Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5856517 | Regulatory Toxicology and Pharmacology | 2015 | 44 Pages |
Abstract
Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor⢠program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals.
Keywords
CFSANSMILESECETOCGHSEECMCCNIEHSOECD3RsTLAHBAADMEHbdCenter for Food Safety and Applied NutritionANNEuropean UnionEuropean Economic CommunityGenetic algorithmQSARSPRDescriptorsRegistration, Evaluation, Authorisation and Restriction of Chemicalsabsorption, distribution, metabolism, and excretionapplicability domainDomain of applicabilityDOAQuantitative structure–activity relationshipREACHStructure–property relationshipOrganisation for Economic Co-operation and Developmentglobally harmonized system of classification and labeling of chemicalsArtificial Neural NetworkArchitectureNational Institute of Environmental Health SciencesNeuronINPUTSimplified Molecular Input Line Entry SystemMolecular weightEnsemble
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Authors
Rajeshwar P. Verma, Edwin J. Matthews,