Semiphysiologically Based Pharmacokinetic Models for the Inhibition of Midazolam Clearance by Diltiazem and Its Major Metabolite

Zhang, Xin; Quinney, Sara K.; Gorski, J. Christopher; Jones, David R.; Hall, Stephen D.

doi:10.1124/dmd.109.026658

Cited by 87 publications

(73 citation statements)

References 44 publications

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“…MI complexes exhibit a signature Soret absorbance at approximately 455 nm (Franklin, 1974). Predictions of the magnitude of drug-drug interactions (DDIs) for diltiazem, fluoxetine, and clarithromycin have been reported using results from in vitro kinetic studies and from measured alkyl amine concentrations in vivo (Mayhew et al, 2000;Galetin et al, 2006;Zhang et al, 2009b).…”

mentioning

confidence: 99%

Sequential Metabolism of Secondary Alkyl Amines to Metabolic-Intermediate Complexes: Opposing Roles for the Secondary Hydroxylamine and Primary Amine Metabolites of Desipramine, (S)-Fluoxetine, and N-Desmethyldiltiazem

Hanson¹,

VandenBrink²,

Babu³

et al. 2010

Drug Metab Dispos

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confidence: 99%

Sequential Metabolism of Secondary Alkyl Amines to Metabolic-Intermediate Complexes: Opposing Roles for the Secondary Hydroxylamine and Primary Amine Metabolites of Desipramine, (S)-Fluoxetine, and N-Desmethyldiltiazem

Hanson¹,

VandenBrink²,

Babu³

et al. 2010

Drug Metab Dispos

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“…Temporal changes in inhibitor concentrations and CYP3A enzyme activities as well as interindividual variability in CYP3A enzyme levels and rate constants of enzyme degradation are not considered. In addition, it is difficult to assess the effects of dosing regimens (e.g., irregular dosing) on the extent of drug interactions using a static model.Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al, 2000;Zhang et al, 2009;Fenneteau et al, 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models.…”

mentioning

confidence: 99%

Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Wang¹

2010

Drug Metab Dispos

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ABSTRACT:Accurate prediction of the extent of mechanism-based CYP3A inhibition is critical in determining the timing of clinical drug interaction studies in drug development. To evaluate the prediction accuracy of the static and Simcyp time-based approaches, 54 clinical drug interactions involving mechanism-based CYP3A inhibitors were predicted using both methods. The Simcyp timebased approach generated better prediction when 0.03 h ؊1 was used as the hepatic CYP3A enzyme degradation rate constant (k deg ) value. Of the predictions 87 and 55% had an error less than 2 and 0. Drug-drug interactions remain an area of focus in drug discovery and development. Mechanism-based inhibition of CYP3A is one of the major causes of clinical drug-drug interactions and generally leads to greater concern, as highlighted by the list of moderate and strong CYP3A inhibitors in the Food and Drug Administration (2006) Drug Interaction Guidance. The inhibitory effects of a mechanism-based CYP3A inhibitor persist long after the compound is eliminated from the body because the recovery of CYP3A enzyme activity requires de novo protein synthesis or slow release of the enzyme from the enzyme-inhibitor complex. Because of the primary role of CYP3A in drug disposition, prediction of the clinical drug interaction potential of a mechanism-based CYP3A inhibitor would be helpful in guiding the timing and design of clinical studies.Several approaches have been developed to predict clinical outcomes of mechanism-based inhibition. The static mathematical model developed by Mayhew et al. (2000) is a commonly used approach to predict mechanism-based drug interactions from in vitro estimated inactivation parameters. Several modifications to the original model have been made to incorporate the effects of intestinal wall metabolism (Wang et al., 2004b), competitive inhibition, and induction (Fahmi et al., 2009). Nonetheless, these static models are only capable of predicting the average magnitude of drug interactions across a population, assuming that the steady state of enzyme inhibition has been reached. Temporal changes in inhibitor concentrations and CYP3A enzyme activities as well as interindividual variability in CYP3A enzyme levels and rate constants of enzyme degradation are not considered. In addition, it is difficult to assess the effects of dosing regimens (e.g., irregular dosing) on the extent of drug interactions using a static model.Several physiologically based pharmacokinetic models (PBPK) were developed to address some of the aforementioned limitations with static models (Kanamitsu et al., 2000;Zhang et al., 2009;Fenneteau et al., 2010). These PBPK models take into account temporal changes in inhibitor and substrate concentrations and enzyme activities as well as the enzyme inhibition concept in the static models. They can be used to simulate drug concentrationtime profiles and to explore the effects of various dosing regimens. Simcyp (Simcyp Limited, Sheffield, UK) is a commercially available absorption, distribution, metabolism, and...

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“…The approved dosing regimen of ticagrelor is 180 mg loading dose followed by 90 mg twice daily thereafter3,4. The daily dose (240 mg) of diltiazem selected for the ticagrelor-diltiazem study was within the approved clinical range (180–480 mg daily19), and was expected to inhibit CYP3A421. The selected dose of ketoconazole (200 mg twice daily) is the maximum clinically approved oral dose23 and is commonly used in phase I drug–drug interaction studies10,25 to inhibit CYP3A4 activity.…”

Section: Methodsmentioning

confidence: 99%

“…Diltiazem is a calcium channel blocker used to treat angina pectoris and mild-to-moderate arterial hypertension19. This compound is an inhibitor of CYP3A activity20,21 and is classified as a moderate inhibitor of this enzyme10. The antifungal agent, ketoconazole22,23, also inhibits CYP3A24 and is considered to be a strong CYP3A inhibitor10.…”

Section: Introductionmentioning

confidence: 99%

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Effect of the CYP3A inhibitors, diltiazem and ketoconazole, on ticagrelor pharmacokinetics in healthy volunteers

Teng

Butler

2013

Journal of Drug Assessment

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ObjectivesTwo open-label, two-period, crossover studies in healthy volunteers were designed to determine the pharmacokinetic interactions between ticagrelor, a P2Y12 receptor antagonist, and a moderate (diltiazem) and a strong (ketoconazole) cytochrome P450 (CYP) 3A inhibitor.MethodsSeventeen volunteers received diltiazem (240 mg once daily) for 14 days. In the second study, ketoconazole (n = 14) 200 mg twice daily was given for 10 days. A single oral 90-mg ticagrelor dose was administered on day 8 (diltiazem) or day 4 (ketoconazole). In each study, volunteers received a single 90-mg oral dose of ticagrelor before or after washout (≥14 days). Pharmacokinetic parameters for ticagrelor, AR-C124910XX (primary metabolite), diltiazem, and ketoconazole were assessed.ResultsCompared with ticagrelor alone, diltiazem co-administration significantly increased the mean maximum concentration (Cmax) and mean area under the plasma concentration–time curve (AUC) for ticagrelor by 69% and 174%, respectively. Diltiazem co-administration reduced Cmax by 38% but had no significant effect on AUC for AR-C124910XX. Cmax and AUC for ticagrelor were increased by 135% and 632%, respectively, by ketoconazole co-administration, whereas these parameters were reduced by 89% and 56%, respectively, for AR-C124910XX. Diltiazem and ketoconazole pharmacokinetic parameters were not significantly affected by the presence of ticagrelor.ConclusionsThese results suggest that ticagrelor can be co-administered with moderate CYP3A inhibitors. However, co-administration of strong CYP3A inhibitors with ticagrelor is not recommended.

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Semiphysiologically Based Pharmacokinetic Models for the Inhibition of Midazolam Clearance by Diltiazem and Its Major Metabolite

Cited by 87 publications

References 44 publications

Sequential Metabolism of Secondary Alkyl Amines to Metabolic-Intermediate Complexes: Opposing Roles for the Secondary Hydroxylamine and Primary Amine Metabolites of Desipramine, (S)-Fluoxetine, and N-Desmethyldiltiazem

Sequential Metabolism of Secondary Alkyl Amines to Metabolic-Intermediate Complexes: Opposing Roles for the Secondary Hydroxylamine and Primary Amine Metabolites of Desipramine, (S)-Fluoxetine, and N-Desmethyldiltiazem

Confidence Assessment of the Simcyp Time-Based Approach and a Static Mathematical Model in Predicting Clinical Drug-Drug Interactions for Mechanism-Based CYP3A Inhibitors

Effect of the CYP3A inhibitors, diltiazem and ketoconazole, on ticagrelor pharmacokinetics in healthy volunteers

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