The use of mechanistic DM‐PK‐PD modelling to assess the power of pharmacogenetic studies –CYP2C9and warfarin as an example

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Abstract
What is already known about this subject• Many studies have shown that genetic polymorphisms of theCYP2C9gene contribute to some of the variability (around 20%) in warfarin dose requirements and therapeutic response to the drug.• It is also clear that this effect must be elicited through differences in the plasma (S)‐warfarin concentration between individuals of different genotypes, although assessing the effects of any single genotype ofCYP2C9on the kinetics of (S)‐warfarin has generally failed.What this study adds• The study aims to simulate the impact of genetic polymorphism in CYP2C9 on both the pharmacokinetics (PK) and pharmacodynamics (PD) of (S)‐warfarin using a mechanistic, population approach to modelling.• The outcomes with respect to the design of studies and their statistical power are compared against those of actual reported studies.• The exercise with warfarin is offered as an example of how prior information on thein vitroPK and PD of new drugs might be used in association with knowledge of relevant genetic polymorphisms and their frequencies to carry out virtual clinical studies as an aid to the design, optimization and powering of subsequent real clinical trials assessing the impact of specific genetic differences.AimTo assess the power ofin vivostudies needed to discern the effect of genotype on pharmacokinetics (PK) and pharmacodynamics (PD) usingCYP2C9and (S)‐warfarin as an example.MethodsInformation on thein vitrometabolism of (S)‐warfarin and genetic variation in CYP2C9 was incorporated into a mechanistic population‐based PK–PD model. The influence of study design on the ability to detect significant differences in PK (AUC0−12 h) and PD (AUEC0−12 hINR) betweenCYP2C9genotypes was investigated.ResultsA study size of 90 (based on the natural abundance of genotypes and uniform dosage) was required to achieve 80% power to discriminate the PK of (S)‐warfarin between wild type (*1/*1) and the combination of all other genotypes. About 250 subjects were needed to detect a difference in anticoagulant response. The power to detect differences between specific genotypes was much lower. Analysis of experimental comparisons of the PK or PD between wild‐type and other individual genotypes indicated that only 21% of cases (20 of 95 comparisons within 11 PD and four PK–PD studies) reported statistically significant differences. This was similar to the percentage expected from our simulations (20%, χ2test,P = 0.80). Simulations of studies enriched with specific genotypes indicated that only three and five subjects were required to detect differences in PK and PD between wild type and the *3/*3 genotype, respectively.ConclusionThe utilization of prior information (includingin vivoenzymology) in clinical trial simulations can guide the design of subsequentin vivostudies of the impact of genetic polymorphisms, and may help to avoid costly, inconclusive outcomes.