Abstract:
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Pharmacokinetic experiments, conducted in the early stages of drug development process, play an important role in assessing the association between different enzyme activities and substrate under study. Nonlinear (e.g. Michealis-Menten model) mixed effects (NLME) models are commonly used for analyzing these experiments. Enzyme activities, related to the human liver microsomes (HLM) used in the experiments, are considered as covariates for modelling the random parameters of the NLME model, which helps to identify important enzymes for metabolizing the substrate. Outcome of this analysis can make important contributions to the future course of the research with the substrate. Designing such pharmacokinetic experiments requires considering the combination of HLM (enzyme activities) and substrate concentration levels as treatment factor and often a rich design, based on all possible treatments, is used in industrial setup. Depending on the type of the model assumed for metabolism of the substrate, three design criteria are proposed. We can show that optimum designs based on the proposed criteria can get the required information in many fewer runs compared to the rich design.
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