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

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 geneticall...

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Autores principales: Shuey, Megan M., Lee, Kyung Min, Keaton, Jacob, Khankari, Nikhil K., Breeyear, Joseph H., Walker, Venexia M., Miller, Donald R., Heberer, Kent R., Reaven, Peter D., Clarke, Shoa L., Lee, Jennifer, Lynch, Julie A., Vujkovic, Marijana, Edwards, Todd L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328805/
https://www.ncbi.nlm.nih.gov/pubmed/37399599
http://dx.doi.org/10.1016/j.ebiom.2023.104674
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author Shuey, Megan M.
Lee, Kyung Min
Keaton, Jacob
Khankari, Nikhil K.
Breeyear, Joseph H.
Walker, Venexia M.
Miller, Donald R.
Heberer, Kent R.
Reaven, Peter D.
Clarke, Shoa L.
Lee, Jennifer
Lynch, Julie A.
Vujkovic, Marijana
Edwards, Todd L.
author_facet Shuey, Megan M.
Lee, Kyung Min
Keaton, Jacob
Khankari, Nikhil K.
Breeyear, Joseph H.
Walker, Venexia M.
Miller, Donald R.
Heberer, Kent R.
Reaven, Peter D.
Clarke, Shoa L.
Lee, Jennifer
Lynch, Julie A.
Vujkovic, Marijana
Edwards, Todd L.
author_sort Shuey, Megan M.
collection PubMed
description 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: 10.13039/100000002National Institutes of Health, 10.13039/501100000265Medical Research Council Integrative Epidemiology Unit at the 10.13039/501100000883University of Bristol, 10.13039/501100000265UK Medical Research Council, 10.13039/100000968American Heart Association, and 10.13039/100000738Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.
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spelling pubmed-103288052023-07-09 A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records Shuey, Megan M. Lee, Kyung Min Keaton, Jacob Khankari, Nikhil K. Breeyear, Joseph H. Walker, Venexia M. Miller, Donald R. Heberer, Kent R. Reaven, Peter D. Clarke, Shoa L. Lee, Jennifer Lynch, Julie A. Vujkovic, Marijana Edwards, Todd L. eBioMedicine Articles 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: 10.13039/100000002National Institutes of Health, 10.13039/501100000265Medical Research Council Integrative Epidemiology Unit at the 10.13039/501100000883University of Bristol, 10.13039/501100000265UK Medical Research Council, 10.13039/100000968American Heart Association, and 10.13039/100000738Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program. Elsevier 2023-07-01 /pmc/articles/PMC10328805/ /pubmed/37399599 http://dx.doi.org/10.1016/j.ebiom.2023.104674 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Shuey, Megan M.
Lee, Kyung Min
Keaton, Jacob
Khankari, Nikhil K.
Breeyear, Joseph H.
Walker, Venexia M.
Miller, Donald R.
Heberer, Kent R.
Reaven, Peter D.
Clarke, Shoa L.
Lee, Jennifer
Lynch, Julie A.
Vujkovic, Marijana
Edwards, Todd L.
A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
title A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
title_full A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
title_fullStr A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
title_full_unstemmed A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
title_short A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
title_sort genetically supported drug repurposing pipeline for diabetes treatment using electronic health records
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328805/
https://www.ncbi.nlm.nih.gov/pubmed/37399599
http://dx.doi.org/10.1016/j.ebiom.2023.104674
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