title: QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.
abstract: A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.
authors.affiliation: Department of Structural and Functional Biology, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, via Dunant 3, 21100 Varese, Italy.
authors.forename: Huanxiang
authors.initials: H
authors.lastname: Liu
authors.forename: Ester
authors.initials: E
authors.lastname: Papa
authors.forename: Paola
authors.initials: P
authors.lastname: Gramatica
mesh_terms: D000465:Algorithms; D000818:Animals; D001667:Binding, Competitive; D007631:Chlordecone / Q000737:chemistry; D003198:Computer Simulation; D004967:Estrogens / Q000378:metabolism*; D006801:Humans; D008956:Models, Chemical; D015394:Molecular Structure; D009930:Organic Chemicals / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000378:metabolism; D012431:Rutin / Q000737:chemistry
chemical_list: D004967:Estrogens; D009930:Organic Chemicals; D011960:Receptors, Estrogen; D012431:Rutin; D007631:Chlordecone