Traditional Chinese Medicine (TCM) doctors always prescribe various herbal formulae tailored to individual patients. However, there is still a lack of appropriate methods to study the rule and potential biological basis underlying the numerous prescriptions. Here we developed an Herb-Compound-Target-Disease coherent network approach to analyze 871 herbal prescriptions from a TCM master, Mr. Ji-Ren Li, in his clinical practice on treatment of rheumatoid arthritis (RA). The core herb networks were extracted from Mr. Li’s prescriptions. Then, we predicted target profiles of compounds in core herb networks and calculated potential synergistic activities among them. We further found that the target sets of core herbs overlapped significantly with the RA related biological processes and pathways. Moreover, we detected a possible connection between the prescribed herbs with different properties such as Cold and Hot and the Western drugs with different actions such as immunomodulatory and hormone regulation on treatment of RA. In summary, we explored a new application of TCM network pharmacology on the analysis of TCM prescriptions and detected the networked core herbs, their potential synergistic and biological activities, and possible connections with drugs. This work offers a novel way to understand TCM prescriptions in clinical practice.
Traditional Chinese Medicine (TCM) is one of the most treasured cultural heritages in China and is also an inseparable part of current medical systems. Herbal formula (
However, quite different from the clinical practice of the Western medicine, herbal formulae are always prescribed by TCM practitioners specifically tailored to individual patients. TCM doctors treat patients mainly based on syndrome differentiation. For different patients, various herbs are grouped and different formulae are prescribed. This tradition and feature make TCM a kind of personalized medicine and cause great difficulty in designing standard protocols such as the randomized controlled trial in TCM studies. The lack of appropriate methods to analyze the rule as well as scientific basis for TCM-doctor-prescribed herbal formulae is therefore one of the major frustrations to find clinical evidence of TCM. How to study the characteristics and prescription rule of such flexible TCM formulae is still a great challenge and requires widely interdisciplinary efforts.
Thanks to the rapid progress of bioinformatics especially complex network theory and technologies, recently, the network approach has become a new avenue and powerful tool to handle intricate problems such as those in TCM. We have been making efforts to build a TCM network pharmacology approach over ten years and established a set of network-based methods for TCM study [
In this work, 871 herbal prescriptions from Mr. Ji-Ren Li’s clinical treatment on patients with rheumatoid arthritis were analyzed from a network point of view. TCM network pharmacology methods were employed to detect the common rules and potential biological basis of these herbal prescriptions. This work is promising as the results nicely show the rationality and validity of Mr. Li’s herbal prescriptions. Moreover, this work provides a new route to interpret the professional experience embedded in numerous and precious TCM herbal prescriptions.
A total of 871 herbal prescriptions from Mr. Ji-Ren Li in the individually clinical treatment of RA patients from 2005 to 2013 were used in this work. The 871 herbal prescriptions were normalized by substituting the polysemes, synonyms, and acronyms of the herbs in the dataset and resulted in a standardized Herb Name list. Each formula contains 15 to 29 herbs and the average number is 18. Compounds of herbs were gathered from the HerbBioMap database (China Copyright of Computer Software, 2011SR076502). The HerbBioMap database contains 10806 compounds for 539 herbs up to now.
As shown in Figure
The Herb-Compound-Target-Disease coherent network analysis framework for studying herbal prescriptions on the treatment of certain diseases.
343 herbs were uniquely identified from all 871 prescriptions. By DMIM scoring [
Top 15 herbs in Mr. Ji-Ren Li’s antirheumatoid arthritis herbal prescriptions.
Chinese name | Latin name | Property | Frequency | Normalized frequency |
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Ji Xue Teng |
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Warm | 503 | 5.10% |
Huo Xue Teng |
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Neutral | 499 | 5.06% |
Huang Qi |
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Warm | 481 | 4.87% |
Dang Gui |
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Warm | 430 | 4.36% |
Quan Xie |
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Neutral | 399 | 4.04% |
Qing Feng Teng |
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Neutral | 396 | 4.01% |
Ku Shen |
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Cold | 323 | 3.27% |
Wu Tou |
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Hot | 306 | 3.10% |
Wu Gong |
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Warm | 287 | 2.91% |
Huang Bai |
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Cold | 283 | 2.87% |
Bi Xie |
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Neutral | 280 | 2.84% |
Ren Dong Teng |
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Cool | 257 | 2.60% |
Qin Jiao |
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Cold | 254 | 2.57% |
Wu Shao She |
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Neutral | 235 | 2.38% |
Pu Gong Ying |
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Cold | 227 | 2.30% |
For the high-frequency network, only the top frequent herbs were treated as nodes and the cooccurrence herbs in herbal prescriptions identified by DMIM were treated as edges. For example, we found that the top 10 frequent herbs with 45 edges are closely interrelated with each other, forming almost all connected graphs. Among them, Ji Xue Teng and Huang Qi (DMIM score = 18.627), Huo Xue Teng and Ji Xue Teng (DMIM score = 16.517) have the highest DMIM scores. This result suggests that the top 10 herbs are always prescribed in pairs in Mr. Li’s practice for RA treatment.
For the intersection herb network, we built every subnetwork by DMIM for each of the top frequent herbs and then intersected the subnetworks, to construct a core network. For example, Figure
The core intersection herb networks constructed from Mr. Ji-Ren Li’s anti-RA herbal prescriptions. (a) The herb subnetworks around each top frequent herb, for example, Huo Xue Teng (
Herbs and herb combinations present in these two types of herb networks may play a central role in Mr. Li’s herbal prescriptions for the treatment of RA.
Taking the core herb networks as examples, we further performed the network target analysis to computationally determine whether the DMIM extracted herb networks displayed synergistic effects. In this step, we collected available compounds for the core herb networks identified from Mr. Li’s prescriptions. The target profile of each compound was predicted by the drugCIPHER method [
Calculation of synergistic activity among compounds collected from the core herb networks (a). A part of the synergistic module from compounds of Qing Feng Teng (
Since the targets of each compound of herbs are available by drugCIPHER, we further verify the effectiveness of the top 15 frequent herbs in Mr. Li’s prescriptions (Table
Overlapped RA-related GO terms between top 15 herb targets and RA genes.
Category | Enriched GO terms | Corrected |
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Herb targets | Response to wounding |
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Angiogenesis |
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Inflammatory response |
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Regulation of cytokine production |
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Immune response |
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Leukocyte activation |
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Regulation of lymphocyte activation |
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Defense response to bacterium |
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Positive regulation of NF- |
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RA genes | Response to wounding |
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Angiogenesis |
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Inflammatory response |
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Regulation of cytokine production |
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Immune response |
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Leukocyte activation |
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Regulation of lymphocyte activation |
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Defense response to bacterium |
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Positive regulation of NF- |
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Network association of RA genes and top 15 frequent herbs’ targets in the enriched KEGG “rheumatoid arthritis” pathway (KEGG:05323).
In TCM, RA patients can be categorized to different syndromes (
Possible connection between RA herbs and drugs in the networks consisting of herbs, herbal compounds, immunomodulatory drugs (a), and hormones (b).
In summary, we developed a TCM network pharmacology strategy and an Herb-Compound-Target-Disease coherent network analysis framework to study herbal prescriptions, the main form of TCM clinical practice. We extracted two types of core herb networks from 871 anti-RA herbal formulae prescribed by a famous TCM doctor. From the core herb networks, our network-based strategy identified cooccurrence herbs and herb pairs with potential synergistic activities. We also predicted the biological basis of the core herb network by calculating the compound’s targets from most frequent herbs. We detected that targets of the commonly prescribed herbs are enriched together with RA genes in critical biological processes and pathway of RA. We also indicated that herbs with different properties in prescriptions may have a potential connection with RA immunomodulatory drugs or hormones in the compound networks. Taken together, these results could help uncover the prescription rules underlying herbal formulae in the clinical practice of TCM practitioners. Moreover, such network-based analyses not only can develop a holistic understanding of herbal remedies but also may facilitate the following pharmacological evaluation of herbal treatments. Clearly, this study is only the first step to learn the common rule of herbal prescriptions. We will continue to conduct in-depth analysis for understanding the scientific basis of herbal prescriptions in our future work. Although more powerful methods, more data on TCM prescriptions, and clinical or experimental evaluations are still required, we believe that this TCM network pharmacology strategy is suitable to study herbal formulae both in the herb level and in the molecular level and thus can serve as a novel approach to further understand the professional experience embedded in TCM prescriptions.
Distance-based mutual information model
Drug-target prediction by correlating protein interaction network and phenotype network
Gene ontology
Mutual information
Network-based identification of multicomponent synergy
Rheumatoid arthritis
Protein-protein interaction
Qing-Luo-Yin
Traditional Chinese Medicine.
The authors declare that they have no competing interests.
Shao Li conceived the study. Yan Li collected the data. Shao Li, Yan Li, Rui Li, and Zibo Ouyang analyzed the data and wrote the paper. All authors read and approved the final paper.
Thanks to Kai Zhang for his help in herbal prescription collection. This work is supported by NSFC project (81225025), the SATCM key discipline of “TCM Bi-Bing” in Yijishan Hospital of Wannan Medical College, the SATCM laboratory of TCM master Ji-Ren Li, the CATCM project, and Anhui’s Key Technologies R&D project (11010402173).