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Learning pharmacokinetic models for in vivo glucocorticoid activation
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Bunte, Kerstin, Smith, David J., Chappell, M. J. (Michael J.), Hassan-Smith, Zaki K., Tomlinson, Jeremy W., Arlt, Wiebke and Tiňo, Peter (2018) Learning pharmacokinetic models for in vivo glucocorticoid activation. Journal of Theoretical Biology, 455 . pp. 222-231. doi:10.1016/j.jtbi.2018.07.025 ISSN 0022-5193.
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Official URL: https://doi.org/10.1016/j.jtbi.2018.07.025
Abstract
To understand trends in individual responses to medication, one can take a purely data-driven machine learning approach, or alternatively apply pharmacokinetics combined with mixed-effects statistical modelling. To take advantage of the predictive power of machine learning and the explanatory power of pharmacokinetics, we propose a latent variable mixture model for learning clusters of pharmacokinetic models demonstrated on a clinical data set investigating 11β-hydroxysteroid dehydrogenase enzymes (11β-HSD) activity in healthy adults. The proposed strategy automatically constructs different population models that are not based on prior knowledge or experimental design, but result naturally as mixture component models of the global latent variable mixture model. We study the parameter of the underlying multi-compartment ordinary differential equation model via identifiability analysis on the observable measurements, which reveals the model is structurally locally identifiable. Further approximation with a perturbation technique enables efficient training of the proposed probabilistic latent variable mixture clustering technique using Estimation Maximization. The training on the clinical data results in 4 clusters reflecting the prednisone conversion rate over a period of 4 h based on venous blood samples taken at 20-min intervals. The learned clusters differ in prednisone absorption as well as prednisone/prednisolone conversion. In the discussion section we include a detailed investigation of the relationship of the pharmacokinetic parameters of the trained cluster models for possible or plausible physiological explanation and correlations analysis using additional phenotypic participant measurements. [Abstract copyright: Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.]
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > Q Science (General) R Medicine > RM Therapeutics. Pharmacology |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Pharmacokinetics, Machine learning, Dehydrogenases, Enzymes | |||||||||||||||
Journal or Publication Title: | Journal of Theoretical Biology | |||||||||||||||
Publisher: | Elsevier | |||||||||||||||
ISSN: | 0022-5193 | |||||||||||||||
Official Date: | 14 October 2018 | |||||||||||||||
Dates: |
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Volume: | 455 | |||||||||||||||
Page Range: | pp. 222-231 | |||||||||||||||
DOI: | 10.1016/j.jtbi.2018.07.025 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 20 September 2018 | |||||||||||||||
Date of first compliant Open Access: | 20 September 2018 | |||||||||||||||
RIOXX Funder/Project Grant: |
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