As a widely practiced clinical medical system, traditional Chinese medicine (TCM) possesses a theoretical and practical foundation accumulated over thousands of years. With growing global interest in natural therapies, TCM is currently facing a historic opportunity for globalization. However, its widespread international acceptance still faces significant challenges due to the lack of standardization, variability in herbal quality, incomplete understanding of its mechanisms of action, and gaps in meeting international regulatory requirements for medicines. With the rapid development of analytical tools and methods, advances in artificial intelligence (AI), and continuous progress in pharmaceutical sciences, comprehensive high-throughput pharmacological research, quality control, and the establishment of regulatory approaches for the complex system of TCM are gradually becoming feasible. This study systematically expounds on the current applications and challenges of modern analytical methods in TCM formulation research from the perspective of analytical technology, covering key aspects including the efficacious components of TCM, multi-component analysis of herbal medicines, application models of modern analytical techniques, correlation analysis between the bioactive constituents of TCM formulations and their pharmacological mechanisms, and the establishment of quality control methods based on efficacy-related substances. • Proposes a framework for clarifying TCM ingredients & pharmacological mechanisms. • Emphasizes multi-component and high-throughput analysis for TCM complexity. • Discusses the correlation between TCM ingredients and pharmacological mechanisms. • Explores the application of advanced analytical tools for TCM ingredients.
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Huaxin Zhou
Nankai University
Fengwen Yang
Tianjin University of Traditional Chinese Medicine
Haoyun Bai
Xi'an Jiaotong University
Journal of Pharmaceutical Analysis
Xi'an Jiaotong University
Tianjin University of Traditional Chinese Medicine
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Zhou et al. (Sun,) studied this question.
synapsesocial.com/papers/69c4cdcdfdc3bde44891a7fa — DOI: https://doi.org/10.1016/j.jpha.2026.101611
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