Integration of high-throughput genotyping data into pharmacometric analyses using nonlinear mixed effects modeling

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

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

AIM

Ethnicity plays a modulating role in atorvastatin pharmacokinetics (PK), with Asian patients reported to have higher exposure compared with Caucasians. Therefore, it is difficult to safely extrapolate atorvastatin PK data and models across ethnic groups. This work aims to develop a population PK model for atorvastatin and its pharmacologically active metabolites specifically for the Japanese population. Subsequently, it aimed to identify genetic polymorphisms affecting atorvastatin PK in this population.

METHODS

Atorvastatin acid (ATA) and ortho-hydroxy-atorvastatin acid (o-OH-ATA) plasma concentrations, clinical/demographic characteristics and genotypes for 18 (3, 3, 1, 1, 7, 2 and 1 in the ABCB1, ABCG2, CYP3A4, CYP3A5, SLCO1B1, SLCO2B1 and PPARA genes, respectively) genetic polymorphisms were collected from 27 Japanese individuals (taking 10 mg atorvastatin once daily) and analysed using a population PK modelling approach.

RESULTS

The population PK model developed (one-compartment for ATA linked through metabolite formation to an additional compartment describing the disposition of o-OH-ATA) accurately described the observed data and the associated population variability. Our analysis suggested that patients carrying one variant allele for the rs2622604 polymorphism (ABCG2) show a 55% (95% confidence interval: 16-131%) increase in atorvastatin oral bioavailability relative to the value in individuals without the variant allele.

CONCLUSION

The current work reports the identification in the Japanese population of a BCRP polymorphism, not previously associated with the PK of any statin, that markedly increases ATA and o-OH-ATA exposure. The model developed may be of clinical importance to guide dosing recommendations tailored specifically for the Japanese.

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

We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated with six genetic markers randomly sampled in each simulated dataset. We compared penalized regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even if sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimization brings a further improvement, and we highlight a direct relationship between η‐shrinkage and loss of genetic signal.

Integrating dynamic mixed-effect modelling and penalized regression to explore genetic association with pharmacokinetics

CONTEXT

In a previous work, we have shown that penalized regression approaches can allow many genetic variants to be incorporated into sophisticated pharmacokinetic (PK) models in a way that is both computationally and statistically efficient. The phenotypes were the individual model parameter estimates, obtained a posteriori of the model fit and known to be sensitive to the study design.

OBJECTIVE

The aim of this study was to propose an integrated approach in which genetic effect sizes are estimated simultaneously with the PK model parameters, which should improve the estimate precision and reduce sensitivity to study design.

METHODS

A total of 200 data sets were simulated under the null and each of the following three alternative scenarios: (i) a phase II study with N=300 participants and n=6 sampling times, wherein six unobserved causal variants affect the drug elimination clearance; (ii) the addition of participants with a residual concentration collected in clinical routine (N=300, n=6 plus N=700, n=1); and (iii) a phase II study (N=300, n=6) in which four unobserved causal variants affect two different model parameters.

RESULTS

In all scenarios the integrated approach detected fewer false positives. In scenario (i), true-positive rates were low and the stepwise procedure outperformed the integrated approach. In scenario (ii), approaches performed similarly and rates were higher. In scenario (iii), the integrated approach outperformed the stepwise procedure.

CONCLUSION

A PK phase II study with N=300 lacks the power to detect genetic effects on PK using genetic arrays. Our approach can simultaneously analyse phase II and clinical routine data and identify when genetic variants affect multiple PK parameters.

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

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.

Identification of the effect of multiple polymorphisms on the pharmacokinetics of simvastatin and simvastatin acid using a population-modeling approach

The aim of this work was to develop a joint population pharmacokinetic model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), that incorporates the effects of multiple genetic polymorphisms and clinical/demographic characteristics. SV/SVA plasma concentrations, demographic/clinical data, and genotypes for 18 genetic variants were collected from 74 individuals (three clinical trials) and analyzed using a nonlinear mixed-effects modeling approach. The structural model that best described the data included a two- and a one-compartment disposition model for SV and SVA, respectively. Age, weight, Japanese ethnicity, and seven genetic polymorphisms-rs4149056 (SLCO1B1), rs776746 (CYP3A5), rs12422149 (SLCO2B1), rs2231142 (ABCG2), rs4148162 (ABCG2), rs4253728 (PPARA), and rs35599367 (CYP3A4)-were identified as significantly affecting model parameters. The developed model was used to assess combinations of these covariates, highlighting specific risk factors associated with altered SV/SVA pharmacokinetics, and consequently myopathy cases that cannot be solely attributed to the rs4149056 CC genotype.

A semi-physiological model of amyloid-β biosynthesis and clearance in human cerebrospinal fluid: a tool for alzheimer's disease research and drug development

Stable isotope labeling kinetics (SILK) was successfully applied to quantify endogenous amyloid-β (Aβ) metabolism in human cerebrospinal fluid (CSF). A semi-physiological model describing Aβ biosynthesis and degradation in human CSF and the impact of the γ-secretase inhibitor semagacestat should be developed and validated based on digitized data from three published SILK studies. Aβ biosynthesis was adequately characterized by six transit compartments. At each transition step, a first-order degradation process was implemented. A two-compartment model best described semagacestat CSF concentration-time profiles. Semagacestat concentrations were linked to the Aβ production by an inhibitory Emax model. For model validation, three individual Aβ profiles from literature were successfully predicted. Model application demonstrated a 35% decreased Aβ elimination rate constant in Alzheimer's disease (AD) patients. Study design optimization revealed that SILK studies could be conducted with significant less sampling points compared to the standard protocol without losing information about the Aβ metabolism, if analyzed by the presented model. In conclusion, the analysis outlined the advantages and opportunities of integrating all available data and knowledge into a semi-physiological model. The model can serve as valuable tool for researchers and clinicians interested in the pathology of AD as well as in the development of new therapeutics for AD.

Genetic Determinants of Dabigatran Plasma Levels and Their Relation to Bleeding

Background—

Fixed-dose unmonitored treatment with dabigatran etexilate is effective and has a favorable safety profile in the prevention of stroke in atrial fibrillation patients compared with warfarin. We hypothesized that genetic variants could contribute to interindividual variability in blood concentrations of the active metabolite of dabigatran etexilate and influence the safety and efficacy of dabigatran.

Methods and Results—

We successfully conducted a genome-wide association study in 2944 Randomized Evaluation of Long-term Anticoagulation Therapy (RE-LY) participants. The CES1 single-nucleotide polymorphism rs2244613 was associated with trough concentrations, and the ABCB1 single-nucleotide polymorphism rs4148738 and the CES1 single-nucleotide polymorphism rs8192935 were associated with peak concentrations at genome-wide significance ( P <9×10 −8 ) with a gene-dose effect. Each minor allele of the CES1 single-nucleotide polymorphism rs2244613 was associated with lower trough concentrations (15% decrease per allele; 95% confidence interval, 10–19; P =1.2×10 −8 ) and a lower risk of any bleeding (odds ratio, 0.67; 95% confidence interval, 0.55–0.82; P =7×10 −5 ) in dabigatran-treated participants, with a consistent but nonsignificant lower risk of major bleeding (odds ratio, 0.66; 95% confidence interval, 0.43–1.01). The interaction between treatment (warfarin versus all dabigatran) and carrier status was statistically significant ( P =0.002), with carriers having less bleeding with dabigatran than warfarin (hazard ratio, 0.59; 95% confidence interval, 0.46–0.76; P =5.2×10 − 5) in contrast to no difference in noncarriers (hazard ratio, 0.96; 95% confidence interval, 0.81–1.14; P =0.65). There was no association with ischemic events, and neither rs4148738 nor rs8192935 was associated with bleeding or ischemic events.

Conclusions—

Genome-wide association analysis identified that carriage of the CES1 rs2244613 minor allele occurred in 32.8% of patients in RE-LY and was associated with lower exposure to active dabigatran metabolite. The presence of the polymorphism was associated with a lower risk of bleeding.

Clinical Trial Registration—

URL: http://www.clinicaltrials.gov . Unique identifier: NCT00262600.