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Bioinformatics for Diseases Management: A Personalized Therapeutics Prospective

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Advances in Personalized Nanotherapeutics

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Abstract

The advancements in omics technologies and the emergence of modern bioinformatics tools transport significant changes in understanding of the mechanism of complex disease and revolutionized the healthcare to better disease diagnostics and management. These developments bring a sea of data for the physician and biological researchers to analyze and overcome the challenges of poor penetration of the available drugs into the diseases. A proper way of data management and integration technology can transform the big biomedical datasets into high-value, cost-effectiveness and rational drug target for effective personalized treatment. Moreover, it can reduce diagnostic costs, improve patient care and help the physician to develop individualistic patient care. Also, it enables the researcher to map disease molecules towards the discovery of distinct biomarkers for effective diagnosis through personalized therapeutics. This chapter provides an overview of available and integrated bioinformatics approaches in the role of effective disease managements, which can further establish safer, accurate, and reliable healthcare for every patient.

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References

  1. Fernald HG, Capriotti E, Daneshjou R, et al. Bioinformatics challenges for personalized medicine. Bioinformatics. 2011;27:1741–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Alyass A, Turcotte M, Meyer D, et al. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics. 2015;8:33.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chen R, Snyder M. Promise of personalized omics to precision medicine. Rev Syst Biol Med. 2013;5(1):73–82.

    Article  Google Scholar 

  4. Musa A, Ghoraie SL, Zhang S-D. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform. 2017:1–18. doi:10.1093/bib/bbw112.

  5. Mullen J, Cockell SJ, Woollard P, et al. An integrated data driven approach to drug repositioning using gene-disease associations. PLoS One. 2016;11(5):e0155811.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lu J-J, Pan W, Hu Y-J, et al. Multi-target drugs: the trend of drug research and development. PLoS One. 2012;7(6):e40262.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mooney SD, Krishnan VG, Evani US. Bioinformatic tools for identifying disease gene and SNP candidates. Methods Mol Biol. 2010;628:307–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang P, Wang F, Hu J, et al. Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Summ Transl Sci Proc. 2014;2014:132–6.

    Google Scholar 

  9. Cornetta K, Brown CG. Perspective: balancing personalized medicine and personalized care. Acad Med. 2013;88(3):309–13.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Brown C. Targeted therapy: an elusive cancer target. Nature. 2016;537:S106–8.

    Article  CAS  PubMed  Google Scholar 

  11. Bibault JE, Fumagalli I, Ferté C, et al. Personalized radiation therapy and biomarker-driven treatment strategies: a systematic review. Cancer Metastasis Rev. 2013;32(3-4):479–92.

    Article  CAS  PubMed  Google Scholar 

  12. Scherer HU, Dörner T, Burmester GR. Patient-tailored therapy in rheumatoid arthritis: an editorial review. Curr Opin Rheumatol. 2010;22(3):237–45.

    Article  PubMed  Google Scholar 

  13. Aslani A-A, Mangematin V. The future of drug discovery and development: shifting emphasis towards personalized medicine. Technol Forecast Soc Change. 2010;77(2):203–17.

    Article  Google Scholar 

  14. Downing JG, Boyle NS, Brinner NK. Information management to enable personalized medicine: stakeholder roles in building clinical decision support. BMC Med Inform Decis Mak. 2009;9:44.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Egea RR, Puchalt NG, Escrivá MM, et al. OMICS: current and future perspectives in reproductive medicine and technology. J Hum Reprod Sci. 2014;7(2):73–92.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shendure J, Lieberman AE. The expanding scope of DNA sequencing. Nat Biotechnol. 2012;30(11):1084–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schatz MC, Langmead B, Salzberg SL. Cloud computing and the DNA data race. Nat Biotechnol. 2010;28(7):691–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schadt EE, Linderman MD, Sorenson J, et al. Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nat Rev Genet. 2011;12(3):224.

    Article  CAS  PubMed  Google Scholar 

  19. Jo H, Jeong J, Lee M, et al. Exploiting GPUs in virtual machine for BioCloud. Biomed Res Int. 2013;2013:1–11.

    Article  Google Scholar 

  20. Nobile SM, Cazzaniga P, Tangherloni A. Graphics processing units in bioinformatics, computational biology and systems biology. Brief Bioinform. 2016:bbw058. doi:10.1093/bib/bbw058.

  21. Potamias G, Lakiotaki K, Katsila T, et al. Deciphering next-generation pharmacogenomics: an information technology perspective. Open Biol. 2014;4(7) doi:10.1098/rsob.140071.

  22. Rodin AS, Gogoshin G, Boerwinkle E. Systems biology data analysis methodology in pharmacogenomics. Pharmacogenomics. 2011;12(9):1349–60.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Loscalzo J. Systems biology and personalized medicine: a network approach to human disease. Proc Am Thorac Soc. 2011;8(2):196–8.

    Article  PubMed  Google Scholar 

  25. Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des. 2014;20(1):23–36.

    Article  CAS  PubMed  Google Scholar 

  26. Talevi A. Multi-target pharmacology: possibilities and limitations of the “skeleton key approach” from a medicinal chemist perspective. Front Pharmacol. 2015;6:205.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kitano H. A robustness-based approach to systems-oriented drug design. Nat Rev Drug Discov. 2007;6:202–10.

    Article  CAS  PubMed  Google Scholar 

  28. Xie L, Xie L, Kinnings SL, et al. Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu Rev Pharmacol Toxicol. 2012;52:361–79.

    Article  CAS  PubMed  Google Scholar 

  29. Lehár J, Krueger AS, Avery W, et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat Biotechnol. 2009;27:659–66.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhao X-M, Iskar M, Zeller G, et al. Prediction of drug combinations by integrating molecular and pharmacological data. PLoS Comput Biol. 2011;7:e1002323.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zanzonia A, Soler-Lópeza M, Aloy P. A network medicine approach to human disease. FEBS Lett. 2009;583(11):1759–65.

    Article  Google Scholar 

  32. Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36:D901–6.

    Article  CAS  PubMed  Google Scholar 

  33. Yang H, Qin C, Li HY, et al. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res. 2016;44:D1069–74.

    Article  CAS  PubMed  Google Scholar 

  34. Thorn CF, Klein TE, Altman RB. PharmGKB: the pharmacogenomics knowledge base. Methods Mol Biol. 2013;1015:311–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kuhn M, von Mering C, Campillos M, et al. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 2008;36:D684–8.

    Article  CAS  PubMed  Google Scholar 

  36. Gao Z, Li H, Zhang H, et al. PDTD: a web-accessible protein database for drug target identification. BMC Bioinform. 2008;9:104.

    Article  Google Scholar 

  37. Günther S, Kuhn M, Dunkel M, et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2008;36:D919–22.

    Article  PubMed  Google Scholar 

  38. Berg JM, Rogers ME, Lyster PM. Systems biology and pharmacology. Clin Pharmacol Ther. 2010;88:1719.

    Article  Google Scholar 

  39. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4:682690.

    Article  Google Scholar 

  40. Luo J, Wu M, Gopukumar D, et al. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights. 2016;8:1–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Schadt EE, Linderman MD, Sorenson J, et al. Computational solutions to large-scale data management and analysis. Nat Rev Genet. 2010;11(9):647–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li Y, Chen J. Big biological data: challenges and opportunities. Genomics Proteomics Bioinformatics. 2014;12(5):187–9.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ng SB, Turner EH, Robertson PD, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461:272–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lupski JR, Reid JG, Gonzaga-Jauregui C, et al. Whole-genome sequencing in a patient with Charcot–Marie–Tooth neuropathy. N Engl J Med. 2010;362:1181–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Howe D, Costanzo M, Fey P, et al. Big data: the future of biocuration. Nature. 2008;455:47–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Belle A, Thiagarajan R, Soroushmehr SMR, et al. Big data analytics in healthcare. Biomed Res Int. 2015;2015:370194.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Alzu’bi A, Zhou L, Watzlaf V. Personal genomic information management and personalized medicine: challenges, current solutions, and roles of HIM professionals. Perspect Health Inf Manag. 2014;11:1c.

    PubMed  PubMed Central  Google Scholar 

  48. Nair BG, Newman SF, Peterson GN, et al. Smart Anesthesia Manager™ (SAM)—a real-time decision support system for anesthesia care during surgery. IEEE Trans Biomed Eng. 2013;60(1):207–10.

    Article  PubMed  Google Scholar 

  49. Gomez-Cabrero D, Abugessaisa I, Maier D, et al. Data integration in the era of omics: current and future challenges. BMC Syst Biol. 2014;8(Suppl 2):I1.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Buil-Bruna N, Sahota T, López-Picazo JM, et al. Early prediction of disease progression in small cell lung cancer: toward model-based personalized medicine in oncology. Cancer Res. 2015;175(12):2416–25.

    Article  Google Scholar 

  51. Oyelade J, Soyemi J, Isewon I, et al. Bioinformatics, healthcare informatics and analytics: an imperative for improved healthcare system. Int J Appl Inform Syst. 2015;8(5):1–6.

    Article  Google Scholar 

  52. Kawamoto K, Lobach DF, Willard HF, et al. A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine. BMC Med Inform Decis Mak. 2009;9:17.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Buntin MB, Jain SH, Blumenthal D. Health information technology: laying the infrastructure for national health reform. Health Aff. 2010;29(6):1214–9.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Academy of Finland through grant 272451, by the Finnish Funding Agency for Innovation through grant 1758/31/2016, and by Center of International Mobility through grant TM-15-9933.

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Authors and Affiliations

  1. Computational Biomodeling Laboratory, Turku Centre for Computer Science, Åbo Akademi University, Agora, Domkyrkotorget 3, 20500, Turku, Finland

    Krishna Kanhaiya

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  1. Krishna Kanhaiya
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Correspondence to Krishna Kanhaiya .

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Editors and Affiliations

  1. Department of Immunology, Center for Personalized Nanomedicine, Institute of Neuro-Immune Pharmacology, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, USA

    Ajeet Kaushik

  2. Department of Immunology, Center for Personalized Nanomedicine, Institute of Neuro-Immune Pharmacology, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, USA

    Rahul Dev Jayant

  3. Department of Immunology, Center for Personalized Nanomedicine, Institute of Neuro-Immune Pharmacology, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida, USA

    Madhavan Nair

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Kanhaiya, K. (2017). Bioinformatics for Diseases Management: A Personalized Therapeutics Prospective. In: Kaushik, A., Jayant, R., Nair, M. (eds) Advances in Personalized Nanotherapeutics . Springer, Cham. https://doi.org/10.1007/978-3-319-63633-7_11

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