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NMR Metabolomics Protocols for Drug Discovery

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NMR-Based Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2037))

Abstract

Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is “fragile” and may be negatively impacted by improper sample collection, storage, and extraction procedures. Similarly, biologically irrelevant conclusions may result from incorrect data collection, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.

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References

  1. Hajduk PJ, Greer J (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 6:211. https://doi.org/10.1038/nrd2220

    Article  CAS  PubMed  Google Scholar 

  2. Pellecchia M, Sem DS, Wüthrich K (2002) Nmr in drug discovery. Nat Rev Drug Discov 1:211. https://doi.org/10.1038/nrd748

    Article  CAS  PubMed  Google Scholar 

  3. Pellecchia M, Bertini I, Cowburn D, Dalvit C, Giralt E, Jahnke W, James TL, Homans SW, Kessler H, Luchinat C, Meyer B, Oschkinat H, Peng J, Schwalbe H, Siegal G (2008) Perspectives on NMR in drug discovery: a technique comes of age. Nat Rev Drug Discov 7(9):738–745

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162(6):1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chessari G, Woodhead AJ (2009) From fragment to clinical candidate—a historical perspective. Drug Discov Today 14(13):668–675. https://doi.org/10.1016/j.drudis.2009.04.007

    Article  CAS  PubMed  Google Scholar 

  6. Harner MJ, Frank AO, Fesik SW (2013) Fragment-based drug discovery using NMR spectroscopy. J Biomol NMR 56(2):65–75. https://doi.org/10.1007/s10858-013-9740-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Huth JR, Mendoza R, Olejniczak ET, Johnson RW, Cothron DA, Liu Y, Lerner CG, Chen J, Hajduk PJ (2005) ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. J Am Chem Soc 127(1):217–224. https://doi.org/10.1021/ja0455547

    Article  CAS  PubMed  Google Scholar 

  8. Powers R (2014) The current state of drug discovery and a potential role for NMR metabolomics. J Med Chem 57(14):5860–5870. https://doi.org/10.1021/jm401803b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Powers R (2009) NMR metabolomics and drug discovery. Magn Reson Chem 47(S1):S2–S11. https://doi.org/10.1002/mrc.2461

    Article  CAS  PubMed  Google Scholar 

  10. Robertson DG, Reily MD, Baker JD (2007) Metabonomics in pharmaceutical discovery and development. J Proteome Res 6(2):526–539. https://doi.org/10.1021/pr060535c

    Article  CAS  PubMed  Google Scholar 

  11. Wishart DS (2008) Applications of metabolomics in drug discovery and development. Drugs R&D 9(5):307–322. https://doi.org/10.2165/00126839-200809050-00002

    Article  CAS  Google Scholar 

  12. Wishart DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15:473. https://doi.org/10.1038/nrd.2016.32

    Article  CAS  PubMed  Google Scholar 

  13. Zhang L, Hatzakis E, Patterson AD (2016) NMR-based metabolomics and its application in drug metabolism and cancer research. Curr Pharmacol Rep 2(5):231–240. https://doi.org/10.1007/s40495-016-0067-9

    Article  CAS  Google Scholar 

  14. Wu W, Zhao S (2013) Metabolic changes in cancer: beyond the Warburg effect. Acta Biochim Biophys Sin 45(1):18–26. https://doi.org/10.1093/abbs/gms104

    Article  CAS  PubMed  Google Scholar 

  15. Gardner SG, Marshall DD, Daum RS, Powers R, Somerville GA (2018) Metabolic mitigation of Staphylococcus aureus Vancomycin intermediate-level susceptibility. Antimicrob Agents Chemother 62(1). https://doi.org/10.1128/aac.01608-17

  16. Gaupp R, Lei S, Reed JM, Peisker H, Boyle-Vavra S, Bayer AS, Bischoff M, Herrmann M, Daum RS, Powers R, Somerville GA (2015) Staphylococcus aureus metabolic adaptations during the transition from a daptomycin susceptibility phenotype to a daptomycin nonsusceptibility phenotype. Antimicrob Agents Chemother 59(7):4226–4238. https://doi.org/10.1128/AAC.00160-15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Verdin E (2015) NAD+ in aging, metabolism, and neurodegeneration. Science 350(6265):1208

    Article  CAS  PubMed  Google Scholar 

  18. Van Norman GA (2016) Drugs, devices, and the FDA: part 1: an overview of approval processes for drugs. JACC Basic Transl Sci 1(3):170–179. https://doi.org/10.1016/j.jacbts.2016.03.002

    Article  PubMed  PubMed Central  Google Scholar 

  19. DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 47:20–33

    Article  PubMed  Google Scholar 

  20. Mohs RC, Greig NH (2017) Drug discovery and development: role of basic biological research. Alzheimers Dement (N Y) 3(4):651–657

    Google Scholar 

  21. Harrison RK (2016) Phase II and phase III failures: 2013–2015. Nat Rev Drug Discov 15:817. https://doi.org/10.1038/nrd.2016.184

    Article  CAS  PubMed  Google Scholar 

  22. Smietana K, Siatkowski M, Moeller M (2016) Trends in clinical success rates. Nat Rev Drug Discov 15(6):379–380. https://doi.org/10.1038/nrd.2016.85

    Article  CAS  PubMed  Google Scholar 

  23. Downing NS, Shah ND, Aminawung JA et al (2017) Postmarket safety events among novel therapeutics approved by the us food and drug administration between 2001 and 2010. JAMA 317(18):1854–1863. https://doi.org/10.1001/jama.2017.5150

    Article  PubMed  PubMed Central  Google Scholar 

  24. Settleman J, Cohen RL (2016) Communication in drug development: “translating” scientific discovery. Cell 164(6):1101–1104. https://doi.org/10.1016/j.cell.2016.02.050

    Article  CAS  PubMed  Google Scholar 

  25. Scannell JW, Bosley J (2016) When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLoS One 11(2):e0147215/0147211-e0147215/0147221. https://doi.org/10.1371/journal.pone.0147215

    Article  CAS  Google Scholar 

  26. Kamb A, Wee S, Lengauer C (2007) Why is cancer drug discovery so difficult? Nat Rev Drug Discov 6(2):115–120. https://doi.org/10.1038/nrd2155

    Article  CAS  PubMed  Google Scholar 

  27. Swinney DC (2004) Opinion: biochemical mechanisms of drug action: what does it take for success? Nat Rev Drug Discov 3(9):801–808. https://doi.org/10.1038/nrd1500

    Article  CAS  PubMed  Google Scholar 

  28. Pink R, Hudson A, Mouries M-A, Bendig M (2005) Opportunities and challenges in Antiparasitic drug discovery. Nat Rev Drug Discov 4(9):727–740. https://doi.org/10.1038/nrd1824

    Article  CAS  PubMed  Google Scholar 

  29. Koul A, Arnoult E, Lounis N, Guillemont J, Andries K (2011) The challenge of new drug discovery for tuberculosis. Nature 469(7331):483–490. https://doi.org/10.1038/nature09657

    Article  CAS  PubMed  Google Scholar 

  30. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214. https://doi.org/10.1038/nrd3078

    Article  CAS  PubMed  Google Scholar 

  31. Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL (2007) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Discov 6(1):29–40. https://doi.org/10.1038/nrd2201

    Article  CAS  PubMed  Google Scholar 

  32. Gebregiworgis T, Powers R (2012) Application of NMR metabolomics to search for human disease biomarkers. Comb Chem High Throughput Screen 15(8):595–610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tennant DA, Durán RV, Gottlieb E (2010) Targeting metabolic transformation for cancer therapy. Nat Rev Cancer 10:267. https://doi.org/10.1038/nrc2817

    Article  CAS  PubMed  Google Scholar 

  34. Shukla SK, Purohit V, Mehla K, Gunda V, Chaika NV, Vernucci E, King RJ, Abrego J, Goode GD, Dasgupta A, Illies AL, Gebregiworgis T, Dai B, Augustine JJ, Murthy D, Attri KS, Mashadova O, Grandgenett PM, Powers R, Ly QP, Lazenby AJ, Grem JL, Yu F, Matés JM, Asara JM, J-w K, Hankins JH, Weekes C, Hollingsworth MA, Serkova NJ, Sasson AR, Fleming JB, Oliveto JM, Lyssiotis CA, Cantley LC, Berim L, Singh PK (2017) MUC1 and HIF-1alpha signaling crosstalk induces anabolic glucose metabolism to impart gemcitabine resistance to pancreatic cancer. Cancer Cell 32(1):71–87. e77. https://doi.org/10.1016/j.ccell.2017.06.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Al-Keilani MS, Al-Sawalha NA (2017) Potential of Phenylbutyrate as adjuvant chemotherapy: an overview of cellular and molecular anticancer mechanisms. Chem Res Toxicol 30(10):1767–1777. https://doi.org/10.1021/acs.chemrestox.7b00149

    Article  CAS  PubMed  Google Scholar 

  36. Choi Y-K, Park K-G (2018) Targeting glutamine metabolism for cancer treatment. Biomol Ther (Seoul) 26(1):19–28

    Article  CAS  Google Scholar 

  37. Seo M, Crochet RB, Lee Y-H (2014) Chapter 14—targeting altered metabolism—emerging cancer therapeutic strategies. In: Neidle S (ed) Cancer drug design and discovery, 2nd edn. Academic Press, San Diego, pp 427–448. https://doi.org/10.1016/B978-0-12-396521-9.00014-0

    Chapter  Google Scholar 

  38. Jones NP, Schulze A (2012) Targeting cancer metabolism—aiming at a tumour’s sweet-spot. Drug Discov Today 17(5):232–241. https://doi.org/10.1016/j.drudis.2011.12.017

    Article  CAS  PubMed  Google Scholar 

  39. Copeland RA, Harpel MR, Tummino PJ (2007) Targeting enzyme inhibitors in drug discovery. Expert Opin Ther Targets 11(7):967–978. https://doi.org/10.1517/14728222.11.7.967

    Article  CAS  PubMed  Google Scholar 

  40. Kinch MS, Umlauf S, Plummer M (2015) An analysis of FDA-approved drugs for metabolic diseases. Drug Discov Today 20(6):648–651

    Article  CAS  PubMed  Google Scholar 

  41. Phan LM, Yeung S-CJ, Lee M-H (2014) Cancer metabolic reprogramming: importance, main features, and potentials for precise targeted anti-cancer therapies. Cancer Biol Med 11(1):1–19. https://doi.org/10.7497/j.issn.2095-3941.2014.01.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mercier KA, Baran M, Ramanathan V, Revesz P, Xiao R, Montelione GT, Powers R (2006) FAST-NMR: functional annotation screening technology using NMR spectroscopy. J Am Chem Soc 128(47):15292–15299. https://doi.org/10.1021/ja0651759

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Powers R, Mercier KA, Copeland JC (2008) The application of FAST-NMR for the identification of novel drug discovery targets. Drug Discov Today 13(3):172–179. https://doi.org/10.1016/j.drudis.2007.11.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Venkitakrishnan RP, Benard O, Max M, Markley JL (2012) Use of NMR saturation transfer difference spectroscopy to study ligand binding to membrane proteins. Methods Mol Biol 914:47–63. https://doi.org/10.1007/978-1-62703-023-6_4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mercier KA, Shortridge MD, Powers R (2009) A multi-step NMR screen for the identification and evaluation of chemical leads for drug discovery. Comb Chem High Throughput Screen 12(3):285–295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Moy FJ, Haraki K, Mobilio D, Walker G, Tabei K, Tong H, Siegel MM, Powers R (2001) MS/NMR: a structure-based approach for discovering protein ligands and for drug design by coupling size exclusion chromatography, mass spectrometry, and nuclear magnetic resonance spectroscopy. Anal Chem 73(3):571–581

    Article  CAS  PubMed  Google Scholar 

  47. Dalvit C, Fagerness PE, Hadden DTA, Sarver RW, Stockman BJ (2003) Fluorine-NMR experiments for high-throughput screening: theoretical aspects, practical considerations, and range of applicability. J Am Chem Soc 125(25):7696–7703. https://doi.org/10.1021/ja034646d

    Article  CAS  PubMed  Google Scholar 

  48. Tiziani S, Kang Y, Choi JS, Roberts W, Paternostro G (2011) Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat Commun 2:545. https://doi.org/10.1038/ncomms1562. https://www.nature.com/articles/ncomms1562#supplementary-information

    Article  CAS  PubMed  Google Scholar 

  49. Forgue P, Halouska S, Werth M, Xu K, Harris S, Powers R (2006) NMR metabolic profiling of Aspergillus nidulans to monitor drug and protein activity. J Proteome Res 5(8):1916–1923

    Article  CAS  PubMed  Google Scholar 

  50. Halouska S, Fenton RJ, Barletta RG, Powers R (2012) Predicting the in vivo mechanism of action for drug leads using NMR metabolomics. ACS Chem Biol 7(1):166–171. https://doi.org/10.1021/cb200348m

    Article  CAS  PubMed  Google Scholar 

  51. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM (2006) Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 78(13):4430–4442. https://doi.org/10.1021/ac060209g

    Article  CAS  PubMed  Google Scholar 

  52. Lindon JC, Keun HC, Ebbels TMD, Pearce JMT, Holmes E, Nicholson JK (2005) The consortium for Metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6(7):691–699. https://doi.org/10.2217/14622416.6.7.691

    Article  CAS  PubMed  Google Scholar 

  53. Torell F, Bennett K, Rännar S, Lundstedt-Enkel K, Lundstedt T, Trygg J (2017) The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples. Metabolomics 13(6):66. https://doi.org/10.1007/s11306-017-1196-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Catala A, Culp-Hill R, Nemkov T, D’Alessandro A (2018) Quantitative metabolomics comparison of traditional blood draws and TAP capillary blood collection. Metabolomics 14(7):100. https://doi.org/10.1007/s11306-018-1395-z

    Article  CAS  PubMed  Google Scholar 

  55. Worley B, Powers R (2013) Multivariate analysis in metabolomics. Curr Metabolomics 1(1):92–107. https://doi.org/10.2174/2213235X11301010092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Worley B, Powers R (2016) PCA as a practical indicator of OPLS-DA model reliability. Curr Metabolomics 4(2):97–103. https://doi.org/10.2174/2213235X04666160613122429

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Worley B, Powers R (2014) MVAPACK: a complete data handling package for NMR metabolomics. ACS Chem Biol 9(5):1138–1144. https://doi.org/10.1021/cb4008937

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Worley B, Halouska S, Powers R (2013) Utilities for Quantifying Separation in PCA/PLS-DA scores plots. Anal Biochem 433(2):102–104. https://doi.org/10.1016/j.ab.2012.10.011

    Article  CAS  PubMed  Google Scholar 

  59. Delaglio F, Grzesiek S, Vuister GW, Zhu G, Pfeifer J, Bax A (1995) NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J Biomol NMR 6(3):277–293

    Article  CAS  PubMed  Google Scholar 

  60. Johnson BA (2004) Using NMRView to visualize and analyze the NMR spectra of macromolecules. In: Downing AK (ed) Protein NMR Techniques. Humana Press, Totowa, NJ, pp 313–352. https://doi.org/10.1385/1-59259-809-9:313

    Chapter  Google Scholar 

  61. Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res 43(W1):W251–W257. https://doi.org/10.1093/nar/gkv380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0-the human Metabolome database in 2013. Nucleic Acids Res 41(D1):D801–D807. https://doi.org/10.1093/nar/gks1065

    Article  CAS  PubMed  Google Scholar 

  63. Markley JL, Anderson ME, Cui Q, Eghbalnia HR, Lewis IA, Hegeman AD, Li J, Schulte CF, Sussman MR, Westler WM, Ulrich EL, Zolnai Z (2007) New bioinformatics resources for metabolomics. World Scientific Publishing Co. Pte. Ltd, Singapore, pp 157–168. https://doi.org/10.1142/9789812772435_0016

    Book  Google Scholar 

  64. Worley B, Powers R (2015) Deterministic multidimensional nonuniform gap sampling. J Magn Reson 261:19–26. https://doi.org/10.1016/j.jmr.2015.09.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D, Djoumbou Y, Liu Y, Deng L, Guo AC, Han B, Pon A, Wilson M, Rafatnia S, Liu P, Wishart DS (2014) SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res 42(D1):D478–D484. https://doi.org/10.1093/nar/gkt1067

    Article  CAS  PubMed  Google Scholar 

  66. Blaise BJ, Correia G, Tin A, Young JH, Vergnaud A-C, Lewis M, Pearce JTM, Elliott P, Nicholson JK, Holmes E, Ebbels TMD (2016) Power analysis and sample size determination in metabolic Phenotyping. Anal Chem 88(10):5179–5188. https://doi.org/10.1021/acs.analchem.6b00188

    Article  CAS  PubMed  Google Scholar 

  67. Nyamundanda G, Gormley IC, Fan Y, Gallagher WM, Brennan L (2013) MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach. BMC Bioinformatics 14:338

    Article  PubMed  PubMed Central  Google Scholar 

  68. Bando K, Kawahara R, Kunimatsu T, Sakai J, Kimura J, Funabashi H, Seki T, Bamba T, Fukusaki E (2010) Influences of biofluid sample collection and handling procedures on GC–MS based metabolomic studies. J Biosci Bioeng 110(4):491–499. https://doi.org/10.1016/j.jbiosc.2010.04.010

    Article  CAS  PubMed  Google Scholar 

  69. Emwas A-HM, Salek RM, Griffin JL, Merzaban J (2013) NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations. Metabolomics 9(5):1048–1072. https://doi.org/10.1007/s11306-013-0524-y

    Article  CAS  Google Scholar 

  70. Lipinski CA (2001) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44(1):235–249. https://doi.org/10.1016/S1056-8719(00)00107-6

    Article  Google Scholar 

  71. Li AP (2001) Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today 6(7):357–366. https://doi.org/10.1016/S1359-6446(01)01712-3

    Article  CAS  PubMed  Google Scholar 

  72. Heller AA, Lockwood SY, Janes TM, Spence DM (2018) Technologies for measuring pharmacokinetic profiles. Annu Rev Anal Chem 11:79–100. https://doi.org/10.1146/annurev-anchem-061417-125611

    Article  CAS  Google Scholar 

  73. Reigner BG, Blesch KS (2002) Estimating the starting dose for entry into humans: principles and practice. Eur J Clin Pharmacol 57(12):835–845

    Article  CAS  PubMed  Google Scholar 

  74. Nair AB, Jacob S (2016) A simple practice guide for dose conversion between animals and human. J Basic Clin Pharm 7(2):27–31

    Article  PubMed  PubMed Central  Google Scholar 

  75. O’Gorman A, Gibbons H, Brennan L (2013) Metabolomics in the identification of biomarkers of dietary intake. Comput Struct Biotechnol J 4:e201301004

    Article  PubMed  PubMed Central  Google Scholar 

  76. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, Dame ZT, Poelzer J, Huynh J, Yallou FS, Psychogios N, Dong E, Bogumil R, Roehring C, Wishart DS (2013) The human urine Metabolome. PLoS One 8(9):e73076. https://doi.org/10.1371/journal.pone.0073076

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Emwas A-H, Luchinat C, Turano P, Tenori L, Roy R, Salek RM, Ryan D, Merzaban JS, Kaddurah-Daouk R, Zeri AC, Nagana Gowda GA, Raftery D, Wang Y, Brennan L, Wishart DS (2015) Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review. Metabolomics 11(4):872–894. https://doi.org/10.1007/s11306-014-0746-7

    Article  CAS  PubMed  Google Scholar 

  78. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS (2011) The human serum metabolome. PLoS One 6(2):e16957. https://doi.org/10.1371/journal.pone.0016957

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Geraghty RJ, Capes-Davis A, Davis JM, Downward J, Freshney RI, Knezevic I, Lovell-Badge R, Masters JRW, Meredith J, Stacey GN, Thraves P, Vias M (2014) Guidelines for the use of cell lines in biomedical research. Br J Cancer 111:1021. https://doi.org/10.1038/bjc.2014.166

    Article  PubMed  PubMed Central  Google Scholar 

  80. Halouska S, Zhang B, Gaupp R, Lei S, Snell E, Fenton RJ, Barletta RG, Somerville GA, Powers R (2013) Revisiting protocols for the NMR analysis of bacterial metabolomes. J Integr OMICS 3(2):120–137. https://doi.org/10.5584/jiomics.v3i2.139

    Article  PubMed  PubMed Central  Google Scholar 

  81. Westerhoff HV, Chen Y-D (1984) How do enzyme activities control metabolite concentrations? Eur J Biochem 142(2):425–430. https://doi.org/10.1111/j.1432-1033.1984.tb08304.x

    Article  CAS  PubMed  Google Scholar 

  82. Worley B, Powers R (2014) Simultaneous phase and scatter correction for NMR datasets. Chemom Intell Lab Syst 131:1–6. https://doi.org/10.1016/j.chemolab.2013.11.005

    Article  CAS  Google Scholar 

  83. Savorani F, Tomasi G, Engelsen SB (2010) Icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson 202(2):190–202. https://doi.org/10.1016/j.jmr.2009.11.012

    Article  CAS  PubMed  Google Scholar 

  84. De Meyer T, Sinnaeve D, Van Gasse B, Tsiporkova E, Rietzschel ER, De Buyzere ML, Gillebert TC, Bekaert S, Martins JC, Van Criekinge W (2008) NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem 80(10):3783–3790. https://doi.org/10.1021/ac7025964

    Article  CAS  PubMed  Google Scholar 

  85. Development Core Team R (2011) R: a language and environment for statistical computing, vol 1

    Google Scholar 

  86. Eriksson L, Trygg J, Wold S (2008) CV-ANOVA for significance testing of PLS and OPLS® models. J Chemom 22(11–12):594–600. https://doi.org/10.1002/cem.1187

    Article  CAS  Google Scholar 

  87. Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, Rutledge DN, Savarin P (2015) PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol BioSyst 11(1):13–19. https://doi.org/10.1039/C4MB00414K

    Article  CAS  PubMed  Google Scholar 

  88. Goodacre R, Broadhurst D, Smilde AK, Kristal BS, Baker JD, Beger R, Bessant C, Connor S, Capuani G, Craig A, Ebbels T, Kell DB, Manetti C, Newton J, Paternostro G, Somorjai R, Sjöström M, Trygg J, Wulfert F (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3(3):231–241. https://doi.org/10.1007/s11306-007-0081-3

    Article  CAS  Google Scholar 

  89. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol 57(1):289–300

    Google Scholar 

  90. Bland JM, Altman DG (1995) Multiple significance tests: the Bonferroni method. BMJ 310(6973):170. https://doi.org/10.1136/bmj.310.6973.170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Xu Q-S, Liang Y-Z (2001) Monte Carlo cross validation. Chemom Intell Lab Syst 56(1):1–11. https://doi.org/10.1016/S0169-7439(00)00122-2

    Article  CAS  Google Scholar 

  92. Rubinson KA (2017) Practical corrections for p(H,D) measurements in mixed H2O/D2O biological buffers. Anal Methods 9(18):2744–2750. https://doi.org/10.1039/C7AY00669A

    Article  CAS  Google Scholar 

  93. Bhinderwala F, Lonergan S, Woods J, Zhou C, Fey PD, Powers R (2018) Expanding the coverage of the Metabolome with nitrogen-based NMR. Anal Chem 90(7):4521–4528. https://doi.org/10.1021/acs.analchem.7b04922

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Ruxton GD (2006) The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behav Ecol 17(4):688–690. https://doi.org/10.1093/beheco/ark016

    Article  Google Scholar 

  95. Fay MP, Proschan MA (2010) Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statist Surv 4:1–39. https://doi.org/10.1214/09-SS051

    Article  Google Scholar 

  96. Hotelling H (1931) The economics of exhaustible resources. J Polit Econ 39(2):137–175

    Article  Google Scholar 

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant Number (1660921). This work was supported in part by funding from the Redox Biology Center (P30 GM103335, NIGMS) and the Nebraska Center for Integrated Biomolecular Communication (P20 GM113126, NIGMS). The research was performed in facilities renovated with the support from the National Institutes of Health (RR015468-01). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA

    Fatema Bhinderwala & Robert Powers

  2. Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE, USA

    Fatema Bhinderwala & Robert Powers

Authors
  1. Fatema Bhinderwala
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  2. Robert Powers
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Correspondence to Robert Powers .

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Editors and Affiliations

  1. Northwest Metabolomics Research Center, Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA

    G. A. Nagana Gowda

  2. Northwest Metabolomics Research Center, Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington, Fred Hutch Cancer Research Center, Seattle, WA, USA

    Daniel Raftery

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Bhinderwala, F., Powers, R. (2019). NMR Metabolomics Protocols for Drug Discovery. In: Gowda, G., Raftery, D. (eds) NMR-Based Metabolomics. Methods in Molecular Biology, vol 2037. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9690-2_16

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  • DOI: https://doi.org/10.1007/978-1-4939-9690-2_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9689-6

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