ArticleArchives of toxicology2026
From toxicogenomics data to cumulative assessment groups: a framework for chemical grouping.
Article 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.
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
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The grouping of chemicals based on common properties or molecular mechanisms of action is pivotal for advancing regulatory toxicology, reducing data gaps, and enabling cumulative risk assessments. This study introduces a novel framework using chemical-gene-phenotype-disease (CGPD) tetramers derived from the Comparative Toxicogenomics Database (CTD). Our approach integrates publicly available toxicogenomics data to identify and cluster chemicals with similar molecular and phenotypic effects. The considered chemicals belong to diverse use groups including pesticides, pharmaceuticals, and industrial chemicals. We validated our method by comparing CGPD tetramer-based clusters with cumulative assessment groups (CAGs) that have been established by EFSA for pesticides and demonstrate strong overlap with established groupings while identifying additional compounds relevant for risk assessment. Key examples include clusters associated with endocrine disruption and metabolic disorders. By bridging omics-derived molecular data with phenotypic and disease endpoints, this framework provides a comprehensive tool for chemical grouping and the support of evidence-based regulatory decision-making to facilitate the transition to next-generation risk assessment methodologies.
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What Socratic 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 Socratic graph.