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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.

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


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.


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

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