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PhenoPredict: a disease phenome-wide drug repositioning approach towards schizophrenia drug discovery
Filetype[PDF - 1.92 MB]


Details:
  • Pubmed ID:
    26151312
  • Pubmed Central ID:
    PMC4589865
  • Funding:
    DP2 HD084068/HD/NICHD NIH HHS/United States
    DP2HD084068/DP/NCCDPHP CDC HHS/United States
    R25 CA094186/CA/NCI NIH HHS/United States
    R25 CA094186-06/CA/NCI NIH HHS/United States
    UL1 RR024989/RR/NCRR NIH HHS/United States
    UL1 TR000439/TR/NCATS NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs.