AimsThe metabolic pathways leading to the formation of prasugrel and clopidogrel active metabolites differ. We hypothesized that decreased CYP2C19 activity affects the pharmacokinetic and pharmacodynamic response to clopidogrel but not prasugrel.Methods and resultsNinety-eight patients with coronary artery disease (CAD) taking either clopidogrel 600 mg loading dose (LD)/75 mg maintenance dose (MD) or prasugrel 60 mg LD/10 mg MD were genotyped for variation in six CYP genes. Based on CYP genotype, patients were segregated into two groups: normal function (extensive) metabolizers (EM) and reduced function metabolizers (RM). Plasma active metabolite concentrations were measured at 30 min, 1, 2, 4, and 6 h post-LD and during the MD period on Day 2, Day 14, and Day 29 at 30 min, 1, 2, and 4 h. Vasodilator-stimulated phosphoprotein (VASP) and VerifyNow™ P2Y12 were measured predose, 2, and 24 ± 4 h post-LD and predose during the MD period on Day 14 ± 3 and Day 29 ± 3. For clopidogrel, active metabolite exposure was significantly lower (P = 0.0015) and VASP platelet reactivity index (PRI, %) and VerifyNow™ P2Y12 reaction unit (PRU) values were significantly higher (P < 0.05) in the CYP2C19 RM compared with the EM group. For prasugrel, there was no statistically significant difference in active metabolite exposure or pharmacodynamic response between CYP2C19 EM and RM. Variation in the other five genes demonstrated no statistically significant differences in pharmacokinetic or pharmacodynamic responses.ConclusionVariation in the gene encoding CYP2C19 in patients with stable CAD contributes to reduced exposure to clopidogrel's active metabolite and a corresponding reduction in P2Y12 inhibition, but has no significant influence on the response to prasugrel.

Variation in individual response to statin therapy has been widely studied for a potential genetic component. Multiple genes have been identified as potential modulators of statin response, but few study findings have replicated. To further examine these associations, 2735 individuals on statin therapy, half on atorvastatin and the other half divided among fluvastatin, lovastatin, pravastatin and simvastatin were genotyped for 43 SNPs in 16 genes that have been implicated in statin response. Associations with lowdensity lipoprotein cholesterol (LDL-C) lowering, total cholesterol lowering, HDL-C elevation and triglyceride lowering were examined. The only significant associations with LDL-C lowering were found with apoE2 in which carriers of the rare allele who took atorvastatin lowered their LDL-C by 3.5% more than those homozygous for the common allele and with rs2032582 (S893A in ABCB1) in which the two groups of homozygotes differed by 3% in LDL-C lowering. These genetic effects were smaller than those observed with the demographic variables of age and gender. The magnitude of all the differences found is sufficiently small that genetic data from these genes should not influence clinical decisions on statin administration.

In the fight against hard-to-treat diseases such as cancer, it is often difficult to discover new treatments that benefit all subjects. For regulatory agency approval, it is more practical to identify subgroups of subjects for whom the treatment has an enhanced effect. Regression trees are natural for this task because they partition the data space. We briefly review existing regression tree algorithms. Then we introduce three new ones that are practically free of selection bias and are applicable to data from randomized trials with two or more treatments, censored response variables, and missing values in the predictor variables. The algorithms extend the GUIDE approach by using three key ideas: (i) treatment as a linear predictor, (ii) chi-squared tests to detect residual patterns and lack of fit, and (iii) proportional hazards modeling via Poisson regression. Importance scores with thresholds for identifying influential variables are obtained as by-products. A bootstrap technique is used to construct confidence intervals for the treatment effects in each node. The methods are compared using real and simulated data.

The advent of high-throughput technologies has proven valuable in the assessment of genetic differences and their effects on drug activation, metabolism, disposition, and transport. However, most studies to date have focused on a small number of genes or few alleles, some of which are rare and therefore observed infrequently or lacked rigorous ethnic characterization, thus reducing the ability to extrapolate within and among populations. In this study, the authors comprehensively assessed the allele frequencies of 165 variants comprising 27 drug-metabolizing enzyme and transporter (DMET) genes from 2188 participants across 3 major ethnic populations: Caucasians, Africans, and East Asians. This sample size was sufficiently large to demonstrate genetic differences among these major ethnic groups while concomitantly confirming similarities among East Asian subpopulations (Korean, Han Chinese, and Japanese). A comprehensive presentation of allele and genotype frequencies is included in the online supplement, and 3 of the most widely studied cytochrome P450 (CYP) genes, CYP2D6, CYP2C19, and CYP2C9; 2 non-CYP enzymes, NAT1 and TMPT; and 2 transporter genes, SLCO1B1 and SLCO2B1, are presented herein according to ethnic classification.

michael.man@pfizer.com