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

Abstract

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.

Integrating Full Bayesian Inference and Student's t‐Distribution Method for Enhanced Outlier Handling in Caffeine Population Pharmacokinetics: Assessing Drug–Drug Interactions with Enasidenib in Relapsed or Refractory AML and MDS Patients

“…20 In contrast, full Bayesian MCMC methods, while widely employed in various scientific communities and capable of integrating prior knowledge and data into analyses, have seen limited application in the PK field. 25,26 Bayesian inference in its essence depends on Bayes rule, that is, the posterior distribution is proportional to the likelihood times the prior information. 26 This approach provides full posterior distributions of parameters, offering a more detailed understanding of parameter uncertainty and greater modeling flexibility, especially beneficial for complex PK models.…”

“…25,26 Bayesian inference in its essence depends on Bayes rule, that is, the posterior distribution is proportional to the likelihood times the prior information. 26 This approach provides full posterior distributions of parameters, offering a more detailed understanding of parameter uncertainty and greater modeling flexibility, especially beneficial for complex PK models. Despite being introduced in NONMEM 7 and higher versions, 20,21 the adoption of full Bayesian methods in PK remains limited.…”

“…In contrast, full Bayesian MCMC methods, while widely employed in various scientific communities and capable of integrating prior knowledge and data into analyses, have seen limited application in the PK field 25,26 . Bayesian inference in its essence depends on Bayes rule, that is, the posterior distribution is proportional to the likelihood times the prior information 26 .…”

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction

“…Various recent studies, including from the author, have described pharmacokinetic models of the bovine mammary gland which embed physiologic information as fixed values, either in a minimally physiologic framework (Cao & Jusko, 2012), for example, Whittem (2019a, 2019b), or the more traditional physiologically based framework, for example Tardiveau et al (2022). A Bayesian framework, implemented, for example, in Stan (Elmokadem et al, 2023), allows for more rigorous implementation of existing information and propagation of its uncertainty (Lin & Chen, 2021).…”

Computational Pharmaceutics: A Comprehensive Review of Past Achievements, Present Applications, and Future Challenges

“…PBPK modeling plays a crucial role in predicting the pharmacokinetic/pharmacodynamic behavior of pharmaceutical formulations (Deepika & Kumar, 2023;Denninger et al, 2023;Elmokadem et al, 2023). By integrating in vitro dissolution data with PBPK tools, such as PK-Sim and GastroPlus®, accurate predictions of human pharmacokinetics can be achieved.…”