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Establishing and Predicting Quality: Process Validation - Stage 1 Brad Evans / Kim Vukovinsky Pfizer May 20, 2015

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

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Stages of Process Validation Pfizer Confidential 3 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 4 http://www.fda.gov/downloads/Drugs/Guidances/UCM070336.pdf

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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

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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

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7 Quality Assurance Process Understanding, Control Strategy, Specifications assurance from the total quality system including the process definition + control strategy + testing tight specifications are not the only way Product Efficacy, Patient safety, Reduced Cost to Society

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Models + Requirements Impurity1 = f(B, C) Impurity2 = f(B) Impurity1 < 0.1% Impurity2 < 0.1% Analysis + Visualization + Decisions Multifactor Understanding: DOE + Data Pfizers Right First Time / QbD Process Statistical Component Risk Assessment

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

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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 Plot of Two Responses vs. Two PPs

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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

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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

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Prospective Process Reliability Estimate (PPRE) John J. Peterson, Guillermo Mir-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.

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Estimated Probability of Passing Original 0.1% Spec Specification Increase to Achieve Quality Requirements Estimated Probability of Passing New 0.3% Spec based on Safety Decision Making - End Process Attribute

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Decision Making - In Process Attribute Set Point Moved to Achieve Cost Target 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.

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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 DoEs 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 16 * 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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Design Space Verification* Option: Verify a region around set- point Option: Verify as required 17 Option: verification at set-point * 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: QAs Process Understanding + data used to assess risk and support decision to commercialize process What coverage, with 90% Confidence, fills Spec window? 18 Is it relevant to combine and analyze PV Stage 1 &2 data? Maybe pH example (not shown) 6.2-6.8 data recorded to tenth: insufficient granularity

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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

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Example Control Strategy for Dissolution 20 This equation opens up different control strategy options

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QbD Process Understanding 21 Design Space as a Mathematical Model Statistics Tools: Visualization, Intervals, Sampling, Simulation, Modeling, DoE Statistics Design Space Control Strategy - PPQ How are design space verification and PPQ related? PPQ Verification Statistics Tools: Sampling Acceptance Criteria Batch Evaluation Science Mechanistic Models Engineering Holistic Control Strategy Statistics tools: Risk mitigation, confidence, process/ product performance Design space can be a mathematical expression of process understanding, which then feeds into the development of an appropriate 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. 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.

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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

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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

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

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References 25 http://www.fda.gov/downloads/Drugs/Guidances/UCM070336.pdf http://www.ispeboston.org/files/handouts_-_morrison.pdf

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References 26 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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27 http://www.iabs.org/index.php/docs/doc_download/386-iabs-setting- specifications-2013t-schofield

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http://www.ispe.org/2015-statistician-forum Pfizer Confidential 28

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Pfizer Confidential 29