Big Data in Predictive Toxicology
CHAPTER 12: Role of Toxicological Big Data to Support Read-across for the Assessment of Chemicals
Published:04 Dec 2019
Special Collection: 2019 ebook collectionSeries: Issues in Toxicology
M. T. D. Cronin and A. Richarz, in Big Data in Predictive Toxicology, ed. D. Neagu and A. Richarz, The Royal Society of Chemistry, 2019, pp. 359-384.
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The toxicity of similar chemicals can be read across to fill data gaps. As such, read-across provides a pragmatic solution to data gap filling and is of considerable interest to reduce the reliance on animal testing for regulatory purposes, or where testing may not be practical or possible. Weaknesses of read-across can be addressed, at least partially, by the use of toxicological data increasingly generated by new approach methodologies on a large scale in the big data era, to provide evidence to support a justification of similarity which extends the current paradigm from chemical to biological and toxicological similarity. This chapter illustrates how these toxicological big data, such as from high-throughput in vitro screening, high content omics technologies and other large-scale bioactivity data compilations, can be used to undertake read-across based on biological and chemical similarity, supporting read-across justifications and mechanistic interpretation, as well as contribute to tackling challenges such as how to perform hazard assessment on mixtures and nanomaterials.