1. Biological Chemistry, Biomedical Sciences Division, Faculty of Natural Sciences, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, UK
2. Department of Chemistry, Umeå University, 901 87 Umeå, Sweden
3. Pfizer Global Research & Development, Centre de Recherche, 37401 Amboise Cedex, France
4. Pfizer Inc., 2800 Plymouth Road, Ann Arbor, Michigan 48105, USA
5. Pfizer Global Research & Development, Ramsgate Road, Sandwich, Kent CT13 9NJ, UK

Correspondence to: Jeremy K. Nicholson¹ Correspondence and requests for materials should be addressed to J.K.N (Email: j.nicholson@imperial.ac.uk).

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^{1, 2}. 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^{3, 4, 5}. 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^{6, 7}. 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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