CON4EI: Evaluation of QSAR models for hazard identification and labelling of eye irritating chemicals. (June 2018) - Geerts, L.; Adriaens, E.; Alépée, N.; Guest, R.; Willoughby, J.A.; Kandarova, H.; Drzewiecka, A.; Fochtman, P.; Verstraelen, S.; Van Rompay, A.R.
Abstract: Assessment of ocular irritation is a regulatory requirement in safety evaluation of industrial and consumer products. Although a number of in vitro ocular irritation assays exist, none are capable of fully categorizing chemicals as stand-alone assays. Therefore, the CEFIC-LRI-AIMT6-VITO CON4EI (CONsortium for in vitro Eye Irritation testing strategy) project was developed to assess the reliability of eight in vitro test methods and computational models as well as establishing an optimal tiered-testing strategy. For three computational models (Toxtree, and Case Ultra EYE_DRAIZE and EYE_IRR) performance parameters were calculated. Coverage ranged from 15 to 58%. Coverage was 2 to 3.4 times higher for liquids than for solids. The lowest number of false positives (5%) was reached with EYE_IRR; this model however also gave a high number of false negatives (46%). The lowest number of false negatives (25%) was seen with Toxtree; for liquids Toxtree predicted the lowest number of false negatives (11%), for solids EYE_DRAIZE did (17%). It can be concluded that the training sets should be enlarged with high quality data. The tested models are not yet sufficiently powerful for stand-alone evaluations, but that they can surely become of value in an integrated weight-of-evidence approach in hazard assessment.