Navigating traditional chinese medicine network pharmacology and computational tools.
ABSTRACT: The concept of "network target" has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.
Project description:With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.
Project description:The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a "one-target, one-drug" mode to a "network-target, multiple-component-therapeutics" mode. Network pharmacology is more effective for establishing a "compound-protein/gene-disease" network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.
Project description:Radix salviae miltiorrhizae (Danshen in Chinese), a classic traditional Chinese medicine (TCM) herb, has been used for centuries to treat liver diseases. In this study, the preventive and curative potential of Danshen aqueous extract on acute/chronic alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD) was studied. The in vivo results indicated that Danshen could alleviate hepatic inflammation, fatty degeneration, and haptic fibrogenesis in ALD and NAFLD models. In the aspect of mechanism of action, the significant reduction in MDA levels in both ALD and NAFLD models implies the decreased levels of oxidative stress by Danshen. However, Danshen treatment could not activate the internal enzymatic antioxidant system in ALD and NAFLD models. To further explore the hepatoprotective mechanism of Danshen, an in silico-based network pharmacology approach was employed in the present study. The pharmacological network analysis result revealed that six potential active ingredients such as tanshinone iia, salvianolic acid b, and Danshensu may contribute to the hepatoprotective effects of Danshen on ALD and NAFLD. The action mechanism may relate with regulating the intracellular molecular targets such as PPAR?, CYP1A2, and MMP2 for regulation of lipid metabolism, antioxidant and anti-fibrogenesis by these potential active ingredients. Our studies suggest that the combination of network pharmacology strategy with in vivo experimental study may provide a forceful tool for exploring the mechanism of action of traditional Chinese medicine (TCM) herb and developing novel bioactive ingredients.
Project description:<h4>Objective</h4>Traditional Chinese medicine (TCM) is a unique and complex medical system that has developed over thousands of years. This article studies the problem of automatically extracting meaningful relations of entities from TCM literature, for the purposes of assisting clinical treatment or poly-pharmacology research and promoting the understanding of TCM in Western countries.<h4>Methods</h4>Instead of separately extracting each relation from a single sentence or document, we propose to collectively and globally extract multiple types of relations (eg, herb-syndrome, herb-disease, formula-syndrome, formula-disease, and syndrome-disease relations) from the entire corpus of TCM literature, from the perspective of network mining. In our analysis, we first constructed heterogeneous entity networks from the TCM literature, in which each edge is a candidate relation, then used a heterogeneous factor graph model (HFGM) to simultaneously infer the existence of all the edges. We also employed a semi-supervised learning algorithm estimate the model's parameters.<h4>Results</h4>We performed our method to extract relations from a large dataset consisting of more than 100,000 TCM article abstracts. Our results show that the performance of the HFGM at extracting all types of relations from TCM literature was significantly better than a traditional support vector machine (SVM) classifier (increasing the average precision by 11.09%, the recall by 13.83%, and the F1-measure by 12.47% for different types of relations, compared with a traditional SVM classifier).<h4>Conclusion</h4>This study exploits the power of collective inference and proposes an HFGM based on heterogeneous entity networks, which significantly improved our ability to extract relations from TCM literature.
Project description:Traditional Chinese Medicine (TCM) has a long history of widespread clinical applications, especially in East Asia, and is becoming frequently used in Western countries. However, owing to extreme complicacy in both chemical ingredients and mechanism of action, a deep understanding of TCM is still difficult. To accelerate the modernization and popularization of TCM, a single comprehensive database is required, containing a wealth of TCM-related information and equipped with complete analytical tools. Here we present YaTCM (Yet another Traditional Chinese Medicine database), a free web-based toolkit, which provides comprehensive TCM information and is furnished with analysis tools. YaTCM allows a user to (1) identify the potential ingredients that are crucial to TCM herbs through similarity search and substructure search, (2) investigate the mechanism of action for TCM or prescription through pathway analysis and network pharmacology analysis, (3) predict potential targets for TCM molecules by multi-voting chemical similarity ensemble approach, and (4) explore functionally similar herb pairs. All these functions can lead to one systematic network for visualization of TCM recipes, herbs, ingredients, definite or putative protein targets, pathways, and diseases. This web service would help in uncovering the mechanism of action of TCM, revealing the essence of TCM theory and then promoting the drug discovery process. YaTCM is freely available at http://cadd.pharmacy.nankai.edu.cn/yatcm/home.
Project description:Backgrounds and Aims:Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI. Materials and Methods:In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM. Results:16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity. Conclusion:Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.
Project description:Traditional Chinese medicine (TCM) has been used to treat tumors for years and has been demonstrated to be effective. However, the underlying molecular mechanisms of herbs remain unclear. This study aims to ascertain molecular targets of herbs prolonging survival time of patients with advanced hepatocellular carcinoma (HCC) based on network pharmacology, and to establish a research method for accurate treatment of TCM. The survival benefit of TCM treatment with Chinese herbal medicine (CHM) was proved by Kaplan-Meier method and Cox regression analysis among 288 patients. The correlation between herbs and survival time was performed by bivariate correlation analysis. Network pharmacology method was utilized to construct the active ingredient-target networks of herbs that were responsible for the beneficial effects against HCC. Cox regression analysis showed CHM was an independent favorable prognostic factor. The median survival time was 13 months and the 5-year overall survival rates were 2.61% in the TCM group, while there were 6 months, 0 in the non-TCM group. Correlation analysis demonstrated that 8 herbs closely associated with prognosis. Network pharmacology analysis revealed that the 8 herbs regulated multiple HCC relative genes, among which the genes affected proliferation (KRAS, AKT2, MAPK), metastasis (SRC, MMP), angiogenesis (PTGS2) and apoptosis (CASP3) etc.
Project description:Herb pair Danggui-Honghua has been frequently used for treatment of blood stasis syndrome (BSS) in China, one of the most common clinical pathological syndromes in traditional Chinese medicine (TCM). However, its therapeutic mechanism has not been clearly elucidated. In the present study, a feasible system pharmacology model based on chemical, pharmacokinetic and pharmacological data was developed via network construction approach to clarify the mechanisms of this herb pair. Thirty-one active ingredients of Danggui-Honghua possessing favorable pharmacokinetic profiles and biological activities were selected, interacting with 42 BSS-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as HMOX1, NOS2, NOS3, HIF1A and PTGS2 were mainly involved in TNF signaling pathway, HIF-1 signaling pathway, estrogen signaling pathway and neurotrophin signaling pathway. The contribution index of every active ingredient also indicated six compounds, including hydroxysafflor yellow A, safflor yellow A, safflor yellow B, Z-ligustilide, ferulic acid, and Z-butylidenephthalide, as the principal components of this herb pair. These results successfully explained the polypharmcological mechanisms underlying the efficiency of Danggui-Honghua for BSS treatment, and also probed into the potential novel therapeutic strategies for BSS in TCM.
Project description:Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions.
Project description:Natural products, including traditional herbal medicine (THM), are known to exert their therapeutic effects by acting on multiple targets, so researchers have employed network pharmacology methods to decipher the potential mechanisms of THM. To conduct THM-network pharmacology (THM-NP) studies, researchers have employed different tools and databases for constructing and analyzing herb-compound-target networks. In this study, we attempted to capture the methodological trends in THM-NP research. We identified the tools and databases employed to conduct THM-NP studies and visualized their combinatorial patterns. We also constructed co-author and affiliation networks to further understand how the methodologies are employed among researchers. The results showed that the number of THM-NP studies and employed databases/tools have been dramatically increased in the last decade, and there are characteristic patterns in combining methods of each analysis step in THM-NP studies. Overall, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was the most frequently employed network pharmacology database in THM-NP studies. Among the processes involved in THM-NP research, the methodology for constructing a compound-target network has shown the greatest change over time. In summary, our analysis describes comprehensive methodological trends and current ideas in research design for network pharmacology researchers.