Bayesian Population Analysis of Age-Related Physiologically Based Pharmacokinetic Model of Pyrethroids in Rats

A Bayesian Population Compartmental Absorption and Transit Modeling Approach to Support Generic Drug Development: Application to Bupropion Hydrochloride Oral Dosage Forms

Well-tempered MCMC simulations for population pharmacokinetic models

Well-Tempered MCMC Simulations for Population Pharmacokinetic Models

A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, in particular in a population context, gives access to powerful inference, including on model structure. Markov Chain Monte Carlo (MCMC) samplers are typically used to estimate the joint posterior parameter distribution of interest. Among MCMC samplers, the simulated tempering algorithm (TMCMC) has a number of advantages: it can sample from sharp multi-modal posteriors; it provides insight into identifiability issues useful for model simplification; it can be used to compute accurate Bayes factors for model choice; the simulated Markov chains mix quickly and have assured convergence in certain conditions.

pksensi: An R package to apply global sensitivity analysis in physiologically based kinetic modeling

Sensitivity analysis (SA) is an essential tool for modelers to understand the influence of model parameters on model outputs. It is also increasingly used in developing and assessing physiologically based kinetic (PBK) models. For instance, several studies have applied global SA to reduce the computational burden in the Bayesian Markov chain Monte Carlo-based calibration process PBK models. Although several SA algorithms and software packages are available, no comprehensive software package exists that allows users to seamlessly solve differential equations in a PBK model, conduct and visualize SA results, and discriminate between the non-influential model parameters that can be fixed and those that need calibration.

PoPKAT: A Framework for Bayesian Population PBPK Analysis

Applying a global sensitivity analysis workflow to improve the computational efficiencies in physiologically-based pharmacokinetic modeling

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias.

U01 FD005838

Enhancing the Reliability, Efficiency, and Usability of Bayesian Population PBPK Modeling