Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for a Molecule in the QbD.

Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for a Molecule in the QbD Pilot Program Ron Taticek, Ph.D., Director, Pharma Technical Regulatory Genentech, a Member of the Roche Group South San Francisco, CA CMC Forum, Bethesda, MD 19 July 2010 © 2009 Genentech, Inc.

PRESENTATION OVERVIEW
Introduction Background on A-MAb and MAb1 Establishing a Design Space for a MAb Experimental Design Progression of Experiments Identifying Design Space Classifying Process Parameters FDA Pilot Program & Lessons Learned on Design Space Opportunities & Challenges Acknowledgements © 2009 Genentech, Inc.

Introduction Roche and Genentech’s QbD activities have both internal and external components: Pharma-wide Steering Committee with multiple teams working on implementing QbD for Biologics, Small Molecules and Drug Conjugates (Limited) Member of the CMC Bio Working Group that wrote the A-MAb Case Study Part of the FDA QbD Pilot Program with 2 applications: late stage MAb (MAb1) & eCP for DS Site Transfer Member of EFPIA Mockestuzumab Team writing mock S2 filing Participating on ISPE PQLI Interactions on QbD with ex-US Health Authorities (EMA, Health Canada) Attendance & Presentation at Key Conferences © 2009 Genentech, Inc.

Comparison of A-MAb and MAb1
Characteristic A-MAb MAb1 IgG1 Humanized Ab Expressed in CHO Cells Effector Function part of MOA Molecule engineered for TPP Liquid formulation; IV Administered Fed-Batch Production Includes Platform Process Elements Oncology Indication Immunology Indication A-MAb & MAb1 Humanized IgG1 © 2009 Genentech, Inc.

A-MAb Risk Assessment Approach
Multiple Assessments Throughout the A-Mab Development Lifecycle for Entire Process Process 2 Process 1 2 Results from the DOE studies provided an understanding of the multidimensional relationships between input process parameters and output quality attributes; clinical manufacturing experiences provides an understanding of process performance and process control at various operational scales Note that Risk Assessment 4 is done with knowledge from full scale manufacturing (15,000 l in this case) and prior knowledge of commercial operation with other Mabs © 2009 Genentech, Inc. 5 5

MAb1: Risk Assessment Approach
Risk Ranking & Filtering (CQA RRF) For CQA Identification Risk Ranking & Filtering for Product Testing Strategy (ATS RRF) Robustness Assessment of Product Testing Strategy (Robustness RRF) Process Development Platform Knowledge Product Understanding Scientific Literature Quality Attributes Potential CQAs (Ph I-III) Final CQAs & Ranges (Registration Dossier) Control Strategy (Registration Dossier) Process Parameters Design of Process Characterization Studies Process Characterization & Linkage Studies Overall Process Design Space & CPP Identification (Registration Dossier) Lifecycle Management of Design Space Risk Ranking & Filtering for PC Study Design (PC/PV RRF) Risk Ranking & Filtering for CPP Identification (CPP RRF) Comparability Decision Tree = Risk Assessment © 2009 Genentech, Inc.

A-MAb and MAb1 Cell Culture Processes
WCB Ampule Seed Train N-3 N-2 N-1 Inoculum Train State that this is an example: We have other scales as well… Centrifuge Harvest Production (N) © 2009 Genentech, Inc. A-MAb Process MAb1 Process

Risk Score to Define Experimental Strategy
MAb1: Risk Ranking & Filtering Tool to Design Characterization Studies Main Effect x Interaction Effect = Risk Score Direct impact to output (CQA, non-CQA or process attribute) Impact of potential interactions with other process parameters on output (CQA, non-CQA or process attribute) Experimental Strategy (multivariate, univariate or none) Impact on Process Attribute or non-CQA (1, 2 & 4) is weighted less than a CQA (1, 4 & 8) Impact is assessed based on likely Design Space ranges Limited/No data result in default to major impact Risk Score to Define Experimental Strategy © 2009 Genentech, Inc.

MAb1: Risk Ranking and Filtering (RRF)
RRF indirectly considers process parameter control capability First consider desired (targeted) acceptable ranges for parameters Based on capabilities of your facilities and provides some flexibility for site transfer Allows for future control space changes to better control CQAs Relate desired range to expected control capability No scoring of capabilities themselves however Main effect scores typically based on product-specific development data Interaction effect scores based on data when available but also on prior knowledge and literature data © 2009 Genentech, Inc.

Multivariate Acceptable Ranges (MARs) Proven Acceptable Ranges (PARs)
MAb1: Definition of Parameter Ranges Multivariate Acceptable Ranges (MARs) MARs apply to both CPPs and non-CPPs MARs for CPPs = design space Derived from multivariate studies or univariate studies (with rationale supporting a lack of interaction between parameters) Proven Acceptable Ranges (PARs) Derived from univariate studies (ICH Q8 Definition) PARs are not part of the Design Space, but are used to resolve manufacturing deviations PAR supported by univariate studies Parameter 1 MAR supported by multivariate studies © 2009 Genentech, Inc. Parameter 2

MAb1: Categorization of Parameters
Unit Operation No. of Parameters Considered Parameters in Multivariate Studies Parameters in Univariate Studies (Broader Ranges or Short Duration Excursions) Parameters Leveraging Platform Knowledge Inoculum Culture (N‑1) 9 Culture temperature pH Seeding density Medium concentration Culture duration Seed density Temperature Culture duration Dissolved oxygen Dissolved oxygen excursions Production Culture (N) 16 Seed density pH Temperature pH shift time Temperature shift time Batch feed level Batch feed timing Medium concentration Culture duration pH Temperature pH excursions Temperature excursions Galactose conc. Run duration Seed density Dissolved oxygen Dissolved oxygen excursions In‑process media hold times Univariate testing provides support for wider PARs and thus facilitates manufacturing in a practical way This outcome will vary based on process knowledge and thus I would not expect it to match A-MAb guidance directly (case-specific) Note: some parameters tested both in multivariate and univariate studies. © 2009 Genentech, Inc.

A-MAb: Example of Risk Assessment Tool for Process Characterization
Use a Fish-bone (Ishikawa) diagram to identify parameters and attributes that might affect product quality and process performance © 2009 Genentech, Inc. 12 12

A-MAb: Mid-Develoment Risk Assessment Approach
Rank parameters based on impact and control capability. 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 pH is red or critical at this stage due to link to glycosylation © 2009 Genentech, Inc. 13 13

MAb1: Characterization Study Approach
Where possible, initial strategy aimed to provide evidence of Design Space (DS) claims by direct testing. Large factor fractional factorial at wide ranges Response surface with potential CPPs at refined ranges Predicted worst - case(s ) to verify DS edges 1 2 3 Large fractional A-MAb example does not seem to have any reference to direct testing of predicted worst cases Large Fractional Factorial Studies with wide ranges Response Surface with Potential CPPs at refined ranges Predicted worst case(s) tested to verify Design Space edges © 2009 Genentech, Inc.

MAb1: Progression of Multivariate Studies
Unit Operation Study 1 Study 2 Study 3 CQAs in Linkage Studies Inoculum Culture (N‑1) Fractional Factoriala (4 factors, 8 runs) Resolution IV Worst‑case acidic variants linked to both target and worst‑case production (2 runs) Acidic variants (Cell Culture process steps linked) Production Culture (N) Fractional Factorial (8 factors, 16 runs) Central Composite (3 factors, 14 runs) Resolution V Worst‑cases for CQAs Worst‑cases for impurity linkage to purification (9 runs) Acidic variants, CHOP, LpA, DNA, Aggregates Run duration is listed in RRF table, but we did not run production cultures for extended duration in production study 2 – we added it back in for study 3 and thus re-introduced it to the design space Resolution IV = main effects fully resolvable, but 2-way interactions are not Resolution V = both main effects and 2-way interactions are fully resolvable CHOP = Chinese hamster ovary protein; LpA = Leached protein A. aCases are assessed after target production cultures are carried out. Run numbers do not include controls or replicates. Run duration extended in all studies except production Study 2. © 2009 Genentech, Inc.

MAb1: Process Characterization
DOE study progression aligned with A-MAb example Initial fractional factorial tested wide ranges Follow up fractional factorial narrowed ranges of parameters ANOVA (model fit) used to assess statistical significance and to estimate parameter effects on CQAs as well as KPIs Progression culminates with testing of predicted worst-case settings of parameters Most at-risk CQA used to determine worst-case condition Fractional factorial designs may not include predicted worst-case conditions A-MAb example may have tested worst-case(s) already as a full factorial design was described Full factorials may be less economical if large numbers of factors are ranked for DOE © 2009 Genentech, Inc.

MAb1: Seed and Inoculum Train Characterization
N-1 step studied in both multivariate and univariate experiments Factors in multivariate study are eligible for inclusion in Design Space Multivariate (DOE) studies support potential Design Space claims Univariate studies support wider ranges for single parameter excursions (for manufacturing support) Inoculated production (N) cultures with resulting N-1 test cultures Growth rate and index, product titer, and viability in N cultures (Key Performance Indicators (KPIs)) Critical Quality Attributes (CQAs) only considered for CPP identification Steps Prior to N-1: not considered for DS inclusion since negligible MAb is produced in those steps (no product quality impact) Univariate testing Analyzed via growth rate in subsequent passage © 2009 Genentech, Inc.

MAb1: N-1 Inoculum Characterization Outcome
Variation in N-1 parameters led to large variation in production culture (N) growth and titers (specific productivity less affected) Important to link N-1 DOE to performance in N culture (similar to A-MAb) © 2009 Genentech, Inc.

MAb1: N-1 Inoculum Characterization Result (Studies 1 and 2)
Initial DOE (Study 1) led to undesirable levels of acidic variants Follow up DOE (Study 2) led to greatly improved acidic variant response All other CQAs were within acceptable levels after Study 2 Worst Case testing discussed in later slide © 2009 Genentech, Inc.

MAb1: N-1 Inoculum Culture Worst-Case Study (3)
N-1 conditions predicted to result in highest levels of acidic variants were tested Worst-case N, for acidic variants, tested in same study Linkage of both worst-case N-1 and worst-case N also tested No cumulative effect in the linkage, N-1 DS conditions are not additive to worst-case N Worst-cases (N-1 and N) included extended run duration Case Acidic Variants (%) Worst Case N-1 28 Worst Case N 38 Worst Case N-1 and Worst Case N Linked 37 No difference in N worst case alone and linkage of N-1 and N worst cases for acidics (37 vs. 38%) Acidics are only at-risk CQA from an N-1 perspective © 2009 Genentech, Inc.

A-MAb Seed Expansion: 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 © 2009 Genentech, Inc. 21 21

A-MAb N-1 Impacts Process Performance but NOT Product
Seed expansion process is not part of the Design Space and is not included in the registered detail © 2009 Genentech, Inc. 22 22

MAb1: Progression of Multivariate Studies
Unit Operation Study 1 Study 2 Study 3 CQAs in Linkage Studies Inoculum Culture (N‑1) Fractional Factoriala (4 factors, 8 runs) Resolution IV Worst‑case acidic variants linked to both target and worst‑case production (2 runs) Acidic variants (Cell Culture process steps linked) Production Culture (N) Fractional Factorial (8 factors, 16 runs) Central Composite (3 factors, 14 runs) Resolution V Worst‑cases for CQAs Worst‑cases for impurity linkage to purification (9 runs) Acidic variants, CHOP, LpA, DNA, Aggregates Run duration is listed in RRF table, but we did not run production cultures for extended duration in production study 2 – we added it back in for study 3 and thus re-introduced it to the design space Resolution IV = main effects fully resolvable, but 2-way interactions are not Resolution V = both main effects and 2-way interactions are fully resolvable CHOP = Chinese hamster ovary protein; LpA = Leached protein A. aCases are assessed after target production cultures are carried out. Run numbers do not include controls or replicates. Run duration extended in all studies except production Study 2. © 2009 Genentech, Inc.

MAb1: Production Culture Study 1
Very large variation in CQAs and KPIs in the initial wide-range fractional factorial (Study 1) Study done in 3 blocks, thus varying cell ages of controls Control results are in dark green (3 independent studies at varying cell age so you see some variation in the controls for Titer) “USL” = upper specification limit Batch feed level includes both concentration of feed and the volume fed to the N reactor “50” means that each of those two parameters were varied +/- so that the overall levels were +/- 50% of the target process © 2009 Genentech, Inc.

3 parameters with the largest effects on the most affected CQA were determined from ANOVA (main effect model fit) of Study 1 data Constricting temperature shift timing allows for wider pH & temperature ranges for an improved acidic variant response (<36% was targeted) Run duration also has a significant effect on acidic variant response as well Late temp shift constricts MARs of pH and temperature © 2009 Genentech, Inc.

The three most significant factors from Study 1 were re-examined in Study 2 (a resolution V central composite design) Run duration removed from the design and was re-introduced in the Worst-Case tests % Acidic Variants Acidic variants were influenced by three parameters (pH, temp, run duration) Design Space © 2009 Genentech, Inc.

Rigorous testing of Design Space claims for all CQAs led to multiple acidic variant level responses that were undesirable Worst-cases predicted from ANOVA models Direction of parameter estimate used to set level of each parameter © 2009 Genentech, Inc.

MAb1: Design Space Uncertainty
Narrowed CQA Acceptance Criteria used to establish a design space Accounts for uncertainty due to scale-down model and design space model uncertainties A simple and practical approach to build process robustness and ensure product quality Results in a meaningful tightening of process parameter ranges Design Space Corresponds to CQA Acceptance Criteria CQA Target Range Builds robustness into process CQA Acceptance Criteria © 2009 Genentech, Inc.

MAb1: Process Parameter Classification
Two categories: Non-CPPs and CPPs A CPP is a parameter which has both a statistically significant and a practical (non-trivial) impact on the CQA. CPPs are related to Design Space-limiting CQAs Restriction relationship for parameters associated with CQAs impacted by multiple steps & that fail worst-case linkage studies CQA Unit Operation G0‑F Acidic Variants Leached Protein A CHOP Viral Safety N‑1 Inoculum Culture None Seed density culture duration NA N Production Culture Seed density media concentration batch feed level pH culture duration temperature Note: Seed train, N-3 and N-2 inoculum cultures, centrifugation/depth filtration do not have CPPs. © 2009 Genentech, Inc.

MAb1: Adaptive Overall Design Space
Bioreactor CEX Control Space management allows for maximum design spaces for both steps pH Elution cond Run duration Elution pH (Control Space Management) Implement an overall design space for MAb1 via linkage studies Implement a restriction relationship allows for the broadening or constraining of the rangess for an individual unit operation while constraining or broadening the rangess of other unit operations impacting the same CQA. Restriction relationship ensures that the combination of acceptable ranges of the unit operations impacting a given CQA are constrained so that all allowable combinations result in all of the CQAs existing comfortably within the CQA target ranges. © 2009 Genentech, Inc.

Example of DOE Results from Screening Study (N=20)
A-MAb: DOE Studies to Define Design Space Example of DOE Results from Screening Study (N=20) Initial screening study performed on all eight parameters identified as either low or medium in the previous risk assessment. Quickly understand which of these truly affect quality and by which amount. Only important parameters taken to follow-up studies. This prediction profiler from JMP shows the magnitude of these effects for all 8 parameters tested and also for the culture duration. © 2009 Genentech, Inc. 31 31

A-Mab: Develop Multivariate Models to define Design Space
Augment Screening Design with Central Composite Design to develop full response surface One model for each CQA: describes relationships with CPPs Intersection of all CQA models define the Design Space For the production bioreactor the limits of Design Space are defined by a subset of CQAs: Galactosylation aFucosylation All other CQAs did not exceed Quality Limits when process operated within Knowledge Space & Design Space This set of plots provides a graphical representation of the response surface models. Since there are four parameters and duration in the design space, a total of 15 subplots were used. Note that only aFucosylation and Galactosylation constrain the design space. This is because all other CQAs were maintained between acceptable limits over the ranges of the parameters tested. Each panel contains a countor plot of either aFucosylation or Galactosylation vs pH and temperature. The upper section contain five of these plots that show how the shape of the contour plots changes with different values for pCO2 and osmolality for a harvest day of 15 days. The mid and lower section contain the equivalent set of plots but for 17 and 19 days. Notice how the plots for a harvest day of 17 days contain the least shaded ares since that is the target harvest day. In addition, for all harvest days, the center point condition of osmo and pCO2 provides the largest space. © 2009 Genentech, Inc. 32

A-MAb: Design Space Based on Process Capability & Bayesian Reliability
Example: Day 15, Osmo=360 mOsm and pCO2=40 mmHg >99% confidence of satisfying all CQAs 50% contour approximates “white” region” in contour plot aFucos >11% pH pH Before showing the design space plots let me give an example of how the Bayesian reliability approach works. To the left we have one of the panels from the figure I showed a couple slides earlier. As I mentioned, this shows the regions where the mean afucosylation and galactosylation are predicted to be outside of acceptable limits. This is the plot for pH and temperature when osmo=360 mOsm and pCO2=40 mmHg To the right is the corresponding plot showing the region where the predicted reliability of the process is equal or higher than 99% (darker-red or maroon colored). You can see how the white space in the left plot roughly approximates the 50% contour in the right plot (the 0.5 label). Therefore, if we were to use the left plot to define the design space the process will have a reliability as low as 50% if we decide to operate close to the limits in the left plot. We strongly advise that when dealing with a process with inherent variability like cell culture we need to use an approach that considers this variability when defining the design space. Also, data from GMP runs, scale-up runs and other sources should also be used in this analysis so that we incorporate our best quantitative estimate of this variability. Galact >40% Temperature (C) Temperature (C) © 2009 Genentech, Inc. 33 33

A-MAb: Classification of Process Parameters
Within Design Space Regulatory-Sensitive Not in Design Space Managed through QMS © 2009 Genentech, Inc.

Design Space: A-MAb versus MAb1
Characteristic A-MAb MAb1 Steps with Design Space Production Bioreactor N-1 Inoculum Bioreactor Parameters Included in Design Space WC-CPPs & CPPs (only WC-CPPs) CPPs Design Space Parameters pH & temperature Culture duration Dissolved CO2 Osmolality Remaining Glucose Seed density Medium concentration Batch feed volume Design Space Limiting CQAs Afucosylation Galactosylation Acidic Variants Address Uncertainty Bayesian Statistics Reduce the acceptable range for limiting CQAs Description of Design Space in License Mathematical Models Ranges and “Restriction Relationships” Scale Increased Scale included via Engineering DSp Small scale model to Full Commercial Scale Covered Representation in Batch Records Ranges Demonstration of Validity at Full Scale 1-2 runs + Continued Process Verification 3-5 runs + TBD © 2009 Genentech, Inc.

Lessons Learned from QbD Pilot Program
BLA will need to include justification of parameter scoring (i.e., platform knowledge, prior knowledge, literature data) for risk assessments supporting design of characterization experiments Scientific literature is used to inform the risk ranking, but needs to be assessed for applicability to the sponsor’s process and product. Sponsor’s data takes precedence over scientific literature Qualification of small scale models needs to be demonstrated & raw material variation being incorporated in the characterization studies It is not clear what parameters are included in a Design Space (DSp) It is not clear how to interpret the ICH definition of Critical Process Parameters (CPPs); i.e., is a CPP a parameter which has both a statistically significant and a practical (non-trivial) impact on the CQA? © 2009 Genentech, Inc.

Lessons Learned from QbD Pilot Program (cont’d)
Design Space only applies to steps where a CQA(s) is impacted (e.g., protein expression, impurity removal) Important to link individual steps through the process to ensure CQAs are maintained Overall Design Space allows operational flexibility as one or more steps can be constrained to provide more flexibility for another step Steps with Design Space are part of license claims with parameter ranges or mathematical model included Steps without Design Space are not part of license claims (other than claiming that they are controlled) and their ranges are managed within the Quality System (HA notification) © 2009 Genentech, Inc.

Opportunities and Challenges
Design Space How best to communicate design space in License and Quality System – data summaries, graphical representations, mathematical equations, etc. What is included in the Design Space? How to define a CPP? Demonstrate true capability of unit operations – measurement uncertainty of probes, equipment functionality, and linkage to small-scale models Continuous Verification Identify best practices and approaches – statistical modeling and engineering Assure that Quality Systems can manage knowledge and change especially for non-critical parameters How to evolve design space as data and knowledge increase during commercial scale manufacturing? © 2009 Genentech, Inc. 39

A-MAb Cell Culture Group
Acknowledgements A-MAb Cell Culture Group Ilse Blumentals, GSK Guillermo Miroquesada, MedImmune Kripa Ram, MedImmune Ron Taticek, Genentech Victor Vinci, Eli Lilly Genentech/Roche MAb1 Brian Horvath Mike Laird Genentech/Roche Greg Blank Mary Cromwell Reed Harris Bernd Hilger Kathy Hsia Brian Kelley Christoph Luedin Lynne Krummen Sherry Martin-Moe Nathan McKnight Paul Motchnik Wassim Nashabeh Mary Sliwkowski Vassia Tegoulia Nathalie Yanze © 2009 Genentech, Inc.

Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for a Molecule in the QbD.

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Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for a Molecule in the QbD.

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Presentation on theme: "Implementing Design Space for the Production Bioreactor Step: Comparing the A-MAb Case Study Approach with the Approach taken for a Molecule in the QbD."— Presentation transcript:

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