CON4EI: Evaluation of QSAR models for hazard identification and labelling of eye irritating chemicals.

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.

CON4EI: Short Time Exposure (STE) test method for hazard identification and labelling of eye irritating chemicals.

Assessment of ocular irritancy is an international regulatory requirement in the safety evaluation of industrial and consumer products. Although many in vitro ocular irritation assays exist, alone they are incapable of fully categorizing chemicals. Therefore, the CEFIC-LRI-AIMT6-VITO CON4EI consortium was developed to assess the reliability of eight in vitro test methods and establish an optimal tiered-testing strategy. One assay selected was the Short Time Exposure (STE) assay. This assay measures the viability of SIRC rabbit corneal cells after 5min exposure to 5% and 0.05% solutions of test material, and is capable of categorizing of Category 1 and No Category chemicals. The accuracy of the STE test method to identify Cat 1 chemicals was 61.3% with 23.7% sensitivity and 95.2% specificity. If non-soluble chemicals and unqualified results were excluded, the performance to identify Cat 1 chemicals remained similar (accuracy 62.2% with 22.7% sensitivity and 100% specificity). The accuracy of the STE test method to identify No Cat chemicals was 72.5% with 66.2% sensitivity and 100% specificity. Excluding highly volatile chemicals, non-surfactant solids and non-qualified results resulted in an important improvement of the performance of the STE test method (accuracy 96.2% with 81.8% sensitivity and 100% specificity). Furthermore, it seems that solids are more difficult to test in the STE, 71.4% of the solids resulted in unqualified results (solubility issues and/or high variation between independent runs) whereas for liquids 13.2% of the results were not qualified, supporting the restriction of the test method regarding the testing of solids.

CON4EI: Slug Mucosal Irritation (SMI) test method for hazard identification and labelling of serious eye damaging and eye irritating chemicals.

Assessment of ocular irritancy is an international regulatory requirement in the safety evaluation of industrial and consumer products. Although many in vitro ocular irritation assays exist, alone they are incapable of fully categorizing chemicals. The objective of CEFIC-LRI-AIMT6-VITO CON4EI (CONsortium for in vitro Eye Irritation testing strategy) project was to develop tiered testing strategies for eye irritation assessment that can lead to complete replacement of the in vivo Draize rabbit eye test (OECD TG 405). A set of 80 reference chemicals was tested with seven test methods, one method was the Slug Mucosal Irritation (SMI) test method. The method measures the amount of mucus produced (MP) during a single 1-hour contact with a 1% and 10% dilution of the chemical. Based on the total MP, a classification (Cat 1, Cat 2, or No Cat) is predicted. The SMI test method correctly identified 65.8% of the Cat 1 chemicals with a specificity of 90.5% (low over-prediction rate for in vivo Cat 2 and No Cat chemicals). Mispredictions were predominantly unidirectional towards lower classifications with 26.7% of the liquids and 40% of the solids being underpredicted. In general, the performance was better for liquids than for solids with respectively 76.5% vs 57.1% (Cat 1), 61.5% vs 50% (Cat 2), and 87.5% vs 85.7% (No Cat) being identified correctly.