Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning

Gideon Koren, Galia Nordon, Kira Radinsky, Varda Shalev

Research output: Contribution to journalArticlepeer-review

Abstract

Despite effective medications, rates of uncontrolled glucose levels in type 2 diabetes remain high. We aimed to test the utility of machine learning applied to big data in identifying the potential role of concomitant drugs not taken for diabetes which may contribute to lowering blood glucose. Success in controlling blood glucose was defined as achieving HgA1c levels < 6.5% after 90-365 days following diagnosis and initiating treatment. Among numerous concomitant drugs taken by type 2 diabetic patients, alpha 1 (α1)-adrenoceptor antagonist drugs were the only group of medications that significantly improved the success rate of glucose control. Searching the published literature, this effect of α1-adrenoceptor antagonists has been shown in animal models, where this class of medications appears to induce insulin secretion. In conclusion, machine learning of big data is a novel method to identify effective antidiabetic effects for potential repurposable medications already on the market for other indications. Because these α1-adrenoceptor antagonists are widely used in men for treating benign prostate hyperplasia (BPH) at age groups exhibiting increased rates of type 2 diabetes, this finding is of potential clinical significance.

Original languageEnglish
Article numbere00529
JournalPharmacology Research and Perspectives
Volume7
Issue number6
DOIs
StatePublished - 1 Dec 2019
Externally publishedYes

Keywords

  • big data analysis
  • diabetes type 2
  • glucose control
  • machine learning
  • α1-adrenoceptor antagonist

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