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Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates.

ABSTRACT: Rifampicin induces both P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4) through regulating common nuclear receptors (e.g., pregnane X receptor). The interplay of P-gp and CYP3A4 has emerged to be an important factor in clinical drug-drug interactions (DDIs) with P-gp-CYP3A4 dual substrates and requires qualitative and quantitative understanding. Although physiologically based pharmacokinetic (PBPK) modeling has become a widely accepted approach to assess DDIs and is able to reasonably predict DDIs caused by CYP3A4 induction and P-gp induction individually, the predictability of PBPK models for the effect of simultaneous P-gp and CYP3A4 induction on P-gp-CYP3A4 dual substrates remains to be systematically evaluated. In this study, we used a PBPK modeling approach for the assessment of DDIs between rifampicin and 12 drugs: three sensitive P-gp substrates, seven P-gp-CYP3A4 dual substrates, and two P-gp-CYP3A4 dual substrates and inhibitors. A 3.5-fold increase of intestinal P-gp abundance was incorporated in the PBPK models to account for rifampicin-mediated P-gp induction at steady state. The simulation results showed that accounting for P-gp induction in addition to CYP3A4 induction improved the prediction accuracy of the area under the concentration-time curve and maximum (peak) plasma drug concentration ratios compared with considering CYP3A4 induction alone. Furthermore, the interplay of relevant drug-specific parameters and its impact on the magnitude of DDIs were evaluated using sensitivity analysis. The PBPK approach described herein, in conjunction with robust in vitro and clinical data, can help in the prospective assessment of DDIs involving other P-gp and CYP3A4 dual substrates. The database reported in the present study provides a valuable aid in understanding the combined effect of P-gp and CYP3A4 induction during drug development.

SUBMITTER: Pan X

PROVIDER: S-EPMC8674000 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND AND OBJECTIVES:Cytochrome P450 2C9 (CYP2C9) is involved in the biotransformation of many commonly used drugs, and significant drug interactions have been reported for CYP2C9 substrates. Previously published physiologically based pharmacokinetic (PBPK) models of tolbutamide are based on an assumption that its metabolic clearance is exclusively through CYP2C9; however, many studies indicate that CYP2C9 metabolism is only responsible for 80-90% of the total clearance. Therefore, these models are not useful for predicting the magnitude of CYP2C9 drug-drug interactions (DDIs). This paper describes the development and verification of SimCYP®-based PBPK models that accurately describe the human pharmacokinetics of tolbutamide when dosed alone or in combination with the CYP2C9 inhibitors sulfaphenazole and tasisulam. METHODS:A PBPK model was optimized in SimCYP® for tolbutamide as a CYP2C9 substrate, based on published in vitro and clinical data. This model was verified to replicate the magnitude of DDI reported with sulfaphenazole and was further applied to simulate the DDI with tasisulam, a small molecule investigated for the treatment of cancer. A clinical study (CT registration # NCT01185548) was conducted in patients with cancer to assess the pharmacokinetic interaction of tasisulum with tolbutamide. A PBPK model was built for tasisulam, and the clinical study design was replicated using the optimized tolbutamide model. RESULTS:The optimized tolbutamide model accurately predicted the magnitude of tolbutamide AUC increase (5.3-6.2-fold) reported for sulfaphenazole. Furthermore, the PBPK simulations in a healthy volunteer population adequately predicted the increase in plasma exposure of tolbutamide in patients with cancer (predicted AUC ratio = 4.7-5.4; measured mean AUC ratio = 5.7). CONCLUSIONS:This optimized tolbutamide PBPK model was verified with two strong CYP2C9 inhibitors and can be applied to the prediction of CYP2C9 interactions for novel inhibitors. Furthermore, this work highlights the utility of mechanistic models in navigating the challenges in conducting clinical pharmacology studies in cancer patients.

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Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates.

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