Study on strategies and methods for discovering risk of traditional Chinese medicine-related liver injury based on real-world data: an example of Corydalis Rhizoma.
10.19540/j.cnki.cjcmm.20250226.401
- Author:
Long-Xin GUO
1
;
Li LIN
2
;
Yun-Juan GAO
2
;
Min-Juan LONG
2
;
Sheng-Kai ZHU
2
;
Ying-Jie XU
2
;
Xu ZHAO
2
;
Xiao-He XIAO
2
Author Information
1. College of Pharmacy, Dali University Dali 671000, China China Military Institute of Chinese Materia,Fifth Medical Center of Chinese PLA General Hospital Beijing 100039, China Department of Hepatology,Fifth Medical Center of Chinese PLA General Hospital Beijing 100039, China.
2. China Military Institute of Chinese Materia,Fifth Medical Center of Chinese PLA General Hospital Beijing 100039, China Department of Hepatology,Fifth Medical Center of Chinese PLA General Hospital Beijing 100039, China.
- Publication Type:Journal Article
- Keywords:
Corydalis Rhizoma;
adverse drug reaction;
big data;
data analysis;
risk discovery
- MeSH:
Corydalis/adverse effects*;
Drugs, Chinese Herbal/adverse effects*;
Humans;
Chemical and Drug Induced Liver Injury/etiology*;
Medicine, Chinese Traditional/adverse effects*;
Rhizome/adverse effects*;
Male;
Female
- From:
China Journal of Chinese Materia Medica
2025;50(13):3784-3795
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, there have been frequent adverse reactions/events associated with traditional Chinese medicine(TCM), especially liver injury related to traditional non-toxic TCM, which requires adequate attention. Liver injury related to traditional non-toxic TCM is characterized by its sporadic and insidious nature and is influenced by various factors, making its detection and identification challenging. There is an urgent need to develop a strategy and method for early detection and recognition of traditional non-toxic TCM-related liver injury. This study was based on national adverse drug reaction monitoring center big data, integrating methodologies such as reporting odds ratio(ROR), network toxicology, and computational chemistry, so as to systematically research the risk signal identification and evaluation methods for TCM-related liver injury. The optimized ROR method was used to discover potential TCM with a risk of liver injury, and network toxicology and computational chemistry were used to identify potentially high-risk TCM. Additionally, typical clinical cases were analyzed for confirmation. An integrated strategy of "discovery via big data, identification via dry/wet method, confirmation via typical cases, and precise risk prevention and control" was developed to identify the risk of TCM-related liver injury. Corydalis Rhizoma was identified as a TCM with high risk, and its toxicity-related substances and potential toxicity mechanisms were analyzed. The results revealed that liver injury is associated with components such as tetrahydropalmatine and tetrahydroberberine, with potential mechanisms related to immune-inflammatory pathways such as the tumor necrosis factor signaling pathway, interleukin-17 signaling pathway, and Th17 cell differentiation. This paper innovatively integrated real-world evidence and computational toxicology methods, offering insights and technical support for establishing a risk discovery and identification strategy for TCM-related liver injury based on real-world big data, providing innovative ideas and strategies for guiding the safe and rational use of medication in clinical practices.
