Efficient process validation strategies for accelerated ... · Accelerating Process Validation –...

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


2500L Phase-III Mfg• one GMP runs (CMO)


Lab & 300L pilot scaleevaluation

(2012) 3000L PPQ• one GMP run• three PPQ runs

rProtein


Tier-I Characterization scale down model, process characterization


(2014-2015) 3000L PPQ• three GMP runs• four PPQ runs

ADC #1

Tech Transfer to ABCfacility fit adjustment

Risk-based process justification



(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






mAb






rProtein





ADC #1






ADC #2



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






mAb






rProtein





ADC #1






ADC #2



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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