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


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., Bouhifd, M., Donley, E., Egnash, L., Kleinstreuer, N., Kroese, E., Liu, Z., Luechtefeld, T., Palmer, J., Pamies, D., Shen, J., Strauss, V., Wu, S. and Hartung, T. 2016. Supporting read-across using biological data. ALTEX - Alternatives to animal experimentation. 33, 2 (May 2016), 167-182. DOI:

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