Establishing and Predicting Quality:
Process Validation - Stage 1
Brad Evans / Kim Vukovinsky
Pfizer
May 20, 2015
Outline*
• What statistical tools are used in PV Stage 1 and how do the
results influence PV Stage 2?
• How is the difference in scale addressed?
• How are design space verification and PPQ related?
• What is the role of variability in determining readiness for
PV? (hmm, what about measurement uncertainty?)
• Is it relevant to combine and analyze PV Stage 1 data with
PV Stage 2 data?
• What data is needed from PV Stage 1 in preparation for PV
Stages 2 & 3?
* Tools and topics are not equally distributed across all
applications, e.g. mAbs, Vaccines, DP, API, Parenterals
2
Stages of Process Validation
Stage 1: Process Design
Stage 2: Process Qualification
Stage 3: Continued Process Verification
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Process Validation Guidance
• Guidance for Industry
– Process Validation: General Principles and Practices
• For purposes of this guidance, process validation is defined
as the collection and evaluation of data, from the process
design stage through commercial production, which
establishes scientific evidence that a process is capable of
consistently delivering quality product
http://www.fda.gov/downloads/Drugs/Guidances/UCM070336.pdf
4
What Statistical tools are used in PV Stage 1?
At a high level:
• Visualization (“I love a good plot” Steve Novick)
• Simple Descriptive Statistics
• Statistical Intervals (Confidence, Prediction, Tolerance)
• Sampling Plans
• Monte Carlo Simulation
• Messy Data Analysis Tools
• Hypothesis Testing
• Modeling
• Design of Experiments
5
… and how do the results influence PV Stage 2?
Design Space and Control Strategy
The ICH Q8 Guidance* defines “Design Space” as:
“The multidimensional combination and interaction of
input variables (e.g. material attributes) and process
parameters that have been demonstrated to provide
assurance of quality”.
However, knowledge of the parameters and their impacts
does not assure quality. It is the Control Strategy that is
critical in Assuring Quality.
* http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q8_R1/Step4/Q8_R2_Guideline.pdf
Quality Assurance … Process Understanding,
Control Strategy, Specifications
Controls
Parameters,
attributes,
GMP,
business
Holistic
control
strategy
Boundaries/
limits
PARs,
design
space,
release limits
Product
Efficacy,
Patient
safety,
Reduced
Cost to
Society
… assurance from the total quality
system including the process definition
+ control strategy + testing … tight
specifications are not the only way
7
Pfizer’s Right First Time / QbD Process
Statistical Component
Risk Assessment
Multifactor Understanding: DOE + Data
Models + Requirements
Impurity1 = f(B, C)
Impurity2 = f(B)
Impurity1 < 0.1%
Impurity2 < 0.1%
Analysis + Visualization + Decisions
Contour Plots: Two Responses, Two Process Parameters
Want to be less than 0.10 for both impurities
Impurity1 (%)
1.00
Impurity2 (%)
1.00
0.50
0.50
0.00
C
C
0.03
0.07
3
0.02
0.00
0.10
0.05
0.09
0.07
0.11
3
-0.50
0.14
-0.50
0.17
-1.00
-1.00
-0.50
0.00
0.50
1.00 -1.00
-1.00
-0.50
0.00
B
B
0.50
1.00
Overlay Plot of Two Responses vs. Two PP’s
• Easy to implement
(Design Expert)
• Lends to “Edge of
Failure” Terminology
• “EOF” is misleading
• Edge represents
mean 50% failure (if
model is perfect)
• Blue dots have very
different OOS rates
Overlay: Two Responses, two Process Parameters
•
The probability of simultaneously
passing the specifications varies
within in the orange region – in
fact it varies throughout the entire
region
•
“Boundary” provides no greater
than 50% probability of passing
•
Probability of meeting ALL specs
decrease in areas of intersecting
requirements
•
Reliability used to describe
passing all Specs
~50% Prob
< 50% Prob
~50% Prob
11
Prospective Process Reliability Estimate (PPRE)
These levels curves now
show the Reliability, the
chance that the batch
can be released
This takes into account the
predictive Distribution,
not simply the Mean
Prospective Process Reliability Estimate (PPRE)
John J. Peterson, Guillermo Miró-Quesada and Enrique del Castillo, “A Bayesian Reliability Approach to
Multiple Response Optimization with Seemingly Unrelated Regression Models”, Quality Technology &
Quantitative Management, Vol. 6, No. 4, pp. 353-369, 2009.
Data points
New Betas
Data Dist
Counting.
Specification Increase to Achieve Quality
Requirements
Decision Making - End Process Attribute
Estimated Probability of Passing
Original 0.1% Spec
Estimated Probability of Passing
New 0.3% Spec based on Safety
Set Point Moved to Achieve Cost Target
Decision Making - In Process Attribute
Process adjusted so in
process response acceptability
is 80%.
Response acceptability at
process end >99.9% - next
unit operations will achieve
goal.
Affects cost but not quality.
Sets up continuous
improvement opportunity; for
Development or
Manufacturing.
How is the difference in scale addressed*?
Two types of parameters:
• Scale dependent: need strategy to assess DOE at scale
(and life cycle change management understanding)
• Scale independent or scalable: parameter that is scale
independent (by model, science, equipment design) - run
DoE’s at lab scale and results apply to scale.
Examples:
• Pressure, temperature are scale independent
• Mixing rpm is scale dependent, w/kg is scale independent
• High Sheer Granulator is scale dependent, Gerties roller
compactors are scale independent
* Garcia, Thomas, et. al. “Verification of Design Space Developed at Subscale”,
Journal of Pharmaceutical Innovation, Vol 7, pg. 13-18 (2012).
16
Design Space Verification*
Option:
verification at
set-point
Option:
Verify as
required
Option: Verify
a region
around setpoint
* Garcia, Thomas, et. al. “Verification of Design Space Developed at Subscale”,
Journal of Pharmaceutical Innovation, Vol.7, pg. 13-18 (2012).
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What is the role of variability in determining
readiness for PV?
• As a next step within the QbD
process, data from relevant
batches are analyzed.
• Create a Process Reliability
Assessment (PRA) plot: QA’s
• Process Understanding + data
used to assess risk and support
decision to commercialize
process
• What coverage, with 90%
Confidence, fills Spec window?
pH example (not shown) 6.2-6.8 data
recorded to tenth: insufficient granularity
Is it relevant to combine and analyze PV Stage 1 &2 data? Maybe 
18
Control Strategy Implementation Activities
Holistic strategy mitigates any risk from a single unit
operation: e.g. in the step, a downstream purge, or an
analytical test. Could include:
– Facility/equipment qualification/ verification
– Validation
• Analytical methods, manufacturing, packaging, cleaning
– Training
• Operators, analysts, engineering/maintenance, technical
support…
• Understanding of product, process and control strategy
– What are the potential risks during processing?
– Which control strategy elements are the most critical?
19
Example Control Strategy for Dissolution
This equation opens up different control strategy options
20
Statistics – Design Space – Control Strategy - PPQ
How are design space verification and PPQ related?
Verification
Statistics tools:
Risk mitigation, confidence,
process/ product performance
Statistics Tools:
Visualization, Intervals,
Sampling, Simulation,
Modeling, DoE
QbD
Process
Understanding
Holistic
Control
Strategy
Engineering
Mechanistic
Models
Science
“Design Space” as a
Mathematical Model
PPQ
Statistics Tools:
Sampling
Acceptance Criteria
Batch Evaluation
The area tools
of theare
design
space
Statistical
useful
Design space
can be
a to
where we plan
toconfidence
operate could
understand
risk,
mathematical
expression
of levels,
be verified during PPQ,
butwith
process
process performance,
understanding,along
which
then
otherwise
PPQ
remains
essentially
other
science & risk
feeds supporting
into the development
of an
the
samerationale
as it should
bedeciding
driven by
based
when
appropriate control strategy. the
processcontrol
understanding
overall
strategy. and the
holistic control strategy.
21
Data Needed from PV Stage 1 in Preparation
for PV Stages 2 & 3
• Product and process knowledge
– Risk assessment, Cause & Effect matrix, experimental
outcomes
– Process performance data from development
• High level knowledge management document
with links to studies, reports etc
– Should be maintained as a lifecycle document
• Control Strategy – what to control
22
Final Thoughts…
• Through PV Stage 1, R&D Science designs the quality level
for the product
• Statistics has an important contribution to Design Space
• PPRE (and many other Statistical tools) are useful to
understand risk, confidence levels, process performance in
developing the control strategy
• Assurance of quality is provided by the control strategy
• Confidence in quality cannot be estimated based on data
alone
• Statistics is part of the solution but not the solution
23
Acknowledgements
• Kim Vukovinsky
•
•
•
•
•
•
•
•
Penny Butterell
Eric Cordi
Tom Garcia
Fasheng Li
Roger Nosal
Greg Steeno
Ke Wang
Tim Watson
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References
http://www.fda.gov/downloads/Drugs/Guidances/UCM070336.pdf
http://www.ispeboston.org/files/handouts_-_morrison.pdf
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References
http://www.mbswonline.com/presentationyear.php?year=2012
http://www.mbswonline.com/presentationyear.php?year=2013
http://www.mbswonline.com/presentationyear.php?year=2014
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http://www.iabs.org/index.php/docs/doc_download/386-iabs-settingspecifications-2013t-schofield
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http://www.ispe.org/2015-statistician-forum
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