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Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation

Ginkgo biloba L. leaves (GBLs) play a substantial role in the treatment of vascular dementia (VD); however, the underlying mechanisms of action are unclear. OBJECTIVE: This study was conducted to investigate the mechanisms of action of GBLs in the treatment of VD through network pharmacology, molecu...

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Autores principales: Pan, Jienuo, Tang, Jiqin, Gai, Jialin, Jin, Yilan, Tang, Bingshun, Fan, Xiaohua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219709/
https://www.ncbi.nlm.nih.gov/pubmed/37233418
http://dx.doi.org/10.1097/MD.0000000000033877
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author Pan, Jienuo
Tang, Jiqin
Gai, Jialin
Jin, Yilan
Tang, Bingshun
Fan, Xiaohua
author_facet Pan, Jienuo
Tang, Jiqin
Gai, Jialin
Jin, Yilan
Tang, Bingshun
Fan, Xiaohua
author_sort Pan, Jienuo
collection PubMed
description Ginkgo biloba L. leaves (GBLs) play a substantial role in the treatment of vascular dementia (VD); however, the underlying mechanisms of action are unclear. OBJECTIVE: This study was conducted to investigate the mechanisms of action of GBLs in the treatment of VD through network pharmacology, molecular docking, and molecular dynamics simulations. METHODS: The active ingredients and related targets of GBLs were screened using the traditional Chinese medicine systems pharmacology, Swiss Target Prediction and GeneCards databases, and the VD-related targets were screened using the OMIM, DrugBank, GeneCards, and DisGeNET databases, and the potential targets were identified using a Venn diagram. We used Cytoscape 3.8.0 software and the STRING platform to construct traditional Chinese medicine–active ingredient–potential target and protein–protein interaction networks, respectively. After gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of potential targets using the DAVID platform, the binding affinity between key active ingredients and targets was analyzed by molecular docking, and finally, the top 3 proteins–ligand pairs with the best binding were simulated by molecular dynamics to verify the molecular docking results. RESULTS: A total of 27 active ingredients of GBLs were screened and 274 potential targets involved in the treatment of VD were identified. Quercetin, luteolin, kaempferol, and ginkgolide B were the core ingredients for treatment, and AKT1, TNF, IL6, VEGFA, IL1B, TP53, CASP3, SRC, EGFR, JUN, and EGFR were the main targets of action. The main biological processes involved apoptosis, inflammatory response, cell migration, lipopolysaccharide response, hypoxia response, and aging. PI3K/Akt appeared to be a key signaling pathway for GBLs in the treatment of VD. Molecular docking displayed strong binding affinity between the active ingredients and the targets. Molecular dynamics simulation results further verified the stability of their interactions. CONCLUSION SUBSECTIONS: This study revealed the potential molecular mechanisms involved in the treatment of VD by GBLs using multi-ingredient, multi-target, and multi-pathway interactions, providing a theoretical basis for the clinical treatment and lead drug development of VD.
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spelling pubmed-102197092023-05-27 Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation Pan, Jienuo Tang, Jiqin Gai, Jialin Jin, Yilan Tang, Bingshun Fan, Xiaohua Medicine (Baltimore) 3800 Ginkgo biloba L. leaves (GBLs) play a substantial role in the treatment of vascular dementia (VD); however, the underlying mechanisms of action are unclear. OBJECTIVE: This study was conducted to investigate the mechanisms of action of GBLs in the treatment of VD through network pharmacology, molecular docking, and molecular dynamics simulations. METHODS: The active ingredients and related targets of GBLs were screened using the traditional Chinese medicine systems pharmacology, Swiss Target Prediction and GeneCards databases, and the VD-related targets were screened using the OMIM, DrugBank, GeneCards, and DisGeNET databases, and the potential targets were identified using a Venn diagram. We used Cytoscape 3.8.0 software and the STRING platform to construct traditional Chinese medicine–active ingredient–potential target and protein–protein interaction networks, respectively. After gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of potential targets using the DAVID platform, the binding affinity between key active ingredients and targets was analyzed by molecular docking, and finally, the top 3 proteins–ligand pairs with the best binding were simulated by molecular dynamics to verify the molecular docking results. RESULTS: A total of 27 active ingredients of GBLs were screened and 274 potential targets involved in the treatment of VD were identified. Quercetin, luteolin, kaempferol, and ginkgolide B were the core ingredients for treatment, and AKT1, TNF, IL6, VEGFA, IL1B, TP53, CASP3, SRC, EGFR, JUN, and EGFR were the main targets of action. The main biological processes involved apoptosis, inflammatory response, cell migration, lipopolysaccharide response, hypoxia response, and aging. PI3K/Akt appeared to be a key signaling pathway for GBLs in the treatment of VD. Molecular docking displayed strong binding affinity between the active ingredients and the targets. Molecular dynamics simulation results further verified the stability of their interactions. CONCLUSION SUBSECTIONS: This study revealed the potential molecular mechanisms involved in the treatment of VD by GBLs using multi-ingredient, multi-target, and multi-pathway interactions, providing a theoretical basis for the clinical treatment and lead drug development of VD. Lippincott Williams & Wilkins 2023-05-26 /pmc/articles/PMC10219709/ /pubmed/37233418 http://dx.doi.org/10.1097/MD.0000000000033877 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 3800
Pan, Jienuo
Tang, Jiqin
Gai, Jialin
Jin, Yilan
Tang, Bingshun
Fan, Xiaohua
Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
title Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
title_full Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
title_fullStr Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
title_full_unstemmed Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
title_short Exploring the mechanism of Ginkgo biloba L. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
title_sort exploring the mechanism of ginkgo biloba l. leaves in the treatment of vascular dementia based on network pharmacology, molecular docking, and molecular dynamics simulation
topic 3800
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219709/
https://www.ncbi.nlm.nih.gov/pubmed/37233418
http://dx.doi.org/10.1097/MD.0000000000033877
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