Predictive Modeling of Aryl Hydrocarbon Receptor (AhR) Agonism

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dc.contributor.authorGoya-Jorge, Elizabeth
dc.contributor.authorGiner, Rosa M.
dc.contributor.authorBarigye, Stephen J.
dc.date.accessioned2023-02-09T12:33:16Z
dc.date.available2023-02-09T12:33:16Z
dc.date.issued2020
dc.description.abstractThe aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolic machinery and detoxification systems. In the recent years, this receptor has attracted interest as a therapeutic target for immunological, oncogenic and inflammatory conditions. In the present report, in silico and in vitro approaches were combined to study the activation of the AhR. To this end, a large database of chemical compounds with known AhR agonistic activity was employed to build 5 classifiers based on the Adaboost (AdB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms, respectively. The built classifiers were examined, following a 10-fold external validation procedure, demonstrating adequate robustness and predictivity. These models were integrated into a majority vote based ensemble, subsequently used to screen an in-house library of compounds from which 40 compounds were selected for prospective in vitro experimental validation. The general correspondence between the ensemble predictions and the in vitro results suggests that the constructed ensemble may be useful in predicting the AhR agonistic activity, both in a toxicological and pharmacological context. A preliminary structure-activity analysis of the evaluated compounds revealed that all structures bearing a benzothiazole moiety induced AhR expression while diverse activity profiles were exhibited by phenolic derivatives.en_US
dc.identifier.citationGoya-Jorge, E., Giner, R. M., Veitía, M. S. I., Gozalbes, R., & Barigye, S. J. (2020). Predictive modeling of aryl hydrocarbon receptor (AhR) agonism. Chemosphere, 256, 127068.https://doi.org/10.1016/j.chemosphere.2020.127068en_US
dc.identifier.issn0045-6535
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7669
dc.language.isoenen_US
dc.publisherChemosphereen_US
dc.subjectAryl hydrocarbon receptoren_US
dc.subjectAgonistic activityen_US
dc.subjectQSARen_US
dc.subjectComputational modelingen_US
dc.subjectBenzothiazolesen_US
dc.subjectFlavonoidsen_US
dc.subjectCoumarinsen_US
dc.titlePredictive Modeling of Aryl Hydrocarbon Receptor (AhR) Agonismen_US
dc.typeArticleen_US
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