American Journal of Pharmacological Sciences
ISSN (Print): 2327-6711 ISSN (Online): 2327-672X Website: https://www.sciepub.com/journal/ajps Editor-in-chief: Srinivas NAMMI
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American Journal of Pharmacological Sciences. 2019, 7(1), 25-38
DOI: 10.12691/ajps-7-1-5
Open AccessArticle

Network Pharmacology Integrated Pharmacokinetics Approach to Decipher the Mechanism of Shankhapushpi Exerted Nootropic Activity

Srinidhi1,

1Department of Biotechnology, Ramaiah Institute of Technology, MSR Nagar, Bangalore, Karnataka, India

Pub. Date: December 27, 2019

Cite this paper:
Srinidhi. Network Pharmacology Integrated Pharmacokinetics Approach to Decipher the Mechanism of Shankhapushpi Exerted Nootropic Activity. American Journal of Pharmacological Sciences. 2019; 7(1):25-38. doi: 10.12691/ajps-7-1-5

Abstract

Shankhapushpi herbs are widely recognized in traditional ayurvedic medicinal practices for their exceptional nootropic activity. Additionally, Shankhapushpi is known to ameliorate neurological, nootropic, and behavioral disorders like Alzheimer’s, dementia, schizophrenia, and attention deficit hyperactivity disorder. Network pharmacology has been widely used to decipher the molecular mechanism of action of complex therapeutic formulations. In this work, network pharmacology integrated pharmacokinetics strategy was employed to understand the memory enhancement activity of Shankhapushpi. Chemical space of Shankhapushpi was identified by data mining and drug-likeness screening (oral bioavailability (OB ≥ 0.5), Blood-Brain Barrier, and Gastro Intestinal permeability) further, the target identification of the screened chemical space was performed by constraint-based database prediction (similarity parameter ≥ 0.85). Genemania was employed to construct, annotate and analyze a protein-protein (P-P) interaction network of the identified Shankhapushpi nootropic targets. Further, a constraint-based (P ≤ 0.05) comparative gene ontology and enrichment analysis of the (P-P) network was conducted using DAVID (FDR ≤ 0.02) and Genemania (FDR ≤ 0.02) to identify the nootropic pathways perturbed by the Shankhapushpi chemical space. DisGeNet and KEGG databases were queried to identify the diseases related to the identified shankhpushpi nootropic targets, and a gene-disease network was constructed. Finally, statistical network analysis results indicated the involvement of Dopaminergic activity, 5-hydroxytryptamine activity, mitogen-activated protein kinase cascade, histone deacetylase activity as the pivotal mechanisms behind the Shankhapushpi exerted nootropic activity. Finally, the Shankhapushpi herbs, phytochemicals, targets, pathways, diseases identified were organized and mapped into various networks for complete visualization, comprehension, and analysis of the Shankhapushpi network biology.

Keywords:
Shankhapushpi Nootropic activity network pharmacology gene ontology neurodegenerative diseases Ayurveda

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