title: In silico methods in the discovery of endocrine disrupting chemicals.
abstract: The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.
authors.affiliation: Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
authors.forename: Anna
authors.initials: A
authors.lastname: Vuorinen
authors.forename: Alex
authors.initials: A
authors.lastname: Odermatt
authors.forename: Daniela
authors.initials: D
authors.lastname: Schuster
mesh_terms: D003198:Computer Simulation; D055808:Drug Discovery; D052244:Endocrine Disruptors / Q000737:chemistry* / Q000494:pharmacology; D021281:Quantitative Structure-Activity Relationship; D011987:Receptors, Steroid / Q000037:antagonists & inhibitors
chemical_list: D052244:Endocrine Disruptors; D011987:Receptors, Steroid
keywords: Case studies; Endocrine disruptors; Virtual screening