In the current climate within the pharmaceutical industry, there is increased pressure to optimize the efficiency of high throughput toxicity screening for lead compound selection. This focus has resulted in a concerted effort to evaluate the role of technologies such as genomics, proteomics, and metabonomics. These technologies have the potential to generate megavariate data sets that contain information that relates to an organism's response to drugs at the gene expression, cellular protein, or metabolic level. The data-rich matrices generated can then be interrogated with appropriate data mining and multivariate analytical tools [1-4]. This chapter addresses the contribution and potential value of nuclear magnetic resonance (NMR)-based metabonomic approaches to toxicity screening and disease diagnosis.
Metabonomic analysis involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification . The concept of metabonomics has evolved over two decades of 1H NMR spectroscopic analysis of the multicomponent metabolic composition of biofluids, cells, and tissues under different physiological and
Integrated Strategies for Drug Discovery Using Mass Spectrometry, Edited by Mike S. Lee © 2005 John Wiley & Sons, Ltd.
pathophysiological conditions [5-14]. To date, high field NMR spectroscopy coupled with advanced chemometric data analysis has been the dominant analytical platform for acquiring metabonomic data. However, high-performance liquid chromatography [HPLC], mass spectrometry [MS], gas chromatography MS [GC-MS] and near infrared [NIR] spectroscopy have also been used to generate metabonomic data [15-21]. High-resolution 1H NMR spectra of biological matrices, such as urine, blood plasma, or tissue samples, generate complex spectral profiles (Figure 5.1) that contain a wealth of metabolic information that relates to the physiological or pathophysiological status of the organism [1,13,22-26]. 1H NMR spectroscopic analysis is nondestructive, cost effective, and typically takes only a few minutes per sample. Little or no sample pretreatment or reagents is required, and is therefore bioanalytically more efficient than the methods used to characterize either the genetic or pro-teomic composition of samples. Typically, 600-800 MHz 1H NMR spectra of biofluids such as urine and plasma contain thousands of signals arising from hundreds of endogenous molecules that represent many biochemical pathways. Toxin- or disease-induced changes in the biochemical composition of these biofluids are reflected by modulations in the pattern of the 1H NMR spectral profiles (Figure 5.2). Application of automated data-reduction algorithms and chemometric analysis to spectral biofluid databases facilitates v v V
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