CON4EI: EpiOcular™ Eye Irritation Test (EpiOcular™ EIT) for hazard identification and labelling of eye irritating chemicals
Assessment of the acute eye irritation potential is part of the international regulatory requirements for testing of chemicals. The objective of the CON4EI project was to develop tiered testing strategies for eye irritation assessment. A set of 80 reference chemicals (38 liquids and 42 solids) was t...
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Veröffentlicht in: | Toxicology in vitro 2018-06, Vol.49, p.21-33 |
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Sprache: | eng |
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Zusammenfassung: | Assessment of the acute eye irritation potential is part of the international regulatory requirements for testing of chemicals. The objective of the CON4EI project was to develop tiered testing strategies for eye irritation assessment. A set of 80 reference chemicals (38 liquids and 42 solids) was tested with eight different methods. Here, the results obtained with the EpiOcular™ Eye Irritation Test (EIT), adopted as OECD TG 492, are shown.
The primary aim of this study was to evaluate of the performance of the test method to discriminate between chemicals not requiring classification for serious eye damage/eye irritancy (No Category) and chemicals requiring classification and labelling. In addition, the predictive capacity in terms of in vivo drivers of classification (i.e. corneal opacity, conjunctival redness and persistence at day 21) was investigated.
EpiOcular™ EIT achieved a sensitivity of 97%, a specificity of 87% and accuracy of 95% and also confirmed its excellent reproducibility (100%) from the original validation. The assay was applicable to all chemical categories tested in this project and its performance was not limited to the particular driver of the classification. In addition to the existing prediction model for dichotomous categorization, a new prediction model for Cat 1 is suggested.
•EpiOcular achieved a sensitivity of 97%, a specificity of 87% and accuracy of 95%.•Excellent reproducibility (100%) obtained in 2 runs.•EpiOcular™ EIT was applicable to all chemical categories tested in this project.•Excellent performance not limited to the particular driver of the classification•A new prediction model for Cat 1 is suggested for liquids and solids. |
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ISSN: | 0887-2333 1879-3177 |
DOI: | 10.1016/j.tiv.2017.07.002 |