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      LC–MS-Based Qualitative Analysis and Pharmacokinetic Integration Network Pharmacology Strategy Reveals the Mechanism of Phlomis brevidentata H.W.Li Treatment of Pneumonia

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          Abstract

          Phlomis brevidentata H.W.Li Radix ( PbR) is a rare traditional Tibetan medicine, and it is widely used in the Chinese Tibetan region for the treatment of pharyngitis, pneumonia, and so forth. Nevertheless, there is very little research on its modern pharmacy, and the active ingredients and mechanisms against these diseases remain unknown. In this study, we employed the qualitative analysis and pharmacokinetic based on LC–MS technology and network pharmacology to explore the active ingredients and mechanisms of PbR for treatment of pneumonia. Ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry (UPLC-Q-TOF/MS) methodology was applied to identify the chemical composition of PbR. Meanwhile, a UPLC-MS/MS method was developed to quantify three active constituents (sesamoside, shanzhiside methyl ester, and barlerin) in rat plasma for the pharmacokinetic analysis after oral administration of PbR. Finally, in order to clarify the anti-pneumonia mechanism of this rare Tibetan medicine, a comprehensive network pharmacology strategy was applied. As a result, a total of 23 compounds were identified in PbR, including 14 iridoid glycosides, 7 phenylethanoid glycosides, and 2 other kinds of compounds. Pharmacokinetic studies have shown that the three compounds exhibit extremely similar pharmacokinetic characteristics, possibly due to their highly analogous chemical structure. We speculate that the iridoid glycosides may be the main active component in PbR. Then, the three iridoid glycoside constituents absorbed into blood were subjected to network pharmacology analysis for treatment of pneumonia. Compound-target-disease, gene ontology bioanalysis, KEGG pathway, and other network pharmacology analysis methods were applied to reveal that five main targets of the three iridoid glycosides, namely, GAPDH, ALB, MAPK1, AKT1, and EGFR, were significant in the regulation of the above bioprocesses and pathways. These results provide a basis for elucidating the bioactive compounds and the pharmacological mechanisms of P. brevidentata H.W.Li radix under clinical applications.

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          Network pharmacology.

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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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              Network Pharmacology: A New Approach for Chinese Herbal Medicine Research

              The dominant paradigm of “one gene, one target, one disease” has influenced many aspects of drug discovery strategy. However, in recent years, it has been appreciated that many effective drugs act on multiple targets rather than a single one. As an integrated multidisciplinary concept, network pharmacology, which is based on system biology and polypharmacology, affords a novel network mode of “multiple targets, multiple effects, complex diseases” and replaces the “magic bullets” by “magic shotguns.” Chinese herbal medicine (CHM) has been recognized as one of the most important strategies in complementary and alternative medicine. Though CHM has been practiced for a very long time, its effectiveness and beneficial contribution to public health has not been fully recognized. Also, the knowledge on the mechanisms of CHM formulas is scarce. In the present review, the concept and significance of network pharmacology is briefly introduced. The application and potential role of network pharmacology in the CHM fields is also discussed, such as data collection, target prediction, network visualization, multicomponent interaction, and network toxicology. Furthermore, the developing tendency of network pharmacology is also summarized, and its role in CHM research is discussed.
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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                01 February 2021
                16 February 2021
                : 6
                : 6
                : 4495-4505
                Affiliations
                []School of Chinese Materia Medica, Beijing University of Chinese Medicine , Beijing 102488, China
                []Tibetan Traditional Medical College , Lhasa 850000, China
                Author notes
                [* ]Email: Sunyk@ 123456bucm.edu.cn ; Fax: +86-5391-2144.
                [* ]Email: shuofengzhang@ 123456sina.com ; Fax: +86-5391-2144.
                Article
                10.1021/acsomega.0c06201
                7893793
                902d1e91-b779-4deb-a740-1591ede986e2
                © 2021 The Authors. Published by American Chemical Society

                This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.

                History
                : 21 December 2020
                : 22 January 2021
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                Custom metadata
                ao0c06201
                ao0c06201

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