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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.

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Presentation on theme: "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."— Presentation transcript:

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? Slide 1

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 Slide 3

4 Creating a Biotech Case Study: “A-Mab” Based on a monoclonal antibody drug substance and drug product – “A-Mab” – Humanized IgG 1 – 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 Why Monoclonal Antibody? Represents a significant number of products in development Good product and process experience in development and manufacture Slide 4

5 Outline and Intent of Case Study Content Structure Introduction Quality Attributes Upstream Downstream Drug Product Control Strategy Regulatory 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 Slide 5

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

7 Background and Linkage to ICH CMC Biotech Working Group

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

9 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 Slide 9

10 To illustrate options to achieve enhanced product and process understanding Demonstrate Industry’s vision for QbD as applied to biotech product realisation To illustrate options to achieve enhanced product and process understanding Demonstrate Industry’s vision for QbD as applied to biotech product realisation Overall Goals of the A-mAb Case Study 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 Slide 10

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 Slide 11

12 Linking Product and Process Understanding Slide 12

13 “Systematic Evaluation” 1.Use of prior platform knowledge and process risk assessments to identify CQAs and those steps that need additional experimentation. 2.Demonstration that laboratory scale models are representative of the full-scale operations. 3.DOE to determine CPPs & KPPs 4.Linkage of process parameters to product Quality Attributes to create a Design Spaces. 5.Final risk assessment and categorization of process parameters to develop control strategy. Tox 500L PhI/PhII 1,000L Optimization DOE I - 2L Optimization DOE II - 2L PhIII 5,000L Platform Knowledge Slide 13

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 Slide 14

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 Slide 15

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

17 CQA Risk Ranking & Filtering Approach 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 Severity = Impact x Uncertainty Slide 17

18 Impact Definition & Scale Impact (Scor e) Biological Activity or Efficacy a PK/PD a ImmunogenicitySafety Very High (20) Very significant change Significant change on PK ATA detected and confers limits on safety Irreversible AEs High (16) Significant change Moderate change with impact on PD ATA detected and confers limits on efficacy Reversible AEs Moderate (12) Moderate change Moderate change with no impact on PD ATA detected with in vivo effect that can be managed Manageable AEs Low (4) Acceptable change Acceptable change with no impact on PD ATA detected with minimal in vivo effect Minor, transient AEs None (2) No change No impact on PK or PD ATA not detected or ATA detected with no relevant in vivo effect No AEs AE = adverse event; ATA = anti-therapeutic antibody a Quantitative criteria should be established for biological activity/efficacy and PK/PD. Significance of the change is assessed relative to assay variability.

19 Uncertainty Definition & Scale Uncertainty (Score) Description (Variants and Host Related Impurities) Description (Process Raw Material) a 7 (Very High) No information (new variant) No information (new impurity) 5 (High) Published external literature for variant in related molecule. --- 3 (Moderate) Nonclinical or in vitro data with this molecule. Data (nonclinical, in vitro or clinical) from a similar class of molecule. Component used in previous processes 2 (Low) Variant has been present in material used in clinical trials. --- 1 (Very Low) Impact of specific variant established in Clinical Studies with this molecule. GRAS or studied in clinical trials GRAS = generally regarded as safe a Assesses the impact of a raw material as an impurity. Impact of the raw material on the product during manufacturing is assessed during process development.

20 AttributeCriticality Aggregation48 Glycosylation48 Deamidation4 Oxidation12 HCP24 DNA12 Protein A12 C-terminal lysine variants (charge variants) 4 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 Only a Subset of Quality Attributes is Evaluated in the Case Study Slide 20

21 A-Mab Case Study Upstream Process Development CMC Biotech Working Group

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

23 A-Mab Batch History ProcessScaleBatches Disposition Clinical Exposure Process 1 500 L2 Pre-clinical studies Process 1 1,000 L3 Phase 1 & 2 Product/process understanding. Process 2 5,000 L5 Phase 3 Confirm end-to-end process performance. Process 2 15,000 L2 Commercial launch supplies Confirm Design Space and Control Strategy at commercial scale Slide 23

24 Upstream Process Steps 1 & 2: Seed expansion Non-Critical based on Risk Assessment 1.No product is accumulated during seed expansion steps. 2.Prior knowledge with platform process (X-Mab, Y-Mab, and Z-Mab) shows that process performance is consistent and robust 3.Prior knowledge also demonstrates that process is flexible: successful use of multiple formats and scales (shake flasks, cell bags, spinners, bioreactors) 4.Risk Assessments of seed steps up to N-2 stage shows no impact on product quality Seed expansion process is not part of the Design Space and is not included in the registered detail Seed Culture StepsProduct Accumulation Risk of Impact to Product Quality Seed Expansion in Spinner or Shake Flasks NegligibleVery Low Seed Expansion in Wave Bag Bioreactor NegligibleVery Low Seed Expansion in Fixed Bioreactor NegligibleVery Low Slide 24

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 Slide 25

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

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 Slide 27

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

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

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

31 Control Strategy for Upstream Production Slide 31

32 Define Engineering Design Space for Production Bioreactor Analogous to the design space defined by scale- independent parameters, the engineering design space is 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 Slide 32

33 Engineering Design Space R a n d a l A l l e n Design Space for scale- independent parameters was developed using qualified scale- down models 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 2L Scale Design Space applicability to multiple operation scales demonstrated using PCA/MVA models 500 L – 25,000 L Slide 33

34 Lifecycle Approach to Validation Slide 34

35 Conclusions Quality by Design Approaches exemplified in the A-Mab upstream process Traditional Upstream Process Development Approaches Process understanding is based on prior knowledge and product specific experience. Process understanding is limited to product-specific empirical information Acceptable operating conditions expressed in terms of a multidimensional Design Space Acceptable operating ranges expressed as univariate proven acceptable ranges Systematic process development based on risk management tools. Process development based on established industry practices. Rational approach to establishing a control strategy supported by process/product understanding Product quality ensured by comprehensive control strategy Control Strategy based on prior experience and precedent Product quality controlled primarily by end-product testing Design space applicable to multiple operational scales Predictability and robustness of process performance at multiple scales is ensured by defining an engineering design space Process performance at multiple scales is demonstrated through empirical experience and end-product testing Lifecycle approach to process validation & continued process verification Continual improvement enabled Use of multivariate (MVA) approaches for process verification. Process validation based on limited and defined number of full-scale batches Primary focus on corrective action Process performance generally monitored using single variable approaches 35

36 Case Study Downstream Process and Drug Product CMC Biotech Working Group

37 Leverages Prior Knowledge with platform process to define Design Space Leverages prior knowledge and A-Mab results to justify a modular approach to viral clearance Downstream Process Design Space based on worst case scenario for A- Mab stability and worst case for viral inactivation Slide 37 Justification of two process changes post-launch : 1.Change resin for Protein A 2. Change from resin to membrane format for AEX Design Space based on multivariate model that links all three purifications steps (Protein A, AEX and CEX)

38 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. Slide 38

39 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 Acceptable Range Full range on axis is range explored in DOE 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 Slide 39

40 Step 1 Step 2 Step 3 Step 4 A-Mab Drug Substance Drug substance preparation/handling Compounding Sterile filtration Filling, stoppering and Capping Packaged A-Mab Drug Product Design spaces  Multiple or single lots/container  Frozen or unfrozen  Unclassified or class 100,000  50-1500 L  Stir time  Hold time  Tank configuration  50-1500 L  Hold time  Filter configuration  Reservoir pressure  Pumping configuration  Capper spring pressure Risk Assessment Design Space Control Strategy Drug product process steps exemplifying QbD supported by optimized formulation design 40

41 A- Mab Case Study Control Strategy CMC Biotech Working Group

42 Control Strategy: Linking Product and Process Understanding Slide 42

43 Control Strategy is based on a final Risk Assessment for each CQA Slide 43

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

45 Drug Substance & Product Release Testing is Only one Element of Control Strategy AttributeTestAcceptance CriteriaReleaseStability IdentityCEX Consistent with Ref Std and No New Peaks YesNo MonomerHPSECNLT 97%Yes AggregatesHPSECNMT 3%Yes Endotoxin (LAL)USP NMT 12.5 EU/mLYesNo Reduced testing in comparison with traditional approaches Example: Drug Substance Release Testing Slide 45

46 A-Mab Case Study Regulatory Considerations CMC Biotech Working Group

47 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 Slide 47

48 Linking Product and Process Understanding to Regulatory Commitments & Process Lifecycle  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 BLA/MAA Design space controls In-process tests Lot release tests Stability commitments Slide 48

49 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 Slide 49

50 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 Slide 50

51 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 Slide 51

52 Assessing Change: Scope of Change is Initially Assessed at the Unit Operation Level Degree to which outputs overlap denotes risk associated with change Movement w/in approved DS Changes outside approved DS Outputs from previous step & other material inputs SameMinor Change Major change Design Space Parameters SameSame, Data not in original filing New Step OutputsSame New Output from previous step DS Parameters Unchanged DS Parameters Changed Output MATERIAL INPUTS (Vendor, Scale, Technology) Unchanged Changed Risk Changes which represent more risk drive more extensive data collection Slide 52

53 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? Slide 53

54 What are Biosimilars? Biosimilars – Are biological products that claim to be similar to an innovator biological product – The innovator’s product is off-patent and no regulatory data protection remains – Are manufactured by a second manufacturer with new cell line, new process and new analytical methods – Require original data for approval

55 EMEA Approach for Biosimilar Medicines: Guideline on Similar Biological Medicinal Products (CHMP/437/04) Overall Approach – Similar biological medicinal products are not generic medicinal products – Comparability studies need to demonstrate the similar nature in terms of quality, safety, and efficacy Biosimilars will be different from the reference – It is not expected that the quality attributes in the biosimilar and reference product will be identical – The biosimilar product may exhibit a different safety profile (in terms of nature, seriousness, or incidence of adverse reactions)

56 US Definition of Biosimilarity Biosimilarity – The biological product is highly similar to the reference product not withstanding minor differences in clinically inactive components – There are no clinically meaningful differences between the biological product and the reference product in terms of the safety, purity, and potency of the product.

57 Criteria for Biosimilar EU Similar nature to reference product based on: – Quality – Safety – Efficacy Should be similar in molecular and biological terms Pharmaceutical form, strength, and route should be the same or if different additional data should be provided Class specific guidelines are referenced US – BPCA Highly similar to reference product based on: – Analytical studies – Animal studies – Clinical study or studies Utilizes same mechanism of action Conditions of use have been approved Route of administration, dosage form, and strength are the same Not all data elements may be necessary Allows for a determination of interchangeability

58 US Definition of Interchangeability The biological product may be substituted for the reference product without the intervention of the health care provider Determination of Interchangeability – Finding of biosimilarity and expectation to produce the same clinical result in any patient – For a product that is administered more than once The risk in terms of safety or diminished efficacy of alternating or switching between use of the biological product and the reference product is not greater than using the reference product alone

59 Example: Changing From a Chromatographic Ion Exchange Resin to a Membrane Replacing AEX Resin with Membrane  Anion Exchange Step Risk Assessment –Small scale DOE studies are performed to define the new design space –Step outputs meet the quality criteria –Design space supports original viral clearance claims  Replacement can be treated modularly Therefore:  No extended product characterization required  Consider impact on stability program  Reporting Category: –Data are in original submission : Annual update –Data are not in original submission: Report, but no pre-approval required for implementation Output from previous step DS Parameters Unchanged DS Parameters Changed Output MATERIAL INPUTS (Vendor, Scale, Technology) Unchanged Changed

60 Example: Changing the Drug Substance Manufacturing Site Moving to a Licensed Facility producing MAbs  Overall Process Risk Assessment for Each Unit Op –Process is unchanged –Site Engineering and Process fit confirmed –Design Spaces are valid –Step outputs confirmed to meet quality criteria Therefore:  Comparability demonstrated  Consider impact on stability program  Reporting Category: Report, but no/minimal pre- approval required for implementation Output from previous step DS Parameters Unchanged DS Parameters Changed Output MATERIAL INPUTS (Vendor, Scale, Technology) Unchanged Changed

61 Stimulus for Discussion An enhanced understanding of product attributes based on prior knowledge, preclinical and clinical data, linked to demonstrated understanding of the process can result in a more rational basis for design of the overall control strategy. – Understanding of CQAs and their linkage to critical process parameters and the design space allows clear identification of the parameters that may effect product safety or effectiveness, and thus require regulatory approval and oversight (i.e., are considered “regulatory commitments”). – Other parameters not associated with CQAs are controlled and monitored in the Quality system to ensure process and product consistency, but are not considered regulatory commitments. – The design space is based on development data generated from small scale lots up to commercial scale lots. This data in its entirety can form the basis for process qualification and validation when coupled with a program of continued process verification. Slide 61

62 Stimulus for Discussion An iterative, risk based approach for managing changes to the manufacturing process can be implemented by leveraging the original approach for creating a design space by linking process parameters to critical quality attributes. – Movement within a design space based on the lack of documented effect on critical quality attributes can be managed within the Quality system. – For movement outside of a design space, the outcome of the risk assessment exercise will facilitate determination of the data required to support the change. The level of regulatory oversight required for the change should be proportional to the level of risk identified Slide 62

63 Product Quality Criticality Assessment must be updated throughout product lifecycle Pharmacovigilance TOX Ph1 Ph 2a Ph 2b Ph 3 Process Development P r o cess C h a r ac t e r i z a t i on T ec h. T r a n s f e r FILE Launch Commercial Manufacturing Assessment of Criticality for Quality Attributes From Ilse Blumentals, GSK

64 Specification Limits Vs. Control Limits Differentiate Specification Limits from Control Limits Based on clinical relevance to provide assurance of safety and efficacy Based on process capability to provide assurance of process consistency Regulatory Commitment Design Space enabled Process Improvements enabled Managed through QMS Process Monitoring Continued Process Verification Product Understanding Process Understanding Design Space Control Space Specification Limits Control Limits CQA 1CQA 2CQA 3 Specifications are linked to clinical relevance not process capability Changes in specifications during product lifecycle reflect improved understanding of relationship between product and clinical relevance Specifications are linked to clinical relevance not process capability Changes in specifications during product lifecycle reflect improved understanding of relationship between product and clinical relevance From Ilse Blumentals, GSK

65 Elements Described in the Filing Risk Assessment Quality Attributes & the outcome of the criticality ranking Platform & historical knowledge Molecule specific Summary ProvidedRegulatory Commitments Process development history Platform Knowledge DOEs Engineering design requirements Design Space Controls Acceptable ranges or descriptive equation CPPs and WC-CPPs Criteria for critical Input/outputs Lot-to-Lot Product Testing Lifecycle Management Routine process monitoring Process verification with extended product characterization to support: ongoing process capability assessment continuous improvement comparability Slide 65

66 Step 2: Consider Impact to Other Unit Operations and Requirements for Extended Characterization Movement in approved DS Change outside approved DS Outputs from previous step & other material inputs SameMinor ChangeMajor change Design Space ParametersSameSame, Data not in original filing New Step OutputsSame Minor ChangesNew Other Unit Operations Affected Single Multiple Meets IP & Lot Release Criteria Yes Lot release met, some IPCs changed Comparability required __________________ Results Observed no Yes, __________ No changes Yes __________ minor changes Yes __________ new peaks Supportive non-clin/clin data no maybeYes No Reporting Notification Pre-approval Reporting Requirement Reporting requirements are based on the reassessment of risk posed by the change including results of new design and testing if necessary Slide 66


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