ArticleeLife2017
Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 50 citations in OpenAlex.
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- Visualization of FDA Adverse Drug Reaction Reports: Development and Usability Study of the VisDrugs Web Server.JMIR formative research · 2025Article
- Exploring common mechanisms of adverse drug reactions and disease phenotypes through network-based analysis.Cell reports methods · 2025Article
- Long-term hepatobiliary disorder associated with trastuzumab emtansine pharmacovigilance study using the FDA Adverse Event Reporting System database.Scientific reports · 2024Article
- DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.bioRxiv : the preprint server for biology · 2024Article
- Article
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- A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs.Nature communications · 2023Article
- Anti-TNF-α therapy induced psoriasis in rheumatoid arthritis patients according to FDA postmarketing surveillance data.Scientific reports · 2023Article
- Characterization and corroboration of safety signals identified from the US Food and Drug Administration Adverse Event Reporting System, 2008-19: cross sectional study.BMJ (Clinical research ed.) · 2022Article
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- Postmarketing safety surveillance data reveals protective effects of botulinum toxin injections against incident anxiety.Scientific reports · 2021Article
- Myocarditis occurrence with cancer immunotherapy across indications in clinical trial and post-marketing data.Scientific reports · 2021Article
- Systemic quinolones and risk of acute liver failure I: Analysis of data from the US FDA adverse event reporting system.JGH open : an open access journal of gastroenterology and hepatology · 2021Article
- Open Targets Platform: supporting systematic drug-target identification and prioritisation.Nucleic acids research · 2021Article
- Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis.Archives of toxicology · 2020Article
- The activities of drug inactive ingredients on biological targets.Science (New York, N.Y.) · 2020Article
- Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.EBioMedicine · 2020Article
- Relevance of physicochemical properties and functional pharmacology data to predict the clinical safety profile of direct oral anticoagulants.Pharmacology research & perspectives · 2020Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.
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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.