Abstract
Modern information technologies have made big data available in safety sciences, i.e., extremely large data sets that may be analyzed only computationally to reveal patterns, trends and associations. This happens by (1) compilation of large sets of existing data, e.g., as a result of the European REACH regulation, (2) the use of omics technologies and (3) systematic robotized testing in a high-throughput manner. All three approaches and some other high-content technologies leave us with big data--the challenge is now to make big sense of these data. Read-across, i.e., the local similarity-based intrapolation of properties, is gaining momentum with increasing data availability and consensus on how to process and report it. It is predominantly applied to in vivo test data as a gap-filling approach, but can similarly complement other incomplete datasets. Big data are first of all repositories for finding similar substances and ensure that the available data is fully exploited. High-content and high-throughput approaches similarly require focusing on clusters, in this case formed by underlying mechanisms such as pathways of toxicity. The closely connected properties, i.e., structural and biological similarity, create the confidence needed for predictions of toxic properties. Here, a new web-based tool under development called REACH-across, which aims to support and automate structure-based read-across, is presented among others.
Highlights
Read-across has been termed an “ugly duckling” (Teubner and Landsiedel, 2015)
REACH, the European chemicals legislation (Regulation (EC) No 1907/2006), has changed the game by first making read-across an official tool and providing a Read-Across Assessment Framework (RAAF) published mid 2015
An essential contribution to the big data appears to come from REACH itself: The mineable REACH database presented in this issue of ALTEX (Luechtefeld et al, 2016a-d) is already the largest toxicological database, and has enormous growth
Summary
Read-across has been termed an “ugly duckling” (Teubner and Landsiedel, 2015). For many it still has the stigma of GOBSAT (“good old boys sitting around the table”), i.e., a very pragmatic discussion trying to take a shortcut and avoid testing by arguing that we know enough from similar substances. In silico approaches based on information on similar substances, such as read-across and grouping / category approaches, often represent a reasonable tool to fill data gaps or prioritize testing and risk management needs. Their enormous saving potential was for example shown in the US High-Production Volume Chemical program (Bishop et al, 2012): The potential consumption of 3.5 million animals in new testing was brought down to approximately 127,000. These methods are not perfect, but more efficient and not necessarily less predictive than testing on animals, which are not little humans on four legs
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