Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Oct 19, 2019
Date Accepted: Mar 31, 2020
Understanding drug repurposing from the perspective of biomedical entities and their evolution: a bibliographic research using aspirin
ABSTRACT
Background:
Nowadays, 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.
Objective:
The purpose of this study was to understand the drug repurposing from the perspective of bio-entities and their evolution.
Methods:
In the work reported in this paper, we extended the bibliometric indicators of bio-entities mentioned in PubMed to detect potential patterns of bio-entities in various phases of drug research, and investigated the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study, since it can be repurposed for many applications. We applied four easy, transparent measures based on entitymetrics: Popularity Index (P1); Promising Index (P2); Prestige Index (P3); and Collaboration Index (CI), to investigate DR for aspirin.
Results:
We found that the maxima of P1, P3 and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which bio-entities interacted with the drug during the various phases of DR, and to analyze the potential driving factors of DR at the entity level. P1 and CI are indicative of the dynamic trends of a specific bio-entity over a long time period, while P2 is more sensitive to immediate changes. P3 reflects early signs of the practical value of bio-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 which can be valuable for enhancing decision making in the field of drug discovery and development.
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