In The Journal of toxicological sciences ; h5-index 0.0
For the safety assessment of pharmaceuticals, initial data management requires accurate toxicological data acquisition, which is based on regulatory safety studies according to guidelines, and computational systems have been developed under the application of Good Laboratory Practice (GLP). In addition to these regulatory toxicology studies, investigative toxicological study data for the selection of lead compound and candidate compound for clinical trials are directed to estimation by computational systems such as Quantitative Structure-Activity Relationship (QSAR) and related expert systems. Furthermore, in the "Go" or "No-Go" decision of drug development, supportive utilization of a scientifically interpretable computational toxicology system is required for human safety evaluation. A pharmaceutical safety evaluator as a related toxicologist who is facing practical decision needs not only a data-driven Artificial Intelligence (AI) system that calls for the final consequence but also an explainable AI that can provide comprehensive information necessary for evaluation and can help with decision making. Through the explication and suggestion of information on the mechanism of toxic effects to safety assessment scientists, a subsidiary partnership system for risk assessment is ultimately to be a powerful tool that can indicate project-vector with data weight for the corresponding counterparts. To bridge the gaps between big data and knowledge, multi-dimensional thinking based on philosophical ontology theory is necessary for handling heterogeneous data for integration. In this review, we will explain the current state and future perspective of computational toxicology related to drug safety assessment from the viewpoint of ontology thinking.
Yamagata Yuki, Yamada Hiroshi, Horii Ikuo
Artificial intelligence, Computational toxicology, Ontology, Safety assessment