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Towards a qAOP framework for predictive toxicology - Linking data to decisions.

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Summary
This summary is machine-generated.

This study introduces a framework for developing quantitative adverse outcome pathways (qAOPs) to improve chemical safety assessments. It harmonizes data interpretation for reliable prediction of chemically induced adverse effects.

Keywords:
Hazard assessmentIn silico dataIn vitro dataPredictive toxicologyWeight of evidence (WoE)quantitative Adverse Outcome Pathway (qAOP)

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Area of Science:

  • Toxicology and Chemical Safety Assessment
  • Computational Toxicology
  • Mechanistic Toxicology

Background:

  • Adverse Outcome Pathways (AOPs) integrate mechanistic data across biological levels using diverse methods (in silico, in vitro, in vivo).
  • Quantifying AOPs (qAOPs) is crucial for reliable prediction of chemical toxicity and advancing chemical safety assessment.
  • Existing digital resources for qAOP development require practical guidance for effective application.

Purpose of the Study:

  • To propose a harmonized framework for the development of quantitative Adverse Outcome Pathways (qAOPs).
  • To provide guidance for regulators and scientists in applying modelling methodologies for qAOP development.
  • To facilitate the reliable prediction of chemically induced adverse effects through quantitative AOPs.

Main Methods:

  • Literature review and synthesis of existing digital resources for qAOP development.
  • Expert consultation and feedback gathering on AOP quantification methodologies.
  • Development and validation of a qAOP framework using three case studies.

Main Results:

  • A proposed framework for quantitative AOP (qAOP) development is presented.
  • The framework integrates diverse mechanistic data and computational approaches.
  • Case studies demonstrate the practical application and utility of the qAOP framework.

Conclusions:

  • The developed framework offers a harmonized approach for qAOP development, benefiting both regulators and researchers.
  • Implementing this framework enhances the reliability of predicting chemical-induced adverse effects.
  • This work supports the advancement of data-driven chemical safety assessment through quantitative mechanistic understanding.