Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir.
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State: Public
Version: Final published version
License: CC BY-NC 4.0
State: Public
Version: Final published version
License: CC BY-NC 4.0
Serval ID
serval:BIB_59A26B555DD0
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir.
Journal
Clinical pharmacology and therapeutics
ISSN
1532-6535 (Electronic)
ISSN-L
0009-9236
Publication state
Published
Issued date
11/2021
Peer-reviewed
Oui
Volume
110
Number
5
Pages
1231-1239
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
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.
Keywords
Adult, Amides/pharmacokinetics, Cobicistat/pharmacokinetics, Cytochrome P-450 CYP3A Inducers/pharmacokinetics, Cytochrome P-450 CYP3A Inhibitors/pharmacokinetics, Drug Interactions/physiology, Female, Glucuronosyltransferase/antagonists & inhibitors, Heterocyclic Compounds, 3-Ring/pharmacokinetics, Humans, Male, Middle Aged, Models, Biological, Piperazines/pharmacokinetics, Pyridones/pharmacokinetics, Ritonavir/pharmacokinetics, Voriconazole/pharmacokinetics, Young Adult
Pubmed
Web of science
Open Access
Yes
Create date
25/08/2023 5:17
Last modification date
06/08/2024 6:02