In cancer chemotherapy, new drugs such as immune checkpoint inhibitors have been introduced into clinical practice, improving treatment outcomes compared to previous years. As more and more patients continue anticancer treatment for a long period, the management of side effects caused by anticancer drugs has become a more important issue. Therefore, the development of treatments for side effects associated with cancer chemotherapy may contribute significantly to the completion of anticancer treatment, as well as improving patients' quality of life. 
In recent years, a drug discovery strategy called drug repositioning (DR) has been implemented, in which a new pharmacological action of an already approved drug used in clinical practice is discovered and developed as a treatment for another disease.
The major advantage is that the time and cost required for drug development can be significantly reduced because approved drugs have already undergone clinical trials and information on safety and pharmacokinetics in humans has been accumulated. To conduct DR research efficiently and rationally, we are conducting "database-driven drug repositioning research," a strategy to narrow down drugs, diseases, and molecules to be targeted for DR by utilizing medical big data, life science databases, and drug discovery support AI system, and to validate them in basic and clinical research.
At this symposium, I would like to present our recent studies and discuss the current status and issues of database-driven pharmacological research.