Host: The Japanese Society of Toxicology
In research and development of pharmaceuticals, critical decision of the developmental strategy determined in each development stage is carried out by the efficacy / safety evaluation and its risk assessment / management. The basis of many of these factors for decision making is the elucidation of the toxic mechanism with mode of action of the drug, which depends on the biomarker setting. Fusion of two factors (Mode of action: mechanism as medicinal effect, Adverse observed pathway: mechanism as toxic action) is indispensable for elucidation of the mechanism of toxicological effects in human body
In terms of “Go”, “No-Go” decision during drug development, supportive utilization of a scientifically interpretable computational toxicology system is required for human safety evaluation and its risk evaluation / management. 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.
