Decision‐making remains challenging in nearly all aspects of health and disease, despite the tremendous information growth and technological advances we enjoy. Decisions made should be better‐informed and cost‐effective, resulting from reliable contextual data interpretation that encapsulates the heterogeneity of living systems. Pharmacometabolomics serves as a holistic and often, big‐data tool to predict the outcome of a xenobiotic intervention in an individual on the basis of a mathematical model that is built on preintervention metabotypes. Such metabolic phenotypes are the net result of the interplay between the genome and the environment, capturing fully interindividual variability. Being an omics approach itself, pharmacometabolomics may also synergize with other omics to empower tailored‐made theranostics and optimise patient stratification and disease management. As a paradigm, cardiovascular diseases grasp such a momentum for pharmacometabolomics.

Key Concepts

  • Decision‐making is of fundamental importance in every aspect of our lives, including both health and disease states.
  • Precision medicine holds promise for optimum patient stratification and tailored‐made theranostics.
  • Pharmacometabolomics is based on individual metabotypes (metabolic phenotypes) prior to a xenobiotic intervention to predict its outcome via mathematical modelling.
  • Metabotypes map interindividual variability, shedding light into the interplay of the genome with the environment.
  • Pharmacometabolomics may be coupled to other omics (pharmacogenomics, transcriptomics, etc.) empowering data reliability.
  • Cardiovascular diseases share a multifactorial pathophysiology in which both genomics and environmental influences play a key role.
  • It remains challenging to predict who will suffer from a cardiovascular disease, who will respond to therapeutic interventions and/or who will experience one or more cardiovascular events.

Keywords: pharmacometabolomics; omics; precision medicine; theranostics; cardiovascular diseases

Figure 1. Moving from medicine to precision medicine, pharmacometabolomics alone or coupled to bioassays or other omics strategies promises to predict the outcome of a xenobiotic intervention via mathematical modeling, taking into account individual metabotypes prior to it. As a result, pharmacometabolomics may delineate disease mechanisms, discover and validate clinically relevant biomarkers and map interindividual variability toward tailored‐made theranostics, maximising drug efficacy and minimising drug toxicity.


Amin AM , Sheau Chin L , Azri Mohamed Noor D , et al. (2017) The personalization of clopidogrel antiplatelet therapy: the role of integrative pharmacogenetics and pharmacometabolomics. Cardiology Research and Practice 2017: 8062796.

Clayton TA , Lindon JC , Cloarec O , et al. (2006) Pharmaco‐metabonomic phenotyping and personalized drug treatment. Nature 440: 1073–1077.

Clayton TA , Baker D , Lindon JC , et al. (2009) Pharmacometabonomic identification of a significant host–microbiome metabolic interaction affecting human drug metabolism. Proceedings of the National Academy of Sciences of the United States of America 106: 14728–14733.

Craig SA (2004) Betaine in human nutrition. The American Journal of Clinical Nutrition 80: 539–549.

Emwas AH (2015) The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods in Molecular Biology 1277: 161–193.

Everett JR , Loo RL and Pullen FS (2013) Pharmacometabonomics and personalized medicine. Annals of Clinical Biochemistry 50: 523–545.

Friesen RW , Novak EM , Hasman D , et al. (2007) Relationship of dimethylglycine, choline, and betaine with oxoproline in plasma of pregnant women and their newborn infants. The Journal of Nutrition 137: 2641–2646.

Go AS , Mozaffarian D , Roger VL , et al. (2013) Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 127: e6–e245.

Gowda GN , Zhang S , Gu H , et al. (2008) Metabolomics‐based methods for early disease diagnostics. Expert Review of Molecular Diagnostics 8 (5): 617–633.

Gowda GA and Djukovic D (2014) Overview of mass spectrometry‐based metabolomics: opportunities and challenges. Methods in Molecular Biology 1198: 3–12.

Ho JE (2017) Harnessing the power of pharmacometabolomics: the metabolic footprint of statins. Circulation. Cardiovascular Genetics 10 (6): e002014.

Kaddurah‐Daouk R , Kristal BS and Weinshilboum RM (2008) Metabolomics: a global biochemical approach to drug response and disease. Annual Review of Pharmacology and Toxicology 48: 653–683.

Kaddurah‐Daouk R , Weinshilboum RM and Pharmacometabolomics Research Network (2014) Pharmacometabolomics: implications for clinical pharmacology and systems pharmacology. Clinical Pharmacology & Therapeutics 95 (2): 154–167.

Kantae V , Krekels EHJ , Esdonk MJV , et al. (2017) Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy. Metabolomics 13 (1): 9.

Katsila T , Konstantinou E , Lavda I , et al. (2016) Pharmacometabolomics‐aided pharmacogenomics in autoimmune disease. eBioMedicine 5: 40–45.

Katsila T and Matsoukas MT (2018) How far have we come with contextual data integration in drug discovery? Expert Opinion in Drug Discovery 13 (9): 791–794.

Mahmood SS , Levy D , Vasan RS , et al. (2014) The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383 (9921): 999–1008.

Markley JL , Brüschweiler R , Edison AS , et al. (2017) The future of NMR‐based metabolomics. Current Opinion in Biotechnology 43: 34–40.

Nicholson J , Lindon J and Holmes E (1999) Metabonomics: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29: 1181–1189.

Lee I (2015) Betaine is a positive regulator of mitochondrial respiration. Biochemical and Biophysical Research Communications 456: 621–625.

Lewis GD , Asnani A and Gerszten RE (2008) Application of metabolomics to cardiovascular biomarker and pathway discovery. Journal of the American College of Cardiology 52 (2): 117–123.

Lewis JP , Yerges‐Armstrong LM , Ellero‐Simatos S , et al. (2013) Integration of pharmacometabolomic and pharmacogenomic approaches reveals novel insights into antiplatelet therapy. Clinical Pharmacology & Therapeutics 94 (5): 570–573.

Li XS , Wang Z , Cajka T , et al. (2018) Untargeted metabolomics identifies trimethyllysine, a TMAO‐producing nutrient precursor, as a predictor of incident cardiovascular disease risk. JCI Insight 3 (6): 99096.

Rhee EP and Gerszten RE (2012) Metabolomics and cardiovascular biomarker discovery. Clinical Chemistry 58 (1): 139–147.

Rizza S , Copetti M , Rossi C , et al. (2014) Metabolomics signature improves the prediction of cardiovascular events in elderly subjects. Atherosclerosis 232 (2): 260–264.

Sabatine MS , Liu E , Morrow DA , et al. (2005) Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112 (25): 3868–3875.

Senn T , Hazen SL and Tang WW (2012) Translating metabolomics to cardiovascular biomarkers. Progress in Cardiovascular Diseases 55 (1): 70–76.

Stegemann C , Drozdov I , Shalhoub J , et al. (2011) Comparative lipidomics profiling of human atherosclerotic plaques. Circulation. Cardiovascular Genetics 4 (3): 232–242.

Tenori L , Hu X , Pantaleo P , et al. (2013) Metabolomic fingerprint of heart failure in humans: a nuclear magnetic resonance spectroscopy analysis. International Journal of Cardiology 168 (4): e113–e115.

Tomas L , Edsfeldt A , Mollet IG , et al. (2018) Altered metabolism distinguishes high‐risk from stable carotid atherosclerotic plaques. European Heart Journal 39 (24): 2301–2310.

Ussher JR , Elmariah S , et al. (2016) The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. Journal of the American College of Cardiology 68 (25): 2850–2870.

van der Vorst EP and Weber C (2018) Metabolomic profiling of atherosclerotic plaques: towards improved cardiovascular risk stratification. European Heart Journal 39 (24): 2311–2313.

Voora D and Shah SH (2016) Pharmacometabolomics meets genetics: a “natural” clinical trial of statin effects. Journal of the American College of Cardiology 67 (10): 1211–1213.

Vorkas PA , Shalhoub J , Isaac G , et al. (2015) Metabolic phenotyping of atherosclerotic plaques reveals latent associations between free cholesterol and ceramide metabolism in atherogenesis. Journal of Proteome Research 14 (3): 1389–1399.

Weng L , Gong Y , Culver J , et al. (2016) Presence of arachidonoyl‐carnitine is associated with adverse cardiometabolic responses in hypertensive patients treated with atenolol. Metabolomics 12 (10): 160.

Wishart DS , Tzur D , Knox C , et al. (2007) HMDB: the human metabolome database. Nucleic Acids Research 35: D521–D526.

Wishart DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nature Reviews Drug Discovery 15 (7): 473–484.

Würtz P , Wang Q , Soininen P , et al. (2016) Metabolomic profiling of statin use and genetic inhibition of HMG‐CoA reductase. Journal of the American College of Cardiology 67 (10): 1200–1210.

Zhang A , Sun H , Xu H , et al. (2013) Cell metabolomics. OMICS: A Journal of Integrative Biology 17 (10): 495–501.

Zhu W , Gregory JC , Org E , et al. (2016) Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk. Cell 165 (1): 111–124.

Further Reading

Balasopoulou A , Patrinos GP and Katsila T (2016) Pharmacometabolomics Informs Viromics toward Precision Medicine. Frontiers in Pharmacology 7: 411.

Dong H , Zhang A , Sun H , et al. (2012) Ingenuity pathways analysis of urine metabolomics phenotypes toxicity of Chuanwu in Wistar rats by UPLC‐Q‐TOF‐HDMS coupled with pattern recognition methods. Molecular Biosystems 8 (4): 1206–1221.

Elbadawi‐Sidhu M , Baillie RA , Zhu H , et al. (2017) Pharmacometabolomic signature links simvastatin therapy and insulin resistance. Metabolomics 13: 11.

Katsila T and Patrinos GP (2015) The implications of metabotypes for rationalizing therapeutics in infants and children. Frontiers in Pediatrics 3: 68.

Katsila T , Liontos M , Patrinos GP , Bamias A and Kardamakis D (2018) The new age of ‐omics in urothelial cancer ‐ re‐wording its diagnosis and treatment. eBioMedicine 28: 43–50.

Krauss RM , Zhu H and Kaddurah‐Daouk R (2013) Pharmacometabolomics of statin response. Clinical Pharmacology & Therapeutics 94 (5): 562–565.

Mitropoulos K , Katsila T , Patrinos GP and Pampalakis G (2018) Multi‐omics for biomarker discovery and target validation in biofluids for amyotrophic lateral sclerosis diagnosis. OMICS: A Journal of Integrative Biology 22 (1): 52–64.

Yerges‐Armstrong LM , Ellero‐Simatos S , Georgiades A , et al. (2013) Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics‐informed pharmacogenomics. Clinical Pharmacology & Therapeutics 94 (4): 525–532.

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
Chalikiopoulou, Constantina, Patrinos, George P, and Katsila, Theodora(Apr 2019) Pharmacometabolomics. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0028113]