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      Network Pharmacology and Molecular Docking to Explore the Mechanism of Kangxian Decoction for Epilepsy

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          Abstract

          Purpose

          Kangxian decoction (KXD) has been used in clinical practice to treat epilepsy. The purpose of this study was to explore the active components of KXD and clarify its antiepileptic mechanism through network pharmacology and molecular docking.

          Methods

          The components of KXD were collected from the Encyclopedia of Traditional Chinese Medicine (ETCM) database and the literature was searched. Then, active ingredients were screened by SwissADME and potential targets were predicted by the SwissTargetPrediction database. Epilepsy-related differentially expressed genes were downloaded from the Gene Expression Omnibus database. A component-target-pathway network was constructed with Cytoscape. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein‒protein interaction network analysis revealed the potential mechanism and critical targets. Receiver operating characteristic (ROC) curves and box plots in microarray data validated the good diagnostic value and significant differential expression of these critical genes. Molecular docking verified the association between active ingredients and essential target proteins.

          Results

          In our study, we screened the important compounds of KXD for epilepsy, including quercetin, baicalin, kaempferol, yohimbine, geissoschizine methyl ether, baicalein, etc. KXD may exert its therapeutic effect on epilepsy through the following targets: PTGS2, MMP9, CXCL8, ERBB2, and ARG1, acting on the following pathways: neuroactive ligand-receptor interactions, arachidonic acid metabolism, IL-17, TNF, NF-kappa B, and MAPK signaling pathways. The molecular docking results showed that the active ingredients in KXD exhibited good binding ability to the key targets.

          Conclusion

          In this study, we explored the possibility that KXD for epilepsy may act on multiple targets through multiple active ingredients, involving neurotransmitters and neuroinflammatory pathways, providing a theoretical basis for subsequent clinical and experimental studies that will help develop effective new drugs to treat epilepsy.

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          Most cited references45

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

            To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.
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              SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules

              Abstract SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most probable protein targets of small molecules. Predictions are based on the similarity principle, through reverse screening. Here, we describe the 2019 version, which represents a major update in terms of underlying data, backend and web interface. The bioactivity data were updated, the model retrained and similarity thresholds redefined. In the new version, the predictions are performed by searching for similar molecules, in 2D and 3D, within a larger collection of 376 342 compounds known to be experimentally active on an extended set of 3068 macromolecular targets. An efficient backend implementation allows to speed up the process that returns results for a druglike molecule on human proteins in 15–20 s. The refreshed web interface enhances user experience with new features for easy input and improved analysis. Interoperability capacity enables straightforward submission of any input or output molecule to other on-line computer-aided drug design tools, developed by the SIB Swiss Institute of Bioinformatics. High levels of predictive performance were maintained despite more extended biological and chemical spaces to be explored, e.g. achieving at least one correct human target in the top 15 predictions for >70% of external compounds. The new SwissTargetPrediction is available free of charge (www.swisstargetprediction.ch).
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                Author and article information

                Contributors
                Journal
                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                ECAM
                Evidence-based Complementary and Alternative Medicine : eCAM
                Hindawi
                1741-427X
                1741-4288
                2022
                23 September 2022
                23 September 2022
                : 2022
                : 3333878
                Affiliations
                1Guangxi University of Chinese Medicine, Nanning 530200, China
                2Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning 530011, China
                3The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning 530023, China
                Author notes

                Academic Editor: Rajeev K. Singla

                Author information
                https://orcid.org/0000-0002-6140-6114
                https://orcid.org/0000-0002-0359-9798
                https://orcid.org/0000-0001-9298-5932
                https://orcid.org/0000-0002-5865-7940
                Article
                10.1155/2022/3333878
                9525756
                36193133
                8ed2e853-a532-4839-b8a5-c26a716ab3cc
                Copyright © 2022 Weitao Wang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 April 2022
                : 12 August 2022
                : 17 August 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 82160849
                Funded by: Guangxi Traditional Chinese Medicine Appropriate Technology Development and Promotion Project
                Award ID: GZSY21-39
                Funded by: Chongzuo City Science and Technology Plan Project
                Award ID: Chongke FC2019009
                Categories
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine

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