Pharmaco-metabonomic phenotyping and personalized drug treatment
Abstract
There is a clear case for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and reduce the number and severity of adverse drug reactions. However, such personalization of drug treatments requires the ability to predict how different individuals will respond to a particular drug/dose combination. After initial optimism, there is increasing recognition of the limitations of the pharmacogenomic approach, which does not take account of important environmental influences on drug absorption, distribution, metabolism and excretion. For instance, a major factor underlying inter-individual variation in drug effects is variation in metabolic phenotype, which is influenced not only by genotype but also by environmental factors such as nutritional status, the gut microbiota, age, disease and the co- or pre-administration of other drugs. Thus, although genetic variation is clearly important, it seems unlikely that personalized drug therapy will be enabled for a wide range of major diseases using genomic knowledge alone. Here we describe an alternative and conceptually new 'pharmaco-metabonomic' approach to personalizing drug treatment, which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the responses of individual subjects. We provide proof-of-principle for this new approach, which is sensitive to both genetic and environmental influences, with a study of paracetamol (acetaminophen) administered to rats. We show pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration.
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Metabolomic biomarkers such as reduced docosahexaenoic acid (DHA) and elevated homocysteine have demonstrated clinical utility.
“Despite variability introduced by diet and comorbid conditions, metabolomic biomarkers such as reduced docosahexaenoic acid (DHA) and elevated homocysteine have demonstrated clinical utility133,194, 195, 196, 197, 198, 199.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [197]
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BackgroundInfluentialCOordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access
Salek, R. M., Neumann, S., Schober, D., Hummel, J., Billiau, K., Kopka, J., Correa, E. S., Reijmers, T. H., Rosato, A., Tenori, L., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0810-y · Cited by 183 · see citing works
“Metabolomics (Bundy et al. 2009; Clayton et al. 2006; Eckhart et al. 2012; Holmes et al. 2008)1 and fluxomics (metabolic flux analysis, Zamboni, Nicola et al. ‘‘13C-based metabolic flux analysis.’’”
BackgroundMethodImplication of gut microbiota metabolites in cardiovascular and metabolic diseases
Brial, F., Lay, A. L., Dumas, M., & Gauguier, D. (2018) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-018-2901-1 · Cited by 197 · see citing works
Emerging field of metabolomics: Big promise for cancer biomarker identification and drug discovery
Patel, S. V., & Ahmed, S. (2014) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2014.12.020 · Cited by 174 · see citing works
Gender‐related and age‐related urinalysis of healthy subjects by NMR‐based metabonomics
Psihogios, N. G., Gazi, I., Elisaf, M., Seferiadis, K., & Bairaktari, E. (2008) · NMR in Biomedicine · doi:10.1002/nbm.1176 · Cited by 161 · see citing works
“A similar concept, ‘pharmaco-metabonomics’, has recently been introduced, which is the prediction of drug- or xenobiotic-induced responses based on the combination of pre-dose metabolite profiling with chemometrics (11).”
BackgroundMetabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine
Kaddurah-Daouk, R. F., & Weinshilboum, R. M. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.134 · Cited by 162 · see citing works
Fonville, J. M., Richards, S. E., Barton, R. H., Boulangé, C. L., Ebbels, T. M. D., Nicholson, J. K., Holmes, E. C., & Dumas, M. (2010) · Journal of Chemometrics · doi:10.1002/cem.1359 · Cited by 156 · see citing works
“Moreover, themodeling of time-sequence systems biology as well as a more complete classification of the range of metabolic processes found in higher animals and how they may interact to determine the outcome of a disease process [69] or drug interaction [35] is required.”
BackgroundMethodSelf-citationSon, H.-S., Kim, K. M., van den Berg, F. V. D., Hwang, G., Park, W., Lee, C.-H., & Hong, Y.-S. (2008) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf801424u · Cited by 157
Meyer, U. A., Zanger, U. M., & Schwab, M. (2013) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100502 · Cited by 157 · see citing works
Metabolomics: Moving to the Clinic
Nordström, A., & Lewensohn, R. (2009) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-009-9156-4 · Cited by 164 · see citing works
“Using rats as a model system, Clayton and co-workers collected pre- and post-dose urine from 65 animals, which were administered a single toxic threshold dose of paracetamol (600 mg kg−1; Clayton et al. 2006).”
BackgroundMethodInfluentialUse of Pharmaco-Metabonomics for Early Prediction of Acetaminophen-Induced Hepatotoxicity in Humans
Winnike, J., Li, Z., Wright, F. A., Macdonald, J. M., O’Connell, T. M., & Watkins, P. B. (2010) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.240 · Cited by 156 · see citing works
Dumas, M., Wilder, S. P., Bihoreau, M., Barton, R., Fearnside, J. F., Argoud, K., Damato, L., Wallis, R., Blancher, C., Keun, H., et al. (2007) · Nature Genetics · doi:10.1038/ng2026 · Cited by 153 · see citing works
Self-citationIndividual Human Phenotypes in Metabolic Space and Time
Bernini, P., Bertini, I., Luchinat, C., Nepi, S., Saccenti, E., Schäfer, H., Schütz, B., Spraul, M., & Tenori, L. (2009) · Journal of Proteome Research · doi:10.1021/pr900344m · Cited by 149 · see citing works
Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery
Lindon, J., & Nicholson, J. (2008) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2007.08.009 · Cited by 152 · see citing works
Self-citationWang, X.-J., Sun, H., Zhang, A., Wang, P., & Han, Y. (2011) · Journal of Separation Science · doi:10.1002/jssc.201100333 · Cited by 149 · see citing works
Cosgrove, B. D., King, B. M., Hasan, M. A., Alexopoulos, L. G., Farazi, P. A., Hendriks, B. S., Griffith, L. G., Sorger, P. K., Tidor, B., Xu, J. J., et al. (2009) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2009.04.002 · Cited by 133 · see citing works
Bictash, M., Ebbels, T. M., Chan, Q., Loo, R. L., Yap, I. K. S., Brown, I. J., de Iorio, M., Daviglus, M. L., Holmes, E., Stamler, J., et al. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.10.001 · Cited by 140 · see citing works
Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer
Wei, S., Liu, L., Zhang, J., Bowers, J., Gowda, G. A. N., Seeger, H., Fehm, T. N., Neubauer, H. J., Vogel, U. F., Clare, S. E., et al. (2012) · Molecular Oncology · doi:10.1016/j.molonc.2012.10.003 · Cited by 156 · see citing works
Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer
Jendoubi, T. (2021) · Metabolites · doi:10.3390/metabo11030184 · Cited by 177 · see citing works
“Other applications of multi-omics analysis include cross-omics biomarker discovery [13–15], patient stratification [16,17], and functional analysis [18,19].”
Mass spectrometry-based metabolomics in health and medical science: a systematic review
Zhang, X., Li, Q.-H., Xu, Z.-D., & Dou, J.-J. (2020) · RSC Advances · doi:10.1039/c9ra08985c · Cited by 192 · see citing works
Pharmacometabonomics as an effector for personalized medicine.
Nicholson, J., Wilson, I., & Lindon, J. (2011) · Pharmacogenomics (London) · doi:10.2217/pgs.10.157 · Cited by 136 · see citing works
Self-citationRecent advances of metabolomics in plant biotechnology
Okazaki, Y., & Saito, K. (2011) · Plant Biotechnology Reports · doi:10.1007/s11816-011-0191-2 · Cited by 151 · see citing works
“…quantities and identities of the metabolites present in extracts or in vivo (Kikuchi et al. 2004; Lindon et al. 2004; Wang et al. 2004; Krishnan et al. 2005; Clayton et al. 2006; Ratcliffe and Shachar-Hill 2006; Sekiyama and Kikuchi 2007; Tian et al. 2007; Hagel et al. 2008; Sekiyama et al. 2010).”
BackgroundApplying computational modeling to drug discovery and development
Kumar, N., Hendriks, B. S., Janes, K. A., de Graaf, D., & Lauffenburger, D. A. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.07.010 · Cited by 131 · see citing works
Research progress on Traditional Chinese Medicine syndromes of diabetes mellitus
Wang, J., Ma, Q., Li, Y., Li, P., Wang, M., Wang, T., Wang, C., Wang, T., & Zhao, B. (2019) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2019.109565 · Cited by 141 · see citing works
Lay, J. O., Liyanage, R., Borgmann, S., & Wilkins, C. L. (2006) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2006.10.007 · Cited by 130 · see citing works
3D Printed Neural Regeneration Devices
Joung, D., Lavoie, N. S., Guo, S.-Z., Park, S. H., Parr, A., & McAlpine, M. C. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201906237 · Cited by 131 · see citing works
Griffin, J. L., & Nicholls, A. W. (2006) · Pharmacogenomics · doi:10.2217/14622416.7.7.1095 · Cited by 134 · see citing works
“Clayton and colleagues recently showed that the use of an individual rat predose metabolomic profile could provide a strong correlate with the extent of acetaminophen metabolizing capability and outcome of galactosamine toxicity [47].”
Background
- Authors
- Andrew Clayton, T., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G., Provost, J.-P., Le Net, J.-L., Baker, D., Walley, R. J., Everett, J. R., & Nicholson, J. K.
- Affiliations
- Imperial College London (GB)
- Umeå University (SE)
- Pfizer (France) (FR)
- Pfizer (United States) (US)
- Pfizer (United Kingdom) (GB)
- ORCID
- Venue
- Nature
- Published
- 2006-04-01
- Volume
- 440
- Issue
- 7087
- Pages
- 1073-1077
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W1968593126
- Cited by
- 824
- References
- 32
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 17 · 2023: 31 · 2022: 27 · 2021: 29 · 2020: 29 · 2019: 32 · 2018: 39 · 2017: 39 · 2016: 50 · 2015: 62 · 2014: 57 · 2013: 47 · 2012: 62
- FWCI
- 53.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Greenwich Academic Literature Archive (University of Greenwich) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Pfizer
- Wellcome Trust
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Keywords
- drug response prediction, urinary metabolites, paracetamol, liver injury, interindividual variability, gut microbiota, chemometrics
- MeSH terms
- Acetaminophen, Animals, Environment, Individuality, Liver, Male, Models, Biological, Magnetic Resonance Spectroscopy, Pharmacogenetics, Phenotype, Rats, Sprague-Dawley, Rats
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