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Drug repositioning for diabetes based on 'omics' data mining.

Ming Zhang1, Heng Luo2, Zhengrui Xi1

  • 1Department of Medicine, Division of Neurology, Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, 60 Leonard Street, Toronto, Ontario, M5T 2S8, Canada.

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This study repurposed existing drugs for diabetes treatment using

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Area of Science:

  • Biomedical Informatics
  • Pharmacology
  • Genomics

Background:

  • Traditional drug development is lengthy and costly.
  • Drug repositioning offers a faster, cheaper, and safer alternative.
  • Identifying new therapeutic uses for existing drugs is crucial for unmet medical needs.

Purpose of the Study:

  • To repurpose marketed drugs and clinical candidates for novel diabetes indications.
  • To leverage clinical 'omics' data for identifying new anti-diabetic targets and therapies.
  • To accelerate the discovery of effective diabetes treatments.

Main Methods:

  • Analyzed genome-wide association studies (GWAS), proteomics, and metabolomics data.
  • Integrated data from Therapeutic Target Database (TTD), OMIM, and PubMed.
  • Utilized Connectivity Map (CMap) for gene expression analysis and drug repurposing.

Main Results:

  • Identified 992 potential anti-diabetic protein targets.
  • Selected 35 druggable proteins, with 5 known diabetes targets.
  • Repurposed 9 drugs for diabetes: 4 for type 1 (targeting COX2) and 2 for type 2 (targeting ADRA2A).

Conclusions:

  • 'Omics' data mining is a powerful strategy for drug repositioning in diabetes.
  • Identified specific drugs and targets for potential new diabetes therapies.
  • Findings may extend to other diseases like Alzheimer's disease.