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A computationally based identification algorithm for estrogen receptor ligands: part 2. Evaluation of a hERalpha

O G Mekenyan1, V Kamenska, P K Schmieder

  • 1Bourgas University "Prof. As. Zlatarov," Laboratory of Mathematical Chemistry, Department of Physical Chemistry, 118010 Bourgas, Bulgaria.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|December 2, 2000
PubMed
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This study evaluated an expert system for predicting chemical binding to estrogen receptors (ERs). The system showed improved accuracy for identifying chemicals with moderate to high ER binding affinity after incorporating a shielding criterion.

Area of Science:

  • Toxicology
  • Computational Chemistry
  • Endocrinology

Background:

  • Mammalian estrogen receptors (ERs) play crucial roles in various physiological processes.
  • Identifying chemicals that can bind to ERs is vital for assessing potential endocrine disruption.
  • Structure-activity relationship (SAR) models offer a computational approach to predict chemical-ER interactions.

Purpose of the Study:

  • To evaluate an expert system's capability in predicting the potential of chemicals to act as ligands for mammalian estrogen receptors (ERs).
  • To assess the accuracy of the SAR-based expert system using binding affinity data from human ERalpha and rodent ERs.
  • To identify factors influencing prediction accuracy and improve the system's performance.

Main Methods:

  • Utilized a previously developed expert system based on a SAR model derived from human ERalpha (hERalpha) relative binding affinity (RBA) data.

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  • Categorized chemicals into RBA ranges: < 0.1, 0.1 to 1, 1 to 10, 10 to 100, and >150% relative to 17β-estradiol.
  • Validated predictions against experimental RBA data from MCF7 cells, and mouse and rat uterine preparations.

    • Main Results:

    • The expert system demonstrated the best prediction accuracy with MCF7 cell-derived ER data.
    • Predictions for mouse and rat ERs were less reliable, particularly for chemicals with RBAs below 10%, often resulting in false positives.
    • Incorporating a 'shielding criterion' for electronegative sites significantly improved prediction accuracy, correctly categorizing 38 of 46 compounds with high RBA values.

    Conclusions:

    • The SAR-based expert system, particularly when enhanced with structural criteria like shielding, can be a credible tool for prioritizing chemicals for ER binding affinity assessment.
    • High-quality, diverse training datasets are essential for developing robust predictive models.
    • Further refinement of the model could improve its reliability across different species and binding affinity ranges.