Learning pharmacokinetic models for in vivo glucocorticoid activation
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...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2018
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| _version_ | 1797055049461923840 |
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| author | Bunte, K Smith, D Chappell, M Hassan-Smith, Z Tomlinson, J Arlt, W Tiňo, P |
| author_facet | Bunte, K Smith, D Chappell, M Hassan-Smith, Z Tomlinson, J Arlt, W Tiňo, P |
| author_sort | Bunte, K |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T19:05:59Z |
| format | Journal article |
| id | oxford-uuid:152682be-23a6-4f0d-95b9-0c8fbcfc335e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:05:59Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:152682be-23a6-4f0d-95b9-0c8fbcfc335e2022-03-26T10:23:56ZLearning pharmacokinetic models for in vivo glucocorticoid activationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:152682be-23a6-4f0d-95b9-0c8fbcfc335eEnglishSymplectic Elements at OxfordElsevier2018Bunte, KSmith, DChappell, MHassan-Smith, ZTomlinson, JArlt, WTiňo, PTo 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. |
| spellingShingle | Bunte, K Smith, D Chappell, M Hassan-Smith, Z Tomlinson, J Arlt, W Tiňo, P Learning pharmacokinetic models for in vivo glucocorticoid activation |
| title | Learning pharmacokinetic models for in vivo glucocorticoid activation |
| title_full | Learning pharmacokinetic models for in vivo glucocorticoid activation |
| title_fullStr | Learning pharmacokinetic models for in vivo glucocorticoid activation |
| title_full_unstemmed | Learning pharmacokinetic models for in vivo glucocorticoid activation |
| title_short | Learning pharmacokinetic models for in vivo glucocorticoid activation |
| title_sort | learning pharmacokinetic models for in vivo glucocorticoid activation |
| work_keys_str_mv | AT buntek learningpharmacokineticmodelsforinvivoglucocorticoidactivation AT smithd learningpharmacokineticmodelsforinvivoglucocorticoidactivation AT chappellm learningpharmacokineticmodelsforinvivoglucocorticoidactivation AT hassansmithz learningpharmacokineticmodelsforinvivoglucocorticoidactivation AT tomlinsonj learningpharmacokineticmodelsforinvivoglucocorticoidactivation AT arltw learningpharmacokineticmodelsforinvivoglucocorticoidactivation AT tinop learningpharmacokineticmodelsforinvivoglucocorticoidactivation |