A Bayesian Approach to the ICH Q8 Definition of Design Space

Abstract

Manufacturers of pharmaceuticals and biopharmaceuticals are facing increased regulatory pressure to understand how their manufacturing processes work and to be able to quantify the reliability and robustness of their manufacturing processes. In particular, the ICH Q8 guidance has introduced the concept of design space. The ICH Q8 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, relatively little has been put forth to date on how to construct a design space from data composed of such variables. This study presents a Bayesian approach to design space based upon a type of credible region first appearing in Peterson's work.This study considers the issues of constructing a Bayesian design space, design space reliability, the inclusion of process noise variables, and utilization of prior information, as well as an outline for organizing information about a design space so that manufacturing engineers can make informed changes as may be needed within the design space.

Key Words:

• Posterior predictive distribution
• Process capability
• Reliability
• Response surface methodology
• Robust parameter design

ACKNOWLEDGMENTS

I would like to thank Gregory Stockdale, Aili Cheng, Paul McAllister, Gillian Amphlett, Michael Denham, Darryl Downing, Valerii Fedorov, and Tim Schofield for their support and advice with regard to this Bayesian design space issue.