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Parameter Estimation and Estimability Analysis in Pharmaceutical Models with Uncertain Inputs
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  • Iman Moshiritabrizi,
  • Kaveh Abdi,
  • Jonathan McMullen,
  • Brian Wyvratt,
  • Kimberley McAuley
Iman Moshiritabrizi
Queen's University
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Kaveh Abdi
Queen's University Faculty of Engineering and Applied Science
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Jonathan McMullen
Merck & Co., Inc.
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Brian Wyvratt
Merck & Co., Inc.
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Kimberley McAuley
Queen's University

Corresponding Author:[email protected]

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A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean-squared-error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Non-estimable parameters are fixed at their initial values to prevent overfitting. The method will aid error-in-variables parameter estimation in many situations involving limited data.
07 Mar 2023Submitted to AIChE Journal
12 Mar 2023Submission Checks Completed
12 Mar 2023Assigned to Editor
12 Mar 2023Review(s) Completed, Editorial Evaluation Pending
18 Mar 2023Reviewer(s) Assigned
13 Apr 2023Editorial Decision: Revise Minor
01 May 20231st Revision Received
17 May 2023Submission Checks Completed
17 May 2023Assigned to Editor
17 May 2023Review(s) Completed, Editorial Evaluation Pending
21 May 2023Reviewer(s) Assigned
05 Jun 2023Editorial Decision: Accept