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Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling
PURPOSE: Recent in vitro studies demonstrated that dasatinib inhibits organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), and organic anion transporting polypeptide 1B1/1B3 (OATP1B1/1B3). We developed a physiologically based pharmacokinetic (PBPK) model to assess dru...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882081/ https://www.ncbi.nlm.nih.gov/pubmed/35147740 http://dx.doi.org/10.1007/s00280-021-04394-z |
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author | Chang, Ming Bathena, Sai Christopher, Lisa J. Shen, Hong Roy, Amit |
author_facet | Chang, Ming Bathena, Sai Christopher, Lisa J. Shen, Hong Roy, Amit |
author_sort | Chang, Ming |
collection | PubMed |
description | PURPOSE: Recent in vitro studies demonstrated that dasatinib inhibits organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), and organic anion transporting polypeptide 1B1/1B3 (OATP1B1/1B3). We developed a physiologically based pharmacokinetic (PBPK) model to assess drug–drug interaction (DDI) potential between dasatinib and known substrates for these transporters in a virtual population. METHODS: The dasatinib PBPK model was constructed using Simcyp(®) Simulator by combining its physicochemical properties, in vitro data, in silico predictions, and pharmacokinetic (PK) results from clinical studies. Model validation against three independent clinical trials not used for model development included dasatinib DDI studies with ketoconazole, rifampin, and simvastatin. The validated model was used to simulate DDIs of dasatinib and known substrates for OCT2 and MATEs (metformin) and OATP1B1/1B3 (pravastatin and rosuvastatin). RESULTS: Simulations of metformin PK in the presence and absence of dasatinib, using inhibitor constant (K(i)) values measured in vitro, produced estimated geometric mean ratios (GMRs) of the maximum observed concentration (C(max)) and area under the concentration–time curve (AUC) of 1.05 and 1.06, respectively. Sensitivity analysis showed metformin exposure increased < 30% in both AUC and C(max) when dasatinib K(i) was reduced by tenfold for OCT2 and MATEs simultaneously, and < 40% with a 20-fold K(i) reduction. The estimated GMRs of C(max) and AUC for pravastatin and rosuvastatin with co-administration of dasatinib were unity (1.00). CONCLUSIONS: This PBPK model accurately described the observed PK profiles of dasatinib. The validated PBPK model predicts low risk of clinically significant DDIs between dasatinib and metformin, pravastatin, or rosuvastatin. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00280-021-04394-z. |
format | Online Article Text |
id | pubmed-8882081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-88820812022-03-02 Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling Chang, Ming Bathena, Sai Christopher, Lisa J. Shen, Hong Roy, Amit Cancer Chemother Pharmacol Original Article PURPOSE: Recent in vitro studies demonstrated that dasatinib inhibits organic cation transporter 2 (OCT2), multidrug and toxin extrusion proteins (MATEs), and organic anion transporting polypeptide 1B1/1B3 (OATP1B1/1B3). We developed a physiologically based pharmacokinetic (PBPK) model to assess drug–drug interaction (DDI) potential between dasatinib and known substrates for these transporters in a virtual population. METHODS: The dasatinib PBPK model was constructed using Simcyp(®) Simulator by combining its physicochemical properties, in vitro data, in silico predictions, and pharmacokinetic (PK) results from clinical studies. Model validation against three independent clinical trials not used for model development included dasatinib DDI studies with ketoconazole, rifampin, and simvastatin. The validated model was used to simulate DDIs of dasatinib and known substrates for OCT2 and MATEs (metformin) and OATP1B1/1B3 (pravastatin and rosuvastatin). RESULTS: Simulations of metformin PK in the presence and absence of dasatinib, using inhibitor constant (K(i)) values measured in vitro, produced estimated geometric mean ratios (GMRs) of the maximum observed concentration (C(max)) and area under the concentration–time curve (AUC) of 1.05 and 1.06, respectively. Sensitivity analysis showed metformin exposure increased < 30% in both AUC and C(max) when dasatinib K(i) was reduced by tenfold for OCT2 and MATEs simultaneously, and < 40% with a 20-fold K(i) reduction. The estimated GMRs of C(max) and AUC for pravastatin and rosuvastatin with co-administration of dasatinib were unity (1.00). CONCLUSIONS: This PBPK model accurately described the observed PK profiles of dasatinib. The validated PBPK model predicts low risk of clinically significant DDIs between dasatinib and metformin, pravastatin, or rosuvastatin. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00280-021-04394-z. Springer Berlin Heidelberg 2022-02-11 2022 /pmc/articles/PMC8882081/ /pubmed/35147740 http://dx.doi.org/10.1007/s00280-021-04394-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Chang, Ming Bathena, Sai Christopher, Lisa J. Shen, Hong Roy, Amit Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
title | Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
title_full | Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
title_fullStr | Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
title_full_unstemmed | Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
title_short | Prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
title_sort | prediction of drug–drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882081/ https://www.ncbi.nlm.nih.gov/pubmed/35147740 http://dx.doi.org/10.1007/s00280-021-04394-z |
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