Abstract
With the continuous advancements of biomedical instruments and the associated ability to collect diverse types of valuable biological data, numerous recent research studies have been focusing on how to best extract useful information from the Big Biomedical Data currently available. While drug design has been one of the most essential areas of biomedical research, the drug design process for the most part has not fully benefited from the recent explosive growth of biological data and bioinformatics tools. With the significant overhead associated with the traditional drug design process in terms of time and cost, new alternative methods, possibly based on computational approaches, are very much needed to propose innovative ways to propose effective drugs and new treatment options. Employing advanced computational tools for drug design and precision treatments has been the focus of many research studies in recent years. For example, drug repurposing has gained significant attention from biomedical researchers and pharmaceutical companies as an exciting new alternative for drug discovery that benefits from the computational approaches. Molecular profiling of diseases can be used to design customised treatments and more effective approaches can be explored based on the individuals’ genotype. With the newly developed Bioinformatics tools, researchers and clinicians can repurpose existing drugs and propose innovative therapies and precision treatment options. This new development also promises to transform healthcare to focus more on individualized treatments, precision medicine and lower risks of harmful side effects. In particular, this potential new era in healthcare presents transformative opportunities to advance treatments for chronic and rare diseases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andronis, C., Sharma, A., Virvilis, V., Deftereos, S., Persidis, A.: Literature mining, ontologies and information visualization for drug repurposing. Brief. Bioinform. 12(4), 357–368 (2011)
Barretina, J., et al.: The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391), 603–607 (2012)
Brown, A.S., Kong, S.W., Kohane, I.S., Patel, C.J.: ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics 17(1), 78 (2016)
Chavali, A.K., D’Auria, K.M., Hewlett, E.L., Pearson, R.D., Papin, J.A.: A metabolic network approach for the identification and prioritization of antimicrobial drug targets. Trends in Microbiol. 20(3), 113–123 (2012)
Chen, H.R., Sherr, D.H., Hu, Z., DeLisi, C.: A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer. BMC Med. Genomics 9(1), 51 (2016)
Cheng, F., et al.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)
Cheng, F., et al.: Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat. Commun. 9(1), 1–12 (2018)
Cheng, F., et al.: A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat. Commun. 10(1), 1–14 (2019)
Chiang, A.P., Butte, A.J.: Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86(5), 507–510 (2009)
Delavan, B., Roberts, R., Huang, R., Bao, W., Tong, W., Liu, Z.: Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discovery Today 23(2), 382–394 (2018)
Dong, G., Zhang, P., Yang, J., Zhang, D., Peng, J.: A systematic framework for drug repurposing based on literature mining. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 939–942. IEEE (2019)
Edgar, R., Domrachev, M., Lash, A.E.: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)
Emig, D., et al.: Drug target prediction and repositioning using an integrated network-based approach. PLoS One 8(4), e60618 (2013)
Folger, O., Jerby, L., Frezza, C., Gottlieb, E., Ruppin, E., Shlomi, T.: Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7(501), 1 (2011)
Fukuoka, Y., Takei, D., Ogawa, H.: A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs. Bioinformation 9(2), 89 (2013)
Iorio, F., Rittman, T., Ge, H., Menden, M., Saez-Rodriguez, J.: Transcriptional data: a new gateway to drug repositioning? Drug Discovery Today 18(7–8), 350–357 (2013)
Johnson, M.A., Maggiora, G.M.: Concepts and Applications of Molecular Similarity. Wiley, Hoboken (1990)
Keane, H., Ryan, B.J., Jackson, B., Whitmore, A., Wade-Martins, R.: Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease. Sci. Rep. 5(1), 1–12 (2015)
Keenan, A.B., et al.: The library of integrated network-based cellular signatures NIH program: system-level cataloging of human cells response to perturbations. Cell Syst. 6(1), 13–24 (2018)
Kim, S., Thapa, I., Zhang, L., Ali, H.: On identifying candidates for drug repurposing for the treatment of ulcerative colitis using gene expression data. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds.) IWBBIO 2019. LNCS, vol. 11465, pp. 513–521. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17938-0_45
Kirk, J., et al.: Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy. Support. Care Cancer 26(8), 2695–2705 (2018). https://doi.org/10.1007/s00520-018-4096-2
Lamb, J., et al.: The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006)
Leinonen, R., Sugawara, H., Shumway, M., Collaboration, I.N.S.D.: The sequence read archive. Nucleic Acids Res. 39(suppl\(\_\)1), D19–D21 (2010)
Li, J., Zheng, S., Chen, B., Butte, A.J., Swamidass, S.J., Lu, Z.: A survey of current trends in computational drug repositioning. Brief. Bioinform. 17(1), 2–12 (2016)
Lotfi Shahreza, M., Ghadiri, N., Mousavi, S.R., Varshosaz, J., Green, J.R.: A review of network-based approaches to drug repositioning. Brief. Bioinform. 19(5), 878–892 (2018)
Moosavinasab, S., et al.: ‘RE: fine drugs’: an interactive dashboard to access drug repurposing opportunities. Database 2016 (2016). https://doi.org/10.1093/database/baw083/2630453. https://academic.oup.com/database/article/
Shim, J.S., Liu, J.O.: Recent advances in drug repositioning for the discovery of new anticancer drugs. Int. J. Biol. Sci. 10(7), 654 (2014)
Shore, N.: Accelerating the use of electronic health records in physician practices. N. Engl. J. Med. 362, 192–195 (2010)
So, H.C., et al.: Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nat. Neurosci. 20(10), 1342 (2017)
Wagner, A.H., et al.: DGidb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 44(D1), D1036–D1044 (2016)
Wang, R., Loscalzo, J.: Network-based disease module discovery by a novel seed connector algorithm with pathobiological implications. J. Mol. Biol. 430(18 Pt A), 2939–2950 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kim, S., Thapa, I., Samadi, F., Ali, H. (2020). Computational Approaches for Drug Design: A Focus on Drug Repurposing. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)