Network Pharmacology-Based Prediction of Active Ingredients and Mechanisms of Lamiophlomis rotata (Benth.) Kudo Against Rheumatoid Arthritis

Jiang, Yunbin; Zhong, Ming; Long, Fei; Yang, Ran; Zhang, Yanfei; Liu, Tonghua

doi:10.3389/fphar.2019.01435

Cited by 44 publications

(37 citation statements)

References 31 publications

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“…Genes. RA target genes were identified by retrieving 4 public databases [16], including TCMSP, erapeutic Target Database (TTD, http://bidd. nus.edu.sg/group/cjttd/) [17], Online Mendelian Inheritance in Man (OMIM, https://omim.org/), and Dis-GeNET (http://www.disgenet.org/).…”

Section: Identification Of Ra Targetmentioning

confidence: 99%

Deciphering the Active Ingredients and Molecular Mechanisms of Tripterygium hypoglaucum (Levl.) Hutch against Rheumatoid Arthritis Based on Network Pharmacology

Jiang

Zhong

Long

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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Tripterygium hypoglaucum (Levl.) Hutch (THH) shows well clinical effect on rheumatoid arthritis (RA), but the active ingredients and molecular mechanisms remain unclear. This work was designed to explore these issues by network pharmacology. Compounds from THH were gathered by retrieving literatures. Compound-related and RA-related genes were identified using databases, and the overlapping genes were identified by Venn diagram. The active ingredients and genes of THH against RA were confirmed by dissecting interactions between overlapping genes and compounds using Cytoscape. SystemsDock website was used to further verify the combining degree of key genes with active ingredients. Pathway enrichment analysis was performed to decipher the mechanisms of THH against RA by Database for Annotation, Visualization and Integrated Discovery. A total of 123 compounds were collected, and 110 compounds-related and 1871 RA-related genes were identified, including 64 overlapping genes. The target genes and active ingredients of THH against RA comprised 64 genes and 17 compounds, the focus of which was PTGS2, triptolide, and celastrol. SystemsDock website indicated that the combing degree of PTGS2 with triptolide or celastrol was very good. The mechanisms of THH against RA were linked to 31 signaling pathways, and the key mechanism was related to inhibition of inflammation response through inactivating TNF and NF-kappa B signaling pathways. This work firstly explored the active ingredients and mechanisms of THH against RA by network pharmacology and provided evidence to support clinical effects of THH on RA.

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Section: Identification Of Ra Targetmentioning

confidence: 99%

Deciphering the Active Ingredients and Molecular Mechanisms of Tripterygium hypoglaucum (Levl.) Hutch against Rheumatoid Arthritis Based on Network Pharmacology

Jiang

Zhong

Long

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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“…In addition, psoralen chemical composition was obtained from Anticancer Herbs database of Systems Pharmacology (Cancer HSP) [13]. In this study, 714 unique compounds were obtained in Shenlian Capsule, which BBD, BZL, BGZ, DS, EZ, FJ, KS, KXR, SL, SDG and WM were collected 14,94,4,202,81,50,113,113,30, 79 and 40 compounds, respectively (Fig. 1B).…”

Section: Establishment Of Chemical Composition Database Of Shenlian Cmentioning

confidence: 99%

“…The OB index is mainly used to predict the relative amount of the compound into the systemic blood after oral absorption. Many active compounds cannot be active because they are insu ciently absorbed into the body after oral absorption [14]. This indicator is a critical indicator of whether a compound is intrinsically active.…”

Section: Oral Bioavailabilitymentioning

confidence: 99%

“…In this study, the potential active compound InChKey number screened above was placed in the Traditional Chinese Medicine Systems Pharmacology Database to obtain a compound to predict the relationship between the compound and the targets. The database uses a large-scale molecular network target prediction technology, mainly using the WES method and SysDT model to obtain the relationship between compounds and targets [14]. Since the targets obtained by this database include multiple species targets and non-standard gene names for gene names, this is not conducive to clearly showing the relationship between compounds and targets.…”

Section: Prediction Of Targets Of Shenlian Capsulementioning

confidence: 99%

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Uncovering the Mechanism of Shenlian Capsules in the Treatment of Non-small Cell Lung Cancer by System Pharmacology

Gong

Wei

et al. 2020

Preprint

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Background: Shenlian Capsule is a Chinese medicine compound approved by the China Food and Drug Administration (CFDA) for the treatment of advanced non-small cell lung cancer(NSCLC). However, due to its complex constituents, cause its effective active compounds and main action mechanisms for treating diseases are still not fully clear. The purpose of this work is to explore the active ingredients and mechanisms of Shenlian Capsule treatment NSCLC through a system pharmacological approach. Methods: First, a database of Shenlian Capsule chemical composition was constructed by retrieving Chinese herbal medicine data. Absorption, distribution, metabolism and excretion (ADME) methods were used to screen potential active compounds. Predict active compound targets with a large-scale molecular network target prediction technology. Clustering of active compounds obtained through cluster analysis by MECODE plug-in, each cluster obtains the main pathways through enrichment analysis method. To get all targets related to survival in NSCLC, the survival related targets were intersected with the compounds target (C-T) network to get the survival related targets for Shenlian Capsule. Finally, a batch molecular docking technique was used to verify the effect of active compounds of Shenlian Capsule on survival-related targets.Results: The Shenlian Capsule C-T network was constructed with 117 potential compounds and 47 targets. Treatment of NSCLC with Shenlian Capsule through enrichment analysis may involve multiple pathways such as anti-inflammatory, immune regulation, regulation of cell cycle, apoptosis, antiviral, cell hypoxia, angiogenesis and so on. Shenlian Capsule has eight survival-related genes in non-small cell lung adenocarcinoma (LUAD) and two survival-related genes in non-small cell squamous cell lung carcinoma (LUSC). It is known through molecular docking that the active compound has lower energy after conformation with survival-related genes, and has lower binding energy and stable binding.Conclusion: In this study, the potential compounds of active compounds in Shenlian Capsule were first predicted using network pharmacology technology. Through the enrichment analysis, the main pathways of the role of Shenlian Capsule were outcropped. Secondly, by combining bioinformatics and network pharmacology, Shenlian Capsule can be regulated to target survival-related targets. Finally, the molecular docking technique shows the relationship between active compounds and survival-related targets after docking. This work provided a scientific basis for the clinical role of Shenlian Capsule in the treatment of NSCLC, provided a research basis for further clarifying the active ingredients and mechanism of Shenlian Capsule in the treatment of NSCLC.

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“…The ingredients of PM were gathered together from TCMID (http://www.megabionet.org/tcmid/), BATMAN-TCM platform and literature analysis related to PM [21]. Then all the chemical molecular structures and the Smiles numbers were veri ed and obtained by the platforms such as Pubchem (https://pubchem.ncbi.nlm.nih.gov/), Chembank (http://chembank.broadinstitute.org/), or Sci nder (https://sci nder.cas.org/) [22,23].…”

Section: Construction Of Pm Compounds Databasementioning

confidence: 99%

Systemic elucidation on the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum based on network pharmacology

Song

Yang

Jing

et al. 2020

Preprint

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Background: Diabetes is a complex metabolic disease characterized by hyperglycemia, plaguing the whole world. However, the action mode of multi-component and multi-target for traditional Chinese medicine (TCM) could be a promising treatment of diabetes mellitus. According to the previous research, the TCM of polygonum multiflorum (PM) showed noteworthy hypoglycemic effect. Up to now, its hypoglycemic active ingredients and mechanism of action are not yet clear. In this study, network pharmacology was employed to elucidate the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum (PM).Methods: First, the compounds with good pharmacokinetic properties were screened from the self-established library of PM, and the targets of these compounds were predicted and collected through database. Relevant targets of diabetes were summarized by searching database. The intersection targets of compound-targets and disease-targets were obtained soon. Secondly, the interaction net between the compounds and the filtered targets was established. These key targets were enriched and analyzed by PPI analysis, molecular docking verification. Thirdly, the key genes were used to find the biologic pathway and explain the therapeutic mechanism by GO and KEGG analysis. Lastly, the part of potential bioactive compounds were under enzyme activity inhibition tests.Results: In this study, 29 hypoglycemic components and 63 hypoglycemic targets of PM were filtrated based on online network database. Then the component-target interaction network was constructed and five key components resveratrol, apigenin, kaempferol, quercetin and luteolin were further obtained. Sequential studies turned out, AKT1, EGFR, ESR1, PTGS2, MMP9, MAPK14, and KDR were the common key targets. Docking studies indicated that the bioactive compounds could stably bind the pockets of target proteins. There were 38 metabolic pathways, including regulation of lipolysis in adipocytes, prolactin signaling pathway, TNF signaling pathway, VEGF signaling pathway, FoxO signaling pathway, estrogen signaling pathway, linoleic acid metabolism, Rap1 signaling pathway, arachidonic acid metabolism, and osteoclast differentiation closely connected with the hypoglycemic mechanism of PM.Conclusion: In summary, the study used systems pharmacology to elucidate the main hypoglycemic components and mechanism of PM. The work provided a scientific basis for the further hypoglycemic effect research of PM and its monomer components, but also provided a reference for the secondary development of PM.

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Network Pharmacology-Based Prediction of Active Ingredients and Mechanisms of Lamiophlomis rotata (Benth.) Kudo Against Rheumatoid Arthritis

Cited by 44 publications

References 31 publications

Deciphering the Active Ingredients and Molecular Mechanisms of Tripterygium hypoglaucum (Levl.) Hutch against Rheumatoid Arthritis Based on Network Pharmacology

Deciphering the Active Ingredients and Molecular Mechanisms of Tripterygium hypoglaucum (Levl.) Hutch against Rheumatoid Arthritis Based on Network Pharmacology

Uncovering the Mechanism of Shenlian Capsules in the Treatment of Non-small Cell Lung Cancer by System Pharmacology

Systemic elucidation on the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum based on network pharmacology

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Network Pharmacology-Based Prediction of Active Ingredients and Mechanisms of Lamiophlomis rotata (Benth.) Kudo Against Rheumatoid Arthritis

Cited by 44 publications

References 31 publications

Deciphering the Active Ingredients and Molecular Mechanisms of Tripterygium hypoglaucum (Levl.) Hutch against Rheumatoid Arthritis Based on Network Pharmacology

Deciphering the Active Ingredients and Molecular Mechanisms of Tripterygium hypoglaucum (Levl.) Hutch against Rheumatoid Arthritis Based on Network Pharmacology

Uncovering the Mechanism of Shenlian Capsules in the Treatment of Non-small Cell Lung Cancer by System Pharmacology

Systemic elucidation on&nbsp;the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum based on network pharmacology

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Systemic elucidation on the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum based on network pharmacology