Computational Models for Eye Irritation Classification of Mixtures

https://ntp.niehs.nih.gov/go/928025

Computational Models for Eye Irritation Classification of Mixtures

NIEHS scientists developed of a set of computational models to predict eye irritation and corrosion (Sedykh et al. 2022). The models were developed using a curated database of in vivo eye irritation studies from the scientific literature and stakeholder-provided data. The database contained over 500 unique substances, including many mixtures, tested at different concentrations. Substances were categorized according to GHS and EPA hazard classifications. Two modeling approaches were used to predict classification of mixtures. A conventional approach generated predictions based on the chemical structure of the most prominent component of the mixture. A mixture-based approach generated predictions by using weighted feature averaging to consider all known components in the mixture. Results suggest that these models are useful for screening compounds for eye irritation potential. Future efforts to increase the models’ utility will focus on expanding their applicability domains and using them in conjunction with other input variables (e.g., in vitro data) to establish defined approaches for eye irritation testing.

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ICCVAM Biennial Report 2020-2021

ICCVAM Biennial Report 2020-2021

Computational Models for Eye Irritation Classification of Mixtures