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 http://ecf.pfizer.com/sites/PharmSciPGSStat/Shared%20Documents/Forms/AllItems.aspx?RootFolder=%2Fsites%2FPharmSciPGSStat%2FShared%20Documents%2FOur%20Publications%20Articles%20Conference%20Presentations%2FExternal%20Conference%20Information%2FISPE%20Statistics%20Process%20Validation%202015

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

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

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

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

… 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. * http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q8_R1/Step4/Q8_R2_Guideline.pdf

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

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

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

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?)

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

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

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 Spec changed from 0.1 to 0.3. (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

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

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. 13-18 (2012).

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. 13-18 (2012).

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) 6.2-6.8 data recorded to tenth: insufficient granularity Is it relevant to combine and analyze PV Stage 1 &2 data? Maybe 

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

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

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

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

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

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

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

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

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

http://www.ispe.org/2015-statistician-forum