title: DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.
abstract: Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.
authors.affiliation: ThinTek, LLC, Palo Alto, CA.
authors.forename: QuanQiu
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Biostatistics, Institute of Computational Biology, School of Medicine, Case Western Reserve University, Cleveland, OH.
authors.forename: Rong
authors.initials: R
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D003715:Dengue / Q000188:drug therapy*; D055808:Drug Discovery; D058492:Drug Repositioning; D006801:Humans