Antiarrhythmic Mechanisms of Chinese Herbal Medicine Dingji Fumai Decoction

Rui

et al. 2020

Preprint

Self Cite

Background: Dingji Fumai decoction (DFD), a traditional herbal mixture, has been widely used to ventricular arrhythmia (VA) in clinical practice in China. However, research on the bioactive components and underlying mechanisms of DFD in VA is still scarce. Methods: Components of DFD were collected from TCMSP, ETCM, and literature. Then, the chemical structures of each component were obtained from PubChem. Next, SwissADME and SwissTargetPrediction were applied for compounds screening and targets prediction of DFD, meanwhile, targets of VA were collected from DrugBank and OMIM. Then, the H-C-T-D network as well as the PPI network were constructed based on the data obtained above. CytoNCA was utilized to filter hub genes and VarElect was used to analyze the relationship between genes and diseases. At last, Metascape was employed for systematic analysis on the potential targets of herbals against VA, and AutoDock was applied for molecular docking to verify the results.Results: A total of 434 components were collected, 168 of which were qualified, and there were 28 shared targets between DFD and VA. Three function modules of DFD were found from the PPI network. Further systematic analysis of shared genes and function modules explained the potential mechanism of DFD in the treatment of VA, molecular docking has verified the interactions. Conclusions: DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed DFD could apply for the treatment of VA and promoted the explaining of DFD for VA in the molecular mechanisms.

Section: Introductionmentioning

confidence: 96%

Network Pharmacology-based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

Rui

et al. 2020

Preprint

Self Cite

“…Moreover, DFD, as a traditional Chinese decoction, treats VA through multicomponent and multitarget, indicating that the underlying mechanisms are complex as described in our present study. Although we have made some identification on the mechanisms [25], 6…”

Section: Discussionmentioning

confidence: 96%

“…Dingji Fumai Decoction (DFD), consisting of Chuanxiong Rhizoma (Chuanxiong), Jujubae Fructus (Dazao), Poria cocos (Schw.) Wolf (Fuling), Cinnamomi Ramulus (Guizhi), Silktree Albizia Bark (Hehuanpi), Osdraconis ( Fossiliaossiamastodi ) (Longgu), Ostrea Gigas Thunberg (Muli), Ziziphi Spinosae Semen (Suanzaoren), Radix Polygalae (Yuanzhi), and Licorice (Gancao), is widely used for VA and obtains encouraging results [ 24 ], and previous experiments revealed that one of the underlying mechanisms is class I antiarrhythmic property [ 25 ]. To make DFD more recognized, it is essential to ensure the efficacy and safety of DFD.…”

Section: Introductionmentioning

confidence: 99%

Network Pharmacology-Based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

Evidence-Based Complementary and Alternative Medicine

et al. 2021

Self Cite

Background. Dingji Fumai Decoction (DFD), a traditional herbal mixture, has been widely used to ventricular arrhythmia (VA) in clinical practice in China. However, research on the bioactive components and underlying mechanisms of DFD in VA is still scarce. Methods. Components of DFD were collected from TCMSP, ETCM, and literature. The chemical structures of each component were obtained from PubChem. Next, SwissADME and SwissTargetPrediction were applied for compounds screening and targets prediction of DFD; meanwhile, targets of VA were collected from DrugBank and Online Mendelian Inheritance in Man (OMIM). Then, the H-C-T-D network and the protein-protein interaction (PPI) network were constructed based on the data obtained above. CytoNCA was utilized to filter hub genes and VarElect was used to analyze the relationship between genes and diseases. At last, Metascape was employed for systematic analysis on the potential targets of herbals against VA, and AutoDock was applied for molecular docking to verify the results. Results. A total of 434 components were collected, 168 of which were qualified, and there were 28 shared targets between DFD and VA. Three function modules of DFD were found from the PPI network. Further systematic analysis of shared genes and function modules explained the potential mechanism of DFD in the treatment of VA; molecular docking has verified the interactions. Conclusions. DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed that DFD could apply to the treatment of VA and promoted the explanation of DFD for VA in the molecular mechanisms.

“…High-performance liquid chromatography (HPLC) fingerprints of GXV HPLC fingerprints of GXV were performed as described previously [25,56,57] with some modifications.…”

Section: Functional Enrichmentmentioning

confidence: 99%

“…Network pharmacology is of great significance for the discovery of effective components and potential targets in TCM and the investigation of its underlying mechanisms, which may help explore the pharmacological properties of herbal medicines [23,24]. In view of the complex composition of TCM, it has the characteristics of multitarget, multichannel and coordination and synergism [25][26][27][28]. With the continuous improvement in the natural science system, the study of in-depth drug disease mechanisms is increasing [29].…”

Section: Introductionmentioning

confidence: 99%

Virtual screening and network pharmacology-based synergistic mechanism identification of multiple components contained in Guanxin V against coronary artery disease

BMC Complement Med Ther

Zhang

2020

Self Cite

Background Guanxin V (GXV), a traditional Chinese medicine (TCM), has been widely used to treat coronary artery disease (CAD) in clinical practice in China. However, research on the active components and underlying mechanisms of GXV in CAD is still scarce. Methods A virtual screening and network pharmacological approach was utilized for predicting the pharmacological mechanisms of GXV in CAD. The active compounds of GXV based on various TCM-related databases were selected and then the potential targets of these compounds were identified. Then, after the CAD targets were built through nine databases, a PPI network was constructed based on the matching GXV and CAD potential targets, and the hub targets were screened by MCODE. Moreover, Metascape was applied to GO and KEGG functional enrichment. Finally, HPLC fingerprints of GXV were established. Results A total of 119 active components and 121 potential targets shared between CAD and GXV were obtained. The results of functional enrichment indicated that several GO biological processes and KEGG pathways of GXV mostly participated in the therapeutic mechanisms. Furthermore, 7 hub MCODEs of GXV were collected as potential targets, implying the complex effects of GXV-mediated protection against CAD. Six specific chemicals were identified. Conclusion GXV could be employed for CAD through molecular mechanisms, involving complex interactions between multiple compounds and targets, as predicted by virtual screening and network pharmacology. Our study provides a new TCM for the treatment of CAD and deepens the understanding of the molecular mechanisms of GXV against CAD.