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

• Published in: Current research in toxicology 2024, Vol.6, p.100158-100158, Article 100158
• Main Authors: Akinola, Lukman K., Uzairu, Adamu, Shallangwa, Gideon A., Abechi, Stephen E., Umar, Abdullahi B.
• Format: Article
• Language: English
• Subjects: Autocorrelation descriptor; Binary logistic regression; Estrogen receptor; Hydroxylated polychlorinated biphenyl; Quantitative structure–activity relationship
• ISSN: 2666-027X; 2666-027X
• DOI: 10.1016/j.crtox.2024.100158

[Display omitted] •Many adverse effects of OH-PCBs are initiated by estrogen receptor (ER) activation.•Identification of ER agonists is crucial in assessing the adverse effects of OH-PCBs.•Classification models to identify ER agonists among OH-PCBs were developed.•The developed models are useful for prioritization of OH-PCBs in toxicity testing. 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 ERβ 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 ERβ agonists among OH-PCB congeners.