Integration of meta-analysis and network pharmacology analysis to investigate the pharmacological mechanisms of traditional Chinese medicine in the treatment of hepatocellular carcinoma
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ABSTRACT:
SUBMITTER: Lin J
PROVIDER: S-EPMC10978761 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:We present a paradigm, combining chemical profiling, absorbed components detection in plasma and network analysis, for investigating the pharmacology of combination drugs and complex formulae. On the one hand, the composition of the formula is investigated comprehensively via mass spectrometry analysis, followed by pharmacological studies of the fractions as well as the plasma concentration testing for the ingredients. On the other hand, both the candidate target proteins and the effective ingredients of the formula are predicted via analyzing the corresponding networks. The most probable active compounds can then be identified by combining the experimental results with the network analysis. In order to illustrate the performance of the paradigm, we apply it to the Danggui-Jianzhong formula (DJF) from traditional Chinese medicine (TCM) and predict 4 probably active ingredients, 3 of which are verified experimentally to display anti-platelet activity, i.e., (Z)-Ligustilide, Licochalcone A and Pentagalloylglucose. Moreover, the 3-compound formulae composed of these 3 chemicals show better anti-platelet activity than DJF. In addition, the paradigm predicts the association between these 3 compounds and COX-1, and our experimental validation further shows that such association comes from the inhibitory effects of the compounds on the activity of COX-1.
| S-EPMC5233960 | biostudies-literature
Project description:Aim We evaluated the developmental process, research status, and existing challenges of network pharmacology. Moreover, we elucidated the corresponding solutions to improve and develop network pharmacology. Methods Research data for the current study were retrieved from the Web of Science. The developmental process of network pharmacology was analyzed using HisCite, whereas cooccurrence analysis of countries, institutions, keywords, and references in literature was conducted using CiteSpace. Results In literature, there was a trend of annual increase of studies on network pharmacology and China was found to be the country with the most published literature on network pharmacology. The main publishing research institutions were universities of traditional Chinese medicine (TCM). The keywords with more research frequency were TCM, mechanisms, molecular docking, and quercetin, among others. Conclusion Currently, studies on network pharmacology are mainly associated with the exploration of action mechanisms of TCM. The main active ingredient in many Chinese medicines is quercetin. This ingredient may lead to deviation of research results, inability to truly analyze active ingredients, and even mislead the research direction of TCM. Such deviation may be because the database fails to reflect the content and composition changes of Chinese medicinal components. The database does not account for interactions among components, targets, and diseases, and it ignores the different pathological states of the disease. Therefore, network pharmacology should be improved from the databases and research methods.
| S-EPMC9217600 | biostudies-literature
Project description:traditional chinese medicine Targeted loci
| PRJNA796441 | ENA
Project description:Because of the complex etiology, the treatment of gastric cancer is a formidable challenge for contemporary medical. The current treatment method focuses on traditional surgical procedures, supplemented by other treatments. Among these other treatments, Traditional Chinese Medicine (TCM) plays an important role. Here, we used the systems pharmacology approach to reveal the potential molecular mechanism of PRGRC on gastric cancer which composes of Pinellia ternata (Thunb.) Breit., Rheum palmatum L., Gentiana scabra Bunge, Radix Aucklandiae and Citrus aurantium L. This approach combines pharmacokinetics analysis with pharmacodynamics evaluation for the active compounds screening, targets prediction and pathways assessing. Firstly, through pharmacokinetic evaluation and target prediction models, 83 potential compounds and 184 gastric cancer-related targets were screened out. Then, the results of network analysis suggested that the targets of PRGRC were mainly involved two aspects: apoptosis and inflammation. Finally, we verified the reliability of the above analysis at the cellular level by using naringenin and luteolin with good pharmacokinetic activity as representative compounds. Overall, we found that PRGRC could influence the development of gastric cancer from a multi-scale perspective. This study provided a new direction for analyzing the mechanism of TCM.
| S-EPMC8589993 | biostudies-literature
Project description:PurposeThis study investigates the clinical benefits of integrating traditional Chinese medicine (TCM) with Transarterial Chemoembolization (TACE) in hepatocellular carcinoma (HCC) treatment via meta-analysis and an exploration of network pharmacology analysis (NPA).MethodsA comprehensive search across different databases retrieved all randomized controlled trials (RCTs) evaluating TCM combined with TACE for HCC. Meta-analysis included 39 RCTs to assess the intervention effects. The bayesian network meta-analysis observed the relative efficacy and potential ranking of various interventions. Active compounds and target genes from frequently used TCM were sourced from the TCMSP database, while HCC disease targets were collected from five public disease databases. Regulatory networks connecting target genes with active components of key herbs were constructed. Following the identification of key genes, we conducted analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to enrich our understanding of their functions. NPA and molecular docking methods were refined to reveal potential interactions between TCM components and their specific targets.ResultsThe combination of TCM with TACE significantly enhances the efficacy and safety of HCC treatment, improving the overall response rate, disease control rate, and overall survival rate, while also reducing the incidence of adverse events. Among the TCM evaluated, Ganfu Formula proved to be the most effective in enhancing patient response rates. Analysis of all included medicinal herbs identified 10 pivotal TCMs and 17 core genes. GO analysis revealed their significance in protein interactions, whereas KEGG analysis highlighted their role in crucial oncological pathways. NPA and molecular docking techniques elucidate the underlying mechanisms of action of TCM components.ConclusionAdding TCM to TACE protocols significantly enhances treatment outcomes and safety in HCC patients by modulating tumor biology and systemic immune responses, highlighting its potential as an effective adjunct therapy. These findings support the inclusion of TCM in standard care regimens, offering potential for improved management of HCC.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024571280.
| S-EPMC11662279 | biostudies-literature
Project description:BackgroundChinese traditional herbal medicine Fuzhengkangai (FZKA) formulation combination with gefitinib can overcome drug resistance and improve the prognosis of lung adenocarcinoma patients. However, the pharmacological and molecular mechanisms underlying the active ingredients, potential targets, and overcome drug resistance of the drug are still unclear. Therefore, it is necessary to explore the molecular mechanism of FZKA.MethodsA systems pharmacology and bioinformatics-based approach was employed to investigate the molecular pathogenesis of EGFR-TKI resistance with clinically effective herb formula. The differential gene expressions between EGFR-TKI sensitive and resistance cell lines were calculated and used to find overlap from targets as core targets. The prognosis of core targets was validated from the cancer genome atlas (TCGA) database by Cox regression. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment is applied to analysis core targets for revealing mechanism in biology.ResultsThe results showed that 35 active compounds of FZKA can interact with eight core targets proteins (ADRB2, BCL2, CDKN1A, HTR2C, KCNMA1, PLA2G4A, PRKCA and LYZ). The risk score of them were associated with overall survival and relapse free time (HR?=?6.604, 95% CI: 2.314-18.850; HR?=?5.132, 95% CI: 1.531-17.220). The pathway enrichment suggested that they involved in EGFR-TKI resistance and non-small cell lung cancer pathways, which directly affect EGFR-TKI resistance. The molecular docking showed that licochalcone a and beta-sitosterol can closely bind two targets (BCL2 and PRKCA) that involved in EGFR-TKI resistance pathway.ConclusionsThis study provided a workflow for understanding mechanism of CHM for against drug resistance.
| S-EPMC6218988 | biostudies-literature
Project description:The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput compound-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between herb/disease inducible gene expression profiles and specific gene signatures for 8233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal component-direct target-indirect target by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.
| S-EPMC11879102 | biostudies-literature
Project description: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.
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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.
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