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J Chem Inf Model. 2018 Aug 27;58(8):1501-1517. doi: 10.1021/acs.jcim.8b00297. Epub 2018 Jul 26.

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

Author information

1
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences , IRCCS - Istituto di Ricerche Farmacologiche Mario Negri , Via la Masa 19 , 20156 Milano , Italy.
2
Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO) , Institut National de l'Environnement Industriel et des Risques (INERIS) , 60550 Verneuil en Halatte , France.

Abstract

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.

PMID:
29949360
DOI:
10.1021/acs.jcim.8b00297

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