Quality by Design

1. Quality by Design Questions to Consider • How can we maximize the benefits to the industry and other stakeholders? • How can we ensure that this will speed up development and reduce the investment for process and product development? • QbD may be implemented in parts or as part of a development philosophy. How can this be implemented during early development? • What is the best way to ensure that smaller enterprises can benefit from the work going on with QbD and facilitate innovation?

2. A-Mab: a Case Study in Bioprocess Development CMC Biotech Working Group

3. Background and Goal • To create a publicly available case study that helps translate the ‘what’ of ICH guidelines into practical ‘how’ for a biological molecule with emphasis on Quality by Design • Started in August 2008 • 7 companies divided across the various sections into teams • GlaxoSmithKline, Abbott, Lilly, Pfizer, Genentech, MedImmune, Amgen • John Berridge, Sam Venugopal, and Ken Seamon, co-facilitators • Combination of regular telecon and in- person meetings • Relentless focus on science and risk-based approaches, not traditional ways • Colleagues from regulatory authorities provided unique insights to help stimulate our case study

4. Creating a Biotech Case Study:“A-Mab” • Based on a monoclonal antibody drug substance and drug product • “A-Mab” • Humanized IgG1 • IV Administered Drug (liquid) • Expressed in Cho Cells • Treatment of NHL • Publicly and freely available as a teaching tool for industry and agencies • Why Monoclonal Antibody? • Represents a significant number of products in development • Good product and process experience in development and manufacture

5. Outline and Intent of Case Study Content Intent Contains pieces/ sections that appear realistic and represent selected QbD principles Illustrates the benefits of a QbD development approach Information represents real data or appropriate fictitious data Not a mock CTD-Q Not a Gold Standard • Structure • Introduction • Quality Attributes • Upstream • Downstream • Drug Product • Control Strategy • Regulatory

6. A-Mab is a Public Document • Publication and Sponsorship • CASSS http://www.casss.org • ISPE http://www.ispe.org • Maintain CMC Working Group interactions • Coordinate workshops • Develop training • Facilitate regulatory interactions

7. Background and Linkage to ICH CMC Biotech Working Group

8. The New Qs underwrite the Quality Paradigm Product and Process Understanding Q8 (R1) Q9, Q10 Q11 Pharmaceutical Quality System Q10 Quality Risk Management Q9 21st Century Quality Paradigm Lower Risk Operations Innovation and Continual Improvement Optimized Change Management Process Enhanced Regulatory Approaches

9. Historical Perspective Historical Perspective • Companies have always used science and risk based processes to develop new products and gain process understanding • But they often did not submit knowledge or information to regulators • Focus on minimum controversy registration, launch and then compliance • Processes became fixed Future Goal • Knowledge management and risk management processes more extensively used, documented and submitted • Intention of clearer communication of product and process understanding • Opportunities for flexibility and post-approval process optimisation • A challenge to do this well • Leads to opportunities

10. Overall Goals of the A-mAb Case Study To illustrate options to achieve enhanced product and process understanding Demonstrate Industry’s vision for QbD as applied to biotech product realisation • Identification of CQAs • Examples of CQA risk ranking tools • Use of prior knowledge and platform technologies • Risk based approaches • Use of DoEs and statistical approaches • To identify CPPs and their linkage to CQAs • Approaches to define and describe Design Spaces • Upstream , Downstream and Drug Product • Rational approach to defining a Control Strategy that reflects product & process understanding and risk • Risk-based, lifecycle approach to managing continual improvement

11. Our Focus is on the key differentiators of QbD (from ICH Q8R1) • An enhanced, quality by design approach to product development would additionally include the following elements: • A systematic evaluation, understanding and refining of the formulation and manufacturing process, including; • Identifying, through e.g., prior knowledge, experimentation, and risk assessment, the material attributes and process parameters that can have an effect on product CQAs; • Determining the functional relationships that link material attributes and process parameters to product CQAs; • Using the enhanced product and process understanding in combination with quality risk management to establish an appropriate control strategy that includes proposals for a design space(s) and/or real-time release testing

12. Linking Product and Process Understanding

13. Platform Knowledge Tox 500L PhI/PhII 1,000L PhIII 5,000L Optimization DOE I - 2L Optimization DOE II - 2L “Systematic Evaluation” • Use of prior platform knowledge and process risk assessments to identify CQAs and those steps that need additional experimentation. • Demonstration that laboratory scale models are representative of the full-scale operations. • DOE to determine CPPs & KPPs • Linkage of process parameters to product Quality Attributes to create a Design Spaces. • Final risk assessment and categorization of process parameters to develop control strategy.

14. “Prior knowledge” • Extensive use of prior knowledge and platform technologies • Previous Mabs extensively leveraged to assist in risk assessments • Seed Expansion from frozen WCB to N-1 Bioreactor not critical and not dependent on process format • Use engineering and process characterization to define design space for production bioreactor • Demonstrate that Design Space is valid at multiple scales of operation • Parametric control of selected critical quality attributes

15. Critical Quality Attributes (CQAs) • One of the greatest challenges is identifying CQAs • In the case study, we focus on severity, not process capability • Risk assessment is based on: • prior knowledge (encompasses laboratory to clinic) • nonclinical studies and biological characterization throughout clinical development • clinical experience • Key Decisions: • Assign a Criticality Level (continuum) instead of critical/non-critical • Criticality based on potential impact to safety and efficacy • Key Issues that were discussed: • Is there a cutoff for critical? • What would make critical into non-critical? • Linkage of QA ranking to Control Strategy

16. Process 2 Process 1 2 Risk Assessment Approach used through A-MAb development lifecycle

17. CQA Risk Ranking & Filtering Approach Severity = Impact x Uncertainty • Severity = risk that attribute impacts safety or efficacy • Assess relative safety and efficacy risks using two factors: • Impact and Uncertainty • Impact = impact on safety or efficacy, i.e. consequences • Determined by available knowledge for attribute in question • More severe impact = higher score • Uncertainty = uncertainty that attribute has expected impact • Determined by relevance of knowledge for each attribute • High uncertainty = high score • Low uncertainty = low score

18. Impact Definition & Scale

19. Uncertainty Definition & Scale

20. Only a Subset of Quality Attributes is Evaluated in the Case Study High Criticality Impacted by multiple steps in the process Exemplify linkage across multiple unit ops through Design Space and Control Strategy High Criticality Primarily impacted by production BioRx ; no clearance or modification in DS or DP Provide example of Parametric Control Low Criticality Impacted by multiple steps in the process Exemplify linkage to Control Strategy Medium Criticality Impacted by multiple steps in DS but not affected by DP Exemplify linkage to Control Strategy

21. A-Mab Case StudyUpstream Process Development CMC Biotech Working Group

22. Upstream Process Leverage Prior Knowledge with platform process Risk-based approach to demonstrate no impact to product quality Engineering and process characterization to define Design Space and Control Strategy Demonstrate that Design Space is applicable to multiple scales of operation • Lifecycle validation approach that includes continued process verification

23. A-Mab Batch History X

24. Upstream Process Steps 1 & 2: Seed expansionNon-Critical based on Risk Assessment Seed expansion process is not part of the Design Space and is not included in the registered detail • No product is accumulated during seed expansion steps. • Prior knowledge with platform process (X-Mab, Y-Mab, and Z-Mab) shows that process performance is consistent and robust • Prior knowledge also demonstrates that process is flexible: successful use of multiple formats and scales (shake flasks, cell bags, spinners, bioreactors) • Risk Assessments of seed steps up to N-2 stage shows no impact on product quality

25. N-1 Seed Impacts Process Performance but NOT Product Quality Seed expansion process is not part of the Design Space and is not included in the registered detail

26. Upstream Process: Production Bioreactor Approach to Define a Design Space Leverage Prior Knowledge and A-Mab Development Experience Data from other MAbs A-Mab Data Process 1 Process 1 Process 2

27. Example of Risk Assessment Approach to Process Characterization Step 1. Use a Fish-bone (Ishikawa ) diagram to identify parameters and attributes that might affect product quality and process performance

28. Example of Risk Assessment Approach Step 2: Rank parameters and attributes from Step 1 based on severity of impact and control capability. Identify interactions to include in DOE studies Potential impact to significantly affect a process attribute such as yield or viability Potential impact to QA with effectivecontrolof parameter or less robust control

29. DOE Studies to Define Design Space: Identify CPPs and Interactions Example of DOE Results

30. Classification of Process Parameters based on Risk Assessment Within Design Space Regulatory-Sensitive Not in Design Space Managed through QMS

31. Control Strategy for Upstream Production

32. Define Engineering Design Space for Production Bioreactor Analogous to the design space defined by scale-independent parameters, the engineering design spaceis a multidimensional combination of bioreactor design characteristics and engineering parameters that provide assurance that the production bioreactor performance will be robust and consistent and will meet product quality targets

33. Design Space applicability to multiple operation scales demonstrated using PCA/MVA models 500 L – 25,000 L n e l l A l a d n a R Engineering Design Space Design Space for scale-independent parameters was developed using qualified scale-down models 2L Scale • Engineering Design Space includes bioreactors of multiple scales and designs (2L -25K L) • Based on keeping microenvironment experienced by cells equivalent between scales • Characterization of bioreactor design, operation parameters, control capabilities, product quality and cell culture process performance provide basis for scientific understanding of the impact of scale/design • Includes bioreactor design considerations and scale-dependent process parameters linked to fluid dynamics and mass transfer

34. Lifecycle Approach to Validation

35. Case StudyDownstream Process and Drug Product CMC Biotech Working Group

36. Downstream Process Leverages Prior Knowledge with platform process to define Design Space Design Space based on worst case scenario for A-Mab stability and worst case for viral inactivation Leverages prior knowledge and A-Mab results to justify a modular approach to viral clearance Design Space based on multivariate model that links all three purifications steps (Protein A, AEX and CEX) • Justification of two process changes post-launch: • Change resin for Protein A 2. Change from resin to membrane format for AEX

37. Multi-step Design Space for Chromatography Columns • Design Space is defined based on model that links performance of the 3 purification steps • HCP clearance example • Model based on results of individual DOE studies • No extrapolation of parameters outside ranges tested allowed in design space • No interaction of parameters from different steps assumed. • Assumption was experimentally verified. • 99.5% prediction interval added to mean predicted HCP levels • To reflect high level of assurance specifications will be met if process operated in design space.

38. Acceptable Range Acceptable range for each step depends on acceptable ranges for other two steps Case 1: If full range allowed in Protein A and CEX, AEX is constrained Case 2: Constraining Protein A and CEX ranges allows full ranges for AEX Case 3: If full range allowed in Protein A and AEX, CEX is constrained Full range on axis is range explored in DOE

39. Drug product process steps exemplifying QbD supported by optimized formulation design A-Mab Drug Substance Design spaces • Multiple or single lots/container • Frozen or unfrozen • Unclassified or class 100,000 Drug substance preparation/handling Step 1 Compounding • 50-1500 L • Stir time • Hold time • Tank configuration Step 2 Risk Assessment Design Space Control Strategy Sterile filtration • 50-1500 L • Hold time • Filter configuration Step 3 • Reservoir pressure • Pumping configuration • Capper spring pressure Step 4 Filling, stoppering and Capping Packaged A-Mab Drug Product

40. A- Mab Case StudyControl Strategy CMC Biotech Working Group

41. Control Strategy: Linking Product and Process Understanding

42. Control Strategy is based on a final Risk Assessment for each CQA

43. Example of Control Strategy for selected CQAs From A-Mab Case Study www.casss.org

44. Drug Substance & Product Release Testing is Only one Element of Control Strategy Example: Drug Substance Release Testing Reduced testing in comparison with traditional approaches

45. A-Mab Case StudyRegulatory Considerations CMC Biotech Working Group

46. Regulatory Aspects of the Case Study • Objectives of the Regulatory section of the case study: • Describe information that is provided in the filing to convey process & product understanding -vs- license commitments • Describe how elements not covered by license commitments will be addressed in the Quality System • Describe how development and monitoring of process knowledge throughout the product’s lifecycle will differ from traditional process validation activities and lead to continued improvement • Propose a general risk-based approach for managing post-approval changes within and outside the design space and provide specific examples

47. Linking Product and Process Understanding to Regulatory Commitments & Process Lifecycle BLA/MAA • The regulatory filing presents a summary of the risk assessment methodology and accumulated process & product knowledge • Regulatory commitments are the critical elements of the overall control strategy developed based on the outcomes of the overall risk assessments • The overall approach to risk-based process management becomes the basis for lifecycle and change management Design space controls In-process tests Lot release tests Stability commitments

48. Justification of the Design Space • The overall knowledge that justifies the Design Space is based on • Product and process specific knowledge • Historical and platform data • Summary of the knowledge that justifies the outcomes of the risk assessment and the limits for design space will be presented in the Process Development History section • Conclusions will be supported by process characterization reports available upon request or inspection • The design space may be applied across many scales, or pieces of equipment (different bioreactors, columns of different widths), provided data sufficient justification is provided in the application • The design space is not “validated” at manufacturing scale in the traditional sense

49. Lifecycle Approach to Process Validation • Begins during development and continues post-launch • Builds on knowledge from multiple scales • Departure from the traditional 3-batch validation approach prior to submission • Process validation encompasses cumulative knowledge • Includes continued process verification • To demonstrate validity of Design Space • To maintain validity of models

50. Lifecycle Management of Process Improvements & Changes • Movements within the design space are managed without regulatory notification • Changes outside the design space will involve a regulatory action • From notification to pre-approval depending on risk assessment • Specific examples addressed in case study • Scale-up of production culture • Replace new chromatography resin with similar from same vendor • Replace new chromatography resin with new technology (membrane) • Manufacturing Site Changes for DS and DP