The aim of this study was to improve the identification of endocrine disrupting chemicals (EDCs) by developing and evaluating in silico tools that predict interactions at the estrogen (E) and androgen (A) receptors, and binding to transthyretin (T). In particular, the study focuses on evaluating the use of the EAT models in combination with a metabolism simulator to study the significance of bioactivation for endocrine disruption. Balanced accuracies of the EAT models ranged from 77-87%, 62-77%, and 65-89% for E-, A-, and T-binding respectively. The developed models were applied on a set of more than 6000 commonly used industrial chemicals of which 9% were predicted E- and/or A-binders and 1% were predicted T-binders. The numbers of E- and T-binders increased 2- and 3-fold, respectively, after metabolic transformation, while the number of A-binders marginally changed. In-depth validation confirmed that several of the predicted bioactivated E- or T-binders demonstrated in vivo estrogenic activity or influenced blood levels of thyroxine in vivo. The metabolite simulator was evaluated using in vivo data from the literature which showed a 50% accuracy for studied chemicals. The study stresses, in summary, the importance of including metabolic activation in prioritization activities of potentially emerging contaminants.
Keywords: Androgen; Endocrine disruptor; Estrogen; Metabolism; QSAR; Transthyretin.
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