ArticleClinical pharmacokinetics2008
General framework for the prediction of oral drug interactions caused by CYP3A4 induction from in vivo information.
Article in Clinical pharmacokinetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed, 109 citations in OpenAlex.
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- Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes.Clinical pharmacokinetics · 2023Article
- General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data.Clinical pharmacokinetics · 2023Article
- Evaluation of the Drug-Drug Interaction Potential of Ensitrelvir Fumaric Acid with Cytochrome P450 3A Substrates in Healthy Japanese Adults.Clinical drug investigation · 2023Article
- Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach.Clinical pharmacokinetics · 2023Article
- Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction.Clinical pharmacokinetics · 2022Article
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- A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids.International journal of molecular sciences · 2022Article
- Effects of Simvastatin on the Metabolism of Vonoprazan in Rats Both in vitro and in vivo.Drug design, development and therapy · 2022Article
- Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches.Metabolites · 2021Review
- Quantitative Prediction of Interactions Mediated by Transporters and Cytochromes: Application to Organic Anion Transporting Polypeptides, Breast Cancer Resistance Protein and Cytochrome 2C8.Clinical pharmacokinetics · 2020Article
- Defining screening panel of functional variants of CYP1A1, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 genes in Serbian population.International journal of legal medicine · 2020Article
- Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions.Pharmacology & therapeutics · 2020Review
- Leveraging genetic interactions for adverse drug-drug interaction prediction.PLoS computational biology · 2019Article
- A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4.Clinical pharmacokinetics · 2019Article
- Diffuse Alveolar Hemorrhage Caused by Warfarin after Rifampicin Discontinuation.Case reports in medicine · 2019Article
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4 authors at 1 institution in 1 country.
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Abstract
backgroundInduction of cytochrome P450 (CYP) 3A4 potentially reduces the blood concentrations of substrate drugs to less than one-tenth, which results in ineffective pharmacotherapy. Although the prediction of drug-drug interactions (DDIs) that are mediated by induction of CYP3A4 has been performed mainly on the basis of in vitro information, such methods have met with limited success in terms of their accuracy and applicability. Therefore, a realistic method for the prediction of CYP3A4-mediated inductive DDIs is of major clinical importance.
objectiveThe objective of the present study was to construct a robust and accurate method for the prediction of CYP3A4-mediated inductive DDIs. Such a method was developed on the basis of the principle applied for prediction of inhibitory DDIs in a previous report. A unique character of this principle is that the extent of alterations in the area under the plasma concentration-time curve (AUC) is predicted on the basis of in vivo information from minimal clinical studies without using in vitro data.
methodsThe analysis is based on 42 DDI studies in humans reported in 37 published articles over the period 1983-2007. Kinetic analysis revealed that the reduction in the AUC of a substrate of CYP3A4 produced by consecutive administration of an inducer of CYP3A4 could be approximated by the equation 1/(1 + CRCYP3A4 * ICCYP3A4), where CRCYP3A4 is the ratio of the apparent contribution of CYP3A4 to the oral clearance of a substrate and ICCYP3A4 is the apparent increase in clearance of a substrate produced by induction of CYP3A4. Using this equation, the ICCYP3A4 was calculated for seven inducers (bosentan, carbamazepine, efavirenz, phenytoin, pioglitazone, rifampicin [rifampin], and St John's wort [hypericum]) on the basis of the reduction in the AUC of a coadministered standard substrate of CYP3A4, such as simvastatin, in ten DDI studies. The CRCYP3A4 was calculated for 22 substrates on the basis of the previously reported method from inhibitory DDI studies using a potent CYP3A4 inhibitor such as itraconazole or ketoconazole.
resultsThe proposed method enabled the prediction of AUC reduction by CYP3A4 induction with any combination of these substrates and inducers (total 154 matches). To assess the accuracy of the prediction, the AUC reductions in 32 studies were analysed. We found that the magnitude of the deviation between the mean values of the observed and predicted AUCs of all substrate drugs was <20% of the AUCs of the respective substrate drugs before administration of the inducers. In addition, rifampicin was found to be the most potent inducer among the compounds analysed in the present study, with an ICCYP3A4 value of 7.7, followed by phenytoin and carbamazepine, with values of 4.7 and 3.0, respectively. The ICCYP3A4 values of the other CYP3A4 inducers analysed in the present study were approximately 1 or less, which suggests that the AUCs of coadministered drugs may not be reduced to less than approximately half, even if the drug is metabolized solely by CYP3A4.
conclusionBy using the method reported in the present study, the susceptibilities of a substrate drug of CYP3A4 to inductive DDIs can be predicted quantitatively. It was indicated that coadministration of rifampicin, phenytoin and carbamazepine may reduce plasma AUCs to less than half for a broad range of CYP3A4 substrate drugs, with CRCYP3A4 values greater than 0.13, 0.21 and 0.33, respectively.
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