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Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine
Drug–drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301358/ https://www.ncbi.nlm.nih.gov/pubmed/37376035 http://dx.doi.org/10.3390/pharmaceutics15061586 |
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author | Marques, Lara Vale, Nuno |
author_facet | Marques, Lara Vale, Nuno |
author_sort | Marques, Lara |
collection | PubMed |
description | Drug–drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory diseases, is metabolized by cytochrome P450 (CYP) enzymes, which can be inhibited or induced by co-administered drugs. Studying DDIs involving salbutamol is crucial for optimizing drug therapy and preventing adverse outcomes. Here, we aimed to investigate CYP-mediated DDIs between salbutamol and fluvoxamine through in silico approaches. The physiologically based pharmacokinetic (PBPK) model of salbutamol was developed and validated using available clinical PK data, whereas the PBPK model of fluvoxamine was previously verified by GastroPlus. Salbutamol–fluvoxamine interaction was simulated according to different regimens and patient’s characteristics (age and physiological status). The results demonstrated that co-administering salbutamol with fluvoxamine enhanced salbutamol exposure in certain situations, especially when fluvoxamine dosage increased. To sum up, this study demonstrated the utility of PBPK modeling in predicting CYP-mediated DDIs, making it a pioneer in PK DDI research. Furthermore, this study provided insights into the relevance of regular monitoring of patients taking multiple medications, regardless of their characteristics, to prevent adverse outcomes and for the optimization of the therapeutic regimen, in cases where the therapeutic benefit is no longer experienced. |
format | Online Article Text |
id | pubmed-10301358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103013582023-06-29 Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine Marques, Lara Vale, Nuno Pharmaceutics Article Drug–drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory diseases, is metabolized by cytochrome P450 (CYP) enzymes, which can be inhibited or induced by co-administered drugs. Studying DDIs involving salbutamol is crucial for optimizing drug therapy and preventing adverse outcomes. Here, we aimed to investigate CYP-mediated DDIs between salbutamol and fluvoxamine through in silico approaches. The physiologically based pharmacokinetic (PBPK) model of salbutamol was developed and validated using available clinical PK data, whereas the PBPK model of fluvoxamine was previously verified by GastroPlus. Salbutamol–fluvoxamine interaction was simulated according to different regimens and patient’s characteristics (age and physiological status). The results demonstrated that co-administering salbutamol with fluvoxamine enhanced salbutamol exposure in certain situations, especially when fluvoxamine dosage increased. To sum up, this study demonstrated the utility of PBPK modeling in predicting CYP-mediated DDIs, making it a pioneer in PK DDI research. Furthermore, this study provided insights into the relevance of regular monitoring of patients taking multiple medications, regardless of their characteristics, to prevent adverse outcomes and for the optimization of the therapeutic regimen, in cases where the therapeutic benefit is no longer experienced. MDPI 2023-05-24 /pmc/articles/PMC10301358/ /pubmed/37376035 http://dx.doi.org/10.3390/pharmaceutics15061586 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Marques, Lara Vale, Nuno Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine |
title | Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine |
title_full | Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine |
title_fullStr | Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine |
title_full_unstemmed | Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine |
title_short | Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine |
title_sort | prediction of cyp-mediated drug interaction using physiologically based pharmacokinetic modeling: a case study of salbutamol and fluvoxamine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301358/ https://www.ncbi.nlm.nih.gov/pubmed/37376035 http://dx.doi.org/10.3390/pharmaceutics15061586 |
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