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Related Experiment Video

Updated: Sep 20, 2025

Human Pluripotent Stem Cell Based Developmental Toxicity Assays for Chemical Safety Screening and Systems Biology Data Generation
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From cellular perturbation to probabilistic risk assessments.

Alexandra Maertens1, Breanne Kincaid1, Eric Bridgeford2

  • 1Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, USA.

ALTEX
|May 26, 2025
PubMed
Summary

Chemical risk assessment is shifting to probabilistic methods, using new technologies like AI and stem cells for more accurate hazard evaluation. This approach integrates variability and uncertainty for a comprehensive understanding of chemical safety.

Keywords:
artificial intelligencebiological variabilitychemical hazard predictioncheminformaticsprobabilistic risk assessmentregulatory toxicologysystems biologyuncertainty metrics

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Area of Science:

  • Toxicology and Risk Assessment
  • Computational Biology
  • Environmental Health

Background:

  • Traditional chemical risk assessment relies on deterministic methods, which often oversimplify complex biological responses and population variability.
  • Advancements in human stem cells, tissue engineering, high-performance computing, cheminformatics, and artificial intelligence (AI) are driving a paradigm shift.
  • These technologies enable a more nuanced understanding of chemical hazards by capturing biological complexity and individual variability.

Purpose of the Study:

  • To summarize a 2023 workshop on the evolution of chemical risk assessment methodologies.
  • To discuss the technological and data-driven enablers of probabilistic risk assessment.
  • To identify challenges in implementing these advanced approaches, focusing on perturbation of biology for hazard estimation.

Main Methods:

  • Review of advancements in human stem cells, tissue engineering, high-performance computing, and cheminformatics.
  • Application of large-scale artificial intelligence (AI) models for toxicological data analysis.
  • Integration of kinetic variability and uncertainty metrics into probabilistic risk models.
  • Focus on perturbation of biological systems as the basis for hazard estimation.

Main Results:

  • Probabilistic methodologies offer a more nuanced understanding of chemical hazards compared to traditional deterministic approaches.
  • New technologies facilitate the integration of biological complexity and population variability into risk models.
  • Uncertainties associated with new technologies impact hazard probability estimations.
  • Probabilistic approaches allow for the integration of kinetic variability and uncertainty metrics.

Conclusions:

  • The future of toxicological risk assessment lies in the successful integration of probabilistic models.
  • These models promise more accurate, comprehensive, and holistic hazard evaluations.
  • Leveraging modern technologies and extensive toxicological data is key to advancing chemical safety assessments.