Comparison of nonlinear mixed effects models and non-compartmental approaches in detecting pharmacogenetic covariates - Université de Rennes Accéder directement au contenu
Article Dans Une Revue AAPS Journal Année : 2015

Comparison of nonlinear mixed effects models and non-compartmental approaches in detecting pharmacogenetic covariates

Résumé

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 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), 6 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 H1. 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.
Fichier principal
Vignette du fichier
Article_AAPSJ_RevisedVersionVF.pdf (1.16 Mo) Télécharger le fichier
Simulated polymorphisms informations.pdf (659.99 Ko) Télécharger le fichier
Supplementary materials.pdf (373.68 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Origine : Fichiers produits par l'(les) auteur(s)
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01119174 , version 1 (21-02-2015)

Identifiants

Citer

Adrien Tessier, Julie Bertrand, Marylore Chenel, Emmanuelle Comets. Comparison of nonlinear mixed effects models and non-compartmental approaches in detecting pharmacogenetic covariates: Approaches to detect pharmacogenetic covariates. AAPS Journal, 2015, 17 (3), pp.597-608. ⟨10.1208/s12248-015-9726-8⟩. ⟨hal-01119174⟩
284 Consultations
285 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More