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High-Throughput Algorithm for Discovering New Drug Indications by Utilizing Large-Scale Electronic Medical Record
Do-Hoon Kim1, Jung-Eun Lee2, Yong-Gil Kim3
1Department of Information Medicine, Asan Medical Center, Seoul, Republic of Korea.
Clinical Pharmacology and Therapeutics
|July 5, 2020
Summary
This study introduces a data-driven algorithm using electronic medical records to identify new uses for existing drugs. The method effectively prioritizes drug repositioning candidates for conditions like diabetes and dyslipidemia.
Area of Science:
- Pharmacology and Data Science
- Drug Discovery and Development
Background:
- Drug repositioning offers a solution to pharmaceutical production challenges.
- Electronic medical record (EMR) databases contain valuable prescription and laboratory data for identifying new drug indications.
Purpose of the Study:
- To develop and validate a novel, high-throughput, data-driven algorithm for identifying and prioritizing drug candidates for repositioning using large-scale EMR data.
- To assess the algorithm's efficacy in identifying drugs affecting key clinical indicators: hemoglobin A1c (HbA1c), low-density lipoprotein (LDL) cholesterol, triglycerides (TGs), and high-density lipoprotein (HDL) cholesterol.
Main Methods:
- Utilized a 5-year EMR database to generate datasets comparing drug on/off periods for individual patients.
- Adjusted for co-administered drug effects and applied one-sample t-tests with Bonferroni correction for statistical analysis.
- Evaluated candidate drugs against known indications, calculating sensitivity and negative predictive values.
Main Results:
- The algorithm analyzed 1,774 drugs, identifying associations with changes in clinical indicators: 45 for increased HDL cholesterol, and 41, 146, and 65 for reduced HbA1c, LDL cholesterol, and TGs, respectively.
- Achieved high sensitivity (0.95-1.00) and negative predictive value (0.95-1.00) when comparing identified candidates with existing drug indications.
- Successfully rediscovered known drugs for diabetes and dyslipidemia and identified novel candidates with promising literature support.
Conclusions:
- The developed algorithm effectively leverages EMR data for high-throughput drug repositioning.
- The findings demonstrate the algorithm's potential to identify both established and novel drug candidates for various clinical conditions.
- This approach can facilitate drug repositioning, utilizing drugs with established safety profiles for new therapeutic indications.
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