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      A bioinformatics investigation into the pharmacological mechanisms of the effect of the Yinchenhao decoction on hepatitis C based on network pharmacology

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          Abstract

          Background

          Globally, more than 170 million people are infected with hepatitis C virus, a major cause of cirrhosis and hepatocellular carcinoma. The Yinchenhao Decoction (YCHD) is a classic formula comprising three herbal medicines. This decoction have long been used in China for clinically treating acute and chronic infectious hepatitis and other liver and gallbladder damp heat-accumulation disorders.

          Methods

          In this study, we identified 32 active ingredients and 200 hepatitis C proteins and established a compound-predicted target network and a hepatitis C protein–protein interaction network by using Cytoscape 3.6.1. Then, we systematically analyzed the potential targets of the YCHD for the treatment of hepatitis C. Finally, molecular docking was applied to verify the key targets. In addition, we analyzed the mechanism of action of the predicted targets by the Kyoto Encyclopedia of Genes and Genomes and gene ontology analyses.

          Results

          This study adopted a network pharmacology approach, mainly comprising target prediction, network construction, module detection, functional enrichment analysis, and molecular docking to systematically investigate the mechanisms of action of the YCHD in hepatitis C. The targets of the YCHD in the treatment of hepatitis C mainly involved PIK3CG, CASP3, BCL2, CASP8, and MMP1. The module and pathway enrichment analyses showed that the YCHD had the potential to influence varieties of biological pathways, including the TNF signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, and pathways in cancer, that play an important role in the pathogenesis of hepatitis C.

          Conclusion

          The results of this study preliminarily verified the basic pharmacological effects and related mechanisms of the YCHD in the treatment of hepatitis C.

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          Most cited references48

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          Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

          Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.
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            Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information

            Extensive drug discovery efforts have yielded many approved and candidate drugs targeting various targets in different biological pathways. Several freely accessible databases provide the drug, target and drug-targeted pathway information for facilitating drug discovery efforts, but there is an insufficient coverage of the clinical trial drugs and the drug-targeted pathways. Here, we describe an update of the Therapeutic Target Database (TTD) previously featured in NAR. The updated contents include: (i) significantly increased coverage of the clinical trial targets and drugs (1.6 and 2.3 times of the previous release, respectively), (ii) cross-links of most TTD target and drug entries to the corresponding pathway entries of KEGG, MetaCyc/BioCyc, NetPath, PANTHER pathway, Pathway Interaction Database (PID), PathWhiz, Reactome and WikiPathways, (iii) the convenient access of the multiple targets and drugs cross-linked to each of these pathway entries and (iv) the recently emerged approved and investigative drugs. This update makes TTD a more useful resource to complement other databases for facilitating the drug discovery efforts. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.
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              Involvement of the bcl-2 gene in human follicular lymphoma.

              Recombinant DNA probes were cloned for the areas flanking the breakpoint on chromosome 18 in cells from a patient with acute lymphocytic leukemia of the B-cell type; cells of this line carry the t(14;18) chromosomal translocation. Two of the probes detected DNA rearrangements in approximately 60 percent of the cases of follicular lymphoma screened. In follicular lymphoma, most of the breakpoints in band q21 of chromosome 18 were clustered within a short stretch of DNA, approximately 2.1 kilobases in length. Chromosome 18-specific DNA probes for the areas flanking the breakpoints also detected RNA transcripts 6 kilobases in length in various cell types. The gene coding for these transcript (the bcl-2 gene) seems to be interrupted in most cases of follicular lymphomas carrying the t(14;18) chromosomal translocation.
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                Author and article information

                Contributors
                lindajyz@163.com
                lxkchuige@163.com
                exogamy@163.com
                weizhou19940530@163.com
                tjh996@163.com
                gsiyu1995@163.com
                cristielove@163.com
                mengziq_2525jy@126.com
                nmh15764346161@163.com
                Journal
                BMC Complement Med Ther
                BMC Complement Med Ther
                BMC Complementary Medicine and Therapies
                BioMed Central (London )
                2662-7671
                12 February 2020
                12 February 2020
                2020
                : 20
                : 50
                Affiliations
                [1 ]ISNI 0000 0001 1431 9176, GRID grid.24695.3c, Department of Clinical Chinese Pharmacy, , School of Chinese Materia Medica, Beijing University of Chinese Medicine, ; No. 11 of North Three-ring East Road, Chao Yang District, Beijing, China
                [2 ]ISNI 0000 0000 8571 0482, GRID grid.32566.34, Evidence-Based Medicine Center, , School of Basic Medical Sciences, Lanzhou University, ; 222 Tianshui South Road, Lanzhou City, China
                Author information
                http://orcid.org/0000-0002-1617-6110
                Article
                2823
                10.1186/s12906-020-2823-y
                7076901
                32050950
                bc171a2d-47b8-4128-9a5f-ebb6d376c124
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 2 October 2019
                : 20 January 2020
                Funding
                Funded by: Innovative Research Group Project of the National Natural Science Foundation of China (CN)
                Award ID: Grant nos. 81473547 and 81673829
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                yinchenhao decoction,hepatitis c,network pharmacology,bioinformatics

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