Clayton T Andrew, Lindon John C, Cloarec Olivier, Antti Henrik, Charuel Claude, Hanton Gilles, Provost Jean-Pierre, Le Net Jean-Loïc, Baker David, Walley Rosalind J, Everett Jeremy R, Nicholson Jeremy K
Biological Chemistry, Biomedical Sciences Division, Faculty of Natural Sciences, Sir Alexander Fleming Building, Imperial College London, South Kensington, London SW7 2AZ, UK.
Nature. 2006 Apr 20;440(7087):1073-7. doi: 10.1038/nature04648.
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.
根据个体患者的特征选择药物治疗方案，以提高疗效并减少药物不良反应的数量和严重程度，这一点是明确的。然而，这种药物治疗的个性化需要具备预测不同个体对特定药物/剂量组合反应的能力。在经历了最初的乐观之后，人们越来越认识到药物基因组学方法的局限性，该方法没有考虑到环境对药物吸收、分布、代谢和排泄的重要影响。例如，药物效应个体差异的一个主要潜在因素是代谢表型的差异，这不仅受基因型影响，还受营养状况、肠道微生物群、年龄、疾病以及其他药物的联合或预先给药等环境因素影响。因此，虽然基因变异显然很重要，但仅靠基因组学知识似乎不太可能实现针对多种主要疾病的个性化药物治疗。在这里，我们描述了一种用于药物治疗个性化的替代性且概念全新的“药物代谢组学”方法，该方法结合给药前代谢物谱分析和化学计量学来建模和预测个体受试者的反应。我们通过对大鼠给予对乙酰氨基酚的研究，为这种对基因和环境影响均敏感的新方法提供了原理验证。我们展示了给药前对尿液药物代谢物谱某一方面的预测以及给药前尿液成分与对乙酰氨基酚给药后肝脏损伤程度之间的关联。
