title: 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.
authors.affiliation: GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania 19426, USA. john.peterson@gsk.com
authors.forename: John J
authors.initials: JJ
authors.lastname: Peterson
mesh_terms: D001499:Bayes Theorem; D001709:Biotechnology; D002626:Chemistry, Pharmaceutical; D004345:Drug Industry; D017408:Guidelines as Topic; D007391:International Cooperation; D012107:Research Design