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 II: Corrosion potential

Abstract

This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA’s CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU’s 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU’s REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor’s three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity.