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* Corresponding author 1 IAME - Infection, Antimicrobiens, Modélisation, Evolution 2 Pharmacocinétique Clinique 3 Department of Genetics, Evolution, and Environment 4 CIC - Centre d-Investigation Clinique Rennes

Abstract : We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic PK studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated to 6 genetic markers randomly sampled in each simulated dataset. We compared penalised regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimisation brings a further improvement, and we highlight a direct relationship between η-shrinkage and loss of genetic signal.

Keywords : Design Clinical drug development. Pharmacogenetics Pharmacokinetics Nonlinear mixed effects models





Autor: Adrien Tessier - Julie Bertrand - Marylore Chenel - Emmanuelle Comets -

Fuente: https://hal.archives-ouvertes.fr/



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