Abstract：
:The 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.
journal_name
Chemospherejournal_title
Chemosphereauthors
Goya-Jorge E,Giner RM,Sylla-Iyarreta Veitía M,Gozalbes R,Barigye SJdoi
10.1016/j.chemosphere.2020.127068subject
Has Abstractpub_date
2020-10-01 00:00:00pages
127068eissn
0045-6535issn
1879-1298pii
S0045-6535(20)31261-3journal_volume
256pub_type
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