WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Numerous cocktails using concurrent administration of several cytochrome P450 (CYP) isoform‐selective probe drugs have been reported to investigate drug–drug interactions in vivo.• This approach has several advantages: characterize the inhibitory or induction potential of compounds in development toward the CYP enzymes identified in vitro in an in vivo situation, assess several enzymes in the same trial, and have complete in vivo information about potential CYP‐based drug interactions. WHAT THIS STUDY ADDS • This study describes a new cocktail containing five probe drugs that has never been published.• This cocktail can be used to test the effects of a new chemical entity on multiple CYP isoforms in a single clinical study: CYP1A2 (caffeine), CYP2C9 (warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol), and CYP3A (midazolam) and was designed to overcome potential liabilities of other reported cocktails.AIMS To assess the pharmacokinetics (PK) of selective substrates of CYP1A2 (caffeine), CYP2C9 (S‐warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol) and CYP3A (midazolam) when administered orally and concurrently as a cocktail relative to the drugs administered alone.METHODS This was an open‐label, single‐dose, randomized, six‐treatment six‐period six‐sequence William's design study with a wash‐out of 7 or 14 days. Thirty healthy male subjects received 100 mg caffeine, 100 mg metoprolol, 0.03 mg kg−1 midazolam, 20 mg omeprazole and 10 mg warfarin individually and in combination (cocktail). Poor metabolizers of CYP2C9, 2C19 and 2D6 were excluded. Plasma samples were obtained up to 48 h for caffeine, metoprolol and omeprazole, 12 h for midazolam, 312 h for warfarin and the cocktail. Three different validated liquid chromatography tandem mass spectrometry methods were used. Noncompartmental PK parameters were calculated. Log‐transformed Cmax, AUClast and AUC for each analyte were analysed with a linear mixed effects model with fixed term for treatment, sequence and period, and random term for subject within sequence. Point estimates (90% CI) for treatment ratios (individual/cocktail) were computed for each analyte Cmax, AUClast and AUC.RESULTS There was no PK interaction between the probe drugs when administered in combination as a cocktail, relative to the probes administered alone, as the 90% CI of the PK parameters was within the prespecified bioequivalence limits of 0.80, 1.25.CONCLUSION The lack of interaction between probes indicates that this cocktail could be used to evaluate the potential for multiple drug–drug interactions in vivo.

On the basis of morphological parameters, biochemical activity and drug transport characteristics, the TC-7 clone appeared to be a valuable alternative to the use of parental Caco-2 cells for drug absorption studies.

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Clopidogrel is a prodrug that needs to be converted to its active metabolite (clopi-H4) in two sequential cytochrome P450 (P450)-dependent steps. In the present study, a dynamic physiologically based pharmacokinetic (PBPK) model was developed in Simcyp for clopidogrel and clopi-H4 using a specific sequential metabolite module in four populations with phenotypically different CYP2C19 activity (poor, intermediate, extensive, and ultrarapid metabolizers) receiving a loading dose of 300 mg followed by a maintenance dose of 75 mg. This model was validated using several approaches. First, a comparison of predicted-to-observed area under the curve (AUC) 0-24 obtained from a randomized crossover study conducted in four balanced CYP2C19-phenotype metabolizer groups was performed using a visual predictive check method. Second, the interindividual and intertrial variability (on the basis of AUC 0-24 comparisons) between the predicted trials and the observed trial of individuals, for each phenotypic group, were compared. Finally, a further validation, on the basis of drug-drug-interaction prediction, was performed by comparing observed values of clopidogrel and clopi-H4 with or without dronedarone (moderate CYP3A4 inhibitor) coadministration using a previously developed and validated physiologically based PBPK dronedarone model. The PBPK model was well validated for both clopidogrel and its active metabolite clopi-H4, in each CYP2C19-phenotypic group, whatever the treatment period (300-mg loading dose and 75-mg last maintenance dose). This is the first study proposing a full dynamic PBPK model able to accurately predict simultaneously the pharmacokinetics of the parent drug and of its primary and secondary metabolites in populations with genetically different activity for a metabolizing enzyme.

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ABSTRACT:Clopidogrel is an antiplatelet agent widely used in cardiovascular diseases and an inactive prodrug that needs to be converted to an active metabolite in two sequential metabolic steps. Several CYP450 isoforms involved in these two steps have been described, although the relative contribution in vivo of each enzyme is still under debate. CYP2C19 is considered to be the major contributor to active metabolite formation. In the current study, net CYP2C19 contribution to the active metabolite formation was determined from exposure of the active metabolite in two clinical studies (one phase I study with well balanced genetic polymorphic populations and a meta-analysis with a total of 396 healthy volunteers) at different clopidogrel doses. CYP2C19 involvements were estimated to be from 58 to 67% in intermediate metabolizers (IMs), from 58 to 72% in extensive metabolizers (EMs), and from 56 to 74% in ultrarapid metabolizers (UMs), depending on the study and the dose. For this purpose, a static model was proposed to estimate the net contribution of a given enzyme to the secondary metabolite formation. This static model was compared with a dynamic approach (Simcyp model) and showed good consistency. In parallel, in vitro investigations showed that omeprazole is a mechanism-based inhibitor of CYP2C19 with K I of 8.56 M and K inact of 0.156 min ؊1 . These values were combined with the net CYP2C19 contribution to the active metabolite formation, through a static approach, to predict the inhibitory effect at 80-mg omeprazole doses in EM, IM, and UM CYP2C19 populations, with good consistency, compared with observed clinical values.

The aim of the study was to assess the magnitude of the CYP3A4 inhibitory effect of 2 dosing regimens of ketoconazole and the influence of the pharmacokinetic properties of the CYP3A4 substrate on the extent of the substrate exposure increase. For this purpose, a clinical study was conducted and PBPK modeling simulations were performed. A crossover study was conducted in healthy subjects. The study was designed to compare the effects of different regimens of reversible CYP3A4 inhibitors, i.e., ketoconazole 400 mg OD, ketoconazole 200 mg BID, on two CYP3A4 substrates, alprazolam and midazolam, reflecting different pharmacokinetic properties in terms of first-pass effect and elimination. In parallel, time-based simulations were performed using the Simcyp population-based Simulator to address the usefulness of modeling to assess interaction clinical study design with CYP3A4 substrates. Comparison of the OD versus BID regimens for ketoconazole showed an opposite trend for the 2 substrates: BID (200 mg) dosing regimen provided the maximal clearance inhibition for alprazolam, while it was OD (400 mg) dosing regimen for midazolam. However, these effects are moderate despite the well-known pharmacokinetic differences between these substrates, suggesting that these differences are not enough. In the other way round, these investigations show how two CYP3A4 substrates can be different without leading to a major impact of the ketoconazole dosing regimen. The clinical findings are consistent with the Simcyp predictions, in particular the opposite trend observed with midazolam and alprazolam and the ketoconazole dosing regimen. These clinical investigations showed the influence of the CYP3A4 substrates' pharmacokinetic properties and the relevance of ketoconazole dose regimen on the magnitude of the interaction ratios. In addition, PBPK Simcyp simulations demonstrated how they can be used to help clinical study design assessment to capture the maximum effect.

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