BACKGROUND
Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research.
OBJECTIVE
The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution.
METHODS
In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P<sub>1</sub>), Promising Index (P<sub>2</sub>), Prestige Index (P<sub>3</sub>), and Collaboration Index (CI).
RESULTS
We found that the maxima of P<sub>1</sub>, P<sub>3</sub>, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P<sub>1</sub> and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P<sub>2</sub> was more sensitive to immediate changes. P<sub>3</sub> reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug.
CONCLUSIONS
In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.