need to provide n+1 time points and include the zero time point. For example, if you want to simulate every 0.125 hour interval up to 24 for the independent variable, then the value for the total number of output data points is 24×23 + 1 = 193.

Interpolation method

Algorithms available include trapezoidal, log linear and mixed log-linear.

C0 Handling See the AUC* method for an explanation of C0 handling.

Regression start

Entry will replace the regression start time in the stripping algorithm.

Regression end

Entry will use this value instead of the last value in the independent variable column.

BLQ handling

See the AUCinter* method for an explanation of the BLQ handling options.

Superposition – Variable Dosage Options

Methods and Models


The convolution method is a simulation method used to predict the blood/plasma concentration when a drug is administered orally. The principle behind this is the following:

No hypothesis for the absorption model (independent-model method) exists.

Absorption is an input function represented by a series of rapid Bolus injections over a brief interval.

In a linear system, the response c to the input value of f is:

c = cδ * f, where f is the input signal and cδ is the unit impulse response. Convolution is the process to obtain c with known cδ and f.

In practice, the kinetic profile of the drug by IV is known (TIV represents IV time; and CIV represents IV concentration).

The desired result is a profile of absorption A(t) or a profile of the rate of absorption dA/dt. In fact, Tos (time corresponding to the oral administration) and A(t) or dA/dt are already known.

The purpose for using such a method is to obtain the profile of the drug (by simulation) if it were given orally, with Cos (concentration for oral solution) as a byproduct.

The equation used in convolution is:

dtin

in

tttt

n

i∫∑ −−

−=

1IV

1IVC

dtdA

D1 = Cos

where:

Cos = Concentration for oral administration predicted by the simulation

DIV = IV bolus dose

dA/dt = Rate of absorption

Convolution Method

General Formula

Methods and Models


CIV = Concentration under IV bolus administration

The conditions of use are:

The reference time is the time corresponding to the oral administration. Before using the convolution method, you might want to use the “Adjust Time” method. This method computes the missing values of CIV at time Tos.

At t = 0, A(t) = 0 or dA/dt =0

There are a variety of convolution options.

The interpolation option compensates for missing values.

No

No value will be computed on a line containing a missing value.

Yes

A value will be computed on the line containing a missing value obtained by interpolation.

For more information, see the AUCcum section in the “AUC* Method” section of this chapter.

Refer to the AUCcum section in the “AUC* Method” section of this chapter.

When there is no value entered for t for the C0 estimate option, C0extrapolated is determined by extrapolating the curve to t=0 obtained through linear regression on the logarithmic transformation using the first several data points.

Convolution Options

Interpolation

AUC

C(IV)0

Methods and Models


T for Lz estimate allows you to specify a start time for terminal slope determination. When there is no value entered for the t for Lz estimate option, the program estimates a suitable starting point for Lz calculation by performing these steps:

1. Kinetica attempts the regression with 3, 4, to n points on the logarithm transformation.

2. Calculate G:

2−−11−

−1=2

n)R)(n(G

where:

R2= coefficient of determination of the regression line on the logarithmic transformation.

n = number of points used for the regression.

3. The number of points used that resulted in the maximum G becomes the starting time point for Lz calculation. (Kinetica starts regression from the last point going backward. For each timepoint it will compute a G value until it reaches Tmax. Then the algorithm will use the largest G value as the starting regression point.)

The T for C0 estimate allows you to specify a start time to execute the backward extrapolation of the IV profile.

When there is no value entered for t for the C0 estimate option, C0-extrapolated is determined by extrapolating the curve to t=0 obtained through linear regression on the logarithmic transformation using the first several data points.

The number of points to include in the calculation is obtained following the same criteria, G, as used for Lz estimation.

T for C0 Estimate and T for Lz Estimate

T for C0 Estimate

Methods and Models


Deconvolution is the process to obtain f with known c and cδ, or cδ with known c and f. In pharmacokinetics, cδ is the drug concentration in the plasma (or blood) resulting from an instantaneous unit input of a drug.

Deconvolution is the reverse process of convolution. General specifications for deconvolution are explained below.

In a linear system, the response C to the input value of f is:

c = cδf

where:

f is the input signal and cδ is the unit impulse response.

This method is applied to oral doses c = cδf, where cδ is the blood/plasma concentration resulting from a unit impulse input; f is the input function. With known cδ and c, f can be estimated from deconvolution analysis.

This example includes both an IV and an OS (oral solution), however the calculation method is the same when it comes to deconvolution. The data/input values are cδ, IV, as well as the concentration obtained by an OS.

If f is considered constant for the intervals between t(i-1) and ti the result is f = fi = constant

Note d Ad t

= f = rate of deconvolution.

Deconvolution Method

Application of Deconvolution

Method of Deconvolution Calculation

Methods and Models


fiti

Ai=

∆∆

cDo

Aiti

AUCostni

n

iv tn titn t i=

=−

− −∑11

1∆∆

∆ ( )( ( ))

where:

Do: initial dose of IV.

Cos: concentration of oral administration.

AUC: area under the curve.

The time reference is based on the oral administration, by interpolation (logarithmic). The two time references can be made to coincide.

∆∆ ∆At

DoCosAUCt

11

1

01=

and A0 = 0

A1 - A0 = ∆A1

Therefore A1

Then, by using:

∆∆

∆∆

∆

∆At

C DoAiti

AUC

AUCn

n

osn tn titn t i

i

n

tn t n=− −

− −

=

−

− −

∑. . ( )

( )

1

1

1

01

with

Methods and Models


A AAt

tn nn

nn= +− 1

∆∆

∆.

We obtain the result An for all n, as well as f.

There are a variety of options for deconvolution.


No

No value will be computed on the line containing a missing value.

Yes

A value will be computed on the line containing a missing value obtained by interpolation.

For more information, see the AUCcum section under the section, “AUC* Method” of this chapter.

Refer to the section “AUC* Options” of this chapter.

CIV (0)

CIV(0)= user-defined

Extrapolated CIV(t=0)

Note For t for C0 estimate (if needed) and T for Lz estimate (if needed), see the section, “Convolution Method” of this chapter.

Deconvolution Options

Interpolation

AUC

Methods and Models


Computes the derivative.

Example: Input columns: t (time) and A (amount of absorbed drug)

Output columns: dA/dt (rate of absorption over time)

This method computes the derivative dA/dt from t and A.

There are a variety of derivation options available.


• No: No value will be computed on the line containing a missing value.

• Yes: A value will be computed on the line containing a missing value obtained by interpolation.

dA use

An-1 and An+1: the derivation is calculated as follows:

( ) ( )

( ) ( )1n1n

1n1n

ttAA

ndtdA

−+

−+

−

−=⎟

⎠⎞

⎜⎝⎛

The derivation of the point at n is calculated by taking the values of the points at (n-1) and (n+1).

An-1 and An: The derivation is calculated as follows:

( ) ( )

( ) ( )

dAdt

nA At t

n n 1

n n 1

⎛⎝⎜

⎞⎠⎟ =

−

−−

−

The derivation of the point at n is calculated by taking the values of the points at (n-1) and at (n).

:AA ∆± dAdt

nAz Aytz ty

⎛⎝⎜

⎞⎠⎟ =

−−

Derivation Method

Derivation Options

Interpolation

Methods and Models


with:

ty = tn – ∆

tz = tn + ∆

( ) ( ) ( )

( ) ( )( )Ay ty t

A At t

A(n 1)n n 1

n n 1n 1= − ⋅

−

−

⎡

⎣⎢⎢

⎤

⎦⎥⎥+−

−

−−

( ) ( ) ( )

( ) ( )( )Az t tz

At

A(n 1)n 1

n 1n 1= − − ⋅

−

−

⎡

⎣⎢⎢

⎤

⎦⎥⎥++

+

++

At

n

n

This method enables you to calculate a linear regression: Y = a + bX

Where:

Y = Y values

X = X values

a = Intercept

b = Slope

r = Coefficient of correlation.

( )( )

( ) ( )r

x x y y

x x y y

i ii

ii

ii

=− −

− −

∑

∑ ∑2 2

Linear Regression Method

Methods and Models


This method enables you to calculate a linear regression through the origin: Y = b X.

where:

Y = Y values

X = X values

b = slope

This method is used when you want to force a line through the origin, even in the absence of data at the point of origin.

Linear Regression by Zero Method

Methods and Models


The Macro to Micro method enables you to get micro constants from fitting methods that produce macro constants. The relationship between macro and micro constants is dependent on the model.

The principles of the one-compartment model are shown in the diagram below.

Figure 7-2. One compartment model schematic

Kel = Lz = alpha

C0 = A

The principles of the two-compartment model are shown in the figure below.

Figure 7-3. Two compartment model schematic

Macro to Micro Method

One-Compartment Models

Two-Compartment Models

Methods and Models


The two-compartment model uses the following constants:

C0 = A + B

21el K

K αβ=

021 C

BAK αβ +=

K12 = α + β - (K21 + Kel)

The figure below is a schematic of a three-compartment model.

Figure 7-4. Three-compartment model schematic

The constants for the three-component micro model are:

C0 = A + B + C

a = α+ β+ γ

Two-Compartment Micro Constants

Three-Compartment Models

Three-Compartment Micro Constants

Methods and Models


CoCABABCb

−+++++

=ββγγαα

CoABCc βγαγαβ ++

=

( )K

b b 4c231

2

=− − −

K21 = -b – K31

3121KKKel αβγ

=

( )( )2131

22131el21

12 KKKKKaK

K−

+−−++=

αγαββγ

K13 = a – (Kel+K12+K21+ K31)

Methods and Models


The Micro to Macro method enables you to get macro constants from fitting methods that produce micro constants. There are relationships between micro and macro constants for different models.

Below is a schematic for one-compartment models.

Figure 7-5. Schematic for a one-compartment model.

The constants used in one-compartment models are:

Kel = α

VcDoseCoA ==

Below is a schematic for two-compartment models.

Figure 7-6. Schematic for a two-compartment model

Micro to Macro Method

One-Compartment Models

Two-Compartment Models

Methods and Models


The constants for two-compartment models are:

a = Kel + K12 + K21

b= a2 - 4 Kel • K21

b)a(21 = +α

β = a – α

cVDoseC0 =

⎟⎟⎠

⎞⎜⎜⎝

⎛−−

=αβα21KC0A

B = CO - A

A schematic for three-compartment models is shown below.

Figure 7-7. Schematic for a three-component model

Three-Compartment Models

Methods and Models


The constants for three-compartment models are given below:

a = Kel + K12 + K21 + K13 + K31

b = Kel • K21 + Kel • K31 + K12 • K31 + K13 • K21 + K21 • K31

c = Kel • K21 • K31

aa13

a=

bb aa13

b2= −

( )cc aa c aa b= + − ⋅3 12

ϕ = −13

13

2Arc tg bbcc

ϕα cos2 ⋅+= bbaa

⎟⎠⎞

⎜⎝⎛ +⋅+= πϕβ

34cos2 bbaa

⎟⎠⎞

⎜⎝⎛ +⋅+= πϕγ

32cos2 bbaa

c

0V

DoseC =

( ) ( )( ) ( )γαβα

αα−⋅−−⋅−

⋅= 31210KK

CA

( ) ( )( ) ( )γβαβ

ββ−⋅−−⋅−

⋅= 31210KK

CB

C = C0 – B – A

Three-Compartment Macro Constants

Methods and Models


The tN% method enables you to obtain the time corresponding to N% of a cumulative curve, using a linear extrapolation.

Condition for use: the point corresponding to the tN% has to be between two values. If this is not the case, Kinetica does not calculate the value of tN%.

tN% = time when YYlast N

100=

⋅

N must be between 1 and 99.


tN% Method

tN% Option

Methods and Models


The tN% of Cmax method enables you to obtain the time corresponding to N% of a maximum point, using a linear extrapolation. It is used generally to obtain t75% of Cmax in bioequivalence studies. TN% of Cmax = time where

100NCmaxC ⋅

=



tN% of Cmax Method

tN% of Cmax Option

Methods and Models


Kinetica offers the ability to quickly and easily generate methods for non-population methods. The interface for writing these methods is called Kinetica Method Editor, located in the Methods pane. You are not obliged to use this editor to create your methods, any text editor will do, but you must use it to compile the methods for use in Kinetica. The methods and models must be written in a VBA-like language (Visual Basic for Applications). We call this language Kinetica Basic.

For more information related to Kinetica Basic, see the Kinetica Basic Reference Guide.

Kinetica Method Editor

Methods and Models


All methods and models in Kinetica are stored in ASCII files with the .BAS suffix.

To open a method/model:

1. Select the Methods pane.

2. Click on Method Editor.

3. Select Open from the File menu. The Open dialog appears.

4. Select the .BAS file containing the appropriate method or model and click Open.

To compile a method/model:

1. Select the Methods pane and click on Method Editor.

2. Open the appropriate method or model and make changes, if required.

3. Select Method Editor then Compile from the Tools menu. Kinetica executes a syntax check. Any errors found will be directed to a message box; otherwise the non-population method/model will be compiled.

Note Errors found during the compilation process will only be displayed in a message box if this was specified in the Report Setup dialog.

Opening Methods/Models

Compiling Methods/Models

Methods and Models


All methods and models in Kinetica are saved in ASCII files with the .BAS suffix.


2. Click on Method Editor.

3. Select Open from the File menu. The Open dialog appears.

4. Select the .BAS file containing the appropriate method or model and click Open.

5. Make the required changes.

6. Select Save or Save As from the File menu.

To copy and paste information:


2. Open the appropriate method or model.

3. Copy by dragging the cursor over the information you want to select.

4. Press and hold down the Ctrl key and then press the C key.

5. Click at the location where you want to paste the information.

6. Press and hold down the Ctrl key and then press the V key.

Saving Changes

Copying and Pasting Information

Methods and Models


To delete information:


2. Open the appropriate method or model.

3. Drag the cursor over the information you want to delete.


• Press the Delete key

• Press the Backspace key

Deleting Information

Methods and Models


We use the concept of “symbolic modeling” to explain how we generate our differential equations. This means that we will create a symbolic representation of our model before generating the interpreted code for compilation in the main program. The Kinetica Designer enables you to save your symbolic model so that you can re-open it at a later date. The file format of the symbolic model is “private.” If you want to include the symbolic model in a report, you must select Copy from the Edit menu to transport it between the clipboard and the destination application (e.g. Word or Excel). When pasted into a different application, the symbolic model will be imported as a picture in the Windows Metafile Format (for additional information related to metafiles, refer to your Windows documentation).

Kinetica Designer

Methods and Models


This dialog is accessed by selecting Designer, then Standard from the Tools menu.

Figure 7-8. Designer Dialog

The menu bar at the top of the Designer screen has three pull-down menus: File, Edit and Help.

The File menu offers six options for manipulating existing models, or models you will create. These options are described in the following table.

Item Description

New Create a new symbolic model. You will be prompted to save any currently existing symbolic model that has not already been saved.

Open Open an existing symbolic model

Save Save the current symbolic model

Save as Rename and save the current symbolic model under a different file name

Designer Dialog

Menu Bar

File Menu

Methods and Models


Item Description

Make Kinetica Basic file

Generate the interpreted source code necessary to compile the current symbolic model for use in an analysis

Exit Close Designer and return to the Interpreter. You will be prompted to save any currently existing symbolic model that has not already been saved.

The Edit menu offers two options for manipulating existing models, or models you will create.

Item Description

Copy Copy the current symbolic model (current contents of the Designer window) to the Windows clipboard facility.

Paste Paste the contents of the clipboard into the Designer window. This event is only available when the contents of the clipboard facility are in the private format recognized by Designer.

This menu displays help information for the current version of designer.

The toolbar offers a series of nine icons. When you select icons, you can position the respective objects in the window in order to create your symbolic model. You can also use the available text box to enter a name for a new model.

Edit Menu

Help Menu

Toolbar

Methods and Models


Figure 7-9. Kinetica Designer Toolbar

The table below describes the different elements of the Kinetica Designer toolbar.

Item Description

Select Enables normal mouse and menu operations.

Add Compartment

Adds a compartment to the symbolic model. Each compartment added is assigned a sequential number. The maximum number of compartments allowed is nine. If you delete a compartment, the remaining compartments are automatically re-numbered in sequential order. The compartment number appears in the center of the compartment symbol.

The following symbol represents each compartment in your symbolic model and is sequentially numbered:

Methods and Models


Item Description

Add Link Add a link to the symbolic model. There are two types of link icons available from the tool bar:

First order link

Michaelis-Menten.

Additionally, each link can appear differently in your symbolic model depending on where you place it (for more information, see the section, “Link Types” of this chapter). The following symbols represent the four different appearances of possible links in your symbolic model:

Define Admin

IV BOLUS. Add an intravenous bolus administration. The following symbol represents an intravenous bolus administration in your symbolic model:

IV INFUSION. Add an intravenous infusion administration. The following symbol represents an intravenous infusion administration in your symbolic model:

Methods and Models


Item Description

EXTRAVASCULAR. Add an extravascular administration. The following symbol represents an extravascular administration in your symbolic model:

Note: You can only specify one administration type per symbolic model.

Add Output Add an output (elimination) object to the symbolic model. You can specify any number of outputs, but can only assign one output per compartment. The following symbol represents an output in your symbolic model:

Specify Sample The sample number appears in the center of the symbol. The following symbol represents a sample (observed compartment) in your symbolic model:

Model Name Enter the name of a new model in this field

Methods and Models


There are two types of link:

• First Order rate

• Michaelis-Menten: CECCEEE

+±=

500

max

Each link has a unique appearance. The appearance of the links in your symbolic model depends on the context.

You can link two compartments with a First Order link or a Michaelis-Menten link. The links can go in both directions. They appear as follows:

First Order Link

Michaelis-Menten Link

Figure 7-10. Schematic of compartment links.

This type of link can only be added between an administration symbol and a compartment. The link can only go in one direction: from the administration symbol to the compartment. The two link types have the following appearance:

First Order Link


Figure 7-11. Administration-compartment links

Link Types

Link between Compartments

Link between IV Bolus, IV Infusion or Extravascular

Administration and Compartment

Methods and Models


You can only link a compartment and a sample with a First Order link. The Michaelis-Menten link can not be used. The link can only go in one direction, from the sample to the compartment.

The link appears as follows:

Figure 7-12. Compartment-Sample link

You can link a compartment and an output using either a First Order link or a Michaelis-Menten link but the link can only go from the compartment to the output.

The link appears as follows:

First Order Link


Figure 7-13. Compartment-Output link

Link between Compartment and Sample

Link between Compartment and Output

Methods and Models


You can perform various operations using the mouse, in conjunction with the available tools.

To add compartments:

1. Select the object you want to add to your model from the toolbar.

2. Select the window area. The corresponding object appears where you clicked in the window.

Note Once you have clicked in the window area, the program automatically moves focus to the Select tool in preparation for your next selection or normal menu and mouse operation.

To add samples:

1. Select the Sample item from the toolbar.

2. Select the window area. The Associate Sample dialog appears.

Figure 7-14. Associate Sample Dialog

3. Enter the sample number and sample name. The name you enter as the sample name must correspond to the name of the sample worksheet in the Dataset workbook containing the relevant sample data.

4. Click OK to confirm your selections and exit the dialog.

Available Tool Operations

Adding Compartments, Administrations, Samples

and Output

Methods and Models


Note If you select Cancel, the sample object is still added to the symbolic model because you are only canceling the Associate Sample dialog. If you do not want the sample object just created, select the object and select the Delete or Backspace key on the keyboard.

To delete compartments, administrations, samples or output:

1. Select the object you want to delete from your model.

2. Select the Delete or Backspace key. The object is deleted, including any associated links.

When you select an object, its border changes from a solid to a dotted line, allowing you to confirm that you are selecting the correct object.

Figure 7-15. Border outline changes when object is selected.

Kinetica Designer is very flexible and lets you easily make changes to your symbolic model. You can make the changes and additions at any time.

Deleting Compartments, Administrations, Samples

or Output

Changing the Symbolic Representation of a

Model

Methods and Models


Changing the sample number is particularly useful if you deleted samples that were initially placed sequentially within your symbolic model. Changing the sample number enables you to reorder the samples.

Note You can not assign a sample number that currently exists but you can specify an existing sample name.

To change the sample name and number:

1. Double-click the sample icon in the symbolic model. The Associate Sample dialog appears.

2. Enter the sample number and name.

3. Click OK to save the information and exit the dialog.

To move compartments, administrations, samples and output:

1. Select the object you want to move.

2. Drag it to another location while holding down the mouse button. The object follows the mouse movement, enabling you to visualize the object in relation to the model and its new destination.

3. When you reach the new destination, release the mouse button. The object remains at this position.

Note While dragging the object, any associated links also move. This enables you to re-visualize the entire model in relation to the object positions.

Changing Sample Names and Numbering

Moving Compartments, Administrations, Samples

and Output

Methods and Models


Follow the instructions below for creating and deleting links.

To create links:

1. Select the appropriate type of link by clicking on one of the link icons from the toolbar.


First Order Link

Figure 7-16. Link types

2. Select the object in the window that will be the link origin (i.e. an administration).

3. While continuing to hold down the mouse button, drag the arrow to the link destination (i.e. a compartment).

4. The link arrow appears. Its appearance will depend on the link you selected from the toolbar. The arrowhead always points towards the link destination.

Figure 7-17. New link created

Creating and Deleting Links

Creating Links

Methods and Models


To delete links:

1. Select the specific link you want to delete.

2. Select the Delete or Backspace key and the link disappears.

3. While continuing to hold down the mouse button, drag the arrow to the link destination (i.e. a compartment).

4. The link arrow appears. Its appearance depends on the link you selected from the toolbar. The arrowhead will always point towards the link destination.

Note When you select a link, it changes from a solid to a dotted line, enabling you to verify that you are deleting the correct link.

Figure 7-18. Selected link path shows a dotted line

Deleting Links

Methods and Models


An example of a method created graphically using the Kinetica Designer appears in the following figure. The Kinetica basic code compiled from this method is described in this chapter.

Figure 7-19. Method Graphically Created using Kinetica Designer

Example of a Designer Method

Methods and Models


If you want to use your symbolic model in a Kinetica analysis, you need to generate the corresponding Kinetica Basic code. (Refer to the Kinetica Basic Reference Guide for more on Kinetica Basic.)

To create a Kinetica Basic file:

1. Create the symbolic representation of your model (see the sections, “Designer Dialog” and “Available Tool Operations” in this chapter).

2. Select Make Kinetica Basic File from the File menu. The Save As dialog appears.

3. Select the file path where you want to save the basic file and enter a file name.

Note You must save the file with a .bas suffix or Kinetica will not be able to open the file. We recommend that you save Kinetica Basic files in the \Kinetica\Models sub-directory because this folder opens by default when you open a Kinetica Basic file.

4. Click Save to create the basic file and exit the Save As dialog. The following message appears: “Do you want to save the changes?” Click Yes to save the visual image and close Designer.

To open the .bas file:

1. Click on Method Editor in the Methods pane.

2. Select Open from the File menu and select the new basic file. The basic code corresponding to the differential system you created in Kinetica Designer appears. You can now edit the

Using Designer to Generate a

Differential Equation

Generating Kinetica Basic Code

Opening the .bas file

Methods and Models


code or compile and run the method. For more information see the section, Compiling Methods/Models.

An example of a Kinetica basic file originally created using Kinetica Designer is shown below:

'Interactive model 'Differential system model generated by KinDiff ' time for observations Dim X as InputColumn ' PLASMA observations Dim Y1 as ColumnToFit ' PLASMA predicted values Dim Y1calc as ComputedColumn Dim Dose as InputNumber Dim V as Parameter Dim k10 as Parameter Dim Z1 as Double Dim DZ1 as Double SUB Fit_Interactive_model () Dim i as Integer Dim ret as Integer Dim hmod as long hmod = NewInteg("Deriv") ret = DeclareComp(hmod, Z1, DZ1) Z1 = Dose/V For i=1 To X_count ' For each time If X_status(i)=0 Then ' Check time status and if OK ret = IntegTo(hmod, X(i)) ' Compute new comp values If Y1_status(i)=0 Then ' If there is an observation Y1calc(i) = Z1 ' Get corresponding computed value Y1calc_status(i) = 0 ' code for Normal value Else Y1calc_status(i) = 3 ' code for Missing value End if End if Next i Y1calc_unit = Y1_unit ' Set units for computed column End Sub Sub Deriv (Byval t as double) DZ1 = -Z1*k10 End Sub


8. Non-Compartmental Analysis

This chapter describes the numerous Non-Compartmental Analysis (NCA) templates that are provided with Kinetica.

Non-Compartmental Analysis


The purpose of non-compartmental analysis (NCA) is to provide an estimate of the kinetic parameters of a drug based on statistical moment theory such as AUC (zero moment), MRT (first moment), etc. The methods associated with non-compartmental analysis algorithms in Kinetica include the following:

• AUC* – all-inclusive first-dose NCA for all types of dosing administration.

• AUC inter* – partial area under the curve computation; this method is often associated with other AUC methods within a template.

• AUC Steady State* – multiple-dose NCA to evaluate exposure over a specified dosing interval.

• AUC Steady State* with Lz – all-inclusive steady-state NCA for all types of dosing administrations for a typical steady-state dosing interval.

• Sparse AUC – sparse sampling NCA based on the Bailer-Satterwaithe algorithm.

• Non-parametric superposition – simulation of multiple dosing regimens without assumption of any compartmental models.

Performing Non-Compartmental

Analysis in Kinetica



This section discusses the AUC* method and how it can be applied to any type of first-dose administration. This method is the all-inclusive first-dose non-compartmental analysis methodology that encompasses all routes of administration. One way of performing non-compartmental analysis is by applying the AUC* method to the time-concentration data.

Follow the steps below to use the AUC* method.

1. Import time-concentration data.

2. Insert a dataset numeric field called Dose.

3. Click Insert Method and select AUC*.

Figure 8-1. Method Selection dialog

4. Map the Input columns and variables for X, Y and Dose. The method will perform an auto-map by matching the variable names. If the variable name is not identical, you need to map the fields manually by moving the cursor close to the right hand side of the white space. Click the area to make the drop-down menu available for selection.

5. Make changes to the output columns and variables, if you wish.

The AUC* Method



6. Click insert then OK.

7. Now that you have successfully inserted the AUC* method, select the Method pane in the left navigation bar.

8. Click the Set button of the AUC* method under the Global Options (see figure below).

Figure 8-2. Setting Global Options in the Methods pane

9. The AUC* Method Global Options dialog box allows you to set the computation settings for the NCA. The Unit tab allows you to set the input and output parameter units.



Figure 8-3. AUC* Method Global Options dialog

10. Once the data are set up, you may perform the noncompartmental analysis by clicking the double-head icon

. Computation results and terminal phase elimination graphs will be produced in the Dataset variable and gallery, respectively.



The AUC from time zero to the last quantifiable measurement can be estimated using the trapezoidal rule (linear rule), log-linear and mixed log linear rules. AUMC calculation follows the same rules as AUC (AUMC is the statistical moment curve where the segmental AUC is multiplied by the time increment).

• Trapezoidal: computes all values in a linear manner.

• Log linear: computes all values prior to Cmax in a linear manner and transforms all values after Cmax to log in its computation.

• Mixed log linear: the computation method where all ascending phases are computed in a linear manner and all descending phases are computed based on their log values.

The AUC from 0 to the first sampling time called AUC0 can be estimated using the following options: C0=0, C0=C1, Extrapolated C0 and no AUC0 computation. The AUC0 calculation is based on the first cell of the Y column when the corresponding X column cell is 0. This computation determines the value for the Y column when the independent variable is 0, and consequently determines whether the route of administration is IV bolus or extravascular. The options available are:

• c0 = c1: applies to IV Bolus, where the concentration at the zero-time point assumes the same concentration for the first sample collection.

• c0 = 0: assumes that the drug concentration at the zero-time point is zero. This condition applies to the IV Infusion and Extravascular routes of administration.

• Extrapolated C0: applies to IV Bolus, where the concentration at the zero-time point is extrapolated based on the log values of the first three data collected.

• No AUC0 computation: assumes no computation of the area under the curve between the zero time point and the first collected time point.

AUC Computation

AUC0 Calculation



The AUC from the last quantifiable measurement to infinity is called AUCextra and can be estimated using the following options: Computed Clast/Lz, Observed Clast/Lz, and no AUCinf calculation. Lz is the slope of the terminal phase using a log scale.

• Computed Clast/Lz: the extrapolated area beyond the last concentration, based on the estimated last concentration obtained from the regression divided by the slope of the regression line.

• Observed Clast/Lz: the extrapolated area beyond the last concentration, based on the observed last concentration divided by the slope of the regression line.

• No AUCinf computation: AUC from zero to infinity is not computed at all.

Ci missing and AUCcum (AUC accumulated) can be estimated using the options Interpolation and No interpolation. Interpolation applies to cases where there are missing data; Kinetica allows you to use linear interpolation (between two points) to estimate the missing value.

The following AUC* method options enable manual entry of values: AUC Log Start Time, C0 Start Time and Lz Start Time.

• AUC Log Start Time: entry overrides Tmax for the start of log AUC computation when AUC computation selected is log-linear. Entry should be a time point in the X column.

• Lz Start Time: entry is used as the start of terminal phase regression and will override the optimization method based on the greatest G-value for the selection of start of terminal regression.

• Lz End Time: entry is used as the end of terminal phase regression.

• C0 Start Time: entry is used as the starting time for the backward regression to the time=0 point for IV bolus

AUC Infinity

AUC Cumulated

Start and End Times



administration. By default, three time points are used in the extrapolation to determine the initial concentration.

The BLQ Data option provides different ways of handling below the limit of quantification data.

• Default: Data with “<” and assumes the value proceeding the less than symbol.

• Set as 0: Data with “<” will assume the value of 0.

• Set as missing: Data with “<” will be treated as missing.

• Box for BLQ before first non-zero normal data = 0, if checked, will treat all BLQ values before the first non-zero data as 0.

• User-defined LLOQ (lower limit of quantification), if checked, will override the BLQ data option and enable user to map the column for the LLOQ values and set BLQ to LLOQ, LLOQ/2, LLOQ/3 and LLOQ/4.

The infusion check box, if checked, allows you to set the infusion duration time or map to a dataset numeric variable for the infusion duration. Note that you need to enter values in the same unit as the time unit. Select the Inf Value radio button if you wish to enter a value manually or select Inf Var Name if you wish to map to a specific field.

The remaining check boxes available in the AUC* Method Global Options window are described below.

If the most optimized G-value lies at the Tmax for the start of terminal phase regression, the next time point with the most optimized G value following Tmax will be used instead, if the box is checked.

BLQ handling

Infusion Box

Other Check Boxes




If the last value is BLQ, with Compute Last AUC Triangle checked, AUClast will include the AUC of the last triangle if the value following the last normal data is flagged as BLQ. Note that AUCall(Obs) always include the last triangle area if the value following the last normal data is flagged as BLQ.

If checked, any cell marked as an outlier will be included in the computation of AUC but not used in the terminal phase regression.

If checked, the individual profile with the estimated terminal phase regression line will be plotted in the gallery view every time AUC* is run. This box is checked by default.

If there is more than one largest concentration value, you may use the first or last Cmax to report Tmax, or use a median Tmax value.

Compute Last AUC triangle

Use outliers in AUC calculation

Show Lz Plot

Multiple Tmax



Kinetica provides a number of Non-Compartmental Analysis (NCA) templates to be used as examples and as working templates for your analyses. You may follow the instructions for each NCA template or the NCA Assistant using the AUC* method.

Three routes of administration are considered; they are represented by templates named for their route:

• IV Bolus

• IV Infusion

• Extravascular.

In addition, the non-compartmental analysis templates enable you to perform unit conversion for the following methods: AUC*, AUCinter* and AUC Steady State*. To use this feature, you need to specify the input units and select the types of units you want for the output variables. The unit management feature will then convert the units to your specifications for the final output variables. For more information, see the sections, “Unit Management for AUC*,” “AUCinter*,” or “AUC Steady State* Methods” in the chapter, “Working with Methods and Models.”

Built-in templates for NCA



The functionality of the IV Bolus template is described in the subsections below.

Inputs are numeric fields and columns where you must enter data so that Kinetica can successfully complete the analysis. Outputs are numeric fields and columns where Kinetica displays the results of the computation. The following input and output information can be found in the Study Info worksheet in the Study pane.

Data input (user-entered dataset numeric field values):

Dose – Amount of drug in the galenic formulation

Data input (user-entered dataset column values):

• T – Time

• C – Concentration

The Set Column Unit and Make ConcUnit methods are used to add units to the columns and assure that Kinetica understands and inserts the correct final measurement units.

Column Output Variable Output

AUC: partial area under the curve


AUCcum: accumulated AUC


AUMC: partial area under the moment curve


AUMCcum: accumulated AUMC


IV Bolus Template

Template Inputs and Outputs

Methods




R: correlation coefficient of linear regression on log transformation


G: criteria used to estimate Lz





HVD: Half-value duration is the period at which the drug concentration is above half Cmax value


G: Criteria used for C0 extrapolation and Lz calculation.




Rsmooth: Ratio between AUC below the curve and AUC above the curve. The output value is always between 0 and 1. A small difference between AUC below the curve and AUC above the curve reveals a good estimation of the real AUC and a good sampling time.

Raccurate: Raccurate =1-AUCpartial/AUC

where:

AUCpartial is the value that has the largest difference from AUC (AUCpartial was calculated using n-1 data points.)










Computed CLast: Last concentration point estimated using the linear equation obtained from linear regression on log- transformed data

TLast: Last time point


B: Slope of the linear equation on log-transformed data


AUCall: If the last sample is at normal status, missing and outlier then, AUCall=AUClast


AUCall=AUClast+AUCtriangle, where: AUClast is from t=0 to the last normal status data AUCtriangle is:

)(2 1 lastlastlast tt

CeAUCtriangl −= +

Clast can be predicted or observed value. tlast+1 is the first BLQ or Zero data sampling time. There are two outputs from two options in AUCall calculation:

to use Clast predicted value, AUCall(CPred)

to use Clast observed value, AUCall(CObs)



The equations used in the methods are listed below.

T1/2 = ln2/k

AUCAUMCMRT =

Clearance: Dose

AUCtot

Vz: Lz.AUCtotDose

⋅

Vss: AUCtotMRTDose ⋅

The following example contains data for one dataset. Forty mg of a drug was administered by IV Bolus. The concentration-time course was sampled from the plasma and expressed in mg/L and h respectively.

To view an IVBolus template:

1. Load Kinetica.

2. Select New from the File menu. The New Analysis dialog appears.

Equations

Viewing an Example of NCA - IV Bolus Template



Figure 8-4. New Analysis Dialog

3. Select the IVBolus analysis found on the Non Compartmental tab.

4. Select the Open with Data check box.

5. Click OK.

Note For this example we entered some data into the relevant template and saved it as a .kdb file. If you open the corresponding file in the template subdirectory, you will see no data. It is ready for your experimental data.

The dataset appears as follows, containing only the raw data:



Figure 8-5. Example Dataset

Before running the analysis, look at a graph of the time versus concentration values to help decide how to set AUC0:

6. Select the T and Civ columns.

7. Select the Dataset Graph button found on the Dataset pane. The following graph appears:



Figure 8-6. Example – Dataset Graph Display


9. In the Methods column, select AUC*.

10. Click Set in the corresponding Global Options column. The AUC* Method Global Options dialog appears.

11. Modify your selections by adjusting AUC and units options.

12. Select the Dataset pane and click the Calculate One button to run the analysis. The results are displayed.

The Lz plot is displayed automatically when the check box is selected. Deselect the check box if you do not want to display the Lz plot.



Figure 8-7. Example – Calculation Results

Note If you have multiple datasets to analyze, you can select Calculate All from the Dataset menu. The results for all datasets are calculated in batch mode and displayed using the gallery feature. You can then make universal changes to the appearance of your graphs.

You can continue to review the data with the graphical tools:

1. Select the T, Civ and AUCcum columns.

Note To select columns that are non-adjacent, hold down Ctrl while selecting the non-contiguous column(s).

2. Click the Show One Graph button. The following graph appears:



Figure 8-8. Example – Single Graph Display

Some methods contain options you can modify. In this template, you can only modify the AUC* method.

To modify options for the AUC* method:

1. Select Methods in the Kinetica Methods pane and select AUC* in the Methods column.


Modifying AUC* Options



Figure 8-9. AUC* Method Global Options Dialog

3. Specify the following from the Options tab:

Field Name Value(s)

AUC Computation Select mixed log-linear, log-linear, or trapezoidal

AUC0 Calculation Select an initial phase extrapolation option: C0=C1, C0=0, Extrapolated C0, or no AUC0 computation

AUC Infinity Select a terminal phase extrapolation option: Computed Clast/Lz, Observed Clast/Lz, or no AUCinf computation

AUC Cumulated Select No Interpolation or Interpolation. Use an interpolation rule when missing values are found.



Field Name Value(s)

AUC Log Start Time Enter a start time for AUC log calculations if you want to override the values previously generated by Kinetica

Lz Start Time Enter a start time for Lz calculations if you want to override the values previously generated by Kinetica.

Lz End Time Enter the last timepoint for the Lz regression calculation.

C0 Start Time Enter a start time for C0 calculations if you want to override the values previously generated by Kinetica.

Show Lz Plot Select the check box to simultaneously plot both the fitted terminal phase and experimental points. Deselect the check box if you do not want to display the Lz graph.


Select the check box to exclude Tmax from the estimation of the terminal slope.

BLQ Data Select Default, Set as 0, or Set as missing. For more information, see the section, “AUC* Options” in the chapter, “Working with Methods and Models”.

The default will take whatever the LQ value (e.g. <0.2 will be calculated as 0.2).

Set as 0 will calculate all BLQ data as 0.

Set as missing will skip the BLQ data and will not use the BLQ in the calculation.

User-defined LLOQ will set all BLQ data as a factor of the entry for LLOQ column. The options include LLOQ, LLOQ/2, LLOQ/3 and LLOQ/4.

Note In order to flag data as BLQ, identify each undetectable data point with a “less than” sign (<) before the data. For more information, see the section, “Displaying Non-Detectable Data Points” in the chapter, “Working with Graphs.”



Field Name Value(s)

BLQ before first non-zero normal data = 0

Select the check box to treat all BLQ before the first quantifiable data as 0.

Use Outliers in AUC Calculation

When enabled, marked outliers are used in the AUC calculations.

Compute last AUC triangle, if last value is BLQ

This option allows the inclusion of the last triangle of the AUC from the last measured value (t-last) to the first BLQ data point for datasets with the trailing measurements being BLQ. This partial AUC is included in the AUC value reported for that profile

To specify IV infusion:

1. Select the Infusion check box.


3. Select Inf Value and enter a numerical value for the infusion in the adjacent field.

4. Select Inf Var Name and select the appropriate variable name for the infusion from the available list. Select this option if the variable already exists in the dataset numerical field (All Variables view in the Study pane).


6. Under the Selected Unit column, specify Time (e.g. h), Concentration (e.g. mg/mL), a Dose (e.g. mg), AUC (e.g. M.h) and any other required units from the drop down list according to your input data. You can change the display of the units under the Display Label column.



Specifying Infusion (applicable to IV infusion only)



• Select Variable Name and select the appropriate variable name for the molecular weight from the available list. Select this option if the variable already exists in the dataset numerical field (All Variables view in the Study pane).

• Select Value and enter a numerical value for the molecular weight in the adjacent field.




The following section provides information on the IV Infusion template.

The following input and output information can be found in the Study Info worksheet in the Study pane:


• Dose – Amount of drug in the galenic formulation

• Tinf – Infusion duration


• T – Time


The Set Column Unit and Make ConcUnit methods are used to add units to the columns and assure that Kinetica understands and inserts the correct final measurement units.


AUC: partial area under the curve


AUCcum: accumulated AUC


AUMC: partial area under the moment curve


AUMCcum: accumulated AUMC


R: correlation coefficient of linear regression on log transformation


IV Infusion Template

Inputs and Outputs

Methods




G: criteria used to estimate Lz






G: Criteria used for C0 extrapolation and Lz calculation.




Rsmooth: Ratio between AUC below the curve and AUC above the curve. The output value is always between 0 and 1. A small difference between AUC below the curve and AUC above the curve reveals a good estimation of the real AUC and a good sampling time.

Raccurate: Raccurate =1-AUCpartial/AUC

where:











Computed CLast: Last concentration point estimated using the linear equation obtained from linear regression on log-transformed data

TLast : Last time point


B: Slope of the linear equation on log-transformed data


AUCall: If the last sample is at normal status, missing and outlier then, AUCall=AUClast


AUCall=AUClast+AUCtriangle, where: AUClast is from t=0 to the last normal status data AUCtriangle is:

)tt(2

CeAUCtriangl last1last

last −= +

Clast can be predicted or observed value. tlast+1 is the first BLQ or Zero data sampling time. There are two outputs from two options in AUCall calculation:

to use Clast predicted value, AUCall(CPred)

to use Clast observed value, AUCall(CObs)



Equations used by the methods:

TLz1

2

2=

ln

2infT

AUCAUMCMRT −=

Cl = DoseAUC

VzDose

AUC Lz=

⋅

VssDose AUMC

AUC

VssDose MRT

AUC

=⋅

=⋅

2

This example contains data for one dataset. An infusion of 7842.35 mg was administered over a period of 0.16667 hours. The concentration-time course was sampled from the plasma. Concentration is expressed in mg/L and time is expressed in h.

To view an example of NCA - IV infusion template:

1. Select New from the Kinetica File menu. The New Analysis dialog appears.

Equations

Viewing an Example of NCA - IV Infusion

Template



Figure 8-10. New Analysis Dialog – IV Infusion

2. Select IV Infusion analysis found on the Non-Compartmental tab.


4. Click OK.

Note For this example, we entered data into the relevant template and saved it as a .kdb file. If you open the corresponding file in the template subdirectory you will see no data. It is ready for your experimental data.

The dataset appears as follows, containing only the raw data that we entered before sending Kinetica:



Figure 8-11. Example – IV Infusion Dataset

Before running the analysis examine the graph displaying the time versus concentration values.

5. Click the Dataset Graph button found in the Dataset pane. The following graph appears:



Figure 8-12. Example – Dataset Graph






Figure 8-13. AUC * Method Global Options

9. Modify your selections by adjusting the AUC and Units options, as required. For more information, see the section, Modifying AUC* Options.

10. Select Calculate One from the Dataset menu to run the analysis.

The Lz plot is displayed automatically when the “Show Lz Plot” check box is selected. Deselect the check box if you do not want to display the Lz plot.



Figure 8-14. Example – Lz Plot Graph

Note If you have multiple datasets to analyze, you can select Calculate All from the Dataset menu. The results for all datasets are calculated in batch mode and displayed using the Gallery feature. You can then make universal changes to the appearance of your graphs.


11. Select the T, C, and AUCcum columns.

Note To select columns that are non-contiguous, hold down the Ctrl key while selecting the non-contiguous column(s).

12. Select the Show One Graph button. The following graph appears:



Figure 8-15. Example – Dataset Graph – IV Infusion

To export your results to Excel:

1. Load Excel.

2. Select the columns or rows you want to export from Kinetica.

3. Select the Export to Excel button.

4. Switch to Excel to see the data displayed in a worksheet.

Exporting Results to Excel



The following section provides information on the Extravascular Route Template.



Dose – Amount of drug in the galenic formulation


• T – Time


The Set Column Unit and MakeConcUnit methods are used to add units to the columns and assure that Kinetica understands and inserts the correct final measurement units required.

This example contains data for one dataset. A drug dose of 8043.43 nmol was orally administered. The concentration-time course was sampled from the plasma. Concentration is expressed in mg/L and time is expressed in h.

To view an example of the NCA - Extravascular template:


Extravascular Route Template

Inputs and Outputs

Methods

Viewing an Example of NCA - Extravascular

Template



Figure 8-16. New Analysis Dialog – Extravascular

2. Select Extravascular analysis, found on the Non-Compartmental tab.


4. Click OK.

Note For this example we entered some data into the relevant template and saved it as a .kdb file. If you open the corresponding file in the template subdirectory you will not see any data. It is ready for your own experimental data.

The dataset group appears as follows, containing only the raw data.



Figure 8-17. Extravascular Dataset

5. Before running the analysis, examine a graph of the time versus concentration values by selecting the dataset graph icon on the left pane.

6. Select the T and C columns.

7. Select the Dataset Graph button found on the Dataset pane. The following appears:



Figure 8-18. Example – Extravascular Dataset Graph






Figure 8-19. AUC * Method Global Options Dialog

11. Modify your selections by adjusting the AUC and Units options, as required. For more information, see the section, “Modifying AUC* Options” in this chapter.

12. Select Calculate One from the Dataset menu to run the analysis.

Note The Lz plot is displayed automatically when the check box is selected. Deselect the check box if you do not want to display the Lz plot.



Figure 8-20. Example - Lz Plot Display

Note If you have multiple datasets to analyze, you can select Calculate All from the Dataset menu. The results for all datasets will be calculated in batch mode and displayed using the Gallery feature. You can make universal changes to the appearance of your graphs.




14. Select the Show One Graph button on the toolbar. The following graph appears:



Figure 8-21. Example – Extravascular Dataset Graph


1. Load Excel.


3. Select the Export to Excel button on the Kinetica toolbar.

4. Switch to Excel. The data is displayed in a worksheet.




The PK parameters available for each administration route are listed in the following table.

Route of Administration

IV Bolus IV Infusion IV Extravascular

PK Parameters

Lz Lz Lz

TLz1

2

2=

ln

TLz1

2

2=

ln

TLz1

2

2=

ln

Tmax Tmax

Cmax Cmax

AUC AUC AUC

AUMC AUMC AUMC

MRTAUMCAUC

= 2

infTAUC

AUMCMRT −=

MRTAUMCAUC

=

Cl = DoseAUC Cl =

DoseAUC

ClF

DoseAUC

=

VzDose

AUC Lz=

⋅ Vz

DoseAUC Lz

=⋅

VzF

DoseAUC Lz

=⋅

VssDose AUMC

AUC

VssDose MRT

AUC

=⋅

=⋅

2

Vss

Dose AUMCAUC

VssDose MRT

AUC

=⋅

=⋅

2

VssF

Dose AUMCAUC

VssF

Dose MRTAUC

=⋅

=⋅

2

For extravascular administration, bioavailability, F, is generally unknown; therefore, parameters containing F are denoted as:

Summary of First Dose PK Parameters

for each Administration Route



ClF

, VzF

and VssF

The real MRT is expressed as:

MRTAUMCAUC

TKalag= − +

⎛⎝⎜

⎞⎠⎟

1

(with Tlag = lag-time, and Ka = absorption constant rate) and not

MRTAUMCAUC

=

But as Tlag and Ka are generally unknown, the following simplified formula is used:

MRTAUMCAUC

=



The Steady State template allows you to obtain pharmacokinetic (PK) parameters by analyzing data using one dosing interval time (Tau). Two methods allow you to calculate PK parameters:

• The AUC Steady State hard-coded method selects one dosage interval for data analysis.

• The ClearanceSS soft-coded method determines estimation of clearance.

• The AUC steady-state with Lz hard-coded method is an extension of the AUC steady-state method. It is designed to perform terminal phase regression and obtain associated pharmacokinetic parameters.

The templates provided for steady state enable analysis of the IV Bolus, IV Infusion and Extravascular administration routes.

The PK parameters computed in the steady state template are described in the following table.

Parameter Description

Tau Dosage interval time

Cmax Maximum drug concentration during the selected dosing interval

Cmin Minimum drug concentration during the dosing interval

Tmax Time corresponding to the first peak of concentration

AUCss Area under the curve during the selected dosing interval at steady state

AUMCss Area under the moment curve during the selected dosing interval at steady state

Steady State Template




Caverage Mean or average steady state drug concentration expressed as:

AUCssTau

%ptf

Peak-through-fluctuation expressed as:

%max min

ptfC C

Caverage= ⋅

−100

%AUCdf Percentage-area-fluctuation expressed as:

%swing Degree of fluctuation expressed as:

%max min

minswing

C CC

= ⋅−

100

Clss Clearance at steady state expressed as:

AUCssenancedosemaClss int

=

(it is Clss/F when the bioavailability F is not equal to 1)

Tstart Dosing interval start time

Tend Dosing interval end time

HVD Half-value duration is the period at which the drug concentration is above half Cmax value.




AUClast AUC from t=Tstart to Tend (last sampling time)

AUCextra extrapolated AUC

AUCtot AUC total (=AUClast+AUCextra)

%AUCextra percentage of AUC extra with respect to AUC total

Lz The elimination rate based on the slope of the terminal phase regression

R linear regression coefficient

G Criteria used for C0 extrapolation and Lz calculation.

Rstart First point used for Lz calculation

Rend Last point used for Lz calculation

Rnbpoint Number of points used in Lz calculation

Rsmooth Ratio between AUC below the curve and AUC above the curve. The output value is always between 0 and 1. A small difference between AUC below the curve and AUC above the curve reveals a good estimation of the real AUC and a good sampling time.

Raccurate Raccurate =1-AUCpartial/AUC

where:


ComputedCLast Last concentration point estimated using the linear equation obtained from linear regression on log- transformed data

Tlast Last time point

Thalf Half-life of elimination

Cstart Concentration at t=tstart




A Intercept of the linear equation of log transformed data

B Slope of the linear equation of log transformed data

R2 Coefficient of determination whose square root is the correlation coefficient

Clss Clearance at steady state expressed as:

AUCssenancedosemaClss int

=

(it is Clss/F when the bioavailability F is not equal to 1)

Vss Volume of distribution at steady state is calculated as Dose/(AUCss*Lz)

MRTlast Steady state mean residence time is calculated as Vss/Clss

Accumulation is calculated as:

τ*11

LzeRac −−

= , where τ is Tstart-Tend





Dose (steady-state) – Maintenance dose


• T – Time


The Set Column Unit and MakeConcUnit methods are used to add units to the columns and assure that Kinetica understands and inserts the correct final measurement units required.

Inputs and Outputs

Methods



Methods Column and Variable Outputs

AUC Steady State AUC = partial area under the curveAUCcum = accumulated area under the curve

AUMC = partial area under the moment curve

AUMCcum = accumulated area under the moment curve

Tau = dosage interval time

Cmax = maximum steady state drug concentration during a dosing interval

Cmin = minimum steady state drug concentration during a dosing interval

Tmax = maximum time corresponding to Cmax

AUCss = area under the curve during a dosing interval at steady state

AUMCss = area under the moment curve during a dosing interval at steady state

Caverage = mean or average steady state drug concentration

%ptf = peak-through-fluctuation

%AUCdf = percentage-area-fluctuation

%swing = degree of fluctuation

Tstart= dosing interval start time

Tend= dosing interval end time

Clearance Steady State

Clss = clearance at steady state



This example contains data for one dataset. At steady state a drug dose of 100 mg was orally administered. The concentration-time course was sampled from the plasma at steady state. Concentration is expressed in mg/L, and time is expressed in h. The PK parameters at steady state were obtained at the dosage interval time (i.e. tau=12h).

To view an example of the NCA - Steady State Template:


Figure 8-22. New Analysis Dialog – Steady State Template

2. Select the Steady State analysis found on the Non Compartmental tab.


4. Click OK.

Note For this example we entered some data into the relevant template and saved it as a .kdb file. If you open the corresponding file in the template subdirectory you will not see any data. It is ready for your own experimental data.

Viewing an Example of NCA - Steady State

Template



The dataset group appears as follows, containing only the raw data that we entered before delivering Kinetica:

Figure 8-23. Dataset Group – Raw Data

Before running the analysis, examine a graph of the time versus concentration values:

1. Select the Dataset Graph button found on the Dataset pane. The following graph appears:



Figure 8-24. Dataset Graph


3. In the Methods column, select AUC Steady State*.

4. Click Set in the corresponding Global Options column. The AUC Steady State Method Global Options dialog appears:



Figure 8-25. AUC Steady State Method Global Options Dialog

5. Enter 48 for t start and 60 for t end. You are now specifying to calculate the steady state parameters between time 48 and 60 hours only.

6. Select Calculate One from the Dataset menu. The results are displayed. Scroll down to find the computed values.

Note If you have multiple datasets to analyze, select Calculate All from the Dataset menu. The results for all datasets will be calculated in batch mode and displayed in the groups. You can scroll through the datasets to see the results for different subjects.



Figure 8-26. Calculated Results Display

You can continue to review the data using the graphical tools:



2. Select the Show One Graph button found on the toolbar. The following graph appears:



Figure 8-27. Single Graph Display


1. Load Excel.


3. Select the Export to Excel button found on the toolbar.

4. Switch to Excel. The data is displayed in a worksheet.

Some methods contain options that you can modify. In this template, you can only modify the AUC Steady State* method.

To modify options for the AUC steady state* method:

1. Select the Methods pane in Kinetica.

2. Locate AUC Steady State * and click Set in the corresponding Global Options column.


Modifying AUC Steady State * Options



Figure 8-28. Modifying AUC Steady State* Options

3. The AUC Steady State Method Global Options dialog appears:

Figure 8-29. AUC Steady State Method Global Options Dialog

4. Select the Options tab, and specify the following information:



AUC Computation Select mixed log-linear, log-linear, or trapezoidal.

BLQ Data Select Default, Set as 0, or Set as missing. For more information, see the section, “AUC* Options” in the chapter, “Working with Methods and Models”.

Note In order to flag data as BLQ, you must first run the analysis, then identify each undetectable data point with a "less than" sign (<) before selecting a BLQ Data indicator. For more information, see the section, “Displaying Non-Detectable Data Points” in the chapter, “Working with Graphs.”

AUC Cumulated Select No Interpolation or Interpolation. Use an interpolation rule when missing values are found.

AUC Log Start Time Enter a start time for AUC calculation (by logarithmic method). By default, Kinetica uses the time corresponding to Cmax in the interval.

t start Enter the time corresponding to the beginning of Tau. If you do not enter a value, Kinetica uses the time corresponding to the first data point.

t end Enter the time corresponding to the end of Tau. If you do not enter a value, Kinetica uses the time corresponding to the last data point.

5. Select the appropriate input and output units from the Units tab.

6. Click OK to save the selections and exit the dialog.



This section describes the steady-state algorithms and how they can be applied to any type of steady-state dose administration. One way to perform non-compartmental analysis is by applying a method to the steady-state time-concentration data. The table below shows the important differences between AUC steady-state* and AUC steady-state with Lz*.

AUC steady-state AUC steady-state with Lz

Multiple-dose profile allowed Profile should contain only the steady-state dosing interval which could be the last dose profile

Computes exposure between any specified start and end time

Computes AUC steady-state based on dosing interval tau

Dose is not required in the method Steady-state dose is required

Half-life and clearance are not computed Half-life, clearance, volume of distribution and parameters derived from terminal phase regression and input dose are computed

Lz graphs not enabled Lz graph with user-defined start and end of regression enabled

A step-by-step example using the AUC steady-state with Lz method is given below:

1. Import time-concentration data

2. Insert a dataset numeric field called Dose

3. Click Insert Method and select AUC Steady State with Lz

The AUC steady-state with Lz* and AUC

steady-state* Methods



Figure 8-30. Selecting AUC Steady State * with Lz method

4. Map the Input columns and variables for X, Y and Dose

5. Make changes to the output column and variable names, if you wish.

6. Click Insert and then click OK.

7. Now that you have successfully inserted the AUC* method, select the Method pane on your left navigation bar.

8. Click the Set button of the AUC Steady State* with Lz method under the Global Options.

9. The AUC Steady State with Lz Method Global Options dialog box appears to allow you to set the computation settings for the NCA. The Unit tab allows you to set the input and output parameter units.

10. Once the data are set-up, you may start analysis by clicking

the double-head icon: .



Figure 8-31. Setting Global Options for the AUC Steady State with Lz method

The AUC from time zero to the last quantifiable measurement can be estimated using trapezoidal rule (linear rule), log linear and mixed log linear. The AUMC calculation follows the same rule as AUC.

• Mixed log linear – refers to the computation method wherein all ascending phases are computed in a linear manner and all descending phases are computed based on their log values;

AUC Computation



• Log linear – computes all values prior to Cmax in a linear manner and transforms all values after Cmax to log in its computation;

• Trapezoidal – computes all values in a linear manner.

The AUC start calculation handles the first cell of the X and Y column. This computation determines the value for the Y column when the independent variable (time) is the start time, consequently determines whether the route of administration is IV bolus or extravascular. These are the following options available:

• cStart = c1 – applies to IV Bolus, where the concentration at the missing zero or first time point assumes the same concentration for the first collection.

• cStart = 0 – assumes that the drug concentration at the zero time point is zero. This condition applies to IV Infusion and Extravascular routes of administration.

• Extrapolated C0 – applies to IV Bolus, where the concentration at the zero time point is extrapolated based on the log values of the first three collected time points.

• No AUC0 computation – assumes no computation of the area under the concentrations between the zero time point and the first collected time point. Computation of the area (AUC) starts at the first available concentration data

• cStart = cTau – use the corresponding concentration value for whatever “T end” is supplied

• cStart based on selected t – the “T for Cstart” is enabled to allow the user to input the time; the corresponding concentration will be used as the starting concentration for the profile.

AUC start Calculation



The AUC from the last quantifiable measurement to infinity called AUCextra can be estimated using the following options: Computed Clast/Lz, Observed Clast/Lz, and no AUCinf calculation where Lz is the slope of the terminal phase using log scale.

• Computed Clast/Lz – the computation of the extrapolated area beyond the last concentration is based on the estimated last concentration obtained from the regression divided by the slope of the regression line;

• Observed Clast/Lz – the extrapolation of the area beyond the last concentration is based on the observed last concentration divided by the slope of the regression line

• No AUCinf computation – AUC from zero to infinity is not computed at all.

Ci missing and AUCcum (AUC accumulated) can be estimated using the following options: Interpolation and No interpolation. Under AUCcum, the two options are Interpolation or No Interpolation. Interpolation applies to cases where there are missing data; Kinetica allows you to use linear interpolation (between the preceeding point and the next point) to estimate the missing value.

The following AUC* method options enable manual entry of values: AUC Log Start Time, C0 Start Time and Lz Start Time.

• AUC Log Start Time – entry will override Tmax for the start of log AUC computation when AUC computation selected is log linear; entry should be a time point in the X column;

• T start – entry is used the start time for the steady-state profile;

• T end – entry is used as the end time for the steady-state profile; the difference between T end and T start becomes the Tau or dosing interval;

AUC Infinity

AUC Cumulated

Start and End Times



• Lz Start Time – entry will be used as the start of terminal phase regression and will override the optimization method based on greatest G-value for the selection of start of terminal regression;

• Lz End Time – entry will be used as the end of terminal phase regression.

• Extrapolated CStart Start Time – entry will be used as the starting time for the backward regression to the Tstart for IV bolus administration; by default, three time points are used in the extrapolation to determine the starting concentration of the steady-state profile.

The BLQ Data option lists different ways how to handle below the limit of quantification data by selecting:

• Default: Data with “<” and assumes the value proceeding the less than symbol.

• Set as 0: Data with “<” will assume the value of 0.

• Set as missing: Data with “<” will be treated as missing.

• Box for BLQ before first non-zero normal data = 0, if checked, will treat all BLQ values before the first non-zero data as 0.

• User-defined LLOQ, if checked, will override the BLQ data option and enable user to map the column for the LLOQ values and set BLQ to LLOQ, LLOQ/2, LLOQ/3 and LLOQ/4.

The Infusion check box, if checked, allows you to set the infusion duration time or map to a dataset a numeric variable for the infusion duration. Note that you must enter values in the same units as the time unit. Select the Inf Value radial button if you wish to enter a value manually, or select the Inf Var Name radial button if you wish to map to a specific field.

BLQ Handling

Infusion Box



The remaining check boxes available in the AUC* Method Global Options window are described below.

If the most optimized G-value lies at the Tmax for the start of terminal phase regression, the next time point with the most optimized G value following Tmax will be used instead, if the box is checked.

If the last value is BLQ, with Compute Last AUC Triangle checked, AUClast will include the AUC of the last triangle if the value following the last normal data is flagged as BLQ. Note that AUCall(Obs) always include the last triangle area if the value following the last normal data is flagged as BLQ.

If checked, any cell marked as an outlier will be included in the computation of AUC but not used in the terminal phase regression.

If checked, the individual profile with the estimated terminal phase regression line will be plotted in the gallery view every time AUC* is run. This box is checked by default.

If there is more than one largest concentration value, you may use the first or last Cmax to report Tmax, or use a median Tmax value.

Other Check Boxes


Compute Last AUC triangle

Use outliers in AUC calculation

Show Lz Plot

Multiple Tmax



To perform sparse AUC computation, you need datasets to be set up such that there are three columns available for Time, Concentration and Animal ID. In the example below, each time point has three concentration data. Notice that the time and concentrations are repeated and taken from different animals. When you import the data into Kinetica, you need to group them such that each individual dataset contains the grouping variable to perform the composite analysis. For example, the specific dataset can be created by grouping animals by gender that had taken the same dose. The Sparse AUC example in the Kinetica\Data folder shows how the method expects the data to be set up.

Time Concentration Animal ID

0 0 101

0 0 102

0 0 103

0.5 4 104

0.5 1.3 105

0.5 3.2 106

1 4.69 107

1 2.07 108

1 6.45 109

2 6.68 104

2 3.83 105

2 6.08 106

4 4.69 101

4 4.06 102

The Sparse AUC* Method



Time Concentration Animal ID

4 6.45 103

6 8.13 104

6 9.54 105

6 6.29 106

8 9.36 107

8 13 108

8 5.48 109

12 5.18 107

12 5.18 108

12 2.79 109

24 1.06 107

24 2.15 108

24 0.827 109

The algorithm used to compute the standard error of the AUC values is based on the trapezoidal (or linear) rule from sparse sampled data. The input variables are the sampling time and the individual drug concentration from plasma or tissue. Note that even though this method computes AUC based on the log linear or trapezoidal rule, it does not apply logarithmic transformation of the data when computing the standard error of the AUC.

The following steps provide a general procedure to perform sparse AUC computation:

1. Import time-concentration data and animal ID.

2. Insert a dataset numeric field called Dose.



3. Click Insert Method and select Sparse AUC*.

4. Map the input columns for X, Y and Animal ID.



7. Now that you have successfully inserted the Sparse AUC* method, select the method pane on your left navigation bar.

8. Click the Set button of the Sparse AUC* method under the Global Options

9. The Sparse AUC* Method Global Options dialog box appears to allow user to set the computation settings for the computation. And the Unit tab allows user to set the input and output parameter units.

10. Once the data are set-up, you may start analysis by clicking

the double head icon: .



Figure 8-32. Sparse AUC* Method Global Options dialog

The AUC from time zero to the last quantifiable measurement can be estimated using trapezoidal rule (linear rule), log-linear and mixed log linear. The AUMC calculation follows the same rule as AUC.

• Mixed log linear – refers to the computation method wherein all ascending phases are computed in a linear manner and all descending phases are computed based on their log values.

• Log linear – computes all values prior to Cmax in a linear manner and transforms all values after Cmax to log in its computation.

• Trapezoidal – computes all values in a linear manner.

AUC Computation



The AUC from 0 to the first sampling time called AUC start can be estimated using the following options: C0=0, C0=C1, Extrapolated C0 and no AUC0 computation. The AUC0 calculation is based on the first cell of the Y column when the corresponding X column cell is 0. This computation determines the value for the Y column when the independent variable is 0, consequently determines whether the route of administration is IV bolus or extravascular. These are the following options available:

• c0 = c1 – applies to IV Bolus, where the concentration at the zero time point assumes the same concentration for the first collection.

• c0 = 0 – assumes that the drug concentration at the zero time point is zero. This condition applies to IV Infusion and Extravascular routes of administration.


• No AUC0 computation – assumes no computation of the area under the concentrations between the zero time point and the first collected time point.

AUC Start Calculation



The following AUC* method options enable manual entry of values – AUC Log Start Time, C0 Start Time and Lz Start Time.

• AUC Log Start Time – entry will override Tmax for the start of log AUC computation when AUC computation selected is log linear; entry should be a time point in the X column.

• Lz Start Time – entry will be used as the start of terminal phase regression and will override the optimization method based on greatest G-value for the selection of start of terminal regression.

• Lz End Time – entry will be used as the end of terminal phase regression.

• C0 Start Time – entry will be used as the starting time for the backward regression to the 0 time point for IV bolus administration; by default, three time points are used in the extrapolation to determine the initial concentration.


• Default – Data with “<” and assumes the value proceeding the less than symbol.

• Set as 0 – Data with “<” will assume the value of 0.

• Set as missing – Data with “<” will be treated as missing.

• Box for BLQ before first non-zero normal data = 0 – if checked, will treat all BLQ values before the first non-zero data as 0.

• User-defined LLOQ – if checked, will override the BLQ data option and enable user to map the column for the LLOQ values and set BLQ to LLOQ, LLOQ/2, LLOQ/3 and LLOQ/4.

Start and End Times

BLQ Handling



Use outliers in AUC calculation – if checked, any cell marked as an outlier will be included in the computation of AUC but not used in the terminal phase regression.

Use Outliers Check Box



The superposition – variable dosage method or the overlay technique allows you to predict concentration data after multiple dosing based on single dose data. The method assumes linearity and proportionality in the data to allow a proportional increase in the dose, if a different dose is given at any interval.

To perform the superposition algorithm, your datasets need to be set up such that the data contains the time and concentration for a single dose, as well as the dose for the profile. Also make sure that you have the Admin dose and Admin time available. The Admin Dose column should contain the multiple doses that you wish to simulate while the Admin Time should contain entries that correspond to the time at which the multiple doses are administered. The Superposition example in the \\Kinetica\Data\Non Compartmental folder contains an example of how the method expects the data to be set up.

The table below shows an example dataset with a set of time-series for sampled concentration and another set of time-series for dose administration.

Time (h) Concentration (ng/mL)

AdminDose (mg) AdminTime (h)

0 0.099 200 0

0.25 2.6546 200 6

0.5 4.09063 300 10

0.75 4.85247

1 5.21021

1.25 5.32743

1.5 5.30317

1.75 5.19728

2 5.0459

2.25 4.87076

The Superposition – Variable Dosage

Method



Time (h) Concentration (ng/mL)

AdminDose (mg) AdminTime (h)

2.5 4.68487

2.75 4.49598

3 4.3086

3.25 4.12536

3.5 3.94768

3.75 3.77631

4 3.61156

4.25 3.4535

4.5 3.30206

4.75 3.15708

5 3.01835

5.25 2.88565

5.5 2.75874

5.75 2.63739

6 2.52137

1. Import time-concentration data and dose information.

2. Insert two columns called Admin Dose and Admin Time by selecting Insert from the menu and then select Column.

3. Click Insert Method and select Superposition – Variable Dosage.



4. Map the input columns for X, Y, Dose, Admin Time and Admin Dose.



7. Now that you have successfully inserted the Superposition – Variable Dosage method, select the method pane on your left navigation bar.

8. Click the Set button of the Superposition – Variable Dosage method under the Global Options

9. The Superposition – Variable Dosage Method Global Options dialog box appears to allow you to set the computation settings for the computation. The Unit tab allows you to set the input and output parameter units.

10. Once the data are set up, you may start analysis by clicking

the double head icon: .



The AUC from time zero to the last quantifiable measurement can be estimated using trapezoidal rule (linear rule), log-linear and mixed log linear. The AUMC calculation follows the same rule as AUC.

• Mixed log linear: refers to the computation method wherein all ascending phases are computed in a linear manner and all descending phases are computed based on their log values.

• Log linear: computes all values prior to Cmax in a linear manner and transforms all values after Cmax to log in its computation.

• Trapezoidal: computes all values in a linear manner.

The time from 0 to the first sampling time called C0 can be estimated using the following options: C0=0, C0=C1, Extrapolated C0 and no AUC0 computation. The AUC0 calculation is based on the first cell of the Y column when the corresponding X column cell is 0. This computation determines the value for the Y column when the independent variable is 0, consequently determines whether the route of administration is IV bolus or extravascular. These are the following options available:

• c0 = c1 – applies to IV Bolus, where the concentration at the zero time point assumes the same concentration for the first collection.

• c0 = 0 – assumes that the drug concentration at the zero time point is zero. This condition applies to IV Infusion and Extravascular routes of administration.


• No AUC0 computation – assumes no computation of the area under the concentrations between the zero time point and the first collected time point.

Interpolation Method

C0 Handling




• Default – Data with “<” and assumes the value proceeding the less than symbol;

• Set as 0 – Data with “<” will assume the value of 0;

• Set as missing – Data with “<” will be treated as missing;

Last Simulated Time Point – enter the last time point where simulation stop

You need to tell the method the total number of points to simulate. The method takes the last simulated time point and divides it by the total number of points minus 1 to increment the time points from the first time point all the way to the last simulated time point. Therefore, you will need to provide n+1 points to include zero time point. For example, if you want to simulate every 0.125 hour up to 24 for the independent variable, the value for the total number of output data points is 24 * 23 + 1 = 193.

Regression Start Time: entry will be used as the start of terminal phase regression and will override the optimization method based on greatest G-value for the selection of start of terminal regression.

Regression End Time: entry will be used as the end of terminal phase regression.

BLQ handling

Last Simulated Time Point

Number of Output Data Points

Start and End Times



The NCA Assistant is a tool for high throughput non-compartmental analyses, enabling you to interactively analyze data using a graphical interface. You can view the time versus concentration data values next to the subject curve. You can customize the appearance of the plot, selecting the style, width, and color for the line. You also have the option to flag outlier and BLQ data points. Selections can be applied to single datasets or all datasets in the study. The non-compartmental parameters are recalculated whenever options are modified.

The NCA Assistant wizard is accessed from the Kinetica Welcome dialog or from the toolbar any time during a working session.

The NCA Assistant wizard is a series of dialogs that simplifies non-compartmental analyses and guides you through the process step-by-step. You: specify X and Y input columns and variables for the analysis, global AUC rules and options, units for input and output variables, and local AUC options.

To use the NCA Assistant wizard, read the instructions in each dialog to help you enter information, and follow the steps in the procedure. You can move back and forth between the dialogs and change information as required until you complete the wizard. You can exit without saving the information at any point before completing the wizard by clicking Cancel.

The NCA Assistant wizard has 5 steps. Steps 1, 3, and 5 can be completed by following the instructions in the dialog and in the procedure. Steps 2 and 4 are explained in detail in their own sections.

Note The NCA Assistant wizard links to the first defined AUC* method it locates in the list of methods.

Working with the NCA Assistant

NCA Assistant Wizard



This dialog is used to specify the calculation rules to apply to a selected study. The available options are listed in the following table.

Calculation Rule

AUC Mixed Log Linear

Log Linear

Trapezoidal

AUC0 c0=0

c0=c1

Extrapolated c0

No AUC0 computation

AUCinf ComputedClast/Lz

ObservedClast/Lz

No AUCinf computation

AUCcum No interpolation

Interpolation

NCA Assistant – Step 2 of 5 Dialog



Calculation Rule

BLQ Data Select Default, Set as 0, or Set as missing. For more information, see the section, “AUC * Options” in the chapter, “Working with Methods and Models”. If you do not specify a BLQ Data indicator, the "Default" option will be used for all AUC computations, by default.

Note In order to flag data as BLQ, you must first run the analysis, then identify each undetectable data point with a less-than sign (<) before selecting a BLQ Data indicator. For more information, see the section, “Displaying Non-Detectable Data Points” in the chapter, “Working with Graphs”. Alternatively, you can identify undetectable data points in the NCA Assistant – Step 4 of 5 dialog. For more information, see the section, “Flagging Data as BLQ (Undetectable)” in this chapter.

Infusion Variable

User-defined selection (available only if the route of administration is IV infusion)

This dialog is used to select local and global AUC calculations to apply to a selected study. You can also view various datasets contained in the study, traversing through each dataset to view the results. These options are described in the following table.

Option Description

View data During an analysis, you can view the graphical plot only, or the graphical plot and the X and Y column values used to plot the graph. To do this, select or deselect the View Data check box. This option is useful when combined with the ability to flag outliers and/or BLQ data points since the flags become visible in the Y data column

NCA Assistant – Step 4 of 5 Dialog



Option Description

Y-Log scale Sets the display for the Y axis to log scale

Local options The Local options area of the dialog is used to specify the following:

• T for AUC log

• T for Lz estimate

• T for Co estimate

• Infusion Value.

These options can be set individually for each dataset.

Recalculate Re-analyze all the AUC parameters.

Apply to all Apply the current calculation options to every dataset in the study.

Results area Contains a list of computed AUC parameters. These calculations are derived from user-defined selections made in real-time on the graphical plot.



You can specify a start time for AUC log calculations by plotting a line indicator on the graphical plot (a blue solid line). You can perform this function by entering a time value in the t for AUC log field.

Your plot should resemble the following:

Figure 8-33. Example – Specifying Time for AUC Log

Specifying Time for AUC Log



You can specify a start time for terminal phase regression analysis by plotting a vertical line on the graphical plot (plots a green solid line). To perform this function, click on the appropriate timepoint to start the Lz plot, or enter a value on the t for Lz estimate field (see figure below). Each time you move the vertical line, the non-compartmental parameters are re-calculated and the results are updated. If you do not specify a terminal phase regression start time, the NCA Assistant will use an automatic calculation to find a suitable result.

Figure 8-34. Example – Specifying Time for Terminal Phase Regression

You can specify a start time for initial phase regression analysis by plotting a line indicator (a green broken line) on the graphical plot (see figure below). You can perform this function by entering a time value in the t for C0 estimate field. If you do not specify an initial phase regression start time, the NCA Assistant will use an automatic calculation to find a suitable result.

Specifying Time for Terminal Phase

Regression

Specifying Time for Initial Phase Regression



Figure 8-35. Example – Specifying Time for Initial Phase Regression

You can specify whether a data point is an outlier or not, therefore, including or excluding the value from the analysis. If you flag a data point as an outlier, the outlier data point changes to a blue circle. Each time you exclude or include a data point the non-compartmental parameters are re-calculated and updated, and the terminal phase regression line indicator is automatically moved.

The original data point values are never deleted or amended. A flag is simply attached to the data value, as can be seen by selecting the View Data check box.

Flagging Data as Outlier



Figure 8-36. Example – Flagging Data as Outlier



You can specify whether a data point is below the limit of quantification (BLQ or undetectable) or not, thereby including or excluding the value from the analysis. Each time you exclude or include a data point, the non-compartmental parameters are re-calculated and updated, and the terminal phase regression line indicator is automatically moved.

The original data point values are never deleted or amended. A flag is simply attached to the data value, as can be seen by selecting the View Data check box.

The default BLQ symbol appears as an hourglass-like shape.

Figure 8-37. Example – Flagging Data as BLQ

Flagging Data as BLQ (Undetectable)



To perform non-compartmental analysis using the NCA assistant:

1. Launch Kinetica. The Welcome to Kinetica dialog appears:

Figure 8-38. Welcome to Kinetica Dialog

2. Select NCA Assistant and click OK. The New Analysis dialog appears.

Figure 8-39. NCA Assistant with Extravascular assistant selected and Open with Data checkbox checked.

Performing Non-compartmental Analysis Using the NCA Assistant



3. Select a non-compartmental analysis type. To open the template with example data, select the Open with Data check box.

Note If you would like your own NCA template to appear in the NCA Assistant, before starting Kinetica place your template (a .ktp file) in the Program Files\Kinetica\Data directory.

4. For this example, we select the Extravascular template, check the Open with Data box, and click OK.

If you do not use the Open with Data option, the following message appears:

Figure 8-40. Kinetica query when you do not select Open with Data.

Do one of the following:

• Click Yes to enter the data manually.

• Click No. Import Assistant imports the data for you from a foreign file or database. The assistant only works if the study contains data.

5. After clicking OK with the Extravascular template, Open with Data selected, the NCA Assistant - Step 1 of 5 dialog appears.



Figure 8-41. NCA Assistant – Step 1 of 5

6. Select the X and Y input columns for the plot from the X column and Y column list boxes. By default, Kinetica selects the first two columns of the first worksheet.

7. Specify the route of drug administration from the Route list box.

8. Specify the dose from the Dose list box and click Next. The NCA Assistant - Step 2 of 5 dialog appears.




9. Select the appropriate AUC options, BLQ indicator (see the section BLQ handling for more on BLQ), and Infusion Variable (if required) from the available lists and click Next. The NCA Assistant - Step 3 of 5 dialog appears.




10. Specify the input units (Time, Concentration and Dose) and select the appropriate units for the output variables.

11. Click Next. The NCA Assistant - Step 4 of 5 dialog appears. A plot of the X and Y columns appears, displaying the regression line derived from an automatic linear regression calculation.




12. Select the options to use for the AUC calculations, as required.

To customize the plot, right-click anywhere in the graph, select Graph Properties and then the appropriate selection from the second popup menu. Make modifications as required. For more information, see the section, Dataset Graph Options in the chapter, Working with Graphs.

Customizing the Plot



To flag outlier data points, right-click the appropriate data point (shown as Point# x value; y value) and select Outlier from the popup menu. The data point changes to a blue circle.

To flag BLQ data points, right-click the appropriate data point (shown as Point# x value; y value) and select Undetectable from the popup menu. The data point changes to an hourglass-like shape.

To send a graph to the Gallery:

1. Right click anywhere in the graph and select Send to Gallery. For more information, see the section, “Working with the Graph Gallery” in the chapter, “Working with Graphs.”

2. Click Next. The NCA Assistant – Step 5 of 5 dialog appears:

Figure 8-45. NCA Assistant – Step 5 of 5 dialog

Flagging Outlier Data Points

Flagging BLQ Data Points

Sending a Graph to the Gallery



3. Click Finish to conclude the NCA Assistant wizard.

4. Kinetica displays the graph Gallery view with the new graph added.


9. Performing Compartmental Analysis

Kinetica is a flexible tool for all kinds of pharmocokinetic/ pharmacodynamic (PK/PD) fitting. Many methods, models and templates are included with the software, and we have designed an interpreted language called Kinetica Basic that enables you to create any model you require. A differential equation solver is supplied and a visual designer enables you to generate the basic code for differential systems interactively drawn on your screen. This chapter guides you through the available models, options, and the generated output.

Performing Compartmental Analysis


In a single dose situation you can execute the fitting method without entering any initial parameter values. The templates and methods for fitting are categorized by the topics discussed below.

Whether you want to obtain a fit using first macro and then micro constants or vice-versa.

There are four primary routes of administration:

• IV Bolus

• Extravascular

• IV Infusion

• Zero Order Input.

Kinetica provides TmaxCalc and CmaxCalc as output variables. CmaxCalc corresponds to the IV extrapolated concentration at t=0 and TmaxCalc=0. Lz always represents the smallest disposition rate constant.

This method fits data for all routes other than IV Bolus and IV Infusion (e.g. oral administration, I.M, etc.). The general condition for use is an input (or absorption) following a first order, therefore Kinetica provides Ka as an output variable. You can fit with or without lag-time. Kinetica also provides TmaxCalc and CmaxCalc as output variables. These two parameters can be different from the TmaxObserved and the CmaxObserved. Lz always represents the smallest disposition rate constant.

The Infusion duration is known, and is an input variable. Kinetica also provides TmaxCalc and CmaxCalc as output variables. These two parameters can be different from the TmaxObserved and the CmaxObserved. Lz always represents the smallest disposition rate constant.

Fitting Data with Kinetica - Single

Dose


IV Bolus

Extravascular

IV Infusion



This method is used when you have a kinetic profile with a zero order input function that is not an IV Infusion (for example oral, I.M administration, etc). The Input duration (equivalent to the Infusion duration in the IV Infusion) is an output variable and is then estimated by Kinetica. TmaxCalc and CmaxCalc are provided as output variables. These two parameters can be different from the TmaxObserved and the CmaxObserved. Lz always represents the smallest disposition rate constant.

By using Macro constant methods, you obtain directly:

• A, alpha

• B, beta

• C, gamma

By using Micro constant methods, you obtain directly:

• Vc = the apparent volume of the central compartment

• Kel = elimination rate constant from the central compartment

• K12, K21 = elimination rate constants from the central compartment to the superficial ( 2nd) compartment, from the superficial compartment to the central compartment respectively

• K13, K31 = elimination rate constants from the central compartment to the deep ( 3rd) compartment, from the deep peripheral compartment to the central compartment respectively

Note Lz always represents the smallest disposition rate constant.

Whenever you use one of the templates supplied for fitting, you will always find a method called “Macro to Micro” after using a model employing macro constants, and you will always find a method called “Micro to Macro” after using a model employing

Zero Order Input

Macro or Micro Constants



Micro constants. You can always obtain all the constants (macro and micro) when using any fitting method.

Except for the methods related to units, Kinetica needs three methods in a Single Dose Fitting template.

The first method is the fitting method depending, on the method and macro/micro constants. It allows you to obtain the following pharmacokinetic parameters that come directly from fitting (except for the macro or micro constants):

• TmaxCalc, CmaxCalc

• AUC, AUMC

• MRT, Lz.

The methods are listed in the following table.


Macroconstants Microconstants

IV Bolus FitMacroIVBolus FitMicroIVBolus

IV Infusion FitMacroIVInf FitMicroIVInf

Extravascular FitMacroExtravascular FitMicroExtravascular

0 Order Input FitMacro0OrderInput FitMicro0OrderInput

The second method enables the conversion of macro to micro constants or micro to macro constants. The two methods are called MacroToMicro and MicroToMacro.

The third method is a method written in the Kinetica Basic Language. The method is called PkExFitDerive (for the extravascular route) or PKIVFitDerive (for all other routes).

The PKExFitDerive or PKIVFitDerive method enables you to obtain additional PK parameters such as:

T1/2 = half-life

Creating a Single Dose Fitting Template



Tabs = Duration of absorption (for the extravascular method only)

Vss = Apparent volume of distribution at steady-state

Vz = Apparent volume of distribution during the terminal phase (Lz)

Cl = Total clearance.

The following example displays these three methods (FitMacroExtravascular, MacroToMicro, and PkExFitDerive) in the Methods view for a Single Dose Extravascular Macro Constants template.

Note Methods are organized and executed sequentially in Kinetica.

Figure 9-1. Methods View for a Single Dose Extravascular Macro Constants template.



For multiple dose, you can not execute the fitting method without entering initial parameters. The templates and methods for fitting contain only Micro constant methods (no Macro constant methods).

In the sample view of the Dataset pane you must have three input columns ready for the fitting: time, concentration and dose. You simply enter the quantity of dose in the cell (Dose column) adjacent to the time the dose was administered.

Time Concentration Dose

0 100

0.5 47.5615

1 45.2419

1.5 43.0354

2 40.9365

3 37.0409

6 27.4406

9 20.3285

12 15.0597

18 8.26494

24 54.5359 100

24.5 51.8762

25 49.3461

25.5 46.9395

26 44.6502

Fitting Data with Kinetica - Multiple

Dose

IV Bolus and Extravascular



Time Concentration Dose

27 40.4012

30 29.9299

33 22.1726

36 16.4259

42 9.01472

48 54.9474 100

48.5 52.2676

49 49.7184

In the sample view of the Dataset pane you must have four input columns ready for the fitting: time, concentration, dose, and infusion duration. You simply enter the duration of the infusion in the cell (Infusion Duration column) adjacent to each dose you entered previously.

Figure 9-2. Sample View – Dataset Pane

IV Infusion



When using the Multiple Dose templates you obtain the following micro constants:

• Vc = Apparent volume of the central compartment

• Kel = Elimination rate constant from the central compartment

• Ka, Tlag (if extravascular route)

• K12, K21 = elimination rate constants from the central compartment to the superficial (2nd ) compartment, from the superficial compartment to the central compartment respectively

• K13, K31 = elimination rate constants from the central compartment to the deep peripheral (3rd) compartment, from the deep peripheral compartment to the central compartment respectively.

Macro constants are A, B, C and alpha, beta, and gamma. Alpha, beta, and gamma are dose independent parameters, while A, B and C are dose-dependent parameters. You are required to enter the dose for macro constants calculation. The results of A, B, C depend on the values for dose that you entered. For simplicity, we suggest that you enter a value of one "1" for dose, to calculate A, B, and C.

Except for the methods concerning units, Kinetica needs three methods in a Multiple Dose Fitting template. The first method is the fitting method depending on the route. This first method in the fitting template only enables you to obtain the pharmacokinetic parameters which come directly from the fitting as follows:

• Ka, tlag (for extravascular route)

• Vc, Kel

• K12, K21, K13, K31.

The names of the methods are the following:

Micro and Macro Constants

Creating a Multiple Dose Fitting Template



• IV Bolus

• FitMultiMicroIVBolus

• IV Infusion

• FitMultiMicroIVInf

• Extravascular

• FitMultiMicroExtravascular.

The second method, MicroToMacro, enables the conversion of micro constants to macro constants.

The third method, CalcThalf, was written in the Kinetica Basic Language and is applicable to one, two, and three compartment models. It enables you to obtain T1/2 alpha, T1/2 beta and T1/2

gamma.

The following example illustrates these three methods (FitMultiMicroIVBolus, MicroToMacro, and CalcThalf123comp) in the Methods view for a Multiple Dose IV Bolus Micro Constants template:

Note Methods are organized and executed sequentially in Kinetica.



Figure 9-3. Methods View for a Multiple Dose IV Bolus Micro Constants



Kinetica includes one, two and three compartment Zero Order Absorption (single dose only), Extravascular, IV Bolus and IV Infusion models for single and multiple dose regimens. These models are included in the library of hard-coded methods. In addition, there are also some soft-coded methods written in Kinetica Basic. You have the option to open these soft-coded methods in the Basic Editor and modify them to meet your own requirements.

The methods hard-coded in C++ are listed in the table below.

Note No models for Multiple dose macro constants are included because they require a dose-independent parameterization.

Hard-Coded Method Name Function in Kinetica

FitMacro0orderinput Single Dose Zero Order Macro Constants

FitMacroExtravascular Single Dose Extravascular Macro Constants

FitMacroIVBolus Single Dose IV Bolus Macro Constants

FitMacroIVInf Single Dose IV Infusion Macro Constants

FitMicro0orderinput Single Dose Zero Order Micro Constants

FitMicroExtravascular Single Dose Extravascular Micro Constants

FitMicroIVBolus Single Dose IV Bolus Micro Constants

FitMicroIVInf Single Dose IV Infusion Micro Constants

FitMultiMicroExtravascular Multiple Dose Extravascular Micro Constants

FitMultiMicroIVBolus Multiple Dose IV Bolus Micro Constants

Available Models



Hard-Coded Method Name Function in Kinetica

FitMultiMicroIVInf Multiple Dose IV Infusion Micro Constants

FitMultiMicro Multiple Dose With Multiple Method Micro Constants

The following models are hard-coded for IV bolus analysis:

(1A) talphaeAC ⋅−⋅=

(1B) tbetatalpha eBeAC ⋅−⋅− ⋅+⋅=

(1C) tgammatbetatalpha eCeBeAC ⋅−⋅−⋅− ⋅+⋅+⋅=

Hard-Coded Models: IV Bolus Macro Constants



The following models are hard-coded for IV infusion analysis:

T = infusion duration

(2A)

( )

( )talphae)Tt(alphaeTalpha

ATtC

talphae1Talpha

ATtC

⋅−−

−⋅−⋅

⋅=≥

⋅−−⋅

⋅=<

(2B)

⎟⎠⎞⎜

⎝⎛⎟

⎠⎞⎜

⎝⎛

⎟⎠⎞⎜

⎝⎛⎟

⎠⎞⎜

⎝⎛

⋅−−−⋅−

⋅⋅

+⋅−

−−⋅−

⋅⋅

=≥

⋅−−⋅⋅

+⋅−

−⋅⋅

=<

tbetae)Tt(betaeTbeta

Btalphae)Tt(alphaeTalpha

ATt

C

tbetae1Tbeta

Btalphae1Talpha

ATt

C

(2C)

( ) ( ) ( )

( ) ( ) ( )tgamma)Tt(gammatbeta)Tt(betatalpha)Tt(alphaTt

tgammatbetatalphaTt

eeTgamma

CeeTbeta

BeeTalpha

AC

e1Tgamma

Ce1Tbeta

Be1Talpha

AC

⋅−−⋅−⋅−−⋅−⋅−−⋅−≥

⋅−⋅−⋅−<

−⋅⋅

+−⋅⋅

+−⋅⋅

=

−⋅⋅

+−⋅⋅

+−⋅⋅

=

The following models are hard-coded for extravascular analysis:

When (t<=lag), C = 0

tl = t - lag

(3A)

( )tlkatlalpha eealphaKa

KaAC ⋅−⋅− −⋅−

⋅=

Hard-Coded Models: IV Infusion Macro Constants

Hard-Coded Models: Extravascular Macro

Constants



(3B)

( ) ( )tlkatlbetatlkatlalpha eebetaKa

KaBeealphaKa

KaAC ⋅−⋅−⋅−⋅− −⋅−

⋅+−⋅−

⋅=

(3C)

( ) ( ) ( )tlkatlgammatlkatlbetatlkatlalpha eegammaKaKa

CeebetaKa

KaBee

alphaKaKa

AC ⋅−⋅−⋅−⋅−⋅−⋅− −⋅−

⋅+−⋅−

⋅+−⋅−

⋅=

When selecting one of the Micro constant templates in Kinetica, we first compute the model using macro constants and then call a MacroToMicro method that converts the results to micro constants. This process is completely automatic. The two steps are described below:

• STEP A - Compute macro constants

Use Equations 1A, 1B, or 1C.

• STEP B - Convert from macro to micro constants depending on the number of compartments

(4) One compartment micro constants

Kel = Lz = alpha

Co = A

(5) Two compartment micro constants

Co = A + B

Kelalpha beta

K21=

⋅

KA beta B alpha

Co21 =⋅ + ⋅

K12 = alpha + beta - (K21 + Kel)

Hard-Coded Models: IV Bolus Micro Constants



(6) Three compartment micro constants

Co = A + B + C

a = alpha + beta + gamma

bC alpha B alpha A gamma B gamma A beta C beta

Co=

⋅ + ⋅ + ⋅ + ⋅ + ⋅ + ⋅−

cC alpha beta B alpha gamma A beta gamma

Co=

⋅ ⋅ + ⋅ ⋅ + ⋅ ⋅

( )K

b b 4c231

2

=− − −

K21 = -b - K31

Kelalpha beta gamma

K K21 31=

⋅ ⋅⋅

( )( )K

beta gamma alpha beta alpha gamma K a Kel K K

K K1221 31 21

2

31 21

=⋅ + ⋅ + ⋅ − ⋅ − ⋅ +

−

K13 = a - (Kel + K12 + K21 + K31)

When selecting one of the Micro constant templates in Kinetica, we first compute the model using macro constants and then call a MacroToMicro method that converts the results to micro constants. This process is completely automatic. The two steps are described below:

• STEP A – Compute macro constants

Use Equation set (2).

• STEP B – Convert from macro to micro constants depending on the number of compartments

Hard-Coded Models: Zero Order and IV Infusion

Micro Constants



Use Equations (4), (5), or (6).

When selecting one of the Micro constant templates in Kinetica we first compute the model using macro constants and then call a MacroToMicro method that converts the results to micro constants. This process is completely automatic. The two steps are described below:

• STEP A – Compute macro constants

Use equations (3A), (3B), or (3C).

• STEP B – Convert from macro to micro constants depending on the number of compartments

Use Equation (6).

Hard-Coded Models: Extravascular Macro

Constants



The table below catalogs the equations for PK parameters for the IV Bolus fitted model.

Parameter One Compartment Two Compartments Three Compartments

Vc Vc

DoseA

=

VcDose

A + B=

Vc

DoseA + B + C

=

AUC AUC

Aalpha

=

AUCA

alphaB

beta= +

AUC

Aalpha

Bbeta

Cgamma

= + +

AUMC AUMC

Aalpha2=

AUMC

Aalpha

Bbeta2 2= +

AUMC

Aalpha

Bbeta

Cgamma2 2 2= + +

MRT MRT

AUMCAUC

=

T T

ln2alpha1/2alpha

=

Tln2beta1/2beta

=

Tln2

gamma1/2gamma=

Cl Cl

DoseAUC

=

V V Cl MRTss = ⋅

V V

ClLzz =

IV Bolus Fitted Model - Equations for PK

Parameters



The following table summarizes the equations for the PK parameters of the IV infusion fitted model:


Vc Vc

DoseA

=

VcDose

A + B=

Vc

DoseA + B + C

=

AUC AUC

Aalpha

=

AUCA

alphaB

beta= +

AUC

Aalpha

Bbeta

Cgamma

= + +

AUMC AUMC

Aalpha 2=

AUMC

Aalpha

Bbeta2 2= +

AUMC

Aalpha

Bbeta

Cgamma2 2 2= + +

MRT MRT

AUMCAUC

=

T T

ln2alpha1/2alpha

=

Tln2beta1/2beta

=

Tln2

gamma1/2gamma=

Cl Cl

DoseAUC

=

V V Cl MRT ss = ⋅

V V

ClLzz =

IV Infusion Fitted Model - Equations for PK

Parameters



The table below summarizes the equations for PK parameters for the extravascular fitted model.


Vc Vc

DoseA

=

VcDose

A + B=

Vc

DoseA + B + C

=

AUC AUC AKa

Ka alpha1

alpha1

Ka=

−−

⎛⎝⎜

⎞⎠⎟ AUC A

KaKa alpha

1alpha

1Ka

=−

−⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

+ B Ka

Ka beta1

beta1

Ka−−

⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

AUC AKa

Ka alpha1

alpha1

Ka=

−−

⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

+ B Ka

Ka beta1

beta1

Ka−−

⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

+ C Ka

Ka gamma1

gamma1

Ka−−

⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

AUMC AUMC AKa

Ka alpha1

alpha1

Ka2 2=−

−⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥ AUMC A

KaKa alpha

1alpha

1Ka2 2=

−−

⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

+−

−⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥B

KaKa beta

1beta

1Ka2 2

AUMC AKa

Ka alpha1

alpha1

Ka2 2=−

−⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥

+−

−⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥B

KaKa beta

1beta

1Ka2 2

+−

−⎛⎝⎜

⎞⎠⎟

⎡

⎣⎢

⎤

⎦⎥C

KaKa gamma

1gamma

1Ka2 2

MRT MRTAUMCAUC

lag1

Ka= − +

⎛⎝⎜

⎞⎠⎟

T T

ln2alpha1/2alpha

=

Tln2beta1/2beta

=

Tln2

gamma1/2gamma=

Cl Cl

DoseAUC

=

V V Cl MRTss = ⋅

V V

ClLzz =

Extravascular Fitted Model - Equations for PK

Parameters



For fitting all cases of PK multiple dose data, Kinetica uses the principle of superposition by following these steps:

1. Calculating the concentrations (Ccalc) for each administration in an independent manner by using the equations computed for the single dose micro constant model corresponding to each method (see the equations for single dose micro constants, Hard-Coded Models: IV Bolus Micro Constants and Hard-Coded Models: Zero Order and IV Infusion Micro Constants in this chapter).

2. Adding the Ccalc computed values for each time.

3. Plotting the curve where Ccalc = f (t) to obtain the total kinetic profile containing all the administrations.

4. Computing the fitting from this curve.

Equations for Fitting Multiple Dose Data



You can select the hard-coded methods for fitting or simulation in the Method Selection dialog. We call these method models because they are specific methods for modeling data rather than simple functions. The soft-coded methods are stored in text files with the *.BAS extension and can be retrieved in the Method Editor. When you choose a method from the Method Selection dialog, you can view the associated input and output parameters and a brief explanation of what the method computes.

To select a method model

1. Load Kinetica and open the appropriate file.


• Select Method from the Insert menu. The Method Selection dialog appears. Choose a hard-coded or a soft-coded method from the available lists.

• Create your own soft-coded model method by clicking Method Editor in the Methods pane

Use Designer to create your method graphically by selecting Designer then Standard from the Tools menu. For more information, see the section, “Kinetica Designer” in the chapter, “Working with Methods and Models.”

Selecting a Method Model



The start values will affect the iterative procedure to converge to a solution. Inadequate start information may result in convergence to an unreliable point, where the final parameter values do not provide the true optimum, but rather a “local” optimum.

You do not have to provide start values for a single-dose study, however, you must enter some start values in the parameter cells of the study or dataset group for multiple dose and steady state datasets. These cells are automatically inserted if you choose a hard-coded method, but you must create them if you insert a soft-coded method. For single dose datasets you can enter your own values, or use the Kinetica stripping algorithm (and in any order you like). We recommend that you use the Kinetica stripping algorithm. It requires no numerical entries to calculate the initial estimates and tries to optimize the start points.

Initial Parameter Estimates



The Stripping algorithm enables you to estimate the parameters of one or two compartment linear models only (J.G. Wagner, Fundamentals of Clinical Pharmacokinetics, Drug Intelligence Pub. Inc. Hamilton, Illinois, 1975). The equations that define the model are assumed to be polyexponentials. The number of exponents is determined by the number of compartments.

The Stripping algorithm starts the calculations using a log-linear regression on the observations pertaining to the terminal elimination phase of the curve. Then the parameters of all phases are calculated by recursive subtraction of the predicted values from the ones observed in the initial portion of the curve.

The algorithm automatically estimates the parameters for any possible combination of the number of points defining the exponential kinetic processes. The final parameters retained are the ones that give the minimal sum of squared differences between the experimental and computed values.

When the stripping algorithm fails, #ERR appears in the initial estimate spreadsheet cells and a message is written in the Dataset Info view for each Dataset stripping failure ("Data not compatible with the stripping assumption, enter some initial values").

Stripping can fail for any of the following reasons:

• If the last point of the curve does not correspond to the terminal phase, e.g. if the last point is around Cmax in an extravascular administration

• If there are not enough points available to compute (minimum number of points is 2 x number of compartments in the most general cases, although there are some exceptions to this rule)

• If a significant random noise is present in the data

• If the kinetics do not follow a multi-exponential model.

Stripping Algorithm

If Stripping Fails



When you insert a method into a study, you can specify the weight using the Options Setup dialog in the Methods view. The default setting is a constant weight (=1) is. The available weighting schemes are listed in the following table.

Label Weighting Scheme

1 Constant (default value)

Yobs 1/ Y observed

Yobs*Yobs 1/ Y observed2

Ycalc 1/ Y predicted

Ycalc*Ycalc 1/ Y predicted2

User-Defined User-defined (available for soft-coded methods only)

This option is available for soft-coded methods only. There are two ways of performing user-defined weighting:

The first way requires a column in the sample view containing the weighting values that will be assigned to each observation. You must insert this column, enter the weight values for each observation, and then reference the column in your soft-coded method.

The second way uses the values calculated in your soft-coded model as weighting. In this case, you do not need to insert a column for weighting. The method will automatically generate that column.

Weighting Schemes

User-Defined Weighting



Kinetica, like many kinetic modeling programs, uses a modified Gauss-Marquardt algorithm for minimization. Marquardt's method represents a compromise between the linearization method and the steepest descent method and appears to combine the best features of both while avoiding their most serious limitations.

Suppose we start from a certain point in the parameter space, ‘b’; if the method of steepest descent is applied, a certain vector direction, δg where g stands for gradient, is obtained from movement away from the initial point. Due to attenuation in the S (b) (S is the objective function to be minimized in b) contours this may be the best local direction in which to move to attain values of S(b) but may not be the best overall direction. However, the best direction must be within 90° of δg or else S(b) will be larger locally.

Marquardt found that for a number of practical problems to be studied, the angle between δg and δt fell in the range 80° - 90°. In other words, the two directions were almost at a right angle. The Marquardt algorithm provides a method for interpolating between the vector δg and δt and for obtaining a suitable step size as well.

Please consult the following references for more discussion of Marquardt’s Principle.

Marquardt, D.W., 'An algorithm for least squares estimation of nonlinear parameters', Journal of the society for Industrial and Applied mathematics, 11, 431 441 (1963)

Press W.H, Teukolsky S.A, Vetterling W.T, and Flannery B.P, “Numerical recipes in C, The Art of Scientific Computing”, Second Edition, Cambridge University Press (1992)

Minimization Algorithm

Marquardt's Principle

References for Further Reading



Kinetica uses a numerical integration algorithm based on the Runge-Kutta-Fehlberg method to compute the regression function values, when the structural model is described by a system of differential equations [Forsythe G.E., Malcolm M.A. and Moler C.B., Computer Methods for Mathematical Computations, N.J., Prentice Hall Inc. 1977].

This is a 5th order method with variable step-size control. Initially a step length satisfying a local error criterion is estimated, then the 4th and 5th order Runge-Kutta approximation of the solution are computed and used to estimate the local error. The 5th order estimation is used as the solution if, and only if, the estimated error is less than a fixed tolerance level. If this is not the case, the step size is reduced until the error criterion is satisfied.

Please consult the following reference for further discussion of the Runge-Kutta-Fehlberg algorithm:


Differential Equation Solver

Reference for Further Reading



Kinetica generates detailed statistics for interpreting the model selection and goodness of fit including: Objective Function, Akaike & Schwartz Criteria, Standard Deviation (S.D.) & Coefficient of Variation (%CV), Correlation Matrix and the Residuals & Weighted Residuals.

The objective function is computed as the sum of squares, where:

( )Ycalc YobsWeight−

∑2

In Kinetica, the best fit is determined by the smallest objective function found.


The Akaike criteria tries to identify the content of specific parameter estimates by relating the coefficient of variation to all the parameters required for the fitting. The Akaike criteria is expressed as:

( )Akaike n n W Yobs Ycalc pii

n

i i= ∗ −⎛⎝⎜

⎞⎠⎟ +

=∑1 2

1

2

The Akaike value is dependent on the size of the data points and the number of observations. In Kinetica the best model selection is determined by the smallest Akaike value found.

Statistics and Goodness of Fit

Objective Function


The Akaike Criteria



The Schwartz criteria is defined as follows:

Schwartz = - Log likelihood + 1/2 Log N

where N = the number of data points

The best model selection is determined by the smallest Schwartz value found.

Note It is always important to check the residual raw values before assuming that the Akaike or Schwartz values are accepted as the best fit indicator.

Consult the following references for further discussion of the Akaike and Schwartz criteria:

Akaike H., “A New Look at Statistical Model Identification”, IEEE Trans. Automat. Contr., 19: 716-723 (1973)

Akaike H., “An Information Criterion (AIC)”, Math Sci, 14: 5-9 (1976)

In Kinetica, Standard Deviation (S.D.) and Coefficient of Variation (%CV) are used as indicators for the goodness of fit.

The S.D. of the computed data can be typically defined as:

( )( )

nnxx

nxxsd i

nc

22

21=

Σ−Σ

=−Σ

=

During a Kinetica analysis a warning message is displayed in a message box and in the Dataset Info view for each dataset analyzed if the calculated Coefficient of Variation (%CV) is greater than 50 for any parameter. A second warning message is generated if the calculated CV% is greater than 1000 for any parameter.

The Schwartz Criteria


Standard Deviation and Coefficient of Variation



The %CV of the computed data can be typically defined as:

%CV = SD / mean x 100

Press W.H, Teukolsky S.A, Vetterling W.T, and Flannery B.P, "Numerical recipes in C, The Art of Scientific Computing", Second Edition, Cambridge University Press (1992)

In Kinetica, the correlation matrix provides an indication of the degree of inter-dependency between the computed estimates of the different parameters. In general, transforming the regression problem into a format involving correlations is a good statistical tool. It makes all the output values computed during the fitting lie between the range -1 to 1. When the values are all of this order the adverse effects of ‘rounding-off’ the error are minimized.

During a Kinetica analysis, a message is displayed in the Dataset Info view for each dataset analyzed if the calculated correlation is greater than 0.99 between two different parameters. This indicates that the application has a strong assumption that you are over-parameterized.

Draper N., and Smith H., “Applied Regression Analysis”, Second Edition, Wiley Interscience (1980)



Correlation Matrix




Residuals are widely used as an important marker to assess the model selection and goodness of fit. A residual value is the difference between the observed Y concentration and the predicted Y concentration that is computed by Kinetica. Kinetica recognizes these residuals with the notation Yobs and Ycalc respectively. The resulting residual is the unexplained model error. In a good fit circumstance this error should be randomly distributed around the Ycalc mean.

Kinetica automatically outputs both the residuals and weighted residuals after an analysis. These values are displayed in the Dataset Info view for each dataset analyzed. In addition, you can view a plot of the residuals by highlighting the Ycalc and Residuals or Weighted Residuals columns, then clicking the "Show one graph" or "Show all graphs" button.


Residuals and Weighted Residuals




PK templates enable you to study PK fitting and how it is applied in Kinetica. Kinetica offers many different types of templates for PK fitting.

In order to understand how to use these templates, we supply a description of each template type and a kdb file containing data. This way you can try each analysis and understand how the application operates before using your own data. Once you try the example, you can open the empty template, cut and paste your own data, and rerun the analysis.

The templates are listed in the following table:

Template Name Use in Kinetica

Macro0orderInput Single Dose Zero Order Input Macro Constants

MacroExtravascular Single Dose Extravascular Macro Constants

MacroIVBolus Single Dose IV Bolus Macro Constants

MacroIVInfusion Single Dose IV Infusion Macro Constants

Micro0orderInput Single Dose Zero Order Input Micro Constants

MicroExtravascular Single Dose Extravascular Micro Constants

MicroIVBolus Single Dose IV Bolus Micro Constants

MicroIVInfusion Single Dose IV Infusion Micro Constants

MultiMicroExtravascular Multiple Dose Extravascular Micro Constants

PK Template Examples



Template Name Use in Kinetica

MultiMicroIVBolus Multiple Dose IV Bolus Micro Constants

MultiMicroIVInf Multiple Dose IV Infusion Micro Constants

MultiDoseMultiRoute Multiple Dose With Multiple Administration Routes



This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose Zero Order Input with Macro Constants. There are no special conditions for using this template.



Dose – Amount of drug administered


• T = Time

• C = Concentration

The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and computes the correct final measurement units required.

The other methods are explained in the following table:


FitMacro0orderInput Ccalc Computed concentration values

Residuals Residuals

Weight Weight

Weighted res.

Weighted Residuals

Single Dose Zero Order Input Macro

Constants Template

Inputs and Outputs

Methods




Input duration

Duration of the input phase

A Coefficients in the sum of exponentials

Alpha Exponent

B Coefficients in the sum of exponentials

Beta Exponent

C Coefficients in the sum of exponentials

Gamma Exponent

AUC Partial area under the curve

AUMC Partial Area under the moment curve

MRT Mean residence time

Lz Smallest (slowest) disposition rate constant

Cmax calc Calculated maximum concentrate

Tmax calc Time where C=Cmax

MacroToMicro Vc Volume in the central compartment

Kel Elimination rate constant from central compartment




K12 Transfer rate constant from central compartment to the superficial compartment

K21 Transfer rate constant from superficial compartment to the central compartment

K13 Transfer rate constant from central compartment to the deep peripheral compartment

K31 Transfer rate constant from deep peripheral compartment to the central compartment

PkIVFitDerive t1/2 alpha Elimination half-life of Alpha

t1/2 beta Elimination half-life of Beta

t1/2 gamma Elimination half-life of Gamma

t1/2 Lz Elimination half-life associated with the terminal slope

t1/2 Kel Elimination half-life of Kel




Vss Apparent volume of distribution at steady state

Vz Apparent volume of distribution during the terminal phase (Lz)

Cl Total clearance

A drug dose of 200,000 µg was administered orally. The concentration-time course was sampled from the plasma, expressed in µg/L and h respectively. A one-compartment model with zero order absorption was used to analyze this example. This example contains data for one dataset.

Note For this example we entered data into the template and saved it as a .kdb file. If you open the corresponding file in the Template subdirectory, you will not see any data. It is ready for your own data.

To view an example of the single-dose zero-order macro constants template:

1. In Kinetica, select Open from the File menu. The Open a Kinetica File dialog appears. By default, the program displays the Data subdirectory.

2. Double click the Fitting directory, select the Macro0orderInput.kdb file and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.

3. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and displayed in the workspace.

Viewing an Example of Single Dose Zero Order Input Macro

Constants Template



To modify options for the FitMacro0orderInput method:

1. First, ensure that the number of compartments (Nb Comp) you select is consistent.


3. Click Set in the Global Options column of the FitMacro0orderInput row. The Options Setup dialog appears.

4. Select from the following table:

Nb Comp Select one, two or three compartments

Weight Select one of the following weighting schemes:


Yobs 1/ Y observed

Yobs* Yobs 1/ Y observed2


Ycalc* Ycalc 1/ Y predicted2

Plot Curve Choose between Yes and No to plot automatic graphs

Execute Choose between Fitting and Simulation. If you choose Simulation you must enter the initial parameter estimates in the parameter cells.


To modify MacroToMicro options:

1. Ensure that the number of compartments (Nb Comp) you select is consistent.

Modifying Options for the FitMacro0orderInput Method

Modifying MacroToMicro Options




3. Click Set in the Global Options column of the MacroToMicro row. The Options Setup dialog appears.

4. Select from the following Nb Comp: One, two, or three compartments.


To plot the residuals:

1. Highlight the C and Residuals (or Weighted Residuals) columns in the appropriate dataset in the Dataset pane, using the mouse and the Ctrl key.

2. Click the Dataset Graph button. The residual plot is displayed.

3. By default, Kinetica joins all points with a line. To suppress the line, select the Style button in the graph window to display the Style Property dialog. Deselect the Line option and click OK.

To view statistics on the data fitting select the Dataset Info view in the Dataset pane.

This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose Extravascular with Macro Constants. There are no special conditions for using this template.

Plotting the Residuals

Viewing Fitting Statistics

Single Dose Extravascular Macro

Constants Template







T = Time

C = Concentration

The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and computes the correct final measurement units required. The other methods are explained in the following table.

Methods Column and Variable Outputs

FitMacroExtravascular Ka Absorption rate constant

Tlag Lag time

Note In Kinetica, the duration of absorption Tabs is calculated as follows: Tabs = 5T½Ka. The absorption phase is considered finished when time > 5T½Ka.

Inputs and Outputs

Methods



A drug dose of 10µg was administered orally. The concentration-time course was sampled from the plasma. Units for time and concentration are expressed in h and µg/L, respectively. A two-compartment model with first order absorption and lag-time was used to analyze this example. This example contains data for one dataset.

Note For this example, we entered some data into the template and saved it as a .kdb file. If you open the corresponding file in the Template subdirectory, you will not see any data. It is ready for your own data.

To view the single dose extravascular macro constants template

1. Launch Kinetica.

2. Select Open from the File menu. The Open a Kinetica File dialog appears. By default, the program displays the Data subdirectory.

3. Double click the Fitting directory.

4. Select the MacroExtravascular.kdb file and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


To modify MacroToMicro options first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options, then follow the steps below.


Viewing an Example of Single Dose

Extravascular Macro Constants Template

Modifying MacroToMicro options






To plot residuals:


2. Click the Dataset Graph button. The residual plot appears.

To view the statistics on the fitting select the Dataset Info view in the Dataset pane.


Viewing Statistics on the Fitting



This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose IV Bolus with Macro Constants. There are no special conditions for using this template.





T = Time

C = Concentration


A drug dose of 496 mg was administered by an IV bolus route. The concentration-time course was sampled from the plasma, expressed in mg/L and min. respectively. A two-compartment model was used to analyze this example. This example contains data for one dataset.

Note For this example we entered some data into the template and saved it as a .kdb file. If you open the corresponding file in the Template subdirectory, you will not see any data. It is ready for your own data.

Single Dose IV Bolus Macro Constants

Template

Inputs and Outputs

Methods

Viewing an Example of Single Dose IV Bolus

Macro Constants Template



To view the single dose IV bolus macro constants template

1. Launch Kinetica.


3. Double click the Fitting directory, select the MacroIVBolus.kdb file and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


To modify FitMacroIVBolusOptions first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.


2. Click Set in the Global Options column of the FitMacroIVBolus row. The Options Setup dialog appears.

3. Select from the following:




Yobs 1/ Y observed


Modifying FitMacroIVBolus Options









To modify MacroToMicro options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.












To view statistics on the fitting select the Dataset Info view in the Dataset pane.




This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose IV Infusion with Macro Constants. There are no special conditions for using this template.




Infusion Duration – Duration of the drug infusion


T = Time

C = Concentration


A drug dose of 1 mg was administered by an IV infusion method over a period of two hours. The concentration-time course was sampled from the plasma, expressed in mg/Land h. respectively. A two-compartment model was used to analyze this example. This example contains data for one dataset.

To view an example of the single dose IV infusion macro constants template:

Single Dose IV Infusion Macro

Constants Template

Inputs and Outputs

Methods

Viewing an Example of Single Dose IV Infusion

Macro Constants Template



1. Launch Kinetica.


3. Double click the Fitting directory, select the MacroIVInfusion.kdb file and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


To modify FitMacroIVInf options:


2. Click Set in the Global Options column of the FitMacroIVInf row. The Options Setup dialog appears.





Yobs 1/ Y observed




Modifying FitMacroIVInf Options







To modify MacroToMicro options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.








To view the fitting statistics select the Dataset Info view in the Dataset pane.






This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose Zero Order Input with Micro Constants. There are no special conditions for using this template.





T = Time

C = Concentration


A drug dose of 1 mg was administered by a subcutaneous route. The concentration-time course was sampled from the plasma, expressed in mg/L and h respectively. A two-compartment model with a zero order input phase was used to analyze this example. This example contains data for one dataset.

To view an example of the single dose zero order input micro constants template

1. Launch Kinetica.

Single Dose Zero Order Input Micro

Constants Template

Inputs and Outputs

Methods

Viewing an Example of Single Dose Zero Order

Input Micro Constants Template




3. Double click the Fitting directory, select the Micro0OrderInput.kdb file and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


To modify FitMicro0orderInput options:


2. Click Set in the Global Options column of the FitMacro0OrderInput row. The Options Setup dialog appears.





Yobs 1/ Y observed




Modifying FitMicro0orderInput Options







To modify MicroToMacro options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.








To view fitting statistics select the Dataset Info view in the Dataset pane.

Modifying MicroToMacro Options


View Statistics on the Fitting



This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose Extravascular with Micro Constants. There are no special conditions for using this template.





T = Time

C = Concentration


A drug dose of 10 µg was administered orally. The concentration-time course was sampled from the plasma, expressed in µg/L and h respectively. A two-compartment model with first order absorption and lag time was used to analyze this example. This example contains data for one dataset.

To view an example of single dose extravascular micro constants template

1. Launch Kinetica.

Single Dose Extravascular Micro Constants Template

Inputs and Outputs

Methods

Viewing an Example of Single Dose

Extravascular Micro Constants Template




3. Double click the Fitting directory, select the MicroExtravascular.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


To modify FitMicroExtravascular options:


2. Click Set in the Global Options column of the FitMicroExtravascular row. The Options Setup dialog appears.





Yobs 1/ Y observed




Modifying FitMicroExtravascular Options







To modify MicroToMacro options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.


2. Click Set in the Global Options column of the MacroToMicro row.The Options Setup dialog appears.






Modifying MicroToMacro Options




To view the fitting statistics select the Dataset Info view in the Dataset pane.




This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose IV Bolus with Micro Constants. There are no special conditions for using this template.





T = Time

C = Concentration


A drug dose of 496 mg was administered by an IV bolus route. The concentration-time course was sampled from the plasma, expressed in mg/L and min. respectively. A two-compartment model was used to analyze this example. This example contains data for one dataset.

To view an example of single dose IV bolus micro constants template

1. Launch Kinetica.

Single Dose IV Bolus Micro Constants

Template

Inputs and Outputs

Methods

Viewing an Example of Single Dose IV Bolus

Micro Constants Template




3. Double click the Fitting directory, select the MicroIVBolus.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


To modify FitMicroIV Bolus options:


2. Click Set in the Global Options column of the FitMicroIVBolus row. The Options Setup dialog appears.





Yobs 1/ Y observed





Modifying FitMicroIV Bolus Options











To plot residuals:




Modifying MacroToMicro options





This template enables you to perform a pharmacokinetic fitting when you have data relative to Single Dose IV Infusion with Micro Constants. There are no special conditions for using this template.






T = Time

C = Drug Concentration


Single Dose IV Infusion Micro

Constants Template

Inputs and Outputs

Methods



A drug dose of 785.7 nmol was administered by an IV infusion route. The concentration-time course was sampled from the plasma, expressed in nmol/L and min. respectively. A three-compartment model was used to analyze this example. This example contains data for one dataset.

To view an example of single dose IV infusion micro constants template:

1. Launch Kinetica.

2. Select Open from the File menu. The Open a Kinetica File dialog appears. By default, the program displays the subdirectory “Data.”

3. Double click the Fitting directory, select the MicroIVInfusion.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.


Viewing an Example of Single Dose IV Infusion




To modify FitMicroIVInf options:


2. Click Set in the Global Options column of the FitMicroIVBolus row. The Options Setup dialog appears.





Yobs 1/ Y observed










Modifying FitMicroIVInf Options






To plot residuals:








This template enables you to perform a pharmacokinetic fitting when you have data relative to Multiple Dose Extravascular with Micro Constants. There are no special conditions for using this template.



Ka – Initial parameter estimate

Vc – Initial parameter estimate

Kel – Initial parameter estimate


T – Time

C – Drug Concentration



Multiple Dose Extravascular Micro Constants Template

Inputs and Out puts

Methods



A drug dose of 10 µg was administered at 0, 4, 8, and 16h; 20 µg at 22, 28, 34, 44, 54, 66, and 74h; and 40 µg at 86 and 98h. The concentration-time course was sampled from the plasma, expressed in µg/L and h respectively. A one-compartment model with first order absorption and no lag time was used to analyze this example. This example contains data for one dataset.

To view an example of multiple dose extravascular micro constants template

1. Launch Kinetica.


3. Double click the Fitting directory, select the MultiMicroExtravascular.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.

4. In the Study All Variables view, enter some initial estimates for the analysis. For this example, you can enter the following values:

Ka = 0.5

Vc = 10

Kel = 0.1.

5. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and displayed in the Gallery.

Viewing an Example of Multiple Dose

Extravascular Micro Constants Template



To change the FitMultiMicroExtravascular options:


2. Click Set in the Global Options column of the FitMultiMicroExtravascular row. The Options Setup dialog appears.



Use Lag Time

Select Yes or No to compute lag time



Yobs 1/ Y observed







Changing FitMultiMicroExtravascular

Options








To plot residuals:









This template enables you to perform a pharmacokinetic fitting when you have data relative to Multiple Dose IV Bolus with Micro Constants. There are no special conditions for using this template.






T – Time



The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and computes the correct final measurement units required. The other methods are explained as follows:


FitMultiMicroIVBolus

Ccalc Computed concentration values

Residuals Residuals

Weight Weight

Multiple Dose IV Bolus Micro

Constants Template

Inputs and Outputs

Methods




Weighted Res.

Weighted Residuals

Vc Volume in the central compartment





K31 Transfer rate constant from deep peripheral compartment to the central

compartment

MicroToMacro A Coefficients in the sum of exponentials

Alpha Exponent


Beta Exponent


Gamma Exponent




CalcThalf (1,2,3 comp)

t1/2 alpha Elimination half-life of Alpha


t1/2 gamma

Elimination half-life of Gamma

A drug dose of 100 mg was administered at 0, 24, and 48h by an IV Bolus route. The concentration-time course was sampled from the plasma, expresed in ng/mL and h respectively. A one-compartment model was used to analyze this example. This example contains data for one dataset.

To view an example of multiple dose IV bolus micro constants template

1. Launch Kinetica.

2. Select Open from the File menu.The Open a Kinetica File dialog appears. By default, the program displays the Data subdirectory.

3. Double click the Fitting directory, select the MultiMicroIVBolus.kdbdirectory, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.

4. In the Study All Variables view, enter some initial estimates for the analysis. In this example, you can enter the following values:

Vc = 1

Kel = 0.2.

Viewing an Example of Multiple Dose IV Bolus




5. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and displayed in the Gallery.

To change FitMultiMicroIVBolus options:


2. Click Set in the Global Options column of the FitMultiIVBolus row. The Options Setup dialog appears.





Yobs 1/ Y observed








Changing FitMultiMicroIVBolus Options








To plot residuals:








This template enables you to perform a pharmacokinetic fitting when you have data relative to Multiple Dose IV Infusion with Micro constants. There are no special conditions for using this template.





K12 – Initial parameter estimate

K21 – Initial parameter estimate

Data input (user-entered dataset column field values):

T – Time




The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and computes the correct final measurement units required. The other methods are described in the following table.

Multiple Dose IV Infusion Micro

Constants Template

Inputs and Outputs

Methods




FitMultiMicroIVInf Ccalc Computed concentration values

Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals

Vc Volume in the central compartment










Alpha Exponent


Beta Exponent


Gamma Exponent




t1/2 gamma Elimination half-life of Gamma

A drug dose of 177 mg was administered at 0, 24,08 and 48,08 h by an IV infusion lasting one hour. Likewise, a dose of 175,3 mg was administered at 1h by an IV infusion lasting three hours. Another dose of 175,2 mg was administered at 25,08 and 49,08h by an IV infusion lasting three hours. The concentration-time course was sampled from the plasma, expressed in mg/L and h respectively. A two-compartment model was used to analyze this example. This example contains data for one dataset.

To view an example of multiple dose IV infusion micro constants template

1. Launch Kinetica.


Viewing an Example of Multiple Dose IV Infusion




3. Double click the Fitting directory, select the MultiMicroIVInfusion.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.

4. In the Study All Variables View, enter some initial estimates for the analysis. In this example, you can enter the following values:

Vc = 5

Kel = 0.5

K12 = 0.5

K21 = 0.1

5. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and the respective plot is generated.

To change FitMultiMicroIVInf options:


2. Click Set in the Global Options column of the FitMultiMicroIVInf row. The Options Setup dialog appears.





Yobs 1/ Y observed


Changing FitMultiMicroIVInf Options



To plot residuals:








This template enables you to perform a pharmacokinetic fitting when you have data relative to multiple dose and multiple method with micro constants. There are no special conditions for using this template. This template contains two views:

Plasma – contains columns for Ccalc, Residuals, Weight, Weighted Res.

Administration – contains columns for information relative to the administration (i.e. time, dose, method and infusion duration).



Dose for Macro Constant – Dose which should always be equal to 1 (used only for obtaining macro constants, it is not the administered dose)


T – Time


Admin Time – Time of drug administration

Admin Dose – Amount of drug administered

Admin Route – Choose to identify the administration method (1 = Extravascular, 2 = IV infusion, 3 = IV Bolus)

Infusion Duration – Duration of drug infusion

Multiple Dose Multi Route Template

Inputs and Outputs




The other methods are described in the following table.


FitMultiMicro Ccalc Computed concentration values

Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals

Ka Absorption rate constant (if there is an oral administration)

Tlag Lag time

F Bioavailability

Volume Volume of distribution





Methods






Alpha Exponent


Beta Exponent


Gamma Exponent




t1/2 gamma

Elimination half-life of Gamma



A drug dose of 0.25 mg was administered by an IV bolus route. At 2h a dose of 1 mg is administered by an IV infusion over a period of six hours. The concentration-time course was sampled from the plasma, expressed in mg/L and h respectively. Two-compartment model was used to analyze this example. This example contains data for one dataset.

To view an example of multiple dose multiple method micro constants template

1. Launch Kinetica.


3. Double click the Fitting directory, select the MultiDoseMultiRoute.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.

4. In the Study All Variables View, enter some initial estimates for the analysis. In this example, you can enter the following values:

Volume = 1

Kel = 0.5

K12 = 0.5

K21 = 0.25.


Viewing an Example of Multiple Dose Multiple

Method Micro Constants Template



To change FitMultiMicro options:


2. Click Set in the Global Options column of the FitMultiMicro row. The Options Setup dialog appears.





Yobs 1/ Y observed










Changing FitMultiMicro Options






To plot residuals:








Kinetica enables you to complete single dose pharmacodynamic (PD) analysis with direct models or link models.

Several models have been developed to link the effect to drug concentration or drug doses when the drug-induced response is generated by a simple or multiple receptor activation. These models are independent of time and describe the balanced relationship between the concentrations and the effects.

The standard library used within Kinetica contains six different models as described below.

The linear model is proportional to concentration.

We compute:

E = S Ce + E0

where:

E = Effect variable

E0 = Baseline effect

S = Slope of the line relating the effect to the concentration

Ce = Concentration to which the effect is related

For the log-linear model we compute:

E = S log (Ce) + E0

where:

E = Effect variable


S = Slope of the line relating the effect to the concentration

Performing PD Analysis

General PD Template

Linear Model

Log-Linear Model




We compute:

E E EC

C ECe

e= + ⋅

+0 50max

where:

E = Effect variable


Emax = Maximum drug induced effect


EC50 = Plasma concentration at 50% of maximal effect

A graphical representation of the Ordinary Emax model is shown below, where E0 is 0, Emax is 100, and EC50 is 20.

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Concentration (ng/mL)

Effe

ct (%

)

Figure 9-4. Ordinary Emax Model with E0=0, Emax=100 and EC50=20

Ordinary Emax Model



We compute:

E E EC

C ECe

e= − ⋅

+0 50max

where:

E = Effect variable





A graphical representation of the Ordinary Emax model is shown below, where E0 is 100, Emax is 100, and EC50 is 20.

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100


Effe

ct (%

)

Figure 9-5. Ordinary Inhibition Emax Model with E0=100, Emax=100 and EC50=20

Ordinary Inhibition Emax Model



We compute the Hill equation:

E E EC

C ECep

ep p= + ⋅+0 50

max

where:

E = Effect variable





n = Sigmoidicity factor (Hill exponent)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100


Effe

ct (%

)

Figure 9-6. Graph of a Sigmoidal Emax Model

Sigmoid Emax Model (Hill)



We compute the Hill equation:

E E EC

C ECep

ep p= − ⋅+0 50

max

where:

E = Effect variable





n = Sigmoidicity factor (Hill exponent)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100


Effe

ct (%

)

Figure 9-7. Graph of a Sigmoidal Inhibition Emax Model

Kinetica provides one template that covers all of these pharmacodynamic models. You can choose a specific model from the options for the method called FitDynamic. This template is

Sigmoid Inhibition Emax Model (Hill)



available for all routesof administration (IV Bolus, IV Infusion, and Extravascular).

Inputs are numeric fields and columns where you must enter data so that Kinetica can successfully complete the analysis. The following input and information can be found in the Study Info worksheet in the Study pane.


S – Slope of the line relating the effect to the concentration

Emax – Maximum drug induced effect

EC50 – Plasma concentration at 50% of maximal effect

N – Hill exponent (sigmoidicity factor)

E0 – Baseline estimate

Note You only need to enter the estimates that are specific to the general model you have selected.


Cp – Drug concentration

Effect – Effect

The Set Column Unit method is used to add unity to the columns and assure that the program understands and inserts the correct final measurement units required.


FitDynamic EffectCalc Calculated effect

Residuals Residuals

Weight Weight

Inputs Required

Methods




Weighted Res

Weighted residuals

S Slope

Emax Maximum drug induced effect

EC50 Plasma concentration at 50% of maximal effect

E0 E0 = Baseline effect (the output variable which appears on the screen depends on the selected model)



In this example, a drug was given to a patient. Plasma concentration (Cp) and effect observations were obtained at steady state. A baseline effect E0 (E0=175) was observed with the absence of a drug. Increased plasma concentration of the drug (mg/L) decreased blood pressure (effect). The Emax inhibition model with E0 equal to 175 was used to analyze this example. This example contains data for one dataset.

To view an example of the general PD template

1. Select Open from the Kinetica File menu. The Open a Kinetica File dialog appears. By default, the program displays the Data subdirectory.

2. Double click the Fitting directory, select the General Dynamics.kdb file, and click Open. The dataset appears in the workspace, containing only the raw data that we entered before sending you the application. You are ready to run the analysis.

Figure 9-8. Dataset view (Plasma view)

3. Enter some initial estimates for the analysis in the All Variables view (Study pane). For this example, you can enter the following values:

Emax = 35

EC50 = 120

E0 = 175

Viewing an Example of the General PD Template



4. Before running the analysis, examine a graph of the Cp versus Effect values. Click the Dataset Graph button. The following graph is displayed:

Figure 9-9. Graph Depicting Cp versus Effect Values


Figure 9-10. Graph Depicting Calculated Results of Analysis



Note If you have multiple datasets when you try your analyses, you can select Calculate All from the toolbar. The results for all datasets will be calculated in batch mode and displayed in the Gallery feature.

For this example, select the Cp, Effect, and Weighted Res columns, then select the Dataset Graph item found in the Dataset pane to view the results.

Now that the analysis is complete, you can export the results to Microsoft Word or Excel.

1. Load Excel and then select the columns or rows you want to export from Kinetica.

2. Select Assistants then Export from the Tools menu.

3. Switch the view to Excel to view the data.

To change the fit dynamic options:


2. Click Set in the Global Options column of the Fit Dynamic row. The Options Setup dialog appears.


PD model Choose one of the following PD models:

Linear

Log-Linear

Emax

Exploring the Data with the Graphical Tools

Exporting the Results

Changing the Fit Dynamic Options




Emax Inhibition

Hill

Hill Inhibition

E0 is Choose one of the following:

User-defined constant

Enter the baseline value E0 in the group (even it is an output variable)

Model parameter

If the baseline is not constant and varies then choose this option, Kinetica will fit E0.



Yobs 1/ Y observed









The pharmacokinetic/pharmacodynamic (PK/PD) model used in Kinetica is the model proposed by Sheiner et al. (1979). This model can be viewed as the combination of three different models: pharmacokinetic, link, and effect. The schema is shown below.

K1e

Link Model

K12

Central Comp. Periph. Comp.

Effect Model Pharmacokinetic Model

Effect Comp.

K21

Ke0 Kel

Figure 9-11. PK/PD schematic

The PK model can be defined using the classical compartment theory. In order to establish a relationship between the drug concentrations in the effect site (Ce) which is usually unknown, and the effect, we need to define a model. The latter enables us to compute the drug concentration in the effect site from the measured drug concentration in the central compartment. This particular model is called the link model and can be defined as follows:

Ke0 Ce Cp

Effect Comp. Central Comp.

Figure 9-12. Link model schematic

Corresponding to:

Performing PK/PD Analysis



dCedt

K Cp K Ce e e= ⋅ − ⋅1 0

where:

Ce – Drug concentration in the effect compartment

Cp – Drug concentration in the central compartment.

The effect site is considered as an additional compartment linked to the plasma compartment by a first order process (rate K1e). It is assumed that only a negligible mass of drug reaches this site. Therefore, the kinetics of the drug is completely unaffected by the presence of this hypothetical compartment. Drug removal is then characterized by Ke0, where:

Ke0 – First order rate constant which characterizes the temporal aspects of equilibration between plasma concentration and effect.

At the steady state, we have:

Cp = Ce e

e

KK

1

0⋅

If Ke0 is great, the Ce profile is parallel to the Cp profile.

In Kinetica, the PD equations used to fit a PD model are the following three hard-coded methods:

FitPK/PD_Extravascular (for the extravascular route)

FitPK/PD_IVB (for the IV bolus route)

FitPK/PD_IVInf (for the IV infusion route).

The equations used in the above methods are described below:



The expression for first order absorption is given as:

( ) ( ) ( ) ( ) ( )⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−⋅−−⋅

−−⋅−⋅−

⋅= ⋅−⋅−=

=

⋅−⋅−∑

∑ tKtKa

e

n

in

i

tKtLi

eie

ee eeKaK

LiKaKaCii

eeLiKLiKa

KaCiiKtCe 00

0

1

1 0

)(

IV Bolus

( ) ( )⎥⎦

⎤⎢⎣

⎡−⋅

−⋅= ∑

=

⋅−⋅−n

i

tKtLi

eie

eeeLiK

CiiKtCe1 0

0)(

IV Infusion (during the perfusion):

( )⎥⎦⎤

⎢⎣

⎡−⋅

⋅−

−+⋅=

⋅−⋅−

=∑ LiKLi

eKLiK

eLiT

CiitCee

tLie

eo

tKn

i

e

0

0

1

01)(

The expression for IV Infusion is:

( )( )

( ) ( )( ) ( )( )

⎥⎥⎦

⎤

⎢⎢⎣

⎡⋅⎟⎟

⎠

⎞⎜⎜⎝

⎛−⋅

⋅−

−++−⋅

−⋅−⋅

⋅= −⋅−⋅−⋅−

−⋅−−⋅−⋅

=∑ TtK

e

TLie

e

TKTtKTtLi

e

tLie

n

i

ee

e eLiKLi

eKLiK

eLi

eeLiKLi

eKT

CiitCe 00

0

0

0

00

0

1

11)(

where:

Ce (t) = Drug concentration in the effect compartment

Cii = Coefficients in the sum of exponentials (A, B or C)

Li = Exponents (alpha, beta or gamma)

Ka = Absorption rate constant

Ke0 = Rate constant for drug removed from the effect compartment

First Order Absorption

Zero Order Absorption

IV Infusion (post perfusion):



Generally, in PK/PD templates, there are two views:

• PK view: to fit PK data

• PD view: to fit PD data with PK parameters.

The principle advantage of this method (to fit PK and PD separately) is that different weights can be attributed for PK and PD fitting. The same weights are not always applicable to PK and PD data.

Note The time corresponding to the plasma concentration can be different from the time corresponding to the effect.



The subsections below discuss the application of the PK/PD Extravascular template.



Ke0 – Rate constant for drug removal from the effect compartment


Data input (user-entered dataset column field values) in the PK view:

T(Cp) – Time


Data input (user-entered dataset column field values) in the PD view:

T (effect) – Effect time

Effect – Drug effect

The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and inserts the correct final measurement units required.

The other methods are explained in the following table.


FitMacroExtravascular CpCalc Computed concentration values

PK/PD Extravascular Template

Inputs Required

Methods




Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals

Ka Absorption rate constant

lag Lag time

A Coefficient in the sum of exponentials

Alpha Exponent

B Coefficient in the sum of exponentials

Beta Exponent

C Coefficient in the sum of exponentials

Gamma Exponent


AUMC Partial area under the moment curve



Cmax calc Extrapolated concentration at 0




Tmax calc Extrapolated time at 0

FitPK/PD_Extravascular Ce Effect concentration

Effectcalc Computed concentration values

Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals

Ke0 Rate constant for drug removal from the effect compartment

S Slope of the line relating the effect to the concentration



n Hill exponent (sigmoidicity factor)

E0 Baseline estimate



In this example, 200 µg of a drug was administered orally. The concentration-time course was sampled from the plasma, expressed in µg/L and h respectively. To fit PK data, a one-compartment model with no lag time was used. To fit PD data, a linear model with E0=0 was used.

To view an example of the PK/PD extravascular template

1. Launch Kinetica.


3. Double click the Fitting directory, select the PK/PD Extravascular.kdb file, and click Open. The dataset appears in the workspace.

Figure 9-13. Dataset view (PK view)

Note PK worksheets always contain input columns T and C.

Viewing an Example of PK/PD Extravascular

Template



Figure 9-14. Dataset view (PD view)

Note PD worksheets always contain input columns T(effect) and Effect.

4. In the All Variables view of the Study pane, enter some initial estimates for the analysis. In this example, you can enter the following values:

Ke0 = 0.18

S = 1

5. Before running the analysis, examine a graph of the T(Cp) versus Cp values.

6. Click the Dataset Graph button. The following graph appears:



Figure 9-15. Dataset Graph Depicting T(Cp) versus Cp values

7. Now plot a graph of the T(effect) versus Effect values. Select the T(effect) and Effect columns from the PD view.

8. Click the Dataset Graph button. The following graph appears:

Figure 9-16. Dataset Graph Depicting T(effect) versus Effect values



9. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and two graphs are generated.

Figure 9-17. Graph of PK Fitting

Figure 9-18. Graph of PD Fitting

Note If you have multiple datasets when you try your analyses, you can select the Calculate All button found on the toolbar. The results for all datasets will be calculated in batch mode and displayed in the groups. You can scroll through the datasets to see the results for different subjects.



In this case we will plot a hysteresis graph after the PK fitting, and then the collapsed hysteresis graph after the PD fitting.

1. Select Select Dataset Graph from the View menu. Select 1 from the Dataset list, PK.CpCalc from the Column X list, and PD.Effect from the Column Y list, then click OK. The hysteresis graph after PK fitting is displayed.

2. Select Select Dataset Graph from the View menu again. This time select 1 from the Dataset list, PD.Effectcalc from the Column X list, and PD.Ce from the Column Y list, then click OK. The collapsed hysteresis graph after the PD fitting is displayed.

Figure 9-19. Hysteresis Graph Generated after the PK Fitting before the PD fitting

Exploring the Data with the Graphical Tools

http://www.PD.Ce



Figure 9-20. Collapsed Hysteresis Graph Generated after the PD fitting

Note You can change the graph scaling to better view the collapse of the hysteresis plot. For more information, see the chapter, “Working with Graphs.”








To change FitMacroExtravascular options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.


2. Click Set in the Global Options column of the FitMacroExtravascular row. The Options Setup dialog appears.


Nb Comp Choose one, two, or three compartments

Use Lag Time

Choose between Yes and No to compute lag time



Yobs 1/ Y observed







Changing FitMacroExtravascular Options



To change FitPK/PD_Extravascular options:


2. Click Set in the Global Options column of the FitPK/PD_Extravascular row. The Options Setup dialog appears.


Nb Comp Choose one, two or three compartments


Linear

Log-Linear

Emax

Emax Inhibition

Hill

Hill Inhibition




Model parameter




Yobs 1/ Y observed

Changing FitPK/PD_Extravascular Options








Execute Choose between Fitting and Simulation. If you choose Simulation, you must enter the initial parameter estimates in the parameter cells.




The subsections below describe the PK/PD IV Bolus template.

Inputs are numeric fields where you must enter data so that Kinetica can successfully complete the analysis. The following input and information can be found in the Study Info worksheet in the Study pane.


Dose – Dose administered

Ke0 – Rate constant for drug removal from the effect compartment

Emax – Maximum drug induced effect

EC50 – Plasma concentration at 50% of maximal effect

E0 – Baseline estimate.

Data input (user-entered dataset column field values) in PK view:

T (Cp) – Time

Cp – Drug Concentration

Data input (user-entered dataset column field values) in PD view:

T(effect) – Effect time


The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and inserts the correct final measurement units required. The remaining methods are described in the following table.

PK/PD IV Bolus Template

Inputs Required

Methods




FitMacroIVBolus CpCalc Computed concentration values

Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals


Alpha Exponent


Beta Exponent


Gamma Exponent







FitPK/PD_IVB Ce Effect concentration





Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals

Ke0 Rate constant for drug removed from the effect compartment





E0 Baseline estimate.



A drug dose of 50 mg was administered by an IV Bolus route. The concentration-time course was sampled from the plasma, expressed in mg/L and h respectively. An Emax inhibitive model was used to fit PK data.

To view an example of the PK/PD IV Bolus template:

1. Launch Kinetica.


3. Double click the Fitting directory, select the PKPD IV Bolus.kdb file, and click Open. The dataset appears in the workspace.

Figure 9-21. Dataset group (PK view)

Viewing an Example of PK/PD IV Bolus Template



Figure 9-22. Dataset group (PD view)


Ke0 = 1

Emax = 100

EC50 = 10

E0 = 100

5. Before running the analysis, examine a graph of the T(Cp) versus Cp values. Click the Dataset Graph button. The following graph appears:



Figure 9-23. Graph Depicting T(Cp) versus Cp values

6. Now plot a graph of the T(effect) versus Effect values. Select the T(effect) and Effect columns from the PD view. Click the Dataset Graph button found on the toolbar. The following graph appears:

Figure 9-24. Graph Depicting the T(effect) versus Effect values

7. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and two graphs are generated.



Figure 9-25. Graph of PK Fitting

Figure 9-26. Graph of PD Fitting

Note If you have multiple datasets, when you try your analyses you can click the Calculate All button from the toolbar. The results for all datasets are calculated in batch mode and displayed in the Graph Gallery. You can scroll through the datasets to see the results for different subjects.







To change FitMacroIVBolus options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.


2. Click Set in the Global Options column of the Fit MacroIVBolus row. The Options Setup dialog appears.





Yobs 1/ Y observed






Changing FitMacroIVBolus Options






To change FitPK/PD_IVB options:


2. Click Set in the Global Options column of the FitPK/PD_IVB row. The Options Setup dialog appears.




Linear

Log-Linear

Emax

Emax Inhibition

Hill

Hill Inhibition




Changing FitPK/PD_IVB Options




Model parameter




Yobs 1/ Y observed









The subsections below describe the PK/PD IV Infusion template.



Dose – Dose administered

Infusion Duration – Duration of drug infusion

Ke0 – Link constant between the central and effect compartments


E0 – Baseline estimate

Data input (user-entered dataset column field values) in the PK view:

T(Cp) – Time


Data input (user-entered dataset column field values) in the PD view:

T (effect) – Effect time


PK/PD IV Infusion Template

Inputs required



The Set Column Unit and MakeConcUnit methods are used to add unity to the columns and assure that the program understands and inserts the correct final measurement units required.


FitMacroIVInf CpCalc Computed concentration values

Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals


Alpha Exponent


Beta Exponent


Gamma Exponent





Methods






FitPK/PD_IVInf Ce Effect concentration


Residuals Residuals

Weight Weight

Weighted Res.

Weighted Residuals

Ke0 Rate constant for drug removed from the effect compartment





E0 Baseline estimate



In this example, one mg of a drug was administered with an infusion duration of two hours. The concentration-time course was sampled from the plasma, expressed in mg/L and h respectively. To fit PK data, a two-compartment model was used. To fit PD data, a linear model with a baseline E0=0 was used.

To view an example of the PK/PD IV infusion template

1. Launch Kinetica.


3. Double click the Fitting directory, select the PKPD IV Infusion.kdb file, and click Open. The dataset appears in the workspace.

Figure 9-27. Dataset View (PK view)

Viewing an Example of PK/PD IV Infusion

Template



Figure 9-28. Dataset View (PD view)


Ke0 = 2

S = 100

E0 = 0

5. Before running the analysis, look at a graph of the T(Cp) versus Cp values. Click the Dataset Graph button. The following graph is displayed:

Figure 9-29. Graph Depicting T(Cp) versus Cp Values



6. Now plot a graph of the T(effect) versus Effect values. Select the T(effect) and Effect columns from the PD view. Click the Dataset Graph button found on the toolbar. The following graph appears:

Figure 9-30. Graph Depicting T(effect) versus Effect Values

7. Select Calculate One from the Dataset menu to run the analysis. The results are plotted and two graphs are generated:

Figure 9-31. PK Graph



Figure 9-32. PD Graph

Note If you have multiple datasets when you try your analyses, you can select the Calculate All button found on the toolbar. The results for all datasets will be calculated in batch mode and displayed in the groups. You can scroll through the datasets to see the results for different subjects.








To change FitMacroIVInf options, first ensure that the number of compartments (Nb Comp) you select is consistent when you modify method options.


2. Click Set in the Global Options column of the Fit MacroIVInf row. The Options Setup dialog appears.





Yobs 1/ Y observed







To change fitting PK/PD IV infusion options:


2. Click Set in the Global Options column of the FitPK/PD_IVInf row. The Options Setup dialog appears.

Changing FitMacroIVInf Options

Changing Fitting PK/PD IV Infusion Options






Linear

Log-Linear

Emax

Emax Inhibition

Hill

Hill Inhibition




Model parameter




Yobs 1/ Y observed






10. Creating Tables and Scripts

The following chapter provides information on creating Tables and Scripts in Kinetica.

Creating Tables and Scripts


You can successfully create and save tables and scripts using the Table Assistant wizard. The Table Assistant creates tables by directly accessing Kinetica files (files with a .kdb extension). A table script file is also generated each time a table is created using the Table Assistant. This script file can be saved in the Tables pane and the current .kdb file. This enables you to build lists of different tables, which can then be re-created by a single mouse click. The tables are re-created using the last copy of computed data in the active .kdb file. The same table script is saved to the template so that users could use the same template with new datasets.

Creating Tables and Scripts using the

Table Assistant



The Table Assistant wizard is accessed by selecting Tools/Assistants/Table from Kinetica’s main menu.

The Table Assistant wizard is a series of dialogs that simplifies the creation of a table and guides you through the process step-by-step. You select a template, variable parameters, descriptive statistics on datasets with the same grouping factors, summary statistics, table formatting and conversion criteria, and save a table script file.

To use the Table Assistant wizard, read the instructions in each dialog to help you enter information and follow the steps in the procedure. You can move back and forth between the dialogs and change information as required until you complete the wizard. You can exit without saving the information at any point before completing the wizard by clicking Cancel.

The Table Assistant wizard has 5 steps. These steps are explained in more detail in the information that follows.

This dialog is used to select a template for the new table. Nine templates, named "Structures" are provided. The content of the templates is listed in the following table.

Structure Group Variables

Cross Variables

ID Variables Variables Descriptive Statistics

Summary Statistics

1 √ √ √ optional


3 √ √ √ √ optional



6 √ √ √ √ √ optional

7 √ √ √ √ √ optional

Table Assistant Wizard

Table Assistant - Step 1 of 5 Dialog



Structure Group Variables

Cross Variables

ID Variables Variables Descriptive Statistics

Summary Statistics



10 √ √ √ NA

11 √ √ √ NA

Note The template you choose will dictate the variables available for selection in the Table Assistant - Step 2 of 5 dialog. It will also determine the availability of the dialogs within the procedure.

The following illustrations provide an example of each of the templates available for selection in the Table Assistant - Step 1 of 5 dialog.

Figure 10-1. Table Structure One

Table Templates



Figure 10-2. Table Structure Two

Figure 10-3. Table Structure Three



Figure 10-4. Table Structure Four

Figure 10-5. Table Structure Five



Figure 10-6. Table Structure Six

Figure 10-7. Table Structure Seven



Figure 10-8. Table Structure Eight

Figure 10-9. Table Structure Nine



Figure 10-10. Table Structure Ten

Figure 10-11. Table Structure Eleven



This dialog is used to select the table variable parameters. These parameters are described in the table below.


Group Variable Identify data in adjacent columns

Appear as a column in the new table

Printed only when the value of a Group Variable changes

You can specify one or more Group Variables, if required

Rows in the new table will be sorted by Group Variables, then ID Variables (if present)

Cross Variable Divides data into separate columns, one for each Cross Variable defined

ID Variable Identify data in adjacent columns (e.g. Period, Age)

You can specify one or more ID Variables, if ID Variables are included in the template you selected

If you choose to include summary statistics for the new table, statistics are NOT included for ID variables

Rows in the table are sorted by ID Variables

Variable Data that is filled into the new table

If you choose to include summary statistics for the new table, statistics ARE included for ID variables

Note The availability of the table variables is dependent on the template selected in the Table Assistant - Step 1 of 5 dialog. If a template does not include a particular variable (e.g. ID Variable), the variable box appears gray to indicate that it is disabled.




This dialog is used to select the descriptive statistics to calculate within datasets with the same grouping factor. For example, you may want to calculate and display the Mean, Min and Max of all datasets within each Treatment.

Note You must select at least one statistical test.

This dialog is used to select the summary descriptive statistics you want to calculate across all datasets. For example, you may want to calculate and display the Mean, Median, Geometric Mean and Standard Deviation of all datasets, regardless of a grouping factor such as Treatment.

This dialog is used to specify the following characteristics for the table:

• Table title

• Page layout preference (Portrait or Landscape)

• Font and font size for column titles and table body text

• Margins (top, bottom, left, and right)

• Datasets to include

For more information, see the section, “Selecting the Datasets Dialog” in this chapter.

This dialog is also used to specify conversion criteria for all data (summary statistics) contained in the table (optional). You can specify:

• Number of decimal places, or

• Number of significant digits with or without scientific notation.






For example, if the value for a sample is 103.9056912367, you select the Number of significant digits with Scientific notation option, and you specify four decimal places for the data display, the notation will appear as follows:

1.039E3

You can specify conversion criteria for each column that appears in the table. If you do not specify conversion criteria for a column, all calculated decimal places for the value are used to display summary statistics for the variable in the final table, by default.

Note Statistics are calculated from raw numbers as they are entered by you, not from formatted values that may appear rounded.



This dialog is accessed by selecting the Dataset Selection check box in the Table Assistant – Step 5 of 5 dialog. The Selecting the Datasets dialog is used to select one or more datasets to include in the new table. You can:

• Manually select one or more datasets

• Select all datasets

• Select subsets of datasets to include in the table, by specifying a single dataset textual or numeric field that filters across all datasets.

Note If you do not make any selections in this dialog, all datasets will be included in the table, by default.

To create tables and scripts using the table assistant wizard and Excel

1. Select Assistants and then Table from the Tools menu. The Table Assistant - Step 1 of 5 dialog appears.

Figure 10-12. Table Assistant – Step 1 of 5

Selecting the Datasets Dialog



2. Select a template from the available list and click Next. The Table Assistant - Step 2 of 5 dialog appears.


3. Select a parameter from one of the available lists by dragging and dropping the variable into the appropriate area.



To specify group variables:

1. Drag the Study Field(s) that you would like to include as group variable(s) to the Group Variables area of the dialog.

2. To change the order of the group variables, click the Up or Down arrow in the Group Variables area of the dialog.

To specify cross-variables:, drag the Study Field(s) that you would like to include as cross variables to the X Cross Variables area of the dialog.

To specify ID variables, drag the Study Field(s) that you would like to include as ID variables to the ID Variables area of the dialog.

Note You must specify at least one ID Variable if ID Variables are included in the template that you selected in the Table Assistant - Step 1 of 5 dialog. If you selected Structure 8 as the template and you wish to use the transpose function that it provides, you must select a Study Field (e.g., SbjName) that uniquely identifies the study subject.

To specify variables:

1. Drag the Study Field(s) that you would like to include as variables to the Variables area of the dialog.

2. To change the order of the variables, click the Up or Down arrow in the Variables area of the dialog.

Specifying Group Variables (optional)

Specifying Cross variables

Specifying ID Variables

Specifying Variables



To specify data set fields:

1. Select a Worksheet name from the available list.

2. Drag the appropriate Column Name for time into the Time area of the dialog (one column name only).

3. Drag the appropriate Column Name for concentration into the Concentration area of the dialog (one column name only).

4. Click Next. The Table Assistant - Step 3 of 5 dialog appears.


Specifying Data Set Fields (available for templates 8 and 9

only)



To select statistics based on grouping factors:


• To select all of the descriptive statistics in the Available list, click on the >> button. The list of available statistics moves to the right-hand side of the screen under the Selected list.

• To move the entire list of selected statistics back to the Available list, click <<.

• To move a statistic from the Available list to the Selected list, highlight the statistic(s) and click >. The descriptive statistics move to Selected.

• To move a statistic from the Selected list to the Available list, highlight the statistic and click <.

2. Highlight the appropriate statistic and click the Up arrow or Down arrow to move a descriptive statistic in the Selected list up or down.


Selecting Statistics Based on Grouping Factors




To select summary statistics:


• To select all of the summary statistics in the Available list, click on the >> button. The list of available statistics moves to the right-hand side of the screen under the Selected list.

• To move the entire list of selected statistics back to the Available list, click <<.

• To move a statistic from the Available list to the Selected list, highlight the statistic(s) and click >. The summary statistics move to Selected.

• To move a statistic from the Selected list to the Available list, highlight the statistic and click <.

2. Highlight the appropriate statistic and click the Up arrow or Down arrow to move a summary statistic in the Selected list up or down.

Selecting Summary Statistics



To specify title and formatting characteristics:

1. Enter a name for the new table in the Table title field. The table title is a text label placed at the top of the table. If you do not enter a name, the Table Assistant uses the default title “EP Table.”

2. Specify page layout, font and font size, and margin preferences for the new table.

3. Click the Symbolic Status Flag check box if you would like to report flagged data with both a symbol and a number (e.g. <0.04), otherwise flagged data will be reported descriptively (e.g. undetectable, outlier).

To specify conversion criteria:

1. Click the Conversion check box.

2. Specify one of the following formats in the corresponding spin box:

• Number of decimal places to display

• Number of significant digits with scientific notation. When this option is chosen, the check box sets the data to be displayed in the scientific format, as opposed to default numeric format.

3. To specify conversion criteria for an individual variable, select the variable from the Choose the variables to format list and click >.

4. Specify the display for the variable using the spin box in the Specify Data Format area of the dialog.

Specifying Title and Formatting Characteristics

Specifying Conversion Criteria (optional)



To specify BLQ handling criteria:

1. Select the option under the drop-down menu for BLQ data

2. If user-defined LLOQ is selected:

a. Map the column for LLOQ values

b. Select the LLOQ setting

The BLQ Data option lists different ways of handling below the limit of quantification data by selecting:

• Default: Data with “<” and assumes the value proceeding the less than symbol;

• Set as 0: Data with “<” will assume the value of 0;

• Set as missing: Data with “<” will be treated as missing;

• Box for BLQ before first non-zero normal data = 0, if checked, will treat all BLQ values before the first non-zero data as 0;

• User-defined LLOQ, if checked, will override the BLQ data option and enable user to map the column for the LLOQ values and set BLQ to LLOQ, LLOQ/2, LLOQ/3 and LLOQ/4.

The export to SigmaPlot option is only available for Tables 2, 8 (transposed), and 9. To specify data export to SigmaPlot for plotting, check the Export to SigmaPlot box.

• The group variable specify the sorting of data to different tables based on the grouping variable.

• ID Variable becomes the column header of the SigmaPlot tables.

Specifying BLQ Handling (optional)

BLQ handling

Export to SigmaPlot (optional)



• If ID variable is not available, the statistical parameters become the column header of the SigmaPlot tables.

To specify data set information to include in the table:

1. Click the Dataset Selection check box. The Selecting the Datasets dialog appears.

Figure 10-17. Table Assistant – Selecting the Datasets Dialog

2. Specify one of the following in the Dataset Selecting area of the dialog:

• Manual Select

• Criteria Setting

• Select All. If you make this selection, all data in the .kdb file will be included in the table.

Specifying Data Set Information to Include in the Table (optional)




• To select all of the datasets in the Available list, click on the >> button. The list of datasets moves to the right-hand side of the screen under the Selected list.

• To move the entire list of selected datasets back to the Available list, click <<.

• To move a dataset from the Available list to the Selected list, highlight the dataset(s) and click >. The datasets move to Selected.

• To move a dataset from the Selected list to the Available list, highlight the dataset and click <.

4. Select the appropriate textual or numeric field from the Field list. Kinetica lets you to select combination of filter fields. For example, select the text field DrugName.

5. Select the appropriate operator from the Operators list. For example, select =.


• For text fields, select the appropriate criteria from the Criteria list. For example, select B.

• For numeric dataset fields, enter the appropriate numerical value.


• Click Cancel to exit the Selecting the Datasets dialog and return to the Table Assistant – Step 5 of 5 dialog.

• Click Finish to complete the Table Assistant wizard, load Excel and generate the table.



To save table script files:

1. Return to Kinetica. A message appears prompting you to save the table script (i.e. store the table creation script inside the Kinetica study (a .kdb file)):

Figure 10-18. Prompt for Saving Table Script

2. Click Yes to save the script. The Export Script Name dialog appears.

Figure 10-19. Export Script Name Dialog


• Click Cancel. The table remains inside Excel.

• Enter a name for the table script in the Export Script Name field. The name is added to the list of other table scripts that may already be embedded inside the kdb file. Click OK. The Excel table script and table appear inside the Tables pane.

Saving Table Script Files



If you have embedded table scripts inside Kinetica, you can recreate the tables without using the Table Assistant.

To recreate a table:

1. Open the appropriate .kdb file. Kinetica loads all table scripts saved in the study file inside the Tables pane.

2. Double click on the appropriate Table Script name in the Tables pane to regenerate the table using the current data in the study.

Regenerating Embedded Tables

from Script Files



You have the option to delete a table script at any time. When you delete a table script, it is removed from the .kdb file and the Tables pane.

To delete a table:

1. Select Embedded Objects then Tables from the Tools menu. The Embedded Script dialog appears.

Figure 10-21. Embedded Table Script Dialog

2. Highlight the table script you want to remove and click Remove.

3. Click OK to exit the dialog. The table script is removed from the Tables pane and the .kdb file.

Deleting a Table Script


11. Population Pharmacokinetics (PK)

Population pharmacokinetics is the study of the variability in drug disposition between individuals when standard dosage regimens are administered. The following chapter provides information on PK within Kinetica.

Population Pharmacokinetics (PK)


Population pharmacokinetics is the study of the variability in drug disposition between individuals when standard dosage regimens are administered. Because of logistical and ethical reasons, only relatively sparse samples are taken and available for analysis in many clinical trials. Therefore, traditional pharmacokinetic analysis, which involves the determination of an individual's pharmacokinetic parameters, is not feasible. The methodology in population pharmacokinetics focuses on the central tendency of the pharmacokinetic information and is capable of analyzing such sparse data. This can lead to a better prediction of the dose-response relationships in future studies.

Kinetica is also a software package for population pharmacokinetic analysis using nonlinear mixed-effect models. It can estimate the parameter distribution for the population, as well as assess the contribution of inter-individual variabilities physiologically and pathologically (covariates) to individual parameter values. Covariates can be age, body weight, hepatic or renal function, or co-administration of other drugs, for example.

Kinetica applies the EM algorithm to conduct nonlinear mixed-effect model analysis.

STEP E: Conditional Expectation (Bayes)

The individual parameters in the model are estimated assuming that they have a known prior distribution (mean + variance) and a known residual error distribution (mean + variance).

STEP M: Likelihood Maximization

Given the individual parameters computed in Step E, the ML posterior population mean and variance are computed.

Introduction to Population PK




Model evaluation can be conducted through residual analysis, the Akaike Criteria (AIC), Schwartz criterion (BIC), objective function (OBJ), or the log likelihood function. The Evaluation Graphs in Kinetica enable you to evaluate the distribution of computed parameters, the distribution of the residual error, the weighting schemes, as well as the prediction of the individual and population profiles, with the option to display the confidence interval. Kinetica estimates the expected individual parameters given the populations' estimated values (using a MAP procedure), and then computes appropriate statistical tests to evaluate the distribution properties of the differences between the expected and the observed data. For more information, see “Appendix A Population Methodology.”

Observations :time,

concentrations, covariates.

Fixed effect parameters : β(k)&

Variance matrix of random effect parameters: C(k)

&Error variance : σ(k)

Individual parameters:βj(k+1)

Variance of random effect

parametersC(k+1)

Start

Stop ?

No

Yes

Fixed effectparameters:

β(k+1)+ +

Error variance : σ(k+1)

Step E

Step M

Figure 11-1. EM Algorithm Flow Chart

Model Evaluation



The features included in Kinetica Population are listed in the following table.

Feature Description

User interface As in Standard Kinetica (see the section, “Using the Kinetica Workspace” in the chapter “Starting Kinetica”)

Import/Export data

As in Standard Kinetica (see the section, “Importing and Exporting Data”)

Graphic engine As in Standard Kinetica (see the chapter, “Working with Graphs”)

Standard analysis All mathematical/statistical analyses in standard Kinetica are available in Kinetica Population

Structural model Kinetica contains a pre-defined library of PK/PD. Write your own models using either the Population Designer or Kinetica Macro language (see the section, “Working with the Population Designer” in this chapter, and Appendix B)

Features of Population PK




Feature Description

Covariate models Two options to build covariate models are offered by Kinetica Population:

The first option is user-defined and enables you to select the PK/PD parameters, then associate them to one or more covariables (for more information, see the sections, “Population Methodology” and “Running the Analysis with Covariable(s)” in this chapter)

The second option makes use of a Stepwise forward method to screen the potential covariates. The final covariate models are obtained through multiple linear regression between the parameters and the covariables (for more information, see Appendix A and the section, “Running the Analysis with Covariable(s)” in this chapter)

Interindividual variability

Parameters can be calculated assuming a normal or a Log Normal distribution (see Appendix A and the section, “Estimating Initial Parameter Values” in this chapter)



Feature Description

Residual error There are three types of residual errors: additive, proportional, and the combination of both additive and proportional

There are four different scenarios for variance:

The variance is a known constant

The variance is an unknown constant

The variance is proportional to the square of the dependent variable

The variance is proportional to the dependent variable

(see Population Methodology – Appendix A)


These values are provided either by the user or by the Initialization Assistant (for a single dose). The Initialization Assistant applies the stripping method to naïve average data (NAD), naïve pooled data (NPD), or individual data (standard two-stage method) to obtain the parameter values. These values are then used as initial estimates for the EM algorithm. For more information, see the section, “Estimating Initial Parameter Values” in this chapter.



Feature Description

Graphic Evaluation

Generates:

Individual observations and predictions

Individual observations together with the population mean curve

Individual observations against individual predictions

Weighted residuals against predicted values

Residuals against predicted values

Residual error distribution

Parameter distribution

For more information, see the section, “Working with Kinetica Population Graphs” in this chapter.

In addition, you can use Kinetica Population to:

• Import/export data and graphs

• Create datasets

• Select or modify the computational methodology by writing user-defined soft-coded methods

• View individual and study results.

Note To use Kinetica Population, you must insert at least one population PK/PD method after entering the input data and inserting the necessary variables.



The wide variety of hard-coded methods in the Population Method directory is listed and categorized based on:

• Route of administration (e.g. IV Bolus, Extravascular (first order absorption), IV Infusion, Zero Order Absorption (single dose only) models for single and multiple dose.

• Compartment number (e.g. 1 Comp, 2 Comp, 3 Comp).

• Type of pharmacodynamic model. For more information related to pharmacodynamic modeling, see Population Methodology – Appendix A.

Each hard-coded method is uniquely designed to generate specific output columns and variables for numerous types of population templates. All population PK/PD methods contain the prefix “PopFit.”

Note The naming conventions for hard-coded methods and templates are identical.

For a complete list of the generated outputs for each method, see the sections, “Single Dose Population PK Methods and Compartments”, “Multiple Dose Population PK Methods and Compartments”, and “PD Population Methods and Output Parameters” in this chapter.

Note You can also create soft-coded methods in the Macro Editor to meet your own requirements. For more information, see the section, “Working with Population Designer” in this chapter and in Appendix B.

The population PK/PD methods (hard-coded in C++) are listed in the following table.

Population Method Function in Kinetica

PopFitMicroIVBolus1comp Single dose intravenous bolus micro constants using 1-compartment model

Kinetica Population PK/PD Methods




PopFitMicroIVBolus2comp Single dose intravenous bolus micro constants using 2-compartment model.

PopFitMicroIVBolus3comp Single dose intravenous bolus micro constants using 3-compartment model.

PopFitMicroExtravascular1comp Single dose extravascular micro constants using 1-compartment model.



PopFitMicroIVInf1comp Single dose intravenous infusion micro constants using 1-compartment model.



PopFitMicro0orderinput 1comp Single dose zero order micro constants using 1-compartment model.

PopFitMicro0orderinput 2comp Single dose zero order micro constants using 2-compartment model.

PopFitMicro0orderinput3comp Single dose zero order micro constants using 3-compartment model.

PopFitBolusInput1comp Single dose intravenous bolus using 1-compartment model. Clearance is used as a parameter.

PopFitBolusInput2comp Single dose intravenous bolus using 2-compartment model. Clearance is used as a parameter.

PopFitFirstOrderinput 1comp Single dose input using 1-compartment model. Clearance is used as a parameter.




PopFitFirstOrderinput 2comp Single dose input using 2-compartment model. Clearance is used as a parameter.

PopFitZeroOrderinput1comp Single dose zero order using 1-compartment model.

PopFitZeroOrderinput2comp Single dose zero order using 2-compartment model.

PopFitLinear Pharmacodynamic linear model.

PopFitLinearInhibition Pharmacodynamic log linear model.

PopFitSigmoidal Pharmacodynamic Sigmoid model.

PopFitSigmoidal Inhibition Pharmacodynamic Sigmoid Inhibition model.

PopFit Emax Pharmacodynamic Emax model.

PopFit Emax Inhibition Pharmacodynamic Emax Inhibition model.

PopFitIVBolus1CompMultiDose Multiple dose intravenous bolus micro constants using 1-compartment model.



PopFitExtravascular1CompMultiDose Multiple dose extravascular micro constants using 1-compartment model.

PopFitExtravascular2CompMultiDose Multiple dose extravascular micro constants using 2-compartment model

PopFitExtravascular3CompMultiDose Multiple dose extravascular micro constants using 3-compartment model.

PopFitIVInfusion1CompMultiDose Multiple dose intravenous infusion micro constants using 1-compartment model.



You can select the hard-coded population PK/PD methods for fitting in the Methods dialog. We call these methods models because they are specific methods for modeling data rather than simple functions. When you choose a population PK/PD method from the Methods dialog, the input and output parameters can be viewed along with a brief explanation of what the method computes.

Note If a population PK/PD method is being shared by more than one worksheet, be sure to rename the output parameters found in the Method dialog. If you do not perform this procedure, the calculations from the second worksheet will override the first.

To select a population PK/PD method:

1. Launch Kinetica and enter the input data.


• Create your own soft-coded model method by clicking Macro Editor in the Study pane.

• Use Designer to create your method graphically by selecting Designer then Population from the Tools menu

3. Select Pop Method from the Insert menu. The Method Selection dialog appears. We will use this option for this example.

Inserting a Population PK/PD

Method




4. Select a population method from the available list (e.g. PopFitMicroExtravascular1comp method) to activate Kinetica Population.

5. Make the appropriate selections under the User names and Parameter columns corresponding to the Input Cols&Vars, Output Cols and In/Out Vars columns by clicking on the associated drop down lists. Parameters have a Yes or No option. If you select Yes, you are prompting Kinetica to calculate the parameter. If you select No, you are specifying that there is an existing value for the parameter and thus the parameter will not be calculated.

6. Click Datasets. The Select Dataset dialog appears.



Figure 11-4. Select Dataset Dialog

7. Use the Ctrl key and mouse to select a particular range of datasets or click Select All to include all datasets.

8. Click OK to exit the Select Dataset dialog.

9. Click OK to exit the Method Selection dialog.



You can view or modify population PK/PD method options after a population PK/PD method is inserted using the Options Setup dialog. All inserted population PK/PD methods contain the same options.

This dialog is accessed by clicking Methods in the Methods pane and then clicking Set under the Global Options column of the appropriate population PK/PD method. The selections in the Options Setup dialog are described in the following table.

Note These options can be modified before and after running an analysis without covariables. For more information, see the sections, “Estimating Initial Parameter Values” and “Running the Analysis without Covariable(s)” in this chapter.

Option Description

Initialization With Population parameters - Select this option to use prior population parameter distribution to run the EM algorithm

With Individual Parameters: If the individual parameters are known, you can select this option to rerun the EM algorithm

Use Simplex for the First Step

Yes – Select this option to use Simplex to search for the minimum point for the first step

No – Select this option if you do not want to use Simplex

Datasets Select this option to choose the set of datasets to analyze

Modifying Global Options

Options Setup Dialog



To modify global options:


2. Click Set under the Global Options column of the appropriate Population PK/PD method. The Options Setup dialog appears.

Figure 11-5. Options Setup Dialog

3. View or modify the options as required then click OK to save the information and exit the dialog.

Modifying Global Options



There are four primary routes of administration pertaining to single dose situations and three primary routes of administration pertaining to multiple dose situations, as described in the following table.


Description

IV Bolus This method is used when the kinetic profile of a drug was generated as a result of an intravenous bolus administration.

Extravascular This method fits data for all routes other than IV Bolus and IV Infusion (e.g. oral administration, I.M, etc.). The general condition for use is an input (or absorption) following a first order. For this reason, Kinetica provides an Output Variable Ka you can fit with or without lag-time.

IV Infusion This method is used when you have a kinetic profile of a drug generated as a result of an administration with a known IV infusion duration value (as an input).

Zero Order Input This method is used when you have a kinetic profile with a zero order input function that is not an IV Infusion. This case is possible with an oral, I.M administration, etc. The Input duration (equivalent to the Infusion duration in the IV Infusion) is an Output Variable and is then estimated by Kinetica.

Note This route of administration is not available for multiple dose situations.

Routes of Administration



The routes of administration can be further subdivided by compartments as we described below in more detail.

• 1Comp (one compartment model)

• 2Comp (two compartment model)

• 3Comp (three compartment model).

They can be further subdivided by PK single dose and PK multiple dose, as follows:

In the case of single dose administration, execution of population data is accomplished by providing input data consisting of Time, Concentration, Dose and *Infusion Duration, as dictated by the Single Dose Population PK/PD method you want to insert.

Note *Infusion Duration is an input when the route of administration is an IV Infusion. Infusion Duration is a calculated output if the route of administration is a Zero Order.

Inputs are numeric or text variables and columns where you must enter data so that Kinetica can successfully complete the analysis. Outputs are numeric or text variables and columns where Kinetica displays the results of the computation.

Note All PK single dose methods require the same variable and column input.

Data input (user-entered dataset numeric or text field values):


Data input (user-entered dataset column values)

T (or X) – Time

C (or Y) – Concentration

Compartment Number (1Comp, 2Comp, 3Comp)

Single Dose Methods



Note By default, Kinetica will identify Time and Concentration values entered under X and Y columns, respectively. If you want to modify these settings, you must first delete the population method. You can do this using the Select Columns and Variables to Remove dialog, accessed by selecting Remove Last Pop Method (or Remove all Pop Methods) from the Edit menu. Alternatively, if you have not yet inserted a method, insert the population PK/PD method and select the appropriate User Names and Parameters in the Method Selection dialog.

This table lists all Single Dose Population Methods and associated output parameters, categorized by compartment number.

Single Dose Population Methods

Output Parameters

Population Method 1 Compartment 2 Compartment 3 Compartment

PopFitMicroIVBolus Volume, Kel Volume, Kel, K12, K21 Volume, Kel, K12, K21, K13, K31

PopFitMicro Extravascular

Volume, Kel, Ka, Lag

Volume, Kel, Ka, Lag, K12, K21

Volume, Kel, Ka, Lag, K12, K21, K13, K31

PopFitMicroIVInf

Volume, Kel Volume, Kel, K12, K21 Volume, Kel, K12, K21, K13, K31

PopFitMicro0orderinput

Volume, Kel, Infusion Duration

Volume, Kel, Infusion Duration, K12, K21

Volume, Kel, Infusion Duration, K12, K21, K13, K31

PopFitBolusInput

Volume, CL Volume, CL, K12, K21

PopFitFirstOrderinput

Volume, CL, Lag, F, Ka

Volume, CL, Lag, F, Ka, K12, K21



Single Dose Population Methods

Output Parameters

PopFitZeroOrderinput

Volume, CL, InfusionDuration

Volume, CL, InfusionDuration, K12, K21

Note Individual parameters are stored in the All Variables worksheet. Population parameters are stored in the Study worksheet. Population parameter names are displayed as “Study.parametername.”

In the case of multiple dose administration, execution of population data is accomplished by providing input data consisting of Time, Concentration, Administered Time, and Administered Dose columns, as dictated by the Multiple Dose Population PK method you want to insert.


T – Time


AdminDose – Amount of drug administered

AdminTime – Time of drug administration

*InfusionDuration – Duration of drug infusion (PopFitIVinfusionMultiDose method only)

Choose to identify the administration route:

• Extravascular

• IV infusion

• IV Bolus.

Multiple Dose Methods

http://Study.parametername.




This table lists all Multiple Dose Population Methods and associated output parameters, categorized by compartment number.

Multiple Dose Population Methods Output Variables

Population Method 1 Compartment 2 Compartment 3 Compartment

PopFitIVBolusCompMulti Dose

Volume, Kel Volume, Kel, K12, K21

Volume, Kel, K12, K21, K13, K31

PopFitExtravascularComp MultiDose

Volume, Kel, Ka, Lag

Volume, Kel, Ka,

Lag, K12, K21

Volume, Kel, Ka, Lag, K12, K21, K13, K31

PopFitIVInfusionComp MultiDose

Volume, Kel Volume, Kel, K12, K21

Volume, Kel, K12, K21, K13, K31

In the case of pharmacodynamic templates, execution of population data is accomplished by providing input data consisting of Concentration and Effect columns, as dictated by the PD population method you want to insert.



Effect – Drug Effect

This table lists all Population PD Methods and associated output parameters.

PD Population Method Output Parameters

All Variables Worksheet

PopFitLinear S, E0

Pharmacodynamic Methods




PD Population Method Output Parameters

PopFitLinearInhibition S, E0

PopFitLogLinear S, E0

PopFitLogLinearInhibition S, E0

PopFitSigmoidal E0, Emax, n, EC50

PopFitSigmoidal Inhibition E0, Emax, n, EC50

PopFit Emax E0, Emax, EC50

PopFit Emax Inhibition E0, Emax, EC50





This table lists output columns for all Population Methods.

Note The output columns for all Kinetica Population PK methods are the same.

Output Column Description

Ycalc Computed values of dependent variable for each individual

Residuals Difference between Ycalc and Yobs for each individual

Weight Weighting for each individual

Weighted Residuals

Residual adjusted according to a selected weighting scheme, e.g. 1,Ycalc, Ycalc2, Yobs, Yobs2 for each individual

Iwres Residual adjusted according to standard deviation calculated from variance matrix

Isd Square root of the diagonal in the variance matrix

Pred Predicted values of dependent variable computed with the population mean values of parameters

Sd Standard deviation of population prediction

Wres Residual adjusted according to standard deviation calculated from variance matrix for population

Kinetica Population Output Columns



In order to understand how to use Kinetica Population templates, we suggest that you study the following example. This way you can try running an analysis to understand how Kinetica Population computes results before using your own data. After you try the example, you can open the empty template and enter your data by, either: a) cutting and pasting the data, or b) importing the data using the Import Assistant. You can then rerun the analysis. For more information related to the Import Assistant, see the chapter, “Importing and Exporting Data.”

The following example is applicable to all Population templates, with small variations. These variations are indicated, as needed.

Note The difference between .kdb and .ktp files: .kdb file: A file that contains calculated results dependent on the method(s) embedded within it. .ktp file: A template that contains methods with no calculated results. A template (or .ktp file) can be used repeatedly for future analysis.

A drug dose of 100 mg was administered via IV Bolus. The concentration-time course was sampled from the plasma, expressed in mg/L and h respectively. A one-compartment model was used to analyze this example.

To open the PopFitMicroIVBolus1comp template:


2. Select the Population tab.

3. Click on the PopFitMicroIVBolus1comp icon and select the Open with Data check box.

Kinetica Population Templates

Running an Example Template

(PopFitMicroIVBolus1comp single dose)



Figure 11-6. New Analysis Dialog showing Population Tab, PopFitMicroIVBolus1comp.ktp, Open with Data selected.

4. Click OK. The data appears in the workspace.

5. Examine the data, as required.

Note When you are working with your own data, we strongly advise that you examine the data carefully. Review graphs, check for outliers, missing values and inconsistencies.

6. To estimate initial parameter values, see the section, “Estimating Initial Parameter Values” in this chapter.



After you open the template, you estimate initial parameter values for the EM algorithm to get the best possible fitting for the data using the Set Initial Parameter Values dialog.

There are two ways of entering initial parameter estimates:

• Manually

• Using the Initialization Assistant.

You can manually enter values for all templates. However, the Initialization Assistant is not available for multiple dose or PD templates.

This dialog is accessed by selecting Set Initial Parameters from the Population menu. The dialog is divided into several sections:

1. Select Inserted Population Method – contains all inserted Population methods within a template. You can select the appropriate method by clicking on the drop down list. The initial parameter values you set in this window and the error model correspond to the population method you selected.

2. Enter Initial Parameter Estimation – applicable to manual data entry (without using the Initialization Assistant). You enter values for each parameter under the Values column. You also have the option to enter values for the remaining columns, as required.

Column Description

Minimum Lower bound in parameter estimation. For normal distribution, the default value is set at value/20. Only used in the random search algorithm.

Maximum Upper bound in parameter estimation. For normal distribution, the default value is set at value*100, by default. Only used in the random search algorithm.

Estimating Initial Parameter Values

Using the Set Initial Parameter Values Dialog



Column Description

Init.Variance Initial estimation of variance for population parameters.

Init.CV 100*population mean/population SD.

Distribution Used to select the population parameter distribution property, either normal or log normal. Convert values interchangeably (Log to Normal). If you choose a log normal distribution, the initial estimates, minimum and maximum are automatically converted from normal distribution.

3. Select Error Model – defines the error variance according to the Sigma and the Weighting function. The Error Variance models are described in the following table.

Column Description

Sig1(fixed constant) Kinetica assumes that the variance of the error model is a known constant.

Sig1 (homoscedastic)

Kinetica assumes that the error variance is the same for all the measurements, but it is unknown.

Sig1*Weight+Sig2 You can set Sig2 to 0 for heteroscedastic error structure. There are 4 different weighting schemes:

~ Y – Kinetica assumes that the error variance is proportional to ycalc or yobs

~ Y^2 – Kinetica assumes that the error variance is proportional to the ycalc^2 or Yobs^2.

Sig1*Weight+Sig2

If Sig2 is not set to 0, this is a combination error structure, homoscedastic+ heteroscedastic.

http://www.Init.CV



Column Description

Link Model This option is used for PK/PD simultaneous fitting.

4. Initialization Assistant Wizard…The Initialization wizard is a series of dialogs designed to aid in the process of estimation using one of the available methods.

To use the Initialization Assistant wizard, read the instructions in each dialog to help you enter information, and follow the steps in the procedure. You can move back and forth between the dialogs and change information as required until you complete the wizard. You can exit without saving the information at any point before completing the wizard.

The Initialization Assistant wizard has 3 steps. Step 2 can be completed by following the directions on the dialog and in the procedure. Steps 1 and 3 are explained in more detail in the following descriptions.

This dialog is used to select an initialization method. These methods are described in the following table.

Initialization Assistant Wizard - Step 1 of 3 Dialog



Initialization Method

Description

Naive Average Data (NAD)

Creates new datasets by calculating the mean time and concentration values for those datasets (or individuals) with the same dosing regimen

Creates initial parameter estimates for the EM algorithm by applying standard compartment PK analysis on the new datasets. If there is more than one new dataset, the mean values of the parameters will be used as initial estimates for the EM algorithm

By selecting this method, when you open the exported .kdb file, you see that the mean values were calculated using the concentration values from all datasets. Therefore, your dataset worksheet appears to contain 1 or several datasets, depending on the dosing regimen (see Initialization Assistant Wizard - Step 3 of 3 Dialog description)



Initialization Method

Description

Naive Pool Data (NPD)

Creates new datasets by gathering/pooling time and concentration values for those datasets (or individuals) with the same dosing regimen

Creates initial parameter estimates for the EM algorithm by applying standard compartment PK analysis on the new datasets. If there is more than one new dataset, the mean values of the parameters will be used as initial estimates for EM algorithm.

If you select this method, when you open the exported .kdb file, all time and concentration values (x and y columns, respectively) for those datasets with the same dosing regimen are displayed (stacked one on top of the other) as one dataset (see Initialization Assistant Wizard - Step 3 of 3 Dialog description)

Two Stage method

Creates initial parameter estimates for the EM algorithm by calculating parameter values one dataset at a time using standard compartment PK analysis

If you select this method, when you open the exported .kdb file, time and concentration values (x and y columns, respectively) for all datasets are displayed in one worksheet (see Initialization Assistant Wizard - Step 3 of 3 Dialog description)

This dialog is also used to select a group. Both NAD and NPD are accomplished using the “dosing regimen” grouping factor. Only those datasets (or individuals) that have the same dosing regimen can be grouped together. Each group corresponds to one dosing regimen.



You also have the option to select Automatic Initialization for Fitting. If you select this option, Kinetica will provide initial values to conduct parameter estimation on the new datasets generated from NAD or NPD, or on each individual dataset in the Two Stage method. These initial values provided by Kinetica are derived from the Stripping method.

This dialog is used to:

• Select Group (required for NAD and NPD methods only)

• View the generated graph

• Export the data to a new .kdb file (optional)

• Review the calculated values for every available group.

There are two ways of adjusting parameter values (for example, if you do not have a good fitting):

• You can manually edit the calculated values and click Reload Graph to see the adjusted plot.

• You can select a parameter from the drop down list (e.g. study.vol, study.kel), and adjust the plot by clicking the up or down arrow (available for NAD and NPD methods only). The percentage of change in the parameter value that occurs each time you click an arrow can be adjusted by manually entering a different numerical value in the field. The value is set to 0.1, by default.

You can adjust parameters for two or more groups simultaneously by clicking the icon in the Select Parameters row. If there is an “X” in the box, Kinetica will recalculate the values. If there is no “X,” the values will not be recalculated.

To estimate initial parameter values:

1. Complete the procedure for running an example template (see the section, “Running an Example Template” (PopFitMicroIVBolus1comp single dose) in this chapter).

Initialization Assistant Wizard - Step 3 of 3 Dialog



2. Select Set Initial Parameters from the Population menu. The Set Initial Parameter Values dialog appears.

Figure 11-7. Set Initial Parameter Values Dialog

3. Set options for Error Variance, Sig1, Sig2 and Weight, if required. After running the Initialization Assistant, you can readjust these values, if necessary.


• Manual entry: Enter values for each parameter in the Value column and any other initial parameters, as needed. Click OK to exit the dialog. To run the analysis, see the section, “Running the Analysis without Covariable(s)” in this chapter.

• Click Initialization Assistant. The Initialization Assistant Step 1 of 3 dialog appears.



Figure 11-8. Initialization Assistant – Step 1 of 3

5. Select one of the following from the Select Initialization Method list:

• Naive Average Data (NAD)

• Naive Pool Data (NPD)

• Two Stage Method.

For this example, we select NAD.

6. If you want to perform automatic fitting, select the Automatic Initialization for Fitting check box. For this example, we perform automatic fitting.

7. Click Group. The Select Group dialog appears.



Figure 11-9. Select Group Dialog

8. Select the appropriate check box and parameter from the drop down list box. For example, select the Dose Group check box and select Dose from the drop down list.

Note For intravenous infusion single dose population methods, you must also select the Infusion Group check box and then select the infusion duration variable from the available list.

9. Click OK to return to the Initialization Assistant Step 1 of 3 dialog.

10. Click Next. The Initialization Assistant Step 2 of 3 dialog appears:



Figure 11-10. Initialization Assistant Step 2 of 3. If “Automatic Initialization for Fitting” was unchecked in Step 1, initial parameter fields are blank and will need to be set manually.


• Manually enter initial estimates under Group.

• If you selected the Automatic Initialization for Fitting in the Step 1 of 3 dialog, the estimated values are already displayed (see figure below).



Figure 11-11. Initialization Assistant Step 2 of 3. “Automatic Initialization for Fitting” box was checked in Step 1 so initial parameter value fields are populated here.

12. Click Next. The Initialization Assistant Step 3 of 3 dialog appears. The results of the fitting are displayed. View the plot. Edit the data, as required.



Figure 11-12. Initialization Assistant - Step 3 of 3

13. To complete the estimation, perform one of the following:

• Click the Back button to return to Step 1 of the Initialization Assistant and repeat the procedure if results are not satisfactory.

• Click Export Data to New KDB.

• Click Finish to conclude the wizard.

14. For this example, click Export to New KDB. The Export Data to New KDB dialog appears.



Figure 11-13. Export Data to New KDB Dialog

15. Click the “…” button under the Select Path column. The Save As dialog appears.


16. Enter a name for the .kdb file to be exported under a folder of your choice and click Save.

17. Click OK to exit the Export Data to New KDB dialog.

18. Click Finish in the Initialization Assistant Step 3 of 3 dialog. The estimated values appear in the Set Initial Parameter Values dialog.



Figure 11-15. Set Initial Parameter Values Dialog after running Initialization Assistant.

19. Readjust the calculated values and options as needed.


21. To run the analysis, see the section, “Running the Analysis without Covariable(s)” in this chapter. While you run the analysis, you can also create different types of graphs (Yobs/Ycalc, Wres/Ycalc, Res/Ycalc, Weighted Residual Distribution, Parameter Distribution), transforming the information generated by the calculated data into graphical representations (see the section, “Working with Kinetica Population Graphs” in this chapter).



After you create initial parameter estimates for the EM algorithm, you are ready to run the analysis without covariables. This procedure must be performed before running an analysis including a covariable.

The population pharmacokinetic parameters together with the individual posterior estimates are computed under the assumption that no dependency exists between the PK parameter and the covariables. The relationship between the posterior individual estimates and the covariables is investigated using graphical exploratory function, statistical analysis, or a forward selection of Stepwise algorithm, after the procedure for running the analysis without covariables is completed.

The analysis is performed using the Running EM dialog. The default setting for the maximum number of iterations is twenty-five. When you reach the maximum, you have the option to add an additional 25 iterations. An iteration is one run of the EM algorithm. You can also change this default setting using the Advance Fitting Options dialog. For more information, see the section, “Performing Advanced Fitting” in this chapter.

To see the final results quickly, select a faster booster speed. To see the results of each iteration, adjust the booster speed to low. If you find the process too fast, adjust the booster speed. You can pause at any time by clicking Pause.

To run the analysis without covariables:

1. Complete the steps for estimating initial parameter values (see the section, “Estimating Initial Parameter Values” in this chapter).

2. Select Run with No Covariables from the Population menu. The Running EM… dialog appears.

Running the Analysis without Covariable(s)



Figure 11-16. Running EM Dialog

3. Adjust the Booster speed as required and click Start.

Note Max speed = one hundred percent of CPU used for calculations.

4. To pause the process at any time and review the data, click Pause. To resume the process click Pause.

5. When the iterations are complete, you will see a green flag or red flag. If you see a red flag, this indicates that an error occurred. Adjust initial parameter estimate values, and rerun the analysis. For more information, see the section, “Estimating Initial Parameter Values” in this chapter.

6. Click Close. The Individual Graphs dialog appears.



Figure 11-17. Individual Graphs Dialog

View each dataset plot by clicking the arrows located at the top of the dialog. You have the following options:

• Report Setup, Select Datasets, Export to Word, Export to Gallery, and Nb of Columns – dictates the number of columns that will be displayed on a page in Word (for more information related to these export features, see the sections, “Exporting Data to Microsoft Word and Microsoft Excel” and “Exporting Data to Microsoft Word” in this chapter).

• Individual CI – plots the dataset curve including the upper and lower confidence interval. By default CI is set to 95%. You can modify this setting by clicking Probability for CI.

• Prediction – plots the individual dataset curve, predicted and observed, with the population mean curve, predicted.

• Individual – plots the individual dataset curve, predicted and observed.



Graphical Evaluation displays the available Kinetica Population graphs (for more information, see the section, “Working with Kinetica Population Graphs” in this chapter).

1. For this example, click Graphical Evaluation and click OK. The following message appears: “Are you sure you want to close the individual graphs dialog box?” Click No to return to the Individual Graphs dialog. Click Yes to view all the graphs in the Gallery item of the Gallery pane.

2. To run the analysis with covariables, see the section, “Running the Analysis without Covariable(s)” in this chapter.

After you run the analysis without covariables, you are ready to run the analysis with covariable(s). Only the covariables showing a correlation with a pharmacokinetic parameter are used in the analysis. The population parameters are now re-estimated taking into account the relationship between the individual parameter and the covariables. You can then compare the results obtained from running the analysis without covariables to the results obtained from running the analysis with covariables.

There are two ways to define a covariate model:

• Manually

• Using Stepwise Inclusion.

Note You can estimate the relationship between a parameter and a covariable before running the analysis with covariables. To do this, select the All Variables worksheet in the Study pane. Highlight a particular parameter that you would like to investigate and one covariable, and click the Show one graph button on the toolbar. A graph is displayed in the Gallery. You can also select Linear Regression from the Statistics menu to generate a linear regression, and determine whether a linear relationship exists between the selected parameter and the covariable. For more information, see the chapter, “Performing Statistical Analysis.”

Graphical Evaluation

Running the Analysis with Covariable(s)



After you complete the procedure, you can repeat the analysis by inserting more methods and variables, as required.

To run the analysis with covariable(s) by defining a covariate model manually


2. Complete the steps for running the analysis without covariables (see the section, “Running the Analysis without Covariable(s)” in this chapter).

3. Select Add with Covariables from the Population menu. The User Defined Covariables dialog appears.

Figure 11-18. User Defined Covariables Dialog

4. Double click a parameter in the Parameters area of the dialog. For this example, we will select study. Kel. The parameter



appears in the Current Expression of the Parameter Model area of the dialog.

Note To change your selection, click Clear and then select another parameter.

5. Double click Add a Constant Value. The equation study.Kel=Theta1 is inserted in the Current Expression of the Parameter Model area of the dialog.


6. Double click a covariable(s) in the Covariables list. For this example, select Age and click Add.

The covariate equation study.kel = Theta1 + Age*Theta2 appears in the Parameter Model List area of the dialog. You can now set initial parameter estimates under the Value column for Theta1 and Theta2.



Figure 11-20. User Defined Covariables

Note To add a covariable in an exponential relationship, click the Power Model box.

7. Enter initial value estimates. For this example, enter 0.1 for Theta1 and -0.01 for Theta2.

8. Repeat the appropriate steps for other PK parameters, as required.



To modify the equation:

1. To change your selection, click Clear all equations and then repeat Steps 38 to 41.


3. Select Run with Covariables from the Population menu. The Running EM dialog appears.

4. Complete the appropriate steps for running the analysis without covariables (see the section, “Running the Analysis without Covariables” in this chapter).

To run the analysis with covariables using stepwise inclusion:



3. Select Add with Covariables from the Population menu. The User Defined Covariables dialog appears.

4. Double click a parameter in the Parameters area of the dialog. For this example, we will select study. Kel. The parameter appears in the Current Expression of the Parameter Model area of the dialog.

Note To change your selection, click Clear and then select another parameter.

Modifying the Equation (optional)

Running the Analysis with Covariable(s) Using Stepwise

Inclusion




5. Click Stepwise Inclusion. The Stepwise dialog appears.

Figure 11-22. Stepwise Dialog



6. Double click the appropriate covariable(s). For this example, double click Age. The covariable moves to the Selected Covariables list. Adjust the Significant Level, if required.

7. Click Run Stepwise. The values are displayed in the Stepwise Output Result list.

Figure 11-23. Example – Stepwise Output Result List

8. Click Insert. The following message appears: “Add covariable equation?”

9. Click Yes to insert the covariable equation(s). The equation(s) is inserted in the Parameter Model List area of the dialog.

10. Repeat the appropriate steps for other PK parameters, as required.



To modify the equation:

1. To change your selection, click Clear all equations and then repeat the appropriate steps.


3. Select Run with Covariables from the Population menu. The Running EM dialog appears.

4. Complete the appropriate steps for running the analysis without covariables (see the section, “Running the Analysis without Covariable(s)” in this chapter).

After you run the analysis with covariables, you have the option to generate descriptive statistics. The Study Info view displays information about the data, estimated parameters and selected options at different stages of the analysis. The window indicates which data model is currently in use, displays the initial parameter values, and the calculated population parameters using the initial estimates. This file can be exported to Word.

To view calculated data in the Study Info view:



3. Complete the steps for running the analysis with covariables (see the section, “Running the Analysis with Covariable(s)” in this chapter).

4. Select the Study pane and click on Study Info.

Modifying the Equation (optional)

Viewing Calculated Data



The default settings for fitting options can be modified using the Advanced Fitting dialog. Default settings vary depending on the method you select. The settings can only be modified for one method at a time. The fitting options are described in the following table.

Fitting Option Description

Criteria for EM EM must be less than the number displayed

Maximum number of iterations for EM

The limit number of iterations for the EM algorithm

Start criteria for E fitting

The initial criteria for E-step minimization

Final criteria for E fitting

The criteria for e-step minimization. If this criterion is satisfied, the E-step is finished.

Maximum number of iterations for E

Maximum number of iterations for the E-step

Minimum value of Lambda for E

The value used for the Levenberg-Marquardt algorithm

Initial value of Lambda for E

The start value of lambda before the application of the Levenberg-Marquardt algorithm

Derivation step The required numerical derivation step

Coefficient for Lambda variation of E

How the value is involved in the variation of lambda

To modify default settings:


Performing Advanced Fitting




3. Complete the steps for running the analysis with covariables (see the section, “Running the Analysis with Covariable(s)” in this chapter).

4. Select Options from the Population menu. The Advanced Fitting Options dialog appears.

Figure 11-24. Advanced Fitting Options Dialog

5. Modify the settings for the fitting options, as required. To return to the initial values, click Set default.

6. Click OK to exit the dialog and save the selections.



You can access the Kinetica Population graphs while running an analysis with or without out covariable(s). For more information, see the sections, “Running the Analysis without Covariable(s)” and “Running the Analysis with Covariable(s)” in this chapter.

You can view the following types of graphs in Kinetica Population:

• Population fitting together with individual datasets

• Population fitting with confidence interval together with individual datasets

• Ycalc versus Vobs

• Wres versus Ycalc

• Res versus Ycalc

• Weighted Residual Distribution

• Parameter Distribution

This plot enables you to see the relationship between Yobs (observed values) and Ycalc (predicted values).

If the fitting is perfect, i.e., Ycalc = Yobs, then the Ycalc versus Yobs plot is a straight line with unit slope. The x=y line is plotted as well.

Working with Kinetica Population

Graphs

Yobs versus Ycalc Plot



0 10 20 30Yobs()

0

10

20

30

Yca

lc()

Ycalc v s. Yobs

Ycalc vs. Yobs

Figure 11-25. Example - Yobs Versus Ycalc Plot

This plot enables you to see the relationship between the Weighted Residual (residual adjusted according to a selected weighting scheme, e.g. 1, Ycalc, Ycalc2, Yobs, Yobs2) and Ycalc.

The plot takes on a band-like shape, if the weighting scheme is appropriate. A small residual value suggests a small difference between Ycalc and Yobs.

Wres versus Ycalc Plot



Figure 11-26. Example – Weighted Residual vs. Ycalc

This plot enables you to see the relationship between the Residual and Ycalc prior to a weighting scheme modification.

If the residual is the band-like shaped graph (Res/Ycalc), this indicates that the selection of homoscedastic residual is appropriate. If the residual has the shape shown in the following diagram, then the heteroscedastic residual should be considered.

Res versus Ycalc Plot



Figure 11-27. Example – Res vs. Ycalc Plot



The following graph is an example of a Weighted Residual Distribution plot. The ideal situation follows a normal distribution.

Figure 11-28. Example – Weighted Residual Distribution Plot

Weighted Residual Distribution Plot



The parameter distribution plot provides the information on the evaluation of parameter distribution assumption. In the EM algorithm, you can assume that the population parameter follows either normal distribution or log normal distribution. With this plotting, you can evaluate your assumption.

The following graph is an example of a Normal Distribution plot. The plot shows an ideal normal distribution. Notice the symmetrical histogram bars.

Figure 11-29. Example – Parameter Distribution Plot

The following graph is an example of an ideal Log Transformed Distribution plot.

Parameter Distribution Plot



Figure 11-30. Example - Log Transformed Distribution Plot

To view Kinetica population graphs:

1. Open the Running Em… dialog, run the iterations, and click Close. The Individual Graphs dialog appears. For more information, see the section, “Running the Analysis without Covariable(s)” or “Running the Analysis with Covariable(s)” in this chapter.

Viewing Kinetica Population Graphs




2. Click Graphic Evaluation. The Select Graphic Evaluation dialog appears. You can change the number of steps (Nb Step) to calculate the frequency for parameter distribution.

Figure 11-32. Select Graph Evaluation Dialog

3. All graphs are selected by default. Deselect the check boxes corresponding to the graphs that you do not want to generate.

4. Click OK to exit the dialog and return to the Individual Graphs dialog.



5. Click OK to exit the dialog. The following message appears: “Are you sure you want to close the individual graphs dialog box?”

6. Click No to return to the Individual Graphs dialog. Click Yes to view all the graphs in the Gallery item of the Gallery pane.



In the model validation step, datasets are split into two groups: one for model building, also called the testing group, and another for model validation, called the validation group. Kinetica allows user to validate population model through (1) the parameter method (Bayesian fit), and (2) the concentration method.

For parameter method, the population model validation obtains the population parameter values and statistics based on the EM algorithm in Kinetica. These results are then used to run Bayesian fit (E-step) on the datasets to be validated. The individual parameters obtained from this step are called Pj,obs.

If there are no covariable equations, the deviation of Pj,obs from the population parameter (Ppred) on the testing group will serve as the criterion for model validation.

If there are covariable equations, the predicted individual parameter value, called Pj,pred will be obtained from the covariable equations (obtained from the testing datasets) combining the covariable information of each subject in the validation dataset group. Then, the deviation of Pj,obs from Pj,pred will serve as the criterion for model validation.

The concentration method uses the parameter values from testing dataset to predict the concentrations for individuals in the validation dataset group, and the 95% confidence interval. The concentration obtained from the prediction is called Ci,j,pred. The measurement of the concentration in the validation group is called Ci,j,obs.

Population Method Validation

Parameter method (Bayesian fit)

Case 1

Case 2

Concentration method



If there are no covariable equations, the parameter values obtained from the testing group will be used to predict the concentration of the validation group (expressed as mean and 95% confidence interval of the mean). These values will be compared with the observed concentration of the validation datasets. The 95% confidence interval will be calculated based on inter-individual variability and intra-individual variability obtained from the results of the testing group.

If there are covariable equations, the predicted individual concentrations for those subjects who belong to the validation dataset group can be calculated using the predicted individual parameters, Pj,pred (as shown in the parameter method, case 2). Then, the predicted concentration for each individual in the validation group is calculated, along with its 95% confidence interval. The predicted individual concentration will be plotted against the observed concentration for the validation dataset group.

For this example, we shall use a template with stored data for population methodology.


2. Select the Population tab.

3. Click on the PopFitMicroIVBolus1comp icon and select the Open with Data check box. Click OK.

Case 1

Case 2

Running Population Validation Using Parameter Method



Figure 11-33. New Analysis Dialog – Population Tab

4. Access population method validation by clicking the following toolbar menu: Population | Validation | Validation Setup.

Figure 11-34. Population Validation Menu Options

5. Population method validation allows the user to choose the population model the user wishes to validate by either entering parameters manually or run EM, then validate. For this example, select Run EM, then validate. The user has the option of selecting model building datasets manually or model building datasets by randomization. In the drop-down



menu box, choose Select model building datasets by randomization. If a population EM model is previously set-up, click Enter parameters manually and go to step 16.

Figure 11-35. Model Validation Dialog

6. Input 400 in the Number of model building datasets box. The remaining datasets will be used for validation. The default setting is the maximum number of datasets to be used for model building.

7. To view the subjects used for validation, click Randomize. The user may add or remove subjects manually by Ctrl-clicking on the subjects. Then click OK.

Figure 11-36. Select Model Building Datasets Dialog



8. To view the other option, choose Select model building datasets manually in the drop-down menu box. Notice that both boxes for the number of model building datasets and number of model validating datasets are now shaded. Click Dataset button to choose the subjects for model building.

Figure 11-37. Model Validation Dialog

9. Select subjects 1 to 330 by shift-click. Click OK.

Figure 11-38. Example – Subject Selection

10. The boxes for the number of model building datasets and number of model validating datasets are automatically calculated for the Model Validation dialog. Click Next.



11. Set initial parameter values and run population analysis identical to those for population models. (See the Running an Example Template (PopFitMicroIVBolus1comp single dose section)).

Figure 11-39. Set Initial Parameter Values Dialog

12. After running the population analysis, the Validation Setup allows the user to choose whether to run EM with covariables immediately, later or not to run EM with covariables. Since this example does not contain demographic value, select Do not run EM with covariables. Click Next.

Figure 11-40. Validation Setup Dialog



13. Kinetica will prompt the user whether to save the previous run. Click Yes.

Figure 11-41. Save EM Results Confirmation Dialog

14. Save the file as PracticePopValidation. Click Save.


15. The dialog: Validation setup is complete. You can now run validation appears. Click OK.



Figure 11-43. Validation Complete Dialog

16. Start validation by clicking Population | Validation | Run Validation…

Figure 11-44. Validation Menu Options

17. Run validation prompts the user to choose the population method and provides the choice of whether to use the parameter estimates or the time-concentration data for validating the remaining datasets. Select PopFitMicroIVBolus1comp and Parameter for Population Method and Validation Method drop-down menus. Click Run.



Figure 11-45. Run Validation Dialog

18. Bayesian Fitting dialog appears. Click Start. When the fitting is finished, click Close.

Figure 11-46. Bayesian Fitting Dialog

19. Individual graphs dialog allows the user to view the datasets used for validation and selection for report set-up, datasets



used for the validation process, export to Word, export to gallery, number of columns in the gallery, level of confidence interval . Click OK.


20. Kinetica verifies whether the user wants to close the individual graphs dialog box. Click Yes.

Figure 11-48. Close Confirmation Dialog

21. Kinetica prompts user whether to save the results. Click Yes.



Figure 11-49. Kinetica Save Dialog

22. The graphs sent to the gallery include: Bayesian parameter prediction versus population estimate, confidence interval of the individual estimate and confidence range for the population estimate, and the distributions of weighted residual errors and parameters.

Figure 11-50. Example – Graph Gallery



For this example we will use the template containing covariables.

1. Select Open from the File menu. Within the Data folder, open the EMValidateConc file.

2. Access population method validation by clicking the following: Population > Validation > Validation Setup.

3. Select Run EM, then validate. Choose Select model building datasets by randomization.

4. Input 15 in the Number of model building datasets box. View the subjects used for validation by clicking Randomize.

5. Close the Select Model Building Datasets dialog. Click Next on the Model Validation dialog.

6. Set initial parameter values and run population analysis identical to those for population models.

7. Select Run EM with covariables now. Click Next.

Figure 11-51. Validation Setup Dialog

8. Save the EM results as EMValidateConcCovar.

9. The following dialog appears. Click OK.

Running Population Validation Using

Concentration Method with Covariable



Figure 11-52. Kinetica EM with Covariables Dialog

10. Another dialog will ask whether the user want to run Validation from the last one. Click Yes.

Figure 11-53. Kinetica Validation Dialog

11. Add BW as a covariable of Volume and Kel as a power function.




12. Save the covariable model.

Figure 11-55. Kinetica Save Dialog

13. Run the model with covariable similar to running a population model.

14. Save the new EM result as EMValidateConcCovar2.

15. Run validation by clicking Population > Validation > Run Validation.



16. Select Concentration under the Validation Method drop-down menu. Make sure that Run with Covariables check box is selected. Click Run.


17. Click OK on the Individual Graphs dialog.




18. Save results and view data under All Variables in the Study pane. The individual worksheet under Dataset pane contains Ycalc, IDeviation, and RMS_Error columns. The Yobs versus Ypred plot is sent to the gallery.



The Population Designer enables you to generate and save a symbolic population model. Unlike Kinetica Designer, Population Designer enables you build a model with multiple dose. In all other aspects, the procedure for using the Population Designer is identical to the procedure provided for the Kinetica Designer. For more information, see the section, “Kinetica Designer” in chapter, “Working with Methods and Models.”

To create a symbolic population model:

1. Select Designer then Population from the Tools menu. The Population Designer appears.

Figure 11-58. Population Designer Dialog

2. Double click one of the icons on the toolbar and click anywhere in the workspace area. The Multidose dialog appears.


• To create a model for single dose, click Cancel.

• To create a model for multiple doses, enter the appropriate worksheet, dose column, and dose time column for the symbolic model and click OK.

Working with Population Designer



Figure 11-59. Multidose Dialog

4. To continue using the Population Designer, see the section, “Kinetica Designer” in the chapter, “Working with Methods and Models.”



You have the option to export population data to Microsoft Word from the Individual Graphs dialog. In order to export the information successfully, ensure that you specify Word as the default destination for information in the Report Setup dialog before you run an analysis with or without covariables. You can access the Report Setup option from the toolbar, by selecting Report Setup from the File menu, or by clicking Report Setup in the Individual Graphs dialog. For more information, see the chapters, “Configuring Kinetica” and “Importing and Exporting Data.”

Note Kinetica uses the Normal template that is loaded by default in your version of Microsoft Word. The format of the exported results depends on how your styles are defined in your Normal template. The tabulation may look skewed when you export to Word. Adjust the tab stops in Word to reorganize the data.

For more information related to the Individual Graphs dialog, see the section, “Running the Analysis without Covariable(s)” in this chapter.

The following information is exported to Microsoft Word from the Individual Graphs dialog:

• The date the report was generated

• Population method name

• Selected error model

• Selected datasets included in the report

• Input columns

• Input study parameters

• Parameter initialization values

• CV initialization values

• Fitting results

Exporting Data to Microsoft Word



• Population parameters

• Population CV

• Population variance

• Statistics for SPPE

• Standard error of estimate

• Parameters criteria results

• Elapsed time for the EM algorithm and the number of iterations

• Warnings associated with the analysis

• Selected graphs in Graphical Evaluation (also exported to the gallery)

• Population CI (Predicted or Individual)

• Covariable equations (analysis with covariables only).

To export data to Microsoft Word:

1. Open the Running Em… dialog, run the iterations and click Close. The Individual Graphs dialog appears. For more information, see the section, “Running the Analysis without Covariable(s)” or “Running the Analysis with Covariable(s)” in this chapter.

2. Select the graphs you want to generate (see the section, “Working with Kinetica Population Graphs” in this chapter).

3. Click OK to exit the Individual Graphs dialog. The following message appears: “Are you sure you want to close the individual graphs dialog box?”

4. Click Yes to view all the graphs in the Gallery item of the Gallery pane and to export data to Word.



Notes


12. Performing Statistical Analysis

The following chapter provides information and instruction on performing statistical analysis within Kinetica. The statistics menu in Kinetica contains basic descriptive statistics, parametric and non-parametric statistical evaluation, average and individual bioequivalence, including balanced and incomplete block designs.

Performing Statistical Analysis


There are no templates in Kinetica for statistical analysis. Generally, you can perform a statistical analysis after pharmacokinetic analysis without using a statistical template. The statistical analysis examples contained in this chapter were obtained by using options selected from the Statistics item on the main menu. The statistical tests used are not methods and do not appear in the Methods view of the Study or Dataset panes. We have supplied several kdb files, one for each statistical test, to explain how to use the test and what type of data should be used.

Statistical Analysis in Kinetica



You can perform an analysis of variance (ANOVA) calculation in four different ways within Kinetica:

• One-way analysis

• Two-way analysis

• Three-way analysis

• Replicate analysis.

Parallel studies can be viewed as one-way analysis of variance or one-factor experiments. In a one-factor experiment observations are taken for p independent groups with q observations in each group (i.e. there are p treatments each with q repetitions).

Crossover studies are a little different. There are many types of crossover studies, but we will focus the analysis on the 2-way and 3-way crossover studies.

A 2-way crossover study, also known as a two-factor experiment, is a two-variable experiment. This means that observations are taken for p independent groups and q blocks, such that there is one experimental value that corresponds to each pi (treatment i, i = 1…p) qj (block j, j=1..q). In other words, if we view the results as a table (pxq) there is one experimental value corresponding to every treatment (p) and block (q).

A 3-way crossover study also known as a three-factor experiment, is a three-variable experiment. This means that observations are taken for p independent groups, q blocks, and r sequences, such that there is one experimental value that corresponds to each pi (treatment i, i = 1…p) qj (block j, j=1..q) and rk (sequence k, k=1…r). In other words, if we view the results as a table (3-D this time with pxqxr) there is one experimental value corresponding to every treatment (pi) in block (qj) in the sequence (rk).

A replicate study involves a subject receiving both the test and the reference product in more than one period. For example, in a fully replicated study the distribution would appear like the following example:

ANOVA



Subject Period 1 Period 2 Period 3 Period 4 Sequence

S1 R T T R 1

S2 T R R T 2

The ANOVA test can be performed in the ANOVA n-way dialog. This dialog is accessed by selecting ANOVA from the Statistics menu.

Figure 12-1. ANOVA n-way dialog

The items that appear in the dialog are described in the following table.

ANOVA n-Way Dialog



Item Format Description

Number of ways Enter a number between 1 and 4 depending of the number of factors in your analysis. The program will then activate the column lists according to your entry.

Data column Select the data on which you want to perform the ANOVA analysis.

(for example: AUC, Cmax, Tmax, T1/2, etc).

Treatment column Select the Treatment field from the list. Generally, the treatment effect is always analyzed.

2, 3 and 4-way columns

Select the Data corresponding to the nature of your factor(s), for example, Subject, Center, etc).

Log transformation for data

Select this option to log-transform your data during the ANOVA analysis. Do not select this option if your data has already been log-transformed or if you do not want to log-transform your data.

Note: Kinetica enables the entry of alpha and numeric values for all analysis fields

When an ANOVA analysis is complete a table of results is written in the Study Info view of the Study pane. For this particular test the organization of this table always appears as follows:

Source df

(Degree of freedom)

SS

(Sum of Squares)

MS

(Mean Square)

F

(Fischer test)

p

(Probability value)

Total 1 2 3 4 *5

Treatment 6 7 8 9 *10



Source df

(Degree of freedom)

SS

(Sum of Squares)

MS

(Mean Square)

F

(Fischer test)

p

(Probability value)

2 Way 11 12 13 14 *15

3 Way 16 17 18 19 *20

4 Way 21 22 23 24 *25

Error 26 27 28 29 *30

*In the ANOVA Table, the value of p for every effect is compared to 0.05 (α = 5%). Kinetica computes and outputs a conclusion according to the following rules:

• If p > 0.05 the difference is not significant, so output the symbol is NS

• If p < 0.05 the difference is significant, so output the symbol is ***.

Note The p values are never generated for the Error and Total fields. The root mean square error is calculated by Sqrt (MS error), and the C.V. is calculated by (number of data * Root Mean Square Error) / x∑ .

The equations used to calculate the data in the previous table (i.e. 1, 2, 3) are listed in the following table:

Note For each of the data x, C = ( )xN∑ 2

where N = total

number of data.

Point Equation Used

1 Total number of data – 1

2 xi2∑ - nC

3 SS total / df (total)



Point Equation Used

4 No calculation is made and no output generated

6 (Number of treatments) –1

7

( )x j

n

ij 1

n 2

ii 1

i

=

=

∑∑

⎛

⎝⎜

⎞

⎠⎟

- C

8 SS treatment / df (treatment)

9 MS treatment / MS error

10 (Number of ways 2) –1

11

( )x j

n

ij 1

n 2

ii 1

i

=

=

∑∑

⎛

⎝⎜

⎞

⎠⎟

- C

12 SStwo way / df (2 way)

13 MStwo way / MS error




Point Equation Used

15

( )x j

n

ij 1

n 2

ii 1

i

=

=

∑∑

⎛

⎝⎜

⎞

⎠⎟

- C

16 SS 3 way / df (3 way)

17 MS 3 way / MS error


19

( )x j

n

ij 1

n 2

ii 1

i

=

=

∑∑

⎛

⎝⎜

⎞

⎠⎟

- C

20 SS 4 way / df (4 way)

21 MS 4 way / MS error

22 df (total) - ( df treatment + dftwo way + df 3 way + df 4 way)

23 SS (total) - ( SS treatment + SStwo way + SS 3 way + SS 4 way)

24 SS error / df (error)




The descriptive statistics are computed with the following equations:

Mean:

Mean = ∑ data / number of data

Standard Deviation:

( )SD

Number of data

Number of data - 1=

− ∑∑ x x2 2/

Standard Error Mean:

SEM = SD

Number of data

GeoMean and GeoSD:

The geometric standard deviation describes how spread out are a set of numbers whose preferred average is the geometric mean. If the geometric mean of a set of numbers A1, A2, ... , An is denoted as µg, then the geometric standard deviation is computed as:

1)ln(ln

exp 12

−

−= ∑ =

nAn

i gig

µσ

where µg is the geometric mean, which is calculated as n

nAAAA ...321 .

The root mean square error is the square root of the mean square error.

The power of the test is the statistical power, which is defined as the complement of type II error.

Presentation of Partial Descriptive Statistics



1 – Power is the Type II error, often designated as β and is defined as the probability of accepting the null given that the alternative is true.

Minimum detectable difference (MDD) evaluates how different observed values of a multimetric index must be in order to be significantly different.

Kinetica gives you the option to compute the reference confidence intervals in accordance with the regulatory agency guidelines. These also provide a further option for considering whether the data was log-transformed or not.

Option Regulatory Agency

FDA Europe

No Log Transformation

[0.8 - 1.2] [0.7 - 1.3]

Log Transformation [0.8 - 1.25] [0.7 - 1.43]

Kinetica computes a 90% standard confidence interval t = t (2 * alpha - df error) where:

Test = New formulation

Ref = Reference formulation.

Confidence Intervals



This is used when the model is multiplicative and a confidence interval is obtained around the difference between two formulations. If requested, a Log-Transformation is computed using the following rule:

If I = (MeanTest - MeanRef) - tMS 2

Number of data for Referror ×

and

J = (MeanTest - MeanRef) + tMS 2

Number of data for Referror ×

Then

The lower CI limit = eI and the upper CI limit = eJ.

The no log transformation option is used when the model is additive and a confidence interval is obtained around the ratio of two formulations. If requested, a No Log-Transformation is computed using the following rule:

The lower CI limit = Mean - t

MS 2Number of data for RefMean

Testerror

Ref

×

and

The upper CI limit = Mean + t

MS 2Number of data for RefMean

Testerror

Ref

×

Kinetica compares the calculated CI with the reference CI. If the calculated CI is inside the reference CI, a message is displayed in the Study Info view of the Study pane stating “Can conclude equivalence”. If the calculated CI is outside the reference CI, a message is displayed stating “Cannot conclude equivalence.”

Log Transformation Option

No Log Transformation Option

Conclusion on the Confidence Intervals



The Schuirmann’s test is used in the case where there is a confidence interval around the difference between two formulations. Two unilateral t-tests are calculated as follows:

t = t (0.05 - df error)

d = MeanTest - MeanRef

SMS

Number data in Referror=

×2

C1 = ln (the lower reference CI limit)

C2 = ln (the upper reference CI limit)

t1 =

t2 = C d

S2 −

The smallest value between t1 and t2 is the lower t and the other is the upper t. A conclusion is then computed using the following rules:

1. If t (lower) ≥ t and t (upper) ≥ t then output the message can conclude equivalence.

2. If t (lower) ≤ t or t (upper) ≤ t then output the message cannot conclude equivalence.

Two One-Sided t-Tests for Factor: Schuirmann's Test



In this example, six patients were treated with three formulations: A, B and C (where C was the Reference formulation, and A and B were the New formulations). Cmax measurements were taken for every patient and formulation.

You can enter numbers or letters in the Subject and Treatment columns. Initials or names of patients, and names of formulations can be entered. There is no obligation to code the patients and formulations by numbers (1, 2, 3, etc…).

The example given is an ANOVA two-way analysis. The data is at Cmax.

To complete an ANOVA two-way analysis:


2. Navigate to the Program Files\Kinetica\Example\Statistics directory, select the Anova.kdb file, and click Open.

3. Select the Study pane. The Study group appears as follows, containing sample data from Anova.kdb:

Example of ANOVA Two-Way Analysis



Figure 12-3. Kinetica Study pane showing Anova.kdb

4. Select ANOVA from the Statistics menu. The ANOVA n-way dialog appears.

5. In the Number of Ways field, select 2 from the list. The first three list boxes (Data Column, Treatment Column and 2 Way Column) are activated.

6. Select data from the Data Column list box, Treatment from the Treatment Column list box, and Subject from the 2 Way Column list box.

7. Select either the Ln Transform of data or Log10 Transform of Data check box.



8. Select the Confidence Intervals check box. This activates the Reference Level and Test Level fields.

9. Enter A in the Reference Level field and B in the Test Level field.

10. Select the Log option [0.8 - 1.25] in the Reference Confidence Intervals area of the dialog.

11. Select the Two One-Sided t-tests for Factor check box. This activates the Reference Level and Test Level fields.

12. Enter A in the Reference Level field and B in the Test Level field. The ANOVA dialog should now appear as follows:

Figure 12-4. ANOVA n-way Dialog

Note The data column contains the Cmax values.




You can view the results of the ANOVA analysis, the Confidence Interval and the Schuirmann’s two one-sided test in the Study Info view of the Study pane.

Schuirmann’s two one-sided test: Let µd be the mean difference of the bioavailabilities, such as AUC and Cmax, of the object drug between the first and last periods depicted as above, θL denotes the lower no-effect boundary, and θU denotes the upper no-effect boundary, then the objective of the drug-drug interaction in a fixed-sequence design can usually be tested in the following two one-sided hypotheses:

Null hypothesis (H0): µd ≤θL or µd ≥θU

Alternative hypothesis (Ha): θL <µd <θU

Assuming logarithmically transformed data would be normally distributed, then H0 is rejected at significance level ⟨and no drug-drug interaction is concluded if 100 (1-2α) % CI of the mean µd

is entirely within (θL, θU ), otherwise H0 fails to be rejected. The choices of no-effect boundaries depend on the specific drugs involved in the study.



When you have a cross-over design (Latin Square), Kinetica makes a distinction between a Latin Square with two formulations and a Latin Square with greater than two formulations.

The Latin Square option enables you to perform an analysis on a conventional two-treatment, two-period randomized crossover design. Following the most recent FDA guidelines for bioequivalence studies, the Subject effect nested in sequence, the Sequence effect, and the Period effect are all considered by the Latin Square ANOVA.

The conditions for use are a crossover design limited to two formulations with no missing values.

The Latin Square with two Formulations test can be performed using the Latin Square 2 Formulations dialog. This dialog is accessed by selecting Latin Square with two Formulations from the Statistics menu.

Some of the main items that appear in the dialog are described in the following table.

Object Description

Data column Select the Data on which you want to perform the ANOVA analysis (for example: AUC, Cmax, Tmax, T1/2, etc).

Subject column Select the Subject field from the list

Treatment column

Select the Treatment field from the list. Generally, the treatment effect is always analyzed.

Sequence column

Select the Sequence field from the list

Log Transformation

See the section, “Log Transformation Option” in this chapter

Latin Square

Latin Square - Two Formulations

Latin Square 2 Formulations Dialog



Object Description

Confidence Intervals

See the section, “Confidence Intervals” in this chapter

Two-One-Sided t-tests for factor

See the section, ”Two One-Sided t-Tests for Factor: Schuirmann's Test” in this chapter

When an ANOVA table is complete for Latin Square testing, a table of results is written in the Study Info view of the Study pane. For this particular test the organization of this table always appears as follows (in this example, T1/2 were the data selected):

Source

df

(Degree of freedom)

SS

(Sum of Squares)

MS

(Mean Square)

F

(Fischer test)

p

(Probability value)

Period 1 2 3 4 *

Subject (Seq) 5 6 7 8 *

Formulation 9 10 11 12 *

Sequence 13 14 15 16 *

Error 17 18 19 20

Total 21 22 23 24

*In the ANOVA Table, the value of p for every effect is compared to 0.05 (α = 5%), and Kinetica computes and outputs a conclusion according to the following rules:

• If p > 0.05 then the difference is not significant, so output the symbol NS

• If p < 0.05 then the difference is significant, so output the symbol ***.

Presentation of ANOVA Table for Latin Square Testing



Note The p values are never generated for the Error and Total fields. The Root Mean Square Error is calculated by Sqrt (MS error) and the CV is calculated by (number of data * Root Mean Square Error) / x∑ .

The equations used to calculate the data that appear in the preceding table (i.e. 1, 2, 3, etc.) are listed in the following table.

Note For each of the data x, C = ( )xN∑ 2

where N = total

number of data.

Point Equation Used

1 (number of periods) –1

2

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C

3 SS period / df (period)

4 MS period / MS error

5 This “Subject” effect is nested in the Sequence.

(number of subjects) - (number of sequences)

6

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C

7 SS subject / df (subject)

8 MS subject / MS error

9 (number of formulations) -1



Point Equation Used

10

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= – C

11 SS formulation / df (formulation)

12 MS formulation / MS error

13 (number of sequences) –1

14

j= 1

n

i

2

ii 1

x ( j )

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣

⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥

= -

x j

n

k

nj

jj

numbersequences( )∑

∑

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣

⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥

=

2

1

In fact, the calculation of the 2nd term means:

mean for 1 sequence number of data in this sequencenumber of data in this sequencej 1

number sequences •⎛⎝⎜

⎞⎠⎟

=∑

15 SS sequence / df (sequence)

16 MS sequence / MS error

17 df (total) - ( df formulation + df subject + df period + df sequence)

18 SS (total) - (SS formulation + SS subject + SS period + SS sequence)


20 no calculation is made and no output generated

21 (total number of data) –1

22 xi2∑ – C



Point Equation Used




Mean:


Standard Deviation:

( )SD

Number of data

Number of data - 1=

− ∑∑ x x2 2/

Standard Error of the Mean:

SEM = SD

Number of data

GeoMean and GeoSD:


1)ln(ln

exp 12

−

−= ∑ =

nAn

i gig

µσ

where µg is the geometric mean, which is calculated as:

nnAAAA ...321 .




In this example, six patients were treated with two formulations A and B in a cross-over design with two sequences, AB and BA. T1/2

was calculated for every patient, formulation and sequence.


The example below is a Latin Square analysis without log-transformation of data.

1. In Kinetica select Open from the File menu. The Open a Kinetica File dialog appears. By default, the Data subdirectory is displayed.

2. Navigate to the Program Files\Kinetica\Example\Statistics directory, select the Latin Square 2 Formulations.kdb file, and click Open.

3. Select the Study pane. The Study group appears as follows, containing only raw data:

Example of Latin Square with Two Formulations



Figure 12-5. Kinetica Study Pane showing Latin Square 2 Formulations.kdb

4. From the Statistics menu select Latin Square, then Two Formulations. The Latin Square 2 Formulations dialog appears.

5. Select T1/2 from the Data Column list box, Subject from the Subject Column list, Treatment from the Treatment Column list, and Sequence from the Sequence Column list. The dialog should appear as below.



Figure 12-6. Latin Square 2 Formulations dialog


You can view the results of the ANOVA analysis for Latin Square design with two formulations in the Study Info view of the Study pane.

In the Latin Square design with greater than two formulations, you can select:

1. A Latin Square design with more than one square, or

2. A simple Latin Square design.

The conditions for use are a cross-over design, no missing values, where all the possible sequences are represented and the data is limited to three formulations. For example, for three studied formulations A, B and C, you must have the sequences ABC, BCA, CAB, ACB, BAC and CBA.

Latin Square - Greater Than Two Formulations

Latin Square - Greater Than Two Formulations with Multiple

Square Design



The Subject effect nested in Sequence, and the Sequence effect are not considered by the Latin Square ANOVA. If you choose this option, you must select the More Than One Square check box in the Latin Square n Formulations dialog.

The Latin Square with Greater than two Formulations test can be performed using the Latin Square n Formulations dialog, accessed by selecting Latin Square then Two Formulations from the Statistics menu. However, now you will see the More Than One Square check box enabled. This option enables you to choose between the multiple square design or the basic single square design. This is the main difference between the Latin Square n Formulations dialog and the Latin Square 2 Formulations dialog.

Presentation of ANOVA Table for Latin Square Three Formulations (Multiple Square Design):

When an ANOVA table is complete for Latin Square testing, a table of results is written in the Study Info view of the Study pane. For this particular test, the organization of this table always appears in the following table (in this example, T1/2 was the data selected).

Source

df

(Degree of freedom)

SS

(Sum of Squares)

MS

(Mean Square)

F

(Fischer test)

p

(Probability value)

Period 1 2 3 4 *

Subject 5 6 7 8 *


Error 13 14 15 16

Total 17 18 19 20

*In the ANOVA Table, the value of p for every effect field is compared to 0.05 (α = 5%), and Kinetica computes and outputs a conclusion according to the following rules:

• If p > 0.05 then the difference is not significant, so output the symbol NS.

Latin Square n Formulations Dialog




Note The p values are never generated for the Error and Total fields.

The Root Mean Square Error is calculated by Sqrt (MS error) and the CV is calculated by (number of data * Root Mean Square Error) / x∑ .

The equations used to calculate the data that appear in the ANOVA table above (i.e. 1, 2, 3, etc.) are listed in the following table.

For each of the data x:

C = ( )x

N∑ 2

where N = total number of data.

Point Equation Used


2

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



5 (number of subjects) – 1



Point Equation Used

6

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



9 (number of formulations) -1

10

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



13 (df total) - ( df formulation + df subject + df period)

14 (SS total) - ( SS formulation + SS subject + SS period)




18 xi2∑ - C






Mean:


Standard Deviation:

( )1 - data ofNumber

data ofNumber /SD

22∑ ∑−=

xx


SEM = SD

Number of data

GeoMean and GeoSD:


1)ln(ln

exp 12

−

−= ∑ =

nAn

i gig

µσ


nnAAAA ...321 .

In this example, six patients were treated with three formulations A, B and C in a cross-over design with six sequences: ABC, BCA, CAB, ACB, BAC and CBA. T1/2 was calculated for every patient, formulation and sequence.

You can enter numbers or letters in the Subject and Treatment columns. Initials or names of patients, and names of formulations


Example of Latin Square Three Formulations with Multiple

Square Design



can be entered. There is no obligation to code the patients and formulations by numbers (i.e. 1, 2, 3).

The example below is a Latin Square analysis without log-transformation of data.

1. Load Kinetica.

2. Select Open from the File menu. The Open dialog appears. By default, the Data subdirectory is displayed.

3. Select the LatinSquare2Formulations.kdb file, found in the Program Files\Kinetica\Example\Statistics directory, and click Open.


Figure 12-7. Latin Square Multiple Squares Dialog



5. Select Latin Square then Two Formulations from the Statistics menu. The Latin Square n Formulations dialog appears.

Figure 12-8. Latin Square n Formulations Dialog

Note The More than one square check box is now enabled. The data column contains the T1/2 values.

6. Select data from the Data column list box, Subject from the Subject column list box, Treatment from the Treatment column list box, Sequence from the Sequence column list box. Finally select the More Than 1 Square checkbox. The dialog appears as follows:



Figure 12-9. Latin Square 3 Formulations Only Dialog

Note The title of the dialog has changed, indicating that with the multiple square design selected you cannot have more than three formulations.

7. Do not select any other options from the dialog. Click OK to exit the dialog and generate the report.

You can view the results of the ANOVA analysis for Latin Square design with three formulations and more than one square in the Study Info view of the Study pane.



If you select this option, do not select the More Than One Square check box. With this design you can have n formulations (as many as you require). All the possible sequences are not represented. The conditions for use are a cross-over design, no missing values, where the number of formulations must be equal to the number of sequences.

Following the last FDA guidelines for bioequivalence studies, the Subject effect nested in sequence, the Sequence effect, and the Period effect are all considered by the Latin Square ANOVA.

The Latin Square with n Formulations and Single Latin Square Design test can be performed in the Latin Square n Formulations dialog.

This dialog is accessed by selecting Latin Square then Two Formulations from the Statistics menu.

Latin Square n Formulations with Single

Latin Square Design

Latin Square n Formulations Dialog



When an ANOVA table is complete for Latin Square testing, a table of results is written in the Study Info view of the Study pane. For this particular test the organization of this table always appears as follows (in this example T1/2 was the data selected):

Source

df

(Degree of freedom)

SS

(Sum of Squares)

MS

(Mean Square)

F

(Fischer test)

p

(Probability value)

Period 1 2 3 4 *

Subject (Seq) 5 6 7 8 *


Sequence 13 14 15 16 *

Error 17 18 19 20

Total 21 22 23 24

*In the ANOVA Table, the value of p for every effect field is compared to 0.05 (α = 5%), and Kinetica computes and outputs a conclusion according to the following rules:

• If p > 0.05 then the difference is not significant, so output the symbol NS.


Note The p values are never generated for the Error and Total fields. The Root Mean Square Error is calculated by Sqrt (MS error) and the CV is calculated by (number of data * Root Mean Square Error) / x∑ .

The equations used to calculate the data that appear in the preceding table (i.e. 1, 2, 3) are listed in the following table.


Presentation of ANOVA Table for Latin Square n Formulations



C = ( )x

N∑ 2


Point Equation Used


2

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



5 This subject effect is nested in the Sequence

(number of subjects) - (number of sequences)

6

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



9 (number of formulations) –1

10

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C





Point Equation Used

13 (number of sequences - 1)

14

j= 1

n

i

2

ii 1

x ( j )

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣

⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥

= -

x j

n

k

nj

jj

numbersequences( )∑

∑

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣

⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥

=

2

1

In fact, the calculation of the 2nd term means:

mean for 1 sequence number of data in this sequencenumber of data in this sequencej 1

number sequences •⎛⎝⎜

⎞⎠⎟

=∑

15 SS sequence / df (sequence)

16 MS sequence / MS error

17 df (total) - ( df formulation + df subject + df period + df sequence)

18 SS (total) - (SS formulation + SS subject + SS period + SS sequence)



21 (total number of data - 1)

22 xi2∑ - C






Mean:


Standard Deviation:

( )SD

Number of data

Number of data - 1=

− ∑∑ x x2 2/


SEM = SD

Number of data

GeoMean and GeoSD:


1)ln(ln

exp 12

−

−= ∑ =

nAn

i gig

µσ


nnAAAA ...321 .

Example of Latin Square n Formulations with Single Square Design

In this example, six patients were treated with three formulations A, Band C in a cross-over design with three sequences: ABC, BCA and CAB. T1/2 was calculated for every patient, formulation and sequence.





The example given is a Latin Square analysis without log-transformation of data.

To complete a Latin Square analysis with Single Square design:

1. Load Kinetica.


3. Select the LatinSquareNFormulations.kdb file, found in the Program Files\Kinetica\Example\Statistics directory, and click Open.

4. Select the Study pane. The Study pane appears as follows, containing only the raw data we entered before sending you the program:



Figure 12-10. Latin Square N Formulations Dialog

5. Select Latin Square then Two Formulations from the Statistics menu. The Latin Square 2 Formulations dialog appears:



Figure 12-11. Latin Square 2 Formulations Dialog

Note The More than 1 square check box is now activated. The data column contains the T1/2 values.

6. Select data from the Data column list box, select Subject from the Subject column list box, select Treatment from the Treatment column list box, and select Sequence from the Sequence column list box. Do NOT select the More than 1 square check box. The dialog appears as follows:



Figure 12-12. Latin Square n Formulations Dialog

Note The title of the dialog has changed, indicating that with Multiple Square Design selected you can not have more than three formulations.

7. Select no other options from the dialog. Click OK to exit the dialog and generate the report.

You can view the results of the ANOVA analysis for Latin Square design with three formulations and one square in the Study Info view of the Study pane.



Incomplete block refers to statistical designs in which the block is complete (i.e. all the treatments are represented in a sequence), but not balanced (i.e. all the possible sequences are not represented). The conditions for use are:

1. The number of formulations must be equal to the number of periods, and

2. The number of formulations must be less than the number of sequences.

The Incomplete Block test is performed using the Incomplete Block dialog. This dialog is accessed by selecting Incomplete Block from the Statistics menu.

When an ANOVA table is complete for Incomplete Block testing, a table of results is written in the Study Info view of the Study pane. For this particular test the organization of this table always appears as follows:

Source

df

(Degree of freedom)

SS

(Sum of Squares)

MS

(Mean Square)

F

(Fischer test)

p

(Probability value)

Period 1 2 3 4 *

Subject 5 6 7 8 *


Error 13 14 15 16

Total 17 18 19 20

*In the ANOVA Table, the value of p for every effect is compared to 0.05 (α = 5%), and Kinetica computes and outputs a conclusion according to the following rules:

• If p > 0.05 then the difference is not significant, so output the symbol NS

Incomplete Block

Incomplete Block Dialog

Presentation of ANOVA Table for Incomplete Block




Note The p values are not generated for the Error and Total fields.

The Root Mean Square Error is calculated by Sqrt (MS error) and the CV is calculated by (number of data * Root Mean Square Error) / x∑ .

The equations used to calculate the data that appear in the preceding table (i.e.1, 2, 3, etc.) are listed in the following table:


C = ( )xN∑ 2


Point Equation Used


2

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



5 (number of subjects) – 1

6

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C


http://i.e.1



Point Equation Used


9 (number of formulations) –1

10

j=1

n

i

2

ii 1

x (j)

n

∑∑

⎛

⎝⎜

⎞

⎠⎟

= - C



13 (df total) – ( df formulation + df subject + df period)

14 (SS total) – (SS formulation + SS subject + SS period)




18 xi2∑ – C






Mean:


Standard Deviation:

( )SD

Number of data

Number of data - 1=

− ∑∑ x x2 2/


SEM = SD

Number of data

GeoMean and GeoSD:


1)ln(ln

exp 12

−

−= ∑ =

nAn

i gig

µσ


nnAAAA ...321 .

In this example, four patients were treated with three formulations A, B and C with four sequences: BAC, CBA, ACB and BCA. Tmax was calculated for every patient, formulation and sequence.

You can enter numbers or letters in the Subject and Treatment columns. Initials or names of patients, and names of formulations


Example of Incomplete Block



can be entered. There is no obligation to code the patients and formulations by numbers (1, 2, 3, etc…).

The example given is an Incomplete Block analysis without log-transformation of data.

1. Load Kinetica.

2. Select Open from the File menu. The Open a Kinetica File dialog appears.

3. Navigate to the Program Files\Kinetica\Example\Statistics directory, select the Unbalanced Block.kdb file and click Open.


Figure 12-13. Kinetica Study Pane with file “Unbalanced Block.kdb” loaded



5. Select Incomplete block… from the Statistics menu. The Incomplete Block dialog appears.

Figure 12-14. Incomplete Block Dialog

6. Select data from the Data column list box, select Subject from the Subject column list box, Treatment from the Treatment column list box and Sequence from the Sequence column list box.

Note The data column contains the Tmax values.

7. Select no other options from the dialog. Click OK to exit the dialog and generate the report.

You can find the results of the ANOVA analysis for Incomplete Block in the Study Info view of the Study pane.



Kinetica enables you to access a non-parametric test, Kruskall-Wallis. The Kruskall-Wallis test is a non-parametric test equivalent to a one-way analysis of variance.

The conditions for use are where the data and groups are independent of each another.

Hypothesis of the test is where:

Ho – Signifies no difference between means

H1 – Signifies a difference between means.

We sort the data by ascending order, noting the group to which each item belongs.

H = 12

N(N +1)Sn

Ni

ii

2

3 1∑ − +( )

where:

H = Criteria in the Kruskall-Wallis test

N = Total number of data

ni = Number of data for each group (1, 2,…,i)

Si = Sum of ranks of data for each group (1, 2,…,i)

( )χ 20,95 (number of groups 1)− −

A comparison between H and χ 2 is made, and the following rules are applied:

If H < χ 2 then accept Ho (difference between means is not significant).

If H > χ 2 then reject Ho (difference between means is significant).

Kruskall-Wallis Test



In the ex-aequo case the data have a rank equal to the mean of their own ranks and H is obtained by:

H =

12N(N + 1)

1 - T

N(N - 1)

ii

Sn

Ni

ii

2

3 1∑∑

− +( )

where:

t: Number of ex-aequo in each group and T: ( t - 1 ) t ( t + 1 ).

In this example, we have two columns of data. One column contains three treatments A, B and C. The other column contains three sets of data for these three formulations (for example, Tmax).

To complete a Kruskall-Wallis analysis with Kinetica:


2. Select the Unbalanced Block.kdb file, found in the Program Files\Kinetica\Example\Statistics directory, and click Open.


Example of Kruskall-Wallis Test



Figure 12-15. Kinetica Study Pane

4. Select Kruskall-Wallis from the Statistics menu. The Kruskall-Wallis dialog appears.

Figure 12-16. Kruskall Wallis Dialog

5. Select “treatment” from the Group column list and “data” from the Data column list.

Figure 12-17. Kruskall Wallis Dialog - Populated




You can view the results of the Descriptive Statistics in the Study Info view of the Study pane.



The Friedman rank sum test is the non-parametric equivalent of ANOVA. It is appropriate for data arising from an unreplicated complete block design, i.e., one in which exactly one observation was collected from each experimental unit, or block, under each treatment. The elements of y are assumed to consist of a group’s effect, plus a blocks effect, plus independent and identically distributed residual errors. The interaction between groups and blocks is assumed to be zero.

In the context of a two-way layout with factors groups and blocks, a typical null hypothesis is that the true location parameter for y, net of the blocks effect, is the same in each of the groups. The alternative hypothesis is that it is different in at least one of the groups.

In the example file, Friedmantest, seven subjects underwent treatment A, B, C, D, and E. The result for each treatment is listed under the column y.

To run the Friedman test:

1. Click Statistics | Friedman…

Figure 12-18. Statistics Menu Options

2. Under the Friedman dialog box, select y for Data Column, t for Treatment Column, and s for Subject Column. Click OK.

Friedman Test



Figure 12-19. Friedman Dialog

3. The result of the test is listed in the Study Info under the Study pane.

Figure 12-20. Example – Test Results Displayed in the Study Pane

The ranks in each group j are summed. Let R(Xij) be the rank assigned to Xij within treatment j. Average ranks are used in the case of ties. Then the Friedman test is



H0: The treatment effects have identical effects

Ha: At least one treatment is different from at least one other treatment

The test statistics used is:

2j

2 )2/)1k(nR()1k(nk

12+−

+= ∑χ

If there are ties, then

4)1k(nk))X(R(

)2

)1k(nR()1k(

2k

1j

2ij

n

1i

k

1j

2j

2

+−

+−−

=

∑∑

∑

==

=χ



You can compute and display tables of descriptive statistics in Kinetica using the Descriptive Statistics dialog.

This dialog is accessed by selecting Descriptive Statistics from the Statistics menu.

When a table is computed with Descriptive Statistics the results are written in the Info view of the Study pane. For this particular test the organization of the table always appears as follows:

Column

x 1

x 2

x 3

.

.

.

x n

N (sample size) 1

Mean (mean data value) 2

HarmoMean (harmonic mean data value) 3

GeoMean (geometric mean data value) 4

SEM (standard error of the mean) 5

SD (standard deviation) 6

Median (middle value found) 7

Min (minimum value found) 8

Descriptive Statistics

Descriptive Statistics Dialog

Presentation of Table for Descriptive Statistics



Column

Max (maximum value found) 9

Note The preceding example displays only one selected column.

The equations used to calculate the data in the Descriptive Statistics Design for data1 table (i.e. 1, 2, 3) are listed in the following table:

Point Equation Used

1 Count of the number of data values present

2 xN∑

3

( )Harmonic mean = 1

1N × ∑ 1

x

4 Geometric mean (x x x ...x )1 2 3 N

1N= • •

5 SEM

SDN

=

6 ( )SD

x x / NN 1

2 2

=−

−∑∑

7 If number of data values is an odd number:

mediann 1

2th=

+

Or

If number of data (N = 2k) is an even number:

( )median

k + k + 12

th

=th



Point Equation Used

8 Search the data for the smallest value

9 Search the data for the largest value

In this example, we have two random data columns, data1 and data2. The example given is the Descriptive Statistics on two selected columns of data.

To generate descriptive statistics:


2. Select the DescriptiveStatistics.kdb file, found in the Program Files\Kinetica\Example\Statistics directory, and click Open.


Example of Descriptive Statistics



Figure 12-21. Study Group Displayed in Study Pane

4. Select Descriptive Statistics from the Statistics menu. The Descriptive Statistics dialog appears.

Figure 12-22. Descriptive Statistics Dialog

5. Highlight the “data1” and “data2” data columns.




You can view the results of the descriptive statistics on the two data columns in the Study Info view of the Study pane.



The hypothesis testing for the paired and unpaired t test generally involves:

1. Locating a sample statistic on an appropriate sampling distribution.

2. Determining the relative distance of the statistic from the mean of the distribution.

For two groups: “Data 1”and “Data 2,”

Data 1:

n1 = Number of data for group 1

m1 = Mean for group 1

Data 2:

n2 = Number of data for group 2

m2 =Mean for group 2.

The hypothesis of the test is that:

H0: m1 = m2

H1: m1 ≠ m2

and:

s2 = variance, where the variance is calculated by:

sx m x m

n n2

2 21 21 2 2

=− + −

+ −∑∑ ( ) ( )

The value of t is calculated by:

The Paired and Unpaired t Test



tm msn

sn

=−

+

1 2

1 2

2 2

The degree of freedom (df) is calculated as n1 + n2 -2.

The t (table) is calculated as t (df ; 0.05) with α = 0.05. The conclusion is derived by the following rules:

• If t < t (table) then the difference is not significant.

• If t > t (table) then the difference is significant.

Note This test is only available if x (belonging to two groups) follows a normal distribution.

The power of a test is the ability of a test to detect false null hypotheses.

Kinetica calculates Z(1-beta) as:

Z alpha Z betam m

n ns( / ) ( )2 1

1 211

12

2

− − =−

+⎛⎝⎜

⎞⎠⎟

Z(alpha/2) = 1.96 with alpha = 0.05. (1-beta) is obtained from Z(1-beta) by the normal Table. The power of the test is represented by (1-beta).

To perform the paired and unpaired t test:


2. Browse through directories and files to locate and open the appropriate .kdb file for the statistical analysis.

Power of the t Test



3. Select Student and Fisher Test from the Statistics menu. The Student and Fisher Test dialog appears.

Figure 12-23. Student and Fisher Test Dialog

4. Select the Dataset Variable(s) or Dataset Column(s) radio button.

5. Select the appropriate dataset variable or column from the Data 1 list.

6. Select the appropriate dataset variable or column from the Data 2 list.

7. Enter a value for α in the Significance field. By default, α is set to 0.05.

8. Enter the name of the Reference formulation in the Ref field, if required.

9. Select the Log Transformation for Data check box, if required. Do not select this option if the data has already been log-transformed.

10. Click Select Dataset. The Calculate Range dialog appears.



Figure 12-24. Calculate Range Dialog

11. Use your mouse and the CTRL key to select datasets individually. Click Select All to include all datasets in the analysis.

12. Click OK to exit the dialog and return to the Student and Fisher Test dialog.

To group datasets:

1. Select the By Group check box and select the appropriate group name from the available list.

2. Click OK to generate the report. You can view the results of the Student t test in the Study Info view of the Study pane.

Grouping Datasets (optional)


A. Population Methodology

The EM algorithm, initially proposed by Dempster, Laird and Rubin (1977), is an iterative procedure developed for finding maximum likelihood estimates for incomplete data. This is a two-step algorithm represented by E-step and M-step. The E-step is given current values of parameter estimates, to obtain the expectation of individual parameters, conditional on the observed data vector. The M-step is to obtain the ML posterior population mean and variance together with the residual error variance, given the individual parameter values. Further applications of this algorithm in linear mixed effect models were performed by Laird and Ware (1982), Strenio et al.(1983), and more recently by Lindstrom and Bates (1988), with fewer assumptions on the variance models and either maximum likelihood (ML) or restricted maximum likelihood (REML) estimation.

Population Methodology


Let m denote the number of individuals j in the studied sample. Let yj denote: the vector of nj measurements performed on individual j given a specific design. Define βj as the (p x 1) vector of parameters for this individual. We assume that for each individual j, j=1 to m:

jjjj ),x(fy ε+β= (1)

where f is a known, possibly nonlinear, function of βj describing the nj vector of responses, xj is the independent variable vector for subject j, and εj is a residual error vector for subject j, normally distributed with mean 0 and covariance matrix Rj. The covariance Rj is assumed to be written:

)(SR jj2

j βσ= (2)

where )(S jj β is, for each individual, a known (nj x nj) matrix depending possibly on the individual parameters, and σ 2 is an unknown parameter to be estimated or is fixed to one if the error is fully specified.

The expression of jβ in equation (1), called covariate model, can be expressed as

jjjj ),Z(h η+β=β (3)

where β is a (r x 1) vector of population parameters, and ηj follows normal distribution with mean 0 and covariance matrix C (p x p).

When the relationships between covariates and parameters are linear, the expression (3) can be written:

jjj Z η+β=β (4)

where Zj is a (p x r) matrix depending on the covariates Zj. When no covariates are included, Zj = I and β is the (p x 1) vector of the mean parameters.

Models and Notation

Covariate Models and Interindividual Variability



The inter-individual variability, jη , in Kinetica Population is assumed to follow either normal distribution or log normal distribution,

• Normal distribution, var),0(N~jη

• Log-normal distribution, )exp( jη , where var),0(N~jη .

In both cases it is assumed that ηj follows normal distribution with mean 0 and covariance matrix C (p x p).

Let *jβ deNote: the individual parameters estimated from the

first stage,

*jjj

*j eZ +η+β=β (5)

Mj is the Variance Matrix of *je . We use the following algorithm

after k steps,

Step E: Produce refined estimates of the jβ :

)(k)βjZ1)(k)(C*jβ

1j(M1))(k)C(1

j(M)1(kjβ

1 −+−−+−=+ −

Step M: Obtain updated estimates of the population parameters:

)1k(j

m

j

)k(jW)1k( +β∑=+β

where, 1)k('j

1j

1)k()k(j )C(Z)Z)C(

m

j'jZ(W −−−∑=

'1 ))1k(jZ)1k(

j)()1k(jZ

m

j

)1k(j(1m1)))k(C(

m

j1

jM(1m)1k(C +β−+β+β−∑ +β−+−+∑ −−=+ −

The iteration stops when the M-step converges; that is, until the difference between successive estimates of β and C is sufficiently small.

The EM Iterative Algorithm - Two Stage Parameter Estimates



In this situation only a few measurements will have been collected on each individual. It is not possible to estimate the parameters using standard procedures. Therefore, in this case, only the population approach can be used.

For equation (1), jjjj ),x(fy ε+β= , with jjjj ),Z(h η+β=β , the problem is to estimate, from m vectors or measurements yj and covariates Zj, the population parameters composed of the components of β, the unknown parameters in C and in some cases σ 2 . The iterative algorithm is composed of two steps; the E-step and the M-step. From starting values of the population parameters β(0), C(0), σ 2 (0) , the starting values βj(0) of the m individual parameters are defined as follows:

βj(0) = h(zj,β(0) )

After k iterations, the algorithm proceeds as follows:

E-step – the individual parameters βj(k+1) are estimated by their maximum a posteriori given the current population parameters β(k), C(k), σ 2 (k) and assuming that the variance matrix of the error is fixed and given by Rj(k) = σ2(k) Sj(βj(k)).

M-step – the population parameters β(k+1), C(k+1), σ 2 (k+1) are estimated by maximum likelihood given the current estimates βj(k+1) of the individual parameters and using a first order expansion of the model fj about βj(k+1), if f is not a linear model.

More specifically, it can be shown that these two steps can be described by the following for linear covariate models.

During the E-step for each individual j, the individual parameters βj(k+1) are estimated through minimization of the following objective function by a standard least-squares algorithm, using βj(k) as starting values:

)β − β( )β − β( + ))β( − )( ))β( − ( = )β −1−1+ )k(j

)k(j

)k(')k(j

)k(j

)k(jj

)k(j

')k(jj

)1k(jj Z)C(Zfy(Rfy( Obj

During the M-step, first β(k+1) is estimated from Zj and βj(k+1) by linear regression; the estimate is the value minimizing the sum over the m individuals:

Population Parameter Estimates - Sparse

Data Situation



( ) ( )⎟⎠⎞⎜

⎝⎛ β−+β∑ ⎟

⎠⎞⎜

⎝⎛ β−+β )k(

jZ1kj

'm

j

)k(jZ1k

j

Then the unknown terms in C are estimated by:

( ) ( ) ( ) ( )∑=

+η+η+−=+ m

1j)

'1kj

1kj

kjC(1m1kC

where ηj(k+1) are the estimates of individual random effects, given by:

( ) ( ) ( )1kjZ1k

j1k

j+β−+β=+η

Cj(k) are the conditional variances of βj(k+1) which correspond to the estimation variances of βj(k+1), usually obtained during the first step, and given by:

1)1k(j

'j

1)k(j

)1k(jj

)k(j ))k(C()(G)R)((GC −+−+ +ββ=

Last, if σ 2 is not known, it is estimated by after k steps:

)))k(jV(I(tre

m

j)'e(((

n1 1)k(2)k(2)k(

j)k(

j)1k(2 −+ σ−σ+∑=σ

where, ),x(fYe )k(jjj,Obs

)k(j β−= , and '

jG)k(jCjGIV j

)k(2j

)k(j +σ=

tr(A) denotes the trace of the matrix A (sum of the diagonal elements). Gj is the Jacobian matrix with parameter βj.



The Bayesian methodology allows the pharmacokinetic parameters for an individual to be estimated, when there is a prior knowledge of the mean and dispersion of the pharmacokinetic parameters in the population to which the selected individual belongs.

Kinetica Population uses a Maximum A-Posteriory Probability (MAP) Bayesian fitting procedure to combine the prior knowledge (the population parameter values) and the individual available information (such as drug sample(s), individual demographic and/or concomitant measurement, usually referred to as covariables) in order to estimate the individual parameters.

The MAP Bayesian procedure estimates the individual parameters minimizing the criteria:

∑∑== σ

β−β+

σ

β− p

1k2k

2kkjnj

1i2i

2iki )ˆ())t,ˆ(f(Yobs

where

Yobsi are the observed concentrations for the individual,

f tk i( , )β are the predicted concentrations estimated using the population information,

σi2 are the measurement error variances,

kβ are the population parameter values,

β k are the parameter values (to be estimated) for individual j,

σk2 are the population parameter variances,

k indicates different parameters for subject j,

nj is the number of samples for subject j.

Note This MAP Bayesian fitting procedure can operate with only a single observation (changed here) )Yobs( i , even though many parameter values are to be fitted.

Individual Parameter Estimates: Bayesian Fit



The EM algorithm computes the maximum likelihood parameter estimate without explicitly evaluating the likelihood function.

In order to compare two hierarchically related models using a likelihood ratio test, the likelihood function is evaluated at the end of the algorithm.

The value of this function can be approximated using a linearization of the model around the individual parameter estimates.

The Logarithm of Likelihood function (LL) can be written as:

∑π−= )n)2(L(21LL jln

where:

∑ β−ββ−β−−β−ββ−β−+−= j ))j),jz(h)(j(jG)j(jfjy(1jV'))j),jz(h)(j(jG)j(jfjy(jVdetlnL

jjjjjj RCGGV += ')()( ββ

nj∑ is the population sample size

Suppose that β j , a model of B with np parameters, is a sub-model of A, a model with np + q parameters, then the difference LL LLA B− can be approximated by a χ2 distribution with q degrees of freedom. Therefore this test can be used to compare two nested models A and B.

Using the likelihood value, one can also compute two criteria:

Akaike criterion: ∑

+−=

j

p

nnLL

AIC)(

Schwartz criterion:

Maximum Likelihood Evaluation and

Comparison of Models

Model Evaluation



∑∑+−

=nj

njnlnLLBIC p ))(2/(

where np is the total number of parameters to be estimated and nj∑ is the population sample size. Therefore, to compare two

models one can choose, according to the principle of parisimony, the model with the smallest criteria.

The following outputs also provide the information for model evaluation:

The estimation of the expected concentration for each individual in a population, which is not necessarily included in the original analysis.

The estimation of the expected individual parameters given the populations estimated values (using a MAP procedure).

The computation of the appropriate statistical tests to evaluate the distribution properties of the differences between the expected and the observed data.

For each concentration, a Standardized Concentration Prediction Error (SCPE) is calculated as follows:

Sd)t,(fYobs

SCPE ijjijij

β−=

Where j refers to the subject, i to the time of the sample.

ijYobs is the observed concentration

)t,(f ijjβ is the MAP predicted concentration

β j is the individual parameter posterior estimate computed using the appropriate covariable dependency

Sd is the predicted standard deviation defined as the diagonal of the variance matrix Vj:



jjjjjj R)'(G*C*)(GV +ββ=

where:

)(G jj β is the Jacobian matrix with β j parameter

Rj is the residual error matrix

For each parameter a value is estimated and the normalized SPPE (Standardized Parameter Prediction Error) values are computed as:

)( k

kkjkjSPPE

βσββ −

=

k = parameter

j= subject

βk= mean population value for parameter k

σ(βk)= population standard deviation

Under the assumption of a correct regression model and unbiased parameter estimates, the SPPEij values should have a mean value of zero and a variance equal to one.



Initial parameter estimates are required as a starting point for EM algorithm. Kinetica Population has two tools for estimating some initial values, one is the Initial Estimate Assistant, and the other is the Simplex method.

Initialization Assistant applies the stripping method to naïve average data (NAD), or naïve pooled data (NPD), or individual data (standard two stage method) to obtain the parameter values. These values are then used as initial estimates for the EM algorithm. These methods are sometime used to analyze population data, however, in Kinetica Population, they are only used as a tool for initial parameter estimation.

Combine all the data as if they came from a single individual (a “reference” individual).

Fit all data from this “reference” individual using classical fitting procedures.

( ) Wi2

prediYobsiYΣφOBJ ⎟⎠⎞⎜

⎝⎛ −=

Obtain the average concentration across individuals at each time point.

Fit model to the averaged concentrations (e.g., weighted least squares) and the parameters that would represent the parameters from a “mean” individual.


Initial Parameter Estimation Assistant

Naive Pooled Data (NPD)

Naive Averaged Data (NAD)



( ) Wi2

prediYavgobsiYΣφOBJ ⎟⎠⎞⎜

⎝⎛ −=

Standard Two Stage (STS)

Step 1: Estimate an individual subject’s PK and/or PD parameters from rich data using standard fitting procedures.

( ) Wij2

predijYobsijYΣφOBJ ⎟⎠⎞⎜

⎝⎛ −=

Step 2: Estimate the population parameters across the subjects (mean, variance, covariance).

The equations for arithmetic mean, µ, and variance, Ω, are given below:

N

2µjφΣ

Ω

NjΣφ

µ

⎟⎠⎞⎜

⎝⎛ −

=

=

The simplex method finds the minimum of an unconstrained multivariable, nonlinear function by minimizing the objective function:

∑ − 2i ))p,x(fy(

where yi represents the observed measurements, f is the structural model and p, the unknown parameters.

The procedure is an extension of the simplex method by Spendley, Hext, and Himsworth ("Sequential Applications of Simplex Designs in Optimization and Evolutionary Operation",

Arithmetic Mean and Variance

Simplex Method



Technometrics, 4, 441-461, 1962). Both methods utilize a regular geometric figure (called a simplex) consisting of N+1 vertices. This method accelerates the simplex method and makes it more general. The procedure is based on the work by J. A. Nelder and R. Mead (“A Simplex Method for Function Minimization,” Computer J., 7, 3080313, 1964). This simplex method adapts itself to the local landscape, using reflected, expanded, and contracted points to locate the minimum. Unimodality is assumed and thus several sets of starting points should be considered. Derivatives are not required.

The algorithm proceeds as follows:

1. A starting point, P1, is selected.

2. A starting “simplex” is constructed consisting of the starting point and the following additional points:

Pj = P1 + Sj, with j = 2, 3, . . . ., N +1

where Sj is determined from the following table

j S j1, S j2, . . . SN j−1, SN j,

2 p q . . . q q

3 q p . . . q q

. . . . .

. . . . .

. . . . .

N q q . . . p q

N+1 q q . . . q p

where N is the total number of variables

a = side length of simplex



p = )11(2

−++ NNN

a

q = )11(2

−+NN

a

Once the simplex is formed, the objective function is evaluated at each point. The worst point (highest value of objective function) is replaced by a new point. Three operations are used: reflection, contraction, and expansion. A reflected point is located first as follows:

Pi j, (reflected) = ))(( ,,, worstPPP jicici −+α

where i = 1,2 . . ., N and α is a positive constant (in Kinetica Population the α value is set to 1.0). Pi c, are the centroid coordinates of all points excluding the worst point and are calculated from the following:

∑=

−−

=K

jjijici worstpp

kP

1,,, )],([

11

i=1,2, . . ., N

where K = N+1.

3. If the reflected point has the worst objective function value of the current points, a contracted point is located as follows:

Pi j, (contracted) = )),(( ,,, worstPPP jicici −− β

where i = 1, 2, . . . , N and β lies between 0 and 1 (in Kinetica Population β is set to 0.5).

If the reflected point is better than the worst point but is not the best point, a contracted point is calculated from the reflected point as follows:

Pi j, (contracted) = )),(( ,,, reflectedPPP jicici −− β

where i = 1, 2, . . . , N.



The objective function is now evaluated at the contracted point. If an improvement over the current points is achieved, the process is restarted. If an improvement is not achieved, the points are moved one half the distance toward the best point;

2/))()(()( ,,, oldPbestPnewP jijiji +=

where i = 1, 2, . . ., N.

The process is then restarted.

4. If the reflected point (calculated in step 3) is the best point, an expansion point is calculated as follows:

jiP, (expansion) = ))(( ,,, cijici PreflectedPP −+ γ

where i = 1, 2, . . ., N and γ is a positive constant (in Kinetica Population γ is set to 2.0). If the expansion point is an improvement over the reflected point, the reflected point is replaced by the expansion point and the process restarted. If the expansion point is not an improvement over the reflected point, the reflected point is retained and the process restarted.

5. The procedure is terminated when the convergence criterion is satisfied or a specified number of iterations has been exceeded.

The convergence criteria is defined as follows:

The program will terminate if EJ<0.01, where:

∑=

−−−=N

iciworstcii NPZPZEJ

1

212

,2

, )/))()((((



At each iteration of the algorithm, the estimation of the individual parameters of step E is performed using a Gauss-Marquardt algorithm [Marquardt, D.W., 'An algorithm for least-squares estimation of nonlinear parameters', Journal of the society for Industrial and Applied Mathematics, 11, 431-441 (1963)], with a relative parameter accuracy set by default at 1%.

Marquardt’s method represents a compromise between the linearization method and the steepest descent method and appears to combine the best features of both while avoiding their most serious limitations.

The principle of Marquardt’s method can be explained briefly as follows:

Suppose we start from a certain point in the parameter space, β. If the method of steepest descent is applied, a certain vector direction, gδ where g stands for gradient, is obtained from movement away from the initial point. Due to attenuation in the S(β) (S is the objective function to be minimized in β) contours this may be the best local direction in which to move to attain values of S(β) but may not be the best overall direction.

However, the best direction must be within 90° of δg or else S(β) will be larger locally. The linearization method leads to another correction vector δ t given by solving

gA t =δ

where

A = P′P

Minimization Algorithm



P = ⎟⎟⎠

⎞⎜⎜⎝

⎛

j

if∂β∂

g = ∑=

−n

i j

iii

ffy1

)(∂β∂

Marquardt found that for a number of practical problems to be studied, the angle between δg and δt fell in the range 80° - 90°. In other words, the two directions were almost at a right angle. The Marquardt algorithm provides a method for interpolating between the vector δg and δt and for obtaining a suitable step size as well.



A general termination strategy for the two-step algorithm is defined as follows. The algorithm terminates after the M-step when the relative change between two iterations for each of the estimated population parameters is lower than 1%. Then another E-step is performed to estimate the individual parameters.

Termination Criteria of EM Algorithm



Kinetica Population utilizes a numerical integration algorithm based on a Runge-Kutta-Fehlberg method [Forsythe G.E., Malcolm M.A. and Moler C.B., Computer Methods for Mathematical Computations, N.J., Prentice Hall Inc. 1977] to compute the regression function values, when the structural model is described by a system of differential equations. This is a 5th order method with variable step-size control. Initially a step length satisfying a local error criterion is estimated, then the 4th and 5th order Runge-Kutta approximation of the solution are computed and used to estimate the local error. The 5th order estimation is used as the solution if, and only if, the estimated error is less than a fixed tolerance level. If this is not the case, the step size is reduced until the error criterion is satisfied.

Differential Equation Solver



Stepwise regression is a useful tool for screening covariates automatically. There are forward selection and backward elimination methods. Each method adds and/or deletes covariates sequentially and systematically on the basis of F-test. In the case of Forward Selection, the initial model contains only a constant term. The procedure selects for entry the covariate that produces the largest R2 of any single covariate. The second covariate is chosen if it produces the largest increase in R2 in the presence of the previous covariate, or the largest partial F. This process is continued until no more variates are admitted to the equation.

The forward selection of stepwise algorithm is described below:

Introduce one covariate, called Cov1, to equation, 1P θ=

Cov12θ1θP += (2)

and calculate FK,T-K-1 =(SSreg/K)/ s2

Then use p-value to check if F is significant with the entry of Cov1.

If F is significant with the entry of Cov1, the second covariate, called Cov2, is added to equation (2)

Cov23θCov12θ1θP ++= (3)

1-K-TCov2,1)RSS(Cov1,/

)PK(Cov2,1)RSS(Cov1,-)RSS(Cov1,1Fchange

−=

Then use p-value to check if Fchange is significant with the entry of Cov2.

Repeat Step 2 until there are no more covariates to be admitted to the equation.

Stepwise Method

Step 1

Step 2

Step 3



In Step 1 to Step 3, 2)YcalcYobs(Rss ∑ −= ,

2)YmeanYcalc(SSreg ∑ −= ,

s2=Rss/(T-K-1)

where K is the number of covariates used in the new equation, and P is the number of covariates in the previous step.



The equations for population methods are identical to those described for other Kinetica hard-coded methods. For more information, see the section, “Available Models” in the chapter, “Performing Compartmental Analysis.”

For the following six methods, where Clearance, CL, is used as a parameter rather than Kel (CL = V*Kel), the expressions of the equations can be obtained by replacing Kel

with CLV

.

• PopFitBolusInput1comp (single dose intravenous micro constants using 1 compartment model)

• PopFitBolusInput2comp (single dose intravenous micro constants using 2 compartment model

• PopFitFirst0orderinput1comp(single dose zero order micro constants using 1 compartment model

• PopFitFirst0orderinput2comp (single dose zero order micro constants using 2 compartment model)

• PopFitZeroOrderinput1comp (single dose zero order micro constants using 1 compartment model

• PopFitZeroOrderinput2comp (single dose zero order micro constants using 2 compartment model

For more information related to these six models, see the chapter, “Performing Compartmental Analysis.”

Population Hard-Coded Equations

Exceptions



Amisaki, T. and Tatuhara, T. 'An alternative two stage method via the EM-algorithm for the estimation of population pharmacokinetics parameters', Journal of Pharmacobio-Dynamics, 11, 335-348 (1988).

Beal, S.L., NONMEM Users Guide VII: Conditional estimation Methods. NONMEM Project Group, University of California - San Francisco (1992).

Beal, S.L. and Sheiner, L.B., 'The NONMEM system', The American Statistician, 34,118-119 (1980).

Davidian, M. and Giltinan, D.M. 'Some general estimation methods for nonlinear mixed-effects models', Journal of Biopharmaceutical Statistics, 3, 23-55 (1993).

Dempster, A.P., Laird, N.M. and Rubin, D.B. 'Maximum likelihood from incomplete data via the EM algorithm', Journal of the Royal Statistical Society, Series B, 39, 1-38 (1977).

Laird, N.M. and Ware, J.H. 'Random-effects models for longitudinal data', Biometrics , 38, 963- 974 (1982).

Lindstrom, M.J. and Bates, D.M. 'Newton-Raphson and EM algorithms for linear mixed-effect models for repeated-measures data', Journal of the American Statistical Association, 83, 1014-1022 (1988).

Lindstrom, M.J. and Bates, D.M. 'Nonlinear mixed effects models for repeated measures data', Biometrics, 46, 673-687 (1990).

Racine-Poon, A., 'A Bayesian approach to nonlinear random effects models', Biometrics, 41, 1015-1023 (1985).

Racine-Poon, A. and Smith, A.F., 'Population models' in Berry, D.A. (Ed.) Statistical Methodology in the pharmaceutical sciences. Marcel Dekker, New York, 1990, pp.139-162.

Sanathanan, L.P. 'Random effects modeling in population kinetic/dynamic analysis', Drug Information Journal, 25, 307-318 (1991).

References



Sheiner, L.B., Rosenberg, B. and Marathe, V.V., 'Estimation of population characteristics of pharmacokinetics parameters from routine clinical data', Journal of Pharmacokinetics and Biopharmaceutics, 5, 445-479 (1977).

Steimer, J.L., Mallet, A., Golmard, J.L. and Boisvieux, J.F., 'Alternative approaches to estimation of population pharmacokinetic parameters; comparison with the nonlinear mixed effect model', Drug Metabolism Reviews, 15, 265-292 (1984).

Steimer, J.L., Mallet, A. and Mentré, F. 'Estimating inter-individual pharmacokinetic variability', in Rowland, M., Sheiner, L.B. and Steimer, J.L. (Eds.) Variability in Drug Therapy: Description, Estimation and Control., Raven Press, New York, 1985, pp. 65-111.

Strenio, J.F., Weisberg, H.I. and Bryck, A.S., 'Empirical Bayes estimation of individual growth-curve parameters and their relationship to covariates', Biometrics, 39, 71-86 (1983).

Vonesh, E.F. and Carter, R.L. 'Mixed-effects nonlinear regression for unbalanced repeated measures', Biometrics, 48, 1-17 (1992).



Notes


B. Kinetica Population Method Writing

You can write soft-coded population methods using the Macro Editor located in the Study pane of Kinetica. When you are finished writing the population method, click the Start Running

the Script button on the toolbar to run/insert the method. The method is added to the list of population methods displayed in the Method Selection dialog.

Following are three examples that will help you to write a population method:

• OSMacro2compBasic – shows you a soft-coded population method similar to the file PopFitFirstOrderInput1comp.

• Multiple Dose Example – provides an example of how to create a multiple dosing regimen of an IV Bolus using macro constants.

• IV RungeKuttaMultidose – gives an example of how to create a multiple dosing regimen of a 1 compartment IV Bolus using micro constants.

Kinetica Population Method Writing


The OSMacro2compBasic method is a basic population method.

'Basic Population Method sub FirstPopMethod() 'Indicate the fitting method name PopMethod.Name = " OSMacro2compBasic" 'Method description (will be used as tool tip in the method selection dialog) PopMethod.AddDescription("My Soft Coded ") PopMethod.AddDescription("Population Method Description") 'Set the algorithm to be called, and set the subroutine name to be called 'where all models are ‘defined for all datasets PopMethod.GeneralAlgoOptions("EM","procmodels") 'Add a parameter or a constant to be used by one of the models PopMethod.AddInOutVar("D") 'Add a parameter or a constant to be used by one of the models PopMethod.AddInOutVar("Ka") 'Add a parameter or a constant to be used by one of the models PopMethod.AddInOutVar("Vd") 'Add a parameter or a constant to be used by one of the models PopMethod.AddInOutVar("Cl") 'Here, Model is the dataset variable number which contains the index of the 'model linked to a dataset PopMethod.AddDatasetModelNumVar("Model") 'Indicate that D is linked to the model which index value is 1 PopMethod.LinkVarToModel("D",1) 'Indicate that D is linked to the model which index value is 2 PopMethod. LinkVarToModel ("D",2) 'Indicate that Ka is linked to the model which index value is 1, Ka is only 'linked to the model which index value is 1 PopMethod. LinkVarToModel ("Ka",1) 'Indicate that Vd is linked to the model which index value is 1 PopMethod. LinkVarToModel ("Vd",1) 'Indicate that Vd is linked to the model which index value is 2 PopMethod. LinkVarToModel ("Vd",2) 'Indicate that Cl is linked to the model which index value is 1 PopMethod. LinkVarToModel ("Cl",1) 'Indicate that Cl is linked to the model which index value is 2 PopMethod. LinkVarToModel ("Cl",2) 'Call the dialog to add this method to the current study PopMethod.AddPopMethodToStudy End sub

OSMacro2compBasic



sub procmodels(nb as integer, Model as integer, dtname as string) 'Input of procmodels: 'nb is the number of time for the current dataset named dtname. 'Model is the index indicating which model has to be selected for this 'dataset. 'example: model index 1: os if Model = 1 then 'Parameters are taken in the order defined by AddInOutVar D = PopParam(1) Ka = PopParam(2) Vd = PopParam(3) Cl = PopParam(4) Kel = Cl/Vd 'PopYCalc is an internal array of double for Kinetica to get the result 'PopIndepVal is an internal array of double which contains all the independent 'values column for the current dataset (named dtname) for i = 1 to nb PopYCalc(i) = D / Vd * (Ka / (Ka - Kel) ) * (exp( -Kel * PopIndepVal (i)) - exp(-Ka * PopIndepVal (i) )) next i end if 'example: model index 2: os if Model = 2 then 'Parameters are taken in the order they are defined by AddInOutVar, but Ka 'doesn't belong to this model so, here is the new order D = PopParam(1) Vd = PopParam(2) Cl = PopParam(3) Kel = Cl/Vd for i = 1 to Note: PopYCalc(i) = D / Vd * exp( -Kel * PopIndepVal (i)) next i end if 'If you want to make a multiple dose fitting, you have to use a line of code 'such as: Dosei = GetValue (dtname, "AdminWorksheet", "DoseColumn", ‘LineNumber , pStatus) where pStatus is a Long which indicates if the dose is 'missing or not ‘(3 = missing,0 = 'normal), AdminWorksheet (dim 'AdminWorksheet as string) is the worksheet where the admin 'column is, 'DoseColumn is the dose column name (dim DoseColumn as string), LineNumber is 'a long, Dosei ‘is the return ‘value (a 'double). 'With this line of code you can get all the doses. 'You can also use GetNbValue (dtname, "AdminWorksheet", "DoseColumn") to get the number 'of 'value in the column DoseColumn for the dataset dtname. The return value is 'a long. End sub



The sample code below is an example of how to create a multiple dosing regimen using macro constants.

'This code is a multiple dose sample for population fitting sub MultidoseSample() 'Method name PopMethod.Name = "MultidoseSample" 'Method description PopMethod.AddDescription("Multidose ") 'Method description PopMethod.AddDescription("Population fitting") 'General options, "procmodel" is the model subroutine to be called PopMethod.GeneralAlgoOptions("EM","procmodel") 'Add a variable to be used for the current model PopMethod.AddInOutVar("V") 'Add a variable to be used for the current model PopMethod.AddInOutVar("Cl") ' Note: when there is only one model you do not have to link ' your variable to an index model. 'Add the model to the study PopMethod.AddPopMethodToStudy End sub ' Subroutine to be called to compute the model sub procmodel(nb as integer, Model as integer, dtname as string) ' "nb" is the number of elements in the internal array PopIndepVal. ' "PopIndepVal" is a internal column of double which contains ' the abscisse of the observed value. ' Here "Model" is only needed if you fit with multiple model (which is not the ' case here). ' "dtname" is the name of the dataset we are going to compute ' PopYCalc (PopYCalc is also an internal array for the EM algorithm to receive the 'computed values). 'variable declaration dim diff as double dim LineNumber as long dim pStatus as long dim nAdmin as integer dim dose as double dim j as integer dim admintime as double

Multiple Dose Example



dim newdtname as string ' get the parameter in the same order as they are defined. V = PopParam(1) Cl = PopParam(2) newdtname = dtname 'Those lines are added in order to plot a population graph. 'When Kinetica wants to plot the population graph, 'it will call this model with "#No dataset" instead of ' a dataset name. Then we have to choose which multiple dose 'column to select. Here we choose, for example, the column dose of dataset 51. ' Note: In our case all the datasets have the same dose column. Then we can 'draw a graph. if dtname = "#NoDataset" then newdtname = "51" end if ' Get the number of values in the column Dose, in the worksheet MultiAdmin, ' for the dataset newdtname nAdmin = GetNbValue(newdtname, "MultiAdmin","Dose") for j = 0 to nAdmin - 1 'Get the value and the status in the column Dose, in the worksheet MultiAdmin at the 'index j for the dataset newdtname dose = GetValue(newdtname, "MultiAdmin","Dose", j, pStatus) 'status conventions are: 'pStatus = 0 for normal; 1 for indetectable; 2 for outlier; 3 for missing; 4 for error. ' Here we check the status if pStatus = 0 then ' Get the value and the status in the column Dose, in the worksheet MultiAdmin at the 'index j for the dataset newdtname admintime = GetValue(newdtname, "MultiAdmin","Time", j, pStatus) ' Now compute PopYCalc for i = 1 to nb diff = ( PopIndepVal(i) - admintime ) if diff >= 0 then PopYCalc(i) = PopYCalc(i) + dose/V*exp(-Cl/V*diff) end if next i end if next j End sub



The sample code below creates a multiple dosing regimen of a 1 compartment IV Bolus using micro constants.

'Global Variables declaration 'Variable for differential system dim Z1 as Double dim DZ1 as Double dim LineNumbe as long dim curNextTimeIndex as long ' Dose administration status dim pStatu as long ' number of administrations for this dataset dim nAdmin as integer ' time of the dose administration dim admintime as double dim diff as double dim Dose as double dim maxTime as double dim NextAdmintime as double dim i as Integer dim ret as Integer dim hmod as long sub Fit_Interactive_model () PopMethod.AddInOutVar("V") PopMethod.AddInOutVar("k10") PopMethod.GeneralAlgoOptions("EM","procmodel") ' Function to be called at the begining of the fitting ' in order to declare the differential system equation PopMethod.BeginFunctionToCall("InitDifferentialEquation") ' Add the method to the study PopMethod.AddPopMethodToStudy End sub dim V as double dim k10 as double sub InitDifferentialEquation() hmod = NewInteg("Deriv") ret = DeclareComp(hmod, Z1, DZ1) End sub 'Function to be called to compute YCalc sub procmodel(nb as integer, Model as integer, dtname as string)

IV RungeKuttaMultidose



'Get parameters value V = PopParam(1) k10 = PopParam(2) Z1 = 0 ' Get the number of the value in the column Dose in the worksheet ' MultiAdmin for the dataset dtname nAdmin = GetNbValue(dtname, "MultiAdmin","Dose") 'for each dose administration time for LineNumber = 0 to nAdmin - 1 ' Get the value and the status in the Dose column ' at the index LineNumber for the current dataset Dose = GetValue(dtname, "MultiAdmin","Dose", LineNumber , pStatus) ' status conventions are: ' pStatus = 0 for normal; 1 for undetectable; 2 for outlier; ' 3 for missing; 4 for error. ' Here we check the status if pStatus = 0 then ' Get the value and the status in the Dose column ' at the index LineNumber for the current dataset admintime = GetValue(dtname, "MultiAdmin","Time", LineNumber , pStatus) curNextTimeIndex = LineNumber maxTime = PopIndepVal(nb) + 1 NextAdmintime = maxTime do if curNextTimeIndex > nAdmin - 2 then exit do end if NextDose = GetValue(dtname, "MultiAdmin","Dose", curNextTimeIndex+1 , pStatus) if pStatus = 0 then NextAdmintime = GetValue(dtname, "MultiAdmin","Time", curNextTimeIndex+1 , pStatus) exit do else NextAdmintime = maxTime end if curNextTimeIndex = curNextTimeIndex + 1 loop ' initial time for Runge Kutta integration SetIntegCurrentTime hmod, admintime Z1 = Z1 + Dose/V For i=1 To nb ' For each independant value diff = ( PopIndepVal(i) - admintime ) 'if current time is after the current admin time, then if diff >= 0 and NextAdmintime > PopIndepVal(i) then ' Compute new comp values ret = IntegTo(hmod, PopIndepVal(i)) PopYCalc(i) = Z1 ' Get corresponding computed value



End If Next i End If Next LineNumber End Sub Sub Deriv (Byval t as double) DZ1 = -Z1*k10 End Sub


A

Absorption, 4, 457, 497, 515, 518

Add Free Comment, 193, 235

Adjusting Columns & Rows, 55

All Properties, 218, 235

All Variables, 45

ANOVA, 659

Applying Axis Scale, 194

Applying Graph Templates, 232

Automatic Graphs, 212

Available Views, 143

B

Batch Graphs, 243

Bayesian fit, 636

C

Cells Dialog Box, 52

Changing Fitting, 546

Changing Your Dataset View, 58

Chart Wizard

Starting, 177 Step 1, 177 Step 2, 183 Step 3, 184 Step 4, 185

Step 5, 188

Column Units, 80

Computational Algorithm

Differential Equation Solver, 736 Initial Parameter Estimates

Method, 729 Minimization Algorithm, 733 Stepwise Regression, 737 Termination Criteria, 735

Concentration method, 636

Configuring Units, 73

Convolution/Deconvolution, 4

Correlation Matrix

Fitting, 447

Creating Mean Curves By Group With Statistics, 207

Creating Standard or Overlay Mean Curves With Statistics, 205

Customizing the Normal Kinetica Template, 66

D

Data Column, 115, 116, 140, 154, 160

Data Columns, 212, 213

Data Format, 130

Data Layout, 100, 108, 129

Data Preview, 143

Data View, 131

Dataset Graph, 47

Dataset Graph Options, 192

Dataset Group, 16, 19, 191, 210, 230

Index

Index


Dataset Pane, 30, 46

Default Data Structure, 61

Delete Commands, 26

Deleting a Column, 30

Deleting a Dataset Numerical Field, 33

Deleting a Study Numeric Field, 31

Deleting a Study Text Field, 32

Deleting a Worksheet, 29

Deleting Methods, 33

Differential Equation Solver, 736

Fitting, 444

E

Edit Menu

Move Columns, 56

EM Iterative Algorithm

Two Stage Parameter Estimates, 721

Emax, 503, 504, 505, 506, 507, 509, 511, 512, 519, 527, 529, 531, 532, 537, 541, 547

Enzyme Kinetmatics, 4

Error, 114, 115, 140, 154, 160, 206, 214

Exchange Pane, 50

Exporting Data, 168, 170

Exporting Data To External Databases, 168

Exporting data to Microsoft Word, 654

Exporting Graphs, 243

Exporting Results to Microsoft Word and Excel, 170

Extract Study Command, 38

Extracting Fields from A Study, 39

Extravascular, 157, 220, 223, 226, 230, 336, 360, 361, 369, 412, 420, 422, 423, 427, 429, 431, 434, 437, 449, 456, 458, 470, 481, 482, 507, 514, 517, 520, 527, 537

Extravascular Fitted Model

Equations for PK Parameters, 437

F

First Order Absorption, 515

FitMacroExtravascular, 422, 429, 457, 517

FitMacroIVInf, 422, 429, 465, 546

FitMicro0orderInput, 468

FitMicroIV, 475

FitMultiMicro, 430, 497

FitMultiMicroExtravascular, 427, 429, 483

FitMultiMicroIVBolus, 427, 429, 485, 488, 493, 500, 511

FitMultiMicroIVInf, 427, 430, 491

Fitting, 4, 426

Correlation Matrix, 447 Differential Equation Solver, 444 Goodness of Fit, 445 Initial Parameter Estimates, 440 Macro or Micro Constants, 421 Marquardt's Principle, 443 Method of Administration

Bolus, 424 Extravascular, 424 IV Infusion, 425

Micro and Macro Constants, 426 Minimization Algorithm, 443 Multiple Dose, 424 PD Analysis, 502 Residuals, 448 Single Dose Zero Order Input Macro Constants Template, 451 Statistics & Goodness of Fit, 445 Stripping, 441 Stripping, Failure of, 441 Weighted Residuals, 448 Weighting Schemes, 442

Index


Formatting Cells, 53

Formatting Kinetica Spreadsheets, 52

Friedman Test, 707

G

Gallery Interface, 233

Gallery Pane, 49

Gauss-Marquardt Algorithm, 733

Generating a parameter distribution plot, 250

Getting Units, 85

Goodness of Fit, 445

Graph Buttons, 212

Graph Gallery, 176, 204, 206, 216, 230, 233, 236, 238, 535

Graph Methods, 214

Graph Properties, 194, 211

H

Hard-Coded Methods, 258

Hill, 505, 506, 507, 512, 519, 527, 531, 537, 541, 547

Hot Graph, 191, 201

Hysterisis Graph, 524

I

Importing Data from ASCII Files, 128

Importing Data from Databases, 142

Importing Data from Excel Files, 107

Importing Data from Watson LIMS, 162

Importing Data in Proprietary Formats, 156

Importing Miscellaneous Data, 165

Importing PCNonlin Files, 167

Importing SIPHAR Files, 166

Importing XPD Files, 165

Individual Parameter Estimates

Bayesian Fit, 724


Fitting, 440 Method, 729

Insert Commands, 17

Inserting and using the Get Column Unit Method, 85

Inserting and Using the MakeRateUnit Method, 82

Inserting and Using the Set Column Unit Method, 80

Inserting and Using the XY Unit Method, 87

IV Bolus, 336, 337, 340, 369, 420, 422, 424, 427, 428, 429, 430, 432, 435, 449, 450, 460, 474, 485, 487, 499, 507, 514, 515, 529, 532

IV Infusion, 336, 350, 353, 354, 369, 404, 420, 421, 422, 427, 429, 430, 431, 433, 436, 449, 450, 464, 477, 478, 490, 492, 507, 514, 515, 539, 542, 546

K

KDB, 64, 237, 239, 240, 241, 572, 573

KDB File, 240

KGG File, 236, 238, 239, 240

Kinetica Basic, 258

Kinetica Insert Commands, 17

Kinetica Main Menu, 16

Index


Kinetica Study Pane, 45

Kinetica Toolbar, 41

Kinetica Views, 45

Kinetica Workspace, 14

L

Latin Square, 673

Latin Square with Greater Than Two Formulations

Statistical Analysis, 680

LIMS, 104, 106

Linear, 403, 502, 511, 527, 537, 547

Linear Model, 502, 520, 542

Log-Linear, 502, 511, 527, 537, 547

M

Macro Editor, 45

Macro or Micro Constants, 421

MacroToMicro, 422, 432, 433, 434, 452, 455, 458, 462, 466, 476, 479, 484, 488, 494, 500

MakeConcUnit, 76, 81, 83, 337, 360, 373, 451, 457, 460, 464, 467, 470, 474, 477, 481, 485, 490, 497, 517, 529, 540

Marquardt's Principle

Fitting, 443

Mean Curve, 45, 46, 191, 199, 200, 201, 202, 210

Merge Results, 133, 147, 157

Message Box, 9

Method Editor, 48

Methodological Background, 719

Methodology

Individual Parameter Estimates Bayesian Fit, 724

Methodological Background, 719 Model Validation, 726 Models and Notation, 720 Population Parameter Estimates

Sparse Data, 722 References, 740

Methods, 6, 258

Methods Group, 48

Methods Menu

LZ Graph, 198

Methods Pane, 48, 91, 93

Micro and Macro Constants

Fitting, 426

Micro Constants, 421, 427, 429, 430, 432, 433, 449, 450, 467, 470, 474, 477, 478, 481, 482, 485, 487, 490, 492, 496, 499

Microsoft Excel, 6, 366, 380, 511, 525, 536, 545

Microsoft Word, 6, 170, 171, 176, 204, 206, 244

Microsoft® Excel, 571, 572

MicroToMacro, 422, 427, 469, 472, 486, 491, 498

Minimization Algorithm, 733

Fitting, 443

Missing, 115, 154, 160

Model Validation, 726

Models, 258

Models and Notation, 720

Modifying Your Graphs, 218

Multiple Dose, 424, 426, 427, 428, 429, 430, 438, 449, 450, 481, 482, 485, 487, 490, 492, 496, 499

Fitting, 424

Multiplying Units, 87

Index


N

NCA Assistant, 42, 336, 402, 407, 411

Non-Compartmental Analysis, 4, 327, 336

Non-Detectable Data, 223

Normal Kinetica Template, 66

O

Observed Concentration Units, 76

Obtaining Simplified Units, 90

ODBC, 106, 142, 144, 168

Opening an Existing Kinetica File, 65

Opening an Existing Kinetica Template, 68

Outlier, 115, 153, 160, 220, 402, 408

Output Data, 9

P

PCNonlin, 41, 165, 167

PD Analysis

Fitting, 502

Performing statistical analysis, 657

Pharm-ABS, 165

Pharmacokinetic/Pharmacodynamic, 513

PK Template Examples, 449

PK/PD Fitting, 260

Plasma View, 67, 509

Plotting Mean Curves, 204

Plotting the Residuals, 456, 459, 462, 466, 469, 472, 476, 480, 484, 489, 495, 501

Population Designer, 652

Population Parameter Estimates

Sparse Data Situation, 722

Population validation, 636

Power model, 620

P-Pharm, 157, 165

Profile Properties, 193, 234

Protecting Kinetica Datasets, 58, 59

Protein Binding, 4

R

Rate Units, 82

Remove All Methods, 27

Remove Last Method, 26

Reports Pane, 49

Residuals, 445, 448, 451, 456, 459, 462, 466, 469, 472, 476, 480, 484, 485, 489, 491, 495, 496, 497, 501, 518, 519, 530, 531, 540, 541

Running population validation using the concentration method with covariable, 647

Running population validation using the parameter method, 637

S

Save As Graph Template, 235

Save Graph As, 242

Save Table Script Files, 572

Saving a File as an Empty Kinetica Template, 71

Saving a Gallery Inside a KDB File, 237

Index


Saving a Gallery to File, 235

Saving Graphs, 241, 242

Saving Kinetica Files, 70

Schwartz criterion, 725

Script Files, 573

Send to Gallery, 194, 233, 417

Set Column Unit, 80, 85, 86, 89, 90, 337, 350, 360, 373, 451, 457, 460, 464, 467, 470, 474, 477, 481, 485, 490, 497, 507, 517, 529, 540

Set Column Unit Method, 80

Show Points Legend, 193, 234

Show Sets Legend, 193, 234, 235

Show Status Legend, 235

Sigmoid, 505, 506

Simple Hard-Coded Methods, 260

Simplex Method, 729

Simplified Units, 90

Single Dose Extravascular Macro Constants Template, 456

Fitting, 456

Single Dose Zero Order Input Macro Constants Template, 451

Fitting, 451

SIPHAR, 165

Soft-Coded Methods, 258

Source Columns, 132, 133, 135, 137, 147, 149, 157

Spaghetti Plot, 45, 191, 196, 197

Sparse Data

Population Parameter Estimates, 722

Specifying Units, 75

Spreadsheet Interface, 52

Standardized Concentration Prediction Error, 726

Standardized Parameter Prediction Error, 727

Statistical Analysis

Latin Square with Greater Than Two Formulations, 680

Statistics and Goodness of Fit

Fitting, 445

Status Column, 115, 116, 140, 154, 160

Stepwise Regression, 737

Stripping

Fitting, 441

Stripping, Failure of

Fitting, 441

Student t test, 715

Study Group, 19, 166, 191, 199

Study Icon, 45, 193, 200, 234, 235

Study Info, 45

Study Objects, 45

Study Pane, 45

Study Variables, 45

T

Table Assistant, 42, 48, 549, 550, 568, 573

Table Info, 48

Tables Group, 48

Tables Pane, 48

Template Examples, PK, 449

templates, 4

Templates, 64, 230

Index


Termination Criteria, 735

Ticks, 218, 219

To Adjust All Columns & Rows, 52

To Adjust Individual Columns & Rows, 52

To Format Cells, 52

Toolbar, 41

U

Undetectable, 115, 153, 159, 410

Unit Configuration, 73

Unit Management for AUC*, AUCinter* or AUC Steady State* Methods, 90

V

VBA Editor, 45, 48

Viewing Mean Curves Without Statistics, 201, 202

W

Weighted Residuals, 445, 448, 451, 456, 459, 462, 466, 469, 472, 476, 480, 484, 486, 489, 491, 495, 497, 501, 518, 519, 530, 531, 540, 541

Weighting Schemes

Fitting, 442

WinNonlin, 165

Word, 43

X

XPD, 165

XY Unit, 88

Z

Zero Order Absorption, 429, 515

Thermo Fisher ScientificP.O. Box 9046Waltham, Massachusetts 02454-9046United States

www.thermo.com/informatics

http://www.thermo.com/informatics

kinetica user manual

Documents