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Updated: Jan 9, 2026

Human Pluripotent Stem Cell Based Developmental Toxicity Assays for Chemical Safety Screening and Systems Biology Data Generation
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Hypothesis-driven approach to developmental toxicity assessment: Using mechanistic information to inform testing.

George Daston1, Matthew Burbank2, Florian Gautier2

  • 1Global Product Stewardship - Human Safety, The Procter & Gamble Co., Mason, OH 45040, USA.

Reproductive Toxicology (Elmsford, N.Y.)
|December 1, 2025
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Summary

Understanding the mode of action (MoA) enhances developmental toxicity testing. This approach uses mechanistic data for more flexible and efficient chemical safety assessments.

Keywords:
Developmental toxicologyHypothesis-drivenMechanismrisk assessment

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Area of Science:

  • Toxicology
  • Pharmacology
  • Computational Chemistry

Background:

  • Traditional developmental toxicity testing uses animal models and standardized protocols.
  • Emerging mechanistic data allows for improved model selection and prediction of outcomes.
  • Read-across methods, utilizing cheminformatics and biological similarity, are key for chemical assessment.

Purpose of the Study:

  • To explore how understanding the mode of action (MoA) can refine developmental toxicity assessments.
  • To demonstrate a shift towards more flexible, hypothesis-driven, and resource-efficient testing strategies.
  • To highlight the integration of in vivo, in vitro, and computational methods.

Main Methods:

  • Investigating the mode of action (MoA) of test agents.
  • Utilizing read-across principles with cheminformatics for analog selection.
  • Employing high-throughput screening (e.g., ToxCast) and transcriptomics.
  • Leveraging induced pluripotent stem cells for human-relevant biological models.

Main Results:

  • Mode of action understanding can identify activity cliffs in chemical series.
  • Metabolism data can reduce the need for testing all similar chemical analogs.
  • Gene expression analysis reveals divergent pharmacology among similar compounds.
  • Mechanistic insights enable hypothesis-driven testing designs.

Conclusions:

  • A mechanistic, hypothesis-driven approach offers a more flexible and efficient alternative to traditional developmental toxicity testing.
  • Integration of diverse data (in silico, in vitro, omics) improves predictive accuracy.
  • This paradigm shift supports more targeted and resource-conscious chemical safety evaluations.