Endocrinol Metab.  2022 Apr;37(2):195-207. 10.3803/EnM.2022.1404.

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

• Lee S
• Jeon S
• Kim HS

• KMID: 2529211
• DOI: http://doi.org/10.3803/EnM.2022.1404

Abstract

Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

Keyword

Drug repositioning; Semantics; Machine learning; Real-world data; Data science

Figure

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  Fig. 1. Flowchart depicting the study selection process. aThe 1st manual review by article type: Excluding review and systemic review articles; bThe 2nd manual review by article topic in the abstract

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