Computational approaches in drug discovery from phytochemicals

Padhy, Ipsa; Mahapatra, Aastha; Banerjee, B.; Sharma, Tripti

doi:10.1016/b978-0-323-90593-0.00014-9

Cited by 2 publications

(1 citation statement)

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“…Network pharmacology aims to discover drug leads and understand mechanism of action of potential drugs through interaction with multiple therapeutic targets [2]. Network pharmacology is considered to be an essential method for development of phytochemicals as potential therapeutic agents because of the potential of natural phytoconstituents to act on multiple targets in various signalling pathways [3]. Network pharmacology elucidates the interaction between drug and targets; drug and drug and the impact of potential drugs on biological pathways and networks.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Therapeutic Potential of Ginkgo Biloba: A Network Pharmacology Approach for Parkinson’s Disease

PRİYA,

TYAGI,

DHANDAYUTHAPANI

et al. 2023

Ankara Ecz. Fak. Derg.

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Objective: The aim of the current study is to identify the major phytoconstituents in Ginkgo biloba that could modulate the role of major therapeutic targets involved in the pathogenesis of Parkinson’s disease using approaches in network pharmacology. Material and Method: The phytoconstituents in Ginkgo biloba and their therapeutic targets and the targets of Parkinson’s disease were identified using various online databases and software. The identified phytoconstituents were subjected to evaluation of several pharmacokinetic properties and druglikeness study. The phytoconstituents with favourable pharmacokinetic and druglikeness properties and targets with better topological parameters were subjected to molecular docking study and MMGBSA analysis. Result and Discussion: This study identified the presence of 125 major phytoconstituents in Ginkgo biloba and out of 125 phytoconstituents, 30 phytoconstituents passed the pharmacokinetics and druglikeness property. The therapeutic targets for these selected phytoconstituents were found to be 468 and the disease targets in PD were found to be 2033. The common targets between phyto-targets and disease targets were found to be 44 targets. Out of 44 common targets, 5 top proteins CNR1, HPGDS, AR, RXRA and HDAC1 were identified on the basis of the topological parameters such as degree centrality and betweenness centrality in the Cytoscape 3.9.1 software. The docking studies and MMGBSA analysis revealed that beta-eudesmol has better interaction with the top 5 therapeutic targets.

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Section: Introductionmentioning

confidence: 99%