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Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir
Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8597021/ https://www.ncbi.nlm.nih.gov/pubmed/33626178 http://dx.doi.org/10.1002/cpt.2221 |
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author | Stader, Felix Battegay, Manuel Marzolini, Catia |
author_facet | Stader, Felix Battegay, Manuel Marzolini, Catia |
author_sort | Stader, Felix |
collection | PubMed |
description | Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir. |
format | Online Article Text |
id | pubmed-8597021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85970212021-12-02 Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir Stader, Felix Battegay, Manuel Marzolini, Catia Clin Pharmacol Ther Research Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir. John Wiley and Sons Inc. 2021-03-29 2021-11 /pmc/articles/PMC8597021/ /pubmed/33626178 http://dx.doi.org/10.1002/cpt.2221 Text en © 2021 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Stader, Felix Battegay, Manuel Marzolini, Catia Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir |
title | Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir |
title_full | Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir |
title_fullStr | Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir |
title_full_unstemmed | Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir |
title_short | Physiologically‐Based Pharmacokinetic Modeling to Support the Clinical Management of Drug–Drug Interactions With Bictegravir |
title_sort | physiologically‐based pharmacokinetic modeling to support the clinical management of drug–drug interactions with bictegravir |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8597021/ https://www.ncbi.nlm.nih.gov/pubmed/33626178 http://dx.doi.org/10.1002/cpt.2221 |
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