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Abstract

Pharmaceutical companies have been using the therapeutic switching strategy in their pharmacological research programs to create novel medications based on the discovery of novel biological targets in recent years. This strategy saves a substantial investment and has a very slight failure ratio. It raises a drug's therapeutic value to its maximum potential, which raises the success rate. Drug repositioning is therefore a useful alternative to the conventional drug discovery procedure. Furthermore, efforts might be undertaken to ascertain the effectiveness of natural, botanical, and herbal components in various illnesses. In addition to offering researchers and students useful productivity softwares and real-world endeavours that they can refer to the contents of this review. When combined with the conceptual framework and software of networking on other fronts, can also facilitate quick and easy software tool selection and practical advice for network pharmacology research projects. A medication may be made to interact with several targets in order to transition a diseased network back to a normal state. Consequently, network pharmacology has proven helpful in directing and supporting medication repositioning. Another trend could be the emergence of many vivid and quantitative networks; if network pharmacology technology is used more and more, future costs will be significantly lower. The basis for future studies on the coping mechanisms of medicinal plants in the therapy of health disorder and the uses of network pharmacology in drug development is laid forth in this review.

Keywords

Network pharmacology, Repurposing of Drugs, Drug development, Software tools, Conventional drug, Therapeutic Switching.

Introduction

Network Pharmacology A state-of-the-art method for examining the intricate relationships between medications, targets, and biological systems is network pharmacology, which integrates systems biology, pharmacology, and network science. 1 It seeks to comprehend how medications alter biological networks to cause both beneficial and harmful consequences. In addition to bringing about significant changes in pharmacological research, networking has also brought about significant opportunities as well as obstacles for medical research.2 Opportunities arise from the expectation that network pharmacology will lead to novel concepts in drug development and research, as well as the ability to handle the intricacy of chemical and biological systems and complete the shift from description to prediction in contemporary drug research.3

a) Biological Network: A biological network is essential for explaining the relationships between the components of biological systems and provides the framework for the development of complex biological systems in organisms.4

b) Network Target: A network target is a critical component, such as a key molecule, key pathway, or key module, that may quantitatively depict the entire regulatory mechanism and link drugs and diseases from mechanism.5 Drug repurposing, also known as  re-tasking, is a tactic for discovering novel applications for authorized or under developmental medications that go beyond the parameters of their initial health issue.6  Preclinical, phase I and II, and regulatory costs may be significantly reduced for a repurposed drug, but they may still be roughly the same as for an innovative drug in the same manifestation.

Lastly, repurposed medications might identify fresh targets and pathways for additional exploitation.7 It is yet unknown how drugs work to treat diseases because it hits several targets with different components.8,9 Numerous studies support drugs therapeutic effectiveness in treating different diseases, but we still don't fully understand the intricate interactions between bio actives and targets, as well as how these interactions affect the disease's mechanistic course. To achieve this goal, the current study used a drug repurposing using network pharmacology method to find various potential bio actives and targeting pathways.

2. Single Drug Singal Target Paradigm in contrast with Network Pharmacology                                               

Despite the prevalent belief of ‘monotargeted therapy’ translational pharmacologists have long recognized that almost whole pharmaceuticals have a variety of outcomes, several of which are beneficial and others of which seem to be indifferent or dangerous.10,11 Biological systems are now known to be complex, robust, homeostatic, and resilient to disturbances. Most diseases also show far larger perturbations and variances than previously believed. A relatively new field known as "polypharmacology" is gaining traction in terms of theory and practice.12 Chemogenomic research and current binding affinity analysis have demonstrated that multiple medications integrate with several targets at fairly similar binding affinities.13,14,15 It is now becoming more and more clear that biological systems are resilient, homeostatic, complex, and redundant, and that most diseases show far broader disruptions. In order to generate polypharmacology networks, data portals of FDA-approved medications and their targets (and effects) have been applied.16,17 This results in a complex pharmacological network that is embedded in biological systems to uncover multivariate effects of drugs on various targets and various diseases.18,19

3. Network Pharmacology Methodology

Network pharmacology involves series of steps designed to explore the interaction between drugs, biological targets and disease networks.

  • Data collection
  • Target Characterization
  • Network Construction
  • Network Analysis
  • Drug repurposing

I. Data collection

Omics Data:

Genomics and Transcriptomics

The "elder of all omics," genomics, remains the most popular domestic unit of methods, particularly in therapeutic settings. Genomics has produced historic outcomes over the last 15 years. To find multifactorial disease-predisposing variants, high-throughput genotyping studies have been created, and extensive integrative genomic studies have been implemented.20,21 Several complex diseases, such as a number of cancers, inflammatory bowel disease, obesity, and others have been linked to numerous genetic susceptibility loci through genome wide association studies.22,23  Transcriptomics analysis has evolved downstream from genotype, concentrating more on what is transcribed in response to particular stimuli. Transcriptomics, as opposed to DNA analysis, considers events that may impact DNA expression and enables a closer examination of epigenetic phenomena.24 Control by non-coding RNAs, like microRNAs, is undoubtedly a key component of the epigenetic modulation of gene activity control. MicroRNAs can be regarded as a component of the epigenome due to their significance in regulating gene expression, and they have been linked to disease susceptibilities, creating novel advancement in diagnostic and therapeutic tools.25

Proteomics

Proteomics is the methodical process of specify all or a selected proper subset of the proteins that make up a cell, organ, or organism.26 Protein modifications and level changes are measured for use in drug discovery, toxic Drug development, toxic event investigation, and diagnostics all use measurements of protein alterations and changes. Understanding the biochemical specifics of proteomic diversity in the body's different organ systems will significantly advance our understanding of human biology and illness.27 The proteome is a primary facilitator of changed cellular responses brought on by exposure to active substances like Phyto complexes, which makes proteomic data especially valuable in pharmacotoxicology.28

Metabolomics

Metabonomics, also known as metabolomics, is the study of how biological systems react metabolically to internal or external stimuli in order to comprehend how complex multicellular systems undergo systemic change.29 In a given biological sample, "metabolomics" refers to a comprehensive analysis of all metabolites, or low molecular weight organic or inorganic compounds, that are substrates or byproducts of enzyme-mediated processes.30 By defining quantities of internalized xenobiotic chemicals and their biotransformation products, metabonomics analytical capabilities make it possible to characterize even the most complex mixtures, such as the food metabolome.31

Table 1: Databases involved in data collection

 

Data Sources

Description

References

Drug Bank

 

One widely interpreted chemo informatics and bioinformatics resource is the DrugBank database. DrugBank compiles a variety of data about potential medications. Along with target information, the majority of this data is derived from pharmacological, chemical, and pharmaceutical sources.

32

ChEMBL

Drug candidates with details on ADMET and binding are included in ChEMBL. This vast amount of data was gathered with the aid of routine literature mining. The database currently has 5.4 million bioactive candidates. The drug discovery processes make use of these data and candidates.

33

 

 

 

 

MATADOR

 

A commonly used database is the Manually Annotated Targets and Drugs Online Resource (MATADOR), which renders information on several interplay between drugs and their targets. By looking for a drug or a protein, one can find information about the both direct and indirect protein-chemical binding.

 

 

 

34

SIDER

The SIDER database gathers data on the frequency of side effects of drug candidates that have already received approval. One of the main goals is also to create classifications that link to additional information, like drug-target associations.

35

canSAR

The cancer research database canSAR includes biology data annotations, chemical agent and 3D structural information.

36

II. Target Characterization

Finding drug-target interactions is seen as a major region of interest in genomic drug discovery.

Its activity is modulated by interactions between small molecules and many pharmacologically significant protein targets. The identification of medicines with distinct targets was made possible by the use of a variety of biological tests for screening large chemical databases at high throughput.37,38 The goal of chemical genomics research was to establish a connection between chemical and genomic worlds, however there isn't much of one. The PubChem database, for instance, contains data on millions of chemicals, but the way these molecules interact with their targets is largely unknown.39 It takes time and money to experimentally ascertain potential drug-target interactions or compound-protein interactions.40,41

Table 2: Databases involved in target characterization

 

Data Sources

Description

References

DINIES

 

A supervised analysis serves as the foundation for the networks for inferring drug-target interactions. A web server called DINIES is used to deduce possible networks of drug-target interactions. Additionally, any a kernel similarity can be created from the data set, and the in-silico drug target prediction is achieved using a variety of cutting-edge machine learning techniques.

42

 

 

 

 

Super Pred

 

The web server SuperPred predicts pharmacological targets and Anatomical Therapeutic Chemical (ATC) codes. Different criteria, including pipeline search, could be utilized to integrate 3D, 2D, and fragment similarity for ATC code prediction. The basis for drug target prediction is the similarity distribution, which uses four input alternatives to estimate various thresholds and probability for a given target.

43

Swiss-Target Prediction

 

Using a combination of 2D and 3D similarity values with known ligands, Swiss Target Prediction is a virtual server that determines the targets of bioactive small molecules.

Swiss Target Prediction can be used to query about five distinct organisms: Homo sapiens, Bos taurus, Rattus norvegicus, Equus caballus, and Mus musculus.

44

III. Network Construction

The most crucial stage in network pharmacology is comprehending the disease's network. Another challenging component of this analysis is how to create a network disease, while some techniques to understand and exploit it for new therapeutic opportunities have been developed.45 Phylogenetic reconstruction,46 gene localization,47 gene fusion,48 correlated evolutionary rate,49 mirror tree,50 correlated mutations,51 homologous structural complexes,52 and main structure prediction are a few of the established methods.53 Phylogenetic profiling is an effective instrument for creating networks and their interactions. Both correlation-based and node-based network mapping are thought to be promising for upcoming discoveries.54

Table 3: Databases involved in network construction

 

Data Sources

Description

References

OMIM

An authoritative, publicly available database of human genes and genetic characteristics, OMIM is updated daily.

 

55

 

DisGeNET

DisGeNET is a platform for discovery that combines data on gene-disease associations from the literature and a number of public data sources.

 

56

 

 

SwissVar

A portal for searching variations in the UniProt Knowledgebase's (UniProtKB) Swiss-Prot entries is called Swiss Var. It compiled all of the data pertaining to a certain gene variant, including a manual annotation of each variant's genotype-phenotype association based on existing literature.

 

 

57

 

STRING

A thorough database to investigate known and anticipated protein-protein interactions.

 

58

 

STITCH

This database explores information about known and predicted chemical-protein interactions by combining data from literature mining, in vitro results, and other resourced databases.

 

59

IV. Analysis of Networks

A network is a meticulously calculated computational model of many connected nodes and pathways.60 A significant amount of network pharmacology is comprised of network analysis, which usually consists of key attribute analysis, spatial analysis, network topology and robustness, energy balance analysis, and structural models.61

Following is typically measured in a network study.

    • Module: A collection of nodes working together to carry out a certain task.62
    • Hub: A highly degreed node.63
    • Degree: how many edges are attached towards a node.64
    • Betweenness: Betweenness is the measure of minimum paths that go through a particular node.

Fig 1: Topology of network

Table 4: Databases involved in network analysis

 

Data sources

Description

References

Cytoscape

 

Database for improved visualization and network construction.

 

65

DAVID

Interpretation, Data Visualization, and Convergent Discovery Database.

 

66

DIP

Database of protein interactor.

Analysis of protein–protein binding network.

 

67

KEGG

 

 

Kyoto Encyclopaedia of Genes and Genomes Pathway investigation to investigate the biological process underlying the use of drugs as disease treatments.

 

68

V. Drug Repurposing

Network analysis helps to identify existing drugs whose target key nodes or pathways leading to potential new therapeutic effect.69

Advantages of drug repurposing:

1. Research and development (R&D) expenses have been drastically reduced.

2. Reduces the time needed to produce new medications because many of the compounds already on the market have been proven safe in humans and don't require Phase-1 clinical studies.

3. To generate hypotheses regarding the effectiveness and mechanisms of action of active compounds, with the aim of promoting optimal health, preventing disease, and providing tailored therapeutics with no adverse reactions.

4. Conditions for which Network Pharmacology can be used for repurposing

1. Investigating the pharmacological mechanisms and active components barrenwort for the regimen of early reproductive hormone disorder using network pharmacology. 70

2. Database analysis indicates that Spleen Strengthen Intestine Clearing Decoction contains 205 related therapeutic targets and 181 active ingredients. Important substances include luteolin, quercetin, and kaempferol. These researchers found that it plays a major function in mucosal immune rebalancing and downregulation of pro-inflammatory genes.71

3. Remedy of IBD with a single medication: One dissertation discovered that 148 target genes and 12 active ingredients in curcuma were eliminated through database searches, and 54 possible targets for IBD treatment were eliminated through the use of molecular docking technology. Using molecular docking technique, 24 key proteins were eliminated. Through the JAK-STAT, MAPK, and PI3K-Akt signaling pathways, these targeted proteins efficiently treat and alleviate ulcerative colitis in order to fulfil the disease's therapeutic goal.

4. Networking, which uses products, is thought to be a promising treatment approach for diabetes mellitus. Investigating the bioactive components of Cycloastragenol as the most effective therapy choice for the use of a network pharmacology-oriented framework in the treatment of non-insulin-dependent diabetic mellitus.

5. Discussed the active ingredients in maidenhair tree leaves, their possible target, and related  biological pathways for relieving ischemic stroke in the context of network pharmacology, so offering a theoretical foundation for further experimental study. According to their research, maidenhair tree leaves protect against strokes, most likely by controlling several functions and targeting numerous targets connected to different biological pathways. Their research offers a novel method for developing novel plant-based medications as well as a crucial resource for comprehending the effectiveness of maidenhair tree leaves in the treatment of CCVDs.

CONCLUSION

An optimized substitute for the conventional medicinal research process is drug repurposing.

Drug repurposing to alleviate frequent and orphan condition is turning into more alluring idea since it uses de-risked compounds and may result in shorter development timelines and lower overall development costs. The advancements as well as prospects in network pharmacology, with an emphasis on the identification of biomarker proteins, drug targets, and pathway analysis to provide quick and easy software for research and to elucidate the relationships between them. We can able to unlock the entire potential of commercially available drugs, transforming the landscape of medicine invention and improving therapeutic outcomes.

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Reference

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Pragati Berad
Corresponding author

Ashokrao Mane College of Pharmacy, Peth Vadgaon, Kolhapur, Maharashtra, 416112, India

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Vikas Dhole
Co-author

Ashokrao Mane College of Pharmacy, Peth Vadgaon, Kolhapur, Maharashtra, 416112, India

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Dr. Sanganna Burli
Co-author

Ashokrao Mane College of Pharmacy, Peth Vadgaon, Kolhapur, Maharashtra, 416112, India

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Prashant Kumbhar
Co-author

Ashokrao Mane College of Pharmacy, Peth Vadgaon, Kolhapur, Maharashtra, 416112, India

Pragati Berad*, Vikas Dhole, Dr. Sanganna Burli, Prashant Kumbhar, Network Pharmacology in Drug Repurposing and Reprofiling: Unraveling the Interplay between Drugs and Targets, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 3, 677-688. https://doi.org/10.5281/zenodo.14996147

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