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QSAR models using a large diverse set of estrogens.

L M Shi¹, H Fang, W Tong

¹ROW Sciences Inc, Jefferson, Arkansas 72079, USA.

Journal of Chemical Information and Computer Sciences

|February 24, 2001

Summary

This study developed a rapid computational method to predict how well chemicals bind to the estrogen receptor (ER). The quantitative structure-activity relationship (QSAR) CoMFA model accurately prioritizes chemicals for endocrine disruptor (ED) testing.

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Area of Science:

Environmental Chemistry
Toxicology
Computational Chemistry

Background:

Endocrine disruptors (EDs) pose risks to human and animal health.
Thousands of chemicals require safety testing, necessitating efficient prioritization methods.
Estrogen receptor (ER) binding is a key mechanism for estrogenic EDs.

Purpose of the Study:

To evaluate quantitative structure-activity relationship (QSAR) models for predicting chemical binding to the ER.
To develop a rapid, integrated system for prioritizing chemicals for ED testing.
To assess the performance of CoMFA and HQSAR models for predicting relative binding affinities (RBAs).

Main Methods:

Developed a four-phase integrated system for ED screening.
Constructed and compared CoMFA and HQSAR models using a dataset of 130 chemicals.

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Utilized leave-N-out cross-validation and external test sets to validate model performance.

Main Results:

The CoMFA model achieved a cross-validated r² of 0.91 and q²LOO of 0.66.
Incorporating a phenol indicator improved the CoMFA model's q²LOO to 0.71.
External validation demonstrated the CoMFA model's utility with q²pred values of 0.71 and 0.62.

Conclusions:

The CoMFA model provides accurate quantitative predictions of ER binding affinities.
This model, as part of a tiered system, enables efficient prioritization of chemicals for ED testing.
The developed QSAR approach significantly aids in managing large chemical datasets for safety assessment.