Supporting read-across using biological data

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Hao Zhu, Mounir Bouhifd, Elizabeth Donley, Laura Egnash, Nicole Kleinstreuer, E. Dinant Kroese, Zhichao Liu, Thomas Luechtefeld, Jessica Palmer, David Pamies, Jie Shen, Volker Strauss, Shengde Wu, Thomas Hartung
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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, biological similarity based on bio­logical data adds extra strength to this process. 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 are becoming available, enabling big data approaches in read-across studies. In the context of developing Good Read-Across Practice guidance, a number of case studies using various big data sources were evaluated to assess the contribution of biological data to enriching read-across. An example is given for the US EPA’s ToxCast dataset which allows read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example is given for REACH registration data that enhances read-across for acute toxicity studies. A different approach is taken using omics data to establish biological similarity: Examples are given for in vitro stem cell models and short-term in vivo repeated dose studies in rats used to support read-across and category formation. These preliminary biological data-driven read-across studies show the way towards the generation of new read-across approaches that can inform chemical safety assessment.

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How to Cite
Zhu, H. (2016) “Supporting read-across using biological data”, ALTEX - Alternatives to animal experimentation, 33(2), pp. 167–182. doi: 10.14573/altex.1601252.
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