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Supporting read-across using biological data.

Hao Zhu1, Mounir Bouhifd2, Elizabeth Donley3

  • 1Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.

ALTEX
|February 11, 2016
PubMed
Summary
This summary is machine-generated.

Biological data enhances chemical read-across for toxicological data gaps. This approach strengthens assessments by using biological similarity alongside chemical properties, paving the way for advanced chemical safety evaluations.

Keywords:
big databiological similarityread-acrosssafety assessment

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Area of Science:

  • Toxicology and Chemical Safety Assessment
  • Computational Toxicology and Big Data Approaches

Background:

  • Read-across typically relies on chemical similarity to fill toxicological data gaps.
  • Incorporating biological similarity, supported by available biological data, significantly strengthens read-across.
  • Emerging large datasets enable big data methodologies for read-across studies.

Purpose of the Study:

  • To demonstrate the utility of biological data in enhancing read-across methodologies.
  • To evaluate case studies for developing Good Read-Across Practice guidance.
  • To explore novel, data-driven read-across approaches for chemical safety.

Main Methods:

  • Evaluation of case studies integrating chemical and biological similarity for read-across.
  • Application of big data approaches using sources like US EPA's ToxCast and REACH registration data.
  • Utilizing omics data (in vitro stem cell models, in vivo rat studies) to establish biological similarity for category formation.

Main Results:

  • Chemically similar substances often exhibit similar in vitro assay results, validating basic read-across.
  • Biological data-driven read-across successfully supported assessments for estrogenic endocrine disruption (uterotrophic assays) and acute toxicity.
  • Omics data facilitated read-across and category formation, demonstrating biological similarity.

Conclusions:

  • Biological data significantly enriches traditional chemical similarity-based read-across.
  • Big data and omics approaches offer powerful tools for advancing read-across in chemical safety.
  • These data-driven methods represent a new generation of read-across applicable to regulatory toxicology.