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Pharmacometabonomics and predictive metabonomics: New tools for personalised medicine

Pharmacometabonomics and predictive metabonomics: New tools for personalised medicine

Everett, Jeremy ORCID: 0000-0003-1550-4482, Lindon, John and Nicholson, Jeremy (2016) Pharmacometabonomics and predictive metabonomics: New tools for personalised medicine. In: Nicholson, Jeremy, Darzi, A, Lindon, John and Holmes, Elaine, (eds.) Metabolic Phenotyping in Personalized and Public Healthcare. Elsevier Science Publishing Co Inc, pp. 137-165. ISBN 9780128003442 (doi:

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Metabonomics is an established technology concerned with the analysis of human and animal biofluids to reveal and understand changes in metabolite profiles resulting from interventions such as drug administration, disease, ageing and genetic change. Typically, these studies are performed by the analysis of metabolites in biofluids such as urine or blood plasma by powerful analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy or liquid chromatography - mass spectrometry (LC-MS). Pharmacometabonomics is a newer technology concerned with the use of pre-dose metabolite profiles to predict responses to drugs prior to dosing. Differences observed in responses to drugs such as varying pharmacokinetics, metabolism, efficacy or toxicity in different sub-groups of patients may be correlated with differences in pre-dose metabolite profiles between those particular patient sub-groups. This then enables prediction of drug response in patients on the basis of pre-dose metabolite profile analysis. Pharmacometabonomics is thus a valuable aid to decision-making in stratified or personalised medicine.

Item Type: Book Section
Uncontrolled Keywords: pharmacometabonomics, pharmacometabolomics, personalised medicine, precision medicine, metabonomics, metabolomics, biofluids, NMR spectroscopy
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Science (SCI)
Last Modified: 13 Jul 2017 13:37

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