Predicting Chemical Ocular Toxicity Using a Combinatorial QSAR Approach

Regulatory
agencies require testing of chemicals and products to
protect workers and consumers from potential eye injury hazards. Animal
screening, such as the rabbit Draize test, for potential environmental
toxicants is time-consuming and costly. Therefore, virtual screening
using computational models to tag potential ocular toxicants is attractive
to toxicologists and policy makers. We have developed quantitative
structure–activity relationship (QSAR) models for a set of
small molecules with animal ocular toxicity data compiled by the National
Toxicology Program Interagency Center for the Evaluation of Alternative
Toxicological Methods. The data set was initially curated by removing
duplicates, mixtures, and inorganics. The remaining 75 compounds were
used to develop QSAR models. We applied both k nearest
neighbor and random forest statistical approaches in combination with
Dragon and Molecular Operating Environment descriptors. Developed
models were validated on an external set of 34 compounds collected
from additional sources. The external correct classification rates
(CCR) of all individual models were between 72 and 87%. Furthermore,
the consensus model, based on the prediction average of individual
models, showed additional improvement (CCR = 0.93). The validated
models could be used to screen external chemical libraries and prioritize
chemicals for in vivo screening as potential ocular toxicants.