Big Data in Predictive Toxicology
CHAPTER 11: Big Data and Biokinetics
Published:04 Dec 2019
Special Collection: 2019 ebook collectionSeries: Issues in Toxicology
M. Yoon, G. Song, H. Clewell, and B. Blaauboer, in Big Data in Predictive Toxicology, ed. D. Neagu and A. Richarz, The Royal Society of Chemistry, 2019, pp. 331-358.
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The new paradigm for toxicity testing and safety assessment replaces the emphasis from being based on apical endpoints in animal studies to an approach incorporating knowledge of modes of action in human cellular systems together with biokinetics. With this shift, there is an increasing demand for rapid evaluation and prediction of biokinetics. The development of an easy-to-use and robust computational approach based on alternative approaches is critically required to fully incorporate biokinetics into modern toxicity testing, particularly to accommodate the need to translate a large amount of information from high-throughput in vitro toxicity assays results. This chapter describes the use of big data to define a comprehensive physiological/biochemical modelling framework to rapidly predict in vivo biokinetics of chemicals. The ultimate goal is to support interpretation of high-volume toxicity data on large numbers of chemicals in an efficient way while increasing in vivo relevance in the context of human safety.