Table Info

Table 2.

Network-based resources for drug repurposing

No.	Tools	Description
1	MANTRA	Tool to identify DR candidates by analyzing the MoA
2	STITCH	Database for physical and functional associations between chemical and targets
3	DGIdb	Resource for browsing, searching, and filtering information on drug-gene interactions and the druggable genome
4	PDOD	DR resource for predicting drugs with opposite effects on altered states of disease genes
5	DeMAND	DR resource for MoA analysis
6	DMAP	CMAP of drug-protein effects

DR: drug-resistant; DGIdb: drug-gene interaction database; PDOD: pulsed dose oxygen delivery; DeMAND: detecting mechanism of action by network dysregulation

Declarations

Acknowledgments

The authors are highly thankful to ISF College of Pharmacy, Moga for providing necessary facilities and encouragement.

Author contributions

PAC: Conceptualization, Writing—original draft. DS: Writing—original draft. AS: Writing—original draft. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

References

Mourenza Á, Gil JÁ, Mateos LM, Letek M. Novel treatments against Mycobacterium tuberculosis based on drug repurposing. Antibiotics. 2020;9:550 [DOI] [PubMed] [PMC]

Ramón-García S, González Del Río R, Villarejo ÁS, Sweet GD, Cunningham F, Barros D, et al. Repurposing clinically approved cephalosporins for tuberculosis therapy. Sci Rep. 2016;6:34293. [DOI] [PubMed] [PMC]

Passi A, Rajput NK, Wild DJ, Bhardwaj A. RepTB: a gene ontology based drug repurposing approach for Tuberculosis. J Cheminform. 2018;10:24. [DOI] [PubMed] [PMC]

Mishra R, Krishan S, Siddiqui AN, Kapur P, Khayyam KU, Sharma M. Potential role of adjuvant drugs on efficacy of first line oral antitubercular therapy: drug repurposing. Tuberculosis. 2020;120:101902. [DOI] [PubMed]

Fatima S, Bhaskar A, Dwivedi VP. Repurposing immunomodulatory drugs to combat Tuberculosis. Front Immunol. 2021;12:645485. [DOI] [PubMed] [PMC]

Akinpelu OI, Lawal MM, Kumalo HM, Mhlongo NN. Drug repurposing: fusidic acid as a potential inhibitor of M. tuberculosis FtsZ polymerization - insight from DFT calculations, molecular docking and molecular dynamics simulations. Tuberculosis. 2020;121:101920. [DOI] [PubMed]

Cardoso NC, Oosthuizen CB, Peton N, Singh V. Drug repurposing for tuberculosis. In: Saxena SK, editor. Drug repurposing - molecular aspects and therapeutic applications. Rijeka: IntechOpen; 2021. [DOI]

Riccardi G, Sala CTuberculosis drug dscovery and development 2019. Basel (Switzerland): MDPI; 2020. [DOI]

Majeed S, Mehraj S, Ahmad Z. Antituberculosis drug repurposing: a new hope for tackling multi- challenging TB in timely manner. In: Saxena SK, editor. Drug repurposing - molecular aspects and therapeutic applications. Rijeka: IntechOpen; 2022. [DOI]

10.

Lee C, Bhakta S. The prospect of repurposing immunomodulatory drugs for adjunctive chemotherapy against tuberculosis: a critical review. Antibiotics. 2021;10:91. [DOI] [PubMed] [PMC]

11.

An Q, Li C, Chen Y, Deng Y, Yang T, Luo Y. Repurposed drug candidates for antituberculosis therapy. Eur J Med Chem. 2020;192:112175. [DOI] [PubMed]

12.

Umapathy D, Soundhararajan R, Srinivasan H. Repurposing of FDA-approved drugs against Mycobacterium tuberculosis target MMA4 and CmaA2. Biointerface Res Appl Chem. 2021;11:14688–96. [DOI]

13.

Baindara P, Agrawal S, Franco OL. Host-directed therapies for malaria and tuberculosis: common infection strategies and repurposed drugs. Expert Rev Anti Infect Ther. 2022;20:849–69. [DOI] [PubMed]

14.

Keener AB. Oldie but goodie: repurposing penicillin for tuberculosis. Nat Med. 2014;20:976–8. [DOI] [PubMed]

15.

Yang X, Alam A, Iqbal N, Raza K. Repurposing of FDA-approved drugs to predict new inhibitors against key regulatory genes in Mycobacterium tuberculosis. Biocell. 2021;45:1569–83. [DOI]

16.

Kumar D, Negi B, Rawat DS. The anti-tuberculosis agents under development and the challenges ahead. Future Med Chem. 2015;7:1981–2003. [DOI] [PubMed]

17.

Oprea TI, Bauman JE, Bologa CG, Buranda T, Chigaev A, Edwards BS, et al. Drug repurposing from an academic perspective. Drug Discov Today Ther Strateg. 2011;8:61–9. [DOI] [PubMed] [PMC]

18.

Singh TU, Parida S, Lingaraju MC, Kesavan M, Kumar D, Singh RK. Drug repurposing approach to fight COVID-19. Pharmacol Rep. 2020;72:1479–508. [DOI] [PubMed] [PMC]

19.

Sleire L, Førde HE, Netland IA, Leiss L, Skeie BS, Enger PØ. Drug repurposing in cancer. Pharmacol Res. 2017;124:74–91. [DOI] [PubMed]

20.

Oprea TI, Mestres J. Drug repurposing: far beyond new targets for old drugs. AAPS J. 2012;14:759–63. [DOI] [PubMed] [PMC]

21.

Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The drug repurposing hub: a next-generation drug library and information resource. Nat Med. 2017;23:405–8. [DOI] [PubMed] [PMC]

22.

Cha Y, Erez T, Reynolds IJ, Kumar D, Ross J, Koytiger G, et al. Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol. 2018;175:168–80. [DOI] [PubMed] [PMC]

23.

Park K. A review of computational drug repurposing. Transl Clin Pharmacol. 2019;27:59–63. [DOI] [PubMed] [PMC]

24.

Farha MA, Brown ED. Drug repurposing for antimicrobial discovery. Nat Microbiol. 2019;4:565–77. [DOI] [PubMed]

25.

Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. Drug databases and their contributions to drug repurposing. Genomics. 2020;112:1087–95. [DOI] [PubMed]

26.

Allison M. NCATS launches drug repurposing program. Nat Biotechnol. 2012;30:571–2. [DOI] [PubMed]

27.

Maitra A, Bates S, Shaik M, Evangelopoulos D, Abubakar I, McHugh TD, et al. Repurposing drugs for treatment of tuberculosis: a role for non-steroidal anti-inflammatory drugs. Br Med Bull. 2016;118:138–48. [DOI] [PubMed] [PMC]

28.

Zhou Y, Wang F, Tang J, Nussinov R, Cheng F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health. 2020;2:e667–76. [DOI] [PubMed] [PMC]

29.

Fiscon G, Paci P. SAveRUNNER: an R-based tool for drug repurposing. BMC Bioinformatics. 2021;22:150. [DOI] [PubMed] [PMC]

30.

Rudrapal M, Khairnar SJ, Jadhav AG. Drug repurposing (DR): an emerging approach in drug discovery. In: Badria FA, editor. Drug repurposing - hypothesis, molecular aspects and therapeutic applications. Rijeka: IntechOpen; 2020. [DOI]

31.

Baker NC, Ekins S, Williams AJ, Tropsha A. A bibliometric review of drug repurposing. Drug Discov Today. 2018;23:661–72. [DOI] [PubMed] [PMC]

32.

Tanoli Z, Seemab U, Scherer A, Wennerberg K, Tang J, Vähä-Koskela M. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Brief Bioinform. 2021;22:1656–78. [DOI] [PubMed] [PMC]

33.

Kumar R, Harilal S, Gupta SV, Jose J, Uddin MS, Shah MA, et al. Exploring the new horizons of drug repurposing: a vital tool for turning hard work into smart work. Eur J Med Chem. 2019;182:111602. [DOI] [PubMed] [PMC]

34.

Masoudi-Sobhanzadeh Y. Computational-based drug repurposing methods in COVID-19. Bioimpacts. 2020;10:205–6. [DOI] [PubMed] [PMC]

35.

Sadeghi S, Lu J, Ngom A. A network-based drug repurposing method via non-negative matrix factorization. Bioinformatics. 2022;38:1369–77. [DOI] [PubMed] [PMC]

36.

Akhoon BA, Tiwari H, Nargotra A. In silico drug design methods for drug repurposing. In: Roy K, editor. In silico drug design. Academic Press; 2019. pp. 47–84. [DOI]

37.

Sarvagalla S, Syed SB, Coumar MS. An overview of computational methods, tools, servers, and databases for drug repurposing. In: Roy K, editor. In Silico drug design. Academic Press; 2019. pp. 743–80. [DOI]

38.

Sadeghi SS, Keyvanpour MR. RCDR: A recommender based method for computational drug repurposing. 2019 5th conference on knowledge based engineering and innovation (KBEI); 2019 Feb 28-Mar 1; Tehran, Iran. NY: IEEE; 2019. pp. 467–71. [DOI]

39.

Sepehrinezhad A, Rezaeitalab F, Shahbazi A, Sahab-Negah S. A computational-based drug repurposing method targeting SARS-CoV-2 and its neurological manifestations genes and signaling pathways. Bioinform Biol Insights. 2021;15:11779322211026728. [DOI] [PubMed] [PMC]

40.

Issa NT, Peters OJ, Byers SW, Dakshanamurthy S. RepurposeVS: a drug repurposing-focused computational method for accurate drug-target signature Predictions. Comb Chem High Throughput Screen. 2015;18:784–94. [DOI] [PubMed] [PMC]

41.

Zheng S, Ma H, Wang J, Li J. A computational bipartite graph-based drug repurposing method. Methods Mol Biol. 2019;1903:115–27. [DOI] [PubMed]

42.

Pan X, Lin X, Cao D, Zeng X, Yu PS, He L, et al. Deep learning for drug repurposing: methods, databases, and applications. WIRES COMPUT MOL SCI. 2022;12:e1597. [DOI]

43.

Rapicavoli RV, Alaimo S, Ferro A, Pulvirenti A. Computational methods for drug repurposing. Adv Exp Med Biol. 2022;1361:119–41. [DOI] [PubMed]

44.

Sanseau P, Koehler J. Editorial: computational methods for drug repurposing. Brief Bioinform. 2011;12:301–2. [DOI] [PubMed]

45.

Naveja JJ, Dueñas-González Á, Medina-Franco JL. Chapter 12 - Drug repurposing for epigenetic targets guided by computational methods. In: Medina-Franco JL, editor. Epi-informatics. Boston: Academic Press; 2016. pp. 327–57. [DOI]

46.

Sosa DN, Derry A, Guo M, Wei E, Brinton C, Altman RB. A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases. Pac Symp Biocomput. 2020;25:463–74. [DOI]

47.

Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61. [DOI] [PubMed] [PMC]

48.

Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm. 2016;13:2524–30. [DOI] [PubMed] [PMC]

49.

Wang Z, Sun H, Yao X, Li D, Xu L, Li Y, et al. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys. 2016;18:12964–75. [DOI] [PubMed]

50.

Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. J Med Chem. 2004;47:1739–49. [DOI] [PubMed]

51.

Elokely KM, Doerksen RJ. Docking challenge: protein sampling and molecular docking performance. J Chem Inf Model. 2013;53:1934–45. [DOI] [PubMed] [PMC]

52.

Banerjee D, Bhattacharyya R. Statin therapy may prevent development of tuberculosis in diabetic state. Med Hypotheses. 2014;83:88–91. [DOI] [PubMed]

53.

Fiorillo M, Lamb R, Tanowitz HB, Cappello AR, Martinez-Outschoorn UE, Sotgia F, et al. Bedaquiline, an FDA-approved antibiotic, inhibits mitochondrial function and potently blocks the proliferative expansion of stem-like cancer cells (CSCs). Aging. 2016;8:1593–606. [DOI] [PubMed] [PMC]

54.

Lougheed KE, Osborne SA, Saxty B, Whalley D, Chapman T, Bouloc N, et al. Effective inhibitors of the essential kinase PknB and their potential as anti-mycobacterial agents. Tuberculosis. 2011;91:277–86. [DOI] [PubMed] [PMC]

55.

Naqvi A, Malasoni R, Srivastava A, Pandey RR, Dwivedi AK. Design, synthesis and molecular docking of substituted 3-hydrazinyl-3-oxo-propanamides as anti-tubercular agents. Bioorg Med Chem Lett. 2014;24:5181–4. [DOI] [PubMed]

56.

Brindha S, Vincent S, Velmurugan D, Ananthakrishnan D, Sundaramurthi JC, Gnanadoss JJ. Bioinformatics approach to prioritize known drugs towards repurposing for tuberculosis. Med Hypotheses. 2017;103:39–45. [DOI] [PubMed]

57.

Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35. [DOI] [PubMed]

58.

Doman TN, McGovern SL, Witherbee BJ, Kasten TP, Kurumbail R, Stallings WC, et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem. 2002;45:2213–21. [DOI] [PubMed]

59.

Jadamba E, Shin M. A systematic framework for drug repositioning from integrated omics and drug phenotype profiles using pathway-drug network. Biomed Res Int. 2016;2016:7147039. [DOI] [PubMed] [PMC]

60.

Jin G, Fu C, Zhao H, Cui K, Chang J, Wong ST. A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy. Cancer Res. 2012;72:33–44. [DOI] [PubMed] [PMC]

61.

Sambarey A, Devaprasad A, Baloni P, Mishra M, Mohan A, Tyagi P, et al. Meta-analysis of host response networks identifies a common core in tuberculosis. NPJ Syst Biol Appl. 2017;3:4. [DOI] [PubMed] [PMC]

62.

Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, et al. NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Res. 2005;33:D562–6. [DOI] [PubMed] [PMC]

63.

Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011;3:96ra76. [DOI] [PubMed] [PMC]

64.

Hebbring SJ. The challenges, advantages and future of phenome-wide association studies. Immunology. 2014;141:157–65. [DOI] [PubMed] [PMC]

65.

Luo Y, Thompson WK, Herr TM, Zeng Z, Berendsen MA, Jonnalagadda SR, et al. Natural language processing for EHR-based pharmacovigilance: a structured review. Drug Saf. 2017;40:1075–89. [DOI] [PubMed]

66.

Xu H, Aldrich MC, Chen Q, Liu H, Peterson NB, Dai Q, et al. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc. 2015;22:179–91. [DOI] [PubMed] [PMC]

67.

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;7:496. [DOI] [PubMed] [PMC]

68.

Liu Z, Guo F, Gu J, Wang Y, Li Y, Wang D, et al. Similarity-based prediction for anatomical therapeutic chemical classification of drugs by integrating multiple data sources. Bioinformatics. 2015;31:1788–95. [DOI] [PubMed]

69.

Napolitano F, Zhao Y, Moreira VM, Tagliaferri R, Kere J, D’Amato M, et al. Drug repositioning: a machine-learning approach through data integration. J Cheminform. 2013;5:30. [DOI] [PubMed] [PMC]

70.

Wang Y, Chen S, Deng N, Wang Y. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One. 2013;8:e78518. [DOI] [PubMed] [PMC]

71.

Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34:D668–72. [DOI] [PubMed] [PMC]

72.

Grange JM, Zumla A. The global emergency of tuberculosis: what is the cause? J R Soc Promot Health. 2002;122:78–81. [DOI] [PubMed]

73.

Bhatt K, Salgame P. Host innate immune response to Mycobacterium tuberculosis. J Clin Immunol. 2007;27:347–62. [DOI] [PubMed]

74.

Mueller P, Pieters J. Modulation of macrophage antimicrobial mechanisms by pathogenic mycobacteria. Immunobiology. 2006;211:549–56. [DOI] [PubMed]

75.

Pandey R, Sharma S, Khuller GK. Nebulization of liposome encapsulated antitubercular drugs in guinea pigs. Int J Antimicrob Agents. 2004;24:93–4. [DOI] [PubMed]

76.

Manning T, Mikula R, Lee H, Calvin A, Darrah J, Wylie G, et al. The copper (II) ion as a carrier for the antibiotic capreomycin against Mycobacterium tuberculosis. Bioorg Med Chem Lett. 2014;24:976–82. [DOI] [PubMed]