Artificial intelligence and network pharmacology based investigation of pharmacological mechanism and substance basis of Xiaokewan in treating diabetes. (September 2020)

• Record Type: Journal Article
• Authors: Zhu, Chunyan; Cai, Tingting; Jin, Ying; Chen, Jiayun; Liu, Guoqiang; Xu, Niusheng; Shen, Rong; Chen, Yuhong; Han, Luying; Wang, Suping; Wu, Caisheng; Zhu, Mingshe
• Is Part Of: Pharmacological research. Volume 159(2020)
• Publication Date: 2020-09
• Subjects: TCM Chinese traditional medicine -- ADME absorption distribution, metabolism and excretion -- PATBS precise-and-thorough background-subtraction -- MS mass spectrometry -- BE-DDA background exclusion data dependent acquisition -- LC liquid chromatography -- HRMS high resolution mass spectrometry -- EIC extracted ion chromatography -- MDF mass defect filter -- PIF product ion filter -- NLF neutral loss filter -- MTSF mass spectral tree similarity filter -- DIA data independent acquisition -- DDA data dependent acquisition -- BS background subtraction -- SWATH sequential window acquisition of all theoretical fragmentation spectra -- T2DM type 2 diabetes mellitus -- GSH-PX glutathione peroxidase -- CAT catalase -- SOD superoxide dismutase -- Lpo lipid peroxide; Chinese traditional medicine -- Network pharmacology -- Metabolites -- Diabetes -- Background exclusion data dependent acquisition -- Non-targeted data mining; Pharmacology -- Periodicals; Pharmacology -- Periodicals; Research -- Periodicals; Médicaments -- Recherche -- Périodiques; Pharmacologie -- Périodiques; 615.105
• DOI: 10.1016/j.phrs.2020.104935
• Languages: English
• ISSNs: 1043-6618

Abstract

Graphical abstract: Highlights: Component screening by intelligent MS data analysis and network pharmacology. BE-DDA for non-targeted acquiring HRMS 2 Data of TCM' s metabolites in vivo . First report on comprehensive study on in vivo exposure of Xiaokewan in rats. Abstract: Xiaokewan is a typical Traditional Chinese medicine (TCM) for diabetes and contains various natural chemicals, such as lignans, flavonoids, saponins, polysaccharides, and western medicine glibenclamide. In the current study, a highly efficient system for screening hypoglycemic efficacy constituents of Xiaokewan has been developed with the integration of intelligent data acquisition, data mining, network pharmacology, and computer assisted target fishing. With the combination of background exclusion data dependent acquisition (BE-DDA) and non-targeted precise-and-thorough background-subtraction (PATBS) techniques, a novel workflow has been established for the non-targeted recognition and identification of TCM constituents in vivo, and has been applied to the exposure study of Xiaokewan in rat. In this case, an interesting correlation among drug, target, and disease can be established, by combining the screening or characterization results with the strategy of network pharmacology and multiple computer assisted techniques. Consequently, five main constituents (puerarin, daidzein, formononetin, deoxyschizandrin and glibenclamide) exposed in vivo have been selected as effective hypoglycemic components. Meanwhile, the network pharmacology experimental results showed that these five constituents could act on various drug targets, such as PI3K, PTP1B, MAPK, AKT, TNF, and NF-κB. These five constituents might be involved in the regulation of β-cell function or exhibit inflammation inhibition ability to relieve the pathophysiological process of disease from multiple links. Furthermore, the pharmacological effects of these five constituents have been verified by diabetic zebrafish model. The zebrafish model results showed that the TCM monomer mixture without glibenclamide exhibited similar hypoglycemic activity with Xiaokewan. Although the monomer mixture with glibenclamide showed better activity than Xiaokewan only, the deoxyschizandrin (TCM constituent of Xiaokewan) exhibited best hypoglycemic performance. In summary, the above results indicated that the application of both intelligent recognition technology in mass spectrometry dataset and computerized network pharmacology might provide a pioneering approach for investigating the substance basis of TCM and searching lead compounds from natural sources.