Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors.

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

Prediction of mutagenicity and carcinogenicity using in silico modelling: A case study of polychlorinated biphenyls.

In silico modelling is an important alternative method for the evaluation of properties of chemical compounds. Basically, two concepts are used in its applications: QSAR modelling for endpoint predictions, and grouping (categorization) of large groups of chemicals. In the presented report we address both of these concepts. As a case study we present the results on a set of polychlorinated biphenyls (PCBs) and some of their metabolites. Their mutagenicity and carcinogenic potency were evaluated with CAESAR and T.E.S.T. models, which are freely available over the internet. We discuss the value and reliability of the predictions, the applicability domain of models and the ability to create prioritized groupings of PCBs as a category of chemicals.