1st Edition

Case Studies in Bayesian Methods for Biopharmaceutical CMC

Edited By Paul Faya, Tony Pourmohamad Copyright 2023
    354 Pages 134 B/W Illustrations
    by Chapman & Hall

    354 Pages 134 B/W Illustrations
    by Chapman & Hall

    The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter). The collection of case studies covers a broad array of CMC topics, including stability analysis, analytical method development, specification setting, process development and optimization, process control, experimental design, dissolution testing, and comparability studies. The analysis of each case study includes a presentation of code and reproducible output. This book is written with an academic level aimed at practicing nonclinical biostatisticians, most of whom have graduate degrees in statistics.

    • First book of its kind focusing strictly on CMC Bayesian case studies

    • Case studies with code and output

    • Representation from several companies across the industry as well as academia

    • Authors are leading and well-known Bayesian statisticians in the CMC field

    • Accompanying website with code for reproducibility

    • Reflective of real-life industry applications/problems

    1. Introduction

    Paul Faya and Tony Pourmohamad

    2. An Overview of Bayesian Computation

    David J. Kahle, John W. Seaman Jr., and James D. Stamey

    3. Basic Bayesian Model Checking

    John W. Seaman Jr., David J. Kahle, and James D. Stamey

    4. Quantitative Decision - Making, a CMC application to analytical method equivalence

    Misbah Ahmed and Mike Denham

    5. Bayesian Dissolution Testing

    Tony Pourmohamad and Robert Richardson

    6. A Non-Normal Bayesian Model for the Estimation and Comparison of Immunogenicity Screening Assay Cut-Points

    David LeBlond and Robert Singer, Lu Xu, and Rong Zeng

    7. Application of Bayesian Hierarchical Models to Experimental Design

    Adam P. Rauk and Paul Faya

    8. Bayesian Prediction for Staged Testing Procedures

    Katherine E.D. Giacoletti and Tara Scherder

    9. A Bayesian Approach to Multivariate Conditional Regression Surrogate Modeling with Application to Real Time Release Testing

    Stan Altan, Dwaine Banton, Hans Coppenolle, Martin Kovarik and Martin Otava, and Christian Schmid

    10. Bayesian Approach for Demonstrating Analytical Similarity

    Harry Yang and Steven Novick

    11. Bayesian Evaluation and Monitoring of Process Comparability

    Ke Wang and Aili Cheng

    12. Bayesian Alternatives to Traditional Methods for Estimating Product Shelf Life and Internal Release Limits

    Perceval Sondag, Ji Young Kim, Laurent Natalis, and Tara Scherder

    13. Application of Bayesian Methods for Specification Setting

    Chris Thompson and Guillermo Miro-Quesada

    14. Calculating Statistical Tolerance Intervals Using SAS

    Richard Lewis and Buffy Hudson-Curtis

    15. A Bayesian Application in Process Monitoring – Establishing Limits for Dosage Units in Early Phase Process Control

    Yanbing Zheng, James Reynolds, Man Tang, Mark Johnson and Hesham Fahmy


    Paul Faya (Ph.D.) is a Director in Discovery and Development Statistics with Eli Lilly and Company, USA.

    Tony Pourmohamad (Ph.D.) is a Principal Statistical Scientist with Genentech, USA, and an Assistant Adjunct Professor in the Department of Statistics at the University of California, Santa Cruz.