Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl
Physiologically‐based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowle...
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Veröffentlicht in: | CPT: Pharmacometrics & Systems Pharmacology 2023-03, Vol.12 (3), p.300-310 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Physiologically‐based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well‐suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters. This tutorial demonstrates a full population Bayesian PBPK analysis framework using R/Stan/Torsten and Julia/SciML/Turing.jl. |
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ISSN: | 2163-8306 2163-8306 |
DOI: | 10.1002/psp4.12926 |