title: Exploiting large-scale drug-protein interaction information for computational drug repurposing.
abstract: BACKGROUND
  Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated.
  
  
  RESULTS
  We developed a computational method termed "drug-protein interaction-based repurposing" (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect.
  
  
  CONCLUSIONS
  Detailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).
authors.affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U,S, Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. RLiu@bhsai.org.
authors.forename: Ruifeng
authors.initials: R
authors.lastname: Liu
authors.forename: Narender
authors.initials: N
authors.lastname: Singh
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Tawa
authors.forename: Anders
authors.initials: A
authors.lastname: Wallqvist
authors.forename: Jaques
authors.initials: J
authors.lastname: Reifman
mesh_terms: D001499:Bayes Theorem; D017277:Drug Approval; D004347:Drug Interactions; D058492:Drug Repositioning / Q000379:methods*; D006973:Hypertension / Q000188:drug therapy; D011506:Proteins / Q000378:metabolism*; D014481:United States
chemical_list: D011506:Proteins