Risk Evaluation of Registered Specifications and Internal Release Limits Using a Bayesian Approach
This article proposes to pursue advanced statistical approaches to quantify risks systematically through a product lifecycle for sound decision making. The work focuses on registered specifications and internal release limits as these are important elements in pharmaceutical development, manufacturing, and supply to ensure product safety, efficacy, and quality. Bayesian inference is explored as a potential valuable approach to enhance risk assessment and related decision making. A Bayesian approach is utilized to predict risks of batch failure and poor process capability associated with registered specifications and internal release limits, leading to a more effective specification setting process. The benefits are demonstrated using a real-life case.
KeywordsRisk assessment Bayesian Specification Release limit Product lifecycle Process verification Robustness Process capability
The work was first presented at the 2015 Nonclinical Biostatistics Conference in October 2015 and then 2016 Midwest Biopharmaceutical Statistics Workshop in May 2016. The authors thank Mary Ann Gorko for guidance and insightful review as the 2016 Midwest Biopharmaceutical Statistics Workshop CMC Section Chair. They acknowledge Dr. Jose Tabora and Dr. Renfei Yan for support to developing the case for demonstration. They also appreciate Dr. Siheng He, Joel Young, Dr. Ronald Behling, Jennifer Walsh, Dr. Lindsay Hobson, and Don Buglino for helpful discussions pertaining to this work.
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