Using Prior Parameter Knowledge in Model‐Based Design of Experiments for Pharmaceutical Production

Abstract

Sequential model‐based design of experiments (MBDoE) uses information from previous experiments to select new experimental conditions. Computation of MBDoE objective functions can be impossible due to a non‐invertible Fisher Information Matrix (FIM). Previously, we evaluated a leave‐out (LO) approach that designed experiments by removing problematic model parameters from the design process. Unfortunately, the LO approach can be computationally expensive due to its iterative nature. In this study, we propose a simplified Bayesian approach that makes the FIM invertible by accounting for prior parameter information. We compare the proposed simplified Bayesian approach to the LO approach for sequential A‐optimal design. Results from a pharmaceutical case study show that the proposed approach is superior, on average, for design of experiments. We suggest that simplified Bayesian MBDoE should be combined with a subset‐selection‐based approach for parameter estimation. This combined methodology gave the best results on average for the case study.