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In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens

The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two prote...

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Autores principales: César-Razquin, Adrián, Girardi, Enrico, Yang, Mi, Brehme, Marc, Saez-Rodriguez, Julio, Superti-Furga, Giulio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6137680/
https://www.ncbi.nlm.nih.gov/pubmed/30245630
http://dx.doi.org/10.3389/fphar.2018.01011
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author César-Razquin, Adrián
Girardi, Enrico
Yang, Mi
Brehme, Marc
Saez-Rodriguez, Julio
Superti-Furga, Giulio
author_facet César-Razquin, Adrián
Girardi, Enrico
Yang, Mi
Brehme, Marc
Saez-Rodriguez, Julio
Superti-Furga, Giulio
author_sort César-Razquin, Adrián
collection PubMed
description The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two protein families: the ATP-Binding Cassette (ABC) transporter family, whose members are often involved in xenobiotic efflux and drug resistance, and the large and heterogeneous family of solute carriers (SLCs). We recently argued that SLCs are collectively a rather neglected gene group, with most of its members still poorly characterized, and thus likely to include many yet-to-be-discovered associations with drugs. We searched publicly available resources and literature to define the currently known set of drugs transported by ABCs or SLCs, which involved ∼500 drugs and more than 100 transporters. In order to extend this set, we then mined the largest publicly available pharmacogenomics dataset, which involves approximately 1,000 molecularly annotated cancer cell lines and their response to 265 anti-cancer compounds, and used regularized linear regression models (Elastic Net, LASSO) to predict drug responses based on SLC and ABC data (expression levels, SNVs, CNVs). The most predictive models included both known and previously unidentified associations between drugs and transporters. To our knowledge, this represents the first application of regularized linear regression to this set of genes, providing an extensive prioritization of potentially pharmacologically interesting interactions.
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spelling pubmed-61376802018-09-21 In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens César-Razquin, Adrián Girardi, Enrico Yang, Mi Brehme, Marc Saez-Rodriguez, Julio Superti-Furga, Giulio Front Pharmacol Pharmacology The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two protein families: the ATP-Binding Cassette (ABC) transporter family, whose members are often involved in xenobiotic efflux and drug resistance, and the large and heterogeneous family of solute carriers (SLCs). We recently argued that SLCs are collectively a rather neglected gene group, with most of its members still poorly characterized, and thus likely to include many yet-to-be-discovered associations with drugs. We searched publicly available resources and literature to define the currently known set of drugs transported by ABCs or SLCs, which involved ∼500 drugs and more than 100 transporters. In order to extend this set, we then mined the largest publicly available pharmacogenomics dataset, which involves approximately 1,000 molecularly annotated cancer cell lines and their response to 265 anti-cancer compounds, and used regularized linear regression models (Elastic Net, LASSO) to predict drug responses based on SLC and ABC data (expression levels, SNVs, CNVs). The most predictive models included both known and previously unidentified associations between drugs and transporters. To our knowledge, this represents the first application of regularized linear regression to this set of genes, providing an extensive prioritization of potentially pharmacologically interesting interactions. Frontiers Media S.A. 2018-09-07 /pmc/articles/PMC6137680/ /pubmed/30245630 http://dx.doi.org/10.3389/fphar.2018.01011 Text en Copyright © 2018 César-Razquin, Girardi, Yang, Brehme, Saez-Rodriguez and Superti-Furga. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
César-Razquin, Adrián
Girardi, Enrico
Yang, Mi
Brehme, Marc
Saez-Rodriguez, Julio
Superti-Furga, Giulio
In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens
title In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens
title_full In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens
title_fullStr In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens
title_full_unstemmed In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens
title_short In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens
title_sort in silico prioritization of transporter–drug relationships from drug sensitivity screens
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6137680/
https://www.ncbi.nlm.nih.gov/pubmed/30245630
http://dx.doi.org/10.3389/fphar.2018.01011
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