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Pharmaco-metabonomic phenotyping and personalized drug treatment

Author

Listed:
  • T. Andrew Clayton

    (Sir Alexander Fleming Building, Imperial College London)

  • John C. Lindon

    (Sir Alexander Fleming Building, Imperial College London)

  • Olivier Cloarec

    (Sir Alexander Fleming Building, Imperial College London)

  • Henrik Antti

    (Umeå University)

  • Claude Charuel

    (Centre de Recherche
    Development, Centre de Recherche)

  • Gilles Hanton

    (Centre de Recherche
    Development, Centre de Recherche)

  • Jean-Pierre Provost

    (Centre de Recherche
    Development, Centre de Recherche)

  • Jean-Loïc Le Net

    (Centre de Recherche
    Development, Centre de Recherche)

  • David Baker

    (Pfizer Inc.)

  • Rosalind J. Walley

    (Pfizer Global Research & Development
    Development)

  • Jeremy R. Everett

    (Pfizer Global Research & Development
    Development)

  • Jeremy K. Nicholson

    (Sir Alexander Fleming Building, Imperial College London)

Abstract

Medicines for one We may one day benefit from 'personalized medicine', which will involve analysing key characteristics of an individual and predicting which drugs will be effective for that patient. Although the pharmacogenomic approach looked promising, its usefulness is limited as it does not take account of potentially important environmental factors. Enter 'pharmaco-metabonomics', which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the response of an individual. A 'proof-of-principle' for this approach, in which the metabolic effects of paracetamol in rats were predicted, is published this week. This approach involves the analysis of a patient's metabolic phenotype, which is influenced not only by genotype but also by factors such as nutritional status, age and disease, so it could be an important step towards personalized human healthcare.

Suggested Citation

  • T. Andrew Clayton & John C. Lindon & Olivier Cloarec & Henrik Antti & Claude Charuel & Gilles Hanton & Jean-Pierre Provost & Jean-Loïc Le Net & David Baker & Rosalind J. Walley & Jeremy R. Everett & J, 2006. "Pharmaco-metabonomic phenotyping and personalized drug treatment," Nature, Nature, vol. 440(7087), pages 1073-1077, April.
    • Handle: RePEc:nat:nature:v:440:y:2006:i:7087:d:10.1038_nature04648
    DOI: 10.1038/nature04648
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    Cited by:

    1. Nicole Bijlsma & Marc M. Cohen, 2016. "Environmental Chemical Assessment in Clinical Practice: Unveiling the Elephant in the Room," IJERPH, MDPI, vol. 13(2), pages 1-27, February.
    2. Rachel Cavill & Atanas Kamburov & James K Ellis & Toby J Athersuch & Marcus S C Blagrove & Ralf Herwig & Timothy M D Ebbels & Hector C Keun, 2011. "Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-12, March.
    3. Melissa K Friswell & Helen Gika & Ian J Stratford & Georgios Theodoridis & Brian Telfer & Ian D Wilson & Andrew J McBain, 2010. "Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    4. Pablo Martinez-Lozano Sinues & Malcolm Kohler & Renato Zenobi, 2013. "Human Breath Analysis May Support the Existence of Individual Metabolic Phenotypes," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-5, April.
    5. Hanafi, Mohamed & Ouertani, Samia Samar & Boccard, Julien & Mazerolles, Gérard & Rudaz, Serge, 2015. "Multi-way PLS regression: Monotony convergence of tri-linear PLS2 and optimality of parameters," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 129-139.
    6. Shinji Fukuda & Yumiko Nakanishi & Eisuke Chikayama & Hiroshi Ohno & Tsuneo Hino & Jun Kikuchi, 2009. "Evaluation and Characterization of Bacterial Metabolic Dynamics with a Novel Profiling Technique, Real-Time Metabolotyping," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-10, March.

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