analytical qbd applied to cge purity assay of a ... · analytical qbd applied to cge purity assay...
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Analytical QbD applied to CGE purity assay of a therapeutic
protein : A step further in Analytical
Lifecycle Management
26.09.2016
Jérémie Cuisenaire
QbD approach 2
* ICH Q8 (R2), Pharmaceutical Development, August 2009
Quality by Design (QbD)
Definition (ICH Q8(R2)*) “A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.” ICHQ8(R2) does not explicitly discuss analytical method development.
QbD approach 3
* ICH Q8 (R2), Pharmaceutical Development, August 2009
Quality by Design (QbD)
Definition (ICH Q8(R2)*) “A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.” ICHQ8(R2) does not explicitly discuss analytical method development. However, concepts can be applied to analytical methods with a systematic and structured approach that includes: - Risk Assessments
- Definition of a Design Space
- Setting–up of a Control Strategy
- Continual improvement:
- Increasing the method Robustness
- Bringing better understanding of method and product
Analytical QbD (AQbD)
QbD for Analytical methods – what does it mean? 4
The different steps of Analytical QbD
P. Nethercote et al. Pharmaceutical manufacturing (2010); USP Stimuli article on lifecycle management of analytical procedures (2013); ICH Q8 (R2), Pharmaceutical Development, August 2009 - ICHQ9, Quality Risk Management September 2006
Analytical Target Profile (ATP)
Technology Selection
Prior Knowledge
Risk Assessment
Screening Experiments
Optimization Experiments
Robustness Studies
Validation of the Assay
Analytical Control Strategy
MODR **
Stage 1: Method Design & Understanding
Stage 2: Method Performance Qualification
Stage 3: Continued Method Verification
CMA *
* Critical Method Attributes ** Method Operable Design Region
Stage 1 – Method design and understanding 5
Analytical Target Profile
* Using Total Error Approach
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Definition The ATP defines the objective of the test and quality requirements for the reportable result. It is a prospective summary of the required characteristics of the reportable result that needs to be achieved to ensure the data is fit for purpose. Example The analytical procedure must be:
- Specific - Able to accurately quantify:
o Main species Acceptance limit: ± 20% with a 5% risk* within a range from A to B% of the % Main species
o Impurities Acceptance limit: ± 50% with a 5% risk* within a range from X to Y% of the % Impurity
- Stability indicating - Provide prepared samples stable for at least 48 hours - Applicable for use in a standard analytical QC laboratory for routine analyses
Stage 1 – Method design and understanding 6
Analytical Target Profile
* Using Total Error Approach
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Definition The ATP defines the objective of the test and quality requirements for the reportable result. It is a prospective summary of the required characteristics of the reportable result that needs to be achieved to ensure the data is fit for purpose. Example The analytical procedure must be:
- Specific - Able to accurately quantify:
o Main species Acceptance limit: ± 20% with a 5% risk* within a range from A to B% of the % Main species
o Impurities Acceptance limit: ± 50% with a 5% risk* within a range from X to Y% of the % Impurity
- Stability indicating - Provide prepared samples stable for at least 48 hours - Applicable for use in a standard analytical QC laboratory for routine analyses
Stage 1 – Method design and understanding 7
Technology Selection
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Definition Identification of an analytical technology for performing the measurements that has the ability to conform to the ATP. Other Factors to take into account:
Equipment and consumables availability
Costs
Analysis time
Second provider
…
Stage 1 – Method design and understanding 8
Technology Selection
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Definition Identification of an analytical technology for performing the measurements that has the ability to conform to the ATP. Other Factors to take into account:
Equipment and consumables availability
Costs
Analysis time
Second provider
… CGE Example:
1. SDS-PAGE - Historical technology
2. Bioanalyzer - Improved technology
3. Capillary Gel Electrophoresis - Continuous improvement technology
Stage 1 – Method design and understanding 9
Prior Knowledge
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Definition Gathering of knowledge and useful information for the initial risk assessment: Literature
Background of molecule (pH/pI, solubility, molecular weight, stability data,
structure of the molecule,…)
Previous experience with similar methods, comparable molecules,..
Stage 1 – Method design and understanding 10
Risk Assessment
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Objectives Identify & Gain understanding on
- How potential sources of variability affect the performances of the analytical
procedure.
- Classify the risks Give a priority numbering and take appropriate actions Using Risk Assessment Tools Process mapping
Ishikawa diagrams
Failure Mode and Effects Analysis (FMEA)
…
Stage 1 – Method design and understanding 11
Risk Assessment - Process Workflow
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Objectives Flow chart to give a simple view of the different steps of the analytical procedure Identification of potential critical procedure parameters
Stage 1 – Method design and understanding 12
Risk Assessment - Process Workflow
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Objectives Flow chart to give a simple view of the different steps of the analytical procedure Identification of potential critical procedure parameters
Reagent 1 out of the fridge
Weighing of the reagent 1
DP sample out of the fridge
DS sample out of the freezer
Desalting if necessary
...
Separation procedure
Analysis sequence prep.
...
Stage 1 – Method design and understanding 13
Risk Assessment - Ishikawa (Fishbone)
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Objectives Based on the method workflow outcome
Visualization tool used to categorize the potential elements / critical method
parameters
Classification according to 6 groups:
o Method
o Manpower
o Environment
o Material
o Measurement
o Instrument
- Separation voltage - Capillary T°
Laboratory T°,..
Stage 1 – Method design and understanding 14
Risk Assessment - FMEA
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Objectives - Evaluate the potential deviation (failure mode) of a variable and the corresponding
impact on the method attributes
How? - Each method attribute is categorized (see previous tools) as:
o Controlled (C)
o Experimental (X)
o Noise (N)
- Each failure is scored for :
o Probability
o Relevance
- Risk = Probability 𝒙𝒙 Relevance
Relevance 2 4 6 8 10
Prob
abili
ty
2 4 8 12 16 20
4 8 16 24 32 40
6 12 24 36 48 60
8 16 32 48 64 80
10 20 40 60 80 100
Stage 1 – Method design and understanding 15
Risk Assessment - FMEA
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Initial scoring Mitigation plan
Scoring after mitigation
Category Method Attributes Potential Failure mode Potential impact on method performance
Classification
Relevance
Probability Risk scoring Mitigation
Relevance after mitigation
Probability after mitigation
Risk scoring after mitigation
Instrument
6 4 24 6 2 12 6 4 24 6 2 12 10 4 40 10 2 20 10 4 40 10 2 20 10 4 40 10 2 20
10 4 40 10 2 20 10 4 40 10 2 20 6 4 24 6 2 12 6 4 24 6 2 12
6 4 24 6 2 12 6 4 24 6 2 12 10 4 40 10 2 20
6 4 24 6 2 12 6 4 24 6 2 12 10 4 40 10 2 20
10 4 40 10 2 20 8 4 32 8 4 32 6 4 24 6 2 12 6 4 24 6 2 12 6 4 24 6 2 12 6 4 24 6 2 12 10 4 40 10 2 20 10 4 40 10 2 20
8 4 32 8 4 32 6 6 36 6 6 8 4 32 8 2 16
6 4 24 6 2 12 6 4 24 6 2 12
8 4 32 8 2 16 8 4 32 8 2 16 6 4 24 6 2 12 8 4 32 8 2 16 6 4 24 6 2 12 6 4 24 6 2 12 8 4 32 8 2 16 6 4 24 6 2 12 10 4 40 10 2 20 10 6 60 10 4 40 8 4 32 8 2 16
Environment
10 2 20 10 2 20 4 2 8 2 2 4 6 2 12 6 2 12 8 2 16 8 2 16 6 2 12 6 2 12
Manpower
8 8 64 8 4 32
10 6 60 10 4 40 10 4 40 10 2 20
8 6 48 8 2 16 8 6 48 8 2 16 8 6 48 8 2 16 8 6 48 8 2 16
8 6 48 8 2 16 8 6 48 8 2 16
Measurement
8 6 48 8 2 16 8 6 48 8 2 16 8 6 48 8 2 16
10 4 40 10 2 20 10 4 40 10 2 20
0 0 0 0
Material
6 4 24 6 2 12 6 4 24 0
8 6 48 6 2 12 10 6 60 10 4 40
6 4 24 6 2 12 0 0
6 4 24 6 2 12 6 4 24 6 2 12 10 4 40 10 2 20
8 4 32 8 2 16 8 6 48 0 8 6 48 0 0 0 0 0 0 0 6 4 24 6 2 12 6 4 24 6 2 12
10 4 40 10 2 20 8 4 32 8 2 16
Method
8 6 48 0 8 4 32 0 6 4 24 0 6 4 24 0 8 4 32 8 2 16 8 4 32 6 2 12 8 4 32 0 10 10 100 0
6 4 24 6 2 12 6 4 24 6 2 12 10 10 100
0 0 6 6 36 0 6 6 36 0 10 10 100 0 6 6 36 6 4 24 6 6 36 0 8 6 48 0 6 4 24 6 2 12 6 4 24 0 8 4 32 8 2 16 8 4 32 8 2 16 8 4 32 8 2 16 6 6 36 6 4 24
Examples - Environment & method parameters
Stage 1 – Method design and understanding 16
Risk Assessment - FMEA
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
N° Method Attributes
Potential Failure mode
Potential impact on method performance Classification Relevance Probability Risk scoring
37 Temperature Temperature regulation Incorrect analysis C 2 2 4
38 Humidity Malfunction of the machine
Potential damage to the equipment, no data
acquired N 6 2 12
57 Separation voltage
Non Optimal voltage
Potential influence on Repeatability /
Reproducibility / Resolution
X 8 4 32
N° Mitigation Relevance after mitigation
Probability after mitigation
Risk scoring after mitigation
57 DoE 6 2 12
Stage 1 – Method design and understanding 17
Screening and Optimization - Design of Experiments
* Douglas Montgomery – Introduction to statistical quality control
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Definition “Design of experiments (DOE) is a test or series of tests in which purposeful changes
are made to the input variables of a process so that we may observe and identify
corresponding changes in the output response”* Objectives Evaluate effect of the most influential parameters
Identify the interactions between parameters
Optimize the best operating condition settings
In Practice:
Stage 1 – Method design and understanding 18
Screening and Optimization - Design of Experiments
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Example DOE n°1: DSD (Definitive Screening Design)
7 parameters studied -17 Experiments
Identification of Main parameters and some interactions
Run A B C D E F G
1 0 1 1 1 1 1 1
2 0 -1 -1 -1 -1 -1 -1
3 1 0 1 1 -1 1 -1
4 -1 0 -1 -1 1 -1 1
5 1 -1 0 1 1 -1 1
6 -1 1 0 -1 -1 1 -1
7 1 -1 -1 0 1 1 -1
8 -1 1 1 0 -1 -1 1
9 1 1 -1 -1 0 1 1
10 -1 -1 1 1 0 -1 -1
11 1 -1 1 -1 -1 0 1
12 -1 1 -1 1 1 0 -1
13 1 1 -1 1 -1 -1 0
14 -1 -1 1 -1 1 1 0
15 1 1 1 -1 1 -1 -1
16 -1 -1 -1 1 -1 1 1
17 0 0 0 0 0 0 0
Parameters A, B and C
statistically significant
Interactions B*C and C*C
and F*G significant
A B C
Stage 1 – Method design and understanding 19
Screening and Optimization - Design of Experiments
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Example DOE n° 2: FFD (Fractional Factorial Design)
5 parameters studied – 5 center conditions – 21 experiments
Tightening the range of each parameter definition of operating range (Impurity) (Purity)
Run A B C F G
1 0 0 0 0 0
2 1 -1 -1 1 1
3 1 -1 -1 -1 -1
4 -1 1 -1 -1 -1
5 1 -1 1 -1 1
6 0 0 0 0 0
7 1 1 -1 1 -1
8 1 1 -1 -1 1
9 -1 -1 -1 -1 1
10 -1 -1 1 1 1
11 0 0 0 0 0
12 -1 -1 1 -1 -1
13 1 -1 1 1 -1
14 1 1 1 -1 -1
15 -1 -1 -1 1 -1
16 0 0 0 0 0
17 1 1 1 1 1
18 -1 1 1 -1 1
19 -1 1 1 1 -1
20 -1 1 -1 1 1
21 0 0 0 0 0
A
G
The method is considered accurate within the range for which the accuracy profile is within the predefined acceptance limits This approach gives the guarantee that each future measurement of unknown samples is included within the tolerance limits with a given risk level (usually 5%)
Stage 2 – Method Performance Qualification 20
Validation of the assay – Total Error Approach
1 The β-expectation tolerance interval is the interval wherein each future measurement will fall with a defined probability β. It represents the location where β% of the future results are expected to lie.
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Acceptance Limits
Relative bias
β-expectation tolerance limits1
Use of E-Noval software - Arlenda
Stage 3 – Continued Method Verification 21
Control strategy
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Control strategy includes the use of control charts as follows Use a control sample in each analytical run
Trend the parameters of interest using control charts
Anticipation of drifts in the analytical methods.
Conclusions 22
ATP
Technology
Knowledge
Risk Assessment
Screening
Optimization
Robustness
Validation
Control Strategy
Acknowledgements
Method Development Team
Annick GERVAIS Marc JACQUEMIN
Cyrille CHERY
Aurélie DELANGLE
Christophe BEAUFAYS
Grégory SCHITTEKATTE
Julie BRAUN
Marc SPELEERS
Sandrine VAN LEUGENHAEGHE
All other team members
Statistician Team Bianca TEODORESCU
Dimitris GAYRAUD
Anastasia KOKOREVA
Carl JONE
Lance SMALLSHAW
Chinedu MADICHIE
23
Questions? 24
Back-up Slides
Stage 2 – Procedure Performance Qualification 26
From descriptive to predictive approach
accuracy standard deviation bias = +
accuracy precision trueness = +
Stage 1 – Method Design and Understanding 27
Screening and optimizing experiments and Robustness studies
Factorial designs
Factors effects/interaction characterization
Screening designs Influent factors determination/ranking
Eg – Plackett & Burman
Prediction in a domain
Response surface designs
Eg – Central Composite Design
Factorial/Response Surface Design
Main factor & interactions
Screening design Main influent factors determination
Controlled factors, ranges, responses from prior knowledge
or risk assessment
Use of statistics