CHAPTER 1. Big Data in Predictive Toxicology: Challenges, Opportunities and Perspectives

Predictive toxicology and model development rely heavily on data to draw upon and have historically suffered from the paucity of available and good quality datasets. The situation has now dramatically changed from a lack of data hampering model development to “data overload”. With high throughput/content screening methodologies being systematically used aiming to understand the mechanistic basis of adverse effects, and increasing use of omics technologies and consideration of (bio)monitoring data, the volume of data is continuously increasing. Big data in predictive toxicology may not have reached the dimension of other areas yet, such as real-time generated data in the health sector, but encompass similar characteristics and related challenges. Pertinent questions in this area are whether the new plethora of data are adequate for use in predictive toxicology and whether they address this area's most urgent problems. This overview chapter looks at the definition and characteristics of big data in the context of predictive toxicology as well as the challenges and opportunities big data present in this field.