Endless possibilities: stem cells and the vision for toxicology testing in the 21st century

Robert E Chapin1, Donald B Stedman

  • 1Developmental and Reproductive Toxicology, Drug Safety R&D, Pfizer, Inc., Groton, Connecticut 06365, USA. Robert.e.chapin@pfizer.com

Insights

The National Research Council

Area of Science:

  • Toxicology and Pharmacology
  • Stem Cell Biology
  • Computational Biology

Background:

  • The National Research Council (NRC) proposes a future for toxicity testing reliant on computational methods and in vitro assays.
  • Advances in stem cell biology offer new models for understanding cellular responses to toxic compounds.
  • Current methods often lack the physiological relevance needed for accurate toxicity prediction.

Purpose of the Study:

  • To outline how stem cells and advanced computational toxicology can fulfill the NRC's vision for toxicity testing.
  • To highlight the potential of in vitro models using mixed cell populations for predicting in vivo toxicities.
  • To discuss the development of predictive models for faster chemical evaluation with reduced animal use.

Main Methods:

  • Utilizing computational algorithms informed by cellular biochemistry and protein interaction pathways.
  • Employing in vitro assays with novel compounds on human cells, including stem cell-derived differentiated cell types.
  • Developing complex in vitro models that mimic tissue environments with interacting cell types.

Main Results:

  • Stem cells provide models for predicting embryo-fetal developmental toxicity.
  • Stem cell-derived differentiated cells will form the basis of more representative in vitro tissue models.
  • Interactions between multiple cell types in vitro enhance the prediction of in vivo toxicities.

Conclusions:

  • Integrating stem cell technology and computational toxicology is key to achieving the NRC's vision.
  • In vitro models using mixed cell populations offer a path toward more accurate and efficient toxicity testing.
  • Iterative development of predictive models promises reduced animal use and faster chemical safety assessments.

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