ArticleArchives of toxicology2024
Utility of in vivo metabolomics to support read-across for UVCB substances under REACH.
Article in Archives of toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 6 citations in OpenAlex.
- Demonstrating the reliability of in vivo metabolomics-based chemical grouping: Part 2 - Consistency of group-specific metabolic effects.Archives of toxicology · 2026Article
- Saccharopine and α-Aminoadipic Acid as Potential Biomarkers for Mitochondrial Stress in Neurons.Cells · 2026Article
- From Reduction to Replacement: Challenges and Proposed Solutions for Animal-Free Safety Assessment of Hydrocarbon UVCB Substances.NAM journal · 2026Review
- Guidance on the use of read-across for chemical safety assessment in food and feed.EFSA journal. European Food Safety Authority · 2025Article
- Unanticipated differences in the rat plasma metabolome of genistein and daidzein.Archives of toxicology · 2025Article
- Assessment of pulmonary fibrosis using weighted gene co-expression network analysis.Frontiers in toxicology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structure-based grouping of chemicals for targeted testing and read-across is an efficient way to reduce resources and animal usage. For substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs), structure-based grouping is virtually impossible. Biology-based approaches such as metabolomics could provide a solution. Here, 15 steam-cracked distillates, registered in the EU through the Lower Olefins Aromatics Reach Consortium (LOA), as well as six of the major substance constituents, were tested in a 14-day rat oral gavage study, in line with the fundamental elements of the OECD 407 guideline, in combination with plasma metabolomics. Beyond signs of clinical toxicity, reduced body weight (gain), and food consumption, pathological investigations demonstrated the liver, thyroid, kidneys (males only), and hematological system to be the target organs. These targets were confirmed by metabolome pattern recognition, with no additional targets being identified. While classical toxicological parameters did not allow for a clear distinction between the substances, univariate and multivariate statistical analysis of the respective metabolomes allowed for the identification of several subclusters of biologically most similar substances. These groups were partly associated with the dominant (> 50%) constituents of these UVCBs, i.e., indene and dicyclopentadiene. Despite minor differences in clustering results based on the two statistical analyses, a proposal can be made for the grouping of these UVCBs. Both analyses correctly clustered the chemically most similar compounds, increasing the confidence that this biological approach may provide a solution for the grouping of UVCBs.
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.