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Cell-Based High-Throughput Screening for Aromatase Inhibitors in the Tox21 10K Library.

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Summary

This study identified 113 potential aromatase inhibitors (AIs) from a large chemical library using a high-throughput assay. Ten novel AI compounds were confirmed, highlighting the assay

Keywords:
AroER tri-screenTox21 10K libraryaromatase enzyme assayenvironmental chemicalsquantitative high throughput screening

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Area of Science:

  • Endocrinology
  • Biochemistry
  • Toxicology

Background:

  • Endocrine disruption can occur through nonreceptor-mediated mechanisms, including inhibition of aromatase, an enzyme crucial for regulating androgen and estrogen balance.
  • Aromatase inhibitors (AIs) are important for understanding and potentially treating hormone-dependent conditions.

Purpose of the Study:

  • To adapt and validate the AroER tri-screen assay for high-throughput screening (HTS) to identify novel aromatase inhibitors.
  • To screen the U.S. Tox21 10K compound library for potential AIs.
  • To characterize the identified AIs for selectivity and novel structures.

Main Methods:

  • Adapted the AroER tri-screen assay from 96-well to 1536-well format for HTS.
  • Screened the Tox21 library using the tri-screen assay in the presence of testosterone to identify potential AIs.
  • Rescreened identified compounds and known actives/inactives across all three assay formats (ERα agonist, AI/ERα antagonist, ERα antagonist).
  • Confirmed potential AIs using a biochemical tritiated water-release aromatase assay and investigated binding characteristics.

Main Results:

  • Screening identified 302 potential AIs from the Tox21 library.
  • Of 333 compounds retested, 113 (34%) were identified as potential AIs, independent of cytotoxicity and ER antagonism.
  • Structure-activity analysis revealed both conventional and novel AI structures.
  • Ten novel AI compounds were confirmed active in the biochemical assay, with four showing low efficacy in HTS only.

Conclusions:

  • The high-throughput AroER tri-screen assay is an accurate and efficient method for identifying selective aromatase interactors in large chemical libraries.
  • The study successfully identified novel potential aromatase inhibitors, including drugs and fungicides.
  • These findings provide a foundation for further investigation into the mechanisms and applications of these novel AIs.