Computational modeling grounded in
reliable experimental data can
help design effective non-animal approaches to predict the eye irritation
and corrosion potential of chemicals. The National Toxicology Program
(NTP) Interagency Center for the Evaluation of Alternative Toxicological
Methods (NICEATM) has compiled and curated a database of in
vivo eye irritation studies from the scientific literature
and from stakeholder-provided data. The database contains 810 annotated
records of 593 unique substances, including mixtures, categorized
according to UN GHS and US EPA hazard classifications. This study
reports a set of in silico models to predict EPA
and GHS hazard classifications for chemicals and mixtures, accounting
for purity by setting thresholds of 100% and 10% concentration. We
used two approaches to predict classification of mixtures: conventional
and mixture-based. Conventional models evaluated substances based
on the chemical structure of its major component. These models achieved
balanced accuracy in the range of 68–80% and 87–96%
for the 100% and 10% test concentration thresholds, respectively.
Mixture-based models, which accounted for all known components in
the substance by weighted feature averaging, showed similar or slightly
higher accuracy of 72–79% and 89–94% for the respective
thresholds. We also noted a strong trend between the pH feature metric
calculated for each substance and its activity. Across all the models,
the calculated pH of inactive substances was within one log10 unit
of neutral pH, on average, while for active substances, pH varied
from neutral by at least 2 log10 units. This pH dependency is especially
important for complex mixtures. Additional evaluation on an external
test set of 673 substances obtained from ECHA dossiers achieved balanced
accuracies of 64–71%, which suggests that these models can
be useful in screening compounds for ocular irritation potential.
Negative predictive value was particularly high and indicates the
potential application of these models in a bottom-up approach to identify
nonirritant substances.