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
Structure
Introduction
Quality Attributes
Upstream
Downstream
Drug Product
Control Strategy
Regulatory
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

6. A-Mab is a Public Document
Publication and Sponsorship
CASSS
ISPE
Maintain CMC Working Group interactions
Coordinate workshops
Develop training
Facilitate regulatory interactions

7. Background and Linkage to ICH
CMC Biotech Working Group 7

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

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

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. “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.
Tox
500L
PhI/PhII
1,000L
Optimization DOE I - 2L
Optimization
DOE II - 2L
PhIII
5,000L
Platform
Knowledge

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

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
Impact (Score)
Biological Activity or Efficacya
PK/PDa
Immunogenicity
Safety
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
aQuantitative 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. 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
Attribute
Criticality
Aggregation 48
Glycosylation
Deamidation 4
Oxidation 12
HCP 24
DNA
Protein A
C-terminal lysine variants (charge variants)
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 Study Upstream Process Development
CMC Biotech Working Group 21

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 22

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

24. Risk of Impact to Product Quality
Upstream Process Steps 1 & 2: Seed expansion Non-Critical based on Risk Assessment
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
Seed Culture Steps
Product Accumulation
Risk of Impact to Product Quality
Seed Expansion in Spinner or Shake Flasks
Negligible
Very Low
Seed Expansion in Wave Bag Bioreactor
Seed Expansion in Fixed Bioreactor
Seed expansion process is not part of the Design Space and is not included in the registered detail

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 effective control of 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 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

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

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

37. Leverages Prior Knowledge with platform process to define Design Space
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 37

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.

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
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

40. Packaged A-Mab Drug Product
Drug product process steps exemplifying QbD supported by optimized formulation design
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 L
Stir time
Hold time
Tank configuration
Filter configuration
Reservoir pressure
Pumping configuration
Capper spring pressure
Risk Assessment
Design Space
Control Strategy

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

42. Control Strategy: Linking Product and Process Understanding

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

44. Example of Control Strategy for selected CQAs
Criticality
Process Capability
Testing
Spec Limits
Other Control Elements
Aggregate
High (48)
High Risk
DS and DP release
Yes
Parametric Control of DS/DP steps
aFucosylation
Low Risk
DS Process Monitoring
Parametric Control of Production BioRx
Host Cell Protein
High (24)
Very Low Risk
Charact.
Comparability
Parametric Control of Prod BioRx, ProA, pH inact, CEX , AEX steps
DNA
Parametric Control of Prod Biox and AEX Steps
Deamidated Isoforms
Low (12) No
From A-Mab Case Study

45. Drug Substance & Product Release Testing is Only one Element of Control Strategy
Example: Drug Substance Release Testing
Attribute
Test
Acceptance Criteria
Release
Stability
Identity
CEX
Consistent with Ref Std
and No New Peaks
Yes No
Monomer
HPSEC
NLT 97%
Aggregates
NMT 3%
Endotoxin (LAL)
USP <85>
NMT 12.5 EU/mL
Reduced testing in comparison with traditional approaches

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

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 47

48. 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

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

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

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

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

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?

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
What 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.

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
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 BiosimilarEU
US – BPCA
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
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
Unchanged
MATERIAL INPUTS (Vendor, Scale, Technology)
Changed
DS Parameters Unchanged
DS Parameters Changed
Output
Output
Output 59

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
Unchanged
MATERIAL INPUTS (Vendor, Scale, Technology)
Changed
DS Parameters Unchanged
DS Parameters Changed
Output
Output
Output 60

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.

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

63. Product Quality Criticality Assessment must be updated throughout product lifecycle
Pharmacovigilance
TOX
Ph1
Ph 2a
Ph 2b
Ph 3
Launch
Commercial
Manufacturing c e s s e c h . P r o c e s s D e v e l o p m e n t P r o T
FILE C h a r a c t e r i z a t i o n T r a n s f e r
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
process consistency
Regulatory Commitment
Design Space enabled
Process Improvements enabled
Managed through QMS
Process Monitoring
Continued Process Verification
Product Understanding
Process Understanding
Control Space
Specification Limits
Control Limits
Design Space
CQA 1
CQA 2
CQA 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
From Ilse Blumentals, GSK

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

66. Change outside approved DS
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
Same
Minor Change
Major change
Design Space Parameters
Same,
Data not in original filing
New
Step Outputs
Minor Changes
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
__________ minor changes
__________ new peaks
Supportive non-clin/clin data
maybe
Reporting requirements are based on the reassessment of risk posed by the change including results of new design and testing if necessary
No Reporting Notification Pre-approval
Reporting Requirement