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Related Experiment Video

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Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity

Timothy E H Allen1, Jonathan M Goodman1, Steve Gutsell2

  • 1Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , United Kingdom.

Chemical Research in Toxicology
|September 14, 2019
PubMed
Summary

This study models chemical interactions at the molecular initiating event (MIE) to predict toxicity. Understanding chemical drivers of MIEs aids in developing safer chemical risk assessments.

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Area of Science:

  • Computational toxicology
  • cheminformatics
  • Pharmacology

Background:

  • Human toxicity risk assessment requires understanding chemical exposure and dose-response relationships.
  • Adverse Outcome Pathways (AOPs) describe toxicity mechanisms, starting with a Molecular Initiating Event (MIE).
  • Modeling MIEs computationally is crucial for predicting chemical safety.

Purpose of the Study:

  • To computationally model the chemistry of MIEs using chemical substructures and 3D-QSAR.
  • To predict molecular activity and understand chemical interactions with biological targets.
  • To support AOP-based chemical risk assessments.

Main Methods:

  • Categorizing chemicals using substructure analysis.
  • Employing 3D Quantitative Structure-Activity Relationships (QSAR) with Comparative Molecular Field Analysis (CoMFA).
  • Developing models for human biological targets: glucocorticoid receptor, mu opioid receptor, COX-2, hERG, and DAT.

Main Results:

  • Models achieved molecular activity estimations within one log unit.
  • Identified key electronic and steric fields influencing MIEs and target interactions.
  • Provided visualized fields for understanding chemical-biological interactions.

Conclusions:

  • The methodology enables quantitative prediction of chemical activity for various MIEs.
  • Understanding MIE chemistry allows for targeted chemical modification to reduce toxicity.
  • This approach enhances AOP-based chemical risk assessment and safety evaluations.