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ALTEX. 2016;33(2):167-82. doi: 10.14573/altex.1601252. Epub 2016 Feb 11.

Supporting read-across using biological data.

Author information

1
Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
2
Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.
3
Stemina Biomarker Discovery Inc., Madison, WI, USA.
4
National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
5
Risk Analysis for Products in Development, TNO Zeist, The Netherlands.
6
US FDA, NCTR, Little Rock, Arkansas, USA.
7
Research Institute for Fragrance Materials, Inc. Woodcliff Lake, New Jersey, USA.
8
BASF Aktiengesellschaft, Experimental Toxicology and Ecology, Ludwigshafen, Germany.
9
Procter & Gamble, Cincinnati, OH, USA.
10
University of Konstanz, CAAT-Europe, Konstanz, Germany.

Abstract

Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.

KEYWORDS:

big data; biological similarity; read-across; safety assessment

PMID:
26863516
PMCID:
PMC4834201
DOI:
10.14573/altex.1601252
[Indexed for MEDLINE]
Free PMC Article

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