Title: Supporting read-across using biological data.

Authors: Zhu, Hao; Bouhifd, Mounir; Donley, Elizabeth; Egnash, Laura; Kleinstreuer, Nicole; Kroese, E Dinant; Liu, Zhichao; Luechtefeld, Thomas; Palmer, Jessica; Pamies, David; Shen, Jie; Strauss, Volker; Wu, Shengde; Hartung, Thomas

Published In ALTEX, (2016)

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.

PubMed ID: 26863516

MeSH Terms: No MeSH terms associated with this publication