ArticlePeerJ2017
Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov.
Article in PeerJ, 2017. 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, 31 citations in OpenAlex.
- Exploring Cutting-Edge Strategies for the Management of Triple-Negative Breast Cancer Through Drug Repurposing.Current topics in medicinal chemistry · 2026Review
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- Near-physiological-temperature serial crystallography reveals conformations of SARS-CoV-2 main protease active site for improved drug repurposing.Structure (London, England : 1993) · 2021Article
- Drug Repurposing for Triple-Negative Breast Cancer.Journal of personalized medicine · 2020Review
- Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology.PloS one · 2020Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.Biochimica et biophysica acta. Reviews on cancer · 2019Review
- An Institutional Program to Increase Compliance with Clinicaltrials.gov Requirements.Therapeutic innovation & regulatory science · 2019Article
- Overcoming Obstacles to Drug Repositioning in Japan.Frontiers in pharmacology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts "Serious Adverse Events" (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).
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.