Network pharmacology of Withania somnifera against stress associated neurodegenerative diseases

Duyu, Taaza; Khanal, Pukar; Dey, Yadu Nandan; Jha, Sajal Kumar

doi:10.1007/s13596-020-00530-x

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Hence, in the present study, using network pharmacology, the role of the active phytoconstituents derived from the five selected ingredients of SWRT that were closely related in the treatment of neurological disorders were analysed. There have been published network pharmacology research on Withania somnifera and Bacopa monnieri's therapeutic action on neurological disorders (Duyu et al, 2021;Jeyasri et al, 2020). Since they are a part of the ingredients of Saraswatharishta formulation, a brief comparison of our findings with these two published pieces of research are mentioned further in the discussion.…”

Section: Discussionmentioning

confidence: 68%

“…The targets identified in a previous study on network pharmacology of Withania somnifera (Duyu et al, 2021) against stress associated neurodegenerative disorders such as Alzheimer's and Parkinson's diseases were PTGS2, PTGS1, CHRM5, CHRNA4, CHRNA7, CHRNB2, ADORA1, KCNQ1, KCNQ2, PRKCD. These targets, PTGS2, PTGS1, CHRM5, CHRNA4, ADORA1 and PRKCD were present in the gene target list identified in this study.…”

Section: Discussionmentioning

confidence: 97%

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Analysing the role of Saraswatarishta in the treatment of neurological disorders based on network pharmacology

et al. 2021

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Ayurvedic medications originated centuries ago and are still prevalent today. Saraswatarishta (SWRT) is a well-known ayurvedic formulation that is often prescribed to control the manifestations of neurological illnesses and disorders such as slurred speech, anxiety, Parkinson's disease (PD) and Alzheimer's disease(AD). However, scientific research on its mode of action has not been studied extensively. Therefore, this study employs network pharmacology to understand better the neuroprotective role of Saraswatarishta (SWRT) in neurological disorders. Out of the 18 ingredients in SWRT, five were considered in this study due to their elevated therapeutic action in neurological disorders. Further, nine active phytoconstituents were chosen from the five selected ingredients. The gene targets of the active phytoconstituents were screened and selected using STITCH, SwissTargetPrediction and ChEMBL. Protein-Protein interaction and Gene Ontology (GO) enrichment analysis were carried out using STRING and g:Profiler, respectively. Cytoscape 3.7.2 was used to create three networks-the compound-target, the target-disease and the compound-target-disease network. Molinspiration and admetSAR2.0 were used to obtain the bioactivity scores and the blood-brain barrier (BBB) probability scores. The three networks indicated that all nine phytoconstituents were linked to the gene targets that encode proteins involved in the pathways of 10 major neurological disorders. This includes Parkinson's disease (PD), Alzheimer's disease (AD), dementia, Huntington disease, epilepsy, schizophrenia, spinocerebellar ataxia, amyotrophic lateral sclerosis (ALS), multiple sclerosis and attention deficit hyperactivity disorder (ADHD). The gene targets were expressed significantly in various central nervous system regions such as the cerebral cortex, cerebellum and amygdala. The bioactivity scores of the phytoconstituents were in the active range along with high BBB probability scores, indicating that the phytoconstituents can potentially cross the BBB and impart therapeutic effects.

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Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 97%

Analysing the role of Saraswatarishta in the treatment of neurological disorders based on network pharmacology

et al. 2021

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“…Though the concept of “lock and key” is an established approach to develop new therapeutic agents against various diseases, it is to be understood that a “single compound may regulate multiple proteins” with the concept of “master key can unlock multiple locks” ( Patwardhan 2014 ; Duyu et al, 2021 ; Khanal & Patil 2021 ), in which the master key can be conceptualized for its broad-spectrum pharmacological activity. Based on this theory, huperzine A-regulated targets were enriched, which identified a total of 101 different pathways.…”

Section: Discussionmentioning

confidence: 99%

Integration of System Biology Tools to Investigate Huperzine A as an Anti-Alzheimer Agent

et al. 2021

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Aim: The present study aimed to investigate huperzine A as an anti-Alzheimer agent based on the principle that a single compound can regulate multiple proteins and associated pathways, using system biology tools.Methodology: The simplified molecular-input line-entry system of huperzine A was retrieved from the PubChem database, and its targets were predicted using SwissTargetPrediction. These targets were matched with the proteins deposited in DisGeNET for Alzheimer disease and enriched in STRING to identify the probably regulated pathways, cellular components, biological processes, and molecular function. Furthermore, huperzine A was docked against acetylcholinesterase using AutoDock Vina, and simulations were performed with the Gromacs package to take into account the dynamics of the system and its effect on the stability and function of the ligands.Results: A total of 100 targets were predicted to be targeted by huperzine A, of which 42 were regulated at a minimum probability of 0.05. Similarly, 101 Kyoto Encyclopedia of Genes and Genomes pathways were triggered, in which neuroactive ligand–receptor interactions scored the least false discovery rate. Also, huperzine A was predicted to modulate 54 cellular components, 120 molecular functions, and 873 biological processes. Furthermore, huperzine A possessed a binding affinity of −8.7 kcal/mol with AChE and interacted within the active site of AChE via H-bonds and hydrophobic interactions.

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“…Selective molecules may have poorer clinical effectiveness than multi-target medications due to phenotypic resilience and network topology [ 17 ]. Many herbal formulations have been analyzed for the probable mechanism in a disease through this approach [ 18 , 19 , 20 ]. Therefore, using combined bioinformatics and network pharmacology methodologies, the goal of this study is to further understand the signaling pathways, associated proteins, and genes that cause rheumatoid arthritis based on the plentiful naturally occurring phytocannabinoids.…”

Section: Introductionmentioning

confidence: 99%

The Abundant Phytocannabinoids in Rheumatoid Arthritis: Therapeutic Targets and Molecular Processes Identified Using Integrated Bioinformatics and Network Pharmacology

Nandi¹,

Das

Dey³

et al. 2023

Life

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The endocannabinoid system consists of several phytocannabinoids, cannabinoid receptors, and enzymes that aid in numerous steps necessary to manifest any pharmacological activity. It is well known that the endocannabinoid system inhibits the pathogenesis of the inflammatory and autoimmune disease rheumatoid arthritis (RA). To the best of our knowledge, no research has been done that explains the network-pharmacology-based anti-rheumatic processes by focusing on the endocannabinoid system. Therefore, the purpose of this study is to further our understanding of the signaling pathways, associated proteins, and genes underlying RA based on the abundant natural endocannabinoids. The knowledge on how the phytocannabinoids in Cannabis sativa affect the endocannabinoid system was gathered from the literature. SwissTarget prediction and BindingDB databases were used to anticipate the targets for the phytocannabinoids. The genes related to RA were retrieved from the DisGeNET and GeneCards databases. Protein–protein interactions (high confidence > 0.7) were carried out with the aid of the string web server and displayed using Cytoscape. The Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis was used to perform enrichment analyses on the endocannabinoid–RA common targets. ShinyGO 0.76 was used to predict the biological processes listed in the Gene Ontology (GO) classification system. The binding affinity between the ligand and the receptors was precisely understood using molecular docking, induced-fit docking, and a molecular dynamics simulation. The network pharmacology analyses predicted that processes like response to oxygen-containing compounds and peptodyl-amino acid modification are related to the potential mechanisms of treatment for RA. These biological actions are coordinated by cancer, neuroactive ligand–receptor interaction, lipids and atherosclerosis, the calcium signaling pathway, and the Rap1 signaling pathway. According to the results of molecular docking, in the context of RA, phytocannabinoids may bind to important target proteins such PIK3CA, AKT1, MAPK9, PRKCD, BRAF, IGF1R, and NOS3. This entire study predicted the phytocannabinoids’ systemic biological characteristics. Future experimental research is needed, however, to confirm the results so far.

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Network pharmacology of Withania somnifera against stress associated neurodegenerative diseases

Cited by 11 publications

References 38 publications

Analysing the role of Saraswatarishta in the treatment of neurological disorders based on network pharmacology

Analysing the role of Saraswatarishta in the treatment of neurological disorders based on network pharmacology

Integration of System Biology Tools to Investigate Huperzine A as an Anti-Alzheimer Agent

The Abundant Phytocannabinoids in Rheumatoid Arthritis: Therapeutic Targets and Molecular Processes Identified Using Integrated Bioinformatics and Network Pharmacology

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