In silico prioritization of transporter-drug relationships from drug sensitivity screens

• Verfasst von: César-Razquin, Adrián [VerfasserIn]
• Verfasst von: Girardi, Enrico [VerfasserIn]
• Verfasst von: Yang, Mi [VerfasserIn]
• Verfasst von: Brehme, Marc [VerfasserIn]
• Verfasst von: Saez-Rodriguez, Julio [VerfasserIn]
• Verfasst von: Superti-Furga, Giulio [VerfasserIn]
• Titel: In silico prioritization of transporter-drug relationships from drug sensitivity screens
• Verf.angabe: Adrián César-Razquin, Enrico Girardi, Mi Yang, Marc Brehme, Julio Saez-Rodriguez and Giulio Superti-Furga
• E-Jahr: 2018
• Jahr: 07 September 2018
• Umfang: 10 S.
• Fussnoten: Gesehen am 02.12.2020
• Titel Quelle: Enthalten in: Frontiers in pharmacology
• Ort Quelle: Lausanne : Frontiers Media, 2010
• Jahr Quelle: 2018
• Band/Heft Quelle: 9(2018) Artikel-Nummer 1011, 10 Seiten
• ISSN Quelle: 1663-9812
• DOI: doi:10.3389/fphar.2018.01011
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: ABC transporters (ATP binding cassette)
• Sach-SW: drug sensitivity and resistance.
• Sach-SW: Drug Transport
• Sach-SW: Regularized linear regression
• Sach-SW: Solute carriers (SLCs)
• K10plus-PPN: 1741733804

Abstract

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 1000 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.