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High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial

Chihae Yang1, James F Rathman2,3, Aleksandra Mostrag2

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Summary
This summary is machine-generated.

This study introduces a digital read-across framework for chemical risk assessment, improving no-observed-adverse-effect level (NOAEL) estimation for data-poor chemicals using chemoinformatics and biological fingerprints.

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Area of Science:

  • Toxicology
  • Computational Chemistry
  • Chemoinformatics

Background:

  • Read-across is an in silico method for chemical risk assessment of data-poor substances.
  • Traditional methods like QSARs are limited for toxicity endpoints with weak chemical-biological interaction data.
  • Estimating no-observed-adverse-effect levels (NOAELs) requires robust methods for analogue selection and similarity assessment.

Purpose of the Study:

  • To develop a novel read-across paradigm for estimating NOAELs.
  • To establish a digitalized framework for efficient assessment of numerous chemicals and metabolites.
  • To validate the framework using a case study of bisphenols and their metabolites.

Main Methods:

  • Utilized chemoinformatics and experimental study quality for analogue selection.
  • Developed analogue quality (AQ) metrics considering structural, physicochemical, ADME, and biological similarity.
  • Employed machine learning (ML) hybrid rules from ToxCast/Tox21 data for biological fingerprinting.
  • Applied a decision theory approach to estimate NOAEL confidence bounds.
  • Created a digitalized workflow for large-scale substance assessment and prioritization.

Main Results:

  • The new paradigm effectively estimates NOAELs by leveraging analogue similarity.
  • Biological fingerprinting using ML enhances the accuracy of target-analogue similarity assessment.
  • The digitalized framework streamlines the assessment of multiple targets and numerous metabolites.
  • The workflow demonstrated successful application in a bisphenol use case.

Conclusions:

  • The developed read-across approach provides a robust method for NOAEL estimation in chemical risk assessment.
  • The digitalized framework significantly improves the efficiency and manageability of large-scale toxicological assessments.
  • This approach offers a valuable tool for handling data-poor chemicals and complex substance mixtures.