Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Ming, Bathena, Sai, Christopher, Lisa J., Shen, Hong, Roy, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
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
_version_ 1784659626351919104
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
work_keys_str_mv AT changming predictionofdrugdruginteractionpotentialmediatedbytransportersbetweendasatinibandmetforminpravastatinandrosuvastatinusingphysiologicallybasedpharmacokineticmodeling
AT bathenasai predictionofdrugdruginteractionpotentialmediatedbytransportersbetweendasatinibandmetforminpravastatinandrosuvastatinusingphysiologicallybasedpharmacokineticmodeling
AT christopherlisaj predictionofdrugdruginteractionpotentialmediatedbytransportersbetweendasatinibandmetforminpravastatinandrosuvastatinusingphysiologicallybasedpharmacokineticmodeling
AT shenhong predictionofdrugdruginteractionpotentialmediatedbytransportersbetweendasatinibandmetforminpravastatinandrosuvastatinusingphysiologicallybasedpharmacokineticmodeling
AT royamit predictionofdrugdruginteractionpotentialmediatedbytransportersbetweendasatinibandmetforminpravastatinandrosuvastatinusingphysiologicallybasedpharmacokineticmodeling