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

dc.contributor.authorShahmohammadi, Ali
dc.contributor.authorMcAuley, Kimberley
dc.date.accessioned2020-08-17T15:20:33Z
dc.date.available2020-08-17T15:20:33Z
dc.date.issued2020-08-11
dc.descriptionThis is the peer reviewed version of the following article: Shahmohammadi, A. and McAuley, K.B. (2020), Using Prior Parameter Knowledge in Model‐Based Design of Experiments for Pharmaceutical Production. AIChE J. Accepted Author Manuscript. doi:10.1002/aic.17021, which has been published in final form at https://doi.org/10.1002/aic.17021. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.en
dc.description.abstractSequential 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.en
dc.identifier.citationShahmohammadi, A. and McAuley, K.B. (2020), Using Prior Parameter Knowledge in Model‐Based Design of Experiments for Pharmaceutical Production. AIChE J. Accepted Author Manuscript. doi:10.1002/aic.17021en
dc.identifier.doihttps://doi.org/10.1002/aic.17021
dc.identifier.urihttp://hdl.handle.net/1974/28011
dc.language.isoenen
dc.publisherWileyen
dc.relationOntario Trillium Scholarshipen
dc.subjectSequential Model-Based DoEen
dc.subjectLO Approachen
dc.subjectSimplified Bayesian Approachen
dc.subjectParameter Estimationen
dc.subjectMathematical Modelingen
dc.titleUsing Prior Parameter Knowledge in Model‐Based Design of Experiments for Pharmaceutical Productionen
dc.typejournal articleen
project.funder.identifierhttp://dx.doi.org/10.13039/100004312en
project.funder.identifierhttp://dx.doi.org/10.13039/501100000236en
project.funder.nameEli Lilly (United States)en
project.funder.nameOntario Trillium Foundationen
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
aic.17021.pdf
Size:
1.55 MB
Format:
Adobe Portable Document Format
Description:
Accepted Author Manuscript
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.77 KB
Format:
Item-specific license agreed upon to submission
Description: