Principal Investigator (PI): MengHuan SONG, Research Associate Professor
Year: 2024
“Pharmacovigilance-related research can not only evaluate and understand issues related to the safety of TCMs from the scientific perspective described in this study to improve the efficiency of risk management of medicine; moreover, the application of artificial intelligence in regulatory science can promote the transformation of regulatory perspectives. That is, transition from passive risk management to active risk prevention and control.”
Regulatory Science Challenge
Pharmacovigilance is the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other possible drug safety-related issues. It is an important aspect of regulatory science. In 2019, China established a nationwide alert system that is comprehensively applicable to various types of drugs such as traditional Chinese medicines (TCMs), chemical drugs, and biological agents. Considering the production process and characteristics of TCM, as well as its frequent adverse drug reactions and other safety issues, it is of far-reaching significance to carry out pharmacovigilance research on TCM. Based on the traditional theoretical system of TCM, most TCMs are multi-component and multi-target preparations, which makes it difficult to identify effective and toxic ingredients. At the same time, in real-world applications, TCMs are often used in combination with chemical drugs, which makes the safety prevention and control of TCMs more difficult. To scientifically and accurately solve different types of safety issues arising from TCM requires a large amount of multi-modal real-world data as the basis for analysis. The application of artificial intelligence in regulatory science provides opportunities to solve the above problems and technical support for pharmacovigilance-related research on TCM. It also responds to the connotation of regulatory science about “developing new methods” to support “drug evaluation.”
Project Description
This study plans to use artificial intelligence-related technical methods to monitor adverse drug events and reactions of TCM, review drug safety reports, investigate interactions between drugs, and identify high-risk population for drug safety problems. This research project intends to reveal the macro status of TCM safety issues and provide reference for decision-making related to TCM supervision; and to clarify and explain important issues related to the microscopic safety of TCM to achieve early identification and prevention of drug safety issues. The research of this project can demonstrate real-world needs regarding drug safety issues and provide a basis for the construction of a digital information system for pharmacovigilance systems. The research results of this project can provide technical support for clinical decision-making on drug use and provide a data basis for the pharmacovigilance system, thereby providing a reference for regulatory decision-making.
