Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Drug Repositioning Based on a Multiplex Network by Integrating Disease, Gene, and Drug Information

Author(s): Gang Zhou, Chenxu Xuan, Yan Wang, Bai Zhang, Hanwen Wu and Jie Gao*

Volume 18, Issue 3, 2023

Published on: 14 March, 2023

Page: [266 - 275] Pages: 10

DOI: 10.2174/1574893618666230223114427

Price: $65

Abstract

Background: The research of new drugs is very expensive and the cycle is relatively long, so it has broad development prospects and good economic benefits to use validated drugs in the treatment of other diseases.

Objective: The purpose of drug repositioning is to identify other indications for existing drugs. In addition to using disease and drug information for drug repositioning, other biomolecular information can also be integrated for drug repositioning. Integrating multiple biomolecular data of different types can improve the predictive performance of drug repositioning models.

Methods: This paper proposes a drug repositioning algorithm based on a multiplex network (DRMN algorithm) by integrating disease, gene, and drug information. DRMN algorithm utilizes known diseasegene and gene-drug associations to connect disease phenotype similarity network, gene expression similarity network, and drug response similarity network. Then they are constructed into a multiplex network, and the importance score of each node is calculated by PageRank (PR) algorithm. Finally, disease- drug association scores are sorted to achieve drug repositioning.

Results: DRMN algorithm is applied to two sets of sample data. Disease-drug association scores are calculated separately from disease PR values and drug PR values in both datasets. In top 50% of association scores, lots of disease-drug association prediction results have been verified by existing results. Compared with other algorithms, DRMN algorithm also shows better performance.

Conclusion: DRMN algorithm can effectively integrate multi-omics data for drug repositioning and obtain better prediction results.

Graphical Abstract

[1]
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]
[2]
Adams CP, Brantner VV. Estimating the cost of new drug development: Is it really 802 million dollars? Health Aff 2006; 25(2): 420-8.
[http://dx.doi.org/10.1377/hlthaff.25.2.420] [PMID: 16522582]
[3]
Ashburn TT, Thor KB. Drug repositioning: Identifying and developing new uses for existing drugs. Nat Rev Drug Discov 2004; 3(8): 673-83.
[http://dx.doi.org/10.1038/nrd1468] [PMID: 15286734]
[4]
Chiang AP, Butte AJ. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther 2009; 86(5): 507-10.
[http://dx.doi.org/10.1038/clpt.2009.103] [PMID: 19571805]
[5]
Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: A method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011; 7(1): 496.
[http://dx.doi.org/10.1038/msb.2011.26] [PMID: 21654673]
[6]
Wang W, Yang S, Li J. Drug target predictions based on heterogeneous graph inference. Pac Symp Biocomput 2013; 53-64.
[PMID: 23424111]
[7]
Bleakley K, Yamanishi Y. Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics 2009; 25(18): 2397-403.
[http://dx.doi.org/10.1093/bioinformatics/btp433] [PMID: 19605421]
[8]
Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput Biol 2012; 8(5): e1002503.
[http://dx.doi.org/10.1371/journal.pcbi.1002503] [PMID: 22589709]
[9]
Perlman L, Gottlieb A, Atias N, Ruppin E, Sharan R. Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol 2011; 18(2): 133-45.
[http://dx.doi.org/10.1089/cmb.2010.0213] [PMID: 21314453]
[10]
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]
[11]
Luo H, Wang J, Li M, et al. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 2016; 32(17): 2664-71.
[http://dx.doi.org/10.1093/bioinformatics/btw228] [PMID: 27153662]
[12]
Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics 2018; 34(11): 1904-12.
[http://dx.doi.org/10.1093/bioinformatics/bty013] [PMID: 29365057]
[13]
Chen J, Zhang L. A survey and systematic assessment of computational methods for drug response prediction. Brief Bioinform 2021; 22(1): 232-46.
[http://dx.doi.org/10.1093/bib/bbz164] [PMID: 31927568]
[14]
Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 2006; 313(5795): 1929-35.
[http://dx.doi.org/10.1126/science.1132939] [PMID: 17008526]
[15]
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]
[16]
Zhao S, Li S. A co-module approach for elucidating drug–disease associations and revealing their molecular basis. Bioinformatics 2012; 28(7): 955-61.
[http://dx.doi.org/10.1093/bioinformatics/bts057] [PMID: 22285830]
[17]
Hu G, Agarwal P. Human disease-drug network based on genomic expression profiles. PLoS One 2009; 4(8): e6536.
[http://dx.doi.org/10.1371/journal.pone.0006536] [PMID: 19657382]
[18]
Iorio F, Bosotti R, Scacheri E, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 2010; 107(33): 14621-6.
[http://dx.doi.org/10.1073/pnas.1000138107] [PMID: 20679242]
[19]
Shang H, Liu ZP. Network-based prioritization of cancer genes by integrative ranks from multi-omics data. Comput Biol Med 2020; 119(1): 103692.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103692] [PMID: 32339126]
[20]
Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, McKusick VA. Online Mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2002; 30(1): 52-5.
[http://dx.doi.org/10.1093/nar/30.1.52] [PMID: 11752252]
[21]
van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JAM. A text-mining analysis of the human phenome. Eur J Hum Genet 2006; 14(5): 535-42.
[http://dx.doi.org/10.1038/sj.ejhg.5201585] [PMID: 16493445]
[22]
Kim S, Chen J, Cheng T, et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res 2019; 47(D1): D1102-9.
[http://dx.doi.org/10.1093/nar/gky1033] [PMID: 30371825]
[23]
Yap CW. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem 2011; 32(7): 1466-74.
[http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294]
[24]
Liu C, Ma Y, Zhao J, et al. Computational network biology: Data, models, and applications. Phys Rep 2020; 846: 1-66.
[http://dx.doi.org/10.1016/j.physrep.2019.12.004]
[25]
Xuan P, Han K, Guo Y, et al. Prediction of potential disease-associated microRNAs based on random walk. Bioinformatics 2015; 31(11): 1805-15.
[http://dx.doi.org/10.1093/bioinformatics/btv039] [PMID: 25618864]
[26]
Page L, Brin S, Motwani R, Winograd T. The PageRank citation ranking: Bringing order to the web. Stanford Digit Libr Working Paper 1998; 6: 102-7.
[27]
Gozgit JM, Wong MJ, Moran L, et al. Ponatinib (AP24534), a multitargeted pan-FGFR inhibitor with activity in multiple FGFR-amplified or mutated cancer models. Mol Cancer Ther 2012; 11(3): 690-9.
[http://dx.doi.org/10.1158/1535-7163.MCT-11-0450] [PMID: 22238366]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy