American Journal of Pharmacological Sciences
ISSN (Print): 2327-6711 ISSN (Online): 2327-672X Website: Editor-in-chief: Srinivas NAMMI
Open Access
Journal Browser
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


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


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.

Shankhapushpi Nootropic activity network pharmacology gene ontology neurodegenerative diseases Ayurveda

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


[1]  P.K. Mukherjee, V. Kumar, N.S. Kumar, M. Heinrich, The Ayurvedic medicine Clitoria ternatea-From traditional use to scientific assessment, J. Ethnopharmacol. 120 (2008) 291-301.
[2]  N.K. Sethiya, M.K.M.M. Raja, S.H. Mishra, Antioxidant markers based TLC-DPPH differentiation on four commercialized botanical sources of Shankhpushpi (A Medhya Rasayana): A preliminary assessment, J. Adv. Pharm. Technol. Res. 4 (2013) 25-30.
[3]  J. Malik, M. Karan, K. Vasisht, Nootropic, anxiolytic and CNS-depressant studies on different plant sources of shankhpushpi, Pharm. Biol. 49 (2011) 1234-1242.
[4]  A. Nahata, U.K. Patil, V.K. Dixit, Effect of Evolvulus alsinoides Linn. on learning behavior and memory enhancement activity in rodents, Phyther. Res. 24 (2010) 486-493.
[5]  N.K. Sethiya, A. Nahata, V.K. Dixit, S.H. Mishra, Cognition boosting effect of Canscora decussata (a South Indian Shankhpushpi), Eur. J. Integr. Med. 4 (2012) e113-e121.
[6]  R.A. Jain, S.H. Shukla, Pharmacognostic evaluation and phytochemical studies on stem of Clitoria ternatea linn, Pharmacogn. J. 3 (2011) 62-66.
[7]  A.L. Hopkins, Network pharmacology: The next paradigm in drug discovery, Nat. Chem. Biol. 4 (2008) 682-690.
[8]  U. Chandran, N. Mehendale, G. Tillu, B. Patwardhan, Network Pharmacology of Ayurveda Formulation Triphala with Special Reference to Anti-Cancer Property, Comb. Chem. High Throughput Screen. 18 (2015) 846-854.
[9]  Y. Nakamura, F. Mochamad Afendi, A. Kawsar Parvin, N. Ono, K. Tanaka, A. Hirai Morita, T. Sato, T. Sugiura, M. Altaf-Ul-Amin, S. Kanaya, KNApSAcK metabolite activity database for retrieving the relationships between metabolites and biological activities, Plant Cell Physiol. 55 (2014).
[10]  U.S. Department of Agriculture, Dr. Duke’s Phytochemical and Ethnobotanical Databases, Agric. Res. Serv. (n.d.) Home page.
[11]  S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B.A. Shoemaker, P.A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, E.E. Bolton, PubChem 2019 update: Improved access to chemical data, Nucleic Acids Res. 47 (2019) D1102-D1109.
[12]  A. Daina, O. Michielin, V. Zoete, SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules, Sci. Rep. 7 (2017).
[13]  F. Cheng, W. Li, Y. Zhou, J. Shen, Z. Wu, G. Liu, P.W. Lee, Y. Tang, AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties, J. Chem. Inf. Model. 52 (2012) 3099-3105.
[14]  D.S. Wishart, Y.D. Feunang, A.C. Guo, E.J. Lo, A. Marcu, J.R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, N. Assempour, I. Iynkkaran, Y. Liu, A. MacIejewski, N. Gale, A. Wilson, L. Chin, R. Cummings, Di. Le, A. Pon, C. Knox, M. Wilson, DrugBank 5.0: A major update to the DrugBank database for 2018, Nucleic Acids Res. 46 (2018) D1074-D1082.
[15]  M.K. Gilson, T. Liu, M. Baitaluk, G. Nicola, L. Hwang, J. Chong, BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology, Nucleic Acids Res. 44 (2016) D1045-D1053.
[16]  A. Gaulton, A. Hersey, M.L. Nowotka, A. Patricia Bento, J. Chambers, D. Mendez, P. Mutowo, F. Atkinson, L.J. Bellis, E. Cibrian-Uhalte, M. Davies, N. Dedman, A. Karlsson, M.P. Magarinos, J.P. Overington, G. Papadatos, I. Smit, A.R. Leach, The ChEMBL database in 2017, Nucleic Acids Res. 45 (2017) D945-D954.
[17]  UniProt: a worldwide hub of protein knowledge, Nucleic Acids Res. 47 (2019) D506–D515.
[18]  K.I. Morley, G.W. Montgomery, The genetics of cognitive processes: Candidate genes in humans and animals, Behav. Genet. 31 (2001) 511-531.
[19]  D. Warde-Farley, S.L. Donaldson, O. Comes, K. Zuberi, R. Badrawi, P. Chao, M. Franz, C. Grouios, F. Kazi, C.T. Lopes, A. Maitland, S. Mostafavi, J. Montojo, Q. Shao, G. Wright, G.D. Bader, Q. Morris, The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function, Nucleic Acids Res. 38 (2010) W214-W220.
[20]  D.W. Huang, B.T. Sherman, R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc. 4 (2009) 44-57.
[21]  S. Wei, X. Zhou, M. Niu, H. Zhang, X. Liu, R. Wang, P. Li, H. Li, H. Cai, Y. Zhao, Network pharmacology exploration reveals the bioactive compounds and molecular mechanisms of Li-Ru-Kang against hyperplasia of mammary gland, Mol. Genet. Genomics. 294 (2019) 1159-1171.
[22]  M. Kanehisa, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Res. 28 (2000) 27-30.
[23]  J. Piñero, J.M. Ramírez-Anguita, J. Saüch-Pitarch, F. Ronzano, E. Centeno, F. Sanz, L.I. Furlong, The DisGeNET knowledge platform for disease genomics: 2019 update., Nucleic Acids Res. (2019).
[24]  P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, B. Schwikowski, T. Ideker, Cytoscape: A software Environment for integrated models of biomolecular interaction networks, Genome Res. 13 (2003) 2498-2504.
[25]  Q. Ge, L. Chen, M. Tang, S. Zhang, L. Liu, L. Gao, S. Ma, M. Kong, Q. Yao, F. Feng, K. Chen, Analysis of mulberry leaf components in the treatment of diabetes using network pharmacology, Eur. J. Pharmacol. 833 (2018) 50-62.
[26]  P. Gong, H. Zhang, W. Chi, W. Ge, K. Zhang, A. Zheng, X. Gao, F. Zhang, An association study on the polymorphisms of dopaminergic genes with working memory in a healthy Chinese Han population, Cell. Mol. Neurobiol. 32 (2012) 1011-1019.
[27]  R. Bernabeu, L. Bevilaqua, P. Ardenghi, E. Bromberg, P. Schmitzt, M. Bianchin, I. Izquierdo, J.H. Medina, Involvement of hippocampal cAMP/cAMP-dependent protein kinase signaling pathways in a late memory consolidation phase of aversively motivated learning in rats, Proc. Natl. Acad. Sci. U. S. A. 94 (1997) 7041-7046.
[28]  I. Bethus, D. Tse, R.G.M. Morris, Dopamine and memory: Modulation of the persistence of memory for novel hippocampal NMDA receptor-dependent paired associates, J. Neurosci. 30 (2010) 1610-1618.
[29]  K.A. Kempadoo, E. V. Mosharov, S.J. Choi, D. Sulzer, E.R. Kandel, Dopamine release from the locus coeruleus to the dorsal hippocampus promotes spatial learning and memory, Proc. Natl. Acad. Sci. U. S. A. 113 (2016) 14835-14840.
[30]  C.H. Bailey, D. Bartsch, E.R. Kandel, Toward a molecular definition of long-term memory storage, Proc. Natl. Acad. Sci. U. S. A. 93 (1996) 13445-13452.
[31]  S.D. Schmidt, C.R.G. Furini, C.G. Zinn, L.E. Cavalcante, F.F. Ferreira, J.A.K. Behling, J.C. Myskiw, I. Izquierdo, Modulation of the consolidation and reconsolidation of fear memory by three different serotonin receptors in hippocampus, Neurobiol. Learn. Mem. 142 (2017) 48-54.
[32]  C.M. Alberini, Transcription factors in long-term memory and synaptic plasticity, Physiol. Rev. 89 (2009) 121-145.
[33]  P.C. Orban, P.F. Chapman, R. Brambilla, Is the Ras-MAPK signalling pathway necessary for long-term memory formation?, Trends Neurosci. 22 (1999) 38-44.
[34]  J.C.P. Yin, T. Tully, CREB and the formation of long-term memory, Curr. Opin. Neurobiol. 6 (1996) 264-268.
[35]  V. Lakhina, R.N. Arey, R. Kaletsky, A. Kauffman, G. Stein, W. Keyes, D. Xu, C.T. Murphy, Genome-wide functional analysis of CREB/Long-term memory-dependent transcription reveals distinct basal and memory gene expression programs, Neuron. 85 (2015) 330-345.
[36]  D.A. Frank, M.E. Greenberg, CREB: A mediator of long-term memory from mollusks to mammals, Cell. 79 (1994) 5-8.
[37]  S. Abdul, N. Adhikari, S. Kotagiri, T. Jha, B. Ghosh, European Journal of Medicinal Chemistry Histone deacetylase 3 inhibitors in learning and memory processes with special emphasis on benzamides, Eur. J. Med. Chem. 166 (2019) 369-380.
[38]  N.M. Grissom, F.D. Lubin, The dynamics of HDAC activity on memory formation., Cellscience. 6 (2009) 44-48. (accessed October 22, 2019).