Predictive Analytics in Driving Growth in Toxicology
Luis G. Valerio · 2024
Predictive data analytics has exploded in recent years with increasing availability and demand for advanced computational software and underlying machine learning methods. This promising approach to improve the efficiency and accuracy of testing is now driving growth in toxicology. In this chapter, predictive data analytics drive growth and impact toxicology are discussed. Current initiatives fueling predictive data analytics, and how the progress and establishment of data science are making it easier for the field of toxicology to engage with these methods are covered. Recognition that predictive data science is a data-driven strategy to reduce the use of animals in toxicological experimentation is also supporting the growth and impact of analytics in toxicology. Other reasons predictive data analytics is driving growth in toxicology include the substantial cost and time savings for in silico techniques compared to conventional animal tests, which leads to maximizing resources at a time when budgets are fragile. The continued interest in predictive data analytics as methods in chemical safety and risk assessment has led to improved access to computer-based predictive technologies such as open-source systems. Finally, the chapter discusses the integration of big data and artificial intelligence, as well as the development of predictive toxicology databases and current initiatives. Overall, predictive data analytics is poised to transform toxicological testing and drive growth in the field.