Well-tempered MCMC simulations for population pharmacokinetic models
- PDF / 3,071,241 Bytes
- 17 Pages / 595.276 x 790.866 pts Page_size
- 34 Downloads / 193 Views
ORIGINAL PAPER
Well-tempered MCMC simulations for population pharmacokinetic models Frederic Y. Bois1
•
Nan-Hung Hsieh2 • Wang Gao3 • Weihsueh A. Chiu2 • Brad Reisfeld4
Received: 21 November 2019 / Accepted: 12 July 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. The main challenge when implementing this approach is to find an adequate scale of auxiliary inverse temperatures (perks) and associated scaling constants. We solved that problem by adaptive stochastic optimization and describe our implementation of TMCMC sampling in the GNU MCSim software. Once a grid of perks is obtained, it is easy to perform posteriortempered MCMC sampling or likelihood-tempered MCMC (thermodynamic integration, which bridges the joint prior and the posterior parameter distributions, with assured convergence of a single sampling chain). We compare TMCMC to other samplers and demonstrate its efficient sampling of multi-modal posteriors and calculation of Bayes factors in two stylized case-studies and two realistic population pharmacokinetic inference problems, one of them involving a large PBPK model. Keywords Thermodynamic integration population pharmacokinetics physiologically-based pharmacokinetic model Bayesian inference Bayes factor computational efficiency Abbreviations CV Coefficient of variation MCMC Markov chain Monte Carlo PBPK Physiologically-based pharmacokinetic PK Pharmacokinetic
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10928-020-09705-0) contains supplementary material, which is available to authorized users. & Frederic Y. Bois [email protected] 1
Certara UK Limited, Simcyp Division, Sheffield, UK
2
Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
3
DRC/VIVA/METO Unit, INERIS, Verneuil en Halatte, France
4
Department Chemical and Biological Engineering, School of Biomedical Engineering, Colorado State University, Fort Collins, CO, USA
SD TI
Standard deviation Thermodynamic integration
Introduction In modeling pharmacokinetics and pharmacodynamics, a Bayesian statistical framework gives access to a range of flexible inference options, including hierarchical non-conjugate models for population modeling [1, 2] o
Data Loading...
