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