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Updated: Jul 3, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Prediction of the health effects of polychlorinated biphenyls (PCBs) and their metabolites using quantitative
P Ruiz1, O Faroon, C J Moudgal
1Agency for Toxic Substances and Disease Registry, Division of Toxicology and Environmental Medicine, Computational Toxicology and Methods Development Unit, 1600 Clifton Road, MS-F32, Atlanta, GA 30333, USA. pruiz@cdc.gov
Abstract:
Polychlorinated biphenyls (PCBs) are a group of 209 persistent environmental contaminants that are slightly different but structurally related. PCBs are known to induce a variety of health effects and often have been toxicologically tested as complex commercial mixtures (Aroclors) but environmental exposure occurs separately to a small number of specific congeners. Recently, the Third National Report on Human Exposures to Environmental Chemicals, an assessment of exposure data of the National Health and Nutrition Examination Survey (NHANES), identified 35 individual PCB congeners in the U.S. population. These types of findings necessitate the toxicity evaluation of individual congeners but adequate toxicity data for most individual PCB congeners are not available. Due to this, a quantitative structure-activity relationship (QSAR) approach was used to assess the potential mutagenesis and carcinogenesis of individual congeners and their possible metabolites. The predictions were analyzed to define the underlying generalizations between the parent PCBs, their metabolites, and some important toxicological endpoints. This analysis reveals that (1) mono and di-chlorinated PCBs and their metabolites can be potential mutagens; (2) PCB benzoquinone metabolites could be carcinogenic but the weight of evidence is poor. These results support the hypothesis that environmental exposure to some PCBs and/or their metabolites could produce mutagenicity and/or carcinogenicity. Hence, these data should be considered as priority toxicological testing data needs. As with all computational toxicology analytical findings, these conclusions must yield to empirical data as they become available.
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