title: Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach.
abstract: The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other bio-relevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of approximately 200,000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor alpha (ERalpha) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.
authors.affiliation: Department of Pharmacology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School (UMDNJ-RWJMS), and UMDNJ Informatics Institute, 675 Hoes Lane, Piscataway, New Jersey 08854, USA.
authors.forename: Ching Y
authors.initials: CY
authors.lastname: Wang
authors.forename: Ni
authors.initials: N
authors.lastname: Ai
authors.forename: Sonia
authors.initials: S
authors.lastname: Arora
authors.forename: Eric
authors.initials: E
authors.lastname: Erenrich
authors.forename: Karthigeyan
authors.initials: K
authors.lastname: Nagarajan
authors.forename: Randy
authors.initials: R
authors.lastname: Zauhar
authors.forename: Douglas
authors.initials: D
authors.lastname: Young
authors.forename: William J
authors.initials: WJ
authors.lastname: Welsh
mesh_terms: D001667:Binding, Competitive; D019295:Computational Biology / Q000379:methods*; D052244:Endocrine Disruptors / Q000633:toxicity*; D047628:Estrogen Receptor alpha / Q000378:metabolism*; D008024:Ligands; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D011869:Radioligand Assay
chemical_list: D052244:Endocrine Disruptors; D047628:Estrogen Receptor alpha; D008024:Ligands