title: Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.
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.
authors.affiliation: Office of Cosmetics and Colors, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD 20740, United States; Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD 20740, United States. Electronic address: Rajeshwar.Verma@fda.hhs.gov.
authors.forename: Rajeshwar P
authors.initials: RP
authors.lastname: Verma
authors.affiliation: Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD 20740, United States.
authors.forename: Edwin J
authors.initials: EJ
authors.lastname: Matthews
mesh_terms: D000826:Animal Testing Alternatives / Q000379:methods*; D000818:Animals; D003358:Cosmetics / Q000008:administration & dosage / Q000633:toxicity*; D005131:Eye Injuries / Q000139:chemically induced*; D007509:Irritants / Q000008:administration & dosage / Q000633:toxicity*; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D011817:Rabbits
chemical_list: D003358:Cosmetics; D007509:Irritants
keywords: Artificial neural network; Eye irritation; QSAR-21; Weight of evidence; c-QSAR