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Prediction of the Impact of Cytochrome P450 2C9 Genotypes on the Drug-Drug Interaction Potential of Siponimod With Physiologically-Based Pharmacokinetic Modeling: A Comprehensive Approach for Drug Label Recommendations.

ABSTRACT: We predicted the drug-drug interaction (DDI) potential of siponimod in presence of cytochrome P450 (CYP)2C9/CYP3A4 inhibitors/inducers in subjects with different CYP2C9 genotypes by physiologically-based pharmacokinetic (PK) modeling. The model was established using in vitro and clinical PK data and verified by adequately predicting siponimod PK when coadministered with rifampin. With strong and moderate CYP3A4 inhibitors, an increased DDI risk for siponimod was predicted for CYP2C9*3/*3 genotype vs. other genotypes area under the curve ratio (AUCR): 3.03-4.20 vs. ? 1.49 for strong; 2.42 vs. 1.14-1.30 for moderate. AUCRs increased with moderate (2.13-2.49) and weak (1.12-1.42) CYP3A4/CYP2C9 inhibitors to the same extent for all genotypes. With strong CYP3A4/moderate CYP2C9 inducers and moderate CYP3A4 inducers, predicted AUCRs were 0.21-0.32 and 0.35-0.71, respectively. This complementary analysis to the clinical PK-DDI studies confirmed the relevant influence of CYP2C9 polymorphism on the DDI behavior of siponimod and represented the basis for the DDI labeling recommendations.

SUBMITTER: Huth F

PROVIDER: S-EPMC6851657 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Prediction of the Impact of Cytochrome P450 2C9 Genotypes on the Drug-Drug Interaction Potential of Siponimod With Physiologically-Based Pharmacokinetic Modeling: A Comprehensive Approach for Drug Label Recommendations.

Huth Felix F Gardin Anne A Umehara Kenichi K He Handan H

Clinical pharmacology and therapeutics 20190811 5

We predicted the drug-drug interaction (DDI) potential of siponimod in presence of cytochrome P450 (CYP)2C9/CYP3A4 inhibitors/inducers in subjects with different CYP2C9 genotypes by physiologically-based pharmacokinetic (PK) modeling. The model was established using in vitro and clinical PK data and verified by adequately predicting siponimod PK when coadministered with rifampin. With strong and moderate CYP3A4 inhibitors, an increased DDI risk for siponimod was predicted for CYP2C9*3/*3 genotyp ...[more]

PMID: 31199498

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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.

Project description:Cytochrome P450 enzymes (P450s) are important in drug metabolism and have been linked to adverse drug reactions. P450s display broad substrate reactivity, and prediction of metabolites is complex. QM/MM studies of P450 reactivity have provided insight into important details of the reaction mechanisms and have the potential to make predictions of metabolite formation. Here we present a comprehensive study of the oxidation of three widely used pharmaceutical compounds (S-ibuprofen, diclofenac, and S-warfarin) by one of the major drug-metabolizing P450 isoforms, CYP2C9. The reaction barriers to substrate oxidation by the iron-oxo species (Compound I) have been calculated at the B3LYP-D/CHARMM27 level for different possible metabolism sites for each drug, on multiple pathways. In the cases of ibuprofen and warfarin, the process with the lowest activation energy is consistent with the experimentally preferred metabolite. For diclofenac, the pathway leading to the experimentally observed metabolite is not the one with the lowest activation energy. This apparent inconsistency with experiment might be explained by the two very different binding modes involved in oxidation at the two competing positions. The carboxylate of diclofenac interacts strongly with the CYP2C9 Arg108 side chain in the transition state for formation of the observed metabolite-but not in that for the competing pathway. We compare reaction barriers calculated both in the presence and in the absence of the protein and observe a marked improvement in selectivity prediction ability upon inclusion of the protein for all of the substrates studied. The barriers calculated with the protein are generally higher than those calculated in the gas phase. This suggests that active-site residues surrounding the substrate play an important role in controlling selectivity in CYP2C9. The results show that inclusion of sampling (particularly) and dispersion effects is important in making accurate predictions of drug metabolism selectivity of P450s using QM/MM methods.

Dataset Information

Prediction of the Impact of Cytochrome P450 2C9 Genotypes on the Drug-Drug Interaction Potential of Siponimod With Physiologically-Based Pharmacokinetic Modeling: A Comprehensive Approach for Drug Label Recommendations.

Publications

Prediction of the Impact of Cytochrome P450 2C9 Genotypes on the Drug-Drug Interaction Potential of Siponimod With Physiologically-Based Pharmacokinetic Modeling: A Comprehensive Approach for Drug Label Recommendations.

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