Phytoestrogens and mycoestrogens bind to the rat uterine estrogen receptor.
ABSTRACT Consumption of phytoestrogens and mycoestrogens in food products or as dietary supplements is of interest because of both the potential beneficial and adverse effects of these compounds in estrogen-responsive target tissues. Although the hazards of exposure to potent estrogens such as diethylstilbestrol in developing male and female reproductive tracts are well characterized, less is known about the effects of weaker estrogens including phytoestrogens. With some exceptions, ligand binding to the estrogen receptor (ER) predicts uterotrophic activity. Using a well-established and rigorously validated ER-ligand binding assay, we assessed the relative binding affinity (RBA) for 46 chemicals from several chemical structure classes of potential phytoestrogens and mycoestrogens. Although none of the test compounds bound to ER with the affinity of the standard, 17beta-estradiol (E(2)), ER binding was found among all classes of chemical structures (flavones, isoflavones, flavanones, coumarins, chalcones and mycoestrogens). Estrogen receptor relative binding affinities were distributed across a wide range (from approximately 43 to 0.00008; E(2) = 100). These data can be utilized before animal testing to rank order estimates of the potential for in vivo estrogenic activity of a wide range of untested plant chemicals (as well as other chemicals) based on ER binding.
Article: Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods.[show abstract] [hide abstract]
ABSTRACT: Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.Journal of Molecular Graphics and Modelling 12/2006; 25(3):313-23. · 2.18 Impact Factor
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ABSTRACT: Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990's by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing. The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at http://edkb.fda.gov/webstart/edkb/index.html.BMC Bioinformatics 01/2010; 11 Suppl 6:S5. · 2.75 Impact Factor
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ABSTRACT: Vascular complications, as a consequence of atherosclerosis, are main causes of morbidity and mortality in patients with diabetes mellitus. There is increasing evidence that lipid peroxidation and oxidative modification of low density lipoprotein (LDL) is important in atherogenesis. In this study we investigated the effect of soybean hypocotyl extract (SHE), rich in isoflavones and saponins, on lipid peroxide (LPO) levels in liver, plasma and lipoproteins in GK diabetic rats, and its efficacy on the reduction of susceptibility of LDL and high density lipoprotein (HDL) to oxidation. The oxidative modification of LDL and HDL was determined with the lag time of copper ion-induced oxidation curve identified by the conjugated dienes. In SHE group which were fed diet containing 40 g/kg of SHE for 16 weeks, LPO levels in liver, plasma and HDL fraction were significantly decreased compared with the control group. The lag phage of LDL oxidation curve was prolonged noticeably by a mean of 27 min in SHE group as compared to the control group, indicating a reduced susceptibility to oxidation. The results suggest that intake of soybean hypocotyl extract might be useful for the prevention and treatment of diabetes mellitus and diabetes-associated diseases.Journal of Clinical Biochemistry and Nutrition 06/2009; 44(3):212-7. · 1.98 Impact Factor