Determination and Sub-categorization of Ocular Irritants Using the EpiOcular Tissue Model – Prediction Models for Liquids and Solids

Silvia Letasiova, Lenka Hudecova, Jan Markus, Yulia Kaluzhny, Mitch Klausner

Assessment of serious eye damage/eye irritation originally involved the use of laboratory animals (OECD TG 405). In 2015, a new test guideline (OECD TG 492) was accepted which enables the use of an in vitro procedure based on reconstructed human cornea-like epithelium (RhCE) to distinguish between chemicals (substances and mixtures) not requiring classification and those that must be labeled for eye irritation or serious eye damage. Chemicals identified as requiring classification for eye irritation/serious eye damage must be further tested to distinguish between eye irritants and those causing serious eye damage. There have been several projects focused on the development of tiered testing strategies for eye irritation assessment which takes in account all drivers of classification. The goal of these projects has been to develop a testing strategy to subcategorize chemicals which: a) do not require labeling for serious eye damage or eye irritancy (NoCategory), b) can cause serious eye damage (Category1 or Cat1), and c) are eye irritants (Category 2 or Cat 2)[1,2].

In the current project, a set of 13 chemicals (7 liquids and 6 solids) that are listed as proficiency chemicals in draft OECD TG 492 B were tested using the RhCE model, EpiOcular. We used a testing strategy developed in CON4EI project and confirmed in ALT 4EI project, which combines the most predictive time-points of EpiOcular time-to-toxicity neat and dilution protocols. Liquids and solids were test separately with different methodologies and prediction models. The set of chemicals consisted of 4 Cat 1 chemicals, 5 Cat 2 chemicals and 4 No Cat chemicals. Using the proposed testing strategy, we were able to correctly identify 100% of Cat 1 chemicals (4/4), 100% of Cat 2 chemicals (5/5) and 100% of No Cat chemicals(4/4).

The testing strategy proposed in CON4EI and verified in ALT 4EI projects to achieve optimal prediction for all three categories–prediction models for liquids and solids seems to be a very promising tool in an integrated testing strategy (ITS) that can discriminate chemicals to No Cat,Cat 2 and Cat 1.


EpiOcular (OCL-200-EIT), OECD TG 492b, sub-categorization, prediction model

Materials Tested

N,N-Diethylethanolamine, Acetic acid (10 %), 2-Butanone, Acetone, Hexadecyltrimethylammonium, chloride (2%), 1,3-Diisopropylbenzene, Dodecane, Magnesium carbonate, Anthracene, 1-Naphthalene acetic acid Na salt, 1,2-Benzisothiazol-3(2H)-one, 4-Carboxybenzaldehyde, 2-Hydroxy-1,4-naphthoquinone

Request a copy of this paper, click here.