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  • Review Article
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Emerging applications of metabolomics in drug discovery and precision medicine

Nature Reviews Drug Discovery volume 15, pages 473–484 (2016)Cite this article

Subjects

Key Points

  • New techniques, such as metabolite imaging, and improved analytical technologies are making metabolomics increasingly useful for a wider range of biomedical and pharmaceutical applications.

  • Metabolomics has already entered the clinic, with applications in newborn screening. Many other metabolomic-based clinical applications and tests are now emerging.

  • Metabolomics is revealing surprising metabolic causes and metabolite biomarkers for several prominant diseases such as diabetes, Alzheimer disease, atherosclerosis and cancer. These findings are identifying previously unsuspected therapeutic targets and novel potential therapeutic strategies.

  • Metabolomics is reducing the cost of toxicological screening, enabling improved clinical trial design, allowing better patient selection and monitoring and shortening the time needed for drugs to move through the development pipeline.

  • Metabolomics is beginning to play a part in precision medicine through the development of personalized phenotyping and individualized drug-response monitoring.

  • The use of metabolomics to phenotype tumours and to design custom cancer therapies represents the most 'cutting-edge' example of metabolomics enabling precision medicine.

Abstract

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

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Figure 1: Metabolites play a central part in disease development.
Figure 2: A decision tree for metabolite-based drug discovery and development using atherosclerosis as an example.

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References

  1. Wild, C. P., Scalbert, A. & Herceg, Z. Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk. Environ. Mol. Mutag. 54, 480–499 (2013).

    CAS  Google Scholar 

  2. Houten, S. M. Metabolomics: unraveling the chemical individuality of common human diseases. Ann. Med. 41, 402–407 (2009).

    CAS  PubMed  Google Scholar 

  3. Wishart, D. S. Applications of metabolomics in drug discovery and development. Drugs R. D. 9, 307–322 (2008).

    CAS  PubMed  Google Scholar 

  4. Kaddurah-Daouk, R., Kristal, B. S. & Weinshilboum, R. M. Metabolomics: a global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol. 48, 653–683 (2008). One of the first comprehensive reviews highlighting the many roles that metabolomics can have in drug discovery and personalized medicine.

    CAS  PubMed  Google Scholar 

  5. Everett, J. R. Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 16, 737–754 (2015).

    CAS  PubMed  Google Scholar 

  6. Kricka, L. J. & Savory, J. International year of Chemistry 2011. A guide to the history of clinical chemistry. Clin. Chem. 57, 1118–1126 (2011).

    CAS  PubMed  Google Scholar 

  7. Carpenter, K. J. A short history of nutritional science: part 3 (1912–1944). J. Nutr. 133, 3023–3032 (2003).

    CAS  PubMed  Google Scholar 

  8. Nicholson, J. K. & Lindon, J. C. & Holmes, E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999). The paper that unofficially launched the field of metabolomics (here referred to as metabonomics).

    CAS  PubMed  Google Scholar 

  9. Verdonk, J. C. et al. Regulation of floral scent production in petunia revealed by targeted metabolomics. Phytochemistry 62, 997–1008 (2003).

    CAS  PubMed  Google Scholar 

  10. Lommen, A. et al. An untargeted metabolomics approach to contaminant analysis: pinpointing potential unknown compounds. Anal. Chim. Acta 584, 43–49 (2007).

    CAS  PubMed  Google Scholar 

  11. Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).

    Article  CAS  PubMed  Google Scholar 

  12. Choi, H. K. et al. Metabolic fingerprinting of wild type and transgenic tobacco plants by 1H NMR and multivariate analysis technique. Phytochemistry 65, 857–864 (2004).

    CAS  PubMed  Google Scholar 

  13. Sanford, K., Soucaille, P., Whited, G. & Chotani, G. Genomics to fluxomics and physiomics — pathway engineering. Curr. Opin. Microbiol. 5, 318–322 (2002).

    CAS  PubMed  Google Scholar 

  14. Han, X. & Gross, R. W. Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J. Lipid Res. 44, 1071–1079 (2003).

    CAS  PubMed  Google Scholar 

  15. Szpunar, J. Metallomics: a new frontier in analytical chemistry. Anal. Bioanal. Chem. 378, 54–56 (2004).

    CAS  PubMed  Google Scholar 

  16. Wild, C. P. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomarkers Prev. 14, 1847–1850 (2005). A seminal paper that links metabolomics with exposure science and molecular epidemiology.

    CAS  PubMed  Google Scholar 

  17. Wishart, D. S. Advances in metabolite identification. Bioanalysis 3, 1769–1782 (2011).

    CAS  PubMed  Google Scholar 

  18. Wishart, D. S. Quantitative metabolomics using NMR. TrAC Trends Anal. Chem. 27, 228–237 (2008).

    CAS  Google Scholar 

  19. Zhang, A., Sun, H., Wang, P., Han, Y. & Wang, X. Modern analytical techniques in metabolomics analysis. Analyst 137, 293–300 (2012).

    CAS  PubMed  Google Scholar 

  20. Dunn, W. B., Bailey, N. J. & Johnson, H. E. Measuring the metabolome: current analytical technologies. Analyst 130, 606–625 (2005).

    CAS  PubMed  Google Scholar 

  21. Psychogios, N. et al. The human serum metabolome. PLoS ONE 6, e16957 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013). A nice example of the power and potential of comprehensive, quantitative metabolomics.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Lindon, J. C. & Nicholson, J. K. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics. Annu. Rev. Anal. Chem. 1, 45–69 (2008).

    CAS  Google Scholar 

  24. Brown, M. et al. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics 27, 1108–1112 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    CAS  PubMed  Google Scholar 

  26. Grebe, S. K. & Singh, R. J. LC-MS/MS in the clinical laboratory — where to from here? Clin. Biochem. Rev. 32, 5–31 (2011).

    PubMed  PubMed Central  Google Scholar 

  27. Lehotay, D. C. et al. LC-MS/MS progress in newborn screening. Clin. Biochem. 44, 21–31 (2011).

    CAS  PubMed  Google Scholar 

  28. Chace, D. H. & Spitzer, A. R. Altered metabolism and newborn screening using tandem mass spectrometry: lessons learned from the bench to bedside. Curr. Pharm. Biotechnol. 12, 965–975 (2011).

    CAS  PubMed  Google Scholar 

  29. Hao, J. et al. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat. Protoc. 9, 1416–1427 (2014).

    CAS  PubMed  Google Scholar 

  30. Ravanbakhsh, S. et al. Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS ONE 10, e0124219 (2015).

    PubMed  PubMed Central  Google Scholar 

  31. Aggio, R., Villas-Boas, S. G. & Ruggiero, K. Metab: an R package for high-throughput analysis of metabolomics data generated by GC-MS. Bioinformatics 27, 2316–2318 (2011).

    CAS  PubMed  Google Scholar 

  32. Ni, Y. et al. ADAP-GC 2.0: deconvolution of coeluting metabolites from GC/TOF-MS data for metabolomics studies. Anal. Chem. 84, 6619–6629 (2012).

    CAS  PubMed  Google Scholar 

  33. Weaver, E. M. & Hummon, A. B. Imaging mass spectrometry: from tissue sections to cell cultures. Adv. Drug Deliv. Rev. 65, 1039–1055 (2013).

    CAS  PubMed  Google Scholar 

  34. Lin, A. P. et al. Metabolic imaging of mild traumatic brain injury. Brain Imag. Behav. 6, 208–223 (2012).

    CAS  Google Scholar 

  35. Tkac, I., Oz, G., Adriany, G., Ugurbil, K. & Gruetter, R. In vivo1H NMR spectroscopy of the human brain at high magnetic fields: metabolite quantification at 4T versus 7T. Magn. Reson. Med. 62, 868–879 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Tu, Z. & Mach, R. H. C-11 radiochemistry in cancer imaging applications. Curr. Top. Med. Chem. 10, 1060–1095 (2010).

    CAS  PubMed  Google Scholar 

  37. Qu, W. et al. Preparation and characterization of L-[5-11C]-glutamine for metabolic imaging of tumors. J. Nucl. Med. 53, 98–105 (2012).

    CAS  PubMed  Google Scholar 

  38. Balog, J. et al. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci. Transl. Med. 5, 194ra93 (2013).

    PubMed  Google Scholar 

  39. Sekula, J., Niziol, J., Rode, W. & Ruman, T. Gold nanoparticle-enhanced target (AuNPET) as universal solution for laser desorption/ionization mass spectrometry analysis and imaging of low molecular weight compounds. Anal. Chim. Acta 875, 61–72 (2015).

    CAS  PubMed  Google Scholar 

  40. Gessel, M. M., Norris, J. L. & Caprioli, R. M. MALDI imaging mass spectrometry: spatial molecular analysis to enable a new age of discovery. J. Proteom. 107, 71–82 (2014). A nice review of the promise and potential of MS-based imaging.

    CAS  Google Scholar 

  41. Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet. 33, 228–237 (2003).

    CAS  PubMed  Google Scholar 

  42. Stranger, B. E., Stahl, E. A. & Raj, T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187, 367–383 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Hall, S. S. Revolution postponed. Sci. Am. 303, 60–67 (2010).

    PubMed  Google Scholar 

  44. Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008).

    CAS  PubMed  Google Scholar 

  45. Cuatrecasas, P. Drug discovery in jeopardy. J. Clin. Invest. 116, 2837–2842 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).

    CAS  PubMed  Google Scholar 

  47. Rappaport, S. M., Barupal, D. K., Wishart, D., Vineis, P. & Scalbert, A. The blood exposome and its role in discovering causes of disease. Environ. Health Perspect. 122, 769–774 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. Mokdad, A. H., Marks, J. S., Stroup, D. F. & Gerberding, J. L. Actual causes of death in the United States, 2000. JAMA 291, 1238–1245 (2004).

    PubMed  Google Scholar 

  49. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2011).

    Google Scholar 

  51. Scalbert, A. et al. The food metabolome: a window over dietary exposure. Am. J. Clin. Nutr. 99, 1286–1308 (2014).

    CAS  PubMed  Google Scholar 

  52. Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA 106, 3698–3703 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Joice, R. et al. Determining microbial products and identifying molecular targets in the human microbiome. Cell. Metab. 20, 731–741 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Lusis, A. J., Mar, R. & Pajukanta, P. Genetics of atherosclerosis. Annu. Rev. Genom. Hum. Genet. 5, 189–218 (2004).

    CAS  Google Scholar 

  55. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011). One of the first in a series of superb papers produced by Stanley Hazen's laboratory that link diet, gut microflora and metabolites to cardiovascular disease.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, Z. et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur. Heart J. 35, 904–910 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Gregory, J. C. et al. Transmission of atherosclerosis susceptibility with gut microbial transplantation. J. Biol. Chem. 290, 5647–5460 (2015).

    CAS  PubMed  Google Scholar 

  59. Warrier, M. et al. The TMAO-generating enzyme flavin monooxygenase 3 is a central regulator of cholesterol balance. Cell Rep. 10, 326–338 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Seyfried, T. N., Flores, R. E., Poff, A. M. & D'Agostino, D. P. Cancer as a metabolic disease: implications for novel therapeutics. Carcinogenesis 35, 515–527 (2014).

    CAS  PubMed  Google Scholar 

  61. Levine, A. J. & Puzio-Kuter, A. M. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science 330, 1340–1344 (2010).

    CAS  PubMed  Google Scholar 

  62. Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting α-ketoglutarate to 2-hydroxyglutarate. Cancer Cell. 17, 225–234 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Yang, M., Soga, T. & Pollard, P. J. Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3658 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Wishart, D. S. Is cancer a genetic disease or a metabolic disease? EBioMedicine 2, 478–479 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. Fu, X. et al. 2-Hydroxyglutarate inhibits ATP synthase and mTOR signaling. Cell. Metab. 22, 508–515 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Shanmugasundraram, K. et al. The oncometabolite fumarate promotes pseudohypoxia through noncanonical activation of NF-κB signaling. J. Biol. Chem. 289, 24691–24699 (2014).

    Google Scholar 

  67. Hu, F. B. et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N. Engl. J. Med. 345, 790–797 (2001).

    CAS  PubMed  Google Scholar 

  68. Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011). One of thefirst in a series of excellent papers produced by Robert Gerszten's laboratory that use metabolomics to identify predictive metabolite biomarkers for developing type 2 diabetes.

    PubMed  PubMed Central  Google Scholar 

  69. Wang, T. J. et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J. Clin. Invest. 123, 4309–4317 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Palmer, N. D. et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J. Clin. Endocrinol. Metab. 100, E463–E468 (2015).

    CAS  PubMed  Google Scholar 

  71. Wurtz, P. et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 36, 648–655 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Neis, E. P., Dejong, C. H. & Rensen, S. S. The role of microbial amino acid metabolism in host metabolism. Nutrients 7, 2930–2946 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Li, X. et al. Chronic leucine supplementation increases body weight and insulin sensitivity in rats on high-fat diet likely by promoting insulin signaling in insulin-target tissues. Mol. Nutr. Food Res. 57, 1067–1079 (2013).

    CAS  PubMed  Google Scholar 

  74. Wishart, D. S. et al. HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).

    CAS  PubMed  Google Scholar 

  75. Shaw, W. Increased urinary excretion of a 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), an abnormal phenylalanine metabolite of Clostridia spp. in the gastrointestinal tract, in urine samples from patients with autism and schizophrenia. Nutr. Neurosci. 13, 135–143 (2010).

    CAS  PubMed  Google Scholar 

  76. Mapstone, M. et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat. Med. 20, 415–418 (2014). An interesting paper that suggests a new metabolomic approach to non-invasively identify early Alzheimer disease.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Steinmeyer, S., Lee, K., Jayaraman, A. & Alaniz, R. C. Microbiota metabolite regulation of host immune homeostasis: a mechanistic missing link. Curr. Allergy Asthma Rep. 15, 524 (2015).

    Google Scholar 

  78. Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Bains, W. Failure reates in drug discovery and development: will we ever get any better? Drug Discovery World 5, 9–18 (2004).

    Google Scholar 

  80. Mullard, A. New drug costs US $2.6 billion to develop. Nat. Rev. Drug. Discov. 13, 877 (2014).

    Google Scholar 

  81. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    CAS  PubMed  Google Scholar 

  82. Kim, J. W. & Dang, C. V. Cancer's molecular sweet tooth and the Warburg effect. Cancer Res. 66, 8927–8930 (2006).

    CAS  PubMed  Google Scholar 

  83. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer: the next generation. Cell 144, 648–674 (2011).

    Google Scholar 

  84. Cai, H. et al. Metabolic dysfunction in Alzheimer's disease and related neurodegenerative disorders. Curr. Alzheimer Res. 9, 5–17 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. de la Monte, S. M. & Wands, J. R. Alzheimer's disease is type 3 diabetes — evidence reviewed. J. Diabetes Sci. Technol. 2, 1101–1113.

  86. Brown, J. M. & Hazen, S. L. The gut microbial endocrine organ: bacterially derived signals driving cardiometabolic diseases. Annu. Rev. Med. 66, 343–359 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Cracuin, S. & Balskus, E. P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl Acad. Sci. USA 109, 21307–21312 (2014).

    Google Scholar 

  88. Baker, J. R. & Chaykin, S. The biosynthesis of trimethylamin-N-oxide. J. Biol. Chem. 237, 1309–1313 (1962).

    CAS  PubMed  Google Scholar 

  89. Copeland, R. A., Harpel, M. R. & Tummino, P. J. Targeting enzyme inhibitors in drug discovery. Expert Opin. Ther. Targets 11, 967–978 (2007).

    CAS  PubMed  Google Scholar 

  90. Morgan, S. L. & Baggott, J. E. Medical foods: products for the management of chronic diseases. Nutr. Rev. 64, 495–501 (2006).

    PubMed  Google Scholar 

  91. Semba, R. D. The historical evolution of thought regarding multiple micronutrient nutrition. J. Nutr. 142, 143S–156S (2012).

    CAS  PubMed  Google Scholar 

  92. Baranano, K. W. & Hartman, A. L. The ketogenic diet: uses in epilepsy and other neurologic illnesses. Curr. Treat. Opt. Neurol. 10, 410–409 (2008).

    Google Scholar 

  93. Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).

    CAS  PubMed  Google Scholar 

  94. Lindon, J. C., Holmes, E. & Nicholson, J. K. Metabonomics in pharmaceutical R&D. FEBS J. 274, 1140–1151 (2007). A comprehensive review of how metabolomics (here referred to as metabonomics) could be used in drug research and development.

    CAS  PubMed  Google Scholar 

  95. Lindon, J. C. et al. The consortium for metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6, 691–699 (2005).

    CAS  PubMed  Google Scholar 

  96. Chen, C., Gonzalez, F. J. & Idle, J. R. LC-MS-based metabolomics in drug metabolism. Drug Metab. Rev. 39, 581–597 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Walker, G. S. et al. Biosynthesis of drug metabolites and quantitation using NMR spectroscopy for use in pharmacologic and drug metabolism studies. Drug Metab. Dispos. 42, 1627–1639 (2014).

    PubMed  Google Scholar 

  98. Tomaszewski, M. et al. High rates of non-adherence to antihypertensive treatment revealed by high-performance liquid chromatography-tandem mass spectrometry (HP LC-MS/MS) urine analysis. Heart 100, 855–861 (2014).

    CAS  PubMed  Google Scholar 

  99. Koster, R. A., Alffenaar, J. W., Greijdanus, B., VanDernagel, J. E. & Uges, D. R. Fast and highly selective LC-MS/MS screening for THC and16 other abused drugs and metabolites in human hair to monitor patients for drug abuse. Ther. Drug Monit. 24, 234–243 (2014).

    Google Scholar 

  100. Guo, A. Y., Ma, J. D., Best, B. M. & Atayee, R. S. Urine specimen detection of concurrent nonprescribed medicinal and illicit drug use in patients prescribed buprenorphine. J. Anal. Toxicol. 32, 636–641 (2013).

    Google Scholar 

  101. Andersen, M. B. et al. Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. J. Proteome Res. 13, 1405–1418 (2014).

    CAS  PubMed  Google Scholar 

  102. Couchman, L., Belsey, S. L., Handley, S. A. & Flanagan, R. J. A novel approach to quantitative LC-MS/MS: therapeutic drug monitoring of clozapine and norclozapine using isotopic internal calibration. Anal. Bioanal. Chem. 405, 9455–9466 (2013).

    CAS  PubMed  Google Scholar 

  103. Coen, M. Metabolic phenotyping applied to pre-clinical and clinical studies of acetaminophen metabolism and hepatotoxicity. Drug Metab. Rev. 47, 29–44 (2015).

    CAS  PubMed  Google Scholar 

  104. Navarrrete, A. et al. Simultaneous online SPE-HPLC-MS/MS analysis of docetaxel, temsirolimus and sirolimus in whole blood and human plasma. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 15, 922–935 (2013).

    Google Scholar 

  105. Pickering, M. & Brown, S. Quantification and validation of HPLC-UV and LC-MS assays for therapeutic drug monitoring of ertapenem in human plasma. Biomed. Chromatogr. 27, 568–575 (2013).

    CAS  PubMed  Google Scholar 

  106. Ubhi, B. K. et al. Targeted metabolomics identifies perturbations in amino acid metabolism that sub-classify patients with COPD. Mol. Biosyst. 8, 3125–3133 (2013).

    Google Scholar 

  107. Puskarich, M. A. et al. Pharmcometabolomics of l-carnitine treatment response phenotypes in patients with septic shock. Ann. Am. Thorac. Soc. 12, 46–56 (2015).

    PubMed  PubMed Central  Google Scholar 

  108. Hou, Y. et al. A metabolomics approach for predicting the response to neoadjuvant chemotherapy in cervical cancer patients. Mol. Biosys. 10, 2126–2133 (2014).

    CAS  Google Scholar 

  109. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012). A fascinating paper describing an 'accidental' case study of disease discovery, monitoring and treatment using multiple omics techniques.

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Flores, M., Glusman, G., Brogaard, K., Price, N. D. & Hood, L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per. Med. 10, 565–576 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Hood, L., Lovejoy, J. C. & Price, N. D. Integrating big data and actionable health coaching to optimize wellness. BMC Med. 13, 4 (2015).

    PubMed  PubMed Central  Google Scholar 

  112. Xia, J., Broadhurst, D. I., Wilson, M. & Wishart, D. S. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 9, 280–299 (2013).

    CAS  PubMed  Google Scholar 

  113. la Marca, G. Mass spectrometry in clinical chemistry: the case of newborn screening. J. Pharm. Biomed. Anal. 101, 174–182 (2014).

    CAS  PubMed  Google Scholar 

  114. Diamandis, E. P. The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem? BMC Med. 10, 87 (2012).

    PubMed  PubMed Central  Google Scholar 

  115. Castaldi, P. J., Dahabreh, I. J. & Ioannidis, J. P. An empirical assessment of validation practices for molecular classifiers. Brief. Bioinform. 12, 189–202 (2011).

    PubMed  PubMed Central  Google Scholar 

  116. Turner, R. M. From the lab to the prescription pad: genetics, CYP450 analysis, and medication response. J. Child Adolesc. Psychiatr. Nurs. 26, 119–123 (2013).

    PubMed  Google Scholar 

  117. Kaddurah-Daouk, R., Weinshilboum, R. & Pharmacometabolomics Research Network. Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine. Clin. Pharmacol. Ther. 98, 71–75 (2015).

    CAS  PubMed  Google Scholar 

  118. Gamazon, E. R., Skol, A. D. & Perera, M. A. The limits of genome-wide methods for pharmacogenomic testing. Pharmacogenet. Genom. 22, 261–272 (2012).

    CAS  Google Scholar 

  119. Sallustio, B. C. LC-MS/MS for immunosuppressant therapeutic drug monitoring. Bioanalysis 2, 1141–1153 (2010).

    CAS  PubMed  Google Scholar 

  120. Brozmanová, H., Perinová, I., Halvová, P. & Grundmann, M. Liquid chromatography–tandem mass spectrometry method for simultaneous determination of cyclosporine A and its three metabolites AM1, AM9 and AM4N in whole blood and isolated lymphocytes in renal transplant patients. J. Sep. Sci. 33, 2287–2293 (2010).

    PubMed  Google Scholar 

  121. Shen, B. et al. Determination of total, free and saliva mycophenolic acid with a LC-MS/MS method: application to pharmacokinetic study in healthy volunteers and renal transplant patients. J. Pharm. Biomed. Anal. 50, 515–521 (2009).

    CAS  PubMed  Google Scholar 

  122. Holt, D. W. et al. Long-term evaluation of analytical methods used in sirolimus therapeutic drug monitoring. Clin. Translplant. 28, 243–251 (2014).

    CAS  Google Scholar 

  123. Moes, D. J., Press, R. R., de Fijter, J. W., Guchelaar, H. J. & den Hartigh, J. Liquid chromatography–tandem mass spectrometry outperforms fluorescence polarization immunoassay in monitoring everolimus therapy in renal transplantation. Ther. Drug Monit. 32, 413–419 (2010).

    CAS  PubMed  Google Scholar 

  124. Ponnayyan Sulochana, S., Sharma, K., Mullangi, R. & Sukumaran, S. K. Review of the validated HPLC and LC-MS/MS methods for determination of drugs used in clinical practice for Alzheimer's disease. Biomed. Chromatogr. 28, 1431–1490 (2014).

    CAS  PubMed  Google Scholar 

  125. Zgheib, N. K., Frye, R. F., Tracy, T. S., Romkes, M. & Branch, R. A. Validation of incorporating flurbiprofen into the Pittsburgh cocktail. Clin. Pharmacol. Ther. 80, 257–263 (2006).

    CAS  PubMed  Google Scholar 

  126. Stewart, N. A., Buch, S. C., Conrads, T. P. & Branch, R. A. A. UPLC-MS/MS assay of the “Pittsburgh cocktail”: six CYP probe-drug/metabolites from human plasma and urine using stable isotope dilution. Analyst 136, 605–612 (2011).

    CAS  PubMed  Google Scholar 

  127. Krauss, R. M., Zhu, H. & Kaddurah-Daouk, R. Pharmacometabolomics of statin response. Clin. Pharmacol. Ther. 94, 562–565 (2013).

    CAS  PubMed  Google Scholar 

  128. Zhu, H. et al. Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder — possible role for methoxyindole pathway. PLoS ONE 8, e68283 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Ellero-Simatos, S. et al. Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometr. Syst. Pharmacol. 3, e125 (2014).

    CAS  Google Scholar 

  130. Yerges-Armstrong, L. M. et al. Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics-informed pharmacogenomics. Clin. Pharmacol. Ther. 94, 525–532 (2013).

    CAS  PubMed  Google Scholar 

  131. Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011). An important paper that shows the influence of genetics on individual metabotypes.

    CAS  PubMed  Google Scholar 

  132. Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Wikoff, W. R. et al. Pharmacometabolomics reveals racial differences in response to atenolol treatment. PLoS ONE 8, e57639 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Walther, Z. & Sklar, J. Molecular tumor profiling for prediction of response to anticancer therapies. Cancer J. 17, 71–79 (2011).

    CAS  PubMed  Google Scholar 

  135. Forbes, S. A. et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–811 (2015).

    CAS  PubMed  Google Scholar 

  136. Hipp, S. J. et al. Molecular imaging of pediatric brain tumors: comparison of tumor metabolism using 18F-FDG-PET and MRSI. J. Neurooncol. 109, 521–527 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Zhan, H., Ciano, K., Dong, K. & Zucker, S. Targeting glutamine metabolism in myeloproliferative neoplasms. Blood Cells Mol. Dis. 55, 241–247 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. Sutinen, E. et al. Kinetics of [11C]choline uptake in prostate cancer: a PET study. Eur. J. Nucl. Med. Mol. Imag. 31, 317–324 (2004).

    CAS  Google Scholar 

  139. Choi, C. et al. A comparative study of short- and long-TE 1H MRS at 3 T for in vivo detection of 2-hydroxyglutarate in brain tumors. NMR Biomed. 26, 1242–1250 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Zhu, Z. J. et al. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat. Protoc. 8, 451–460 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Haug, K. et al. MetaboLights — an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).

    CAS  PubMed  Google Scholar 

  142. Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res. 43, W251–W257 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Yang, M., Soga, T. & Pollard, P. J. Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3659 (2013). A well-written review regarding the discovery and emerging importance of oncometabolites in cancer and cancer treatment.

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Yang, M., Soga, T., Pollard, P. J. & Adam, J. The emerging role of fumarate as an oncometabolite. Front. Oncol. 2, 85 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Morin, A., Letouze, E., Gimenez-Roqeuplo, A. P. & Favier, J. Oncometabolites-driven tumorigenesis: From genetics to targeted therapy. Int. J. Cancer 135, 2237–2248 (2014).

    CAS  PubMed  Google Scholar 

  147. Khan, A. P. et al. The role of sarcosine metabolism in prostate cancer progression. Neooplasia 15, 491–501 (2013).

    CAS  Google Scholar 

  148. Chen, K. T. et al. AMPA receptor–mTOR activation is required for the antidepressant-like effects of sarcosine during swim tests in rats: insertion of AMPA receptor may play a role. Front. Behav. Neurosci. 9, 162 (2015).

    PubMed  PubMed Central  Google Scholar 

  149. Lee, A. S. Glucose-regulated proteins in cancer: molecular mechanisms and therapeutic potential. Nat. Rev. Cancer 14, 263–276 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Wolf, A. et al. Hexokinase 2 is a key mediator of aerobic glycolysis and promotes tumor growth in human glioblastoma multiforme. J. Exp. Med. 208, 313–326 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Wise, D. R. & Thompson, C. B. Glutamine addiction: a new therapeutic target in cancer. Trends Biochem. Sci. 35, 427–433 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Miller, D. M., Thomas, S. D., Islam, A., Muench, D. & Sedoris, K. c-Myc and cancer metabolism. Clin. Cancer Res. 18, 5546–5553 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Zhang, J. et al. Asparagine plays a critical role in regulating cellular adaptation to glutamine depletion. Mol. Cell 56, 205–218 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Awwad, H. M., Geisel, J. & Obeid, R. The role of choline in prostate cancer. Clin. Biochem. 45, 1548–1553 (2012).

    CAS  PubMed  Google Scholar 

  155. Choi, S. Y., Collins, C. C., Gout, P. W. & Wang, Y. Cancer-generated lactic acid: a regulatory, immunosuppressive metabolite? J. Pathol. 230, 350–355 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Gillies, R. J. & Gatenby, R. A. Metabolism and its sequelae in cancer evolution and therapy. Cancer J. 21, 88–96 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The author wishes to thank Genome Canada, the Canadian Institutes for Health Research (CIHR) and Alberta Innovates for financial support.

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Authors and Affiliations

  1. Department of Biological Sciences, CW 405, Biological Sciences Building, University of Alberta, Edmonton, T6G 2E9, Alberta, Canada

    David S. Wishart

  2. Department of Computing Science, 2–21 Athabasca Hall University of Alberta, Edmonton, T6G 2E8, Alberta, Canada

    David S. Wishart

  3. National Institute of Nanotechnology, National Research Council, Edmonton, T6G 2M9, Alberta, Canada

    David S. Wishart

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PowerPoint slides

Glossary

Metabolic phenotyping

The characterization of a cell, organism or biological system using metabolomics or metabolic profiling. Metabolic phenotyping is a method of describing the phenotype using chemical or metabolite readouts as a proxy for an organism's observable biochemical traits.

Endogenous metabolites

Metabolites that are biosynthesized or potentially biosynthesized by the host organism and/or its endogenous microflora. Endogenous metabolites also include xenobiotics that have been metabolically transformed by the host.

Exogenous metabolites

Xenobiotic metabolites or chemicals that the host (and/or its endogenous microflora) is not capable of biosynthesizing or that have not yet been metabolically transformed.

Exposomics

A branch of omics science that involves the study of the complete collection of environmental exposures (chemicals, foods, pollutants and pathogens) that a human is exposed to from conception onwards, which is referred to as the exposome.

Coulometric array detectors

Multi-array electrochemical detection systems for detecting redox-active compounds as they elute from a high performance liquid chromatography (HPLC) column. Chemicals or metabolites react with specific electrodes in the detector depending on their redox potential.

Inductively coupled plasma mass spectrometers

Mass spectrometers that are specifically designed to detect and quantify metals at very low concentrations. Metal ions are ionized by inductive heating to create an electrically conductive plasma that is then sent to a conventional mass spectrometer for detection.

Evaporative light-scattering detectors

(ELSDs). Instruments that detect compounds eluting from a high-performance liquid chromatography (HPLC) system on the basis of light scattering rather than ultraviolet absorption or fluorescence. ELSDs permit the detection of far more compounds than other optical techniques.

Secondary ion MS

(SIMS). A mass spectrometry (MS) technique that can be used to analyse and image the composition of thin films. Ions (that is, secondary ions) are generated by sputtering the surface of the sample with an intense ion beam.

Desorption electrospray ionization MS

(DESI-MS). A mass spectrometry technique (MS) that uses atmospheric pressure ion sources to ionize samples in open air under ambient conditions. It is a combination of both electrospray and desorption ionization techniques wherein ionization occurs by spraying an electrically charged mist onto the sample surface.

Microbiome

The collection of microorganisms that reside in or on a larger organism, a larger organ or within a specific environmental niche.

Epigenome

The collection of chemical compounds that act on DNA as well as the collection of chemical modifications to DNA (and histones) that direct and/or alter the original instructions in the genome.

Atherotoxin

An agent (specifically, a chemical, protein or pathogen) that damages arteries leading to atherosclerosis or cardiovascular disease.

Glutaminolysis

A metabolic process involving the catabolism of glutamine to generate energy as well as nitrogen and carbon byproducts. It is an important energy pathway for tumour cells.

Mammalian target of rapamycin

(mTOR). A serine/threonine kinase that acts as a master controller of cell metabolism, cell growth, cell proliferation, cell survival and protein synthesis.

Chemometrics

A field of information science that extracts useful information from chemical data using statistical or data-driven techniques.

Microbiomics

A branch of omics science that involves the study of the microbiome.

Pharmacometabolomics

A branch of metabolomics that involves the metabolomic analysis of both pharmaceutical compounds and endogenous metabolites after the administration of a pharmaceutical compound.

Intronic SNP

A single nucleotide polymorphism (SNP) found in an intron or a non-coding region of a gene.

Metabotypes

The metabolic equivalent of phenotypes. A metabotype is a metabolic profile that defines or classifies an individual's biochemical state at a given point in time.

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Wishart, D. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15, 473–484 (2016). https://doi.org/10.1038/nrd.2016.32

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