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Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling

OBJECTIVES: The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with...

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Autores principales: Montanha, Maiara Camotti, Fabrega, Francesc, Howarth, Alice, Cottura, Nicolas, Kinvig, Hannah, Bunglawala, Fazila, Lloyd, Andrew, Denti, Paolo, Waitt, Catriona, Siccardi, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481493/
https://www.ncbi.nlm.nih.gov/pubmed/34635995
http://dx.doi.org/10.1007/s40262-021-01067-1
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author Montanha, Maiara Camotti
Fabrega, Francesc
Howarth, Alice
Cottura, Nicolas
Kinvig, Hannah
Bunglawala, Fazila
Lloyd, Andrew
Denti, Paolo
Waitt, Catriona
Siccardi, Marco
author_facet Montanha, Maiara Camotti
Fabrega, Francesc
Howarth, Alice
Cottura, Nicolas
Kinvig, Hannah
Bunglawala, Fazila
Lloyd, Andrew
Denti, Paolo
Waitt, Catriona
Siccardi, Marco
author_sort Montanha, Maiara Camotti
collection PubMed
description OBJECTIVES: The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis. METHODS: A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (C(trough)) clinical cut-off of 150 ng/mL. RESULTS: The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV C(trough), with > 95% of individuals predicted to achieve C(trough) above the clinical cut-off. CONCLUSIONS: The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the C(trough) of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40262-021-01067-1.
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spelling pubmed-94814932022-09-18 Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling Montanha, Maiara Camotti Fabrega, Francesc Howarth, Alice Cottura, Nicolas Kinvig, Hannah Bunglawala, Fazila Lloyd, Andrew Denti, Paolo Waitt, Catriona Siccardi, Marco Clin Pharmacokinet Original Research Article OBJECTIVES: The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis. METHODS: A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (C(trough)) clinical cut-off of 150 ng/mL. RESULTS: The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV C(trough), with > 95% of individuals predicted to achieve C(trough) above the clinical cut-off. CONCLUSIONS: The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the C(trough) of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40262-021-01067-1. Springer International Publishing 2021-10-12 2022 /pmc/articles/PMC9481493/ /pubmed/34635995 http://dx.doi.org/10.1007/s40262-021-01067-1 Text en © The Author(s) 2022, corrected publication 2022 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research Article
Montanha, Maiara Camotti
Fabrega, Francesc
Howarth, Alice
Cottura, Nicolas
Kinvig, Hannah
Bunglawala, Fazila
Lloyd, Andrew
Denti, Paolo
Waitt, Catriona
Siccardi, Marco
Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
title Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
title_full Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
title_fullStr Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
title_full_unstemmed Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
title_short Predicting Drug–Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling
title_sort predicting drug–drug interactions between rifampicin and ritonavir-boosted atazanavir using pbpk modelling
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481493/
https://www.ncbi.nlm.nih.gov/pubmed/34635995
http://dx.doi.org/10.1007/s40262-021-01067-1
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