Evidence map›Paper›PMID 37022445›Full record

ArticleArchives of toxicology2023

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved.

Teresa Martínez-Sena, Erika Moro, Marta Moreno-Torres, Guillermo Quintás, Jan Hengstler, José V Castell

Open access · hybridAbstract read
In one paragraph

Article in Archives of toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.9field-weighted citation impact, top 4% 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

16 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Development andACS omega · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Review
  8. Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  9. Article
  10. Review
  11. Plant Metabolomics: The Future of Anticancer Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2024
    Review
  12. Article
  13. SynthesizedNanomaterials (Basel, Switzerland) · 2024
    Article
  14. Article
  15. Article
  16. Review
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

6 authors at 3 institutions in 2 countries.

Teresa Martínez-SenaInstituto de Investigación Sanitaria del Hospital La Fe (IIS La Fe), Unidad Mixta de Hepatologia Experimental, Valencia, Spain.
Erika MoroInstituto de Investigación Sanitaria del Hospital La Fe (IIS La Fe), Unidad Mixta de Hepatologia Experimental, Valencia, Spain.
Marta Moreno-TorresInstituto de Investigación Sanitaria del Hospital La Fe (IIS La Fe), Unidad Mixta de Hepatologia Experimental, Valencia, Spain.
Guillermo QuintásHealth and Biomedicine, Leitat Technological Center, Valencia, Spain.
Jan HengstlerLeibniz Research Centre for Working Environment and Human Factors at the Technical University of Dortmund (IfADo), Dortmund, Germany.
José V CastellInstituto de Investigación Sanitaria del Hospital La Fe (IIS La Fe), Unidad Mixta de Hepatologia Experimental, Valencia, Spain. jose.castell@uv.es.ORCID http://orcid.org/0000-0003-1179-6430
Universitat de València · ESLeitat Technological Center · ESTU Dortmund University · DE

Funding

Horizon 2020 681002Instituto de Salud Carlos III FI18/00260Ministerio de Ciencia e Innovación IJC2018-036209-I
6 · The paper itself

Abstract

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

Indexed as

Chemical and Drug Induced Liver InjuryDrug-Related Side Effects and Adverse ReactionsFatty LiverAnimalsHepatocytesHep G2 CellsHumansDrug hepatotoxicityHepG2 cellsIn vitroMechanisms of hepatotoxicityMetabolomics

Identifiers

PMID37022445
PMCPMC10182947
OpenAlexW4362657696

What OpenQuestion holds

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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.