QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis

Nonalcoholic hepatic
steatosis is a worldwide epidemiological concern
since it is among the most prominent hepatic diseases. Indeed, research
in toxicology and epidemiology has gathered evidence that exposure
to endocrine disruptors can perturb cellular homeostasis and cause
this disease. Therefore, assessing the likelihood of a chemical to
trigger hepatic steatosis is a matter of the utmost importance. However,
systematic in vivo testing of all the chemicals humans
are exposed to is not feasible for ethical and economical reasons.
In this context, predicting the molecular initiating events (MIE)
leading to hepatic steatosis by QSAR modeling is an issue of practical
relevance in modern toxicology. In this article, we present QSAR models
based on random forest classifiers and DRAGON molecular descriptors
for the prediction of in vitro assays that are relevant
to MIEs leading to hepatic steatosis. These assays were provided by
the ToxCast program and proved to be predictive for the detection
of chemical-induced steatosis. During the modeling process, special
attention was paid to chemical and toxicological data curation. We
adopted two modeling strategies (undersampling and balanced random
forests) to develop robust QSAR models from unbalanced data sets.
The two modeling approaches gave similar results in terms of predictivity,
and most of the models satisfy a minimum percentage of correctly predicted
chemicals equal to 75%. Finally, and most importantly, the developed
models proved to be useful as an effective in silico screening test for hepatic steatosis.