創薬・医薬品開発中の意思決定に必要とされるComputational toxicologyとは？

抄録

As a base of safety assessment of pharmaceuticals, the 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 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 the estimation by computational systems such as QSAR and related expert systems.

Furthermore, in the “Go”, “No-Go” decision of drug development, supportive utilization of a scientifically interpretable computational toxicology system is required for human safety evaluation. Pharmaceutical safety evaluator as a related toxicologist who is facing to practical decision does not need a data-driven AI (Artificial Intelligence) system that calls for the final consequence, rather requires an explainable AI that can provide comprehensive information necessary for evaluation and can help decision making. Through the explication and suggestion of information on the mechanism of toxic effects to safety assessment scientists, ultimately a subsidiary partnership system for risk assessment is to be a powerful tool that can indicate project-vector with data weight for the corresponding counterparts.

To bridge the gaps between the big-data and the knowledge, multi-dimensional thinking based on philosophical ontology theory is necessary to handle heterogeneous data such as interpretable computational toxicology related to drug safety assessment.