Efficient process validation strategies for accelerated programsKartik SubramanianOctober 8th, 2018
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Introduction to AbbVieWe discover, develop and deliver medicines in therapeutic areas where we have proven expertise and where we can have an impact.
These Areas Include:• Immunology• Oncology• Virology• Neuroscience ~29,000
Employees globally14Manufacturing facilities
8Research & Development facilities
In 2017, AbbVie medicines helped 26+ million patients In 200+countries treating 30 conditions
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Biologics Discovery & Development Clinical & commercial manufacturing of AbbVie and Third Party products
AbbVie Bioresearch Center (ABC)
AbbVie Bioresearch Center - Worcester, MA
ABC cGMP Manufacturing• 2 x 3,000 L bioreactors in 2 suites• 5 x 6,000 L bioreactors in 2 suites• 2 x 1,000 L all single use suite• 5 independent purification suites• Class B bulk fill suite
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Differentiated Strategy Tailored to Pipeline NeedsRequires flexible platform technologies and approaches
• Oncology– Potent ADCs directed toward specific cancers, low kg demands– Need option for accelerated development pathway– Focus on speed & product quality
• Neuroscience– Large populations with high doses, potential for extreme kg demands– Long development timelines– Focus on cost & product quality
• Immunology– Large populations, moderate kg demands– Traditional development pathway– Focus on cost & product quality
• Novel Formats– Potential to challenge our platforms, extending timelines– Focus on product quality
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CMC timeline – Three phases of development activities
Discovery Non-Clinical Phase 1 Phase 2 File and
Launch Life Cycle Management
Clinical Timeline
Interim DataBT Designation
CMC ActivitiesLaunch ready process at
Phase I
• Product characterization assessment
• Cell line development and characterization
• Platform process focused on product quality
• Commercial Assay development
Post-Launch improvements for manufacturing excellence
• Process optimization for yield/cost of goods
• Reduced testing of attributes• Flexibility: resin aging,
solution hold-times, alternates
Rapid Process Characterization for
BLA submission
• Prior Knowledge• Platform process• Minimum changes• High-throughput
tools
Do more at risk Do more with successBe efficient
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Accelerating Process Validation – I: Introduction to Case Studies
Commercial process development
6000L Phase-III Mfg• one engineer run• four GMP runs
Process Design scale down model, process characterization
300L pilot scaleevaluation
(2008) 6000L PV• two engineer runs• four PV runs• four GMP runs
mAb
Commercial process development
2500L Phase-III Mfg• one GMP runs (CMO)
Process Design scale down model, process characterization
Lab & 300L pilot scaleevaluation
(2012) 3000L PPQ• one GMP run• three PPQ runs
rProtein
Commercial process development
Tier-I Characterization scale down model, process characterization
300L pilot scaleevaluation
(2014-2015) 3000L PPQ• three GMP runs• four PPQ runs
ADC #1
Tech Transfer to ABCfacility fit adjustment
Risk-based process justification
Process Design scale down model, process characterization
Lab & 300L pilot scaleevaluation
(2016-2017) 1000L PPQ• four PPQ runs
ADC #2
Tech Transfer to ABC
Tier-II Characterizationprocess characterization
Case Study 2Case Study 1
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Accelerating Process Validation – Strategy Why and how?
Objective: Accelerated start of Process Performance Qualification (PPQ) runs
Benefit
Risk
PPQ mAb material for DS and DP PPQs
PPQ material available for stability time points
Efficient for manufacturing to combine clinical manufacturing and PPQ
Invalid PPQ- Fail to meet an attribute release criterion
Process Changes Post-PPQ- Tighten an IPC criterion after PPQ- Tighten a parameter range after PPQ
May limit opportunity to fully optimize process
Alternative strategy: Leverage pre-PPQ representative clinical manufacturing batches instead
Platform Processes
Product understanding
Predictive Scale-Down Models
Efficient Experimental
Design
Scale-up Methodology
Process Analytics and Modeling
Rapid and Efficient approach to Process Understanding needed
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Accelerating Process Validation – Case study 1
Commercial process development
6000L Phase-III Mfg• one engineer run• four GMP runs
Process Design scale down model, process characterization
300L pilot scaleevaluation
(2008) 6000L PV• two engineer runs• four PV runs• four GMP runs
mAb
Commercial process development
2500L Phase-III Mfg• one GMP runs (CMO)
Process Design scale down model, process characterization
Lab & 300L pilot scaleevaluation
(2012) 3000L PPQ• one GMP run• three PPQ runs
rProtein
Commercial process development
Tier-I Characterization scale down model, process characterization
300L pilot scaleevaluation
(2014-2015) 3000L PPQ• three GMP runs• four PPQ runs
ADC #1
Tech Transfer to ABCfacility fit adjustment
Risk-based process justification
Process Design scale down model, process characterization
Lab & 300L pilot scaleevaluation
(2016-2017) 1000L PPQ• four PPQ runs
ADC #2
Tech Transfer to ABC
Tier-II Characterizationprocess characterization
Case Study 2Case Study 1
• Oncology ADC asset for high unmet medical need
• Legacy mAb process to be quickly transitioned for the ADC format application
• No Pre-PPQ large scale runs with commercial process
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Scale down model (SDM) qualification - UpstreamApproach:
• Define SDM based on prior development experience/engineering parameters
• Perform SDM qualification in parallel to PPQ runs
• Used SDM for Tier I Process characterization prior to qualification
• 3L satellites (n=19) performed in parallel with GMP manufacturing runs (n=7) – Statistical treatment
• Scale down model predictive with routine process variability e.g. Raw material changes
Results:• Process Performance comparable between scale
• No notable offsets between scales for PQ attributes
Methodology:
Equivalent performance
Equivalent Product Quality
Viab
le C
ell D
ensi
ty
Time (Days)
Tite
r(g/
l)
3L 3000L
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Process Characterization Experimentation Approach - UpstreamFrom Univariate to Multivariate
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UnivariateDefinitive
Screening DesignResponse
Surface Design
When? Tier I for pCPPs (Pre-PPQ)
Tier II (PPQ to BLA)
Post Tier II
What? All parameters identified by risk
assessment
High/medium impact parameters based on
univariate studies
High impact parameters based on
univariate and Definitive screening
Rationale Simple design/analysis
Parameter excursions typically 1 at a time
Purpose Set PARs/NORs
Initial criticality assessment
Efficient DOE design
Mid-level resolution
Incorporate interactions into criticality assessment
Verify operating ranges are valid (multi-variate space)
Higher-level resolution
Develop predictive models using high impact parameters
Process optimization/continuous improvement
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Definitive Screening DesignsEfficient experimentation
• Hybrid design that looks to combine the efficiency of screening design with the increased resolution of a response surface design
• Advantages:• Efficiency -> 2n+1 runs for even number of factors / 2n+3 for odd• 3 level factor analysis -> can detect nonlinearity for each factor • Desirable resolution of 2-factor interactions with little confounding
• Limitations:• Dependent on effect sparsity to yield reliable models(knowledge from univariate studies done before facilitate expt. design)
Mirrored Pair
A B C D E F G Order
N=15 runs
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Evolution of risk assessmentsGuide evolution of knowledge
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Tier I Process Characterization
Tier II Process Characterization
Seve
rity
of e
ffect
(p
roce
ss p
aram
eter
s
CQA)
Seve
rity
x O
ccur
renc
e x
Det
ecta
bilit
yPPQ protocol to include all high/medium impact PPs
Finalize CPPs
Seve
rity
of e
ffect
(p
roce
ss p
aram
eter
s
CQA)
Verify control strategy
Criticality risk assessment - I
Criticality risk assessment - II
Full Process FMEA
Manufacturing experience
Pre-Characterization Risk assessment
Like
lihoo
d of
impa
ct(p
roce
ss p
aram
eter
s
CQA)
Define process characterization study design
CQA Assessment
Keep the team focused on what needs to be understood and mitigate risks
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Knowledge Management/DocumentationRationale for Process Control Strategy Definition
StepsTier I (Univariate
Experiments) Results
Preliminary evaluation of parameter criticality
Implementation of control strategy in PPQ runs
Tier II Results (Multivariate/Linkage)
Refined control strategy
Verify that control strategy is sufficient
DocumentationTier I Characterization
Reports
Criticality Assessment and Process Justification Report
PPQ protocols and reports
Tier II Characterization Reports
Revised Criticality Assessment Report
Manufacturing Process Risk Assessment (FMEA)
• Control strategy evolution documentation is a key activity• Tiered approach adds some redundancy in terms of documentation
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Capa
cita
nce
(pF/
cm)
Time (days)
Titer (% variance
of target)
PQ attribute (% variance of average)
Run1 6 5Run2 3 -13Run3 -1 1Run4 -2 7
• Example of using automation of temperature shift capacitance• 4 x PPQ runs each with different complex raw materials (RM) combination
Capacitance profiles Process Output Variance
Late temp shift
Early temp shift
• Process Validated with capacitance based process decisions to manage RM variability
• Small scale RM evaluation + automation of temp shift based on capacitance Process consistency
• Consistency of PQ attribute, material needs and manufacturing schedule (harvest day) all achieved despite different RM
Use of PAT to manage raw material variabilityCapacitance based temperature shifts
Capacitance Probe
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Accelerating Process Validation – Case study 2
Commercial process development
6000L Phase-III Mfg• one engineer run• four GMP runs
Process Design scale down model, process characterization
300L pilot scaleevaluation
(2008) 6000L PV• two engineer runs• four PV runs• four GMP runs
mAb
Commercial process development
2500L Phase-III Mfg• one GMP runs (CMO)
Process Design scale down model, process characterization
Lab & 300L pilot scaleevaluation
(2012) 3000L PPQ• one GMP run• three PPQ runs
rProtein
Commercial process development
Tier-I Characterization scale down model, process characterization
300L pilot scaleevaluation
(2014-2015) 3000L PPQ• three GMP runs• four PPQ runs
ADC #1
Tech Transfer to ABCfacility fit adjustment
Risk-based process justification
Process Design scale down model, process characterization
Lab & 300L pilot scaleevaluation
(2016-2017) 1000L PPQ• four PPQ runs
ADC #2
Tech Transfer to ABC
Tier-II Characterizationprocess characterization
Case Study 2
Case Study 1
• Acquired Oncology ADC asset for high unmet medical need
• Transfer of process to commercial site to enable PPQ to support BLA in case of accelerated filing
• No prior process platform knowledge – go directly to PPQ w/o any large scale runs
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Solid engineering understandingModeling – ‘in-silico’ experiments
Commercial Manu. Scale
1000 L
Phase II Manu. Scale
200 L
Phase II - 200 L Commercial – 1000 L
Bioreactor SUB system A SUB system B
Gassing strategy
O2: Micro-sparger (DO)Air: Drilled hole (pCO2)
O2: Drilled hole (DO)Air: Drilled hole (DO & pCO2)
pH control Deadband Setpoint
Harvest Cell setting + depth filtration Depth filtration
Weeks1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
1st technical team telecon
1st thaw of thePPQ campaign
4.5 months
No Engineering run
Rapid Technical Transfer Upstream example of rapid and successful scale-up
Mass transfer Mixing
Ph2 200L 1000 L
Spec
CQA1
Challenge:
Productivity Product quality
Speed enabled through expertise and close collaboration between development and manufacturing
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Enhanced PPQ StrategyComparison to Case Study 1 and key themes
• Monitor more parameters in PPQ runs
• Define preliminary criticality based on risk assessment
• Narrow operating ranges specified to ensure product quality
• Collected more in-process samples to enhance understanding
• Any changes to criticality or NOR as a result of process characterization required retrospective evaluation of PPQ data
• Leveraged principles of process validation life-cycle for continued monitoring with post-PPQ commercial runs (under CPV)
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Enhanced PPQ StrategyExample of cell culture workflow
Step 1. PPRA
% DO assessed as non-critical process parameter (no data)
Step 2. Univariate experiment Harvest titer fails acceptance criteria
Step 3. Multivariate experiment
Repeat with 25% DO condition PASS
Failure of multiple PQ acceptance criteria
Current NOR: 20-60%
DO range tested 10 -70%
DO range tested 20-60% (NOR)
PAR: 25-70%
NOR tightened to 30-50%
Identified as CPP based on PC results
PPQ protocol: NOR 20-60% need to verify PPQ within revised range
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Lesson Learned #1 – Early emphasis on Analytics/Testing Start discussions around analytics early Ideally: Process characterization performed with final methods Alignment with QC; if high-throughput methods desired, solid data package
demonstrating equivalence with in-process samplesNo analytical changes during characterization Platform assays
Peak identification/reporting format established early on to avoid re-analysis of samples Avoid relying on specifications where possible due to potential changes triggering
reanalysis and document revisions Understand expected level of assay variability early on; use internal controls (BDS
and in process) Consider cell bank testing timeline and assays early
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Lesson Learned #2 – Standardized work practices and alignment
Standardized report and BLA templates
Tiered approach to Process Characterization
Commercial development by exception
Platform Process launch ready for FIH
Knowledge ManagementHigh-throughput tools and automation
Experienced TeamActivity Prioritization
Strategic Alignment Establish standards prior to initiating activity Consistency:
Standardize data entry and lab activities to control variability and reduce potential for errors
Potential areas for streamlining: Documentation ELN entries/templates, alignment of report and BLA
structures Terminology – identify defining documents and
ensure involvement of those impacted in approval. No changes!
Data review process – everything requires a source document
Maintain standard practices across upstream/downstream as well as DS/DP where possible to provide consistent strategy within and between programs
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Future Opportunity #1 – Leverage high-Throughput SDM
One Process Scheme Throughout
Late Discovery FIH Dev Late Process Dev Proc Char & PV
Process definition based on platform
Process optimization & 1° screening DoE
characterization
Manufacturability/ platform assessment
2° Refining DoE & non-chromat. characterization, continuous improvement
High-throughput µSDM application
• Increase overall development efficiency• Gain commercial process experience and understanding earlier• Reduce process characterization time demand
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Future Opportunity #2 – Predictive Modeling
Gas transfer
Oxygen Utilization
Lactate metabolism
Carbon-di-oxide equilibrium
pH control
Bioreactor Physicochemical
Model
Predict effect of air cap and metabolism on pCO2 levels
CFD Models
Integration of these different model systems is next step
Goal is to use these to enhance process understanding in an efficient way.
Predictive models + focused experiments
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Final thoughts..• Expedited clinical programs require innovative strategies for commercial process
development, and process validation
• Needs to be a balance between speed and ensuring critical CMC aspects are met while maintaining high quality
• Approaches for Process Validation (PV)/control strategy have been established throughout industry
– However, it may be necessary to have options depending on program timeline What other approaches may be taken w.r.t Process Validation? Types of CMC activities can be deferred to post-approval with a plan To what extent prior knowledge can be leveraged for definition of control
strategy
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