Evidence map›Paper›PMID 34345836›Full record

ArticleCurrent research in toxicology2020

Development of an adverse drug event network to predict drug toxicity.

Qier Wu, Olivier Taboureau, Karine Audouze

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current research in toxicology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 4 pooled it
0.7field-weighted citation impact, top 24% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 4 syntheses or guidelines pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Qier WuUniversité de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France.
Olivier TaboureauUniversité de Paris, BFA, CNRS UMR 8251, ERL Inserm U1133, CNRS UMR 8251, F-75013 Paris, France.
Karine AudouzeUniversité de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France.
Centre National de la Recherche Scientifique · FRInserm · FRUniversité Paris Cité · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.

Indexed as

ADE, adverse drug eventADR, adverse drug reactionAdverse event networkAOP, adverse outcome pathwayComputational toxicologyFAERS, FDA Adverse Event Reporting SystemFDA, Food and Drug AdministrationHMS-PCI, high-throughput mass spectrometric protein complex identificationLRT, Likelihood Ratio TestMedDRA, Medical Dictionary for Regulatory ActivitiesNetwork sciencePPAN, protein-protein association networkPredictive toxicityPT, Preferred TermpullS, pull-down scoreQSAR, Quantitative structure-activity relationshipsSE, side effectSOC, System Organ ClassSystem toxicologyTAP–MS, tandem-affinity-purification method coupled to mass spectrometrywS, weighted score

Identifiers

PMID34345836
PMCPMC8320634
OpenAlexW3034975687

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.