1. Design Space: Case Study for a Downstream Process Post Approval
Tamas Blandl
Amgen Process Development

2. Topics to be covered
Sources of process knowledge: univariate and multivariate data
Unit operation interactions
Manufacturing data in model refinement
Confidence level at design space boundaries
Non-critical parameters
How design space information is used in risk management
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Tamas Blandl – July 19, 2010, Washington DC

3. Ideal state: Comprehensive process understanding
Design space is comprehensive process understanding
Product Quality Impact – QbD
Cover all relevant quality attributes
Cover all relevant operational variables
Business impact
Cover process performance (titer, cell viability, yield, filterability)
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4. Cover all relevant quality attributes: Influence points identified across the process
Checkmarks highlight where process understanding is required
Same matrix used for Control Strategy
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5. Define Operating conditions
Cover all relevant operational variables: steps in mapping Unit Operation Design Space
Prioritize operational parameters for experimental evaluation; relevant quality attributes considered – FMEA
Risk based filtering
Screening studies, Interaction DOEs; relevant quality attributes studied – Evaluate main effects and interactions
Generate Data
Diagnostics and refinement to generate RSM equations - Data based statistical model building
Analyze data
Identify constraints
Define operational parameter constraints based on impact to quality attributes - Design Space
Operational ranges based on design space plus process performance – Simulate/confirm outcomes
Define Operating conditions
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6. Multiple sources of knowledge may form the basis of comprehensive process understanding
Multivariate lab and/or pilot scale data
For unit operations with complex multi-parameter controls
Interactions between operational parameters may be reasonably expected
Quality attribute behavior can be modeled via process models
Univariate lab and/or pilot scale data
For unit operations with limited complexity
Interactions between operational parameters are not expected
Manufacturing scale process monitoring data
If sufficient run history is available to evaluate process variability
For quality attributes that are associated with facility specific microbial background levels, such as endotoxin, bioburden, mycoplasma, etc, which can not be extrapolated from process models at lab or pilot scale
Other molecules/processes, ie platform knowledge
Quality attribute behavior expected to be similar to prior molecules, other processes
Direct applicability of the data is confirmed
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7. Case Study: Impact of Multiple Unit Operations on Aggregate
Column 1
Knowledge: Multivariate
Impact:
Medium
Constraint:
Equation 1
Viral Inactivation
Knowledge: Multivariate
Impact:
High
Constraint:
Equation 2
Column 2
Knowledge: Multivariate
Impact:
High
Constraint:
Equation 3
TFF
Knowledge: Univariate
Impact:
None
Constraint:
DS/DP Storage
Knowledge: Univariate, Formulation robustness
Impact:
Low
Constraint:
Shelf life
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8. Viral Inactivation Unit Operation Design Space
Column 1 pool aggregate level was part of DOE as input variable
Multivariate constraint
Represented by multi-term equation
Term of Column 1 pool aggregate level included
Design Space constraint:
Aggregate (%) = function (pH, Temp, Protein conc, Time, load Aggregate) ≤ x% (numerical limit)
Load aggregate level part of DOE
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9. Use of manufacturing data
Manufacturing and pilot scale data are used as additional center point replicates
Opportunity to compare averages (center point responses) and variability (model error vs. error at mfg scale)
Statistical treatment of scale as a variable allows adjusting trends to the manufacturing average (blocking)
Design space models can be refined through the product lifecycle
Blocking: Trend centered on commercial scale mean
Simulation output: Rate of excursions
Simulation:
Load aggregate at worst case
Other parameters at observed values and distribution
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10. Assurance of quality at the boundaries of Design Space
Statistical response surface models predict average response
At boundary 50% of observations are out
Use adjusted quality attribute limits
Design space equations expressed at upper/lower Individual Confidence Intervals
Equations are adjusted to use ICI terms, ie 95%
At boundary 95% of observations are in
Set operational ranges based on Monte Carlo simulations at real life distribution of operational variables to predict frequency of excursions
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11. Column 2 aggregate multidimensional response surface: Design space constraint
Complex multivariate constraint:
Represented by multi-term equation
Term of Load aggregate level included as input variable
Design Space constraint:
Aggregate (%) = function(Equil Wash Mol, Conditioning Mol, Elution Mol, Equil Wash pH, Conditioning pH, Elution pH, Temp, Mass load, load Aggregate) ≤ x% (numerical limit)
Separation not sensitive to load aggregate
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12. TFF aggregate impact: univariate approach resulted in no constraint
Screening study shows small reproducible increase in aggregate
Not sensitive to operating parameters
Concentration
TMP
Pump passage #
Conversion ratio
Temperature
pH adjustment/titration effect
Univariate study at center point vs. worst case conditions comparable – No constraint
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13. Storage/Stability Effect on Aggregate Univariate Approach: Shelf Life Constraint
Aggregate increase observed
Univariate constraint on storage time
Intermediate pool holds
No/minimal Aggregate increase observed
Will not exceed knowledge space: maximum hold times
Select operational ranges, ie individual hold times, based on cumulative effects
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14. Linking of unit operations
Quality attribute behavior across the whole process is adequately described
Separate quality attribute DSp equation for each unit operation
QA level in intermediates included as a variable for the next step
Any univariate effects are accounted for
Stability
Intermediate hold
TFF
Unit operation acceptable levels are determined considering quality attribute behavior across the whole process
Operational ranges (OR) are selected together
Cumulative effect modeled based on conditions
Ensure OR scenario provides acceptable level
Excursions can be modeled
Future state: can build in adaptive responses
If unit operation OR changes
Evaluate impact to downstream steps
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15. Non-critical parameters
Variability does not impact product quality attribute outcomes
Not part of multivariate or univariate restrictions: not part of the design space
Comprehensive approach used to identify them as non-critical
Risk based screening
Data based screening
Still controlled within a range
Range based on mfg procedure/equipment tolerances
Subject to change control
Supporting data required
Change outcome monitored
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16. Risk management throughout process design lifecycle
Occurrence Matrix:
Stage 1: Checkmarks
Relevant Quality Attributes for each unit operation are identified
Initial identification based on platform knowledge, Process Development results, scientific principles
Stage 2: Occurrence Scores
Scoring definitions allow assignment of scores with limited information
Scores range medium to high
Stage 3: Updated Occurrence Scores
As comprehensive knowledge is built, scores are updated to reflect more detailed understanding
Low scores are given to robust unit ops, full range of scores used
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17. Decision tree developed to assign occurrence, based on yes/no answers
Occurrence questions:
Is the quality attribute impacted
Is there comprehensive knowledge
Are there constraints
Is the process robust
Is the process close to the edge of failure
Is a quality attribute excursion likely
Is there a low Cpk/Ppk observed/expected
Is there process redundancy
SME evaluation of design space or available knowledge
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18. Capture process knowledge in risk matrix
Updated occurrence scores after Process Characterization
High RPN score:
Opportunity to increase process capability
Opportunity to enhance testing
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19. Summary Design space is comprehensive process understanding
Knowledge basis may be
Multivariate studies
Univariate studies
Process history analysis
Platform knowledge
Quality attribute behavior across the whole process is adequately described
Risk management approach used throughout process design lifecycle
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20. Acknowledgments Chulani Karunatilake Marc Better Toshi Mori Bajwa
Ruoheng Zhang
Megumi Noguchi
Dongmei Szeto
Ken Hamamoto
Xinfeng Zhang
Andy Howe
Duane Bonam
Bob Kuhn
Abe Germansderfer
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Tamas Blandl – July 19, 2010, Washington DC