Modeling methodologies developed for studying pharmacokinetic(PK)/pharmacodynamic(PD) processes confront many challenges related in part to the severe restrictions on the number and type of measurements that are available from laboratory experiments and clinical trials, as well as the variability in the experiments and the uncertainty associated with the processes themselves. Bayesian methods have provided a framework for PK/PD modeling and drug development that can address some of the above-mentioned challenges. This paper presents two illustrations of the application of Bayesian methods: the first involves a population modeling study of the cellular kinetics of the antiretroviral compound Lamivudine in the PBMCs of HIV-1 infected adolescents; the second uses a population mixture modeling approach to identifying hidden subpopulations that can not be identified by available measured covariates.

The analytical solution for multi-compartment models with a non-zero initial condition is complex because of the inter-compartmental transfer. An elegant solution and its implementation in the ‘wnl' R package can be useful in solving examples of textbooks and developing software of therapeutic drug monitoring, pharmacokinetic simulation, and parameter estimation. This solution uses Laplace transformation, convolution, matrix inversion, and the fact that the general solution of an inhomogeneous ordinary differential equation is the sum of a homogenous and a particular solution, together.
Drug Monitoring