The use of mechanistic DM‐PK‐PD modelling to assess the power of pharmacogenetic studies –CYP2C9 and warfarin as an example

• Published in: British journal of clinical pharmacology Vol. 64; no. 1; pp. 14 - 26
• Main Authors: Dickinson, Gemma L., Lennard, Martin S., Tucker, Geoffrey T., Rostami‐Hodjegan, Amin
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.07.2007; Blackwell Science; Blackwell Science Inc
• Subjects: Anticoagulants - administration & dosage; Anticoagulants - pharmacokinetics; Aryl Hydrocarbon Hydroxylases - genetics; Biological and medical sciences; Blood Coagulation - genetics; clinical trial simulation; Cytochrome P-450 CYP2C9; cytochrome P450; drug metabolism; Genotype; Humans; in silico; Medical sciences; modelling and simulation; Models, Biological; pharmacogenetics; Pharmacokinetics & Pharmacodynamics; Pharmacology. Drug treatments; Polymorphism, Genetic; Warfarin - administration & dosage; Warfarin - pharmacokinetics; Human; Drug; Pharmacogenetics; Warfarin; Enzyme; Isozyme; Coumarine derivatives; Cytochrome P450; Anticoagulant; Metabolism; Silica; Antivitamin K; Simulation; drug metabolism; Genetics; Clinical trial; modelling and simulation; Pharmacokinetics; in silica; CYP2C9 gene; clinical trial simulation
• DOI: 10.1111/j.1365-2125.2007.02850.x
• ISSN: 0306-5251

What is already known about this subject • Many studies have shown that genetic polymorphisms of the CYP2C9 gene 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 of CYP2C9 on 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 the in vitro PK 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. Aim To assess the power of in vivo studies needed to discern the effect of genotype on pharmacokinetics (PK) and pharmacodynamics (PD) using CYP2C9 and (S)‐warfarin as an example. Methods Information on the in vitro metabolism 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 h INR) between CYP2C9 genotypes was investigated. Results A 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%, χ2 test, 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. Conclusion The utilization of prior information (including in vivo enzymology) in clinical trial simulations can guide the design of subsequent in vivo studies of the impact of genetic polymorphisms, and may help to avoid costly, inconclusive outcomes.