Traditional Chinese Medicine Network Pharmacology Research Articles

Research and development practice of traditional Chinese medicine based on network target theory and technology

Network targets theory and technology have transcended the limitations of the "single gene, single target" model, aiming to decipher the mechanisms of traditional Chinese medicine(TCM) based on biological network from the perspective of informatics and system. As the core of TCM network pharmacology, with the development of computer science and high-throughput experimental techniques, the network target theory and technology are beginning to exhibit a trend of organic integration with artificial intelligence technology and high-throughput multi-modal multi-omics experimental techniques. Taking the network target analysis of TCM like Yinqiao Qingre Tablets as a typical case, network target theory and technology have achieved the systematic construction, in-depth analysis, and high-throughput multi-modal multi-omics validation of multi-level biological networks spanning from traditional Chinese and Western phenotypes to tissues, cells, molecules, and traditional Chinese and Western medicines. This development helps to address critical issues in the analysis of mechanisms of TCM, including the discovery of key targets, identification of functional components, discovery of synergistic effects among compound ingredients, and elucidation of the regulatory mechanisms of formulae. It provides powerful theoretical and technological support for advancing clinical precision diagnosis and treatment, precise positioning of TCM, and precise research and development of TCM. Thus, a new paradigm of TCM research gradually emerges, combining big data and artificial intelligence(AI) with the integration of human experience and scientific evidence.

Exploring the mechanism of Jianpi Huatan recipe in protecting hepatocellular carcinoma based on network pharmacology

Ethnopharmacological relevanceJianpi Huatan Recipe (JPHTR) is an effective prescription for delaying progression of hepatocellular carcinoma (HCC) provided by Longhua Hospital affiliated to Shanghai University of traditional Chinese Medicine, and it is consisted of nine traditional Chinese drugs, but the protective mechanism of JPHTR against HCC progression is unclear. Aim of the studyTo study the mechanism of JPHTR preventing the progression of HCC based on the network pharmacology. Materials and methodsThe chemical component and potential gene targets of JPHTR and the important gene targets of HCC were obtained by retrieving traditional Chinese medicine network pharmacology analysis system (TCMNPAS) database. The data obtained from the database are used to construct the drugs-chemical component-targets network and protein-protein interaction network by using Cytoscape software and STRING database. The potential targets of JPHTR and HCC targets were imported into TCMNPAS-related modules in order to obtain the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment pathways. Finally, we used HCC rat model to verify the vital signaling pathways predicted by network pharmacology. ResultsA total of 197 potential compounds and 721 potential targets of JPHTR and 611 important gene targets of HCC were obtained. Through the experiment in vivo, it was found that JPHTR can reduce the serum levels of alanine aminotransferase, aspartate aminotransferase and alkaline phosphatase, reduce the lipid droplets and inflammatory injury of liver tissue, and reduce the mRNA expression of Interleukin-6 (Il-6), Janus tyrosine Kinase2 (Jak2) and Forkhead box O3 (Foxo3) in FOXO pathway in the liver, thus delaying the development of HCC. ConclusionThrough network pharmacology and rat experiments, it is preliminarily confirmed that JPHTR may delay the progression of HCC by regulating the expression of Il-6/Jak2/Foxo3 in FOXO signal pathway, which is expected to be a new therapeutic target for the protection of HCC.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research.

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Qinggan Huoxue Recipe attenuates Alcoholic Liver Disease by suppressing PI3K/AKT signaling pathway based on network pharmacology.

Qinggan Huoxue Recipe (QGHXR) is originated from Xiao Chaihu Decotion. Many experimental studies have confirmed that QGHXR can significantly alleviate the symptoms of alcoholic liver disease (ALD), but the detailed mechanism is still unclear. Using traditional Chinese medicine network pharmacology analysis system database and animal experiments, we found that 180 potentially chemical compositions and 618 potential targets were screened from the prescription, which shared 133 signal pathways with ALD. Through animal experiments, it was found that QGHXR could reduce the liver total cholesterol (TC), serum TC, alanine aminotransferase, aspartate aminotransferase of ALD mice, reduce the lipid droplets and inflammatory injury of liver tissue. Meanwhile, it can also increase PTEN, decrease PI3K and AKT mRNA levels. In this study, we obtained the targets and pathways of QGHXR in the treatment of ALD, and preliminatively verified that QGHXR may improve ALD through PTEN/PI3K/AKT signaling pathway.

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach.

Objectives The Traditional Chinese Medicine (TCM) formula Yi-Fei-Jian-Pi-Tang (YFJPT) has been demonstrated effective against Corona Virus Disease 2019 (Covid-19). The aim of this article is to make a thorough inquiry about its active constituent as well as mechanisms against Covid-19 via TCM network pharmacology. Methods All the ingredients of YFJPT are obtained from the pharmacology database of the TCM system. The genes which are associated with the targets are obtained by utilizing UniProt. The herb-target network is built up by utilizing Cytoscape. The target protein-protein interaction network is built by utilizing the STRING database and Cytoscape. The critical targets of YFJPT are explored by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results The outcomes show that YFJPT might has 33 therapeutic targets on Covid-19, namely, interleukin 2 (IL2), heme oxygenase 1 (HMOX1), interleukin 4 (IL4), interferon gamma (FNG), α nuclear factor of kappa light polypeptide gene enhancer in Bcells inhibitor, alpha (NFKBIA), nuclear factor-k-gene binding (NFKB), nitric oxide synthase 3 (NOS3), intercellular adhesion molecule 1 (ICAM1), hypoxia inducible factor 1 subunit alpha (HIF1A), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), interleukin 10 (IL10), jun proto-oncogene (JUN), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), tumor protein p53 (TP53), interleukin 1 beta (IL1B), AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), interleukin 6 (IL6), erb-b2 receptor tyrosine kinase 2 (ERBB2), RELA proto-oncogene (RELA), NF-κB subunit, caspase 8 (CASP8), peroxisome proliferator activated receptor alpha (PPARA), TIMP metallopeptidase inhibitor 1 (TIMP1), transforming growth factor beta 1 (TGFB1), interleukin 1 alpha (IL1A), signal transducer and activator of transcription 1 (STAT1), mitogen-activated protein kinase 8 (MAPK8), myeloperoxidase (MPO), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), and NFE2 like bZIP transcription factor 2 (NFE2L2). The gene enrichment analysis prompts that YFJPT most likely contributes to patients related to Covid-19 by regulating the pathways of cancers. Conclusions That will lay a foundation for the clinical rational application and further experimental research of YFJPT.

Traditional Chinese medicine network pharmacology: development in new era under guidance of network pharmacology evaluation method guidance

Traditional Chinese medicine(TCM) has unique advantages in the prevention and treatment of diseases owing to its holistic view and more than 2 000 years of experience in the clinical use of natural medicine. The "holistic" characteristic of TCM gives birth to a new generation of research paradigm featuring "network" and "system", which has been developing rapidly in the era of biomedical big data and artificial intelligence. Network pharmacology, a representative research field, provides new ideas and methods for the research of the interdiscipline of artificial intelligence and medicine, the analysis of massive biomedical data, and the transformation from data to knowledge. TCM plays an important role in proposing the core theory of "network target" and promoting the establishment and development of network pharmacology, and has taken the lead in formulating the first international standard of network pharmacology--Network Pharmacology Evaluation Method Guidance. In terms of theory, network target can systematically link drugs and diseases and quantitatively interpret the overall regulatory mechanism of drugs. In the aspect of method, network pharmacology is developing towards a research model that combines computational, experimental, and clinical approaches. This review introduces the resent important progress of TCM network pharmacology in predicting drug targets, understanding the biological basis of drugs and diseases, and searching for disease and syndrome biomarkers. Under the guidance of Network Pharmacology Evaluation Method Guidance, the development of network pharmacology is expected to become more and more standardized and healthy. Network target will help produce more high-quality research outcomes in TCM and effectively boost the modernization and internationalization of TCM.

Traditional Chinese Medicine Network Pharmacology in Cardiovascular Precision Medicine.

Cardiovascular disease is a major disease affecting human health, and its pathogenesis is caused by many factors. Through the use of "omics" technology, precision medicine is playing an increasingly important role in the prevention and treatment of cardiovascular diseases. Dialectical treatment with traditional Chinese medicine (TCM will result in personalized treatment, which is consistent with precision medicine to a certain extent. However, due to the multitarget, multipath, and multistep characteristics of TCM, its mechanism of action is not easy to elucidate. Network pharmacology can be used to predict the mechanism, toxicity and metabolic characteristics of TCM. This review summarizes commonly used bioinformatics resources for cardiovascular diseases and TCM, as well as the opportunities and challenges of TCM in cardiovascular precision medicine, with special emphasis on network pharmacology methods.

FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from “experience based, macro” to “data based, macro-micro combined” as well as the development of TCM network pharmacology.

Compound Dihuang Granule Inhibits Nigrostriatal Pathway Apoptosis in Parkinson's Disease by Suppressing the JNK/AP-1 Pathway.

Compound Dihuang Granule (CDG) is widely used in traditional Chinese medicine (TCM) for the treatment of Parkinson’s disease (PD). It has been shown to alleviate PD symptoms. However, the molecular mechanisms of its action have not been established. To establish the molecular mechanisms of CDG against PD, we used TCM network pharmacology methods to predict its molecular targets and signaling pathways, followed by experimental validation. The Core Protein protein interaction (PPI) network of the 150 intersections between CDG and PD-related genes, comprising 23 proteins, including CASP3 (caspase-3), MAPK8 (JNK), FOS (c-Fos), and JUN (c-Jun). KEGG and GO analyses revealed that apoptotic regulation and MAPK signaling pathways were significantly enriched. Since c-Jun and c-Fos are AP-1 subunits, an important downstream JNK effector, we investigated if the JNK/AP-1 pathway influences CDG against apoptosis through the nigrostriatal pathways in PD rat models. Molecular docking analysis found that the top three bioactive compounds exhibiting the highest Degree Centrality following online database and LC-MS analysis had high affinities for JNK. Experimental validation analysis showed that CDG decreased the number of rotating laps and suppressed the levels of phosphorylated c-Jun, c-Fos, and JNK, as well as the number of TUNEL positive cells and the cleaved caspase-3 level in the nigrostriatal pathway. Furthermore, CDG treatment elevated the number of TH neurons, TH expression level, and Bcl-2/Bax protein ratio in a 6-OHDA-induced PD rat. These findings are in tandem with those obtained using SP600125, a specific JNK inhibitor. In conclusion, CDG suppresses the apoptosis of the nigrostriatal pathway and relieves PD symptoms by suppressing the JNK/AP-1 signaling pathway.

TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches.

Traditional Chinese medicine (TCM) is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of diseases. The holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and system. TCM gave birth to a new method featuring holistic and systematic "network target", a core theory and method of network pharmacology. TCM is also an important research object of network pharmacology. TCM network pharmacology, which aims to understand the network-based biological basis of complex diseases, TCM syndromes and herb treatments, plays a critical role in the origin and development process of network pharmacology. This review introduces new progresses of TCM network pharmacology in recent years, including predicting herb targets, understanding biological foundation of diseases and syndromes, network regulation mechanisms of herbal formulae, and identifying disease and syndrome biomarkers based on biological network. These studies show a trend of combining computational, experimental and clinical approaches, which is a promising direction of TCM network pharmacology research in the future. Considering that TCM network pharmacology is still a young research field, it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.

Traditional Chinese Medicine Network Pharmacology in Cardiovascular Precision Medicine

Bentham Science - STM publisher of online and print journals, and related print/online book series. Bentham Science answers the information needs of scientists in the fields of pharmaceutical, biomedical, medical, engineering, technology, computer and social sciences.

Traditional Chinese medicine network pharmacology study on exploring the mechanism of Xuebijing Injection in the treatment of coronavirus disease 2019.

As a representative drug for the treatment of severe community-acquired pneumonia and sepsis, Xuebijing (XBJ) injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019 (COVID-19), but its treatment mechanism for COVID-19 is still unclear. Therefore, this study aims to explore the potential mechanism of XBJ injection in the treatment of COVID-19 employing network pharmacology and molecular docking methods. The corresponding target genes of 45 main active ingredients in XBJ injection and COVID-19 were obtained by using multiple database retrieval and literature mining. 102 overlapping targets of them were screened as the core targets for analysis. Then built the PPI network, TCM-compound-target-disease, and disease-target-pathway networks with the help of Cytoscape 3.6.1 software. After that, utilized DAVID to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to predict the action mechanism of overlapping targets. Finally, by applying molecular docking technology, all compounds were docked with COVID-19 3 CL protease(3CLpro), spike protein (S protein), and angiotensin-converting enzyme II (ACE2). The results indicated that quercetin, luteolin, apigenin and other compounds in XBJ injection could affect TNF, MAPK1, IL6 and other overlapping targets. Meanwhile, anhydrosafflor yellow B (AHSYB), salvianolic acid B (SAB), and rutin could combine with COVID-19 crucial proteins, and then played the role of anti-inflammatory, antiviral and immune response to treat COVID-19. This study revealed the multiple active components, multiple targets, and multiple pathways of XBJ injection in the treatment of COVID-19, which provided a new perspective for the study of the mechanism of traditional Chinese medicine (TCM) in the treatment of COVID-19.

A survey of web resources and tools for the study of TCM network pharmacology

BackgroundTraditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi‐component, multi‐target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM.ResultsIn this paper we introduce state‐of‐the‐art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis.ConclusionsThis review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective.

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.

Identification of the anti-tumor activity and mechanisms of nuciferine through a network pharmacology approach.

Nuciferine is an aporphine alkaloid extracted from lotus leaves, which is a raw material in Chinese medicinal herb for weight loss. In this study we used a network pharmacology approach to identify the anti-tumor activity of nuciferine and the underlying mechanisms. The pharmacological activities and mechanisms of nuciferine were identified through target profile prediction, clustering analysis and functional enrichment analysis using our traditional Chinese medicine (TCM) network pharmacology platform. The anti-tumor activity of nuciferine was validated by in vitro and in vivo experiments. The anti-tumor mechanisms of nuciferine were predicted through network target analysis and verified by in vitro experiments. The nuciferine target profile was enriched with signaling pathways and biological functions, including "regulation of lipase activity", "response to nicotine" and "regulation of cell proliferation". Target profile clustering results suggested that nuciferine to exert anti-tumor effect. In experimental validation, nuciferine (0.8 mg/mL) markedly inhibited the viability of human neuroblastoma SY5Y cells and mouse colorectal cancer CT26 cells in vitro, and nuciferine (0.05 mg/mL) significantly suppressed the invasion of 6 cancer cell lines in vitro. Intraperitoneal injection of nuciferine (9.5 mg/mL, ip, 3 times a week for 3 weeks) significantly decreased the weight of SY5Y and CT26 tumor xenografts in nude mice. Network target analysis and experimental validation in SY5Y and CT26 cells showed that the anti-tumor effect of nuciferine was mediated through inhibiting the PI3K-AKT signaling pathway and IL-1 levels in SY5Y and CT26 cells. By using a TCM network pharmacology method, nuciferine is identified as an anti-tumor agent against human neuroblastoma and mouse colorectal cancer in vitro and in vivo, through inhibiting the PI3K-AKT signaling pathways and IL-1 levels.

Application of drug-target prediction technology in network pharmacology of traditional Chinese medicine

In recent years, network pharmacology has been developed rapidly, and especially, the concept of ″network target″ has brought a new era in the field of traditional Chinese medicine (TCM). The integrity and systematicness emphasized in network pharmacology comply with the characteristics of holistic view and treatment in Chinese medicine. It can provide deeper insights into the underlying mechanisms of TCM theories, including the illustration on action mechanism of Chinese medicine, selection of pharmacodynamic materials and the combination principles of various Chinese herbs, etc. Therefore, this theory is more suitable for TCM academic characteristics and practical conditions. The key problem in network pharmacology is how to efficiently and quickly identify the interactions between large amounts of drugs and target proteins. As an efficient and high throughput way, drug-target prediction technology can reduce costs, quickly predict the component targets, and provide foundation for the application of TCM network pharmacology. In view of the large amount of compounds and target databases, different prediction methods and technologies have been developed, and used to predict the drug-target interactions. Many virtual screening technologies have been successfully applied to network pharmacology. Based on different prediction principles, drug-target prediction technology can be generally divided into four types： ligand-based prediction, receptor-based prediction, machine learning and combined prediction. In this paper, we are going to review the prediction methods of drug-target interactions and give acomprehensive elaboration of their application in network pharmacology of TCM, hoping to provide beneficial references for various Chinese medicine researchers.

Herb Network Analysis for a Famous TCM Doctor's Prescriptions on Treatment of Rheumatoid Arthritis.

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 network pharmacology: theory, methodology and application

Traditional Chinese medicine network pharmacology: theory, methodology and application

Network Pharmacology in Traditional Chinese Medicine

Network Pharmacology in Traditional Chinese Medicine

Traditional Chinese medicine network pharmacology: theory, methodology and application

Traditional Chinese medicine (TCM) has a long history of viewing an individual or patient as a system with different statuses, and has accumulated numerous herbal formulae. The holistic philosophy of TCM shares much with the key ideas of emerging network pharmacology and network biology, and meets the requirements of overcoming complex diseases, such as cancer, in a systematic manner. To discover TCM from a systems perspective and at the molecular level, a novel TCM network pharmacology approach was established by updating the research paradigm from the current “one target, one drug” mode to a new “network target, multi-components” mode. Subsequently, a set of TCM network pharmacology methods were created to prioritize disease-associated genes, to predict the target profiles and pharmacological actions of herbal compounds, to reveal drug-gene-disease co-module associations, to screen synergistic multi-compounds from herbal formulae in a high-throughput manner, and to interpret the combinatorial rules and network regulation effects of herbal formulae. The effectiveness of the network-based methods was demonstrated for the discovery of bioactive compounds and for the elucidation of the mechanisms of action of herbal formulae, such as Qing-Luo-Yin and the Liu-Wei-Di-Huang pill. The studies suggest that the TCM network pharmacology approach provides a new research paradigm for translating TCM from an experience-based medicine to an evidence-based medicine system, which will accelerate TCM drug discovery, and also improve current drug discovery strategies.