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

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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 (H1), 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 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.

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Abbreviations

FWER:

Family wise error rate

LOQ:

Limit of quantification

NCA:

Noncompartmental analysis

NLMEM:

Nonlinear mixed effects model

PK:

Pharmacokinetics

SNP:

Single nucleotide polymorphism

α:

Type I error

β:

Effect size coefficient

λ, γ, ξ:

Penalisation parameters

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Acknowledgments

Adrien Tessier received funding from Institut de Recherches Internationales Servier. The authors thank Laurent Ripoll and Bernard Walther from Institut de Recherches Internationales Servier for their advices in pharmacogenetics. The authors would also like to thank Herve Le Nagard for the use of the computer cluster services hosted on the “Centre de Biomodélisation UMR1137”.

Author information

Authors and Affiliations

  1. INSERM, IAME, UMR 1137, Faculté de médecine Paris Diderot Paris 7 - site Bichat, 16 rue Henri Huchard, 75018, Paris, France

    Adrien Tessier & Emmanuelle Comets

  2. Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, 75018, Paris, France

    Adrien Tessier & Emmanuelle Comets

  3. Division of Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France

    Adrien Tessier & Marylore Chenel

  4. University College London, Genetics Institute, London, UK

    Julie Bertrand

  5. INSERM CIC 1414, Université Rennes 1, Rennes, France

    Emmanuelle Comets

Authors
  1. Adrien Tessier
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  2. Julie Bertrand
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  3. Marylore Chenel
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  4. Emmanuelle Comets
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Corresponding author

Correspondence to Adrien Tessier.

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Tessier, A., Bertrand, J., Chenel, M. et al. Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates. AAPS J 17, 597–608 (2015). https://doi.org/10.1208/s12248-015-9726-8

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  • DOI: https://doi.org/10.1208/s12248-015-9726-8

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