Parameter Estimation for Physiologically Based Pharmacokinetics Model Using Bayesian Inference

摘要

Physiologically based pharmacokinetics (PBPK) model can predict absorption, degradation, execration and other metabolism in drug delivery system. Thus it can be useful for regulating dose and estimating drug concentration at a particular time during the clinical demonstration. PBPK model is expressed as a set of differential equation with various parameters. Bio-chip experimental data are often noisy and sparse. This makes it difficult to estimate parameters with conventional least squares approaches. The resulting parameters often have a large confidence region. This work presents a Bayesian inference algorithm with an objective function suitable for PBPK model. A Markove Chain Monte Carlo (MCMC) method is employed to estimate the posterior distribution of the parameters. We illustrate the approach with a Tegafur delivery system.