Although drugs may interact with a single protein target, their activity depends on a cascade of molecular signals through multiple intertwined signaling pathways. A deep understanding of how a drug works is essential for discovering new treatments and minimizing adverse effects, but it is challenging to combine intertwined pathways into one network.
To tackle this challenge, Zhongming Zhao, Ph.D., and colleagues developed a novel computational framework – the Drug-specific Signaling Pathway Network (DSPathNet) – that uses algorithms to combine data about drug targets and drug-induced gene expression. The investigators used the antidiabetic drug metformin to illustrate the computational approach.
Their results, reported in PLOS Computational Biology, showed that the metformin network was enriched with disease genes for both type 2 diabetes and cancer (metformin also has anticancer activity). The findings provide insight into the molecular action of metformin and serve as a model for constructing drug-specific signal transduction networks to understand drug action and identify novel drug targets.
This project is partially supported by the National Institutes of Health (grants LM011177, LM010685, CA172294, CA090949, CA095103, CA098131, CA068485, GM092618, TR000445), the Cancer Prevention & Research Institute of Texas Rising Star Award, 2013 NARSAD Young Investigator Award, American Cancer Society Institutional Research Grant pilot project and Ingram Professorship Funds.
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