Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in...

Area of Science:

• Environmental toxicology
• Computational toxicology
• Biomedical sciences

Background:

• Environmental chemicals can disrupt estrogen function, leading to adverse health effects like cancer.
• High-throughput screening (HTS) is crucial for identifying and characterizing such substances efficiently.

Purpose of the Study:

• To further develop and apply the E-Morph Assay for automated robotic HTS.
• To screen a library of industrial chemicals for novel estrogenic activities.

Main Methods:

• Screened 430 industrial chemicals, biocides, and plant protection products using the E-Morph Screening Assay.
• Utilized in silico similarity searches and conformal prediction (CP) models to identify and evaluate potential estrogenic substances.
• Validated results against the ToxCast dataset and developed CP ER models.

Main Results:

• Identified 27 known estrogenic substances with high correlation to ToxCast ER Agonist Score (r=+0.95).
• Detected potential novel estrogenic activities in 10 primary hits and 9 structurally similar substances.
• Achieved 71-73% concordance with reference data and high predictivity (up to 87%) for ER active substances.

Conclusions:

• The E-Morph Assay combined with in silico methods provides an efficient proof-of-concept for analyzing large substance libraries.
• This integrated approach prioritizes substances with potential estrogenic activity for further regulatory testing.
• The method aids in identifying endocrine-disrupting chemicals for improved public health and environmental safety.