PhenoPredict: a disease phenome-wide drug repositioning approach towards schizophrenia drug discovery

Details

• Alternative Title:
  J Biomed Inform
• Personal Author:
  Xu, Rong ; Wang, QuanQiu
• 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.
• Keywords:
  Algorithms Antipsychotic Agents Area Under Curve Computational Biology Databases, Factual Disease Phenotype Drug Delivery Systems Drug Discovery Drug Repositioning Knowledge Bases Phenotype Reproducibility Of Results Schizophrenia Software Systems Biology United States United States Food And Drug Administration

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• Source:
  J Biomed Inform. 56:348-355
• Pubmed ID:
  26151312
• Pubmed Central ID:
  PMC4589865
• Document Type:
  Journal Article
• Funding:
  UL1 RR024989/RR/NCRR NIH HHSUnited States/ ; DP2HD084068/DP/NCCDPHP CDC HHSUnited States/ ; R25 CA094186/CA/NCI NIH HHSUnited States/ ; R25 CA094186-06/CA/NCI NIH HHSUnited States/ ; DP2 HD084068/HD/NICHD NIH HHSUnited States/ ; UL1 TR000439/TR/NCATS NIH HHSUnited States/
• Volume:
  56
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:c09fdcb0e573f08992d9bc3afa3c8ad4a24a738e4d8b683e767347e5d5a2598e
• Download URL:
  https://stacks.cdc.gov/view/cdc/34918/cdc_34918_DS1.pdf
• File Type:
  [PDF - 1.92 MB ]