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Food Chem Toxicol. 2018 Feb;112:526-534. doi: 10.1016/j.fct.2017.04.008. Epub 2017 Apr 12.

Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Author information

1
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA; Laboratory of Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, 74605-170, Brazil.
2
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA; Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine.
3
National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD, 20850, USA.
4
Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine.
5
Laboratory of Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, 74605-170, Brazil.
6
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA. Electronic address: alex_tropsha@unc.edu.

Abstract

Computational models have earned broad acceptance for assessing chemical toxicity during early stages of drug discovery or environmental safety assessment. The majority of publicly available QSAR toxicity models have been developed for datasets including mostly drugs or drug-like compounds. We have evaluated and compared chemical spaces occupied by cosmetics, drugs, and pesticides, and explored whether current computational models of toxicity endpoints can be universally applied to all these chemicals. Our analysis of the chemical space overlap and applicability domain (AD) of models built previously for twenty different toxicity endpoints showed that most of these models afforded high coverage (>90%) for all three classes of compounds analyzed herein. Only T. pyriformis models demonstrated lower coverage for drugs and pesticides (38% and 54%, respectively). These results show that, for the most part, historical QSAR models built with data available for different toxicity endpoints can be used for toxicity assessment of novel chemicals irrespective of the intended commercial use; however, the AD restriction is necessary to assure the expected prediction accuracy. Local models may need to be developed to capture chemicals that appear as outliers with respect to global models.

KEYWORDS:

Chemical space; Cosmetics; Drugs; Pesticides; Prediction; QSAR models

PMID:
28412406
PMCID:
PMC5638676
DOI:
10.1016/j.fct.2017.04.008
[Indexed for MEDLINE]
Free PMC Article

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