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Related Experiment Videos

Systems Toxicology: Real World Applications and Opportunities.

Thomas Hartung1,2, Rex E FitzGerald3, Paul Jennings4

  • 1Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health , Baltimore, Maryland 21205, United States.

Chemical Research in Toxicology
|April 1, 2017
PubMed
Summary

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This summary is machine-generated.

Systems Toxicology shifts xenobiotic effect assessment from empirical endpoints to pathways and networks. Integrating in vitro and in vivo data with computational modeling is key, but linking network changes to toxicity remains a challenge.

Area of Science:

  • Toxicology
  • Computational Biology
  • Biotechnology

Background:

  • Systems Toxicology redefines characterizing adverse biological effects of xenobiotics.
  • It moves from empirical endpoints to adverse outcome pathways and perturbed networks.
  • This approach integrates in vitro and in vivo toxicity data with computational modeling.

Purpose of the Study:

  • To summarize limitations and opportunities in emerging Systems Toxicology applications.
  • To highlight the critical dependence on data reliability and relevance.
  • To address the challenge of linking network perturbations to phenotypic toxicity.

Main Methods:

  • Integration of in vitro and in vivo toxicity data.
  • Application of computational modeling and "big data" analysis.

Related Experiment Videos

  • Utilizing omics measurements for integrative analysis of molecular data.

    • Main Results:

    • Significant advances in in vitro test systems and bioanalytical strategies have been made.
    • Data reliability and relevance are ongoing concerns for the evolving approach.
    • A frontier challenge is linking observed network perturbations to phenotypes.

    Conclusions:

    • Systems Toxicology requires integrating diverse data with computational approaches.
    • Ensuring data quality is crucial for the reliability of pathway-based toxicity assessments.
    • Understanding pathways and networks is essential for linking molecular changes to adverse outcomes.