Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Abstract

Abstract. Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the pharmacokinetics (PK) of a compound under development having a nonlinear bioavailability along with genotypes for 176 single nucleotide polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H 1 ), six SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario, 200 PK data sets were simulated under the null hypothesis (no gene effect) and H 1 . We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.

Modelling of atorvastatin pharmacokinetics and the identification of the effect of a BCRP polymorphism in the Japanese population

“…In contrast to a noncompartmental data analysis approach, the use of nonlinear mixed effects modelling allows the investigation of genetic effects even in the case where sparse data are available (as in the current study). Additional advantages also arise in a model-based approach such as increased statistical power to detect a genetic effect and improved interpretation of its underlying mechanism [31]. However, it should be clearly noted that in order to enjoy all the benefits from such an approach, studies that are adequately designed in terms of sample size and sampling times are needed.…”

“…Techniques for the incorporation of multiple SNPs in nonlinear mixed effects models have been recently proposed and compared [31] including both stepwise forward inclusionbackward elimination procedures [32] and penalised regressions [33]. In this study, the number of tested SNPs is relatively small and a computationally intensive stepwise procedure (extensively described in a previous work [18]) was employed for covariate model building.…”

Combined Analysis of Phase I and Phase II Data to Enhance the Power of Pharmacogenetic Tests

“…On the other hand, phase I studies generally provide good quality PK information, allowing characterization of the PK profile of the drug. We showed that from the different approaches used at this stage to estimate PK parameters, nonlinear mixed effects models (NLMEM) could be considerably more powerful than noncompartmental analyses (NCA) for complex PK models . Our simulations also showed that increasing the sample size, as in phase II studies, would improve the power to detect genetic variants.…”

“…Genetic data offer some unique challenges, in particular because they may lead to a very unbalanced number of subjects, which impacts the power of tests in pharmacogenetic analyses . In a previous simulation work, we showed that typical phase I studies have low power to detect genetic effects because of the limited sample size . On the other hand, phase I studies generally provide good quality PK information, allowing characterization of the PK profile of the drug.…”

“…A motivating example was provided by IRIS (Institut de Recherches Servier), a pharmaceutical industry, to generate realistic genetic and PK data. We compared two association methods, a penalized regression method and a stepwise procedure …”