A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records

Megan M Shuey, Kyung Min Lee, Jacob Keaton, Nikhil K Khankari, Joseph H Breeyear, Venexia M Walker, Donald R Miller, Kent R Heberer, Peter D Reaven, Shoa L Clarke, Jennifer Lee, Julie A Lynch, Marijana Vujkovic, Todd L Edwards

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

BACKGROUND: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery.

METHODS: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR).

FINDINGS: After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25).

INTERPRETATION: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions.

FUNDING: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

Original languageEnglish
Article number104674
Pages (from-to)104674
JournalEBioMedicine
Volume94
Early online date1 Jul 2023
DOIs
Publication statusE-pub ahead of print - 1 Jul 2023

Bibliographical note

Funding Information:
National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.The authors wish to acknowledge the efforts of Eric Tortenson and Max Breyer for their assistance with the initial data extraction from Vanderbilt University Medical Center Synthetic Derivative. Figs. 1 and 2 were created with BioRender.com. The authors would like to acknowledge the funders that supported resource data collection and the investigator efforts. Specifically, the work was supported using resources and facilities of the US Department of Veterans Affairs (VA), Veterans Health Administration, Cooperative Studies Program, grant number 825-MS-DI-33848, and used resources and facilities at the VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13–457 for the discovery self-controlled case series experiment. While the replication dataset, Vanderbilt University Medical Center Synthetic Derivative, is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the CTSA grant UL1TR000445 from National Center for Advancing Translational Sciences/National Institutes of Health. M.M.S. was supported by AHA 17SFRN33520017 and VA Merit I01 BX005399-01A1. N.K.K. is supported by NIH R00 CA215360. V.W. is supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK [MC_UU_00011/4] and the COVID-19 Longitudinal Health and Wellbeing National Core Study, which is funded by the UK Medical Research Council (MC_PC_20059). M.V. and P.R. are supported by 2I01BX003362-03A1. M.V. is additionally supported by R01DK134575.

Funding Information:
The authors wish to acknowledge the efforts of Eric Tortenson and Max Breyer for their assistance with the initial data extraction from Vanderbilt University Medical Center Synthetic Derivative. Figs. 1 and 2 were created with BioRender.com . The authors would like to acknowledge the funders that supported resource data collection and the investigator efforts. Specifically, the work was supported using resources and facilities of the US Department of Veterans Affairs (VA), Veterans Health Administration , Cooperative Studies Program , grant number 825-MS-DI-33848 , and used resources and facilities at the VA Informatics and Computing Infrastructure (VINCI), VA HSR RES 13–457 for the discovery self-controlled case series experiment. While the replication dataset, Vanderbilt University Medical Center Synthetic Derivative, is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the CTSA grant UL1TR000445 from National Center for Advancing Translational Sciences / National Institutes of Health . M.M.S. was supported by AHA 17SFRN33520017 and VA Merit I01 BX005399-01A1. N.K.K. is supported by NIH R00 CA215360. V.W. is supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK [ MC_UU_00011/4 ] and the COVID-19 Longitudinal Health and Wellbeing National Core Study , which is funded by the UK Medical Research Council ( MC_PC_20059 ). M.V. and P.R. are supported by 2I01BX003362-03A1. M.V. is additionally supported by R01DK134575.

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© 2023 The Authors

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