Ebbels Timothy M D, Keun Hector C, Beckonert Olaf P, Bollard Mary E, Lindon John C, Holmes Elaine, Nicholson Jeremy K
Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, United Kingdom.
J Proteome Res. 2007 Nov;6(11):4407-22. doi: 10.1021/pr0703021. Epub 2007 Oct 4.
Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.
体内药物毒性的检测和分类是一个昂贵且耗时的过程。代谢物组学分析正成为该领域的一项关键支持工具,因为它为毒性损伤的特征描述和反应机制提供了独特视角。作为代谢物组学毒理学联盟(COMET)项目的一部分,构建了一个庞大的代谢和病理学数据库。我们选择了80种处理方式,利用来自1652只实验大鼠的12935份尿液样本的核磁共振光谱,构建一个毒性预测建模系统。化合物的结构和活性各不相同,但重点是选择肝毒素和肾毒素。我们基于以下假设制定了一个两阶段策略:(a)不良反应会产生与正常动物不同的代谢物组学图谱,(b)对于具有相似生理效应的处理方式,这种差异是相似的。为了解决第一阶段的问题,我们使用经过特殊预处理的1H核磁共振光谱的主成分分析,建立了正常尿液的多变量模型。该模型显示毒性的发生与异常代谢物组学图谱之间具有高度相关性。在第二阶段,我们扩展了一种密度估计方法“CLOUDS”,以计算各处理方式之间的多维相似性。至关重要的是,该技术允许对每种处理方式在多只动物和多个时间点上进行无分布的相似性估计,所得的相似性矩阵显示肝毒素与其他处理方式之间存在分离。利用相似性矩阵,我们能够正确识别两种“盲法”处理方式的靶器官,即使在亚毒性水平也是如此。为了进一步验证该方法,我们随后采用留一法,利用矩阵中最相似的三个匹配项来预测显示出显著反应的毒性主要器官(肝脏或肾脏)。在能够进行预测的情况下,错误率为8%。对肝脏和肾脏毒性的敏感性分别为67%和41%,而相应的特异性分别为77%和100%。在某些情况下,由于药物相关代谢物信号的干扰(18%)、组织病理学或尿液反应不一致(11%)、真正的类别重叠(8%)或与任何其他处理方式缺乏相似性(2%),无法进行预测。这项研究是迄今为止对代谢物组学方法用于临床前毒理学评估的最大规模验证,证实了该方法为快速的体内药物毒性筛查提供了实际应用价值。