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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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“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]
In the citing paper
“Adult humans are characterized by individual metabolic phenotypes [37,38,39,40,41]; there are areas of the metabolome unique to each individual that remain stable over time. “Metabolic profiles” can be detected that allow us to predict which individuals may present ways of metabolizing drugs that make them more susceptible to presenting DILI [17].”
From Metabolomic Analysis of Pediatric Patients with Idiosyncratic Drug-Induced Liver Injury According to the Updated RUCAM (2023) · cites this paper as [37]
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Stable isotope-resolved metabolomics and applications for drug development
Fan, T. W. ‐., Lorkiewicz, P., Sellers, K., Moseley, H., Higashi, R. M., & Lane, A. N. (2011) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2011.12.007 · Cited by 215
“…2005, 2009a, 2009b, 2009c; Verhoeckx et al., 2004; Whitfield et al., 2004; Harrigan et al., 2005; Lee & Go, 2005; Lutz, 2005; Rochfort, 2005; Andrew Clayton et al., 2006; Griffin, 2006a, 2006b; Schlotterbeck et al., 2006; Beger et al., 2009; Wikoff et al., 2009; Fan, 2010; Griffiths et al., 2010;…”
Son, H.-S., Hwang, G., Kim, K. M., Ahn, H.-J., Park, W., van den Berg, F. V. D., Hong, Y.-S., & Lee, C.-H. (2009) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf803388w · Cited by 200
Recent developments in environmental metabolomics
Viant, M. R. (2008) · Molecular BioSystems · doi:10.1039/b805354e · Cited by 206
Ahmed, M. U., Saaem, I., Wu, P. C., & Brown, A. S. (2013) · Critical Reviews in Biotechnology · doi:10.3109/07388551.2013.778228 · Cited by 240
“Individuals exhibiting high pre-dose urinary levels of p-cresol sulfate, a gut bacterial co-metabolite, produced low post-dose urinary ratios of acetaminophen sulfate to acetaminophen glucuronide (Clayton et al., 2006, 2009).”
BackgroundPlant metabolomics and its potential application for human nutrition
Hall, R. D., Brouwer, I. D., & Fitzgerald, M. A. (2007) · Physiologia Plantarum · doi:10.1111/j.1399-3054.2007.00989.x · Cited by 208
Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes
Connor, S. C., Hansen, M. K., Corner, A. S., Smith, R. F., & RYAN, T. E. (2010) · Molecular BioSystems · doi:10.1039/b914182k · Cited by 192
Metabolomics-Based Systems Biology and Personalized Medicine: Moving Towards n = 1 Clinical Trials?
van der Greef, J., Hankemeier, T., & McBurney, R. N. (2006) · Pharmacogenomics · doi:10.2217/14622416.7.7.1087 · Cited by 179
“In addition, even in controlled animal experiments, it has been found that differences in microflora [29] strongly influence the metabolite fingerprint of the individual animals.”
BackgroundPharmacometabolomics: Implications for Clinical Pharmacology and Systems Pharmacology
Kaddurah-Daouk, R. F., & Weinshilboum, R. M. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.217 · Cited by 190
Big data: the next frontier for innovation in therapeutics and healthcare
Issa, N. T., Byers, S. W., & Dakshanamurthy, S. (2014) · Expert Review of Clinical Pharmacology · doi:10.1586/17512433.2014.905201 · Cited by 92
“Similarly, Clayton et al. developed personalized pharmacometabolomics, where pre-dose urinary metabolite profiles are predictive of drug-induced hepatocellular toxicity [17] .”
BackgroundCan Pharmacogenetics Help Rescue Drugs Withdrawn from the Market?
Shah, R. R. (2006) · Pharmacogenomics · doi:10.2217/14622416.7.6.889 · Cited by 179
Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions
Blais, E. M., Rawls, K. D., Dougherty, B. V., Li, Z. I., Kolling, G. L., Ye, P., Wallqvist, A., & Papin, J. A. (2017) · Nature Communications · doi:10.1038/ncomms14250 · Cited by 182
Inter-species transplantation of gut microbiota from human to pigs
Pang, X., Hua, X., Yang, Q., Ding, D., Che, C., Cui, L., Jia, W. P., Bucheli, P., & Zhao, L. (2007) · The ISME Journal · doi:10.1038/ismej.2007.23 · Cited by 182
“Genetically homogenous animals can have different metabolisms if they have structurally different gut microbiota, which in turn determines their individualized drug responses (Nicholson et al., 2005; Clayton et al., 2006).”
BackgroundHu, X.-G., Ouyang, S.-H., Mu, L., An, J., & Zhou, Q.-X. (2015) · Environmental Science and Technology · doi:10.1021/acs.est.5b02102 · Cited by 181
Dumas, M., Kinross, J. K., & Nicholson, J. K. (2013) · Gastroenterology · doi:10.1053/j.gastro.2013.11.001 · Cited by 187
Self-citation1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death
Mäkinen, V., Soininen, P., Forsblom, C. M., Parkkonen, M., Ingman, P., Kaski, K. K., Groop, P., & Ala‐Korpela, M. (2008) · Molecular Systems Biology · doi:10.1038/msb4100205 · Cited by 158
“Animal models have provided encouraging results and further justification for the metabonomic NMR approach (Williams et al, 2005; Clayton et al, 2006; Salek et al, 2007), but more experience from human populations is needed (Griffin and Nicholls, 2006; Ala-Korpela, 2007).”
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 185
“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 201
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
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
“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
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 165
“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
Metabolomics: Moving to the Clinic
Nordström, A., & Lewensohn, R. (2009) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-009-9156-4 · Cited by 167
“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
Dumas, M., Wilder, S. P., Bihoreau, M., Barton, R. H., Fearnside, J., Argoud, K., D’Amato, L., Wallis, R. H., Blancher, C., Keun, H. C., et al. (2007) · Nature Genetics · doi:10.1038/ng2026 · Cited by 151
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 151
Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery
Lindon, J. C., & Nicholson, J. K. (2007) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2007.08.009 · Cited by 162
Self-citationWang, X., Sun, H. J., Zhang, A., Wang, P., & Han, Y. (2011) · Journal of Separation Science · doi:10.1002/jssc.201100333 · Cited by 151
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 134
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
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 163
Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer
Jendoubi, T. (2021) · Metabolites · doi:10.3390/metabo11030184 · Cited by 181
“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
Pharmacometabonomics as an Effector for Personalized Medicine
Nicholson, J. K., Wilson, I. D., & Lindon, J. C. (2010) · Pharmacogenomics · doi:10.2217/pgs.10.157 · Cited by 138
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 153
“…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
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 146
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
3D Printed Neural Regeneration Devices
Joung, D., Lavoie, N., Guo, S., Park, S. H., Parr, A. M., & McAlpine, M. C. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201906237 · Cited by 144
Griffin, J. L., & Nicholls, A. W. (2006) · Pharmacogenomics · doi:10.2217/14622416.7.7.1095 · Cited by 137
“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
Metadata
- 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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