ArticlePharmacology research & perspectives2019
Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning.
Article in Pharmacology research & perspectives, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- From In Silico Hypothesis to Validation: The Role of Real-World Evidence in the Preliminary Verification of AI-Generated Drug-Repositioning Candidates: A Comprehensive Review.Journal of clinical medicine · 2026Review
- Computational drug repurposing based on electronic health records: a scoping review.NPJ digital medicine · 2022Article
- A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus.Endocrinology and metabolism (Seoul, Korea) · 2022Article
- Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms.Healthcare (Basel, Switzerland) · 2021Article
- Chloroquine for Covid 19: introducing drug repurposing to medical students.International journal of medical education · 2020Article
- Insulin Resistance in Osteoarthritis: Similar Mechanisms to Type 2 Diabetes Mellitus.Journal of nutrition and metabolism · 2020Review
- Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning.Pharmacology research & perspectives · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.