Evidence mapPaperPMID 41369776Full record

ReviewArchives of toxicology2026

Mechanism-based new approach methodologies for in vitro detection of chemical-induced liver steatosis.

Anouk Verhoeven, Julen Sanz-Serrano, Mathieu Vinken

Abstract readReview
PubMed Publisher
In one paragraph

Review in Archives of toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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.

Anouk VerhoevenDepartment of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.ORCID 0000-0003-1275-9498
Julen Sanz-SerranoDepartment of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.ORCID 0000-0002-0503-2752
Mathieu VinkenDepartment of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium. mathieu.vinken@vub.be.ORCID 0000-0001-5115-8893

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver steatosis resulting from chemical exposure is a growing clinical concern, pointing to the need for early detection in chemical safety assessments. New approach methodologies (NAMs) based on adverse outcome pathway (AOPs) networks are emerging tools to assess the safety of chemicals as an alternative to animal studies. Amongst others, NAM can include a human-based in vitro system linked to a battery of complementary assays, each measuring an individual key event within the respective AOP network. These AOP-based NAMs allow to acquire mechanistic insight, while facilitating the translation of in vitro data into outcomes relevant to hazard assessment. The current review provides pragmatic guidance for developing NAMs entailing an AOP network-driven in vitro testing platform suitable for steatogenic hazard assessment of chemicals, intended for use in academic, industrial and regulatory settings. In a first part, underlying mechanisms for highly specific key events for liver steatosis are described. In a second part, a workflow is provided for the establishment of NAMs, including the selection of human-based in vitro cell models, functional in vitro assays, reference control chemicals, exposure and treatment conditions. In a third part, future perspectives for AOP-based liver steatosis risk assessment are outlined.

Indexed as

Adverse Outcome PathwaysChemical and Drug Induced Liver InjuryFatty LiverToxicity TestsAnimalsAnimal Testing AlternativesHumansRisk AssessmentAdverse outcome pathwaysHazard assessmentIn vitro test batteriesNew approach methodologiesSteatosis

Identifiers

What Socratic holds

Textmetadata
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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 Socratic graph.