A novel Gibbs maximum a posteriori (GMAP) approach on Bayesian nonlinear mixed-effects population pharmacokinetics (PK) models.

In this paper, various Bayesian Monte Carlo Markov chain (MCMC) methods and the proposed algorithm, the Gibbs maximum a posteriori (GMAP) algorithm, are compared for implementing the nonlinear mixed-effects model in pharmacokinetics (PK) studies. An intravenous two-compartmental PK model is adopted to fit the PK data from the midazolam (MDZ) studies, which recruited twenty-four individuals with nine different time points per subject. The three-stage hierarchical nonlinear mixed model is constructed. Data analysis and model performance comparisons show that GMAP converges the fastest and provides reliable results. At the mean time, data augmentation (DA) methods are used for the Random-walk Metropolis method. Data analysis shows that the speed of the convergence of Random-walk Metropolis can be improved by DA, but all of them are not as fast as GMAP. The performance of GMAP and various MCMC algorithms are compared through Midazolam data analysis and simulation.