• Users Online: 114
  • Print this page
  • Email this page


 
 Table of Contents  
EDITORIAL
Year : 2021  |  Volume : 6  |  Issue : 4  |  Page : 197-199

Bioinformatics and network pharmacology: Scope and relevance in Ayurveda research


Editor-in-Chief, Journal of Drug Research in Ayurvedic Sciences, Central Council for Research in Ayurvedic Sciences (CCRAS), Ministry of Ayush, Government of India, New Delhi, India

Date of Submission11-Feb-2021
Date of Acceptance11-Feb-2022
Date of Web Publication17-May-2022

Correspondence Address:
Narayanam Srikanth
Director General (Additional Charge), Central Council for Research in Ayurvedic Sciences (CCRAS), Ministry of Ayush, Government of India, Janakpuri, New Delhi
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jdras.jdras_28_22

Rights and Permissions

How to cite this article:
Srikanth N. Bioinformatics and network pharmacology: Scope and relevance in Ayurveda research. J Drug Res Ayurvedic Sci 2021;6:197-9

How to cite this URL:
Srikanth N. Bioinformatics and network pharmacology: Scope and relevance in Ayurveda research. J Drug Res Ayurvedic Sci [serial online] 2021 [cited 2022 Jun 29];6:197-9. Available from: http://www.jdrasccras.com/text.asp?2021/6/4/197/345395





The advancement in the research field of biology has shifted the paradigm from “one––target, one––drug” mode to “network––target multiple component––therapeutics” mode and thus a new field of science called “Network pharmacology” has emerged which focuses on targeting multiple steps in a physiological regulatory network. Network pharmacology is an interdisciplinary science based on pharmacology, network biology, systems biology, bioinformatics, computational science, and other related scientific disciplines. Network pharmacology acts by the way of screening individual constituents against drug molecules in database, identification of individual metabolites, mapping of the protein–protein interaction networks (target identification), and mapping the synergy among the metabolites and the multicomponent network and their associated pathways.[1],[2],[3] Researchers found that along with Network pharmacology bioinformatics plays a vital role in predicting potential biomarkers and providing therapeutic options. Collectively, network pharmacology integrated with bioinformatics is a novel strategy to study ingredient identification, target prediction, and therapeutic options.[4],[5]

The system of Ayurveda focuses on establishing and maintaining balance of the life energies within us, rather than focusing on individual symptoms. The medicines used in this system usually contain more than one drug with a strategy to provide holistic health benefits through a multimodal therapeutic approach. Multiple bioactive compounds present in these polyherbal formulations are capable of modulating several disease targets. These complex relationships between these bioactive, target diseases, and genes can be studied with the help of network pharmacology [Figure 1].
Figure 1: Role of bioinformatics and network pharmacology in drug development and generation of evidence on safety and efficacy of interventions of traditional medicines

Click here to view


There is a need to inculcate contemporary scientific knowledge and techniques for better understanding of comprehensive knowledge of Ayurveda pharmacology for its global acceptance. Establishing the pharmacokinetics and pharmacodynamics of Ayurveda single- and multidrug-based therapeutics is a big challenge owing to the presence of numerous active phytoconstituents. These issues can be addressed through a multidisciplinary approach by involving basic sciences such as Chemistry, Molecular Biology, Pharmacoepidemiology Biotechnology, Ethnopharmacology, Ayurvedic Drug Discovery, Reverse Pharmacology, and various other areas.

A Working template for carrying out a Network pharmacology study usually has the following steps, viz. (1) Compounds Database Building (using online databases, viz. PubMed, Embase, and Google Scholar), (2) Screening Potential Active Ingredients for evaluating Drug-Likeness, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Evaluation (using SwissADME http://www.swissadme.ch/),[6] (3) Target Genes (using Swiss Target Prediction http://www.swisstargetprediction.ch/)[7],[8] and Protein–Protein Interaction Network Prediction (using STRING),[9] (4) Gene Function and Pathway Enrichment Analysis with Gene Ontology (GO) enrichment analysis and Kyoto Encyclopaedia of Genes and Genomes (KEGG)[10] pathway analysis, (5) Disease Ontology Analysis (using https://disease-ontology.org/),[11] (6) Molecular Docking Analysis (using Auto Dock vina and other such related tools, and (7) Ingredient-target-disease (I-T-D) network Construction (using tools such as Cytoscape).[12] The findings from these various steps are further analyzed using open-source tools, viz. “R software” etc., with suitable packages for interpretation of the results and deciding up on further course of action in terms of wet-lab work. Studies carried out on these lines can help us to explain multicomponent interaction with multiple targets, regulation of multiple biological processes, and synergistic effect of Ayurvedic formulae containing multiple ingredients with multiple chemical constituents.

The bioinformatics and network pharmacology approaches would be well adopted in validating the approaches from traditional medicine through different possible ways such as:

  1. Understanding of the mechanism of action


  2. Generation of evidence and safety through reverse pharmacology and reverse innovation of known leads from Ayurveda/Traditional Medicine.


  3. Generation of basic leads for designing the management/treatment of multigenic complex diseases


  4. Known drugs for new indications/medications for other diseases.


Given the rapid progress in bioinformatics, systems biology, and polypharmacology, network-based drug discovery is considered to be a promising approach for cost-effective drug development. Based on the above, the Central Council for Research in Ayurvedic Sciences (CCRAS), under the Ministry of Ayush has taken initiative through a collaborative project entitled “Mechanistic investigation on the efficacy and mode of action of Ashwagandha and Yogarajguggulu using a hybrid proteomics cheminformatics network medicine approach for the treatment of osteoarthritis” in collaboration with Indian Institute of Technology (IIT), Guwahati. As the computational and systems biology will provide new insight into the multiple target identification and thus increases the global acceptance of Ayurveda as well as Ayush systems, these efforts may lead to better understanding of Ayurveda pharmacology and Ayurveda pharmaceutics.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Chandran U, Mehendale N, Patil S, Chaguturu R, Patwardhan B. Network Pharmacology. Innov Approach Drug Discov2017;5:127-64. doi: 10.1016/B978-0-12-801814-9.00005-2.  Back to cited text no. 1
    
2.
Hopkins AL. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol 2008;4:682-90.  Back to cited text no. 2
    
3.
Lai X, Wang X, Hu Y, Su S, Li W, Li S. Editorial: Network pharmacology and traditional medicine. Front Pharmacol 2020;11:1194.  Back to cited text no. 3
    
4.
Zhao J, Mo C, Shi W, Meng L, Ai J. Network pharmacology combined with bioinformatics to investigate the mechanisms and molecular targets of Astragalus Radix-Panax notoginseng herb pair on treating diabetic nephropathy. Evid Based Complement Alternat Med 2021;2021:9980981.  Back to cited text no. 4
    
5.
Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: From computational models to experimental design principles. Curr Pharm Des 2014;20:23-36.  Back to cited text no. 5
    
6.
Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, druglikeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017;7:42717.  Back to cited text no. 6
    
7.
Daina A, Michielin O, Zoete V. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 2019;47:W357-64.  Back to cited text no. 7
    
8.
Gfeller D, Zoete V. Protein homology reveals new targets for bioactive small molecules. Bioinformatics 2015;31:2721-7.  Back to cited text no. 8
    
9.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017;45:D362-8.  Back to cited text no. 9
    
10.
Yu G, Wang LG, Han Y, He QY. Clusterprofiler: An R package for comparing biological themes among gene clusters. Omics 2012;16:284-7.  Back to cited text no. 10
    
11.
Schriml LM, Mitraka E, Munro J, Tauber B, Schor M, Nickle L, et al. Human Disease Ontology 2018 update: Classification, content and workflow expansion. Nucleic Acids Res 2019;47:D955-62.  Back to cited text no. 11
    
12.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504.  Back to cited text no. 12
    


    Figures

  [Figure 1]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
References
Article Figures

 Article Access Statistics
    Viewed277    
    Printed23    
    Emailed0    
    PDF Downloaded85    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]