title: Analysis of publically available skin sensitization data from REACH registrations 2008-2014.
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.
authors: Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.
authors: Thomas
authors: T
authors: Luechtefeld
authors: Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.
authors: Alexandra
authors: A
authors: Maertens
authors: The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.
authors: Daniel P
authors: DP
authors: Russo
authors: CAAT-Europe, University of Konstanz, Konstanz, Germany.
authors: Costanza
authors: C
authors: Rovida
authors: The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.|Department of Chemistry, Rutgers University at Camden, NJ, USA.
authors: Hao
authors: H
authors: Zhu
authors: Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.|CAAT-Europe, University of Konstanz, Konstanz, Germany.
authors: Thomas
authors: T
authors: Hartung
mesh_terms: D000826:Animal Testing Alternatives; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D017449:Dermatitis, Allergic Contact; D015386:Hazardous Substances / Q000633:toxicity*; D051379:Mice
chemical_list: D015386:Hazardous Substances
keywords: allergic contact dermatitis; animal testing alternatives; chemical safety; computational toxicology; in silico