'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data
'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data
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DOI：
10.1080/004982599238047
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发表时间：
1999-11-01
期刊：
影响因子：
1.8
通讯作者：
Holmes, E
中科院分区：
文献类型：
--
作者：
Nicholson, JK;Lindon, JC;Holmes, E
药物发现科学的快速发展，由基于组合库的合成方案推动，导致药物安全性评价过程的压力增加。一旦潜在的药物通过了初级生物筛选程序，就需要最大限度地减少候选药物化合物从产品开发管道中的损失（称为"损耗“）。因此，人们正在积极寻找新的分析技术，以最大限度地提高基于安全性和可靠性的先导化合物选择的效率，并最大限度地降低总体损耗率。目前的生物分析方法包括分别使用所谓的基因组学和蛋白质组学方法，在基因水平或细胞蛋白质表达水平上测量生命系统对药物的反应。目前，基因组学和蛋白质组学都是昂贵和劳动密集型的，但可能是研究外源性暴露的生物反应的不同水平的强大工具。然而，即使结合起来，基因组学和蛋白质组学也不能提供理解生命系统中整合的细胞功能所需的信息范围，因为两者都忽略了整个生物体的动态代谢状态。因此，提出了一种新的NMR basedmetabonomic的方法，旨在增强和补充的信息，通过测量的遗传和蛋白质组学的反应，外源性暴露。代谢组学被定义为定量测量生命系统对病理生理刺激或遗传修饰的动态多参数代谢反应。这一概念源于过去20年来应用1H-NMR光谱研究生物体液、细胞和组织的多组分代谢组成的工作（例如Nicholson等人，1983年，1985年; Bales等人，1984年; Gartland等人，1989年; Nicholson和Wilson，1989年; Moka等人，1998年）。利用模式识别（PR）、专家系统和相关生物信息学工具的研究也用于解释和分类复杂的NMR生成的代谢数据集（Gartland等，1991; Holmes等，1992，1994，1998 a，B; Anthony等，1994; Spraul等，1997; Beckwith-Hall等，1998）。这项工作在其他研究领域也有重要的背景，特别是代谢控制分析（Kacser和Burns 1973，Kacser 1993，Goodacre等1996），还有一个相关的概念“代谢组”，代表细胞的总小分子补体。然而，代谢组学研究的是检测，
The rapid evolution of drug discovery science, fuelled by combinatorial librarybased synthesis programmes, has led to increased pressure on the drug safety evaluation process. Once potential drugs have passed the primary biological screening procedures, losses of drug candidate compounds from the product development pipeline (known asattrition’) need to be minimized. Hence, there is an intensive search for new analytical technologies that will maximize eæciency of lead compound selection based both on eæcacy and safety and will minimize overall attrition rates. Current bioanalytical approaches include measurements of responses of living systems to drugs either at the genetic level or at the level of expression of cellular proteins, using so-called genomic and proteomic methods respectively. At present both genomics and proteomics are expensive and labour-intensive, yet potentially are powerful tools for studying diåerent levels of the biological response to xenobiotic exposure. However, even in combination, genomics and proteomics do not provide the range of information needed for an understanding of the integrated cellular function in living systems, since both ignore the dynamic metabolic status of the whole organism. Thus, a new NMR-basedmetabonomic’approach is proposed that is aimed at the augmentation and complementation of the information provided by measuring the genetic and proteomic responses to xenobiotic exposure. Metabonomics is deŪned asthe quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modiŪcation’. This concept has arisen from work on the application of" H-NMR spectroscopy to study the multicomponent metabolic composition of bioŊuids, cells and tissues over the past two decades (eg Nicholson et al. 1983, 1985, Bales et al. 1984, Gartland et al. 1989, Nicholson and Wilson 1989, Moka et al. 1998). Also studies utilizing pattern recognition (PR), expert systems and related bio-informatic tools are used to interpret and classify complex NMR-generated metabolic data sets (Gartland et al. 1991, Holmes et al. 1992, 1994, 1998a, b, Anthony et al. 1994, Spraul et al. 1997, Beckwith-Hall et al. 1998). There is also a signiŪcant background to this work in other research Ūelds, notably metabolic control analysis (Kacser and Burns 1973, Kacser 1993, Goodacre et al. 1996), and there is a related concept of theMetabolome’that represents the total small molecule complement of a cell. However, metabonomics deals with detecting,