Comparison of Model-Based Tests and Selection Strategies to Detect Genetic Polymorphisms Influencing Pharmacokinetic Parameters

Abstract

We evaluate by simulation three model-based methods to test the influence of a single nucleotide polymorphism on a pharmacokinetic parameter of a drug: analysis of variance (ANOVA) on the empirical Bayes estimates of the individual parameters, likelihood ratio test between models with and without genetic covariate, and Wald tests on the parameters of the model with covariate. Analyses are performed using the FO and FOCE method implemented in the NONMEM software. We compare several approaches for model selection based on tests and global criteria. We illustrate the results with pharmacokinetic data on indinavir from HIV-positive patients included in COPHAR 2-ANRS 111 to study the gene effect prospectively. Only the tests based on the EBE obtain an empirical type I error close to the expected 5%. The approximation made with the FO algorithm results in a significant inflation of the type I error of the LRT and Wald tests.

Key Words:

• Genetics
• Model selection
• NONMEM
• Nonlinear mixed effects model
• Pharmacokinetics
• Test

ACKNOWLEDGMENTS

We would like to thank the COPHAR 2-ANRS 111 scientific committee (investigators Pr. D. Salmon and Dr. X. Duval, pharmacology; Pr. J. M. Tréluyer, methodology; and Pr. F. Mentré) for giving us access to the PK data of the indinavir arm in order to build our simulations and to illustrate our topic. We would also like to thank the IFR02 of INSERM and Hervé Le Nagard for the use of the “centre de biomodélisation.”

Notes

K is the number of data sets on which the test could be performed. ∗Estimate significantly different from 5%.

K is the number of data sets on which the test could be performed. The corrected power was obtained using the fifth percentile of the empirical distribution of the test statistic under H ₀ as the cut-off value for the test.

K is the number of data sets on which the test could be performed. ¹ M _base: {β₀ = β₁ = β₂ = 1} (CC = CT = TT) model with no gene effect. M _recessive: {β₀ = β₁ = 1, β₂ ≠ 1} (CC = CT ≠ TT), reduced model. M _dominant: {β₀ = 1, β₁ = β₂ ≠ 1} (CC ≠ CT = TT), reduced model. M _mult: {β₀ = 1, β₁ ≠ β₂ ≠ 1} (CC ≠ CT ≠ TT), complete model.

K is the number of data sets on which the test could be performed. ¹ M _base: {β₀ = β₁ = β₂ = 1} (CC = CT = TT) model with no gene effect. M _recessive: {β₀ = β₁ = 1, β₂ ≠ 1} (CC = CT ≠ TT), reduced model. M _dominant: {β₀ = 1, β₁ = β₂ ≠ 1} (CC ≠ CT = TT), reduced model. M _mult: {β₀ = 1, β₁ ≠ β₂ ≠ 1} (CC ≠ CT ≠ TT), complete model. Results obtained with FO are not presented for these strategies because of their poor performance under H ₀ (Table 3).