Bayesian <scp>PBPK</scp> modeling using R/Stan/Torsten and Julia/<scp>SciML</scp>/Turing.Jl-Reference-Cited by-同舟云学术

Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl

Published:2023-02-05 Issue:3 Volume:12 Page:300-310
ISSN:2163-8306
Container-title:CPT: Pharmacometrics & Systems Pharmacology
language:en
Short-container-title:CPT Pharmacom & Syst Pharma

Author:

Elmokadem Ahmed¹^ORCID,Zhang Yi²,Knab Timothy¹,Jordie Eric¹,Gillespie William R.¹^ORCID

Affiliation:

1. Metrum Research Group Tariffville Connecticut USA

2. Sage Therapeutics, Inc. Cambridge Massachusetts USA

Abstract

AbstractPhysiologically‐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.

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12926

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