Statistical Computation Tools in Pharmaceutical Drug Development and Manufacturing Life Cycle Fasheng Li Ke Wang Pharmaceutical Statistics Worldwide R&D, Pfizer, Inc 40th Annual MBSW Muncie, IN May 24, 2017

Motivation Statisticians often accumulate software in their daily work of actively providing statistical support Script: R, SAS, Matlab, JMP, etc. Template: MS Excel (w/wo VBA macros) or other spread sheets Considerations of what to do with these software pieces Align approaches across an organization Re-usability Sharing Validation Version control Computing resources (e.g. HPC grids) 2

Outline Overview of statistical computational applications (tools) developed in support of drug development and Chemistry, Manufacturing, and Controls Regulatory Affairs (Reg CMC) Why When How Internal Web Statistical Tool Cases (R program based) Evaluation of product stability and predicting shelf life or clinical use period Process parameter criticality assessment Design space exploration through designed experiment and parametric bootstrapping Conclusions/Remarks 3

Statistical Computational Challenges Statisticians in drug development and Regulatory CMC areas are facing more and more challenges: Higher work load Quicker turnaround More consistency and alignment of approaches More demanding on data modeling/mining More system traceability/repeatability Less man power resources 4

Statistical Computational Solutions Utilize accumulated software pieces (R or other based) to develop web-based statistical computational tools Standardized approaches Efficient Consistent Centralized software Version controlled Easy maintenance Validated/Verified software Accuracy/Reproducibility Easy to deal with regulatory queries Utilized HPC or Cloud Computing Resource Increased computational capability Powerful simulation work Much shorter computation time 5

Statistical Computational Tools 6

Internal Online Application Examples 7

Statistical Computational Tool - App 1 Evaluation of product stability and predicting shelf life or clinical use period 8 Stability

Statistical Computational Tool - App 1 Evaluation of product stability and predicting shelf life or clinical use period Design and collect stability data Predict Expected Shelf Life or Use-period Linear Regression Model Fit 9 Stability

Statistical Computational Tool - App 1 An Internal Online Application was set up 10 Stability

Statistical Computational Tool - App 1 11 Stability

Statistical Computational Tool - App 1 Result – displayed in web browser Result – linked downloadable PDF 12 Stability

Statistical Computational Tool - App 1 Evaluation of product stability and predicting shelf life or clinical use period Align statistical analyses of stability data Offer a quick and convenient web-based tool to generate Scatterplots of stability data Shelf life estimation for registration batches Typical three batches per package configuration; Guided by ICH Q1E Use Period Estimation for Clinical Batches Typically a single batch Guided by internal SOPs R program based Version controlled by the platform software (EASA) Easy to maintain for feature updates Run on HPC or Cloud grid computers 13 Stability

A Statistical Tool for Assessment of Criticality of Process Parameters Statistical Computational Tool - App 2 A Statistical Tool for Assessment of Criticality of Process Parameters 14 CPP

Statistical Computational Tool - App 2 A Statistical Tool (CPP) for Assessment of Criticality of Process Parameters Ref: Wang et al., Statistical Tools to Aid in the Assessment of Critical Process Parameters. Pharmaceutical Technology. 2016. 40 (3): 36-44. Assesses Statistical and Practical Significance Data from One-factor-at-a-time or Design of Experiments Process: Step 1: Process Risk Evaluation Evaluate a multifactor data set without focusing on a single parameter Determine how close results are to a target/specification Z Score Assessment Step 2: Parameter Effect Size Calculation Uses the Fitted Statistical Model to Quantify Individual Parameter Effects Quantifies the Effect Size - Impact of a Parameter on a Response Compares the Effect Size to the Specification with the 20% Rule 15 CPP

Statistical Computational Tool - App 2 A Statistical Tool (CPP) for Assessment of Criticality of Process Parameters Z Score Assessment For a CQA, y, with one-sided limit, such as L, the Z score is calculated as 𝑍= |𝑚𝑒𝑎𝑛(𝑦)−𝐿| 𝑠 Decision: a threshold of 2-6 was used If Z > 6, all of the statistical significant parameters are deemed Not Critical If Z < 2, all of the statistical significant parameters are deemed Critical If Z is between 2 and 6, parameter effect sizes are needed for further evaluation 16 CPP

Statistical Computational Tool - App 2 A Statistical Tool (CPP) for Assessment of Criticality of Process Parameters Parameter Effect Size The effect size (Δ) of a process parameter is defined as the maximum change of the predicted CQA caused by the process parameter change when other factors are fixed at all of their possible levels Analytically Numerically (by grid search in R) Decision: the 20% Rule If the effect size (Δ) is <= 20% of specification range, the process parameter is deemed Not Critical to the CQA If the effect size (Δ) is > 20% of specification range, the process parameter is deemed Critical to the CQA 17 CPP

Statistical Computational Tool - App 2 An Internal Online Application was set up 1. DOE Data File (.csv) 2. A Model File (.csv) 18 CPP

Statistical Computational Tool - App 2 An Internal Online Application: Further features to allow enhancement of the analyses and outputs 19 CPP

Statistical Computational Tool - App 2 Result – displayed in web browser Result – linked downloadable PDF 20 CPP

Statistical Computational Tool - App 2 A Statistical Tool (CPP) for Assessment of Criticality of Process Parameters 21 CPP

Prospective Process Reliability Estimate Statistical Computational Tool - App 3 Prospective Process Reliability Estimate Design space exploration through designed experiment and parametric bootstrapping 22 PPRE

Statistical Computational Tool - App 3 Process Prospective Reliability Estimate Driven by Quality by Design Concept - ICH Q8-Q10 Scientific, risk-based, holistic, and proactive approach to pharmaceutical development Deliberate design effort from product conception through commercialization Full understanding of how product attributes and process parameters relate to product performance Visualization of Design Space Multidimensional – many process parameters Multiple responses – many quality attributes with specifications Pros/Cons of Traditional Overlay Space Limitations Operating region is defined on average (50% probability) The risk along the edge of the region may not be acceptable Limited information for decision making E.g. want a higher probability to meet specifications Lack of flexibility No alternatives to mean prediction of responses/attributes Tedious/erroneous to get a summary from the software Advantages Readily available in commercial software (e.g. Design Expert) No extra programing needs 23 PPRE

Statistical Computational Tool - App 3 Prospective Process Reliability Estimate For a CQA (y) with a two-sided spec (Lspec and Uspec), the probability to meet specification at 𝑋 ℎ can be estimated as P(y >= Lspec & y <= Uspec |  , 𝛴 , 𝑋 ℎ ) For multiple CQAs, the confirmatory rate can be calculated as the probability to simultaneously meet all specifications at given levels of 𝑋 ℎ Ref: Peterson et al. A Bayesian reliability approach to multiple response optimization with seemingly unrelated regression models. Quality Technology & Quantitative Management. 2009. 6 (4): 353-369. 24 PPRE

Statistical Computational Tool - App 3 Prospective Process Reliability Estimate Pros/Cons of Traditional Overlay Space Limitations Operating region is defined on average (50% probability) The risk along the edge of the region may not be acceptable Limited information for decision making E.g. want a higher probability to meet specifications Lack of flexibility No alternatives to mean prediction of responses/attributes Tedious/erroneous to get a summary from the software Advantages Readily available in commercial software (e.g. Design Expert) No extra programing needs Parallel computing algorithm implemented in the R code 25 PPRE

Statistical Computational Tool - App 3 An Internal Online Application was set up 1. DOE Data File (.csv) 2. A Model File (.csv) 26 26 PPRE

Statistical Computational Tool - App 3 An Internal Online Application: Further features to allow choose of analyses and enhance outputs Result – Displayed in browser 27 PPRE

Summary Statisticians (and Scientists) in drug development and Reg CMC areas can perform routine statistical analyses with Increased consistency Improved efficiency Better alignment of statistical analyses Easily retrieved results By Standardizing statistical approaches Centralizing software pieces Validating/Verifying software pieces Utilizing high performance computer resource Deploying web-based applications

Acknowledgment Kim Vukovinsky, Senior Director, Pharmaceutical Statistics, Worldwide R&D, Pfizer Inc. 29