Environmental Health Perspectives Volume 105, Number 10, October 1997
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Quantitative Structure-Activity Relationships (QSARs) for Estrogen Binding to the Estrogen Receptor: Predictions across Species
Weida Tong,
1
Roger Perkins,
1
Richard Strelitz,
1
Elizabeth R. Collantes,
2
Susan Keenan,
2
William J. Welsh,
2
William S. Branham,
3
and Daniel M. Sheehan
3
1
R.O.W Sciences, Jefferson, AR 72079 USA
2
Department of Chemistry, University of Missouri-St. Louis, St. Louis, MO 63121 USA
3
Division of Reproductive and Developmental Toxicology, National Center for Toxicological Research, Jefferson, AR 72079 USA
Abstract
The recognition of adverse effects due to environmental endocrine disruptors in humans and wildlife has focused attention on the need for predictive tools to select the most likely estrogenic chemicals from a very large number of chemicals for subsequent screening and/or testing for potential environmental toxicity. A three-dimensional quantitative structure-activity relationship (QSAR) model using comparative molecular field analysis (CoMFA) was constructed based on relative binding affinity (RBA) data from an estrogen receptor (ER) binding assay using calf uterine cytosol. The model demonstrated significant correlation of the calculated steric and electrostatic fields with RBA and yielded predictions that agreed well with experimental values over the entire range of RBA values. Analysis of the CoMFA three-dimensional contour plots revealed a consistent picture of the structural features that are largely responsible for the observed variations in RBA. Importantly, we established a correlation between the predicted RBA values for calf ER and their actual RBA values for human ER. These findings suggest a means to begin to construct a more comprehensive estrogen knowledge base by combining RBA assay data from multiple species in 3D-QSAR based predictive models, which could then be used to screen untested chemicals for their potential to bind to the ER. Another QSAR model was developed based on classical physicochemical descriptors generated using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. The predictive ability of the CoMFA model was superior to the corresponding CODESSA model.
Key words
: CODESSA, CoMFA, endocrine disruptors, estrogen receptor, estrogens, quantitative structure-activity relationships, QSAR, relative binding affinity, species-to-species extrapolation, xenoestrogens.
Environ Health Perspect
105:1116-1124 (1997).
Address correspondence to W. Tong, R.O.W. Sciences, Inc., 3900 NCTR Road, Mail Code-910, Jefferson, AR 72079 USA.
The authors gratefully acknowledge the generous support of the Food and Drug Administration's Office of Women's Health.
Received 1 April 1997; accepted 1 July 1997.
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Last Update: October 2, 1997
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