We use cookies to improve your experience. By continuing to browse this site, you accept our cookie policy.×
Skip main navigation
Aging Health
Bioelectronics in Medicine
Biomarkers in Medicine
Breast Cancer Management
CNS Oncology
Colorectal Cancer
Concussion
Epigenomics
Future Cardiology
Future Medicine AI
Future Microbiology
Future Neurology
Future Oncology
Future Rare Diseases
Future Virology
Hepatic Oncology
HIV Therapy
Immunotherapy
International Journal of Endocrine Oncology
International Journal of Hematologic Oncology
Journal of 3D Printing in Medicine
Lung Cancer Management
Melanoma Management
Nanomedicine
Neurodegenerative Disease Management
Pain Management
Pediatric Health
Personalized Medicine
Pharmacogenomics
Regenerative Medicine

Application of metabonomics in drug development

    Hector C Keun

    † Author for correspondence

    Imperial College London, Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, South Kensington, London, SW7 2AZ, UK.

    &
    Toby J Athersuch

    Imperial College London, Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine, South Kensington, London, SW7 2AZ, UK.

    Published Online:https://doi.org/10.2217/14622416.8.7.731

    Metabolic profiling (metabonomics/metabolomics) is the untargeted analysis of metabolic composition in a biological sample, and is principally aimed at biomarker discovery. The frequent use of noninvasive biofluid analysis in metabonomics is suited to the clinic and facilitates dynamic monitoring. Analytical protocols for metabolic biomarkers are potentially robust because a metabolite is the same chemical entity irrespective of its origin, facilitating ‘bench-to-bedside’ translational research. Metabonomics can make an impact at several points in the drug-development process: target identification; lead discovery and optimization; preclinical efficacy and safety assessment; mode-of-action and mechanistic toxicology; patient stratification; and clinical pharmacological monitoring. This review describes and exemplifies the latest developments in each of these areas, including the impact of new data and chemical analytical techniques. The future goals for metabonomics are the validation of existing biomarkers, in terms of mechanism and translation to man, together with a focus on characterizing the individual (‘personalized healthcare’).

    Papers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers.

    Bibliography

    • Nicholson JK, Lindon JC, Holmes E: ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica29(11),1181–1189 (1999).
    • Fiehn O: Metabolomics – the link between genotypes and phenotypes. Plant Mol. Biol.48,155–171 (2002).
    • Raamsdonk LM, Teusink B, Broadhurst D et al.: A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotech.19(1),45–50 (2001).
    • Griffin JL, Williams HJ, Sang E, Clarke K, Rae C, Nicholson JK: Metabolic profiling of genetic disorders: a multitissue 1H nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue. Anal. Biochem.293(1),16–21 (2001).
    • Robertson DG: Metabonomics in toxicology: a review. Toxicol. Sci.85(2),809–822 (2005).
    • Keun HC: Metabonomic modeling of drug toxicity. Pharmacol. Ther.109(1–2),92–106 (2006).
    • Brindle JT, Antti H, Holmes E et al.: Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med.8(12),1439–1444 (2003).
    • Forgue P, Halouska S, Werth M, Xu K, Harris S, Powers R: NMR metabolic profiling of Aspergillus nidulans to monitor drug and protein activity. J. Proteome Res.5(8),1916–1923 (2006).• Use of metabolic profiling in a drug-discovery mode, via a functional genomics-like strategy.
    • Glunde K, Ackerstaff E, Mori N et al.: Choline phospholipid metabolism in cancer: consequences for molecular pharmaceutical interventions. Mol. Pharmaceutics3(5),496–506 (2006).
    • 10  Glunde K, Serkova NJ: Therapeutic targets and biomarkers identified in cancer choline phospholipid metabolism. Pharmacogenomics7(7),1109–1123 (2006).
    • 11  Griffin JL, Kauppinen RA: Tumour metabolomics in animal models of human cancer. J. Proteome Res.6(2),498–505 (2007).
    • 12  Glunde K, Ackerstaff E, Natarajan K, Artemov D, Bhujwall ZM: Real-time changes in 1H and 31P NMR spectra of malignant human mammary epithelial cells during treatment with the anti-inflammatory agent indomethacin. Magn. Reson. Med.48(5) 819–825 (2002).
    • 13  Adebodun F: Phospholipid metabolism and resistance to glucocorticoid-induced apoptosis in a human leukemic cell line: a 31P-NMR study using a phosphonium analog of choline. Cancer Lett.140(1–2),189–194 (1999).
    • 14  Al-Saffar NMS, Troy H, Ramírez de Molina A et al.: Noninvasive magnetic resonance spectroscopic pharmacodynamic markers of the choline kinase inhibitor MN58b in human carcinoma models. Cancer Res.66(1),427–434 (2006).•• Exemplifies the successful translation of pharmacodynamic metabolic markers, and the development of drugs against a metabolic target.
    • 15  Espina JR, Shockcor JP, Herron WJ et al.: Detection of in vivo biomarkers of phospholipidosis using NMR-based metabonomic approaches. Magn. Reson. Chem.39(9),559–565 (2001).
    • 16  Garrod S, Humpher E, Connor SC et al.: High-resolution 1H NMR and magic angle spinning NMR spectroscopic investigation of the biochemical effects of 2-bromoethanamine in intact renal and hepatic tissue. Magn. Reson. Med.45(5),781–790 (2001).
    • 17  Holmes E, Bonner FW, Sweatman BC et al.: Nuclear magnetic resonance spectroscopy and pattern recognition analysis of the biochemical processes associated with the progression of and recovery from nephrotoxic lesions in the rat induced by mercury(II) chloride and 2-bromoethanamine. Mol. Pharmacol.42(5),922–930 (1992).
    • 18  Beckwith-Hall BM, Nicholson JK, Nicholls AW et al.: Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem. Res. Toxicol.11(4),260–272 (1998).
    • 19  Lindon JC, Keun HC, Ebbels TMD et al.: The Consortium for Metabonomic Toxicology (COMET): aims, activities and achievements. Pharmacogenomics6(7),691–699 (2005).
    • 20  Sinha G: Trying to catch troublemakers with a metabolic profile. Science310(5750),965–966 (2005).
    • 21  Dieterle F, Schlotterbeck G, Ross A, Niederhauser U, Senn H: Application of metabonomics in a compound ranking study in early drug development revealing drug-induced excretion of choline into urine. Chem. Res. Toxicol.19(9),1175–1181 (2006).• Shows how metabonomics can contribute to lead selection/optimization.
    • 22  Soga T, Baran R, Suematsu M et al.: Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption. J. Biol. Chem.281(24),16768–16776 (2006).•• An example of how noninvasive surrogate markers can be screened for and validated using a metabolic profiling approach.
    • 23  Clayton TA, Lindon JC, Cloarec O et al.: Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature440(7087),1073–1077 (2006).•• First proof-of-principle experiment to show that pretreatment metabolic profiles can be predictive of response to drug exposure.
    • 24  Holmes E, Tsang TM, Huang JT et al.: Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Med.3(8),1420–1428 (2006).• Evidence for metabolism revealing clinical subpopulations.
    • 25  Dumas ME, Barton RH, Toye A et al.: Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl Acad. Sci. USA103(33),12511–12516 (2006).• Detailed example of interactions between genomes at the metabolic level.
    • 26  Stella C, Beckwith-Hall B, Cloarec O et al.: Susceptibility of human metabolic phenotypes to dietary modulation. J. Proteome Res.5(10),2780–2788 (2006).
    • 27  Robosky LC, Wells DF, Egnash LA et al.: Metabonomic identification of two distinct phenotypes in Sprague–Dawley (Crl:CD(SD)) rats. Toxicol. Sci.87(1),277–284 (2005).
    • 28  Nicholson JK, Wilson ID: Understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov.2(8),668–676 (2003).
    • 29  Plumb RS, Granger JH, Stumpf CL et al.: A rapid screening approach to metabonomics using UPLC and oa-TOF mass spectrometry: application to age, gender and diurnal variation in normal/Zucker obese rats and black, white and nude mice. Analyst130(6),884–849 (2005).
    • 30  Ullsten S, Danielsson R, Bäckström D, Sjöberg P, Bergquist J: Urine profiling using capillary electrophoresis–mass spectrometry and multivariate data analysis. J. Chromatogr. A1117(1),87–93 (2006).
    • 31  Casado B, Zanone C, Annovazzi L, Iadarola P, Whalen G, Baraniuk JN: Urinary electrophoretic profiles from chronic fatigue syndrome and chronic fatigue syndrome/fibromyalgia patients: a pilot study for achieving their normalization. J. Chromatogr. B Analyst Technol. Biomed. Life Sci.814(1),43–51 (2005).
    • 32  Szymañska E, Markuszewski MJ, Capron X et al.: Increasing conclusiveness of metabonomic studies by cheminformatic preprocessing of capillary electrophoretic data on urinary nucleoside profiles. J. Pharm. Biomed. Anal.43(2),413–420 (2007).
    • 33  Ellis DI, Goodacre R: Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy. Analyst131,875–885 (2006).
    • 34  Torgrip RJO, Lindberg J, Linder M et al.: New modes of data partitioning based on PARS peak alignment for improved multivariate biomarker/biopattern detection in 1H-NMR spectroscopic metabolic profiling of urine. Metabolomics2(1),1–19 (2006).
    • 35  Craig A, Cloarec O, Holmes E, Nicholson JK, Lindon JC: Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal. Chem.78(7) 2262–2267 (2006).
    • 36  Halouska S, Powers R: Negative impact of noise on the principal component analysis of NMR data. J. Magn. Reson.178(1) 88–95 (2006).
    • 37  Keun HC, Ebbels TMD, Antti H et al.: Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling. Anal. Chim. Acta490(1–2) 265–276 (2003).
    • 38  Cloarec O, Dumas ME, Craig A et al.: Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal Chem.77(5),1282–1289 (2005).
    • 39  Crockford DJ, Holmes E, Lindon JC et al.: Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. Anal. Chem.78(2),363–371 (2006).• Outlines a strategy for metabolite identification that directly integrates complementary analytical data.
    • 40  Pan Z, Gu H, Talaty N et al.: Principal component analysis of urine metabolites detected by NMR and DESI-MS in patients with inborn errors of metabolism. Anal. Bioanal. Chem.387,539–549 (2007).
    • 41  Rantalainen M, Cloarec O, Beckonert O et al.: Statistically integrated metabonomic–proteomic studies on a human prostate cancer xenograft model in mice. J. Proteome Res.5(10),2642–2655 (2006).
    • 42  Visapaa H, Bui M, Huang Y et al.: Correlation of Ki-67 and gelsolin expression to clinical outcome in renal clear cell carcinoma. Urology61(4),845–850 (2003).
    • 43  Thor AD, Edgerton SM, Liu S, Moore DH, Kwiatkowski DJ: Gelsolin as a negative prognostic factor and effector of motility in erbB-2-positive epidermal growth factor receptor-positive breast cancers. Clin. Cancer Res.7(8),2415–2424 (2001).
    • 44  Shieh DB, Godleski J, Herndon JE et al.: Cell motility as a prognostic factor in Stage I nonsmall cell lung carcinoma: the role of gelsolin expression. Cancer85(1) 47–57 (1999).
    • 45  Yang J, Tan D, Asch HL et al.: Prognostic significance of gelsolin expression level and variability in non-small cell lung cancer. Lung Cancer46(1),29–42 (2004).
    • 46  Craig A, Sidaway J, Holmes E et al.: Systems toxicology: integrated genomic, proteomic and metabonomic analysis of methapyrilene induced hepatotoxicity in the rat. J. Proteome Res.5(7),1586–1601 (2006).
    • 47  Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol.19(3),1720–1730 (1999).
    • 48  Keun HC, Ebbels TMD, Antti H et al.: Analytical reproducibility in 1H NMR-based metabonomic urinalysis. Chem. Res. Toxicol.15(11),1380–1386 (2002).
    • 49  Fell D: Understanding the Control of Metabolism. Portland Press Ltd, London, UK (1997).
    • 50  Lindon JC, Nicholson JK, Holmes E et al.: Summary recommendations for standardization and reporting of metabolic analyses. Nat. Biotechnol.23(10),833–838 (2005).