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ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Ethnopharmacology
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1551531
Understanding the Ancient Classic and Famous Prescriptions via the Property of Chinese Materia Medica
Provisionally accepted- 1Beijing University of Chinese Medicine, Beijing, China
- 2Beijing International Center for Mathematical Research, Peking University, Beijing, Beijing, China
- 3School of Mathematical Sciences, Faculty of Science, Peking University, Beijing, Beijing Municipality, China
- 4College of Pharmacy, The Ohio State University, Columbus, Ohio, United States
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Background: Ancient Classic and Famous Prescriptions (ACFPs), derived from Traditional Chinese Medicine (TCM) Classics, are widely utilized due to their precise therapeutic effects and distinctive clinical advantages. Existing research predominantly focuses on individual prescriptions, lack of systematic exploration of medication patterns within the official ACFPs Catalogue. The Property of Chinese Materia Medica (PCMM), a multidimensional representation of medicinal properties, offers a novel perspective for systematically analyzing TCM formulas. Objective: This study aims to investigate the implicit medication patterns of ACFPs from the PCMM perspective, establish a feature extraction model based on the Property Combination of Chinese Materia Medica (PCCMM), and evaluate its effectiveness in representing and reconstructing ACFPs.Methods: Based on the Chinese Pharmacopoeia (ChP), we constructed a CMM-PCCMM network as the forward feature extraction process. We formulated the backward process as a constrained combinatorial optimization problem to rebuild ACFPs from their PCCMMs. We evaluated the performance of PCCMM in reconstructing ACFPs using the Jaccard similarity coefficient. Furthermore, we tested the capability of PCCMM to distinguish ACFPs from random pseudo-formulas and classify ACFPs according to deficiency syndromes. Finally, we conducted frequency analysis, association rule analysis, distance analysis, and correlation analysis to explore the implicit medication patterns of ACFPs based on PCCMM.: Numerical experiments showed that PCCMM effectively represented and reconstructed ACFPs, achieving an average Jaccard similarity coefficient above 0.8. PCCMM outperformed the nomenclature of CMM in distinguishing ACFPs from random pseudo-formulas 1 Dan Qin et al. and classifying deficiency syndromes. Frequency analysis revealed that high-frequency CMMs were mainly tonic medicines, while high-frequency PCCMMs predominantly mapped to the Even-Sweet-Spleen meridian. The association rule analysis based on PCCMM yielded significantly more implicit compatibility rules than CMM alone. Distance and correlation analyses identified synergistic CMM-pairs and PCCMM-pairs, such as Jujubae Fructus (Dazao) & Zingiberis Rhizoma Recens (Shengjiang), consistent with clinical experience. Conclusion: The PCCMM-based feature extraction model provides a quasi-equivalent representation of TCM formulas, effectively capturing implicit medication patterns within ACFPs. PCCMM outperforms traditional CMM methods in formula reconstruction, classification, and medication pattern mining. This study offers novel insights and methodologies for systematically understanding TCM formulas, guiding clinical application.
Keywords: Ancient Classic and Famous Prescription, Chinese materia medica, Property of Chinese materia medica, Medication pattern, feature extraction, Combinatorial Optimization ACFP Ancient Classic and Famous Prescription ChP Chinese Pharmacopoeia CMM Chinese Medicinal Material
Received: 25 Dec 2024; Accepted: 14 Apr 2025.
Copyright: © 2025 Qin, Zhang, Du, WANG, Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yun Wang, Beijing University of Chinese Medicine, Beijing, China
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