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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
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851657/ https://www.ncbi.nlm.nih.gov/pubmed/31199498 http://dx.doi.org/10.1002/cpt.1547 |
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author | Huth, Felix Gardin, Anne Umehara, Kenichi He, Handan |
author_facet | Huth, Felix Gardin, Anne Umehara, Kenichi He, Handan |
author_sort | Huth, Felix |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6851657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68516572019-12-12 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 Gardin, Anne Umehara, Kenichi He, Handan Clin Pharmacol Ther Research 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. John Wiley and Sons Inc. 2019-08-11 2019-11 /pmc/articles/PMC6851657/ /pubmed/31199498 http://dx.doi.org/10.1002/cpt.1547 Text en © 2019 Novartis Pharma AG. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Huth, Felix Gardin, Anne Umehara, Kenichi He, Handan 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851657/ https://www.ncbi.nlm.nih.gov/pubmed/31199498 http://dx.doi.org/10.1002/cpt.1547 |
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