Optimal design in population kinetic experiments by set-valued methods
Population pharmacometric experiments are used to study subject processes of drug absorption, distribution, elimination, and effect/side-effect, and how these vary across subjects in a population. Such knowledge is important in order to propose relevant dosing strategies and guidelines for drugs. Proper system modelling and design of experiments are crucial in order to maximise information extraction and thereby determine model parameters as exact as possible. It is often desirable to use sparse sampling in late stages of population kinetic analysis studies (e.g., phases II and III of clinical trials in drug development), mainly due to limited resources and practical reasons. To obtain accurate and precise parameter estimates from sparse data, formulating and solving optimal experimental design problems is of importance. The aim of the project is to develop efficient algorithms for optimal design in this area. We focus on non-linear mixed effect models, and the techniques will be based on work by Warwick Tucker on interval analysis.
The project is carried out by Peter Gennemark, CIM, and supervised by Warwick Tucker (Dept. of Mathematics, Uppsala University) and Andrew Hooker (Dept. of Pharmaceutical Biosciences, Uppsala).