Table · dataset · 2018
Non-animal methods to predict skin sensitization (I): the Cosmetics Europe database
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<p>Cosmetics Europe, the European Trade Association for the cosmetics and personal care industry, is conducting a multi-phase program to develop regulatory accepted, animal-free testing strategies enabling the cosmetics industry to conduct safety assessments.
Description
Based on a systematic evaluation of test methods for skin sensitization, five non-animal test methods (DPRA (Direct Peptide Reactivity Assay), KeratinoSens<sup>TM</sup>, h-CLAT (human cell line activation test), U-SENS<sup>TM</sup>, SENS-IS) were selected for inclusion in a comprehensive database of 128 substances.
Existing data were compiled and completed with newly generated data, the latter amounting to one-third of all data. The database was complemented with human and local lymph node assay (LLNA) reference data, physicochemical properties and use categories, and thoroughly curated. Focused on the availability of human data, the substance selection resulted nevertheless resulted in a high diversity of chemistries in terms of physico-chemical property ranges and use categories.
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Predictivities of skin sensitization potential and potency, where applicable, were calculated for the LLNA as compared to human data and for the individual test methods compared to both human and LLNA reference data. In addition, various aspects of applicability of the test methods were analyzed. Due to its high level of curation, comprehensiveness, and completeness, we propose our database as a point of reference for the evaluation and development of testing strategies, as done for example in the associated work of Kleinstreuer et al. We encourage the community to use it to meet the challenge of conducting skin sensitization safety assessment without generating new animal data.</p>
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- DOI doi.org/10.6084/m9.figshare.5933326.v2 ↗
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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Topics
Provenance · 1 source records, 18 field assertions
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|---|---|---|---|
| figshare | oai:figshare.com:article/5933326 | 11 d ago | JSON v1 |
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