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Using Prior Parameter Knowledge in Model-Based Design of Experiments for Pharmaceutical Production
  • Ali Shahmohammadi,
  • Kimberley McAuley
Ali Shahmohammadi
University of Texas at Austin
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Kimberley McAuley
Queens University
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Abstract

Sequential model-based design of experiments (MBDoE) uses information from previous experiments to select run conditions for new experiments. Computation of the objective functions for popular MBDoE can be impossible due to a non-invertible Fisher Information Matrix (FIM). Previously, we evaluated a leave-out (LO) approach that design experiments by removing problematic model parameters from the design process. However, the LO approach can be computationally expensive due to its iterative nature and some model parameters are ignored. In this study, we propose a simple Bayesian approach that makes the FIM invertible by accounting for prior parameter information. We compare the proposed Bayesian approach to the LO approach for designing sequential A-optimal experiments. Results from a pharmaceutical case study show that the Bayesian approach is superior, on average, to the LO approach for design of experiments. However, for subsequent parameter estimation, a subset-selection-based LO approach gives better parameter values than the Bayesian approach.

Peer review status:ACCEPTED

24 Mar 2020Submitted to AIChE Journal
26 Mar 2020Submission Checks Completed
26 Mar 2020Assigned to Editor
08 Apr 2020Reviewer(s) Assigned
19 May 2020Editorial Decision: Revise Major
24 Jun 20201st Revision Received
07 Jul 2020Submission Checks Completed
07 Jul 2020Assigned to Editor
08 Jul 2020Reviewer(s) Assigned
09 Aug 2020Editorial Decision: Accept