ArticleALTEX2016
Analysis of publically available skin sensitization data from REACH registrations 2008-2014.
Article in ALTEX, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 61 citations in OpenAlex.
- Article
- ChemSkin DB 2.0: a comprehensive reference chemical database for skin corrosion and irritation.Archives of toxicology · 2026Article
- The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data.Chemical research in toxicology · 2023Article
- First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability.Molecular diversity · 2022Article
- Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring.Chemical science · 2022Review
- STopTox: AnEnvironmental health perspectives · 2022Article
- Use of new approach methodologies (NAMs) to meet regulatory requirements for the assessment of industrial chemicals and pesticides for effects on human health.Frontiers in toxicology · 2022Review
- Review
- Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.Artificial intelligence in the life sciences · 2021Article
- Same-chemical comparison of nonanimal eye irritation test methods: Bovine corneal opacity and permeability, EpiOcular™, isolated chicken eye, ocular Irritection®, OptiSafe™, and short time exposure.Toxicology in vitro : an international journal published in association with BIBRA · 2021Review
- Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools?Chemical research in toxicology · 2021Article
- Evaluation of the global performance of eight in silico skin sensitization models using human data.ALTEX · 2021Article
- Human-Derived In Vitro Models Used for Skin Toxicity Testing Under REACh.Handbook of experimental pharmacology · 2021Article
- Disclosing Main authors and Organisations collaborations in bioprinting through network maps analysis.Journal of biomedical semantics · 2020Article
- Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.ALTEX · 2020Article
- Mitochondria as a target of organophosphate and carbamate pesticides: Revisiting common mechanisms of action with new approach methodologies.Reproductive toxicology (Elmsford, N.Y.) · 2019Article
- Predicting toxicity of chemicals: software beats animal testing.EFSA journal. European Food Safety Authority · 2019Article
- Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.Chemical research in toxicology · 2019Review
- Perspectives onApplied in vitro toxicology · 2018Review
- Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.Toxicological sciences : an official journal of the Society of Toxicology · 2018Article
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 at 1 institution in 1 country.
Funding
Abstract
The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization. A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found. A chemical similarity map was produced using PubChem's 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel's module recognition algorithm revealing wide module-dependent variation. Approximately 31% of mapped chemicals are Michaell's acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value. A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity. This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction.
Indexed as
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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.