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Lifecycle Approach to Process ValidationBest Practices and Strategies
July 19th, 2016
Patrick Donohue, Drug Product Development, Janssen R&D
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DisclaimerThe contents of this presentation are my personal views and do not reflect those of Johnson & Johnson or its affiliates.
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Do we really know how to link process capability with in vivo performance?
Adapted from Ken Hinds, PhD (PDA/FDA JRC, 2014)
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Stage 1: Focus on Criticality
Are all CQAs (CPPs/CMAs) created equally?
– 2011 FDA Process Validation Guidance: “With a lifecycle approach to process validation that employs risk based decision making throughout that lifecycle, the perception of criticality as a continuum rather than a binary state is more useful.”
Adapted from Ken Hinds, PhD (PDA/FDA JRC, 2014)
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Focus on Criticality
Definition: Criticality is defined as a classification of an item, (e.g., process, equipment, parameter) that expresses significance given to the impact of that item, and should be controlled or monitored to ensure product quality, safety or efficacy. (PDA TR54)
Criticality = Impact x Uncertainty
Impact scored via science and prior knowledge Uncertainty scored via statistics and process experience Criticality as a guidepost for monitoring and control
Adapted from Dr Mike Long, MBB (PDA/FDA JRC, 2014)
Focus on Criticality
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Control strategy driven by relative risk of process parameters and material attributes
Objectives:– Understand how process
parameter input variation ties to product output variation
– Understand how starting material input variation ties to product output variation
– Design a manufacturing process that consistently delivers acceptable product output variation
Adapted from Ken Hinds, PhD (PDA/FDA JRC, 2014)
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Focus on Risk
Do all risks require the same level of mitigation/control?
– PDA TR54: “application of risk management activities relevant to the type and level of risk inherent in each process will enable product realization, establish and maintain a state of control, and facilitate process improvement.”
Adapted from Ken Hinds, PhD (PDA/FDA JRC, 2014)
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Focus on Risk
Definition: Risk is defined as the combination of the probability of occurrence of harm and the severity of that harm (ICH Q9)
Risk = Severity x Occurrence
Each attribute identified as critical to quality should be assessed by severity and occurrence
Severity scored via science and prior knowledge Occurrence scored via statistics and process experience Risk as a guidepost for monitoring and control
Adapted from Dr. Mike Long, MBB (personal communication)
Focus on Risk
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Control strategy driven by relative risk of product attributes
Objectives:– Understand how product
attribute variation ties to clinical performance
– Establish clinically relevant specifications linking clinical performance to product quality
Adapted from Ken Hinds, PhD (PDA/FDA JRC, 2014)
Input (CQAs)O
utpu
t (sa
fety
/effi
cacy
)
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Process Validation Sequence
Process Understanding Process Design Process
QualificationCommercial Manufacture
Monitoring & Improvement
Stage 1 Stage 2 Stage 3
Based on product quality and patient safety
Is the process known?
Are the variables known?
When is confidence achieved?
What is looked for and for how long?
Risk Assessment
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Process Validation Stage 1
Process Design:
The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities. (FDA 2011 Validation Guidance)
Stage 1a - Process DevelopmentStage 1b - Process Characterization (PDA TR60)
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Process Validation Directives
Manufacturer’s Should:
Understand the sources of variation
Detect the presence and degree of variation
Understand the impact of variation on the process and ultimately on product attributes
Control the variation in a manner commensurate with the risk it represents to the process and product
– FDA 2011 Validation Guidance
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Sources of Variation
“Homogeneity within a batch and consistency between batches are goals of process validation activities.Validation offers assurance that a process is reasonably protected against sources of variability that could affect production output, cause supply problems, and negatively affect public health.”
– FDA 2011 Validation Guidance
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Sources of Variation
Within-batch (Intra-batch) variability– Can be observed with enhanced/extended sampling
Between-batch (Inter-batch) variability– Can be observed with reduced sampling or enhanced sampling
Analytical method variability– Can be observed with measurement system analysis techniques,
e.g. Gage R&R
Unknown variability– Unavoidable– e.g. variation in sample handling, shipping, etc.
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Sampling Strategy
Standardization of within-batch sampling locationsExample of stratified random sampling:– Beginning first 10% of manufactured units– Middle middle 20% of manufactured units– End last 10% of manufactured units
End Middle Beginning
Samples Samples Samples
Conveyor
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Proceed to Stage 2
• Defined from QTPP, CQAs, platform knowledge, etc.• Continuously updated during developmentProcess Design
• Estimate Variation• Determine sample size
Sampling Plan Creation
• Explore Design Space• Challenge Process Parameters
Manufacturing & Testing
• Identify Sources of Variability• Quantify impact to CQAsData Analysis
• Repeat as appropriate during process simulation studies, engineering runs, clinical runs, etc.Repeat
Stage 1: Process Design Cycle
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Process Validation Stage 2
Process Qualification
During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing. (FDA 2011 Validation Guidance)
Stage 2a – Facility, Equipment, Utility Design & QualificationStage 2b – Process Performance Qualification
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Stage 2: Focus on Risk
During Stages 1 & 2 the focus is on ‘beta risk’
– “Guilty until proven innocent”, a.k.a. consumer’s risk
– In process validation, beta probability is the chance a batch is released given that one or more of the manufactured units have failing attribute levels
– In statistics, beta = probability of committing a Type II Error
Adapted from M. Johnson, ISPE Process Validation Conference, October 2013
EMB
98.0
97.5
97.0
96.5
96.0
EMB
EMB
EMB
EMB
Batch 1
Sampling Location
CQA
1
Batch 2 Batch 3 Batch 4 Batch 5
Spec
Individual Value Plot of CQA 1
Panel variable: Batch
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Stage 2: PPQ Success Criteria
All results must meet release specifications
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Stage 2: PPQ Success Criteria
Some statistical tools may be unsuitable for the application of objective pass/fail criteria
At this stage, limited data from clinical and/or engineering runs exist further knowledge is needed about the breadth of variation from raw material attributes and process parameters
IF SETTING OBJECTIVE STATISTICAL LIMITSTHEN PROCEED WITH CAUTION!
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Stage 2: PPQ Data Analysis
Batch-Specific Tolerance Intervals (TI)– May be used to directly measure batch homogeneity– A range of values is estimated for the majority of units produced in a
single batch
Analysis of Variance (ANOVA)– May be used to directly measure batch homogeneity and consistency– May be used to partition variability into ‘Variance Components’– May be used to estimate differences between sampling locations
(B/M/E) or batches
Continuous data should NOT be rounded before analysis– Rounding favored for official documentation (e.g. C of A), but
unfavorable for statistical analysis may generate statistics that significantly misrepresent an attribute’s variability
Stage 2: CPV Planning
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Within-batch variation and between-batch variation observed during PPQ can be used to formulate a CPV plan
– “The increased level of scrutiny, testing, and sampling should continue through the process verification stage as appropriate, to establish levels and frequency of routine sampling and monitoring for the particular product and process.”
2011 FDA Guidance, “Process Validation: General Principles and Practices”
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Stage 2B Enhanced Sampling Analysis
Expected CQA within- and between-batch variability
Unexpected CQA within- and between-batch variability
Yes. CQA meets next order of coverage using TImethod
Release sampling only for Stage 3A
Continue enhanced sampling for Stage 3A
No. CQA does not meet next order of coverage using TImethod
Continue enhanced sampling for Stage 3A
Continue enhanced sampling for Stage 3A
Adapted from Dr. Mike Long, MBB (personal communication)
Stage 2: CPV Planning
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Example criteria for batch-specific TIs
Adapted from Dr. Mike Long, MBB (personal communication)
Stage 2: CPV Planning
Attribute Severity
Stage 2b TIIdeal Confidence / Coverage
Stage 2b TIExceptional Confidence / Coverage
High 95 / 95 95 / 99
Medium 95 / 90 95 / 95
Low 95 / 80 95 / 90
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Process Validation Stage 3
Continued Process Verification
Ongoing assurance is gained during routine production that the process remains in a state of control. (FDA 2011 Validation Guidance)
Stage 3a – Short Term CPVStage 3b – Long Term CPV
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Stage 3: Focus on Risk
During Stage 3 the focus begins shifting to ‘alpha risk’
– “Innocent until proven guilty”, a.k.a. producer’s risk
– In process validation, alpha probability is the chance of rejecting a batch given that none of the manufactured units have failing attribute levels
– In statistics, alpha = probability of committing a Type I Error
Adapted from M. Johnson, ISPE Process Validation Conference, October 2013
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Statistical Process Validation
Given the right investment of time and resources:
Improved understanding of the manufacturing process
Improved ability to detect issues and find root causes
Improved product quality
Decreased consumer risk
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Acknowledgements
Kenneth Hinds Mike Long Darin Furgeson John Motzi
Backup Slides
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Sampling and Testing
Analytical Testing Order
Can increase one’s ability to detect signals from the manufacturing process, versus signals from the assay, by stratifying the sample run sequence
Natural tendency to segregate samples by sampling location and test UNFAVORABLE FOR HIGHLY VARIABLE ASSAYS
– Sampling location comparisons within individual batches become less clear as to their cause, from the process or from the assay.
