Article (Scientific journals)
Predictive modeling of aryl hydrocarbon receptor (AhR) agonism
Goya-Jorge, Elizabeth; Giner, Rosa M; Veitía, Maité Sylla-Iyarreta et al.
2020In Chemosphere, 256, p. 127068
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Keywords :
Aryl hydrocarbon receptor; Agonistic activity; QSAR; Computational modeling; Benzothiazoles; Flavonoids; Coumarins; Polyphenols; Triterpenes
Abstract :
[en] 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.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Goya-Jorge, Elizabeth  ;  Université de Liège - ULiège > Département de sciences des denrées alimentaires (DDA) > Gestion de la qualité dans la chaîne alimentaire
Giner, Rosa M;  University of Valencia > Department of Pharmacology > Professor
Veitía, Maité Sylla-Iyarreta;  Conservatoire National des Arts et Métiers (Cnam) > Equipe de Chimie Moléculaire du Laboratoire Génomique, Bioinformatique et Chimie Moléculaire (EA 7528) > Professor
Gozalbes, Rafael;  ProtoQSAR SL > PhD
Barigye, Stephen J;  ProtoQSAR SL > PhD
Language :
English
Title :
Predictive modeling of aryl hydrocarbon receptor (AhR) agonism
Publication date :
21 October 2020
Journal title :
Chemosphere
ISSN :
0045-6535
eISSN :
1879-1298
Publisher :
Elsevier, Oxford, United Kingdom
Volume :
256
Pages :
127068
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 722634 - PROTECTED - PROTECTion against Endocrine Disruptors; Detection, mixtures, health effects, risk assessment and communication.
Name of the research project :
PROTECTED - PROTECTion against Endocrine Disruptors; Detection, mixtures, health effects, risk assessment and communication
Funders :
CE - Commission Européenne [BE]
Union Européenne [BE]
Funding number :
722634
Funding text :
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 722634.
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since 12 June 2021

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