Learning pharmacokinetic models for in vivo glucocorticoid activation
Author(s)
Type
Journal Article
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.
Date Issued
2018-10-14
Date Acceptance
2018-07-21
Citation
Journal of Theoretical Biology, 2018, 455, pp.222-231
ISSN
0022-5193
Publisher
Elsevier
Start Page
222
End Page
231
Journal / Book Title
Journal of Theoretical Biology
Volume
455
Copyright Statement
© 2018 The Authors. Published by Elsevier Ltd.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/30048717
PII: S0022-5193(18)30349-7
Subjects
11 beta-HSD activity
11-BETA-HSD1
AGE
Biology
Clustering
CUSHINGS-DISEASE
Dynamic systems
Expectation maximization
Gaussian mixture model
Identifiability analysis
In vivo glucocorticoid activation
INHIBITION
Life Sciences & Biomedicine
Life Sciences & Biomedicine - Other Topics
Mathematical & Computational Biology
Partially observed time series analysis
Perturbation analysis
Pharmacokinetics
Probabilistic models
Science & Technology
TISSUE
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2018-07-23