Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1784791279825059840 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9481493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT montanhamaiaracamotti predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT fabregafrancesc predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT howarthalice predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT cotturanicolas predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT kinvighannah predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT bunglawalafazila predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT lloydandrew predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT dentipaolo predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT waittcatriona predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling AT siccardimarco predictingdrugdruginteractionsbetweenrifampicinandritonavirboostedatazanavirusingpbpkmodelling |