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Current Drug Research Reviews

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ISSN (Print): 2589-9775
ISSN (Online): 2589-9783

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Review Article

Network-based Drug Repurposing: A Critical Review

Author(s): Nagaraj Selvaraj, Akey Krishna Swaroop, Bala sai soujith Nidamanuri, Rajesh R Kumar, Jawahar Natarajan and Jubie Selvaraj*

Volume 14, Issue 2, 2022

Published on: 31 March, 2022

Page: [116 - 131] Pages: 16

DOI: 10.2174/2589977514666220214120403

Price: $65

Abstract

New drug development for a disease is a tedious, time-consuming, complex, and expensive process. Even if it is done, the chances for success of newly developed drugs are still very low. Modern reports state that repurposing the pre-existing drugs will have more efficient functioning than newly developed drugs. This repurposing process will save time, reduce expenses and provide more success rate. The only limitation for this repurposing is getting a desired pharmacological and characteristic parameter of various drugs from vast data about medications, their effects, and target mechanisms. This drawback can be avoided by introducing computational methods of analysis. This includes various network analysis types that use various biological processes and relationships with various drugs to simplify data interpretation. Some of the data sets now available in standard, and simplified forms include gene expression, drug-target interactions, protein networks, electronic health records, clinical trial results, and drug adverse event reports. Integrating various data sets and interpretation methods allows a more efficient and easy way to repurpose an exact drug for the desired target and effect. In this review, we are going to discuss briefly various computational biological network analysis methods like gene regulatory networks, metabolic networks, protein-protein interaction networks, drug-target interaction networks, drugdisease association networks, drug-drug interaction networks, drug-side effects networks, integrated network-based methods, semantic link networks, and isoform-isoform networks. Along with this, we briefly discussed the drug's limitations, prediction methodologies, and data sets utilised in various biological networks for drug repurposing.

Keywords: Drug repurposing, biological network analysis methods, network analysis, data sets, predicting methods, drug development.

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Graphical Abstract

[1]
Park K. A review of computational drug repurposing. Transl Clin Pharmacol 2019; 27(2): 59-63.
[http://dx.doi.org/10.12793/tcp.2019.27.2.59] [PMID: 32055582]
[2]
Lotfi SM, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. A review of network-based approaches to drug repositioning. Brief Bioinform 2018; 19(5): 878-92.
[http://dx.doi.org/10.1093/bib/bbx017] [PMID: 28334136]
[3]
Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 2011; 12(4): 303-11.
[http://dx.doi.org/10.1093/bib/bbr013] [PMID: 21690101]
[4]
Gumpinger A. Machine learning on molecular networks to decipher the genetics underlying complex traits. PhD dissertation Zurich: ETH 2020.
[5]
Alaimo S, Pulvirenti A. Network-based drug repositioning: Approaches, resources, and research directions. In: Computational methods for drug repurposing. New York, NY: Humana Press 2019; pp. 97-113.
[6]
Arrell DK, Terzic A. Network systems biology for drug discovery. Clin Pharmacol Ther 2010; 88(1): 120-5.
[http://dx.doi.org/10.1038/clpt.2010.91] [PMID: 20520604]
[7]
Alm E, Arkin AP. Biological networks. Curr Opin Struct Biol 2003; 13(2): 193-202.
[http://dx.doi.org/10.1016/S0959-440X(03)00031-9] [PMID: 12727512]
[8]
Somolinos FJ, León C, Guerrero-Aspizua S. Drug repurposing using biological networks. Processes (Basel) 2021; 9(6): 1057.
[http://dx.doi.org/10.3390/pr9061057]
[9]
Junker BH, Schreiber F. Analysis of biological networks. NewYork: John Wiley & Sons 2011.
[10]
Wu Z, Wang Y, Chen L. Network-based drug repositioning. Mol Biosyst 2013; 9(6): 1268-81.
[http://dx.doi.org/10.1039/c3mb25382a] [PMID: 23493874]
[11]
Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics 2008; 24(2): 282-4.
[http://dx.doi.org/10.1093/bioinformatics/btm554] [PMID: 18006545]
[12]
Akhoon BA, Tiwari H, Nargotra A. In silico drug design methods for drug repurposing. In: Kunal R, Ed. In Silico Drug Design. London: Academic Press 2019; pp. 47-84.
[http://dx.doi.org/10.1016/B978-0-12-816125-8.00003-1]
[13]
Macneil LT, Walhout AJ. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res 2011; 21(5): 645-57.
[http://dx.doi.org/10.1101/gr.097378.109] [PMID: 21324878]
[14]
Sanchez-Osorio I, Ramos F, Mayorga P, Dantan E. Foundations for modeling the dynamics of gene regulatory networks: A multilevel-perspective review. J Bioinform Comput Biol 2014; 12(1): 1330003.
[http://dx.doi.org/10.1142/S0219720013300037] [PMID: 24467752]
[15]
Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 2008; 9(10): 770-80.
[http://dx.doi.org/10.1038/nrm2503] [PMID: 18797474]
[16]
Levine M, Davidson EH. Gene regulatory networks for development. Proc Natl Acad Sci USA 2005; 102(14): 4936-42.
[http://dx.doi.org/10.1073/pnas.0408031102] [PMID: 15788537]
[17]
Passi A, Jolly B, Sharma T, Pandya A, Bhardwaj A. Data-driven systems level approaches for drug repurposing: Combating drug re-sistance in priority pathogens. In: Kunal R, Ed. In Silico Drug Design. 2019; pp. 229-53.
[18]
Bernal A, Daza E. Metabolic networks: Beyond the graph. Curr Comput Aided Drug Des 2011; 7(2): 122-32.
[http://dx.doi.org/10.2174/157340911795677611] [PMID: 21539508]
[19]
Wagner A, Fell DA. The small world inside large metabolic networks. Proc Biol Sci 2001; 268(1478): 1803-10.
[http://dx.doi.org/10.1098/rspb.2001.1711] [PMID: 11522199]
[20]
Lacroix V, Cottret L, Thébault P, Sagot MF. An introduction to metabolic networks and their structural analysis. IEEE/ACM Trans Comput Biol Bioinform 2008; 5(4): 594-617.
[http://dx.doi.org/10.1109/TCBB.2008.79] [PMID: 18989046]
[21]
Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 2018; 46(15): 7542-53.
[http://dx.doi.org/10.1093/nar/gky537] [PMID: 30192979]
[22]
Hatzimanikatis V, Li C, Ionita JA, Henry CS, Jankowski MD, Broadbelt LJ. Exploring the diversity of complex metabolic networks. Bioinformatics 2005; 21(8): 1603-9.
[http://dx.doi.org/10.1093/bioinformatics/bti213] [PMID: 15613400]
[23]
Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, Goliaei B, Peyvandi AA. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench 2014; 7(1): 17-31.
[PMID: 25436094]
[24]
Raman K. Construction and analysis of protein-protein interaction networks. Autom Exp 2010; 2(1): 2.
[http://dx.doi.org/10.1186/1759-4499-2-2] [PMID: 20334628]
[25]
Kuchaiev O, Rašajski M, Higham DJ, Pržulj N. Geometric de-noising of protein-protein interaction networks. PLOS Comput Biol 2009; 5(8): e1000454.
[http://dx.doi.org/10.1371/journal.pcbi.1000454] [PMID: 19662157]
[26]
Ozdemir ES, Halakou F, Nussinov R, Gursoy A, Keskin O. Methods for discovering and targeting druggable protein-protein interfaces and their application to repurposing. In: Quentin V, Ed. Computational Methods for Drug Repurposing. New York, NY: Humana Press 2019; pp. 1-21.
[27]
Yu W, Yan Y, Liu Q, Wang J, Jiang Z. Predicting drug-target interaction networks of human diseases based on multiple feature infor-mation. Pharmacogenomics 2013; 14(14): 1701-7.
[http://dx.doi.org/10.2217/pgs.13.162] [PMID: 24192119]
[28]
Cichonska A, Rousu J, Aittokallio T. Identification of drug candidates and repurposing opportunities through compound-target interaction networks. Expert Opin Drug Discov 2015; 10(12): 1333-45.
[http://dx.doi.org/10.1517/17460441.2015.1096926] [PMID: 26429153]
[29]
Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug-target interaction networks from the integration of chemi-cal and genomic spaces. Bioinformatics 2008; 24(13): i232-40.
[http://dx.doi.org/10.1093/bioinformatics/btn162] [PMID: 18586719]
[30]
Chen H, Zhang Z. A semi-supervised method for drug-target interaction prediction with consistency in networks. PLoS One 2013; 8(5): e62975.
[http://dx.doi.org/10.1371/journal.pone.0062975] [PMID: 23667553]
[31]
Wu Z, Li W, Liu G, Tang Y. Network-based methods for prediction of drug-target interactions. Front Pharmacol 2018; 9: 1134.
[http://dx.doi.org/10.3389/fphar.2018.01134] [PMID: 30356768]
[32]
Deng Y, Xu X, Qiu Y, Xia J, Zhang W, Liu S. A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics 2020; 36(15): 4316-22.
[http://dx.doi.org/10.1093/bioinformatics/btaa501] [PMID: 32407508]
[33]
Liu S, Tang B, Chen Q, Wang X. Drug-drug interaction extraction via convolutional neural networks. Comput Math Methods Med 2016; 2016: 6918381.
[http://dx.doi.org/10.1155/2016/6918381] [PMID: 26941831]
[34]
Zhao Z, Yang Z, Luo L, Lin H, Wang J. Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics 2016; 32(22): 3444-53.
[http://dx.doi.org/10.1093/bioinformatics/btw486] [PMID: 27466626]
[35]
Zhang W, Yue X, Huang F, Liu R, Chen Y, Ruan C. Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network. Methods 2018; 145: 51-9.
[http://dx.doi.org/10.1016/j.ymeth.2018.06.001] [PMID: 29879508]
[36]
Huang YF, Yeh HY, Soo VW. Inferring drug-disease associations from integration of chemical, genomic and phenotype data using net-work propagation. BMC Med Genomics 2013; 6(Suppl. 3): S4.
[http://dx.doi.org/10.1186/1755-8794-6-S3-S4] [PMID: 24565337]
[37]
Martínez V, Navarro C, Cano C, Fajardo W, Blanco A. DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med 2015; 63(1): 41-9.
[http://dx.doi.org/10.1016/j.artmed.2014.11.003] [PMID: 25704113]
[38]
Oh M, Ahn J, Yoon Y. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS One 2014; 9(10): e111668.
[http://dx.doi.org/10.1371/journal.pone.0111668] [PMID: 25356910]
[39]
Lee S, Lee KH, Song M, Lee D. Building the process-drug–side effect network to discover the relationship between biological Processes and side effects. BMC Bioinformatics 2011; 12(2): 1-12.
[http://dx.doi.org/10.1186/1471-2105-12-S2-S2]
[40]
Wu Z, Lu W, Yu W, et al. Quantitative and systems pharmacology 2. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches. Pharmacol Res 2018; 129: 400-13.
[http://dx.doi.org/10.1016/j.phrs.2017.11.005] [PMID: 29133212]
[41]
Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance 2018; 1(6): e201800098.
[http://dx.doi.org/10.26508/lsa.201800098] [PMID: 30515477]
[42]
Sutherland JJ, Webster YW, Willy JA, et al. Toxicogenomic module associations with pathogenesis: a network-based approach to under-standing drug toxicity. Pharmacogenomics J 2018; 18(3): 377-90.
[http://dx.doi.org/10.1038/tpj.2017.17] [PMID: 28440344]
[43]
Cheng F. Cardio-oncology: Network-based prediction of cancer therapy-induced. Adv Comput Toxicol 2019; 30: 75.
[http://dx.doi.org/10.1007/978-3-030-16443-0_5]
[44]
Wu Q, Taboureau O, Audouze K. Development of an adverse drug event network to predict drug toxicity. Curr Res Toxicol 2020; 1: 48-55.
[http://dx.doi.org/10.1016/j.crtox.2020.06.001] [PMID: 34345836]
[45]
March-Vila E, Pinzi L, Sturm N, et al. On the integration of in silico drug design methods for drug repurposing. Front Pharmacol 2017; 8: 298.
[http://dx.doi.org/10.3389/fphar.2017.00298] [PMID: 28588497]
[46]
Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform 2017; 18(2): 333-47.
[PMID: 26944082]
[47]
Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 2014; 30(20): 2923-30.
[http://dx.doi.org/10.1093/bioinformatics/btu403] [PMID: 24974205]
[48]
Hodos RA, Kidd BA, Shameer K, Readhead BP, Dudley JT. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med 2016; 8(3): 186-210.
[http://dx.doi.org/10.1002/wsbm.1337] [PMID: 27080087]
[49]
Emig D, Ivliev A, Pustovalova O, et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS One 2013; 8(4): e60618.
[http://dx.doi.org/10.1371/journal.pone.0060618] [PMID: 23593264]
[50]
Mullen J, Cockell SJ, Tipney H, Woollard PM, Wipat A. Mining integrated semantic networks for drug repositioning opportunities. PeerJ 2016; 4: e1558.
[http://dx.doi.org/10.7717/peerj.1558] [PMID: 26844016]
[51]
Luo Y, Zhao X, Zhou J, et al. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun 2017; 8(1): 573.
[http://dx.doi.org/10.1038/s41467-017-00680-8] [PMID: 28924171]
[52]
Tian Z, Teng Z, Cheng S, Guo M. Computational drug repositioning using meta-path-based semantic network analysis. BMC Syst Biol 2018; 12(9)(Suppl. 9): 134.
[http://dx.doi.org/10.1186/s12918-018-0658-7] [PMID: 30598084]
[53]
Kanza S, Frey JG. A new wave of innovation in Semantic web tools for drug discovery. Expert Opin Drug Discov 2019; 14(5): 433-44.
[http://dx.doi.org/10.1080/17460441.2019.1586880] [PMID: 30884989]
[54]
Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLOS Comput Biol 2012; 8(7): e1002574.
[http://dx.doi.org/10.1371/journal.pcbi.1002574] [PMID: 22859915]
[55]
Tseng YT, Li W, Chen CH, et al. IIIDB: A database for isoform-isoform interactions and isoform network modules. BMC Genomics 2015; 16(2): 1-7.
[http://dx.doi.org/10.1186/1471-2164-16-S2-S10]
[56]
Ma J, Wang J, Ghoraie LS, Men X, Haibe-Kains B, Dai P. Network-based approach to identify principal isoforms among four cancer types. Mol Omics 2019; 15(2): 117-29.
[http://dx.doi.org/10.1039/C8MO00234G] [PMID: 30720033]

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