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Optimizing androgen receptor prioritization using high-throughput assay-based activity models.

Ronnie Joe Bever¹, Stephen W Edwards², Todor Antonijevic³

¹U.S. Environmental Protection Agency, Washington, DC, United States.

Frontiers in Toxicology

|March 26, 2024

Summary

This summary is machine-generated.

This study developed a data-driven method to optimize chemical screening assays, reducing resource needs by 52% while maintaining high sensitivity for endocrine disruptor detection.

Keywords:

androgen receptor computational toxicology endocrine disruption highthroughput screening tiered testing

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Area of Science:

Environmental toxicology
Computational toxicology
Chemical screening

Background:

Computational models aid chemical prioritization for the U.S. EPA's Endocrine Disruptor Screening Program (EDSP).
Existing androgen receptor (AR) pathway models require optimization for efficiency and cost-effectiveness.
Assay availability and chemical diversity pose challenges for current screening models.

Purpose of the Study:

To demonstrate a data processing method for determining optimal minimal assay batteries.
To establish a uniform evaluation method for minimal assay batteries against AR pathway models.
To integrate chemical cluster analysis into assay battery performance evaluation.

Main Methods:

Compared two previously published AR pathway models (11- and 14-assay).
Investigated assay subsets to optimize testing strategies for cost and sensitivity.
Incorporated chemical structure-based clustering into a multi-stage testing workflow.

Main Results:

An expanded 14-assay model showed higher sensitivity for antagonists; the 11-assay model favored agonists.
Identified critical assays: 3 for antagonism, 2 for agonism.
A minimum of 9 assays are needed for 95% sensitivity for both agonism and antagonism.
Chemical clustering reduced the average assays needed per chemical by 52% in a multi-stage workflow.
In silico predictions further reduced resource requirements.

Conclusions:

A data-driven approach using chemical clustering and multi-mechanism consideration enhances chemical screening efficiency.
This case study validates a proof-of-concept for optimizing assay batteries under the EDSP.
Efficient screening maximizes chemical throughput and enables data-driven prioritization for further testing.