ArticleArchives of toxicology2025
Framework for classifying chemicals for repeat dose toxicity using NAMs.
Article in Archives of toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Regulatory integration of new approach methodologies for human-relevant developmental and reproductive toxicity (DART) assessment: A systematic cross-sector review.Toxicology reports · 2026Review
- ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.Molecular diversity · 2026Article
- From Reduction to Replacement: Challenges and Proposed Solutions for Animal-Free Safety Assessment of Hydrocarbon UVCB Substances.NAM journal · 2026Review
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
EPAA's 'NAM Designathon 2023' challenge for human toxicity sought to identify a classification system capable of categorising chemicals based on their bioactivity and bioavailability properties determined using non-animal methodologies (Worth et al. 2025). The proposal is made to classify chemicals into three levels of concern: low concern could be used without restriction, medium concern requiring assessment to establish safe use levels and high concern being candidates requiring risk management (Berggren and Worth in Regul Toxicol Pharmacol 142:105431, https://doi.org/10.1016/j.yrtph.2023.105431 , 2023). We developed a NAMs based classification system for "human systemic toxicity" mainly focussed on repeat dose toxicity, similar to the assessment carried out in classification for 'Specific Target Organ Toxicity-Repeated Exposure' (STOT-RE) based on ECETOC's Tiered Approach integrating three lines of evidence: In silico predictions, In vitro bioavailability and PBK modelling, In vitro bioactivity assays. The first stage employed an in silico approach, covering several toxicity endpoints across various (Q)SAR in silico models to identify indicators of toxicity. Bioavailability was categorised by simulating 14-day plasma C
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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.