Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling

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dc.contributor.authorZhang, Tonglien
dc.contributor.authorTyson, John J.en
dc.date.accessioned2022-08-22T13:47:29Zen
dc.date.available2022-08-22T13:47:29Zen
dc.date.issued2022-02en
dc.description.abstractIndividual biological organisms are characterized by daunting heterogeneity, which precludes describing or understanding populations of 'patients' with a single mathematical model. Recently, the field of quantitative systems pharmacology (QSP) has adopted the notion of virtual patients (VPs) to cope with this challenge. A typical population of VPs represents the behavior of a heterogeneous patient population with a distribution of parameter values over a mathematical model of fixed structure. Though this notion of VPs is a powerful tool to describe patients' heterogeneity, the analysis and understanding of these VPs present new challenges to systems pharmacologists. Here, using a model of the hypothalamic-pituitary-adrenal axis, we show that an integrated pipeline that combines machine learning (ML) and bifurcation analysis can be used to effectively and efficiently analyse the behaviors observed in populations of VPs. Compared with local sensitivity analyses, ML allows us to capture and analyse the contributions of simultaneous changes of multiple model parameters. Following up with bifurcation analysis, we are able to provide rigorous mechanistic insight regarding the influences of ML-identified parameters on the dynamical system's behaviors. In this work, we illustrate the utility of this pipeline and suggest that its wider adoption will facilitate the use of VPs in the practice of systems pharmacology.en
dc.description.notesThe work was supported by Grant 1016183 ARMY W911NF-20-1-0192 to TZ.en
dc.description.sponsorship[1016183 ARMY W911NF-20-1-0192]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s10928-021-09798-1en
dc.identifier.eissn1573-8744en
dc.identifier.issn1567-567Xen
dc.identifier.issue1en
dc.identifier.pmid34985622en
dc.identifier.urihttp://hdl.handle.net/10919/111577en
dc.identifier.volume49en
dc.language.isoenen
dc.publisherSpringer/Plenum Publishersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectQuantitative systems pharmacologyen
dc.subjectVirtual patientsen
dc.subjectMachine learningen
dc.subjectBifurcation analysisen
dc.subjectNonlinear dynamicsen
dc.subjectHypothalamic-pituitary-adrenal axisen
dc.titleUnderstanding virtual patients efficiently and rigorously by combining machine learning with dynamical modellingen
dc.title.serialJournal of Pharmacokinetics and Pharmacodynamicsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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