1. Establishing and Predicting Quality: Process Validation - Stage 1
Brad Evans / Kim Vukovinsky
Pfizer
May 20, 2015
Kims slides from PV Conference…Kim to place on Sharepoint / add to references. (location to be noted in the minutes from Monday staff mtg)
Start at Slide 21 next time

2. 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?
Could change the questions
* Tools and topics are not equally distributed across all applications, e.g. mAbs, Vaccines, DP, API, Parenterals

3. Stages of Process Validation
Stage 1: Process Design
Stage 2: Process Qualification
Stage 3: Continued Process Verification

4. 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
Information vs data (ie model)

5. 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

6. … and how do the results influence PV Stage 2
… 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. *

7. Holistic control strategy
Quality Assurance … Process Understanding, Control Strategy, Specifications
Controls
Parameters, attributes, GMP, business
Boundaries/ limits
PARs, design space, release limits
Holistic control strategy
Product Efficacy, Patient safety, Reduced Cost to Society
Process understanding + Control Strategy gives quality (“don’t test way into compliance”) or tight spec that we have truncate the actual output..
Especially if Specs are just arbirtrary.
… assurance from the total quality system including the process definition + control strategy + testing … tight specifications are not the only way

8. Multifactor Understanding: DOE + Data
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

9. Contour Plots: Two Responses, Two Process Parameters
Want to be less than 0.10 for both impurities

10. 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
Makes it seem “binary”
Scale dependent / scale independent (Ie does small scale predict large scale?)

11. 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

12. 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
Why is *this* 50% Contour so far from the previous 50% Contour?
Covariance of outputs

13. 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 , 2009.
Data points New Betas Data Dist Counting.

14. 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
Spec changed from 0.1 to (0.1% was “default spec” but perhaps not well thought out)
0.1 would have been hard to meet with high reliability.
Increase Spec And 0.3 was still safe so we changed Spec
Change Set point up to Northwest

15. 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.
Different example where Moving set point was “the fix”
Grey out the CP-xxx contours
Still have Release Assays

16. 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
Sometimes the Lab Scale DOE should not be run if the connection Lab to full is so poor.
If we ever had enough data at full scale (at different settings) to KNOW that we match well with Lab,
Then we wouldn’t need small
* Garcia, Thomas, et. al. “Verification of Design Space Developed at Subscale”, Journal of Pharmaceutical Innovation, Vol 7, pg (2012).

17. Design Space Verification*
Option: Verify as required
Option: verification at set-point
Option: Verify a region around set-point
At set point or around set point
As required: change in raw material, equipment causes us to run but still inside region (not an accidental move)
* Garcia, Thomas, et. al. “Verification of Design Space Developed at Subscale”, Journal of Pharmaceutical Innovation, Vol.7, pg (2012).

18. 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?
Replace with an example that doesn’t have 0 twice
X and S are quite variable with small n
Is it relevant to combine: ISPE paper might recommend up to 21 validation lots (ouch!)
Stage 1 data *might* come from full scale so use as PV data / part of PV data If appropriate
pH example (not shown) data recorded to tenth: insufficient granularity
Is it relevant to combine and analyze PV Stage 1 &2 data? Maybe 

19. 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?
Not just tightening the Specs

20. Example Control Strategy for Dissolution
Process Knowledge + Set Point + Control Strategy drives the Quality
API: controlled in high shear
Particle size controlled FBRM
This equation opens up different control strategy options

21. Holistic Control Strategy PPQ
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
Holistic Control Strategy
PPQ
QbD
Process Understanding
Statistics Tools:
Sampling
Acceptance Criteria
Batch Evaluation
Some of the text boxes were overlapping / hidden
Perhaps when I changed the .ptt slide layout they got misaligned?
Animation mode takes care of it
Look on sharepoint
The area of the design space where we plan to operate could be verified during PPQ, but otherwise PPQ remains essentially the same as it should be driven by process understanding and the holistic control strategy.
Statistical tools are useful to understand risk, confidence levels, process performance, along with other supporting science & risk based rationale when deciding the overall control strategy.
Engineering
Design space can be a mathematical expression of process understanding, which then feeds into the development of an appropriate control strategy.
Mechanistic
Models
Science
“Design Space” as a
Mathematical Model

22. 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

23. 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

24. Acknowledgements Kim Vukovinsky Penny Butterell Eric Cordi Tom Garcia
Fasheng Li
Roger Nosal
Greg Steeno
Ke Wang
Tim Watson

25. References http://www.fda.gov/downloads/Drugs/Guidances/UCM070336.pdf

26. References http://www.mbswonline.com/presentationyear.php?year=2012

27. http://www. iabs. org/index