title: Drug repositioning for diabetes based on 'omics' data mining.
abstract: Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical 'omics' data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that 'omics' data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer's disease.
authors.affiliation: Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada.
authors.forename: Ming
authors.initials: M
authors.lastname: Zhang
authors.affiliation: University of Arkansas at Little Rock/University of Arkansas for Medical Sciences Bioinformatics Graduate Program, 2801 S. University Ave., Little Rock, AR, 72204, United States of America.
authors.forename: Heng
authors.initials: H
authors.lastname: Luo
authors.affiliation: Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada.
authors.forename: Zhengrui
authors.initials: Z
authors.lastname: Xi
authors.affiliation: Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada.
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Rogaeva
mesh_terms: D058669:Adrenergic alpha-2 Receptor Antagonists / Q000494:pharmacology; D052246:Cyclooxygenase 2 Inhibitors / Q000494:pharmacology; D057225:Data Mining / Q000379:methods; D062313:Databases, Pharmaceutical / Q000706:statistics & numerical data; D003920:Diabetes Mellitus / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D058492:Drug Repositioning / Q000379:methods*; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D007004:Hypoglycemic Agents / Q000494:pharmacology*; D055432:Metabolomics / Q000706:statistics & numerical data; D040901:Proteomics / Q000706:statistics & numerical data; D016194:Receptors, N-Methyl-D-Aspartate / Q000819:agonists
chemical_list: D058669:Adrenergic alpha-2 Receptor Antagonists; D052246:Cyclooxygenase 2 Inhibitors; D007004:Hypoglycemic Agents; D016194:Receptors, N-Methyl-D-Aspartate