Population pharmacokinetics analysis in Lixoft Monolix softwares

Платова, А. И.

doi:10.37489/2587-7836-2021-3-36-51

Cited by 1 publication

(1 citation statement)

References 26 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This tutorial demonstrated a practical application of building population Bayesian PBPK models using two different open‐source approaches in two different programming languages, namely R/Stan/Torsten and Julia/SciML/Turing.jl. In contrast to tools applying nonlinear mixed‐effects modeling to calculate point estimates in pharmacometric analysis, like the open‐source R package nlmixr 17 or the commercial software Monolix, 27 the approaches presented here carry the advantage of being able to run full Bayesian analyses to infer parameter posterior distributions that characterize the uncertainty around these parameters rather than just inferring the point estimates. Full Bayesian approaches also allow for the incorporation of the investigator's prior knowledge of the system, which can be substantial in PBPK applications.…”

Section: Discussionmentioning

confidence: 99%

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

Elmokadem

Zhang

Knab

et al. 2023

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%