Advertisement

Omics-Based Nanomedicine

  • Chirasmita Nayak
  • Ishwar Chandra
  • Poonam Singh
  • Sanjeev Kumar Singh
Chapter

Abstract

Traditionally “one-size-fits-all” paradigm for all patients within the disease has the limit of successful treatment. Personalized medicine aims to individualize therapeutic interventions, in combination of ex vivo omics data (i.e., genomics, proteomics, metabolomics, etc.) profiling and in vivo imaging insights on the type, the stage, and the grade of the disease called as theranostic. Personalized medicine has led to the discovery of various biomarkers that can detect the early stage of disease as well as response of bioactive molecules. Nanomedicine, the application of nanotechnology in medicine, holds great promise for diagnosing, treating, and preventing disease and traumatic injury and of relieving pain using molecular tools and molecular knowledge of the human body. Personalized nanomedicine has the power of integration of nanomedicine and molecular biomarkers to improve diagnosis, disease management, and prognosis as well as individualized drug selection by reducing side effects and cytotoxicity. In this book chapter, we have discussed about the leading technologies available for personalized nanomedicine and immense potential combination of nanomedicine with high-throughput omics technology.

Keywords

Omics Nanomedicine NGS Personalized medicine Biomarkers 

Notes

Acknowledgments

SKS, CN, and IC thank the Department of Biotechnology (DBT), New Delhi for providing financial support.

Conflict of Interest

The author(s) declare that there is no conflict of interest.

References

  1. Abken H (2015) Adoptive therapy with CAR redirected T cells: the challenges in targeting solid tumors. Immunotherapy 7(5):535–544CrossRefGoogle Scholar
  2. Accessed from U.S. Food and Drug Administration on 07/01/2017. https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm574058.htm
  3. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422(6928):198–207CrossRefGoogle Scholar
  4. Agyeman AA, Ofori-Asenso R (2015) Perspective: does personalized medicine hold the future for medicine? J Pharm Bioallied Sci 7(3):239CrossRefGoogle Scholar
  5. AkilaKesavan G (2014) Nanotechnology and its applications. Scitech J 1(06):12–13Google Scholar
  6. Albert R, Jeong H, Barabási AL (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382CrossRefGoogle Scholar
  7. Al-Mozaini MA, Mansour MK (2016) Personalized medicine: is it time for infectious diseases? Saudi Med J 37(12):1309CrossRefGoogle Scholar
  8. Altadill T, Campoy I, Lanau L, Gill K, Rigau M, Gil-Moreno A, Reventos J, Byers S, Colas E, Cheema AK (2016) Enabling metabolomics based biomarker discovery studies using molecular phenotyping of exosome-like vesicles. PLoS One 11(3):e0151339CrossRefGoogle Scholar
  9. Araujo RP, Liotta LA, Petricoin EF (2007) Proteins, drug targets and the mechanisms they control: the simple truth about complex networks. Nat Rev Drug Discov 6(11):871–880CrossRefGoogle Scholar
  10. Araújo NP, Silva Kuhn GC, Svartman M (2017) Integrating next generation sequencing, bioinformatics and cytogenomics in the study of Brazilian mammals. Next Gener Seq Appl 4:147Google Scholar
  11. Bainbridge MN, Wang M, Burgess DL, Kovar C, Rodesch MJ, D’Ascenzo M, Kitzman J, Wu YQ, Newsham I, Richmond TA, Jeddeloh JA (2010) Whole exome capture in solution with 3 Gbp of data. Genome Biol 11(6):R62CrossRefGoogle Scholar
  12. Bellazzi R, Zupan B (2008) Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 77(2):81–97CrossRefGoogle Scholar
  13. Bethune MT, Joglekar AV (2017) Personalized T cell-mediated cancer immunotherapy: progress and challenges. Curr Opin Biotechnol 48:142–152CrossRefGoogle Scholar
  14. Bhardwaj A, Bhardwaj A, Misuriya A, Maroli S, Manjula S, Singh AK (2014) Nanotechnology in dentistry: present and future. J Int Oral Health 6(1):121PubMedPubMedCentralGoogle Scholar
  15. Bhati A, Garg H, Gupta A, Chhabra H, Kumari A, Patel T (2012) Omics of cancer. Asian Pac J Cancer Prev 13(9):4229–4233CrossRefGoogle Scholar
  16. Boisseau P, Loubaton B (2011) Nanomedicine, nanotechnology in medicine. Comptes Rendus Phys 12(7):620–636CrossRefGoogle Scholar
  17. Buyse M, Loi S, Van't Veer L, Viale G, Delorenzi M, Glas AM, Saghatchiand’Assignies M, Bergh J, Lidereau R, Ellis P, Harris A (2006) Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98(17):1183–1192CrossRefGoogle Scholar
  18. Cai W, Gao T, Hong H, Sun J (2008) Applications of gold nanoparticles in cancer nanotechnology. Nanotechnol Sci Appl 1:17CrossRefGoogle Scholar
  19. Chan CW, Law BM, So WK, Chow KM, Waye MM (2017) Novel strategies on personalized medicine for breast cancer treatment: an update. Int J Mol Sci 18(11):2423CrossRefGoogle Scholar
  20. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148(6):1293–1307CrossRefGoogle Scholar
  21. Chuang HY, Lee E, Liu YT, Lee D, Ideker T (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3(1):140PubMedPubMedCentralGoogle Scholar
  22. DeVita VT, Canellos GP (2011) Hematology in 2010: new therapies and standard of care in oncology. Nat Rev Clin Oncol 8(2):67–68CrossRefGoogle Scholar
  23. Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, Ford JM, Lydon NB, Kantarjian H, Capdeville R, Ohno-Jones S, Sawyers CL (2001) Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl J Med 2001(344):1031–1037CrossRefGoogle Scholar
  24. Duncan R (2006) Polymer conjugates as anticancer nanomedicines. Nat Rev Cancer 6(9):688CrossRefGoogle Scholar
  25. Engin HB, Hofree M, Carter H. (2014) Identifying mutation specific cancer pathways using a structurally resolved protein interaction network. In: Pacific symposium on biocomputing co-chairs, pp 84–95Google Scholar
  26. Fornaguera C, García-Celma MJ (2017) Personalized nanomedicine: a revolution at the nanoscale. J Personalized Med 7(4):12CrossRefGoogle Scholar
  27. Garraway LA, Lander ES (2013) Lessons from the cancer genome. Cell 153(1):17–37CrossRefGoogle Scholar
  28. Giese B, Klaessig F, Park B, Kaegi R, Steinfeldt M, Wigger H, Gleich A, Gottschalk F (2018) Risks, release and concentrations of engineered nanomaterial in the environment. Sci Rep 8(1):1565CrossRefGoogle Scholar
  29. Glass JI, Assad-Garcia N, Alperovich N, Yooseph S, Lewis MR, Maruf M, Hutchison CA, Smith HO, Venter JC (2006) Essential genes of a minimal bacterium. Proc Natl Acad Sci USA 103(2):425–430CrossRefGoogle Scholar
  30. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537CrossRefGoogle Scholar
  31. Grada A, Weinbrecht K (2013) Next-generation sequencing: methodology and application. J Invest Dermatol 133(8):e11CrossRefGoogle Scholar
  32. Gubin MM, Zhang X, Schuster H, Caron E, Ward JP, Noguchi T, Ivanova Y, Hundal J, Arthur CD, Krebber WJ, Mulder GE (2014) Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515(7528):577CrossRefGoogle Scholar
  33. Gubin MM, Artyomov MN, Mardis ER, Schreiber RD (2015) Tumor neoantigens: building a framework for personalized cancer immunotherapy. J Clin Invest 125(9):3413–3421CrossRefGoogle Scholar
  34. Halappanavar S, Vogel U, Wallin H, Yauk CL (2018) Promise and peril in nanomedicine: the challenges and needs for integrated systems biology approaches to define health risk. Wiley Interdiscip Rev Nanomed Nanobiotechnol 1:10(1)Google Scholar
  35. Han JD (2008) Understanding biological functions through molecular networks. Cell Res 18(2):224–237CrossRefGoogle Scholar
  36. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70CrossRefGoogle Scholar
  37. Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18(1):83CrossRefGoogle Scholar
  38. He ML, Mir PS, Beauchemin KA, Ivan M, Mir Z (2005) Effects of dietary sunflower seeds on lactation performance and conjugated linoleic acid content of milk. Can J Anim Sci 85(1):75–83CrossRefGoogle Scholar
  39. Holzinger A, Dehmer M, Jurisica I (2014) Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions. BMC Bioinforma 15(6):I1CrossRefGoogle Scholar
  40. Hong H, Zhang W, Su Z, Shen J, Ge W, Ning B, Fang H, Perkins R, Shi L, Tong W (2013) Next-generation sequencing (NGS): a revolutionary technology in pharmacogenomics and personalized medicine. In: Barh D, Dhawan D, Ganguly NK (eds) Omics for personalized medicine. Springer, New Delhi, pp 39–61, ISBN 978-81-322-1183-9CrossRefGoogle Scholar
  41. Horgan RP, Kenny LC (2011) ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynaecol 13(3):189–195Google Scholar
  42. Hunyadi Murph SE (2017) An Introduction to Nanotechnology. In: Hunyadi Murph S, Larsen G, Coopersmith K (eds) Anisotropic and shape-selective nanomaterials. Nanostructure science and technology. Springer, Cham, pp 3–5, Print ISBN 978-3-319-59661-7Google Scholar
  43. Hyun BR, McElwee JL, Soloway PD (2015) Single molecule and single cell epigenomics. Methods 72:41–50CrossRefGoogle Scholar
  44. Iacobuzio-Donahue CA (2009) Epigenetic changes in cancer. Annu Rev Pathol Mech Dis 4:229–249CrossRefGoogle Scholar
  45. Ideker T, Sharan R (2008) Protein networks in disease. Genome Res 18(4):644–652CrossRefGoogle Scholar
  46. Jones S, Anagnostou V, Lytle K, Parpart-Li S, Nesselbush M, Riley DR, Shukla M, Chesnick B, Kadan M, Papp E, Galens KG (2015) Personalized genomic analyses for cancermutation discovery and interpretation. Sci Transl Med 7(283):283ra53CrossRefGoogle Scholar
  47. Jung KH, Lee KH (2015) Molecular imaging in the era of personalized medicine. J Pathol Transl Med 49(1):5CrossRefGoogle Scholar
  48. Kchouk M, Gibrat JF, Elloumi M (2017) Generations of sequencing technologies: from first to next generation. Biol Med 9(3)Google Scholar
  49. Kell DB (2007) Metabolomic biomarkers: search, discovery and validation. Expert Rev Mol Diagn 7(4):329–333CrossRefGoogle Scholar
  50. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8(2):e1002375CrossRefGoogle Scholar
  51. Kouzarides T (2007) Chromatin modifications and their function. Cell 128(4):693–705CrossRefGoogle Scholar
  52. Kumar Khanna V (2012) Targeted delivery of nanomedicines. ISRN Pharmacol 10:2012Google Scholar
  53. LaBaer J, Ramachandran N (2005) Protein microarrays as tools for functional proteomics. Curr Opin Chem Biol 9(1):14–19CrossRefGoogle Scholar
  54. Lai-Cheong JE, McGrath JA (2011) Next-generation diagnostics for inherited skin disorders. J Investig Dermatol 131(10):1971–1973CrossRefGoogle Scholar
  55. Lee MS, Flammer AJ, Lerman LO, Lerman A (2012) Personalized medicine in cardiovascular diseases. Kor Circ J 42(9):583–591CrossRefGoogle Scholar
  56. Liu X, Zhou L (2014) Mini review: the application of omics in targeted anticancer biopharmaceuticals development. Austin. J Biomed Eng 1(1):1–8Google Scholar
  57. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR (2005) MicroRNA expression profiles classify human cancers. Nature 435(7043):834–838CrossRefGoogle Scholar
  58. Matsumura Y, Maeda H (1986) A new concept for macromolecular therapeutics in cancer chemotherapy: mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs. Cancer Res 46(12 Part 1):6387–6392PubMedGoogle Scholar
  59. Mayeux R (2004) Biomarkers: potential uses and limitations. NeuroRx 1(2):182–188CrossRefGoogle Scholar
  60. Milone MC, Fish JD, Carpenito C, Carroll RG, Binder GK, Teachey D, Samanta M, Lakhal M, Gloss B, Danet-Desnoyers G, Campana D (2009) Chimeric receptors containing CD137 signal transduction domains mediate enhanced survival of T cells and increased antileukemic efficacy in vivo. Mol Ther 17(8):1453–1464CrossRefGoogle Scholar
  61. Mody VV, Siwale R, Singh A, Mody HR (2010) Introduction to metallic nanoparticles. J Pharm Bioallied Sci 2(4):282CrossRefGoogle Scholar
  62. Morel NM, Holland JM, van der Greef J, Marple EW, Clish C, Loscalzo J, Naylor S (2004) Primer on medical genomics Part XIV: introduction to systems biology—a new approach to understanding disease and treatment. In: Mayo clinic proceedings May 31, Elsevier, vol. 79, no. 5, pp 651–658CrossRefGoogle Scholar
  63. Moreth J, Mavoungou C, Schindowski K (2013) Passive anti-amyloid immunotherapy in Alzheimer’s disease: what are the most promising targets? Immun Ageing 10(1):18CrossRefGoogle Scholar
  64. Morgan D (2011) Immunotherapy for Alzheimer’s disease. J Intern Med 269(1):54–63CrossRefGoogle Scholar
  65. O’Shea K, Cameron SJ, Lewis KE, Lu C, Mur LA (2016) Metabolomic-based biomarker discovery for non-invasive lung cancer screening: a case study. Biochim Biophys Acta (BBA)-Gen Subj 1860(11):2682–2687CrossRefGoogle Scholar
  66. Pagel JM, West HJ (2017) Chimeric antigen receptor (CAR) T-cell therapy. JAMA Oncol 3(11):1595CrossRefGoogle Scholar
  67. Papin JA, Stelling J, Price ND, Klamt S, Schuster S, Palsson BO (2004) Comparison of network-based pathway analysis methods. Trends Biotechnol 22(8):400–405CrossRefGoogle Scholar
  68. Pearson ER (2016) Personalized medicine in diabetes: the role of ‘omics’ and biomarkers. Diabet Med 33(6):712–717CrossRefGoogle Scholar
  69. Pillai S, Gopalan V, Lam AK (2017) Review of sequencing platforms and their applications in phaeochromocytoma and paragangliomas. Crit Rev Oncol Hematol 116:58–67CrossRefGoogle Scholar
  70. Rakesh M, Divya TN, Vishal T, Shalini K (2015) Applications of nanotechnology. J Nanomedine Biotherapeutic Discov 5:131.  https://doi.org/10.4172/2155-983X.1000131 CrossRefGoogle Scholar
  71. Reynolds C, Barrera D, Jotte R, Spira AI, Weissman C, Boehm KA, Pritchard S, Asmar L (2009) Phase II trial of nanoparticle albumin-bound paclitaxel, carboplatin, and bevacizumab in first-line patients with advanced nonsquamous non-small cell lung cancer. J Thorac Oncol 4(12):1537–1543CrossRefGoogle Scholar
  72. Rochfort S (2005) Metabolomics reviewed: a new “omics” platform technology for systems biology and implications for natural products research. J Nat Prod 68(12):1813–1820CrossRefGoogle Scholar
  73. Sadelain M, Brentjens R, Rivière I (2013) The basic principles of chimeric antigen receptor design. Cancer Discov 3(4):388–398CrossRefGoogle Scholar
  74. Sawyers CL (2007) Cancer: mixing cocktails. Nature 449(7165):993–996CrossRefGoogle Scholar
  75. Schnackenberg LK, Beger RD (2007) Metabolomic biomarkers: their role in the critical path. Drug Discov Today Technol 4(1):13–16CrossRefGoogle Scholar
  76. Segal E, Friedman N, Kaminski N, Regev A, Koller D (2005) From signatures to models: understanding cancer using microarrays. Nat Genet 37:S38–S45CrossRefGoogle Scholar
  77. Shu Y, Sheardown SA, Brown C, Owen RP, Zhang S, Castro RA, Ianculescu AG, Yue L, Lo JC, Burchard EG, Brett CM (2007) Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Investig 117(5):1422CrossRefGoogle Scholar
  78. Shulaev V (2006) Metabolomics technology and bioinformatics. Brief Bioinform 7(2):128–139CrossRefGoogle Scholar
  79. Silva GA (2004) Introduction to nanotechnology and its applications to medicine. Surg Neurol 61(3):216–220CrossRefGoogle Scholar
  80. Singleton AB (2011) Exome sequencing: a transformative technology. Lancet Neurol 10(10):942–946CrossRefGoogle Scholar
  81. Smith AJ, Oertle J, Warren D, Prato D (2016) Chimeric antigen receptor (CAR) T cell therapy for malignant cancers: summary and perspective. J Cell Immunother 2(2):59–68CrossRefGoogle Scholar
  82. Soulières D, Greer W, Magliocco AM, Huntsman D, Young S, Tsao MS, Kamel-Reid S (2010) KRAS mutation testing in the treatment of metastatic colorectal cancer with anti-EGFR therapies. Curr Oncol 17(Suppl 1):S31PubMedPubMedCentralGoogle Scholar
  83. Souslova T, Marple TC, Spiekerman AM, Mohammad AA (2013) Personalized medicine in Alzheimer’s disease and depression. Contemp Clin Trials 36(2):616–623CrossRefGoogle Scholar
  84. Tai W, Mahato R, Cheng K (2010) The role of HER2 in cancer therapy and targeted drug delivery. J Control Release 146(3):264–275CrossRefGoogle Scholar
  85. Tebani A, Afonso C, Marret S, Bekri S (2016) Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations. Int J Mol Sci 17(9):1555CrossRefGoogle Scholar
  86. Toebes M, Coccoris M, Bins A, Rodenko B, Gomez R, Nieuwkoop NJ, van de Kasteele W, Rimmelzwaan GF, Haanen JB, Ovaa H, Schumacher TN (2006) Design and use of conditional MHC class I ligands. Nat Med 12(2):246CrossRefGoogle Scholar
  87. Toledo RA, Qin Y, Cheng ZM, Gao Q, Iwata S, Silva G, Prasad M, Ocal IT, Rao S, Aronin N, Barontini MB (2015) Recurrent mutations of chromatin remodeling genes and kinase receptors in pheochromocytomas and paragangliomas. Clin Cancer Res clincanres-1841Google Scholar
  88. Torchilin VP (2005) Recent advances with liposomes as pharmaceutical carriers. Nat Rev Drug Discov 4(2):145CrossRefGoogle Scholar
  89. Van De Vijver MJ, He YD, Van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009CrossRefGoogle Scholar
  90. Van El CG, Cornel MC, Borry P, Hastings RJ, Fellmann F, Hodgson SV, Howard HC, Cambon-Thomsen A, Knoppers BM, Meijers-Heijboer H, Scheffer H (2013) Whole-genome sequencing in health care. Eur J Hum Genet 21:S1–S5PubMedPubMedCentralGoogle Scholar
  91. van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B, van Dijk LJ, Behjati S, Hilkmann H, el Atmioui D, Nieuwland M (2013) Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol Off J Am Soc Clin Oncol 31(32)Google Scholar
  92. Van’t Veer LJ, Dai H, Van De Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, Van Der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536CrossRefGoogle Scholar
  93. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW (2013) Cancer genome landscapes. Science 339(6127):1546–1558CrossRefGoogle Scholar
  94. Vogenberg FR, Barash CI, Pursel M (2010) Personalized medicine: part 1: evolution and development into theranostics. Pharm Ther 35(10):560Google Scholar
  95. Vucic EA, Thu KL, Robison K, Rybaczyk LA, Chari R, Alvarez CE, Lam WL (2012) Translating cancer ‘omics’ to improved outcomes. Genome Res 22(2):188–195CrossRefGoogle Scholar
  96. Wakaskar RR (2017) Passive and active targeting in tumor microenvironment. Int J Drug Dev Res 9(2):19Google Scholar
  97. Wang EC, Wang AZ (2014) Nanoparticles and their applications in cell and molecular biology. Integr Biol 6(1):9–26CrossRefGoogle Scholar
  98. Wang L, Xie XQ (2016) Cancer genomics: opportunities for medicinal chemistry? Future Med Chem 8(4):357–359CrossRefGoogle Scholar
  99. Wang K, Xu C (2017) Applications of next-generation sequencing in cancer research and molecular diagnosis. J Clin Med Genom 5:147Google Scholar
  100. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63CrossRefGoogle Scholar
  101. Wang Q, Lu Q, Zhao H (2015) A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Front Genet 6:149PubMedPubMedCentralGoogle Scholar
  102. Winblad B, Graf A, Riviere ME, Andreasen N, Ryan JM (2014) Active immunotherapy options for Alzheimer’s disease. Alzheimers Res Ther 6(1):7CrossRefGoogle Scholar
  103. Wraith DC (2017) The future of immunotherapy for cancer and autoimmune diseases: a 20 year perspective. Front Immunol 8:1668CrossRefGoogle Scholar
  104. Yadav SP (2007) The wholeness in suffix -omics, -omes, and the word om. J Biomol Tech 18(5):277PubMedPubMedCentralGoogle Scholar
  105. Yu KH, Snyder M (2016) Omics profiling in precision oncology. Mol Cell Proteomics 15(8):2525–2536CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Chirasmita Nayak
    • 1
  • Ishwar Chandra
    • 1
  • Poonam Singh
    • 2
  • Sanjeev Kumar Singh
    • 1
  1. 1.Computer Aided Drug Design and Molecular Modelling Lab, Department of BioinformaticsAlagappa UniversityKaraikudiIndia
  2. 2.Corrosion & Materials Protection DivisionC.S.I.R – Central Electrochemical Research Institute (CECRI)KaraikudiIndia

Personalised recommendations