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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Related Experiment Video

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Screening for Endocrine Activity in Water Using Commercially-available In Vitro Transactivation Bioassays
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Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis.

M J Foster1,2, G Patlewicz1, I Shah1

  • 1Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.

Computational Toxicology (Amsterdam, Netherlands)
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

This study develops computational models to predict how chemicals affect steroid hormone production using existing assay data. These methods efficiently identify potential endocrine disruptors among thousands of untested chemicals for further screening.

Keywords:
Steroidogenesischemotypesin silico screeningread-acrossstructure-activity relationships

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Area of Science:

  • Toxicology and pharmacology
  • Computational chemistry
  • Endocrinology

Background:

  • High-throughput screening (HTS) data for steroid hormone biosynthesis is available for many chemicals.
  • Existing methods for assessing chemical effects on steroidogenesis are limited by data availability.
  • Predictive models are needed to prioritize untested chemicals for HTS.

Purpose of the Study:

  • To develop and validate in silico models for predicting chemical effects on steroid hormone biosynthesis.
  • To identify structural features and physicochemical properties associated with endocrine disruption.
  • To prioritize a large set of untested chemicals for experimental evaluation.

Main Methods:

  • Utilized existing high-throughput human adrenocortical carcinoma (HT-H295R) assay data.
  • Constructed quantitative structure-activity relationships (QSARs) and machine learning models.
  • Employed ToxPrint chemotypes, physicochemical properties, random forest (RF), and nearest neighbor (NN) algorithms.

Main Results:

  • Individual chemotypes showed high specificity but low sensitivity for estrogen and androgen synthesis modulators.
  • The best RF model achieved 71% balanced accuracy (BA) for predicting maxmMd outcomes.
  • Nearest neighbor models demonstrated superior performance with BAs of 85% (chemotype) and 81% (Morgan fingerprints).
  • 1241 out of 6302 untested chemicals were identified as putative estrogen and androgen modulators.

Conclusions:

  • In silico approaches, particularly NN models, can effectively predict chemical effects on steroid biosynthesis.
  • These computational methods efficiently prioritize thousands of chemicals for screening, aiding in the identification of endocrine disruptors.
  • The study provides a valuable tool for assessing the endocrine-disrupting potential of data-poor chemicals.