POPULATION PHARMACOKINETIC MODELING ON A SUPERCOMPUTER

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DESCRIPTION (Taken from project abstract): In this resource-related research project, we propose to: 1) Implement on a supercomputer our current nonparametric EM and iterative Bayesian software for population pharmacokinetic modeling, currently running on PC's; 2) Implement extensions of this software to include large nonlinear pharmacokinetic and pharmacodynamic (PK/PD) models of drug effect and toxicity; 3) Develop and implement new software to compute optimally informative schedules and protocols for monitoring serum drug levels and measuring drug effects in multicenter clinical drug trials; and 4) Develop and implement a friendly graphical user interface (GUI) and communication package for access to the supercomputer over the World Wide Web and the Internet, as a fast and efficient research resource. Population PK/PD modeling is the key to understanding how drugs behave in people. It provides the basic structure for understanding what happens in clinical drug trials, and for the intelligent design of clinical drug trials to evaluate treatment of a disease process. However, there are currently two significant bottlenecks in such modeling efforts: 1) current software has been unfriendly for researchers to use, and 2) it takes a long time to analyze large data sets. As the programs are usually run on PC's, investigators have been significantly limited in the number of data sets from large clinical trials which they can analyze. A national research resource with a friendly GUI on the researcher's PC that can easily be connected to a supercomputer, available over the Web and the Internet, will be highly useful to many workers in the NIH, in academic centers, and in the pharmaceutical industry. With this resource they will be able easily and rapidly to analyze their data and estimate population parameter distributions and individual parameter values. Further, the software proposed to obtain the most informative and cost-effective strategies for measuring serum levels and drug effects, both therapeutic and toxic, will provide an easy and rapid method for optimal and most cost-effective design of protocols for multicenter drug trials and concentration controlled clinical trials. The population models and optimal monitoring schedules will also readily link to existing software for new "multiple model" design of drug regimens for optimally precise achievement of desired clinical goals, minimizing patient variability, both in "concentration controlled" and in "effect controlled" clinical drug trials.