ArticleJMIR medical informatics2020
Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 28 citations in OpenAlex.
- Drug repurposing in cancer research: a bibliometric analysis.Discover oncology · 2025Article
- Article
- Benchmarking large language models for biomedical natural language processing applications and recommendations.Nature communications · 2025Article
- Article
- Drug Repurposing as an Effective Drug Discovery Strategy: A Critical Review.Drug design, development and therapy · 2025Review
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- Pollen-derived microcapsules for aspirin microencapsulation:RSC advances · 2022Article
- Drug repositioning: A bibliometric analysis.Frontiers in pharmacology · 2022Review
- Repurposing Drugs to Treat Heart and Brain Illness.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Analyzing knowledge entities about COVID-19 using entitymetrics.Scientometrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug 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.
objectiveThe purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution.
methodsIn 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
resultsWe found that the maxima of P
conclusionsIn-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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.