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Comparison of nonlinear mixed effects models and non-compartmental approaches in detecting pharmacogenetic covariates
- Source :
- AAPS Journal, AAPS Journal, American Association of Pharmaceutical Scientists, 2015, 17 (3), pp.597-608. ⟨10.1208/s12248-015-9726-8⟩
- Publication Year :
- 2015
- Publisher :
- HAL CCSD, 2015.
-
Abstract
- International audience; 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.
- Subjects :
- penalised regression
Genotype
Population
Biological Availability
Pharmaceutical Science
Single-nucleotide polymorphism
Biology
Models, Biological
Polymorphism, Single Nucleotide
Lasso (statistics)
[SDV.SP.MED]Life Sciences [q-bio]/Pharmaceutical sciences/Medication
Covariate
Statistics
False positive paradox
Humans
Computer Simulation
False Positive Reactions
noncompartmental analysis
education
False Negative Reactions
Probability
pharmacogenetics
education.field_of_study
Linear model
Area under the curve
nonlinear mixed effects models
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Regression
Phenotype
Nonlinear Dynamics
[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics
Area Under Curve
pharmacokinetics
[STAT.ME]Statistics [stat]/Methodology [stat.ME]
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 15507416
- Database :
- OpenAIRE
- Journal :
- AAPS Journal, AAPS Journal, American Association of Pharmaceutical Scientists, 2015, 17 (3), pp.597-608. ⟨10.1208/s12248-015-9726-8⟩
- Accession number :
- edsair.doi.dedup.....d85c65c492fc587187e8907817a9b086