Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models.

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERß dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERß agonists among OH-PCB congeners.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor ß dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

In silico prediction of nuclear receptor binding to polychlorinated dibenzofurans and its implication on endocrine disruption in humans and wildlife.

Polychlorinated dibenzofurans (PCDFs) are known to cause endocrine disruption in humans and wildlife but the mechanisms underlying this disruption have not been adequately investigated. In this paper, the susceptibility of the endocrine system to disruption by PCDF congeners via nuclear receptor binding was studied using molecular docking simulation. Findings revealed that some PCDF congeners exhibit high probabilities of binding to androgen receptor in its agonistic and antagonistic conformations. In depth molecular docking analysis of the receptor-ligand complexes formed by PCDFs with androgen receptor in its agonistic and antagonistic conformations showed that, these complexes were stabilized by electrostatic, van der Waals, pi-effect and hydrophobic interactions. It was also observed that PCDF molecules mimic the modes of interaction observed in androgen-testosterone and androgen-bicalutamide complexes, utilizing between 65 and 83% of the amino acid residues used by the co-crystallized ligands for binding. This computational study suggests that some PCDF congeners may act as agonists and antagonists of androgen receptor in humans and wildlife via inapproprate binding to the receptor.

A computational insight into endocrine disruption by polychlorinated biphenyls via non-covalent interactions with human nuclear receptors.

Production of polychlorinated biphenyls (PCBs) was banned a long time ago because of their harmful health effects but humans continue to be exposed to residual PCBs in the environment. In this study, the susceptibility of human nuclear receptors to binding by PCBs was investigated using molecular docking simulation. Findings revealed that PCBs belonging to ortho-substituted, mono-ortho-substituted and non-ortho-substituted congeners could bind to agonistic conformations of androgen (AR), estrogen (ER α and ER ß), glucocorticoid (GR) and thyroid hormone (TR α and TR ß) receptors as well as antagonistic conformation of androgen receptor (AR an) but only ortho-substituted and mono-ortho-substituted PCBs could bind to estrogen receptors in their antagonistic conformations (ER α an and ER ß an). Further molecular docking analyses showed that PCBs mimic the modes of interaction observed for the co-crystallized ligands in the crystal structures of the affected receptors, utilizing 81%, 83%, 78%, 60%, 75%, 60%, 86%, 100% and 75% of the amino acid residues utilized by the co-crystallized ligands for binding in AR, AR an, ER α, ER α an, ER ß, ER ß an, GR, TR α and TR ß respectively. This computational study suggests that PCBs may cause endocrine disruption via formation of non-covalent interactions with androgen, estrogen, glucocorticoid and thyroid hormone receptors.

Theoretical study on endocrine disrupting effects of polychlorinated dibenzo-p-dioxins using molecular docking simulation.

Polychlorinated dibenzo-p-dioxins (PCDDs) are hypothesized to exert their toxic effects in wildlife and humans via endocrine disruption. However, very scanty information is available on the underlying molecular interactions that trigger this disruption. In this study, molecular docking simulation was used to predict the susceptibility of 12 nuclear receptors to disruption via PCDD bindings. Findings revealed that androgen (AR and AR an), estrogen (ER α and ER ß), glucocorticoid (GR) and thyroid hormone (TR α and TR ß) receptors are the most probable protein targets that bind to PCDDs. Further molecular docking analyses showed that PCDD molecules mimic the modes of interaction observed for the co-crystallized ligands of the affected receptors, resulting in the formation of ligand-receptor complexes that were stabilized through electrostatic, van der Waals, pi-effect and hydrophobic interactions with 18, 17, 17, 16, 18, eight and four amino acid residues in the active sites of AR, AR an, ER α, ER ß, GR, TR α and TR ß respectively. The commonalities of these interacting amino acid residues with those utilized by dihydrotestosterone in AR, bicalutamide in AR an, 17ß-estradiol in ER α, 17ß-estradiol in ER ß, cortisol in GR, thyromimetic GC-1 in TR α and thyromimetic GC-1 in TR ß are 86%, 74%, 94%, 80%, 82%, 50% and 43% respectively. The results obtained in this study provide supporting evidence that PCDD molecules may interfere with the endocrine system via binding interactions with some vital amino acid residues in the binding pockets of AR, ERs, GRs and TRs.