Publication of the book Silent Spring in 1962 by Rachel Carson is credited with raising public awareness of the dangers of pesticides and other man-made chemicals (xenobiotics) on humans, wildlife and the ecosystem. Intensive efforts in recent decades by the environmental and toxicological communities, and cognizant regulatory agencies, revealed that endocrine disrupting chemicals (EDCs) can cause cancerous tumors, birth defects, and other developmental disorders.
Nuclear hormone receptors, in particular the estrogen and androgen receptors, were recognized as key mediators of these abnormalities. A major outcome of this initiative has been the development of computational models to screen, identify, and predict natural and man-made chemicals that may behave as EDCs by binding specific nuclear receptors and either activating or inhibiting biological pathways in an aberrant manner.
A specific case in point is estrogen, whose physiological roles 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. In silico approaches are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing.
In one example, the Welsh lab (headed by Snowdon’s founder) devised a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity [Wang CY, Ai N, Arora S, Erenrich E, Nagarajan K, Zauhar R, Young D, Welsh WJ. (2006). Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach. Chem Res Toxicol. 19(12):1595-601.]. This procedure incorporates Shape Signatures, a proprietary 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, Welsh and coworkers implemented 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.
Selected examples of other published studies by the Welsh lab and Snowdon:
- Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species (Tong, et al.)
- Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor (Tong, et al.)
- Comparison of estrogen receptor alpha and beta subtypes based on comparative molecular field analysis (CoMFA) (Xing, et al.)
- Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: predicting the estrogenic activity of xenoestrogens (Yu, et al.)
- Interaction of organophosphate pesticides and related compounds with the androgen receptor (Tamura, et al.)
- QSAR models in receptor-mediated effects: the nuclear receptor superfamily (Fang, et al.)
- Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology (Perkins, et al.)
- Computational models for predicting the binding affinities of ligands for the wild-type androgen receptor and a mutated variant associated with human prostate cancer. (Ai, et al.)
- Bisphenol A and its analogues activate human pregnane X receptor (Sui, et al.)
- Polyester monomers lack ability to bind and activate both androgenic and estrogenic receptors as determined by in vitro and in silico methods. (Osimitz, et al.)
- Pregnane X receptor mediates dyslipidemia induced by the HIV protease inhibitor amprenavir in mice (Helsley, et al.)
- Cardiovascular outcomes and the physical and chemical properties of metal ions found in particulate matter air pollution: a QICAR study (Meng, et al.)