Advertisement

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

  • Jeremy R. EverettEmail author
Chapter
Part of the Handbook of Experimental Pharmacology book series (HEP, volume 260)

Abstract

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

Keywords

Metabolic phenotyping Metabolomics Metabonomics Metabotypes NMR spectroscopy Personalised medicine Pharmacometabolomics Pharmacometabonomics Precision medicine Systems medicine  

Notes

Acknowledgements

I would like to acknowledge productive and enjoyable collaborations with Professor Jeremy Nicholson, Professor John Lindon, Professor Ian Wilson, Professor Elaine Holmes and Professor Elizabeth Shephard over the past 35 years or more. I have gained very much from these stimulating interactions.

Glossary

Area under the curve (AUC)

The integral over time of the concentration of a drug in blood plasma: a measure of the exposure of a patient to the drug.

Capillary electrophoresis (CE)

An electrophoretic separation methodology based on molecular charge and mobility that can be hyphenated to mass spectrometry.

Cmax

The maximal blood plasma concentration achieved by a drug.

Diagnosis

The characterisation of an organism, disease state, phenotype or response to an intervention.

GC

Gas chromatography: a powerful method for the separation of volatile compounds. For use in metabonomics, pre-derivatisation of metabolites is required in order to achieve volatility.

HDL

High density lipoprotein.

HPLC

High performance liquid chromatography: a powerful analytical separation technology often hyphenated with mass spectrometry.

LDL

Low density lipoprotein.

Metabolic entropy

The degree of disorder of metabolite concentrations in an individual or in a group of subjects.

Metabolic phenotype

Multicomponent metabolic characteristics that result from the cumulative interactions of genetic variation, gene products and environmental exposures and that can be related directly to disease risks and therapeutic responses: also known as the metabotype.

Metabolic trajectory

The changes in metabolite concentrations over time in response to an intervention.

Metabolite

A compound in a biological matrix of an organism that is produced in that organism by an enzymatic pathway.

Metabolome

The full set of metabolites within, or that can be secreted from, a biological system such as a cell type or tissue.

Metabolomics

Metabolic profiling defined in an observational fashion as “a comprehensive analysis in which all the metabolites of a biological system are identified and quantified”.

Metabonome

The full set of metabolites contained within an organism, i.e. the sum of all the metabolomes.

Metabonomics

Metabolic profiling defined in an experimental fashion as “the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification”.

Metabotype

A probabilistic, multiparametric description of an organism in a given physiological state based on analysis of its cell types, biofluids and tissues: see metabolic phenotype.

Microbiome

The collection of microorganisms present both in and on an organism, in a variety of environmental niches.

MS

Mass spectrometry: a sensitive analytical methodology for the detection and characterisation of metabolites in biological matrices.

Multivariate analysis: MVA

Multivariate (statistical) analysis: a method for the analysis of multiple variables in an experiment or observation at a time and the simplification of the analysis problem by reduction of the large number of initial variables to a small number of key factors.

NMR spectroscopy

Nuclear magnetic resonance spectroscopy: the most powerful method for molecular structure identification in solution, including metabolites in biological fluids.

OPLS-DA

Orthogonal projection to latent structures with discriminant analysis: a supervised (and therefore potentially biased) approach to multivariate data analysis with the aim of finding metabolites that are statistically significantly discriminating between two groups, e.g. responders and non-responders, and which also discards metabolite variations that are orthogonal to the group discrimination.

Personalised medicine

The use of genomic, molecular and clinical information to select treatments or medicines that are more likely to be both effective and safe for that patient: also known as precision medicine or stratified medicine.

Pharmacogenomics

The prediction of the effects of a drug on the basis of individual genetic profiles.

Pharmacokinetics (PK)

The measurement of the time course of the absorption, distribution, metabolism and excretion of a drug.

Pharmacometabolomics

This term is used synonymously with pharmacometabonomics (see below), but is sometimes erroneously used to describe the investigation of the effects of a drug on an organism: this is just diagnostic metabonomics.

Pharmacometabonomics

The prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles.

Phenotype

The quantitative or qualitative measurement of specific parameters or traits that characterise individual functional biological classes or groups.

Predictive metabolic phenotyping or predictive metabonomics

The prediction of the outcome of an intervention in an individual based on a mathematical model of pre-intervention metabolite profiles. The intervention could be a change in diet, exercise, the passage of time, surgical treatment, etc. Pharmacometabonomics is one case of predictive metabonomics, which covers the prognosis of any intervention.

Principal components analysis (PCA)

An unsupervised (and therefore unbiased) multivariate statistical method for analysing high dimensional data, such as spectral data from metabonomics experiments. The PCA effects a drastic dimensionality reduction and transformation so that new principal components readily display the variance present in the dataset and therefore patterns in the data like clusters or groupings can be readily discerned and outliers identified.

Prognosis

The prediction of disease onset, disease outcome or the outcome of an intervention such as drug treatment.

T2DM

Type 2 diabetes mellitus.

Therapeutic index (TI)

The TI measures the ratio of the effective dose of a drug for 50% of patients (expressed as ED50) to the toxic dose expressed as the TD50. Usually a minimal TI of 10 is required in drug development: some companies will aim for a more conservative TI of 30.

UPLC

Ultra-performance liquid chromatography: a more efficient and effective form of HPLC using smaller column packings and higher pressures.

VLDL

Very low density lipoprotein.

References

  1. Abo R, Hebbring S, Ji Y, Zhu H, Zeng ZB, Batzler A, Jenkins GD, Biernacka J, Snyder K, Drews M, Fiehn O, Fridley B, Schaid D, Kamatani N, Nakamura Y, Kubo M, Mushiroda T, Kaddurah-Daouk R, Mrazek DA, Weinshilboum RM (2012) Merging pharmacometabolomics with pharmacogenomics using ‘1000 Genomes’ single-nucleotide polymorphism imputation: selective serotonin reuptake inhibitor response pharmacogenomics. Pharmacogenet Genomics 22:247–253.  https://doi.org/10.1097/FPC.0b013e32835001c9 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alejandro Vargas D, Dario Prieto M, Jose Martinez-Valencia A, Cossio A, Burgess KEV, Burchmore RJS, Adelaida Gomez M (2019) Pharmacometabolomics of meglumine antimoniate in patients with cutaneous leishmaniasis. Front Pharmacol 10:657.  https://doi.org/10.3389/fphar.2019.00657 CrossRefGoogle Scholar
  3. Allalou A, Nalla A, Prentice KJ, Liu Y, Zhang M, Dai FF, Ning X, Osborne LR, Cox BJ, Gunderson EP, Wheeler MB (2016) A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes. Diabetes 65:2529–2539.  https://doi.org/10.2337/db15-1720 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Amin AM, Chin LS, Teh CH, Mostafa H, Noor DAM, Kader M, Hay YK, Ibrahim B (2017) H-1 NMR based pharmacometabolomics analysis of urine identifies metabolic phenotype of clopidogrel high on treatment platelets reactivity in coronary artery disease patients. J Pharm Biomed Anal 146:135–146.  https://doi.org/10.1016/j.jpba.2017.08.018 CrossRefPubMedGoogle Scholar
  5. Amin AM, Chin LS, Teh C-H, Mostafa H, Noor DAM, Kader MASKA, Hay YK, Ibrahim B (2018) Pharmacometabolomics analysis of plasma to phenotype clopidogrel high on treatment platelets reactivity in coronary artery disease patients. Eur J Pharm Sci 117:351–361.  https://doi.org/10.1016/j.ejps.2018.03.011 CrossRefPubMedGoogle Scholar
  6. Andersson U, Lindberg J, Wang S, Balasubramanian R, Marcusson-Stahl M, Hannula M, Zeng C, Juhasz PJ, Kolmert J, Backstrom J, Nord L, Nilsson K, Martin S, Glinghammar B, Cederbrant K, Schuppe-Koistinen I (2009) A systems biology approach to understanding elevated serum alanine transaminase levels in a clinical trial with ximelagatran. Biomarkers 14:572–586.  https://doi.org/10.3109/13547500903261354 CrossRefPubMedGoogle Scholar
  7. Austdal M, Tangeras LH, Skrastad RB, Salvesen KA, Austgulen R, Iversen A-C, Bathen TF (2015) First trimester urine and serum metabolomics for prediction of preeclampsia and gestational hypertension: a prospective screening study. Int J Mol Sci 16:21520–21538.  https://doi.org/10.3390/ijms160921520 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Backshall A, Sharma R, Clarke SJ, Keun HC (2011) Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine. Clin Cancer Res 17:3019–3028.  https://doi.org/10.1158/1078-0432.ccr-10-2474 CrossRefPubMedGoogle Scholar
  9. Balashova EE, Maslov DL, Lokhov PG (2018) A metabolomics approach to pharmacotherapy personalization. J Pers Med 8:28.  https://doi.org/10.3390/jpm8030028 CrossRefPubMedCentralGoogle Scholar
  10. Bawadikji AA, Teh C-H, Sheikh Abdul Kader MAB, Abdul Wahab MJB, Syed Sulaiman SA, Ibrahim B (2019) Plasma metabolites as predictors of warfarin outcome in atrial fibrillation. Am J Cardiovasc Drugs.  https://doi.org/10.1007/s40256-019-00364-2
  11. Beckonert O, Keun HC, Ebbels TMD, Bundy JG, Holmes E, Lindon JC, Nicholson JK (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2:2692–2703.  https://doi.org/10.1038/nprot.2007.376 CrossRefPubMedGoogle Scholar
  12. Bharti SK, Roy R (2012) Quantitative H-1 NMR spectroscopy. Trac-Trend Anal Chem 35:5–26.  https://doi.org/10.1016/j.trac.2012.02.007 CrossRefGoogle Scholar
  13. Blasco H, Patin F, Descat A, Garcon G, Corcia P, Gele P, Lenglet T, Bede P, Meininger V, Devos D, Gossens JF, Pradat P-F (2018) A pharmaco-metabolomics approach in a clinical trial of ALS: identification of predictive markers of progression. PLoS One 13:e0198116.  https://doi.org/10.1371/journal.pone.0198116 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Bro R, Kamstrup-Nielsen MH, Engelsen SB, Savorani F, Rasmussen MA, Hansen L, Olsen A, Tjonneland A, Dragsted LO (2015) Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics 11:1376–1380.  https://doi.org/10.1007/s11306-015-0793-8 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Broadhurst DI, Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2:171–196.  https://doi.org/10.1007/s11306-006-0037-z CrossRefGoogle Scholar
  16. Burt T, Nandal S (2016) Pharmacometabolomics in early-phase clinical development. CTS Clin Trans Sci 9:128–138.  https://doi.org/10.1111/cts.12396 CrossRefGoogle Scholar
  17. Cao Z, Miller MS, Lubet RA, Grubbs CJ, Beger RD (2019) Pharmacometabolomic pathway response of effective anticancer agents on different diets in rats with induced mammary tumors. Meta 9.  https://doi.org/10.3390/metabo9070149
  18. Chen YH, Xu J, Zhang RP, Abliz Z (2016) Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS. Bioanalysis 8:981–997.  https://doi.org/10.4155/bio-2015-0010 CrossRefPubMedGoogle Scholar
  19. Clayton T, Lindon J, Cloarec O, Antti H, Charuel C, Hanton G, Provost J, Le Net J, Baker D, Walley R, Everett J, Nicholson J (2006) Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440:1073–1077.  https://doi.org/10.1038/nature04648 CrossRefPubMedGoogle Scholar
  20. Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK (2009) Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci U S A 106:14728–14733.  https://doi.org/10.1073/pnas.0904489106 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Coen M, Goldfain-Blanc F, Rolland-Valognes G, Walther B, Robertson DG, Holmes E, Lindon JC, Nicholson JK (2012) Pharmacometabonomic investigation of dynamic metabolic phenotypes associated with variability in response to galactosamine hepatotoxicity. J Proteome Res 11:2427–2440.  https://doi.org/10.1021/pr201161f CrossRefPubMedGoogle Scholar
  22. Condray R, Dougherty GG, Keshavan MS, Reddy RD, Haas GL, Montrose DM, Matson WR, McEvoy J, Kaddurah-Daouk R, Yao JK (2011) 3-Hydroxykynurenine and clinical symptoms in first-episode neuroleptic-naive patients with schizophrenia. Int J Neuropsychopharmacol 14:756–767.  https://doi.org/10.1017/s1461145710001689 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Cunningham K, Claus SP, Lindon JC, Holmes E, Everett JR, Nicholson JK, Coen M (2012) Pharmacometabonomic characterization of xenobiotic and endogenous metabolic phenotypes that account for inter-individual variation in isoniazid-induced toxicological response. J Proteome Res 11:4630–4642.  https://doi.org/10.1021/pr300430u CrossRefPubMedGoogle Scholar
  24. Dai D, Tian Y, Guo H, Zhang P, Huang Y, Zhang W, Xu F, Zhang Z (2016) A pharmacometabonomic approach using predose serum metabolite profiles reveals differences in lipid metabolism in survival and non-survival rats treated with lipopolysaccharide. Metabolomics 12.  https://doi.org/10.1007/s11306-015-0892-6
  25. de Oliveira FA, Shahin MH, Gong Y, McDonough CW, Beitelshees AL, Gums JG, Chapman AB, Boerwinkle E, Turner ST, Frye RF, Fiehn O, Kaddurah-Daouk R, Johnson JA, Cooper-DeHoff RM (2016) Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach. Metabolomics 12.  https://doi.org/10.1007/s11306-016-1076-8
  26. Deelan J et al (2019) A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat Commun 10:1–8.  https://doi.org/10.1038/s41467-019-11311-9 CrossRefGoogle Scholar
  27. Dona AC, Kyriakides M, Scott F, Shephard EA, Varshavi D, Veselkov K, Everett JR (2016) A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput Struct Biotechnol J 14:135–153.  https://doi.org/10.1016/j.csbj.2016.02.005 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Dong B, Jia J, Hu W, Chen Q, Jiang C, Pan J, Huang Y, Xue W, Gao H (2013) Application of H-1 NMR metabonomics in predicting renal function recoverability after the relief of obstructive uropathy in adult patients. Clin Biochem 46:346–353.  https://doi.org/10.1016/j.clinbiochem.2012.11.012 CrossRefPubMedGoogle Scholar
  29. Ellero-Simatos S, Lewis JP, Georgiades A, Yerges-Armstrong LM, Beitelshees AL, Horenstein RB, Dane A, Harms AC, Ramaker R, Vreeken RJ, Perry CG, Zhu H, Sanchez CL, Kuhn C, Ortel TL, Shuldiner AR, Hankemeier T, Kaddurah-Daouk R (2014) Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometrics Syst Pharmacol 3:e125.  https://doi.org/10.1038/psp.2014.22 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Emwas A-H, Luchinat C, Turano P, Tenori L, Roy R, Salek RM, Ryan D, Merzaban JS, Kaddurah-Daouk R, Zeri AC, Gowda GAN, 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 11:872–894.  https://doi.org/10.1007/s11306-014-0746-7 CrossRefPubMedGoogle Scholar
  31. Evans CR, Karnovsky A, Puskarich MA, Michailidis G, Jones AE, Stringer KA (2019) Untargeted metabolomics differentiates L-carnitine treated Septic shock 1-year survivors and nonsurvivors. J Proteome Res 18:2004–2011.  https://doi.org/10.1021/acs.jproteome.8b00774 CrossRefPubMedGoogle Scholar
  32. Everett JR (2015) Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 16:737–754.  https://doi.org/10.2217/pgs.15.20 CrossRefPubMedGoogle Scholar
  33. Everett JR (2016) From metabonomics to pharmacometabonomics: the role of metabolic profiling in personalized medicine. Front Pharmacol 7:15.  https://doi.org/10.3389/fphar.2016.00297 CrossRefGoogle Scholar
  34. Everett JR, Loo RL, Pullen FS (2013) Pharmacometabonomics and personalized medicine. Ann Clin Biochem 50:523–545.  https://doi.org/10.1177/0004563213497929 CrossRefPubMedGoogle Scholar
  35. Everett JR, Lindon JC, Nicholson JK (2016) Pharmacometabonomics and predictive metabonomics: new tools for personalized medicine. In: Holmes E, Jeremy K, Darzi AW, Lindon JC (eds) Metabolic phenotyping in personalized and public healthcare. Academic Press, London, pp 138–165Google Scholar
  36. Everett JR, Holmes E, Veselkov KA, Lindon JC, Nicholson JK (2019) A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis. Trend Pharmacol Sci.  https://doi.org/10.1016/j.tips.2019.08.004
  37. Fiehn O (2002) Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol 48:155–171CrossRefGoogle Scholar
  38. Fischer K, Kettunen J, Wurtz P, Haller T, Havulinna AS, Kangas AJ, Soininen P, Esko T, Tammesoo M-L, Maegi R, Smit S, Palotie A, Ripatti S, Salomaa V, Ala-Korpela M, Perola M, Metspalu A (2014) Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons. PLoS Med 11:e1001606.  https://doi.org/10.1371/journal.pmed.1001606 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Gamage N, Barnett A, Hempel N, Duggleby RG, Windmill KF, Martin JL, McManus ME (2006) Human sulfotransferases and their role in chemical metabolism. Toxicol Sci 90:5–22.  https://doi.org/10.1093/toxsci/kfj061 CrossRefPubMedGoogle Scholar
  40. Gao Y, Li W, Chen J, Wang X, Lv Y, Huang Y, Zhang Z, Xu F (2019) Pharmacometabolomic prediction of individual differences of gastrointestinal toxicity complicating myelosuppression in rats induced by irinotecan. Acta Pharm Sin B 9:157–166.  https://doi.org/10.1016/j.apsb.2018.09.006 CrossRefPubMedGoogle Scholar
  41. Gowda GAN, Raftery D (2017) Recent advances in NMR-based metabolomics. Anal Chem 89:490–510.  https://doi.org/10.1021/acs.analchem.6b04420 CrossRefGoogle Scholar
  42. Gupta M, Neavin D, Liu D, Biernacka J, Hall-Flavin D, Bobo WV, Frye MA, Skime M, Jenkins GD, Batzler A, Kalari K, Matson W, Bhasin SS, Zhu H, Mushiroda T, Nakamura Y, Kubo M, Wang L, Kaddurah-Daouk R, Weinshilboum RM (2016) TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Mol Psychiatry 21:1717–1725.  https://doi.org/10.1038/mp.2016.6 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Hao D, Sarfaraz MO, Farshidfar F, Bebb DG, Lee CY, Card CM, David M, Weljie AM (2016a) Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment. Metabolomics 12:58.  https://doi.org/10.1007/s11306-016-0961-5 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Hao D, Sarfaraz MO, Farshidfar F, Bebb DG, Lee CY, Card CM, David M, Weljie AM (2016b) Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment (vol 12, 58, 2016). Metabolomics 12:122.  https://doi.org/10.1007/s11306-016-1068-8 CrossRefGoogle Scholar
  45. He C, Liu Y, Wang Y, Tang J, Tan Z, Li X, Chen Y, Huang Y, Chen X, Ouyang D, Zhou H, Peng J (2018) H-1 NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers. J Chromatogr B Anal Technol Biomed Life Sci 1095:15–23.  https://doi.org/10.1016/j.jchromb.2018.07.016 CrossRefGoogle Scholar
  46. Huang Q, Aa J, Jia H, Xin X, Tao C, Liu L, Zou B, Song Q, Shi J, Cao B, Yong Y, Wang G, Zhou G (2015) A pharmacometabonomic approach to predicting metabolic phenotypes and pharmacokinetic parameters of atorvastatin in healthy volunteers. J Proteome Res 14:3970–3981.  https://doi.org/10.1021/acs.jproteome.5b00440 CrossRefPubMedGoogle Scholar
  47. Ji Y, Hebbring S, Zhu H, Jenkins GD, Biernacka J, Snyder K, Drews M, Fiehn O, Zeng Z, Schaid D, Mrazek DA, Kaddurah-Daouk R, Weinshilboum RM (2011) Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther 89:97–104.  https://doi.org/10.1038/clpt.2010.250 CrossRefPubMedGoogle Scholar
  48. Jiang L, Lee SC, Ng TC (2018) Pharmacometabonomics analysis reveals serum formate and acetate potentially associated with varying response to gemcitabine-carboplatin chemotherapy in metastatic breast cancer patients. J Proteome Res 17:1248.  https://doi.org/10.1021/acs.jproteome.7b00859 CrossRefPubMedGoogle Scholar
  49. Jove M, Mauri-Capdevila G, Suarez I, Cambray S, Sanahuja J, Quilez A, Farre J, Benabdelhak I, Pamplona R, Portero-Otin M, Purroy F (2015) Metabolomics predicts stroke recurrence after transient ischemic attack. Neurology 84:36–45CrossRefGoogle Scholar
  50. Kaddurah-Daouk R, Baillie RA, Zhu HJ, Zeng ZB, Wiest MM, Nguyen UT, Watkins SM, Krauss RM (2010) Lipidomic analysis of variation in response to simvastatin in the cholesterol and pharmacogenetics study. Metabolomics 6:191–201.  https://doi.org/10.1007/s11306-010-0207-x CrossRefPubMedPubMedCentralGoogle Scholar
  51. Kaddurah-Daouk R, Boyle SH, Matson W, Sharma S, Matson S, Zhu H, Bogdanov MB, Churchill E, Krishnan RR, Rush AJ, Pickering E, Delnomdedieu M (2011) Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept. Transl Psychiatry 1:1–7.  https://doi.org/10.1038/tp.2011.22 CrossRefGoogle Scholar
  52. Kaddurah-Daouk R, Bogdanov MB, Wikoff WR, Zhu H, Boyle SH, Churchill E, Wang Z, Rush AJ, Krishnan RR, Pickering E, Delnomdedieu M, Fiehn O (2013) Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo. Transl Psychiatry 3:e223.  https://doi.org/10.1038/tp.2012.142 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Kaddurah-Daouk R, Weinshilboum R, Pharmacometabolomics Res N (2015) Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine. Clin Pharmacol Ther 98:71–75.  https://doi.org/10.1002/cpt.134 CrossRefPubMedPubMedCentralGoogle Scholar
  54. Kaddurah-Daouk R, Hankemeier T, Scholl EH, Baillie R, Harms A, Stage C, Dalhoff KP, Jurgens G, Taboureau O, Nzabonimpa GS, Motsinger-Reif AA, Thomsen R, Linnet K, Rasmussen HB, INDICES Consortium, Pharmacometabolomics Research Network (2018) Pharmacometabolomics informs about pharmacokinetic profile of methylphenidate. CPT Pharmacometrics Syst Pharmacol 7:525–533.  https://doi.org/10.1002/psp4.12309 CrossRefPubMedPubMedCentralGoogle Scholar
  55. Kapoor SR, Filer A, Fitzpatrick MA, Fisher BA, Taylor PC, Buckley CD, McInnes IB, Raza K, Young SP (2013) Metabolic profiling predicts response to anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis. Arthritis Rheum 65:1448–1456.  https://doi.org/10.1002/art.37921 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Karas-Kuzelicki N, Smid A, Tamm R, Metspalu A, Mlinaric-Rascan I (2014) From pharmacogenetics to pharmacometabolomics: SAM modulates TPMT activity. Pharmacogenomics 15:1437–1449.  https://doi.org/10.2217/pgs.14.84 CrossRefPubMedGoogle Scholar
  57. Keun HC, Sidhu J, Pchejetski D, Lewis JS, Marconell H, Patterson M, Bloom SR, Amber V, Coombes RC, Stebbing J (2009) Serum molecular signatures of weight change during early breast cancer chemotherapy. Clin Cancer Res 15:6716–6723.  https://doi.org/10.1158/1078-0432.ccr-09-1452 CrossRefPubMedGoogle Scholar
  58. Kienana M, Benz-de Bretagne I, Nadal-Desbarats L, Blasco H, Gyan E, Choquet S, Montigny F, Emond P, Barin-Le Guellec C (2016) Endogenous metabolites that are substrates of Organic Anion Transporter’s (OATs) predict methotrexate clearance. Pharmacol Res 118:121.  https://doi.org/10.1016/j.phrs.2016.05.021 CrossRefGoogle Scholar
  59. Kim B, Lee JW, Hong KT, Yu KS, Jang IJ, Park KD, Shin HY, Ahn HS, Cho JY, Kang HJ (2017) Pharmacometabolomics for predicting variable busulfan exposure in paediatric haematopoietic stem cell transplantation patients. Sci Rep 7:1711.  https://doi.org/10.1038/s41598-017-01861-7 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Kind T, Fiehn O (2010) Advances in structure elucidation of small molecules using mass spectrometry. Bioanal Rev 2:23–60.  https://doi.org/10.1007/s12566-010-0015-9 CrossRefPubMedPubMedCentralGoogle Scholar
  61. Kwon HN, Kim M, Wen H, Kang S, Yang H-J, Choi M-J, Lee HS, Choi D, Park IS, Suh YJ, Hong S-S, Park S (2011) Predicting idiopathic toxicity of cisplatin by a pharmacometabonomic approach. Kidney Int 79:529–537.  https://doi.org/10.1038/ki.2010.440 CrossRefPubMedGoogle Scholar
  62. Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalized patients - a meta-analysis of prospective studies. JAMA 279:1200–1205.  https://doi.org/10.1001/jama.279.15.1200 CrossRefPubMedGoogle Scholar
  63. Lee JW, Aminkeng F, Bhavsar AP, Shaw K, Carleton BC, Hayden MR, Ross CJD (2014) The emerging era of pharmacogenomics: current successes, future potential, and challenges. Clin Genet 86:21–28.  https://doi.org/10.1111/cge.12392 CrossRefPubMedPubMedCentralGoogle Scholar
  64. Lee J, Yoon SH, Yi S, Kim AH, Kim B, Lee S, Yu K-S, Jang I-J, Cho J-Y (2019) Quantitative prediction of hepatic CYP3A activity using endogenous markers in healthy subjects after administration of CYP3A inhibitors or inducers. Drug Metab Pharmacokinet 34:247–252.  https://doi.org/10.1016/j.dmpk.2019.04.002 CrossRefPubMedGoogle Scholar
  65. Lewis JP, Yerges-Armstrong LM, Ellero-Simatos S, Georgiades A, Kaddurah-Daouk R, Hankemeier T (2013) Integration of pharmacometabolomic and pharmacogenomic approaches reveals novel insights into antiplatelet therapy. Clin Pharmacol Ther 94:570–573.  https://doi.org/10.1038/clpt.2013.153 CrossRefPubMedPubMedCentralGoogle Scholar
  66. Li H, Ni Y, Su M, Qiu Y, Zhou M, Qiu M, Zhao A, Zhao L, Jia W (2007) Pharmacometabonomic phenotyping reveals different responses to xenobiotic intervention in rats. J Proteome Res 6:1364–1370.  https://doi.org/10.1021/pr060513q CrossRefPubMedGoogle Scholar
  67. Lin YS, Kerr SJ, Randolph T, Shireman LM, Senn T, McCune JS (2016) Prediction of intravenous busulfan clearance by endogenous plasma biomarkers using global pharmacometabolomics. Metabolomics 12:161.  https://doi.org/10.1007/s11306-016-1106-6 CrossRefPubMedPubMedCentralGoogle Scholar
  68. Lindon JC, Wilson ID (2016) The development of metabolic phenotyping - a historical perspective. In: Holmes E, Nicholson JK, Darzi A, Lindon JC (eds) Metabolic phenotyping in personalized and public healthcare. Elsevier, Oxford pp 17–48Google Scholar
  69. Lindon J, Nicholson J, Holmes E, Everett J (2000) Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson 12:289–320.  https://doi.org/10.1002/1099-0534(2000)12:5<289::AID-CMR3>3.0.CO;2-W CrossRefGoogle Scholar
  70. Lindon JC, Nicholson JK, Holmes E (2007) The handbook of metabonomics and metabolomics. Elsevier, AmsterdamGoogle Scholar
  71. Lindon J, Nicholson JK, Holmes E (2019) The handbook of metabolic phenotyping. Elsevier, OxfordGoogle Scholar
  72. Liu L, Cao B, Aa J, Zheng T, Shi J, Li M, Wang X, Zhao C, Xiao W, Yu X, Sun R, Gu R, Zhou J, Wu L, Hao G, Zhu X, Wang G (2012) Prediction of the pharmacokinetic parameters of Triptolide in rats based on endogenous molecules in pre-dose baseline serum. PLoS One 7:e43389.  https://doi.org/10.1371/journal.pone.0043389 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Maltesen RG, Hanifa MA, Kucheryavskiy S, Pedersen S, Kristensen SR, Rasmussen BS, Wimmer R (2016) Predictive biomarkers and metabolic hallmark of postoperative hypoxaemia. Metabolomics 12:87.  https://doi.org/10.1007/s11306-016-1018-5 CrossRefGoogle Scholar
  74. Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS (2017) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34–40.  https://doi.org/10.1016/j.copbio.2016.08.001 CrossRefPubMedGoogle Scholar
  75. Martinez-Avila JC, Garcia Bartolome A, Garcia I, Dapia I, Tong HY, Diaz L, Guerra P, Frias J, Carcas Sansuan AJ, Borobia AM (2018a) Pharmacometabolomics applied to zonisamide pharmacokinetic parameter prediction (vol 14, 70, 2018). Metabolomics 14:87.  https://doi.org/10.1007/s11306-018-1378-0 CrossRefPubMedGoogle Scholar
  76. Martinez-Avila JC, Garcia-Bartolome A, Garcia I, Dapia I, Tong HY, Diaz L, Guerra P, Frias J, Carcas Sansuan AJ, Borobia AM (2018b) Pharmacometabolomics applied to zonisamide pharmacokinetic parameter prediction. Metabolomics 14:70.  https://doi.org/10.1007/s11306-018-1365-5 CrossRefPubMedGoogle Scholar
  77. McPhail MJW, Shawcross DL, Lewis MR, Coltart I, Want EJ, Antoniades CG, Veselkov K, Triantafyllou E, Patel V, Pop O, Gomez-Romero M, Kyriakides M, Zia R, Abeles RD, Crossey MME, Jassem W, O’Grady J, Heaton N, Auzinger G, Bernal W, Quaglia A, Coen M, Nicholson JK, Wendon JA, Holmes E, Taylor-Robinson SD (2016) Multivariate metabotyping of plasma predicts survival in patients with decompensated cirrhosis. J Hepatol 64:1058–1067.  https://doi.org/10.1016/j.jhep.2016.01.003 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Miolo G, Muraro E, Caruso D, Crivellari D, Ash A, Scalone S, Lombardi D, Rizzolio F, Giordano A, Corona G (2016) Phamacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer. Oncotarget 7:39809.  https://doi.org/10.18632/oncotarget.9489 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Nam HW, Karpyak VM, Hinton DJ, Geske JR, Ho AMC, Prieto ML, Biernacka JM, Frye MA, Weinshilboum RM, Choi DS (2015) Elevated baseline serum glutamate as a pharmacometabolomic biomarker for acamprosate treatment outcome in alcohol-dependent subjects. Transl Psychiatry 5:e621.  https://doi.org/10.1038/tp.2015.120 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Navarro SL, Randolph TW, Shireman LM, Raftery D, McCune JS (2016) Pharmacometabonomic prediction of busulfan clearance in hematopoetic cell transplant recipients. J Proteome Res 15:2802–2811.  https://doi.org/10.1021/acs.jproteome.6b00370 CrossRefPubMedPubMedCentralGoogle Scholar
  81. Neavin D, Kaddurah-Daouk R, Weinshilboum R (2016) Pharmacometabolomics informs pharmacogenomics. Metabolomics 12:121.  https://doi.org/10.1007/s11306-016-1066-x CrossRefPubMedPubMedCentralGoogle Scholar
  82. Ni Y, Zhao L, Yu H, Ma X, Bao Y, Rajani C, Loo LM, Shvetsov YB, Yu H, Chen T, Zhang Y, Wang C, Hu C, Su M, Xie G, Zhao A, Jia W, Jia W (2015) Circulating unsaturated fatty acids delineate the metabolic status of obese individuals. EBioMedicine 2:1513–1522.  https://doi.org/10.1016/j.ebiom.2015.09.004 CrossRefPubMedPubMedCentralGoogle Scholar
  83. Nicholson JK, Wilson ID, Lindon JC (2011) Pharmacometabonomics as an effector for personalized medicine. Pharmacogenomics 12:103–111.  https://doi.org/10.2217/pgs.10.157 CrossRefPubMedGoogle Scholar
  84. Nicholson JK, Darzi A, Holmes E, Lindon JC (eds) (2016) Metabolic phenotyping in personalized and public healthcare. Academic Press, LondonGoogle Scholar
  85. Oh J, Yi S, Gu N, Shin D, Yu K-S, Yoon SH, Cho J-Y, Jang I-J (2018) Utility of integrated analysis of pharmacogenomics and pharmacometabolomics in early phase clinical trial: a case study of a new molecular entity. Genom Inform 16:52–58.  https://doi.org/10.5808/gi.2018.16.3.52 CrossRefGoogle Scholar
  86. Park J-E, Jeong G-H, Lee I-K, Yoon Y-R, Liu K-H, Gu N, Shin K-H (2018) A pharmacometabolomic approach to predict response to metformin in early-phase type 2 diabetes mellitus patients. Molecules 23:E1579.  https://doi.org/10.3390/molecules23071579 CrossRefPubMedGoogle Scholar
  87. Phapale PB, Kim SD, Lee HW, Lim M, Kale DD, Kim YL, Cho JH, Hwang D, Yoon YR (2010) An integrative approach for identifying a metabolic phenotype predictive of individualized pharmacokinetics of tacrolimus. Clin Pharmacol Ther 87:426–436.  https://doi.org/10.1038/clpt.2009.296 CrossRefPubMedGoogle Scholar
  88. Phua LC, Goh S, Tai DWM, Leow WQ, Alkaff SMF, Chan CY, Kam JH, Lim TKH, Chan ECY (2017) Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine. Cancer Chemother Pharmacol 81:277.  https://doi.org/10.1007/s00280-017-3475-6 CrossRefPubMedGoogle Scholar
  89. Phua LC, Goh S, Tai DWM, Leow WQ, Alkaff SMF, Chan CY, Kam JH, Lim TKH, Chan ECY (2018) Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine. Cancer Chemother Pharmacol 81:277–289.  https://doi.org/10.1007/s00280-017-3475-6 CrossRefPubMedGoogle Scholar
  90. Pirmohamed M (2014) Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Annu Rev Genomics Hum Genet 15:349–370.  https://doi.org/10.1146/annurev-genom-090413-025419 CrossRefPubMedGoogle Scholar
  91. Puskarich MA, Finkel MA, Karnovsky A, Jones AE, Trexel J, Harris BN, Stringer KA (2015) Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock. Ann Am Thorac Soc 12:46–56.  https://doi.org/10.1513/AnnalsATS.201409-415OC CrossRefPubMedPubMedCentralGoogle Scholar
  92. Puskarich MA, Evans CR, Karnovsky A, Das AK, Jones AE, Stringer KA (2018) Septic shock nonsurvivors have persistently elevated acylcarnitines following carnitine supplementation. Shock 49:412–419.  https://doi.org/10.1097/shk.0000000000000997 CrossRefPubMedPubMedCentralGoogle Scholar
  93. Rahmioglu N, Le Gall G, Heaton J, Kay KL, Smith NW, Colquhoun IJ, Ahmadi KR, Kemsley EK (2011) Prediction of variability in CYP3A4 induction using a combined H-1 NMR metabonomics and targeted UPLC-MS approach. J Proteome Res 10:2807–2816.  https://doi.org/10.1021/pr200077n CrossRefPubMedGoogle Scholar
  94. Reverter E, Lozano JJ, Alonso C, Berzigotti A, Seijo S, Turon F, Baiges A, Martinez-Chantar ML, Mato JM, Martinez-Arranz I, La Mura V, Hernandez-Gea V, Bosch J, Garcia-Pagan JC (2019) Metabolomics discloses potential biomarkers to predict the acute HVPG response to propranolol in patients with cirrhosis. Liver Int 39:705.  https://doi.org/10.1111/liv.14042 CrossRefPubMedGoogle Scholar
  95. Rotroff DM, Shahin MH, Gurley SB, Zhu H, Motsinger-Reif A, Meisner M, Beitelshees AL, Fiehn O, Johnson JA, Elbadawi-Sidhu M, Frye RF, Gong Y, Weng L, Cooper-DeHoff RM, Kaddurah-Daouk R (2015) Pharmacometabolomic assessments of atenolol and hydrochlorothiazide treatment reveal novel drug response phenotypes. CPT Pharmacometrics Syst Pharmacol 4:669–679.  https://doi.org/10.1002/psp4.12017 CrossRefPubMedPubMedCentralGoogle Scholar
  96. Salari K, Watkins H, Ashley EA (2012) Personalized medicine: hope or hype? Eur Heart J 33:1564–1570.  https://doi.org/10.1093/eurheartj/ehs112 CrossRefPubMedPubMedCentralGoogle Scholar
  97. Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BS, van Ommen B, Pujos-Guillot E, Verheij E, Wishart D, Wopereis S (2009) Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics 5:435–458.  https://doi.org/10.1007/s11306-009-0168-0 CrossRefPubMedPubMedCentralGoogle Scholar
  98. Shah RR, Smith RL (2015) Addressing phenoconversion: the Achilles’ heel of personalized medicine. Br J Clin Pharmacol 79:222–240.  https://doi.org/10.1111/bcp.12441 CrossRefPubMedPubMedCentralGoogle Scholar
  99. Shahin MH, Gong Y, Frye RF, Rotroff DM, Beitelshees AL, Baillie RA, Chapman AB, Gums JG, Turner ST, Boerwinkle E, Motsinger-Reif A, Fiehn O, Cooper-DeHoff RM, Han X, Kaddurah-Daouk R, Johnson JA (2017) Sphingolipid metabolic pathway impacts thiazide diuretics blood pressure response: insights from genomics, metabolomics, and lipidomics. J Am Heart Assoc 7:e006656.  https://doi.org/10.1161/JAHA.117.006656 CrossRefPubMedPubMedCentralGoogle Scholar
  100. Shin KH, Choi MH, Lim KS, Yu KS, Jang IJ, Cho JY (2013) Evaluation of endogenous metabolic markers of hepatic CYP3A activity using metabolic profiling and midazolam clearance. Clin Pharmacol Ther 94:601–609.  https://doi.org/10.1038/clpt.2013.128 CrossRefPubMedGoogle Scholar
  101. Shin KH, Ahn LY, Choi MH, Moon JY, Lee J, Jang IJ, Yu KS, Cho JY (2016) Urinary 6β-hydroxycortisol/cortisol ratio most highly correlates with midazolam clearance under hepatic CYP3A inhibition and induction in females: a pharmacometabolomics approach. AAPS J 18:1254–1261.  https://doi.org/10.1208/s12248-016-9941-y CrossRefPubMedGoogle Scholar
  102. Sjoberg RL, Bergenheim T, Moren L, Antti H, Lindgren C, Naredi S, Lindvall P (2015) Blood metabolomic predictors of 1-year outcome in subarachnoid hemorrhage. Neurocrit Care 23:225–232.  https://doi.org/10.1007/s12028-014-0089-2 CrossRefPubMedGoogle Scholar
  103. Stebbing J, Sharma A, North B, Athersuch TJ, Zebrowski A, Pchejetski D, Coombes RC, Nicholson JK, Keun HC (2012) A metabolic phenotyping approach to understanding relationships between metabolic syndrome and breast tumour responses to chemotherapy. Ann Oncol 23:860–866.  https://doi.org/10.1093/annonc/mdr347 CrossRefPubMedGoogle Scholar
  104. Sun Y, Kim JH, Vangipuram K, Hayes DF, Smith EML, Yeomans L, Henry NL, Stringer KA, Hertz DL (2018) Pharmacometabolomics reveals a role for histidine, phenylalanine, and threonine in the development of paclitaxel-induced peripheral neuropathy. Breast Cancer Res Treat 171:657–666.  https://doi.org/10.1007/s10549-018-4862-3 CrossRefPubMedPubMedCentralGoogle Scholar
  105. Tan GG, Zhao BB, Li YQ, Liu X, Zou ZL, Wan J, Yao Y, Xiong H, Wang YY (2017) Pharmacometabolomics identifies dodecanamide and leukotriene B4 dimethylamide as a predictor of chemosensitivity for patients with acute myeloid leukemia treated with cytarabine and anthracycline. Oncotarget 8:88697–88707.  https://doi.org/10.18632/oncotarget.20733 CrossRefPubMedPubMedCentralGoogle Scholar
  106. Trupp M, Zhu H, Wikoff WR, Baillie RA, Zeng ZB, Karp PD, Fiehn O, Krauss RM, Kaddurah-Daouk R (2012) Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS One 7:e38386.  https://doi.org/10.1371/journal.pone.0038386 CrossRefPubMedPubMedCentralGoogle Scholar
  107. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453.  https://doi.org/10.1038/nm.2307 CrossRefPubMedPubMedCentralGoogle Scholar
  108. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, Doering A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost H-G, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8:615.  https://doi.org/10.1038/msb.2012.43 CrossRefPubMedPubMedCentralGoogle Scholar
  109. Waters E, Wang JH, Redmond HP, Wu QD, Kay E, Bouchier-Hayes D (2001) Role of taurine in preventing acetaminophen-induced hepatic injury in the rat. Am J Physiol Gastrointest Liv Physiol 280:G1274–G1279CrossRefGoogle Scholar
  110. Watson DG (2013) A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans. Comput Struct Biotechnol J 4:1–10.  https://doi.org/10.5936/csbj.201301005 CrossRefGoogle Scholar
  111. Wehrens R, Salek R (2019) Metabolomics: practical guide to design and analysis, 1st edn. Chapman and Hall/CRC Press, Boca RatonCrossRefGoogle Scholar
  112. Weng L, Gong Y, Culver J, Gardell SJ, Petucci C, Morse AM, Frye RF, Turner ST, Chapman A, Boerwinkle E, Gums J, Beitelshees AL, Borum PR, Johnson JA, Garrett TJ, McIntyre LM, Cooper-DeHoff RM (2016) Presence of arachidonoyl-carnitine is associated with adverse cardiometabolic responses in hypertensive patients treated with atenolol. Metabolomics 12.  https://doi.org/10.1007/s11306-016-1098-2
  113. Wilson ID (2009) Drugs, bugs, and personalized medicine: pharmacometabonomics enters the ring. Proc Natl Acad Sci U S A 106:14187–14188CrossRefGoogle Scholar
  114. Wilson ID (2015) In: Everett JR, Lindon JC, Harris RK (eds) NMR in pharmaceutical science, 1st edn. Wiley, HobokenGoogle Scholar
  115. Winnike JH, Li Z, Wright FA, Macdonald JM, O’Connell TM, Watkins PB (2010) Use of pharmaco-metabonomics for early prediction of acetaminophen-induced hepatotoxicity in humans. Clin Pharmacol Ther 88:45–51.  https://doi.org/10.1038/clpt.2009.240 CrossRefPubMedGoogle Scholar
  116. Wishart DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15:473–484.  https://doi.org/10.1038/nrd.2016.32 CrossRefPubMedGoogle Scholar
  117. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, Sajed T, Johnson D, Li CR, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang YJ, Badran H, Grant J, Serra-Cayuela A, Liu YF, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A (2018). HMDB 4.0: the human metabolome database for 2018) Nucleic Acids Res 46:D608–D617.  https://doi.org/10.1093/nar/gkx1089 CrossRefPubMedGoogle Scholar
  118. Yerges-Armstrong LM, Ellero-Simatos S, Georgiades A, Zhu H, Lewis JP, Horenstein RB, Beitelshees AL, Dane A, Reijmers T, Hankemeier T, Fiehn O, Shuldiner AR, Kaddurah-Daouk R, Pharmacometabolomics Research Network (2013) Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther 94:525–532.  https://doi.org/10.1038/clpt.2013.119 CrossRefPubMedPubMedCentralGoogle Scholar
  119. Zhang P, Li W, Chen J, Li R, Zhang Z, Huang Y, Xu F (2017a) Branched-chain amino acids as predictors for individual differences of cisplatin nephrotoxicity in rats: a pharmacometabonomics study. J Proteome Res 16:1753.  https://doi.org/10.1021/acs.jproteome.7b00014 CrossRefPubMedGoogle Scholar
  120. Zhang ZX, Gu H, Zhao HZ, Liu YH, Fu S, Wang ML, Zhou WJ, Xie ZY, Yu HH, Huang ZH, Gao XY (2017b) Pharmacometabolomics in endogenous drugs: a new approach for predicting the individualized pharmacokinetics of cholic acid. J Proteome Res 16:3529–3535.  https://doi.org/10.1021/acs.jproteome.7b00218 CrossRefPubMedGoogle Scholar
  121. Zhu H, Bogdanov MB, Boyle SH, Matson W, Sharma S, Matson S, Churchill E, Fiehn O, Rush JA, Krishnan RR, Pickering E, Delnomdedieu M, Kaddurah-Daouk R, Network PR (2013) Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder - possible role for methoxyindole pathway. PLoS One 8:e68283.  https://doi.org/10.1371/journal.pone.0068283 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Medway Metabonomics Research GroupUniversity of GreenwichKentUK

Personalised recommendations