Presentation on theme: "Quality by Design Questions to Consider"— Presentation transcript:
1Quality 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?
2A-Mab: a Case Study in Bioprocess Development CMC Biotech Working Group
3Background and GoalTo 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 DesignStarted in August 20087 companies divided across the various sections into teamsGlaxoSmithKline, Abbott, Lilly, Pfizer, Genentech, MedImmune, AmgenJohn Berridge, Sam Venugopal, and Ken Seamon, co-facilitatorsCombination of regular telecon and in- person meetingsRelentless focus on science and risk-based approaches, not traditional waysColleagues from regulatory authorities provided unique insights to help stimulate our case study
4Creating a Biotech Case Study: “A-Mab” Based on a monoclonal antibody drug substance and drug product“A-Mab”Humanized IgG1IV Administered Drug (liquid)Expressed in Cho CellsTreatment of NHLPublicly and freely available as a teaching tool for industry and agenciesWhy Monoclonal Antibody?Represents a significant number of products in developmentGood product and process experience in development and manufacture
5Outline and Intent of Case Study ContentIntentStructureIntroductionQuality AttributesUpstreamDownstreamDrug ProductControl StrategyRegulatoryContains pieces/ sections that appear realistic and represent selected QbD principlesIllustrates the benefits of a QbD development approachInformation represents real data or appropriate fictitious dataNot a mock CTD-QNot a Gold Standard
6A-Mab is a Public Document Publication and SponsorshipCASSSISPEMaintain CMC Working Group interactionsCoordinate workshopsDevelop trainingFacilitate regulatory interactions
7Background and Linkage to ICH CMC Biotech Working Group7
8The New Qs underwrite the Quality Paradigm Product and Process UnderstandingQ8 (R1)Q9, Q10 Q11Quality Risk ManagementQ9Pharmaceutical Quality SystemQ1021st Century Quality ParadigmLower Risk OperationsInnovation and Continual ImprovementOptimized Change Management ProcessEnhanced Regulatory Approaches
9Historical Perspective Companies have always used science and risk based processes to develop new products and gain process understandingBut they often did not submit knowledge or information to regulatorsFocus on minimum controversy registration, launch and then complianceProcesses became fixedFuture GoalKnowledge management and risk management processes more extensively used, documented and submittedIntention of clearer communication of product and process understandingOpportunities for flexibility and post-approval process optimisationA challenge to do this wellLeads to opportunities
10Overall Goals of the A-mAb Case Study To illustrate options to achieve enhanced product and process understandingDemonstrate Industry’s vision for QbD as applied to biotech product realisationIdentification of CQAsExamples of CQA risk ranking toolsUse of prior knowledge and platform technologiesRisk based approachesUse of DoEs and statistical approachesTo identify CPPs and their linkage to CQAsApproaches to define and describe Design SpacesUpstream , Downstream and Drug ProductRational approach to defining a Control Strategy that reflects product & process understanding and riskRisk-based, lifecycle approach to managing continual improvement
11Our 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
13“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 & KPPsLinkage of process parameters to product Quality Attributes to create a Design Spaces.Final risk assessment and categorization of process parameters to develop control strategy.Tox500LPhI/PhII1,000LOptimization DOE I - 2LOptimizationDOE II - 2LPhIII5,000LPlatformKnowledge
14“Prior knowledge”Extensive use of prior knowledge and platform technologiesPrevious Mabs extensively leveraged to assist in risk assessmentsSeed Expansion from frozen WCB to N-1 Bioreactor not critical and not dependent on process formatUse engineering and process characterization to define design space for production bioreactorDemonstrate that Design Space is valid at multiple scales of operationParametric control of selected critical quality attributes
15Critical Quality Attributes (CQAs) One of the greatest challenges is identifying CQAsIn the case study, we focus on severity, not process capabilityRisk assessment is based on:prior knowledge (encompasses laboratory to clinic)nonclinical studies and biological characterization throughout clinical developmentclinical experienceKey Decisions:Assign a Criticality Level (continuum) instead of critical/non-criticalCriticality based on potential impact to safety and efficacyKey Issues that were discussed:Is there a cutoff for critical?What would make critical into non-critical?Linkage of QA ranking to Control Strategy
16Risk Assessment Approach used through A-MAb development lifecycle Process 2Process 1 2
17CQA Risk Ranking & Filtering Approach Severity = Impact x UncertaintySeverity = risk that attribute impacts safety or efficacyAssess relative safety and efficacy risks using two factors:Impact and UncertaintyImpact = impact on safety or efficacy, i.e. consequencesDetermined by available knowledge for attribute in questionMore severe impact = higher scoreUncertainty = uncertainty that attribute has expected impactDetermined by relevance of knowledge for each attributeHigh uncertainty = high scoreLow uncertainty = low score
18Impact Definition & Scale Impact (Score)Biological Activity or EfficacyaPK/PDaImmunogenicitySafetyVery High (20)Very significant changeSignificant change on PKATA detected and confers limits on safetyIrreversible AEsHigh(16)Significant changeModerate change with impact on PDATA detected and confers limits on efficacyReversible AEsModerate (12)Moderate changeModerate change with no impact on PDATA detected with in vivo effect that can be managedManageable AEsLow(4)Acceptable changeAcceptable change with no impact on PDATA detected with minimal in vivo effectMinor, transient AEsNone(2)No changeNo impact on PK or PDATA not detected or ATA detected with no relevant in vivo effectNo AEsAE = adverse event; ATA = anti-therapeutic antibodyaQuantitative criteria should be established for biological activity/efficacy and PK/PD. Significance of the change is assessed relative to assay variability.
19Uncertainty Definition & Scale Uncertainty (Score)Description (Variants and Host Related Impurities)Description (Process Raw Material)a7(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 processes2(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 trialsGRAS = generally regarded as safea 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.
20Only a Subset of Quality Attributes is Evaluated in the Case Study High CriticalityImpacted by multiple steps in the processExemplify linkage across multiple unit ops through Design Space and Control StrategyAttributeCriticalityAggregation48GlycosylationDeamidation4Oxidation12HCP24DNAProtein AC-terminal lysine variants (charge variants)High CriticalityPrimarily impacted by production BioRx ; no clearance or modification in DS or DPProvide example of Parametric ControlLow CriticalityImpacted by multiple steps in the processExemplify linkage to Control StrategyMedium CriticalityImpacted by multiple steps in DS but not affected by DPExemplify linkage to Control Strategy
21A-Mab Case Study Upstream Process Development CMC Biotech Working Group21
22Upstream ProcessLeverage Prior Knowledge with platform processRisk-based approach to demonstrate no impact to product qualityEngineering and process characterization to define Design Space and Control StrategyDemonstrate that Design Space is applicable to multiple scales of operationLifecycle validation approach that includes continued process verification22
23X A-Mab Batch History Process Scale Batches 500 L 2 1,000 L 3 5,000 L DispositionClinicalExposureProcess 1500 L2Pre-clinical studies1,000 L3Phase 1 & 2Product/process understanding.Process 25,000 L5Phase 3Confirm end-to-end process performance.15,000 LCommercial launch suppliesConfirm Design Space and Control Strategy at commercial scaleX
24Risk of Impact to Product Quality Upstream Process Steps 1 & 2: Seed expansion Non-Critical based on Risk AssessmentNo 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 robustPrior 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 qualitySeed Culture StepsProduct AccumulationRisk of Impact to Product QualitySeed Expansion in Spinner or Shake FlasksNegligibleVery LowSeed Expansion in Wave Bag BioreactorSeed Expansion in Fixed BioreactorSeed expansion process is not part of the Design Space and is not included in the registered detail
25N-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
26Upstream Process: Production Bioreactor Approach to Define a Design Space Leverage Prior Knowledge and A-Mab Development ExperienceData from other MAbsA-Mab DataProcess 1Process 1Process 2
27Example 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
28Example 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 studiesPotential impact to significantly affect a process attribute such as yield or viabilityPotential impact to QA with effective control of parameter or less robust control
29DOE Studies to Define Design Space: Identify CPPs and Interactions Example of DOE Results
30Classification of Process Parameters based on Risk Assessment Within Design SpaceRegulatory-SensitiveNot in Design SpaceManaged through QMS
32Define 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
33Engineering Design Space Design Space for scale-independent parameters was developed using qualified scale-down modelsDesign Space applicability to multiple operation scales demonstrated using PCA/MVA models500 L – 25,000 LRandlAe2L ScaleEngineering Design Space includes bioreactors of multiple scales and designs (2L -25K L)Based on keeping microenvironment experienced by cells equivalent between scalesCharacterization of bioreactor design, operation parameters, control capabilities, product quality and cell culture process performance provide basis for scientific understanding of the impact of scale/designIncludes bioreactor design considerations and scale-dependent process parameters linked to fluid dynamics and mass transfer
35ConclusionsQuality by Design Approaches exemplified in the A-Mab upstream processTraditional Upstream Process Development ApproachesProcess understanding is based on prior knowledge and product specific experience.Process understanding is limited to product-specific empirical informationAcceptable operating conditions expressed in terms of a multidimensional Design SpaceAcceptable operating ranges expressed as univariateproven acceptable rangesSystematic 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 understandingProduct quality ensured by comprehensive control strategyControl Strategy based on prior experience and precedentProduct quality controlled primarily by end-product testingDesign space applicable to multiple operational scalesPredictability and robustness of process performance at multiple scales is ensured by defining an engineering design spaceProcess performance at multiple scales is demonstrated through empirical experience and end-product testingLifecycle approach to process validation & continued process verificationContinual improvement enabledUse of multivariate (MVA) approaches for process verification.Process validation based on limited and defined number of full-scale batchesPrimary focus on corrective actionProcess performance generally monitored using single variable approaches
36Case Study Downstream Process and Drug Product CMC Biotech Working Group36
37Leverages Prior Knowledge with platform process to define Design Space Downstream ProcessLeverages Prior Knowledge with platform process to define Design SpaceDesign Space based on worst case scenario for A-Mab stability and worst case for viral inactivationLeverages prior knowledge and A-Mab results to justify a modular approach to viral clearanceDesign 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 AEX3737
38Multi-step Design Space for Chromatography Columns Design Space is defined based on model that links performance of the 3 purification stepsHCP clearance exampleModel based on results of individual DOE studiesNo extrapolation of parameters outside ranges tested allowed in design spaceNo interaction of parameters from different steps assumed.Assumption was experimentally verified.99.5% prediction interval added to mean predicted HCP levelsTo reflect high level of assurance specifications will be met if process operated in design space.
39Acceptable 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 constrainedAcceptable RangeCase 2: Constraining Protein A and CEX ranges allows full ranges for AEXCase 3: If full range allowed in Protein A and AEX, CEX is constrainedFull range on axis is range explored in DOE
40Packaged A-Mab Drug Product Drug product process steps exemplifying QbD supported by optimized formulation designStep 1Step 2Step 3Step 4A-Mab Drug SubstanceDrug substance preparation/handlingCompoundingSterile filtrationFilling, stoppering and CappingPackaged A-Mab Drug ProductDesign spacesMultiple or single lots/containerFrozen or unfrozenUnclassified or class 100,000LStir timeHold timeTank configurationFilter configurationReservoir pressurePumping configurationCapper spring pressureRisk AssessmentDesign SpaceControl Strategy
41A- Mab Case Study Control Strategy CMC Biotech Working Group41
42Control Strategy: Linking Product and Process Understanding
43Control Strategy is based on a final Risk Assessment for each CQA
44Example of Control Strategy for selected CQAs CriticalityProcess CapabilityTestingSpec LimitsOther Control ElementsAggregateHigh (48)High RiskDS and DP releaseYesParametric Control of DS/DP stepsaFucosylationLow RiskDS Process MonitoringParametric Control of Production BioRxHost Cell ProteinHigh (24)Very Low RiskCharact.ComparabilityParametric Control of Prod BioRx, ProA, pH inact, CEX , AEX stepsDNAParametric Control of Prod Biox and AEX StepsDeamidated IsoformsLow (12)NoFrom A-Mab Case Study
45Drug Substance & Product Release Testing is Only one Element of Control Strategy Example: Drug Substance Release TestingAttributeTestAcceptance CriteriaReleaseStabilityIdentityCEXConsistent with Ref Stdand No New PeaksYesNoMonomerHPSECNLT 97%AggregatesNMT 3%Endotoxin (LAL)USP <85>NMT 12.5 EU/mLReduced testing in comparison with traditional approaches
46A-Mab Case Study Regulatory Considerations CMC Biotech Working Group46
47Regulatory 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 commitmentsDescribe how elements not covered by license commitments will be addressed in the Quality SystemDescribe how development and monitoring of process knowledge throughout the product’s lifecycle will differ from traditional process validation activities and lead to continued improvementPropose a general risk-based approach for managing post-approval changes within and outside the design space and provide specific examples47
48Linking Product and Process Understanding to Regulatory Commitments & Process Lifecycle BLA/MAAThe regulatory filing presents a summary of the risk assessment methodology and accumulated process & product knowledgeRegulatory commitments are the critical elements of the overall control strategy developed based on the outcomes of the overall risk assessmentsThe overall approach to risk-based process management becomes the basis for lifecycle and change managementDesign space controlsIn-process testsLot release testsStability commitments
49Justification of the Design Space The overall knowledge that justifies the Design Space is based onProduct and process specific knowledgeHistorical and platform dataSummary 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 sectionConclusions will be supported by process characterization reports available upon request or inspectionThe 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 applicationThe design space is not “validated” at manufacturing scale in the traditional sense
50Lifecycle Approach to Process Validation Begins during development and continues post-launchBuilds on knowledge from multiple scalesDeparture from the traditional 3-batch validation approach prior to submissionProcess validation encompasses cumulative knowledgeIncludes continued process verificationTo demonstrate validity of Design SpaceTo maintain validity of models
51Lifecycle Management of Process Improvements & Changes Movements within the design space are managed without regulatory notificationChanges outside the design space will involve a regulatory actionFrom notification to pre-approval depending on risk assessmentSpecific examples addressed in case studyScale-up of production cultureReplace new chromatography resin with similar from same vendorReplace new chromatography resin with new technology (membrane)Manufacturing Site Changes for DS and DP
52Assessing Change: Scope of Change is Initially Assessed at the Unit Operation Level Movement w/in approved DSChanges outside approved DSOutputs from previous step & other material inputsSameMinor ChangeMajor changeDesign Space ParametersSame,Data not in original filingNewStep OutputsOutput from previous stepUnchangedMATERIAL INPUTS (Vendor, Scale, Technology)ChangedDS Parameters UnchangedDS Parameters ChangedOutputOutputOutputDegree to which outputs overlap denotes risk associated with changeRiskChanges which represent more risk drive more extensive data collection
53Quality 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?
54What are Biosimilars? Biosimilars Are biological products that claim to be similar to an innovator biological productThe innovator’s product is off-patent and no regulatory data protection remainsAre manufactured by a second manufacturer with new cell line, new process and new analytical methodsRequire original data for approvalWhat are the characteristics of biosimilars?First of all a biosimilar medicine is a biological product that claims to be similar to an innovator’s biological product. It is also a product that claims to be similar once the innovator’s product is off patent.There is no additional regulatory data protection.The active ingredients of chemical medicines can sometimes be purchased as a commodity by those developing generics, whereas biosimilar manufacturers are starting totally from scratch with different cell-line constructs and processes.However, it is important to note that the biosimilar is manufactured by a second manufacturer and not the innovator manufacturer. Due to the protection of trade secrets which include manufacturing data and proprietary information, the 2nd manufacturer has no access to cell lines with which the innovator’s products are produced and therefore has to develop his own unique cell line that has its own unique manufacturing process and its own unique analytical methods. This will become important as we get further into the discussion.It is therefore necessary to establish original data to support pre-clinical and clinical efficacy. In contrast to generic drugs, these drugs will require clinical data to support their approval.
55EMEA Approach for Biosimilar Medicines: Guideline on Similar Biological Medicinal Products (CHMP/437/04)Overall ApproachSimilar biological medicinal products are not generic medicinal productsComparability studies need to demonstrate the similar nature in terms of quality, safety, and efficacyBiosimilars will be different from the referenceIt is not expected that the quality attributes in the biosimilar and reference product will be identicalThe biosimilar product may exhibit a different safety profile (in terms of nature, seriousness, or incidence of adverse reactions)
56US Definition of Biosimilarity The biological product is highly similar to the reference product not withstanding minor differences in clinically inactive componentsThere are no clinically meaningful differences between the biological product and the reference product in terms of the safety, purity, and potency of the product.
57Criteria for Biosimilar EUUS – BPCASimilar nature to reference product based on:QualitySafetyEfficacyShould be similar in molecular and biological termsPharmaceutical form, strength, and route should be the same or if different additional data should be providedClass specific guidelines are referencedHighly similar to reference product based on:Analytical studiesAnimal studiesClinical study or studiesUtilizes same mechanism of actionConditions of use have been approvedRoute of administration, dosage form, and strength are the sameNot all data elements may be necessaryAllows for a determination of interchangeability
58US Definition of Interchangeability The biological product may be substituted for the reference product without the intervention of the health care providerDetermination of InterchangeabilityFinding of biosimilarity and expectation to produce the same clinical result in any patientFor a product that is administered more than onceThe 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
59Example: Changing From a Chromatographic Ion Exchange Resin to a Membrane Replacing AEX Resin with MembraneAnion Exchange Step Risk AssessmentSmall scale DOE studies are performed to define the new design spaceStep outputs meet the quality criteriaDesign space supports original viral clearance claimsReplacement can be treated modularlyTherefore:No extended product characterization requiredConsider impact on stability programReporting Category:Data are in original submission : Annual updateData are not in original submission: Report, but no pre-approval required for implementationOutput from previous stepUnchangedMATERIAL INPUTS (Vendor, Scale, Technology)ChangedDS Parameters UnchangedDS Parameters ChangedOutputOutputOutput59
60Example: Changing the Drug Substance Manufacturing Site Moving to a Licensed Facility producing MAbsOverall Process Risk Assessment for Each Unit OpProcess is unchangedSite Engineering and Process fit confirmedDesign Spaces are validStep outputs confirmed to meet quality criteriaTherefore:Comparability demonstratedConsider impact on stability programReporting Category: Report, but no/minimal pre-approval required for implementationOutput from previous stepUnchangedMATERIAL INPUTS (Vendor, Scale, Technology)ChangedDS Parameters UnchangedDS Parameters ChangedOutputOutputOutput60
61Stimulus 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.
62Stimulus 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
63Product Quality Criticality Assessment must be updated throughout product lifecycle PharmacovigilanceTOXPh1Ph 2aPh 2bPh 3LaunchCommercialManufacturingcessech.ProcessDevelopmentProTFILECharacterizationTransferAssessment of Criticality for Quality AttributesFrom Ilse Blumentals, GSK
64Specification Limits Vs. Control Limits Differentiate Specification Limits from Control LimitsBased on clinical relevanceto provide assurance ofsafety and efficacyBased on process capabilityprocess consistencyRegulatory CommitmentDesign Space enabledProcess Improvements enabledManaged through QMSProcess MonitoringContinued Process VerificationProduct UnderstandingProcess UnderstandingControl SpaceSpecification LimitsControl LimitsDesign SpaceCQA 1CQA 2CQA 3Specifications are linked to clinical relevance not process capabilityChanges in specifications during product lifecycle reflect improved understanding of relationship between product and clinical relevanceFrom Ilse Blumentals, GSK
65Elements Described in the Filing Summary ProvidedRegulatory CommitmentsLot-to-Lot Product TestingRisk AssessmentProcess development historyPlatform KnowledgeDOEsEngineering design requirementsLifecycle ManagementRoutine process monitoringProcess verification with extended product characterization to support:ongoing process capability assessmentcontinuous improvementcomparabilityQuality Attributes & the outcome of the criticality rankingPlatform & historical knowledgeMolecule specificDesign Space ControlsAcceptable ranges or descriptive equationCPPs and WC-CPPsCriteria for critical Input/outputs
66Change outside approved DS Step 2: Consider Impact to Other Unit Operations and Requirements for Extended CharacterizationMovement in approved DSChange outside approved DSOutputs from previous step & other material inputsSameMinor ChangeMajor changeDesign Space ParametersSame,Data not in original filingNewStep OutputsMinor ChangesOther Unit Operations AffectedSingleMultipleMeets IP & Lot Release CriteriaYesLot release met, some IPCs changedComparability required__________________ Results ObservednoYes,__________ No changes__________ minor changes__________ new peaksSupportive non-clin/clin datamaybeReporting requirements are based on the reassessment of risk posed by the change including results of new design and testing if necessaryNo Reporting Notification Pre-approvalReporting Requirement