Supporting read-across using biological data

Abstract

Summary 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.

The need for strategic development of safety sciences

“…-The Johns Hopkins Center for Alternatives to Animal Testing (CAAT) aims, outside of the pressures of regulating or being regulated, to be an engine of change in the safety sciences and other areas of animal use, overcoming the limitations of animal-based approaches and accelerating the uptake of new technologies by collaboration with all stakeholder groups. CAAT has started a number of collaborative programs to advance safety sciences, which include the Human Toxome Collaboration (see above), the Evidence-based Toxicology Collaboration (see below), the Good Cell Culture Practice Collaboration (Pamies et al, 2017) building on earlier work steered by ECVAM (Coecke et al, 2005), the Good ReadAcross Practice Collaboration (Patlewicz et al, 2014, Ball et al, 2016Zhu et al, 2016), the Refinement Collaboration (Zurlo and Hutchinson, 2014) and others. CAAT's transatlantic think tank for toxicology (t 4 ) has organized more than 30 workshops to advance concepts of toxicology such as integrated testing strategies (Hartung et al, 2013b;Rovida et al, 2015b), epithelial barrier models (Gordon et al, 2015), 3D cell cultures (Alépée et al, 2014), microphysiological systems (Marx et al, 2016), high-content imaging (van Vliet et al, 2014), and has commissioned a number of white papers.…”

Rebooting the generally recognized as safe (GRAS) approach for food additive safety in the US

“…This might in the future help determine concern levels and could also be combined with read-across considerations (Zhu et al, 2016). However, up to now, neither a publicly available process for subjecting a substance of interest to such standardized testing nor a process of deriving concern levels has been established.…”

Thresholds of Toxicological Concern – Setting a threshold for testing below which there is little concern

“…Its broad use under REACH and possibly new regulations elsewhere prompted the development of Good Read-Across Practice only most recently (Ball et al, 2016;Zhu et al, 2016). The TTC concept could, however, be seen as an extension of approaches such as read-across and chemical categories as it uses other chemicals' data to intrapolate for an untested chemical.…”

“…But, all together, the enormously increasing availability of toxicological data (Hartung, 2016;Zhu et al, 2016) fuels the opportunities for TTC. There is still a lot to be done as to general data-sharing, data curation, machine-readability, harmonized ontologies (Hardy et al, 2012a,b), etc.…”