Exploiting polypharmacology for drug target deconvolution (original) (raw)
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Abstract
Protein kinase inhibitors represent a major class of anticancer drugs, which are notoriously unspecific. Efforts to exploit the polypharmacology of inhibitors for target deconvolution have met with little success. Our significant contribution is to apply regularized regression to kinase expression and kinase profiling on a large set of inhibitors. By selecting a set of optimally designed kinase inhibitors that span a broad range of kinase specificities, we identified relevant kinases from our model in six cell lines; we then empirically validated a set of specific kinases that regulate cancer cell migration. Using this model, we predict a cell type-specific response to previously untested inhibitors. Broadly, these approaches should prove useful in identifying novel targets and in rational cancer therapy.
Publication:
Proceedings of the National Academy of Science
Pub Date:
April 2014
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