scholarly journals A Systems Biology-Based Approach to Uncovering Molecular Mechanisms Underlying Effects of Traditional Chinese Medicine Qingdai in Chronic Myelogenous Leukemia, Involving Integration of Network Pharmacology and Molecular Docking Technology

2018 ◽  
Vol 24 ◽  
pp. 4305-4316 ◽  
Author(s):  
Chao Zhou ◽  
LiJuan Liu ◽  
Jing Zhuang ◽  
JunYu Wei ◽  
TingTing Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Systems Biology ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Chronic Myelogenous Leukemia ◽  
Molecular Mechanisms ◽  
Myelogenous Leukemia ◽  
Network Pharmacology

scholarly journals Advancing Drug Discovery and Development from Active Constituents of Yinchenhao Tang, a Famous Traditional Chinese Medicine Formula

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Aihua Zhang ◽  
Hui Sun ◽  
Shi Qiu ◽  
Xijun Wang
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Synergistic Effects ◽  
Molecular Networks ◽  
Agile Development ◽  
Therapeutic Effects ◽  
Systematic Analysis

Traditional Chinese medicine (TCM) formula has been playing a very important role in health protection and disease control for thousands of years. Guided by TCM syndrome theories, formula are designed to contain a combination of various kinds of crude drugs that, when combined, will achieve synergistic efficacy. However, the precise mechanism of synergistic action remains poorly understood. One example is a famous TCM formula Yinchenhao Tang (YCHT), whose efficacy in treating hepatic injury (HI) and Jaundice syndrome, has recently been well established as a case study. We also conducted a systematic analysis of synergistic effects of the principal compound using biochemistry, pharmacokinetics and systems biology, to explore the key molecular mechanisms. We had found that the three component (6,7-dimethylesculetin (D), geniposide (G), and rhein (R)) combination exerts a more robust synergistic effect than any one or two of the three individual compounds by hitting multiple targets. They can regulate molecular networks through activating both intrinsic and extrinsic pathways to synergistically cause intensified therapeutic effects. This paper provides an overview of the recent and potential developments of chemical fingerprinting coupled with systems biology advancing drug discovery towards more agile development of targeted combination therapies for the YCHT.

scholarly journals A Network Pharmacology-Based Study on the Anti-Lung Cancer Effect of Dipsaci Radix

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jiayan Wu ◽  
Shengkun Hong ◽  
Xiankuan Xie ◽  
Wangmi Liu
Keyword(s):  
Lung Cancer ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Active Compounds

Objective. Dipsaci Radix (DR) has been used to treat fracture and osteoporosis. Recent reports have shown that myeloid cells from bone marrow can promote the proliferation of lung cancer. However, the action and mechanism of DR has not been well defined in lung cancer. The aim of the present study was to define molecular mechanisms of DR as a potential therapeutic approach to treat lung cancer. Methods. Active compounds of DR with oral bioavailability ≥30% and drug-likeness index ≥0.18 were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform. The potential target genes of the active compounds and bone were identified by PharmMapper and GeneCards, respectively. The compound-target network and protein-protein interaction network were built by Cytoscape software and Search Tool for the Retrieval of Interacting Genes webserver, respectively. GO analysis and pathway enrichment analysis were performed using R software. Results. Our study demonstrated that DR had 6 active compounds, including gentisin, sitosterol, Sylvestroside III, 3,5-Di-O-caffeoylquinic acid, cauloside A, and japonine. There were 254 target genes related to these active compounds as well as to bone. SRC, AKT1, and GRB2 were the top 3 hub genes. Metabolisms and signaling pathways associated with these hub genes were significantly enriched. Conclusions. This study indicated that DR could exhibit the anti-lung cancer effect by affecting multiple targets and multiple pathways. It reflects the traditional Chinese medicine characterized by multicomponents and multitargets. DR could be considered as a candidate for clinical anticancer therapy by regulating bone physiological functions.

scholarly journals Potential Molecular Mechanisms of Chaihu-Shugan-San in Treatment of Breast Cancer Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Kunmin Xiao ◽  
Kexin Li ◽  
Sidan Long ◽  
Chenfan Kong ◽  
Shijie Zhu
Keyword(s):  
Breast Cancer ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Endocrine Resistance ◽  
Protein Domain ◽  
Network Pharmacology ◽  
Complementary And Alternative Therapy ◽  
Cell Substrate ◽  
Catabolic Process

Breast cancer is one of the most common cancers endangering women’s health all over the world. Traditional Chinese medicine is increasingly recognized as a possible complementary and alternative therapy for breast cancer. Chaihu-Shugan-San is a traditional Chinese medicine prescription, which is extensively used in clinical practice. Its therapeutic effect on breast cancer has attracted extensive attention, but its mechanism of action is still unclear. In this study, we explored the molecular mechanism of Chaihu-Shugan-San in the treatment of breast cancer by network pharmacology. The results showed that 157 active ingredients and 8074 potential drug targets were obtained in the TCMSP database according to the screening conditions. 2384 disease targets were collected in the TTD, OMIM, DrugBank, GeneCards disease database. We applied the Bisogenet plug-in in Cytoscape 3.7.1 to obtain 451 core targets. The biological process of gene ontology (GO) involves the mRNA catabolic process, RNA catabolic process, telomere organization, nucleobase-containing compound catabolic process, heterocycle catabolic process, and so on. In cellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction are highly correlated with breast cancer. In the molecular function category, most proteins were addressed to ubiquitin-like protein ligase binding, protein domain specific binding, and Nop56p-associated pre-rRNA complex. Besides, the results of the KEGG pathway analysis showed that the pathways mainly involved in apoptosis, cell cycle, transcriptional dysregulation, endocrine resistance, and viral infection. In conclusion, the treatment of breast cancer by Chaihu-Shugan-San is the result of multicomponent, multitarget, and multipathway interaction. This study provides a certain theoretical basis for the treatment of breast cancer by Chaihu-Shugan-San and has certain reference value for the development and application of new drugs.

scholarly journals Navigating Traditional Chinese Medicine Network Pharmacology and Computational Tools

2013 ◽  
Vol 2013 ◽  
pp. 1-23 ◽  
Author(s):  
Ming Yang ◽  
Jia-Lei Chen ◽  
Li-Wen Xu ◽  
Guang Ji
Keyword(s):  
Systems Biology ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Models ◽  
Research Process ◽  
Modern Medicine ◽  
Machine Learning Algorithms ◽  
Network Pharmacology ◽  
Research Approach ◽  
Computational Tools

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.

scholarly journals Integrating Data Mining, Network Pharmacology, and Molecular Docking Verification to Investigate the Molecular Mechanism of Traditional Chinese Medicine Prescriptions for Treating Male Infertility

2021 ◽  
Author(s):  
Xue Bai ◽  
Yibo Tang ◽  
Qiang Li ◽  
Guimin Liu ◽  
Dan Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Male Infertility ◽  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Oxidant Stress ◽  
Estrogen Signaling ◽  
Network Pharmacology

Abstract Background: Male infertility (MI) affects almost 5% adult men worldwide, and 75% of these cases are unexplained idiopathic. There are limitations in the current treatment due to the unclear mechanism of MI, which highlight the urgent need for a more effective strategy or drug. Traditional Chinese Medicine (TCM) prescriptions have been used to treat MI for thousands of years, but their molecular mechanism is not well defined. Methods: Aiming at revealing the molecular mechanism of TCM prescriptions on MI, a comprehensive strategy integrating data mining, network pharmacology, and molecular docking verification was performed. Firstly, we collected 289 TCM prescriptions for treating MI from National Institute of TCM Constitution and Preventive Medicine for 6 years. Then, Core Chinese Materia Medica (CCMM), the crucial combination of TCM prescriptions, was obtained by the TCM Inheritance Support System from China Academy of Chinese Medical Sciences. Next, the components and targets of CCMM in TCM prescriptions and MI-related targets were collected and analyzed through network pharmacology approach.Results: The results showed that the molecular mechanism of TCM prescriptions for treating MI are regulating hormone, inhibiting apoptosis, oxidant stress and inflammatory. Estrogen signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway are the most important signaling pathways. Molecular docking experiments were used to further validate network pharmacology results. Conclusions: This study not only discovers CCMM and the molecular mechanism of TCM prescriptions for treating MI, but may be helpful for the popularization and application of TCM treatment.

scholarly journals Study on the Mechanism of Baimai Ointment in the Treatment of Osteoarthritis Based on Network Pharmacology and Molecular Docking with Experimental Verification

2021 ◽  
Vol 12 ◽  
Author(s):  
Yingyin Zhu ◽  
Wanling Zhong ◽  
Jing Peng ◽  
Huichao Wu ◽  
Shouying Du
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Animal Experiments ◽  
Tibetan Medicine ◽  
Blue Staining ◽  
Network Pharmacology ◽  
Enzyme Linked Immunosorbent Assay

Purpose: The external preparation of the Tibetan medicine formula, Baimai ointment (BMO), has great therapeutic effects on osteoarthritis (OA). However, its molecular mechanism remains almost elusive. Here, a comprehensive strategy combining network pharmacology and molecular docking with pharmacological experiments was adopted to reveal the molecular mechanism of BMO against OA.Methods: The traditional Chinese medicine for systems pharmacology (TCMSP) database and analysis platform, traditional Chinese medicine integrated database (TCMID), GeneCards database, and DisGeNET database were used to screen the active components and targets of BMO in treating OA. A component–target (C-T) network was built with the help of Cytoscape, and the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment through STRING. Autodock Tools which was used to dock the key components and key target proteins was analyzed. Animal experiments were performed to verify the key targets of BMO. Hematoxylin–eosin and toluidine blue staining were used to observe the pathology of joints. Protein expression was determined using enzyme-linked immunosorbent assay.Results: Bioactive compounds and targets of BMO and OA were screened. The network analysis revealed that 17-β-estradiol, curcumin, licochalone A, quercetin, and glycyrrhizic acid were the candidate key components, and IL6, tumor necrosis factor (TNF), MAPK1, VEGFA, CXCL8, and IL1B were the candidate key targets in treating OA. The KEGG indicated that the TNF signaling pathway, NF-κB signaling pathway, and HIF-1 signaling pathway were the potential pathways. Molecular docking implied a strong combination between key components and key targets. The pathology and animal experiments showed BMO had great effects on OA via regulating IL6, TNF, MAPK1, VEGFA, CXCL8, and IL1B targets. These findings were consistent with the results obtained from the network pharmacology approach.Conclusion: This study preliminarily illustrated the candidate key components, key targets, and potential pathways of BMO against OA. It also provided a promising method to study the Tibetan medicine formula or external preparations.

The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine

2019 ◽  
Vol 14 (3) ◽  
pp. 200-210 ◽  
Author(s):  
Lin Liu ◽  
Hao Wang
Keyword(s):  
Systems Biology ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Interdisciplinary Approach ◽  
The Body ◽  
Biomedical Data ◽  
Modulation Effect ◽  
Bioactive Components ◽  
New Perspective

Background:Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.Objective:This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.Conclusion:Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

scholarly journals Network pharmacology for the identification of phytochemicals in traditional Chinese medicine for COVID-19 that may regulate interleukin-6

2020 ◽  
Author(s):  
Chun Liang ◽  
Wenhao Niu ◽  
Feng Wu ◽  
Wenyue Cao ◽  
Zonggui Wu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Binding Affinity ◽  
Ursolic Acid ◽  
Interleukin 6 ◽  
Chemical Constituents ◽  
Gene Clusters ◽  
Network Pharmacology ◽  
Biological Processes

Objective: “Three formulas and three medicines,” namely, Jinhua Qinggan Granule, Lianhua Qingwen Capsule, Xuebijing Injection, Qingfei Paidu Decoction, HuaShi BaiDu Formula, and XuanFei BaiDu Granule, were proven to be effective for coronavirus disease 2019 (COVID-19) treatment. This study aimed to identify the active chemical constituents of this traditional Chinese medicine (TCM) and investigate their mechanisms through interleukin-6 (IL-6) integrating network pharmacological approaches.Methods: We collected the compounds from all herbal ingredients of the previously mentioned TCM, but those that could downregulate IL-6 were screened through the network pharmacology approach. Then, we modeled molecular docking to evaluate the binding affinity between compounds and IL-6. Furthermore, we analyzed the biological processes and pathways of compounds. Lastly, we screened out the core genes of compounds through the construction of the protein-protein interaction network and the excavation of gene clusters of compounds.Results: The network pharmacology research showed that TCM could decrease IL-6 using several compounds, such as quercetin, ursolic acid, luteolin, and rutin. Molecular docking results showed that the molecular binding affinity with IL-6 of all compounds except γ-aminobutyric acid was < −5.0 kJ/mol, indicating the potential of numerous active compounds in TCM to directly interact with IL-6, leading to an anti-inflammation effect. Finally, Cytoscape 3.7.2 was used to topologize the biological processes and pathways of compounds, revealing potential mechanisms for COVID-19 treatment.Conclusion: These results indicated the positive effect of TCM on the prevention and rehabilitation of COVID-19 in at-risk people. Quercetin, ursolic acid, luteolin, and rutin could inhibit COVID-19 by downregulating IL-6.

Exploring the mechanisms of Drynariae Rhizoma on treating memory impairment through network pharmacology

2021 ◽  
Author(s):  
Wangmi Liu ◽  
Jiayan Wu
Keyword(s):  
Molecular Docking ◽  
Nerve Growth Factor ◽  
Chinese Medicine ◽  
Growth Factor ◽  
Traditional Chinese Medicine ◽  
Memory Impairment ◽  
Network Pharmacology ◽  
Active Compounds ◽  
Major Health Problem ◽  
Nerve Growth

Abstract Background Memory impairment continues to be a major health problem and increases with age, especially in the elderly population worldwide. However, a causal mechanism has not been clearly identified. Currently, an interaction between bone and brain, the so-called “bone-brain crosstalk,” has emerged. We used a network pharmacology approach to explore the potential mechanisms of Drynariae Rhizoma (DR), a traditional Chinese medicine for fracture treatment, for therapeutic intervention of human conditions associated with memory impairment. Methods The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to screen out the active compounds of DR, and the targets of the active compounds were predicted using PharmMapper. Targets related to memory impairment were downloaded from the DisGeNET database. The compound-target network and protein-protein interaction network were built by NetworkAnalyst and Cytoscape software. Gene ontology analysis and Reactome pathway enrichment analysis were performed using NetworkAnalyst. SYBYL-X software was used to perform molecular docking simulation. Results Our study demonstrated that DR had 7 active compounds. There were 60 target genes related to these active compounds as well as to memory impairment. Signalling by nerve growth factor was among the top 3 enriched Reactome terms. Akt1 was an important signalling hub gene belonging to signalling by nerve growth factor pathway. Molecular docking results showed that the one of the active compounds, xanthogalenol, exhibited acceptable affinities to Akt1. Conclusion This study demonstrated the molecular mechanism that DR may alleviate memory impairment via regulation of Akt1 and signalling by nerve growth factor pathway. These results offer new ideas in searching for novel strategies for the treatment of memory impairment.

scholarly journals Network Pharmacology Analysis to Identify Phytochemicals in Traditional Chinese Medicines That May Regulate ACE2 for the Treatment of COVID-19

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wenhao Niu ◽  
Feng Wu ◽  
Haiming Cui ◽  
Wenyue Cao ◽  
YuChieh Chao ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Gallic Acid ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Peroxisome Proliferator ◽  
Active Components ◽  
Peroxisome Proliferator Activated Receptor ◽  
Effective Components

“Three formulas and three medicines,” which include Jinhua Qinggan granule, Lianhua Qingwen capsule/granule, Xuebijing injection, Qingfei Paidu decoction, HuaShiBaiDu formula, and XuanFeiBaiDu granule, have been proven to be effective in curbing coronavirus disease 2019 (COVID-19), according to the State Administration of Traditional Chinese Medicine. The aims of this study were to identify the active components of “Three formulas and three medicines” that can be used to treat COVID-19, determine their mechanism of action via angiotensin-converting enzyme 2 (ACE2) by integrating network pharmacological approaches, and confirm the most effective components for COVID-19 treatment or prevention. We investigated all the compounds present in the aforementioned herbal ingredients. Compounds that could downregulate the transcription factors (TFs) of ACE2 and upregulate miRNAs of ACE2 were screened via a network pharmacology approach. Hepatocyte nuclear factor 4 alpha (HNF4A), peroxisome proliferator-activated receptor gamma (PPARG), hsa-miR-2113, and hsa-miR-421 were found to regulate ACE2. Several compounds, such as quercetin, decreased ACE2 expression by regulating the aforementioned TFs or miRNAs. After comparison with the compounds present in Glycyrrhiza Radix et Rhizoma, quercetin, glabridin, and gallic acid present in the herbal formulas and medicines were found to alter ACE2 expression. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to search for possible molecular mechanisms of these compounds. In conclusion, traditional Chinese medicine (TCM) plays a pivotal role in the prevention and treatment of COVID-19. Quercetin, glabridin, and gallic acid, the active components of recommended TCM formulas and medicines, can inhibit COVID-19 by downregulating ACE2.

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