Journal of ethnopharmacology · 2025
Review on traditional Chinese medicine-induced liver injury.
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Wu Jingwen, Zhu Yan, Du Xinke, Li Qi, Pan Chen, Zhang Yushi, Liu Suyan, Yue Xingnan, Liang Aihua, Han Jiayin
Аннотация
ETHNOPHARMACOLOGICAL RELEVANCE: In recent years, with more widespread use of traditional Chinese medicine (TCM) and continuous improvement of adverse drug reaction monitoring systems, reports of liver injury caused by TCMs, natural drugs, and related formulations are increasing and attracting broad attention. Investigating the characteristics involved in TCM-induced liver injury, understanding the underlying mechanisms correlated and scientifically assessing the potential hepatotoxicity are crucial for enhancing the safety of TCMs in clinical use.
AIM OF THE STUDY: This review aims to show the characteristics of TCM-induced liver injury recorded in clinical reports, and summarize the underlying mechanisms and predictive technologies involved in TCM-induced hepatotoxicity.
METHODS: Literature of clinical reports, mechanisms investigating and predictive technologies related with TCM-induced liver injury was retrieved from multiple databases (CBM, CNKI, PubMed, Wanfang Data, Web of Science, and Google Scholar), as well as doctoral and master's theses and official websites, covering publications from 1982 to 2025.
RESULTS: Through literature review, 481 articles reporting 571 cases of TCM-induced liver injury were collected and descriptively summarized. 265 TCMs were reported, with single TCM herb accounting for 26.97 % and TCM herb formulas for 73.03 %. Hepatocellular injury was the most common type of liver damage. Among the reported TCMs, Polygonum multiflorum Thunb., Psoralea corylifolia L. and Gynura japonica Thunb. Juel were most frequently documented. The mechanisms related to TCM-induced liver injury mainly included mitochondrial dysfunction, endoplasmic reticulum stress, bile acid homeostasis disruption, gut microbiota dysbiosis, CYP450 enzyme metabolic abnormalities, and idiosyncratic immune responses.
CONCLUSION: This review summarizes TCMs that are most frequently reported in published cases of liver injury and outlines the major underlying mechanisms involved. Although current regulatory guidelines mandate hepatotoxicity evaluation of TCMs, predictive approaches remain imperfect. Emerging models such as organoids and computational tools are being explored to address these limitations, with the aim of improving clinical applicability through more accurate risk prediction and mechanistic investigation.
AIM OF THE STUDY: This review aims to show the characteristics of TCM-induced liver injury recorded in clinical reports, and summarize the underlying mechanisms and predictive technologies involved in TCM-induced hepatotoxicity.
METHODS: Literature of clinical reports, mechanisms investigating and predictive technologies related with TCM-induced liver injury was retrieved from multiple databases (CBM, CNKI, PubMed, Wanfang Data, Web of Science, and Google Scholar), as well as doctoral and master's theses and official websites, covering publications from 1982 to 2025.
RESULTS: Through literature review, 481 articles reporting 571 cases of TCM-induced liver injury were collected and descriptively summarized. 265 TCMs were reported, with single TCM herb accounting for 26.97 % and TCM herb formulas for 73.03 %. Hepatocellular injury was the most common type of liver damage. Among the reported TCMs, Polygonum multiflorum Thunb., Psoralea corylifolia L. and Gynura japonica Thunb. Juel were most frequently documented. The mechanisms related to TCM-induced liver injury mainly included mitochondrial dysfunction, endoplasmic reticulum stress, bile acid homeostasis disruption, gut microbiota dysbiosis, CYP450 enzyme metabolic abnormalities, and idiosyncratic immune responses.
CONCLUSION: This review summarizes TCMs that are most frequently reported in published cases of liver injury and outlines the major underlying mechanisms involved. Although current regulatory guidelines mandate hepatotoxicity evaluation of TCMs, predictive approaches remain imperfect. Emerging models such as organoids and computational tools are being explored to address these limitations, with the aim of improving clinical applicability through more accurate risk prediction and mechanistic investigation.