Design Space:
Case Study for a Downstream
Process Post Approval
Tamas Blandl
Amgen Process Development
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
CASSS CMC Strategy Forum QbD
Tamas Blandl – July 19, 2010, Washington DC
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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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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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Cover all relevant operational variables: steps
in mapping Unit Operation Design Space
•
Prioritize operational parameters for
experimental evaluation; relevant quality
attributes considered – FMEA
•
Screening studies, Interaction DOEs; relevant
quality attributes studied – Evaluate main
effects and interactions
•
Diagnostics and refinement to generate RSM
equations - Data based statistical model
building
•
Define operational parameter constraints based
on impact to quality attributes - Design Space
•
Operational ranges based on design space plus
process performance – Simulate/confirm
outcomes
Risk based filtering
Generate Data
Analyze data
Identify constraints
Define Operating
conditions
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Multiple sources of knowledge may form the
basis of comprehensive process understanding
•
Multivariate lab and/or pilot scale data
•
•
•
•
Univariate lab and/or pilot scale data
•
•
•
•
For unit operations with limited complexity
Interactions between operational parameters are not expected
Quality attribute behavior can be modeled via process models
Manufacturing scale process monitoring 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
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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Case Study: Impact of Multiple Unit
Operations on Aggregate
Column 1
Viral
Inactivation
Column 2
TFF
DS/DP
Storage
Knowledge:
Multivariate
Knowledge:
Multivariate
Knowledge:
Multivariate
Knowledge:
Univariate
Impact:
Medium
Impact:
High
Impact:
High
Impact:
None
Constraint:
Equation 1
Constraint:
Equation 2
Constraint:
Equation 3
Constraint:
None
Knowledge:
Univariate,
Formulation
robustness
Impact:
Low
Constraint:
Shelf life
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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:
•
Load aggregate level
part of DOE
Aggregate (%) = function (pH,
Temp, Protein conc, Time, load
Aggregate) ≤ x% (numerical limit)
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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
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Simulation:
Load aggregate at worst case
Other parameters at observed
values and distribution
Assurance of quality at the boundaries
of Design Space
•
Statistical response surface models predict
average response
•
At boundary 50% of observations are out
•
•
Design space equations
expressed at upper/lower
Individual Confidence Intervals
•
Equations are adjusted 2.5
to use
ICI terms, ie 95%
1.5
•
0.5
At boundary 95% of observations
are in
20.27
CASSS CMC Strategy Forum QbD Mass
Load (g/L)
Tamas Blandl – July 19, 2010, Washington DC
Random
-0.08
Elution
Molarity (%)
Random
4.1072
Elution pH
10
Random
4.3
4.2
4.1
4
10
3.9
5
0
-5
30
-10
25
20
15
10
Aggregates
(%)
1.333485
±0.088121
•
Defect
Aggregates (%)
Rate
0.0002
Use adjusted quality
attribute limits
Set operational ranges
based on Monte Carlo
simulations at real life
distribution of operational
variables to predict
frequency of excursions
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
1.5
1
0.5
•
6
pH
Conditioning
20
Temp
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)
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18.5
Mass Load
4
Load Aggregate
& LPA
Mfg Scale
Block
3
2
1
6
5
4
3
2
21
19
17
25
15
23
21
19
17
6.6
15
6.2
5.8
6
pH Elution
Small Scale
6
pH equil/
w ash
5.4
5.4
5.6
5.8
6
6.2
6.4
6.6
5.4
5.6
5.8
6
6.2
6.4
6.6
1.2
1.1
1.15
1.11
Conditioning
Mol (10%) - M
Pilot Scale
300
Elution Mol
(10%) -mM
Mfg Scale
570
Equil Wash
Mol. (10%) -mM
1.05
0
520
540
560
580
600
620
270
280
290
300
310
320
330
1
SEC (%
Aggregate)
0.702258
[0.53129,0.89704]
Prediction Profiler
1
Block 2
Separation not
sensitive to load
aggregate
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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Storage/Stability Effect on Aggregate
Univariate Approach: Shelf Life Constraint
• 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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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
•
•
•
•
•
Unit operation acceptable levels are determined
considering quality attribute behavior across the whole
process
Operational ranges (OR) are selected together
•
•
•
•
•
Stability
Intermediate hold
TFF
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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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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Risk management throughout
process design lifecycle
• 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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Occurrence
Matrix:
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
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SME evaluation of
design space or
available knowledge
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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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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Acknowledgments
• Chulani Karunatilake
• Ken Hamamoto
• Marc Better
• Xinfeng Zhang
• Toshi Mori Bajwa
• Andy Howe
• Ruoheng Zhang
• Duane Bonam
• Megumi Noguchi
• Bob Kuhn
• Dongmei Szeto
• Abe Germansderfer
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