Uncovering the pharmacology of Ginkgo biloba folium in the cell-type-specific targets of Parkinson’s disease

Yan, Yu-Chen; Xu, Zhiheng; Wang, Jian; Yu, Wenbo

doi:10.3389/fphar.2022.1007556

Cited by 7 publications

(4 citation statements)

References 110 publications

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“…[ Activities that fight cancer, inflammation and free radicals [31] Ginkobiloba extract Ginkobiloba leaves are rich in terpenoids and flavonoids…”

Section: Decreased Levels Of Inflammatory Cytokinesmentioning

confidence: 99%

A review on the nutraceuticals of Parkinson's disease

Vella,

Blundell

2024

FNDS

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Parkinson’s disease is a neurological condition that progresses over time and causes both motor and non-motor symptoms. Both patients and healthcare professionals must deal with the numerous challenges of this disease. In recent years, there has been a growing interest in the potential therapeutic effects of nutraceuticals—natural chemicals with alleged health benefits—in the treatment of Parkinson's disease (PD). Parkinson’s disease is influenced by a variety of factors, including aging, genetics and exposure to certain environmental toxins. Additionally, neuroinflammation, oxidative stress, mitochondrial dysfunction and protein aggregation play pivotal roles in the development of PD. The review integrated results from a variety of studies investigating the effects of different nutraceuticals, from preclinical models to clinical trials. This review focused on several key nutritional elements and dietary modifications that have a beneficial effect on a number of the pathogenic pathways, such as mitochondrial dysfunction and the antioxidant pathway, which are associated with the onset and progression of Parkinson's Disease. Hereby, we aim to explore the current body of research on nutraceutical interventions for Parkinson's disease, with a focus on their neuroprotective and symptomatic alleviation properties. Although numerous studies have been conducted on various nutraceuticals in relation to Parkinson's disease, currently there is no definitive evidence of their specific benefits in this condition. It is therefore strongly recommended that further studies and additional research be conducted in this area to gain a clearer understanding of the potential therapeutic effects.

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“…[ Activities that fight cancer, inflammation and free radicals [31] Ginkobiloba extract Ginkobiloba leaves are rich in terpenoids and flavonoids…”

Section: Decreased Levels Of Inflammatory Cytokinesmentioning

confidence: 99%

A review on the nutraceuticals of Parkinson's disease

Vella,

Blundell

2024

FNDS

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“…In oncology, network pharmacology has been widely used in drug screening and development, drug repurposing, and the study of drug mechanisms. For example, by constructing interaction and signal transduction networks, potential drug targets can be predicted, and new therapeutic drugs can be discovered ( Hao and Xiao, 2014 ; Wang et al, 2020 ; Yan et al, 2022 ). In addition, through the simulation of network pharmacology, drug safety and efficacy can be evaluated, and treatment plans can be optimized to improve personalized treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the research landscape: Mapping frontiers and hot spots in anti-cancer research using bibliometric analysis and research network pharmacology

Han,

Li,

et al. 2023

Front. Pharmacol.

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Introduction: Network pharmacology has emerged as a forefront and hotspot in anti-cancer. Traditional anti-cancer drugs are limited by the paradigm of “one cancer, one target, one drug,” making it difficult to address the challenges of recurrence and drug resistance. However, the main advantage of network pharmacology lies in its approach from the perspective of molecular network relationships, employing a “one arrow, multiple targets” strategy, which provides a novel pathway for developing anti-cancer drugs. This study employed a bibliometric analysis method to examine network pharmacology’s application and research progress in cancer treatment from January 2008 to May 2023. This research will contribute to revealing its forefront and hotspots, offering new insights and methodologies for future investigations.Methods: We conducted a literature search on network pharmacology research in anti-cancer (NPART) from January 2008 to May 2023, utilizing scientific databases such as Web of Science Core Collection (WoSCC) and PubMed to retrieve relevant research articles and reviews. Additionally, we employed visualization tools such as Citespace, SCImago Graphica, and VOSviewer to perform bibliometric analysis.Results: This study encompassed 3,018 articles, with 2,210 articles from WoSCC and 808 from PubMed. Firstly, an analysis of the annual national publication trends and citation counts indicated that China and the United States are the primary contributing countries in this field. Secondly, the recent keyword analysis revealed emerging research hotspots in “tumor microenvironment,” “anti-cancer drugs,” and “traditional Chinese medicine (TCM). “ Furthermore, the literature clustering analysis demonstrated that “calycosin,” “molecular mechanism,” “molecular docking,” and “anti-cancer agents” were widely recognized research hotspots and forefront areas in 2023, garnering significant attention and citations in this field. Ultimately, we analyzed the application of NPART and the challenges.Conclusion: This study represents the first comprehensive analysis paper based on bibliometric methods, aiming to investigate the forefront hotspots of network pharmacology in anti-cancer research. The findings of this study will facilitate researchers in swiftly comprehending the current research trends and forefront hotspots in the domain of network pharmacology in cancer research.

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“…With the emergence of various computational methods, there have been great potential opportunities to integrate existing resources to explore therapeutic drugs [ 11 ]. Drug discovery involves the extraction of compounds that act specifically and closely on target proteins, for which deep learning (DL) is one of such powerful artificial intelligence (AI) tools [ 12 ]. DL has built prediction models of protein-protein interaction (PPI), compound property, and drug-target interaction et al DeepPurpose (DP), one of the deep learning algorithms, has been used in numerous studies in the field of drug research and development [ [13] , [14] , [15] ].…”

Section: Introductionmentioning

confidence: 99%