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Pharmacometabonomics: the prediction of drug effects using metabolic profiling

Pharmacometabonomics: the prediction of drug effects using metabolic profiling

Everett, Jeremy ORCID: 0000-0003-1550-4482 (2019) Pharmacometabonomics: the prediction of drug effects using metabolic profiling. In: Barrett, James E., Page, Clive P. and Michel, Martin C., (eds.) Concepts and Principles of Pharmacology: 100 Years of the Handbook of Experimental Pharmacology. Handbook of Experimental Pharmacology, 260 . Springer, Cham, Switzerland, pp. 263-300. ISBN 978-3030353612 (doi:https://doi.org/10.1007/164_2019_316)

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Abstract

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of 21st century healthcare.

Item Type: Book Section
Uncontrolled Keywords: pharmacometabonomics, pharmacometabolomics, precision medicine, personalised medicine, metabonomics, metabolomics, metabotypes, metabolic phenotyping, systems medicine, NMR spectroscopy
Subjects: R Medicine > RS Pharmacy and materia medica
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Science (SCI)
Last Modified: 17 Feb 2020 14:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/26955

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