In the development of pharmaceuticals and other chemical substances, it is important to evaluate their efficacy and safety. There is a growing trend toward reducing reliance on traditional in vivo testing using animals for safety assessments and utilizing new evaluation methods, such as in vitro and in silico testing, to refine human safety assessments. Furthermore, in medical and environmental fields, there is a growing demand for the utilization of vast amounts of information. This has led to the development of data-driven approaches that utilize large-scale medical information and artificial intelligence (AI). Machine learning enables computers to learn from known data, discover new patterns, and predict unknown data. This technology is also useful for in silico prediction of chemical toxicity and adverse reactions in humans. Recently, explainable AI, which presents the basis for forecasts obtained from machine learning models in a user-understandable manner, has attracted attention and is a useful technology for decision-making support. We have developed machine learning models focusing on a quantitative structure–activity relationship approach to predict toxicity and adverse reactions based on the structural information of chemical substances. Furthermore, we have begun to develop a model to predict package insert revisions based on post-marketing adverse reaction information. These efforts will contribute to solving regulatory science issues regarding the appropriate use of chemical substances such as pharmaceuticals.
Kaori Ambe (Sat,) studied this question.