1. CMC Forum Washington, DC Presented by Victor Vinci, Eli Lilly
Control Strategy for Glycosylation Using a QbD Approach: Monoclonal Antibody with Effector Function from the A-Mab Case Study
CMC Forum Washington, DC
Workshop I - CQAs
July , 2010
Presented by Victor Vinci, Eli Lilly

2. CMC BWG – A-Mab Case Study Working Group Members
Amgen Team: Joseph Phillips (Lead), Bob Kuhn
Abbott Team: Ed Lundell (Lead), Hans-Juergen Krause, Christine Rinn, Michael Siedler, and Carsten Weber
Eli Lilly Team: Victor Vinci (Lead), Michael DeFelippis, John R Dobbins, Matthew Hilton, Bruce Meiklejohn, and Guillermo Miroquesada
Genentech Team: Lynne Krummen (Lead), Sherry Martin-Moe, and Ron Taticek
GSK Team: Ilse Blumentals (Lead), John Erickson, Alan Gardner, Dave Paolella, Prem Patel, Joseph Rinella, Mary Stawicki, Greg Stockdale
MedImmune Team: Mark Schenerman (Lead), Sanjeev Ahuja, Laurie Kelliher , Cindy Oliver , Kripa Ram, Orit Scharf, and Gail Wasserman
Pfizer Team: Leslie Bloom (Lead) and Amit Banerjee, Carol Kirchhoff, Wendy Lambert, Satish Singh
Facilitator Team: John Berridge, Ken Seamon, and Sam Venugopal
Plus help from many others
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3. Creating a Biotech Case Study: “A-Mab”
Based on a monoclonal antibody drug substance and drug product
“A-Mab”
Humanized IgG1 (w/ effector function)
IV Administered Drug (liquid)
Expressed in CHO Cells
Treatment of NHL
Molecule designed to maximize clinical outcomes and minimize impact on quality attributes (TPP)
Publically and freely available as a teaching tool for industry and agencies at CASSS or ISPE
Why Monoclonal Antibody?
Represents a significant number of products in development
Good product and process exp. in dev. & manufacture
Reasonable level of complexity
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4. QbD Development Paradigm Creation of a Control Strategy
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5. CQA Risk Ranking & Filtering Tool A Continuum of Criticality (Tool #1 Ex.)
Assess relative safety and efficacy risks using two factors:
Impact and Uncertainty used to rank risks
Impact = impact on safety or efficacy, i.e. consequences
Determined by available knowledge for attribute in question (prior, clinical, etc)
More severe impact = higher score
Impact on biological activity, PK/PD, immunogenicity, adverse effects
Uncertainty = uncertainty that attribute has expected impact
Determined by relevance of knowledge for each attribute
High uncertainty = high score (no information with variant or published lit. only)
Low uncertainty = low score (data from material used in clinical trials)
Severity = risk that attribute impacts safety or efficacy
Severity = Impact x Uncertainty
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6. Criticality Ratings for Glycosylation
Attribute
Criticality
Aggregation 60
aFucosylation
Galactosylation 48
Deamidation 4
Oxidation 12
HCP 36
DNA 6
Protein A 16
C-terminal lysine variants (charge variants)
Glycoslyation - High Criticality
Example is for afucosylation and galactosylation; other glycan structures require individual consideration
Primarily impacted by production BioRx
No clearance or modification in DS
Not impacted by DP process or stability
Note: Assessment at beginning of development
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7. Platform and Product Specific Experience Claimed Acceptable Range
Attribute
Prior Knowledge
In-vitro Studies
Non-clinical Studies
Clinical Experience
Claimed Acceptable Range
Galactose Content
Clinical experience of 10-40% G0 for Y-Mab, another antibody with CDC activity as part of MOA; no negative impact on clinical outcome
0-100% gal content has statistical correlation w/ CDC activity w/ A-Mab
Studies show that 100% G0 or 100%G2 have comparable ADCC
No animal studies
10-30%
10-40%
aFucosylation
1-11%; Clinical experience with X-Mab and Y-Mab; both X-Mab and Y-Mab have ADCC as part of MOA
A-Mab with 2-13% afucosylation tested in ADCC assay; linear correlation; %
Animal model available; modeled material (15%) shows no significant difference from 5%
5-10%;
Phase II and Phase III
2-13%
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8. CQA Linkage to Process Knowledge
afucosylation and galactosylation are assigned as CQAs due to linkage to ADCC and CDC activity and proposed NHL therapeutic need
Analytical characterization method for afucosylation and galactosylation is CE-LIF:
Bioassay development led to a robust assay with a linear correlation between aFuc (2-13%) and ADCC activity (bioassay range of 70 – 130%)
Bioassay for CDC showed no impact over the range of galactosylation (10 – 40%) produced in clinical material
Ranges of afucosylation and galactosylation can be ensured by control of bioreactor process parameters found to have influence on these structures.
Release testing with Biopotency assay for drug product (acceptance criterion 70 – 130%) confirms appropriate product quality
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9. Influence of Glycosylation on ADCC and CDC Effector Functions
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10. Experimental Design Progression of Studies for Production Bioreactor
Prior knowledge and risk assessments inform designed experiments:
Risk analysis tools guide informed assessments
Risk assessment links product attributes with parameters
DOE’s allow understanding of the impact of process parameters and attributes
Risk assessments are iterative and continue through the lifecycle of product
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11. Risk Assessment Approach
Multiple Assessments Throughout the
A-Mab Development Lifecycle for Entire Process
Process 2
Process 1 2
You Are Here
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12. Example of Risk Assessment Tool Approach to Process Characterization
Step 1. Use a Fish-bone (Ishikawa) diagram to identify parameters and attributes that might affect product quality and process performance
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13. A-Mab: Mid-Development Risk Assessment Approach
Rank parameters and attributes from Step 1 based on severity of impact and control capability. Identify interactions to include in DOE studies
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
Note: pH is red or
critical at this stage due to linkage to
glycosylation
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14. MCC Bioreactor Control Strategy Elements by System - pH
Raw Materials (Reg/QMS) – vendor qualification; media (or buffer) make-up based on instructions, weight based; pH check post make-up Equipment (QMS) – bioreactor design (probe type/placement), probe vendor qualification, receipt verification, linked to IQ/OQ and PV for bioreactor Automation (QMS) – control loop qualified (CSV) and controlled via DCS, alert/action alarms aligned with process, data monitored continuously and archived DOE and Models (QMS/Reg) – small-scale models use parameter ranges intended for large-scale; confirm during pivotal and commercial tech transfer In Process/Operations (QMS) – pH probe calibration (pre-run), batch record instructions on how to do daily check and adjustment, data trended Specification Limits/Tests (Reg/QMS) – Control Strategy in place, validated methods reflecting QbD analytical development Process Verification/Continuous Monitoring (QMS/Reg) – MVA (PLS) or SPC monitoring of performance over manufacturing lifecycle
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15. Continuity of Ranges Attributes and Parameters in Study
Levels of CQAs:
CQA
Lower Limit
Higher Limit
Afucosylation (%) 2 13
Galactosylation (%) 10 40
Parameter Ranges:
Platform (2 liters and at-scale FHD) pH 6.6 – 7.1 (initial set pt*)
Screening Study (Central Composite) pH 6.6 – 7.1 (2 liter)
Design Space Proposal pH (commercial)
Batch Record (Pivotal and Comm.) pH (initial ref pt)
Automation Alarms pH 6.85 lo/pH 6.95 hi alert-control space
pH 6.7 lo/pH 7.1 action-design space
*Note that pH variable is set at initial as ref pt and moves through low (base) and high (acid or CO2) control
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16. DOE Studies to Define Design Space
Bringing Together Process and Product Attributes
Example of DOE Results from Screening Study (Process 2). 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.
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17. Moving Toward Design Space Follow-up Studies and Analysis
Augment the screening design to enable estimation of a full response surface: all main effects two-way interactions quadratic effects Additional runs form Central Composite Design (when comb. w/ previous runs): 8 additional runs form full factorial on important parameters. 8 axial points allow to estimate non-linear relationships 4 parameters and 6 QA’s (responses) 8 center points total
The four parameters identified to have a meaningful relationship with any of the CQAs, in this case temperature, pH, Osmolality and pCO2, were taken into a follow-up study.
This follow-up study built on the initial set of runs (N=20) to augment the design to a full Central Composite Design. In total N=40 small scale bioreactors were needed to define the response surface model.
We purposefully blocked the runs in groups of 10, to indicate that the work could be done with a smaller number of bench reactors. Defining the design space took 10 bioreactors dedicated for 12 weeks (3 weeks per block).
N=40 total bioreactor runs
(4 blocks of 10, ~12 weeks)
Response surface model captures all input – output relationships and is suitable to define the design space
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18. Develop Multivariate Models to define Design Space
A better way to look at the data:
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
*Note that DO and Feed Conc from earlier study are controlled in same range
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.
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19. Design Space Based on Process Capability Understanding Variability
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)
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20. Risk Assessment Approach
Multiple Assessments Throughout the
A-Mab Development Lifecycle for Entire Process
Process 2
Process 1 2
You Are Here
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21. Control Strategy for Upstream Production
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22. Example of Control Strategy for Selected CQAs
Criticality
Process Capability
Testing
Criteria
Other Control Elements
Aggregate
High (48)
High Risk
DS and DP release
Yes
Parametric Control of DS/DP steps
aFucosylation
High (60)
Low Risk
DS Process Monitoring
Parametric Control of Production BioRx
Galactosylation
Host Cell Protein
High (24)
Very Low Risk
Charact.
Comparability
Parametric Control of Prod BioRx, ProA, pH inact, CEX , AEX steps
DNA
Parametric Control of Prod Biox and AEX Steps
Deamidated Isoforms
Low (12) No
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23. Lifecycle Management of Design Space Dynamic Modeling
Challenge:
Data from a limited number of batches is required for process validation
ex: n=5 or more for 3 bioreactors ; costly and often critical path
Limited replicates are not statistically significant – at best test the “system” including facility, equipment, process, operators, etc
Alternative Lifecycle Approach or Continuous Process Verification:
Quality Mgt System assures site’s readiness and compliance
Use 1 or 2 batches to confirm or demonstrate validity of design space
Utilize a multivariate statistical partial least squares (PLS) model for continuous process verification as commercial experience grows in number of runs
Scheduled reviews of product quality data trends and design space validity during the product lifecycle
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24. Design Space Linkage to Critical Attributes
Successful acceptance or utilization of our evolving view of design space relies on linking the multiple elements of documented knowledge and systems: Facilitated formal attribute rankings and parameter risk assessments to guide DOEs Linkage of critical attributes and parameter ranges used Delineation of how lifecycle oversight (control strategy) of critical and non-critical parameters and specification/limit testing occurs Movement to best practices for engineering first principles/mechanistic models and statistical modeling as they apply to QbD paradigm
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25. Upstream Development Team
Ilse Blumentals GSK Guillermo Miroquesada MedImmune Kripa Ram MedImmune Ron Taticek Genentech Victor Vinci Lilly Special thanks to Mike DeFelippis *Help from many others – CMC BWG member company reps and internal resources at each company
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