No abstract

Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

In various pharmaceutical applications, repeated measurements are taken from each subject, and model parameters are estimated from the collected data. Examples include dose response modeling and PK/PD studies with serial blood sampling, among others. The quality of the information in an experiment is reflected in the precision of estimates of model parameters, which is traditionally measured by their variance-covariance matrix. In this article, we concentrate on the example of a clinical PK study where multiple blood samples are taken for each enrolled patient, which leads to nonlinear mixed effects regression models with multiple responses. The sampling scheme for each patient is considered a multidimensional point in the space of admissible sampling sequences. We demonstrate how to optimize the precision of parameter estimates by finding the best number and allocation of sampling times. It is shown that a reduced number of samples may be taken without significant loss of precision of parameter estimates. Moreover, our approach allows for taking experimental costs into account, which leads to a more meaningful comparison of sampling schemes and to potential cost savings.

No abstract

We discuss optimal experimental design issues for nonlinear models arising in dose response studies. The optimization is pegormed with respect to various criteria which depend on the Fisher information matrix. Special attention is given to models with a variance component that depends on unknown parameters.