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Bioprocess Development Case Study: A-Mab
CMC Biotech Working Group
CMC Strategy Forum Europe 2009 Lisbon April 2009
Ken SeamonUniversity of Cambridge
Quality By Design:An Opportunity
A concept discussed in ICH documents
Q8 Pharmaceutical DevelopmentQ9 Quality Risk ManagementQ11 Pharmaceutical Quality System
“Means that product and process performance characteristics are scientifically designed to meet specific objectives, not empirically derived from performance of test batches.” (Janet Woodcock, 2004)ACE Case study demonstrated QbD principles for a small molecule P2 dataset
Greater complexity for biological molecules
What do you do?How do you communicate it?What are the regulatory implications?
Pharmaceutical Development Case Study: “ACE Tablets” Prepared by Conformia CMC‐IM Working Group January 22, 2008 Draft 1.0 The contents of this document are not yet intended for public distribution. A final version of this document will be published in April 2008 and will be made available to the public. If you are reading this case study now, we kindly request that you not send the case study to anyone else yet and provide any feedback you have in writing to: CMC‐IM Working [email protected]
Creating 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 NHL
Case Study is being designed to illustrate key principles of Quality by DesignWill be available as a teaching tool for industry and agencies
Why Mab?Represents a significant number of products in developmentGood product and process experience in the use and manufacture
Background
Started working on Case Study in August 2008Combination of monthly telecon and in- person meetings 7 companies divided across the various sections into teams
GlaxoSmithKline, Abbott, Lilly, Pfizer, Genenetech, MedImmune, AmgenJohn Berridge, Anjali Kataria, Sam Venugopal, and Ken Seamon, co-facilitators
Illustrate different approaches on application of QbD principles for biopharm development
Topic 1: Introduction
Case Study Structure & Content
Discussion Topics1. Introduction2. Quality Attributes3. Upstream4. Downstream5. Drug Product6. Control Strategy7. Regulatory
ContentProvide realistic examples
Will contain pieces/ sections that appear realistic and represent selected QbDprinciples Will illustrate the benefits of a QbDdevelopment approach Information represents real data or appropriate fictitious data
Not a mock CTDNot a Gold StandardPresent different tools and approachesCreate a study guide that includes further rationale and background to the case study and points/questions for discussion
60
70
80
90
100
110
120
130
140
CD
C (%
)
0 10 20 30 40 50Galactosylation (%)
FHD PlatformToxPh1/2Proc Optim IProc Optim IIPhase 3Characterization ICharacterization IIComm Launch
Run (Name)
Linear Fit
CDC (%) = 102.36851 - 0.0771186 Galactosylation (%)
InterceptGalactosylation (%)
Term102.4
-0.1
Estimate3.20.1
Std Error31.9-0.8
t Ratio<.0001*0.4396
Prob>|t|
Parameter Estimates
Linear Fit
Bivariate Fit of CDC (%) By Galactosylation (%)
1
3
5
Tite
r (g/
L)3.
7590
83±0
.051
545
258
11
aFuc
os6.
1377
14±0
.184
866
5
20
35
50
Gal
act
(%)
27.0
0591
±0.6
1749
2
5e+5
6.5e+5
8e+5
9.5e+5
HC
P (p
pm)
7055
74.9
±111
44.6
1
1000
2000
3000
DN
A (p
pm)
2014
.255
±43.
6593
3
15
25
35
CEX
% A
V
30.8
8437
±0.5
4323
7
1.0
1.4
1.8
Aggr
egat
es(%
)1.
4457
7±0
.041
279
Prediction Profiler
3434
.5 3535
.5 36
35.352Temperature
(C)
30 40 50 60 70
50DO (%)
40 70 100
130
160
70CO2 (%)
6.6
6.8
6.9
7.1
6.85pH
.8 11.
21.
41.
6
1.2[Medium]
(X)
360
380
400
420
440
400Osmolality(mOsm)
9 11 13 15
12Feed (X)
.7 .9 1.1
1.3
1IVCC (e6cells/mL)
15 16 17 18 19
17Culture
Duration(days)
-0.1 .2 .5 .8 1.1
0.21Curvature
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.250.250.25
0.25
0.50.50.5
0.5
0.70.7
0.7
0.7
0.80.8
0.8
0.8
0.90.9
0.9
0.9
0.950.95
0.95
0.95
0.99
0.99
0.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.250.25
0.25
0.5
0.50.5
0.7
0.7
0.7
0.8
0.8
0.8
0.9
0.9
0.9
0.95
0.95
0.95
0.99
0.99
0.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.25
0.25
0.25
0.5
0.5
0.5
0.5
0.7
0.7
0.7
0.7
0.8
0.8
0.8
0.8
0.9
0.9
0.9
0.9
0.95
0.95
0.95
0.95
0.99
0.99
0.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.25
0.25
0.25
0.50.5
0.5
0.5
0.70.7
0.7
0.7
0.80.8
0.8
0.8
0.90.9
0.9
0.9
0.950.95
0.95
0.95
0.990.99
0.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.80.80.8
0.90.90.9
0.950.95
0.95
0.990.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.9
0.9
0.95
0.95
0.95
0.990.99
0.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.25
0.5
0.5
0.70.7
0.7
0.80.8
0.8
0.8
0.90.9
0.9
0.9
0.950.95
0.95
0.95
0.990.99
0.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.90.950.99
0.99
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.90.950.99
0.99
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
CO
2 (m
mH
g)
100
40
70
Day 15 Osmolality (mOsm)360 440400
For each plot y=pH (6.6,7.1) x=Temp (34,36)
Topic 1: Introduction
General Approach for Development1. Use of prior platform knowledge
and process risk assessments to identify those steps that need additional experimentation.
2. Demonstration that laboratory scale models are representative of the full-scale operations.
3. Univariate and multivariate DOE to determine process capability and linkages.
4. Linkage of process parameters to product Quality Attributes to create a Design Spaces.
5. Final risk assessment and categorization of process parameters to develop control strategy.
Tox500L
PhI/PhII1,000L
Optimization DOE I - 2L
OptimizationDOE II - 2L
PhIII5,000L
PlatformKnowledge
25
30
35
40
Gal
acto
syla
tion
32.0
2279
±0.9
3055
5
35
35.5 36
36.5 37
36Temperature
30 40 50 60 70
50DO
40 50 60 70 80 90 100
70Dissolved
CO2
3.8 4
4.2
4.4
4.6
4.8
4.3Split Ratio
90 95 100
105
110
100Basal Strength
(Dilution)
400
420
440
460
480
440Osmo
90 95 100
105
110
100Feed Strength
(Dilution)
86 88 90 92 94
90Feed
Neutralization
16.8
17.2
17.6 18
17.3778Duration
Prediction Profiler
40
60
80
100
Dis
solv
ed C
O2
Galactosylation
400 420 440 460 480Osmo
40
60
80
100
Dis
solv
ed C
O2 Galactosylation
400 420 440 460 480Osmo
40
60
80
100
Dis
solv
ed C
O2
Productivity
aFucosylation
Galactosylation
400 420 440 460 480Osmo
40
60
80
100
Dis
solv
ed C
O2
Galactosylation
400 420 440 460 480Osmo
Topic 2: Quality Attributes
Product Profile (Quality)
Humanized IgG1Treatment for indolent (slow-growing) non-Hodgkin’s Lymphoma (NHL) in adults only (not immunosuppressed)MOA: binds to surface antigen (Lymph-1) and kills B cells via both CDC and ADCC IV administration at 10 mg/kg; dosing weekly; 6 cyclesSterile liquid formulation in a vial
ManufacturingProduced in CHO cells using selective agentDeveloped using a platform process based on experience with five different monoclonal antibodiesCommercial production at an approved manufacturing facility
Topic 2: Quality Attributes
Risk Assessment of Quality Attributes
Focused on Illustrating These QbD Principles: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 categorizing QAsCriticality based on potential impact to safety and efficacy
Key Issues:Categorization of QAs (critical vs non-critical) or Is there a cutoff?Linkage of QA ranking to Control Strategy
Topic 2: Quality Attributes Subset of Attributes for Evaluation in Case Study
Attribute CriticalityAggregation 48Glycosylation 48Deamidation 4Oxidation 12HCP 24DNA 12Protein A 12C-terminal lysine variants (charge variants)
4
High CriticalityAffected by stress on the moleculeAffected by multiple steps in the process
High CriticalityAffected principally by USNo process clearance or modification in DS or DP
Low Criticality based on laboratory, nonclinical, and clinical dataLow clearance through DS processCan be formed naturally through DS and DP
Low CriticalityDerived from USCleared through DS and not affected by DP
Topic 3: Upstream
Principles and Data ExemplifiedFocused on Illustrating QbD Principles:
Prior Knowledge and platform 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 operationParametric control of selected critical quality attributes
Lifecycle validation approach that includes continuous process verification
Key IssuesApplication of prior knowledge and development data to validation of commercial processDemonstrate that Design Space is scale-independent
Topic 3: Upstream
Process Flow Diagram
S e e d C u ltu re E x p a n s io nin d is p o s a b le s h a k e f la s k s a n d /
o r b a g s
S e e d C u ltu re E x p a n s io n in f ix e ds tir re d ta n k re a c to rs
N -1 S e e d C u ltu re B io re a c to r3 ,0 0 0 L W V
P ro d u c t io n B io re a c to r1 5 ,0 0 0 L W V
H a rv e s t C e n tr i fu g a t io n & D e p th F il t ra t io n
N u tr ie n t F e e d
S e e d M a in te n a n c e
T h a wW o rk in g C e ll B a n k
C la r if ie d B u lk
S e e d M a in te n a n c e
G lu c o s e F e e d s
S T E P 1
S T E P 2
S T E P 3
S T E P 4
Platform data to support use of different platforms for Seed culture
Detailed investigation to demonstrate design space and control strategy
Topic 3: Upstream
Lifecycle Approach to Validation1. Multivariate model based on process characterization (e.g. DOE) - Model 1
A comprehensive Design Space based on 2-L characterization studies as well as 500-L, and 5000L experience for A-Mab. Includes scale-independent operational
parameters: iVCC, temp, pH, pCO2 etc
2. Design Space for Scale-up, based on BioRx engineering parameters- Model 2Based on engineering characterization and DOE studies. Establish 2L as a reliable model system by: a) Establishing hydrodynamic similarity and ensuring appropriate
equipment design and operation; b) Establishing scalability through demonstration of overlapping performance of either scale in a MVA model that includes process inputs,
outputs and product quality – for previous aMAb product (Model 3)
3. Demonstration of scalability and Design Space for A-Mab by execution of 2 batches at the intended commercial scale (15K)
4. Use continuous process verification during routine monitoring to demonstrate that process is in state of control
Build MVA model for A-Mab; define acceptance criteria
1. Multivariate model based on process characterization (e.g. DOE) - Model 1A comprehensive Design Space based on 2-L characterization studies as well as 500-
L, and 5000L experience for A-Mab. Includes scale-independent operational parameters: iVCC, temp, pH, pCO2 etc
2. Design Space for Scale-up, based on BioRx engineering parameters- Model 2Based on engineering characterization and DOE studies. Establish 2L as a reliable model system by: a) Establishing hydrodynamic similarity and ensuring appropriate
equipment design and operation; b) Establishing scalability through demonstration of overlapping performance of either scale in a MVA model that includes process inputs,
outputs and product quality – for previous aMAb product (Model 3)
3. Demonstration of scalability and Design Space for A-Mab by execution of 2 batches at the intended commercial scale (15K)
4. Use continuous process verification during routine monitoring to demonstrate that process is in state of control
Build MVA model for A-Mab; define acceptance criteria
Topic 4: Downstream
Downstream ProcessStandard 3-column platform process
Centrifuge, Protein A, low pH, 2-IEX, Viral Filter (VF), UF/DFFocused on Illustrating
Design SpaceIncorporate several examples of risk assessmentUse of internal prior knowledge and literature
Modular virus clearance based on company data (pH inactivation, VF)Dual sourced raw materials (Viral Filter, Protein A)Alternate technology(AEX membrane for AEX resin)Design space independent of site & scaleLinkage of the design space and control strategy to upstream anddrug productUtilize QbD through small-scale process characterization to reduce drug substance release testing
Topic 4: Downstream
QbD Elements and Process Flow Diagram
UnprocessedHarvest
Centrifugation and Depth filtration
Protein A Chromatography
Low pH Incubation
Cation Exchange Chromatography
Anion Exchange Chromatography
Nanofiltration
Ultrafiltration and Diafiltration
Final Filtration, Bottling and
Freezing
A-mAbDrug Substance
•Lack of Impact of process variables on QAs•Linkage to CEX for HCP clearance•Use of alternate sources of resin
•Complete study on all QAs•Constraint on design space based on HCP worst case
•Use of alternate technology and impact on viral clearance•Evaluation of design space that includes QAs and viral clearance..
•Use of prior knowledge for viral clearance.
•No modification of glycosylation variants•No clearance of deamidated species
Data from several antibodies to make a generic claim for viral clearance and design space•Worst case scenario for the A-mAb stability
Topic 5: Drug Product
QbD and Drug ProductCovering drug product development and manufacturing process Areas of focus are:
Design SpaceLifecycle Management Control Strategy
Link DS and DP into one overall control strategyEstablish sound rationale that allows reduced DP release testingEmploy more PAT tools for IPCsReduce release testing to those attributes that are impacted by drug product manufacturingProvide clear rationale for stability testing
ChallengesUtilize QbD principles to reduce the burden on formal stability studies (for both drug substance and drug product)
Use formal stability programs as more confirmatory studiesApply development studies to justify the design space as well as changes within the design space
Changes in primary container, site, equipment, scale, raw materials
Step 1
Step 2
Step 3
Step 4
A-mAb Drug Substance
Drug substance preparation/handling
Compounding
Sterile filtration
Filling, stopperingand Capping
Packaged A-mAbDrug Product
Design space
•Multiple or single lots/container•Frozen or unfrozen•Unclassified or class 100,000
•50-1500 L•Stir time•Hold time•Tank configuration
•50-1500 L•Hold time•Filter configuration
•Reservoir pressure•Pumping configuration•Capper spring pressure
Risk Assessment
Design Space
Control Strategy
Topic 5: Drug ProductDrug product process steps exemplifying QbD supported by optimized formulation design
Topic 6: Control Strategy
Rationale for Control StrategyFocused on Illustrating these QbD Principles:
Links between criticality of QA and process development, characterization (design space) and controlsApproach to define control strategy based on criticality of QA and process capability
Risk assessments for creating appropriate control strategyProcess and analytics risk assessment for identifying critical process steps with respect to their impact on QAs with high criticalityProcess risk assessment (FMEA, Fishbone etc.) for identifying critical and key process parameters
Define elements of control strategy: end product testing, process monitoring, product characterization, stability testing, in process analysis and monitoring, validation testing, etc
Topic 6: Control Strategy
Steps Leading to Control Strategy
Re-assess as product knowledge
and process understanding
increase
Severity (Define CQA)
ProbabilityPotential safety
or efficacy impact?
Prior productknowledge broader than
process capability?
Raw material control
Procedural controls
Lot release testing
Stability testing
Detectability
Analytical methods capable of monitoring?
Process validation(commercial)
In-process testing
Operational parameters
Characterization testing
Develop Control Strategy
Risk Assessment
Identify All Product Quality Attributes
Re-assess as product knowledge
and process understanding
increase
Severity (Define CQA)
ProbabilityPotential safety
or efficacy impact?
Prior productknowledge broader than
process capability?
Raw material control
Procedural controls
Lot release testing
Stability testing
Detectability
Analytical methods capable of monitoring?
Process validation(commercial)
In-process testing
Operational parameters
Characterization testing
Develop Control Strategy
Risk Assessment
Identify All Product Quality Attributes
Conclusions and Summary
Impact of QbD and Implication for Industry and Regulators
QbDQbDCriticality of
Quality Attributes
Criticality of Quality
Attributes
Control StrategyControl Strategy
Design SpaceDesign Space
TestingTesting
Product and Process
Understanding
Product and Process
Understanding
Continual Process
Improvements
Continual Process
Improvements
Lifecycle Management
Lifecycle Management
•Reduced final product testing•Reduced tests for stability
•Greater flexibility to make changes within design space•Raw Materials•Process Parameters
•Scale and site changes
Activity Output Result Area of Impact Implications
PredictabilityPredictability
Conclusions and Summary
QbD Concepts exemplified in A-Mab case study
Quality by Design Approach Traditional Approach
Thorough process understanding is based on prior knowledge and product specific experience.
Process understanding is limited to product-specific empirical information
Use of multivariate experiments to provide understanding of the relationships between process parameters and product quality attributes. Acceptable operating conditions expressed in terms of a multidimensional Design Space
Some experiments conducted using single-variable approaches. Acceptable operating ranges expressed as univariate Proven Acceptable Ranges
Risk management tools used to guide process development decisions and experiments
Process development plans based on established industry practices
Risk-based control strategy supported by thorough process/product understanding. Product Quality ensured by control strategy.
Control Strategy based on established industry practices. Product quality controlled primarily by end-product testing
Design space applicable to multiple operational scales. 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 which includes continuous process verification to demonstrate that process remains in state of control. Use of multivariate approaches for process verification.
Process validation based on limited number of full-scale batches. Process performance generally monitored using single variable approaches
Conclusions and Summary
Challenges (actually opportunities)Focusing on science not limiting to current regulatory paradigmsAllowing for different approaches such as for risk assessmentsIntegration of new science and data into CTD submissionsImpact of QbD approach on regulatory commitments and reportingUse of better understanding for managing manufacturing lifecycle
Conclusions and Summary
Current Status
TimelineDraft 0.1 May 2009Draft 1 June 2009Draft 2 July 2009Publication Summer 2009
Disclaimer: The slides and data are examples or representations from the current draft and may change as the final document is assembled.
A Great Deal of Thanks!!!!
Company RepresentativesGlaxoSmithKline MedImmuneAbbott PfizerEli Lilly AmgenGenentech
Scientific discussions with Regulatory Scientists at FDAFacilitators
John BerridgeAnjali KatariaKen SeamonSam Venugopal
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