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CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

Kamel Mansouri1, Ahmed Abdelaziz, Aleksandra Rybacka

  • 1National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.

Environmental Health Perspectives
|February 25, 2016
PubMed
Summary
This summary is machine-generated.

The Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) used computational models to screen thousands of chemicals for endocrine disruption potential. This approach effectively prioritized chemicals for further testing, improving environmental health risk assessment.

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Area of Science:

  • Environmental Chemistry
  • Computational Toxicology
  • Endocrinology

Background:

  • Human exposure to numerous man-made chemicals raises concerns about potential endocrine disruption.
  • Many environmental chemicals can mimic natural hormones, interfering with the endocrine system.
  • A significant gap exists in testing the estrogen receptor (ER) activity of these chemicals.

Purpose of the Study:

  • To develop and demonstrate the efficacy of predictive computational models for screening chemicals.
  • To prioritize a large number of chemicals for further evaluation in endocrine disruptor screening programs.
  • To assess the estrogen receptor (ER) activity of thousands of environmental chemicals.

Main Methods:

  • The Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) involved 17 international groups.
  • A consensus approach combined 40 categorical and 8 continuous predictive models (QSPR and docking).
  • Models were trained on U.S. EPA data and validated using literature-curated chemical sets.

Main Results:

  • Individual models demonstrated high prediction reliability, with scores ranging from 0.69 to 0.85.
  • The consensus model identified 4,001 chemicals (12.3%) as high-priority for ER activity.
  • An additional 6,742 chemicals (20.8%) were flagged as potential ER actives requiring further testing.

Conclusions:

  • A consensus of in silico approaches can effectively screen large chemical libraries for ER activity.
  • This computational strategy significantly enhances the prioritization of chemicals for toxicological evaluation.
  • The CERAPP model provides a scalable framework for future screening of other environmental endpoints.