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Artificial intelligence (AI) is revolutionizing toxicology, offering more human-relevant chemical safety predictions than animal testing. An AI-powered "e-validation" framework ensures these advanced models remain trustworthy and effective for regulatory use.

Keywords:
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Area of Science:

  • Toxicology and Chemical Safety Science
  • Computational Toxicology
  • Regulatory Science

Background:

  • Artificial intelligence (AI) integration signifies a paradigm shift in toxicology, moving beyond workflow automation to redefine risk assessment and regulatory decision-making.
  • New Approach Methodologies (NAMs) are increasingly converging with AI, driving innovation in chemical safety.
  • Traditional animal-based testing methods face limitations in reproducibility and scalability.

Purpose of the Study:

  • To explore the convergence of AI and NAMs in toxicology.
  • To highlight key AI trends like multimodal learning, causal inference, explainable AI (xAI), generative modeling, and federated learning.
  • To introduce a novel AI-powered validation framework (e-validation) for ensuring the reliability of AI toxicology models.

Main Methods:

  • Review of current AI trends in toxicology, including multimodal learning, causal inference, xAI, generative modeling, and federated learning.
  • Introduction and operationalization of the e-validation framework based on TREAT principles (Trustworthiness, Reproducibility, Explainability, Applicability, Transparency).
  • Incorporation of AI-powered modules for chemical selection, virtual study simulation, mechanistic cross-validation, and post-validation surveillance.

Main Results:

  • AI and NAMs enable more human-relevant, mechanistically grounded, and ethically aligned toxicological predictions.
  • The e-validation framework addresses challenges in validating dynamic AI models, ensuring ongoing reliability.
  • Ethical considerations, including bias and equity audits, are critical for responsible AI adoption, with a co-pilot model proposed for human-AI collaboration.

Conclusions:

  • The infrastructure, economic, and policy landscape is aligned for global AI-based toxicology deployment.
  • AI-driven toxicology promises more accurate, inclusive, and human-centric chemical safety assessments.
  • The future of toxicology lies in reinventing the field as an adaptive, transparent, and ethically grounded science.