Table · dataset · 2018
Non-animal methods to predict skin sensitization (II): an assessment of defined approaches
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<p>Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals.
Description
Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients.
The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple <i>in vitro</i> and <i>in chemico</i> skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain <i>in silico</i> predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization.
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Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools.
Multiple non-animal testing strategies incorporating <i>in vitro, in chemico</i>, and <i>in silico</i> inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.</p>
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- DOI doi.org/10.6084/m9.figshare.5933323.v3 ↗
DOI / persistent id · from figshare com
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- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
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Provenance · 1 source records, 19 field assertions
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