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Related Experiment Video

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Quantitative adverse outcome pathway (qAOP) models for toxicity prediction.

Nicoleta Spinu1, Mark T D Cronin1, Steven J Enoch1

  • 1School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.

Archives of Toxicology
|May 20, 2020
PubMed
Summary

Quantitative adverse outcome pathway (qAOP) models are emerging tools for chemical risk assessment. This review analyzes qAOP definitions and models, highlighting the need for a development framework to ensure regulatory acceptance.

Keywords:
Bayesian networkComputational approachKey event relationshipPredictive toxicologyQuantitative adverse outcome pathway (qAOP)Response-response relationship

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Area of Science:

  • Environmental toxicology
  • Computational chemistry
  • Risk assessment

Background:

  • Quantitative adverse outcome pathway (qAOP) concepts are increasingly relevant for regulatory chemical risk assessment.
  • Existing qAOP models lack a standardized framework for development and evaluation.
  • Computational predictive tools based on qAOPs are gaining traction.

Purpose of the Study:

  • To analyze existing definitions of qAOPs in scientific literature.
  • To establish common features for qAOP models based on published definitions.
  • To identify and evaluate current qAOP models and associated software tools.

Main Methods:

  • Literature review of qAOP definitions and models.
  • Comparative analysis of five probabilistic and ten mechanistic qAOPs against defined common features.
  • Assessment of available software tools for qAOP development.

Main Results:

  • Five probabilistic and ten mechanistic qAOPs were evaluated.
  • Common features for qAOP models were identified from literature definitions.
  • An overview of qAOP advancement and potential for toxicity assessment was provided.

Conclusions:

  • The qAOP concept shows promise for advancing toxicity assessment in chemical risk evaluation.
  • Further work is essential for the validation, harmonization, and regulatory acceptance of qAOP models.
  • A standardized framework is needed to guide the development and assessment of qAOPs.