Pharmacogenetics (PGt) studies the proportion of interindividual variability in drug response explained by genetic variations. Pharmacogenetics relates especially the genotypes of single nucleotide polymorphisms to pharmacokinetic (PK) variability of a drug. In the hopes to individualise treatments, genetic data is collected in many clinical trials. There is no consensus on methodology to study the effect of genetics on PK, especially during drug development. We investigate and compare methods for PGt analyses in PK early phase studies, to propose approaches enhancing the detection of genetic effects. In a first simulation based on a motivating example, we compare different methods used in PGt to detect the simulated effect of several genetic variants: methods to estimate the PK phenotype (noncompartmental analysis and nonlinear mixed effects models (NLMEM)); association methods (stepwise procedure and three penalised regressions: ridge regression, Lasso and HyperLasso). In a second simulation study we propose practical study designs to improve detection power of genetic variants during drug development. In a third study we assess through simulations an approach to correct the shrinkage in PK phenotype estimated through NLMEM which results in a reduced power to detect genetic variants. Through these different simulation studies, we propose recommendations for PGt analyses in PK studies during drug development.
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