Exploiting polypharmacology for drug target deconvolution

被引:72
作者
Gujral, Taranjit Singh [1 ]
Peshkin, Leonid [1 ]
Kirschner, Marc W. [1 ]
机构
[1] Harvard Univ, Sch Med, Dept Syst Biol, Boston, MA 02115 USA
基金
美国国家卫生研究院;
关键词
systems pharmacology; regularized regression; perturbation biology; predictive modeling; cancer cell migration; CELL-MIGRATION; CANCER CELLS; RECEPTOR; COACTIVATION; REVEALS; CYCLE;
D O I
10.1073/pnas.1403080111
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Polypharmacology (action of drugs against multiple targets) represents a tempting avenue for new drug development; unfortunately, methods capable of exploiting the known polypharmacology of drugs for target deconvolution are lacking. Here, we present an ensemble approach using elastic net regularization combined with mRNA expression profiling and previously characterized data on a large set of kinase inhibitors to identify kinases that are important for epithelial and mesenchymal cell migration. By profiling a selected optimal set of 32 kinase inhibitors in a panel against six cell lines, we identified cell type-specific kinases that regulate cell migration. Our discovery of several informative kinases with a previously uncharacterized role in cell migration (such as Mst and Taok family of MAPK kinases in mesenchymal cells) may represent novel targets that warrant further investigation. Target deconvolution using our ensemble approach has the potential to aid in the rational design of more potent but less toxic drug combinations.
引用
收藏
页码:5048 / 5053
页数:6
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