Objective: Drug repurposing, the application of existing therapeutics to new indications, holds promise in achieving rapid clinical effects at a much lower cost than that of de novo drug development. The aim of our study was to perform a more comprehensive drug repurposing prediction of diseases, particularly cancers.
Methods: Here, by targeting 4,096 human diseases, including 384 cancers, we propose a greedy computational model based on a heterogeneous multilayer network for the repurposing of 1,419 existing drugs in DrugBank. We performed additional experimental validation for the dominant repurposed drugs in cancer.
Results: The overall performance of the model was well supported by cross-validation and literature mining. Focusing on the top-ranked repurposed drugs in cancers, we verified the anticancer effects of 5 repurposed drugs widely used clinically in drug sensitivity experiments. Because of the distinctive antitumor effects of nifedipine (an antihypertensive agent) and nortriptyline (an antidepressant drug) in prostate cancer, we further explored their underlying mechanisms by using quantitative proteomics. Our analysis revealed that both nifedipine and nortriptyline affected the cancer-related pathways of DNA replication, the cell cycle, and RNA transport. Moreover, in vivo experiments demonstrated that nifedipine and nortriptyline significantly inhibited the growth of prostate tumors in a xenograft model.
Conclusions: Our predicted results, which have been released in a public database named The Predictive Database for Drug Repurposing (PAD), provide an informative resource for discovering and ranking drugs that may potentially be repurposed for cancer treatment and determining new therapeutic effects of existing drugs.
Keywords: Database; drug repurposing; machine learning; network random walk; proteomics analysis.
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