how to create a pat data management platform to … to create a pat data management platform to...

© Siemens AG 2011. All rights reserved.

How to create a PAT data management platform to support Continuous Production8th Annual PAT and QbD Conference,London, February 16th, 2011

Jan Verelst


Industry Sector

February 16, 2011 8th Annual PAT/QbD Conference - London

Back in time


Industry Sector

February 16, 2011 8th Annual PAT/QbD Conference - London

What caused this disaster ?

Information :

� Hidden

� Critical

� Knowledge

Knowledge

Data

Information


Industry Sector

Page 5 February 16, 2011 8th Annual PAT/QbD Conference - London

Oral Solid Dosage today

Granulator

Dryer

Coating

Delay Delay Delay

1 to 2

months to

release

Blender

Raw Material

Tablet press

Delay

Blender


Industry Sector


PAT: Key Enabler for Continuous Manufacturing and Real Time Product Release

Raw Material

Dryer

Granulator Tablet press

Blender

Qualitycheck

SIPATSIPAT

Coating

Right First Time

Real time release

In/At line check


Industry Sector


Process feed

Ho

ld /

rele

ase

LabLabTemp., Speed,

Liquid addition,

Compression Force, …

Process output

LIMSLIMS

Sample

Classic controlClassic control

Process Analyzer

monitoringmonitoring

processprocess datadata

monitoringmonitoring

product product qualityquality

PATPAT

mathematicalmathematical

translationtranslation

RealReal--timetime

releasereleaseAdvanced Advanced

ControlControl

Quality build in by design

Right first time

PAT Basics – Holistic Approach

Closed loopClosed loop

controlcontrol


Industry Sector


SIPAT functions


Industry Sector


SIPAT Collectors

Collector = Interface to read data from external systems as input to SIPAT

Method

Standard available collectors within SIPAT

� Instrument collectors (dedicated interfaces to range of analytical instruments)

� Process collector (OPC DA Client)

� File collectors (XML, SPC, JCAMP)

� PAT Collector (results from other SIPAT Method)

� Databrowse collectors (ODBC, SQL, OLEDB)


Industry Sector


Process Analyzers

Standard analyzer connection available with actual productivity pack:

� Kaiser Raman (Raman RXN1, RXN3, RXN3L, RXN4R and PhAT); Kaizer’s Analyser Control V4.2

� Thermo Fischer Antaris and Antaris II family (via Result software)

� PAA Ltd with GranuMet XP (acoustic emission measurement), Version 4

� Bruker NIR via OPUS V6.1 software

� Bruker NIR via OPUS 6.5 embedded DLL

� Malvern Insitec – Laser Diffraction PSD (RT Sizer software)

� Malvern Morphology G3

� Mettler Toledo collector for ReactIR 4000 (FT-IR) � via IC4 software

� Mettler Toledo MonARC (FT-IR) � via IC4 software

� Mettler Toledo Lasentec FBRM (PSD) � via IC4 software

� Zeiss collector MCS 500 serie (photodiode array UV/visible spectrophotometer)

� Zeiss collector MCS 600 serie (NIR)

� Brimrose Luminar 5030 and 4030 AOTF-NIR

� Expo ePAT 601 (including an Axsun NIR)

� Dr. Schleuniger tablet tester (Tandem using Dr. Schleuniger – Pharmatron - Bruker)

� ABB FTPA2000

� Bruker LancIR via embedded DLL 6.5.92

New productivity packs will be released on a frequent basis


Industry Sector


SIPAT: data collection in real time

Alignment of Collectors

NIR

Process value(e.g. Temp)

Measurement

duration

Start batch

Startup off set Measurement rate

Negative Validity

Range

REFERENCE COLLECTOR

Positive Validity Range

Aggregation function :

Average

Last Value

Maximum

Middle

Minimum

First Value

Nearest to reference


Industry Sector


SIPAT: data collection in real timePrinciple

�Data alignment

�Contextualization

�Rules separating non-representativedata

SIPAT

Raw Material

Characteristics

LIMS

Process

+

Context data

SC

AD

A

Analytical

Data

NIR, PSD

Analyzer


Industry Sector


SIPAT functions


Industry Sector


SIPAT: data monitoring and control in real timeApplication

SIPAT brings all data (in-line, at-line, on-line and off-line) in relation for prediction

Quelle: Siemens IT Solutions and Services

Quantitative prediction

Qualitative prediction

Real Time

Prediction

Alig

nm

ent

Meta &

context data

Raw Material

Data / IPC data (Moisture, …)

Other Application data

Instrument data

(NIR, Raman, PSD, …)

Process data (pH, Temp, Pressure, …)


Industry Sector


SIPAT Data Model

SIPAT MethodSIPAT MethodCollector DataCollector Data CalculationsCalculations

VisualizationsVisualizations

Info DataInfo Data

Perform Calculations

Make Predictions

� using

� Models

� Basic Calculations

� Advanced Calculations

Input from

� Analytical Instruments

� Process/Automation Level

� Raw Materials

� Other Systems

� Previous process step

� using Collectors

Static data

Semi-static data

Meta-data

Manual/automatic entry

Configure operator screens

Using Charting types


Industry Sector


SIPAT: data monitoring and control in real timePrinciple

�Data alignment



�Statistical RT Models

�e.g. PLS, PCA

�Chemometrics(SIMCA, Matlab,

Unscrambler,

PharmaMV,…)

engine embedded

in SIPAT

�CQA

Aggregation of CQA over process context

- Average,

- Standard deviation

On line Monitoring

Out of control detection

Deviation tracking

AP

CR

TR

SIPAT

Raw Material

Characteristics

LIMS

Process

+

Context data

SC

AD

A

Analytical

Data

NIR, PSD

Analyzer

Critical

to

QualtyAttribute


Industry Sector


SIPAT functions


Industry Sector


SIPAT: data miningPrinciple

SIPAT

Analyser Data ( NIR, Raman, PSD, …)

Process Data Predicted data

Off- line lab data

(RM,process samples,…)

External application

Meta & context data Other data

- Batch number

- Trial number

- Process step

- Product

- Campaign

…. Processoptimization


Industry Sector


SIPAT: data miningModel creation & process optimization

Turning data into knowledge

� Improved process understanding

� Base of QbD

Model Builder

� Integrate all relevant PAT data

� Send to Chemometrics application

� Umetrics - Simca

� Camo - Unscrambler

� Matlab

� Perceptive Engineering - PharmaMVProcess

optimization


Industry Sector


SIPAT functions


Industry Sector


ReportingAutomated or ad hoc


Industry Sector


ReportingTable output


Industry Sector


ReportingGraphical output


Industry Sector


ReportingGraphical output - spectrum


Industry Sector


ReportingGraphical output – batch comparison


Industry Sector


Why SIPAT ?

SIPAT

Increase process understanding / manage variability

Bring all data (in-line, at-line, on-line and off-line) in relation

Provide on-line predictions on product/process quality

Shift from lab testing towards in process testing

1 common interface for all PAT tools

Close the gap between R&D and Manufacturing


Industry Sector


Project context

GEA Pharma Systems

� Process Know-How

� Supply specific equipment +

instrumentation to handle a specific

production operation

� Focus on equipment specific

optimization

� Equipment automation specification

(PLC, HMI, drive, instrumentation,

...)

� Equipment validation

� Equipment specific 21 CFR Part 11

functionality

� Worldwide Packaged Unit Support

Siemens� Industry know How

� Standard platform

� Scalable solutions

� Easy integration

� GMP supporting functions

� Cost effective solutions

� Long term Support (investment protection)

� Worldwide Product Support

End-User

� Product Know-How

� Specify required machinery to fulfill a specific production operation

� Equipment must comply with the own

specific norms and standards

� Focus on the optimized

functioning of the

complete production line

� Easy integration of

the control level into

Plant-IT

End-user

Supplier

OEM


Industry Sector


PAT Instruments

NDC - Moisture

NIR (J&M LHP)

Moisture+ C. uniformity

Malvern

Particle Size

At line analyzer

Weight, Hardness,Thickness, Assay


Industry Sector


MES LIMS

SCADA

PLC / Motion Control

PROCESS

ANALYZERS

SIPAT

Line

SITE

(2)

(5)

(1) (3)

(4)

(6)

High Level System Architecture

�Control Analyzers

�Multivariate Data handling

�On-line monitoring of the Critical to Quality Attributes (CQA)

�Deviation Tracking CQA

�Out of Control Detection & input to APC

� Supervisory & Control

� Alarm handling

� Line Recipe handling

� Trending Univariate Data

� Deviation Tracking Critical Process Parameters

�Real Time Releasereporting


Industry Sector


Traditional PAT in solid dosage

•

Drying

control

Process

parameters

Milling Blending

Lubrication

control

Process

parameters

Compression

control

Process

parameters

Coating

control

Process

parameters

Dispense

& Blend

control

Process

parameters

� API

� Excipients

Wet

Granulation

control

Process

parameters

Liquid addition

Lubricant excipient

Coating solution

Moisture

Bulk Physical

Defects

Test against

specifications

Bulk Physical

Defects

Content

Uniformity

Packaging

control

Process

parameters

Particle size

Sampling &

Off-line analysis


Industry Sector


End product quality predictions

•

Lubricant excipient

Drying

control

Process

parameters

Blending /

Lubrication

control

Process

parameters

Compression

control

Process

parameters

Coating

control

Process

parameters� Api

� Excipients

Dispense

& Blend

control

Process

parameters

Liquid addition

Wet

Granulation

control

Process

parameters

Coating solution

Moisture

(NIR)

Assay

Dissolution

Disintegration

Packaging

control

Process

parameters

Particle size

(Malvern)Weight

Hardness

Thickness

Input material

characteristics

Visual

Inspection

Coating

thickness

Content

Uniformity


Industry Sector


Lot 1 Lot 2

Lot 1 Lot 2

Lot 1 Lot 2

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4 F4C5

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2

API

Excipient 1

Excipient 2

Granulation

Ingredients

Drum 1 Drum 2 Drum 3Output

Time

Drying

Millling/Blending

Compression

Coating

Material Residence Time distribution Product Plug tracking


Industry Sector


Lot 1 Lot 2

Lot 1 Lot 2

Lot 1 Lot 2

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4 F4C5

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3 F4C4

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2 F4C3

F1C1 F1C2 F1C3 F1C4 F1C5 F1C6 F2C1 F2C2 F2C3 F2C4 F2C5 F2C6 F3C1 F3C2 F3C3 F3C4 F3C5 F3C6 F4C1 F4C2

API

Excipient 1

Excipient 2

Granulation

Ingredients

Drum 1 Drum 2 Drum 3Output

Time

Drying

Millling/Blending

Compression

Coating

Material Residence Time distribution Real Time Release floating window

Release window


Industry Sector


SIPAT: data monitoring and control in real timePrinciple

�Data alignment



�Statistical Real Time Models

�e.g. PLS, PCA

�Chemometrics

(SIMCA, Matlab,

Unscrambler,

PharmaMV,…)

engine embedded in SIPAT

�CQA

Aggregation of CQA over process context

- Average,

- Standard deviation

On line Monitoring

Out of control detection

Deviation tracking

AP

CR

TR

SIPAT

Raw Material

Characteristics

LIMS

Process

+

Context data

SC

AD

A

Analytical

Data

NIR, PSD

Analyzer

Critical

to

QualtyAttribute


Industry Sector


37

Data Driven Manufacturing

• SiPAT allows to measure inline the CQA’s, real time

• Increased number of measurements

Test case of 50hrs run @25kg/hr=1250kg material

- Moisture measured 960 times

- Particles size distribution

measurements: 960 times

- Content uniformity: 100 times

- Tablet weight: each tablet (1,8 Mio)

�Enhanced product security

�Data correlation across units


Industry Sector


Results of the trials

Main findings of the feasibility study:

� Reduced process development time

� “Unlimited” number of reference batches as a basis for model creation

� Real Time Release enabling

� Quality information available on tablet level rather than on batch level

� Increased process understanding

� Through Real Time Quality Assurance

� Time-based process: scalability improvement

� Savings in construction, space, energy, maintenance

� Increase of yield and equipment efficiency

� Reduction of scrap, waste & rework

� Reduction of raw material

� Reduction of human interference (cost, safety)

� …


Industry Sector


Article available on request


Industry Sector


Better process understanding…leads to a more efficient result !


Jan Verelst

Siemens

Industrial IT

+32 475 432 560

[email protected]

Thank you for your attention!

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