Summary. Background: Thienopyridines are metabolized to active metabolites that irreversibly inhibit the platelet P2Y 12 adenosine diphosphate receptor. The pharmacodynamic response to clopidogrel is more variable than the response to prasugrel, but the reasons for variation in response to clopidogrel are not well characterized. Objective: To determine the relationship between genetic variation in cytochrome P450 (CYP) isoenzymes and the pharmacokinetic/pharmacodynamic response to prasugrel and clopidogrel. Methods: Genotyping was performed for CYP1A2, CYP2B6, CYP2C19, CYP2C9, CYP3A4 and CYP3A5 on samples from healthy subjects participating in studies evaluating pharmacokinetic and pharmacodynamic responses to prasugrel (60 mg, n = 71) or clopidogrel (300 mg, n = 74). Results: In subjects receiving clopidogrel, the presence of the CYP2C19*2 loss of function variant was significantly associated with lower exposure to clopidogrel active metabolite, as measured by the area under the concentration curve (AUC 0-24 ; P = 0.004) and maximal plasma concentration (C max ; P = 0.020), lower inhibition of platelet aggregation at 4 h (P = 0.003) and poor-responder status (P = 0.030). Similarly, CYP2C9 loss of function variants were significantly associated with lower AUC 0-24 (P = 0.043), lower C max (P = 0.006), lower IPA (P = 0.046) and poor-responder status (P = 0.024). For prasugrel, there was no relationship observed between CYP2C19 or CYP2C9 loss of function genotypes and exposure to the active metabolite of prasugrel or pharmacodynamic response. Conclusions: The common loss of function polymorphisms of CYP2C19 and CYP2C9 are associated with decreased exposure to the active metabolite of clopidogrel but not prasugrel. Decreased exposure to its active metabolite is associated with a diminished pharmacodynamic response to clopidogrel.

Prasugrel and clopidogrel inhibit platelet aggregation through active metabolite formation. Prasugrel's active metabolite (R-138727) is formed primarily by cytochrome P450 (CYP) 3A and CYP2B6, with roles for CYP2C9 and CYP2C19. Clopidogrel's activation involves two sequential steps by CYP3A, CYP1A2, CYP2C9, CYP2C19, and/or CYP2B6. In a randomized crossover study, healthy subjects received a loading dose (LD) of prasugrel (60 mg) or clopidogrel (300 mg), followed by five daily maintenance doses (MDs) (15 and 75 mg, respectively) with or without the potent CYP3A inhibitor ketoconazole (400 mg/day). Subjects had a 2-week washout between periods. Ketoconazole decreased R-138727 and clopidogrel active metabolite Cmax (maximum plasma concentration) 34-61% after prasugrel and clopidogrel dosing. Ketoconazole did not affect R-138727 exposure or prasugrel's inhibition of platelet aggregation (IPA). Ketoconazole decreased clopidogrel's active metabolite AUC0-24 (area under the concentration-time curve to 24 h postdose) 22% (LD) to 29% (MD) and reduced IPA 28% (LD) to 33% (MD). We conclude that CYP3A4 and CYP3A5 inhibition by ketoconazole affects formation of clopidogrel's but not prasugrel's active metabolite. The decreased formation of clopidogrel's active metabolite is associated with reduced IPA.

Human immunodeficiency virus (HIV) protease inhibitors (PIs) are inhibitors of CYP3A enzymes, but the mechanism is poorly defined. In this study, time-and concentration-dependent decreases in activity as defined by maximum rate of inactivation (k inact ) and inhibitor concentration that gives 50% maximal inactivation (K I ) of CYP3A by amprenavir, indinavir, lopinavir, nelfinavir, ritonavir, and saquinavir were quantified using testosterone 6␤-hydroxylation as a marker for CYP3A activity with recombinant CYP3A4(ϩb 5 ), recombinant CYP3A5, and pooled human liver microsomes (HLMs). All the PIs, except indinavir, displayed inactivation with CYP3A4(ϩb 5 ) and HLMs. Ritonavir was the most potent (K I ϭ 0.10 and 0.17 M) and demonstrated high k inact values (0.32 and 0.40 min Ϫ1 ) with both CYP3A4(ϩb 5 ) and HLMs. Ritonavir was not significantly depleted by high-affinity binding with CYP3A4(ϩb 5 ) and confirmed that estimation of reversible inhibition was confounded with irreversible inhibition. For CYP3A5, nelfinavir exhibited the highest k inact (0.47 min Ϫ1 ), but ritonavir was the most potent (K I ϭ 0.12 M). Saquinavir and indinavir did not show time-and concentration-dependent decreases in activity with CYP3A5. Spectrophototmetrically determined metabolic intermediate complex formation was observed for all of the PIs with CYP3A4(ϩb 5 ), except for lopinavir and saquinavir. The addition of nucleophilic and free aldehyde trapping agents and free iron and reactive oxygen species scavengers did not prevent inactivation of CYP3A4(ϩb 5 ) by ritonavir, amprenavir, or nelfinavir, but glutathione decreased the inactivation by saquinavir (17%) and catalase decreased the inactivation by lopinavir (39%). In conclusion, all the PIs exhibited mechanism-based inactivation, and predictions of the extent and time course of drug interactions with PIs could be underestimated if based solely on reversible inhibition.

Prasugrel pharmacodynamics and pharmacokinetics after a 60-mg loading dose (LD) and daily 10-mg maintenance doses (MD) were compared in a 3-way crossover study to clopidogrel 600-mg/75-mg and 300-mg/75-mg LD/MD in 41 healthy, aspirin-free subjects. Each LD was followed by 7 days of daily MD and a 14-day washout period. Inhibition of platelet aggregation (IPA) was assessed by turbidometric aggregometry (20 and 5 microM ADP). Prasugrel 60-mg achieved higher mean IPA (54%) 30 minutes post-LD than clopidogrel 300-mg (3%) or 600-mg (6%) (P < 0.001) and greater IPA by 1 hour (82%) and 2 hours (91%) than the 6-hour IPA for clopidogrel 300-mg (51%) or 600-mg (69%) (P < 0.01). During MD, IPA for prasugrel 10-mg (78%) exceeded that of clopidogrel (300-mg/75-mg, 56%; 600-mg/75-mg, 52%; P < 0.001). Active metabolite area under the concentration-time curve (AUC0-tlast) after prasugrel 60-mg (594 ng.hr/mL) was 2.2 times that after clopidogrel 600-mg. Prasugrel active metabolite AUC0-tlast was consistent with dose-proportionality from 10-mg to 60-mg, while clopidogrel active metabolite AUC0-tlast exhibited saturable absorption and/or metabolism. In conclusion, greater exposure to prasugrel's active metabolite results in faster onset, higher levels, and less variability of platelet inhibition compared with high-dose clopidogrel in healthy subjects.

ABSTRACT:Conventional methods to forecast CYP3A-mediated drug-drug interactions have not employed stochastic approaches that integrate pharmacokinetic (PK) variability and relevant covariates to predict inhibition in terms of probability and uncertainty. Empirical approaches to predict the extent of inhibition may not account for nonlinear or non-steady-state conditions, such as first-pass effects or accumulation of inhibitor concentration with multiple dosing. A physiologically based PK model was developed to predict the inhibition of CYP3A by ketoconazole (KTZ), using midazolam (MDZ) as the substrate. The model integrated PK models of MDZ and KTZ, in vitro inhibition kinetics of KTZ, and the variability and uncertainty associated with these parameters. This model predicted the time-and dose-dependent inhibitory effect of KTZ on MDZ oral clearance. The predictive performance of the model was validated using the results of five published KTZ-MDZ studies. The model improves the accuracy of predicting the inhibitory effect of increasing KTZ dosing on MDZ PK by incorporating a saturable KTZ efflux from the site of enzyme inhibition in the liver. The results of simulations using the model supported the KTZ dose of 400 mg once daily as the optimal regimen to achieve maximum inhibition by KTZ. Sensitivity analyses revealed that the most influential variable on the prediction of inhibition was the fractional clearance of MDZ mediated by CYP3A. The model may be used prospectively to improve the quantitative prediction of CYP3A inhibition and aid the optimization of study designs for CYP3A-mediated drug-drug interaction studies in drug development.Metabolism-based pharmacokinetic (PK) interactions are well recognized as a source of clinically significant adverse drug reactions Huang and Lesko, 2004). The early forecast of clinically significant metabolism-based pharmacokinetic drug-drug interactions is an increasingly important aspect of drug development. The assessment of drug-drug interaction potential before the conduct of a clinical trial often involves simple algebraic calculations. One such calculation is the ratio of the expected clinical exposure to the in vitro inhibition constant (K i ) of a new drug entity for a specific cytochrome P450 (Sahajwalla et al., 1999;Bjornsson et al., 2003). The primary sources of quantitative errors associated with this prediction approach are attributed to the experimental procedures used to determine the in vitro parameters, the variability in the intrinsic factors associated with PK properties of the inhibitor (e.g., absorption kinetics, plasma protein binding, and tissue partition coefficients), or the substrate (i.e., the fraction of total clearance attributed to the elimination pathway of interest), the extrinsic factors associated with the clinical study designs (e.g., dosing scheme, sampling design, and population demographics), and, most notably, the uncertainty in the effective concentration of the inhibitor at the enzyme site. In addition, complex drug disposition proper...