Big Data in Predictive Toxicology
CHAPTER 2: Biological Data in the Light of Toxicological Risk Assessment
Published:04 Dec 2019
Special Collection: 2019 ebook collectionSeries: Issues in Toxicology
V. Vitcheva, in Big Data in Predictive Toxicology, ed. D. Neagu and A. Richarz, The Royal Society of Chemistry, 2019, pp. 38-68.
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The practice of modern toxicology has generated and continues to generate enormous amounts of data to be used for safety evaluation and decision-making in human toxicological and ecotoxicological risk assessment as well as elucidation of possible modes of action (MoA) and adverse outcome pathways (AOP). The major goal of toxicity testing is to generate and collect appropriate results from a battery of studies and to assess, interpret and use the data correctly. Although these data were not generated at high speed, large collections of data have been assembled over the years. In addition, new in vitro methods generate high-throughput bioactivity data, together with omics technologies, for elucidation of mechanisms and support of chemical safety assessment. This chapter gives an overview on the different types of studies carried out to generate biological data by in vivo and in vitro testing and toxicity assays, focusing on data generated according to classic toxicological hazard assessment for human health, as an overview of requirements for regulatory toxicological evaluation that predictive toxicology would target. These data are generated mostly in rather time-consuming studies; however, high-throughput in vitro technologies have increased the volume, speed and variety of data generation. The main principles of the tests, the toxicity endpoints and the possible applications of the data obtained along with the main steps in data quality assessment and interpretation are discussed.