In silico models have been considered as a promising tool that can be used to screen and identify potential endocrine disrupting chemicals (EDCs) effectively and assist finally realizing the sustainable assessments and management of EDCs. However, most of the current modeling efforts are focused on constructing predictive models for the end points related to nuclear receptors. Considerable actions are needed to model the interfering effects of compounds on the nonreceptor mediated targets, e.g., hormone transporters. Here, a tiered screening approach was proposed and derived based on classification models and quantitative structure–activity relationship (QSAR) models for identifying potential disruptors of human transthyretin (hTTR), a critical thyroid hormone transporter. Regarding classification models, the sensitivity, specificity, and predictive accuracy of all five optimum models for corresponding training sets and validation sets were >0.800, implying that they had good classification performance. Concerning quantitative prediction, k-nearest neighbor and multiple linear regression QSAR models were developed. The statistical parameters of all constructed QSAR models met the acceptable criteria (e.g., leave-one out cross validation Q 2 (Q 2 LOO) and bootstrapping coefficient (Q 2 BOOT) > 0.600, determination coefficient (R 2) > 0.700, externally explained variance (Q 2 EXT), concordance correlation coefficient (CCC) > 0.850), indicating that those QSAR models had good robustness, goodness-of-fit, and external prediction ability. Finally, an extra data set with 38 data was used to evaluate the predictive performance of the tiered approach. The assessment results showed that more than 65% of compounds can be classified correctly. Thus, the tiered approach proposed here can be employed to distinguish whether a given compound within the applicability domain of corresponding models is a potential hTTR disruptor or not.

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The available information about whether wastewater-derived disinfection byproducts (DBPs) could elicit potential endocrine-related detrimental effects on aquatic organisms was scarce. Herein, the potential disrupting effects and underlying binding mechanism of 14 wastewater-derived aliphatic and aromatic DBPs and 12 other substances on Chinese rare minnow (Gobiocypris rarus) transthyretin (CrmTTR) were tested and revealed by in vitro and in silico methods. The amino acid sequences of CrmTTR were determined, and the recombinant CrmTTR with a molecular mass of 66.3 kDa was expressed and purified. In vitro assay results indicated that eight selected aromatic DBPs exhibited detectable CrmTTR disrupting ability. Meanwhile, six aliphatic DBPs were not CrmTTR binders. Molecular modeling results implied that hydrophobic hydrogen bonds and/or ionic pair interactions were non-negligible. Four binary classification models with high classification performance were constructed. A significant positive linear relationship was observed for the binding affinity data from CrmTTR and human TTR (n = 18, r = 0.922, p < 0.0001). However, the binding affinity for 13 out of 18 tested compounds with CrmTTR was higher than that with human TTR. All the results highlighted that some wastewater-derived DBPs may be potential disruptors on the aquatic organism endocrine system, and interspecies variation should not be neglected in future determination of the potential endocrine disrupting effects of wastewater-derived DBPs.