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This study developed predictive models to assess chemical repeated-dose toxicity (RDT) without animal testing. These validated models offer a reliable alternative for hazard screening and regulatory decision-making.

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Area of Science:

  • Toxicology
  • Computational Chemistry
  • Regulatory Science

Background:

  • Synthetic organic chemicals pose risks to human health and the environment.
  • Experimental repeated-dose toxicity (RDT) assessment is costly, time-consuming, and ethically challenging.
  • Reliable and early hazard assessment is crucial for managing chemical risks.

Purpose of the Study:

  • To develop and validate classification-based predictive models for subchronic oral repeated-dose toxicity (RDT).
  • To provide alternatives to traditional animal testing for chemical toxicity evaluation.
  • To identify structural motifs associated with RDT.

Main Methods:

  • Compiled RDT data (NOAEL, LOAEL) from eChemPortal and J-CHECK, adhering to OECD test guidelines and GLP.
  • Developed and evaluated multiple machine learning algorithms using multicriteria analysis (SRD).
  • Performed substructure analysis to identify toxicity-related structural motifs and experimentally validated models.

Main Results:

  • Selected models achieved training set accuracies from 0.665 to 0.902 and test set accuracies from 0.642 to 0.682.
  • Identified eight significant structural motifs, including chlorine- and amine-group-containing aromatic systems.
  • Models were experimentally validated and applied to screen pesticides for potential toxicity.

Conclusions:

  • Developed predictive models serve as viable alternatives to animal testing for subchronic oral RDT assessment.
  • The models demonstrate strong statistical performance and experimental validation, suitable for preliminary hazard screening and regulatory use.
  • Utilizing harmonized test data enhances the models' acceptance for regulatory decision-making.