In this review, metabonomics, a combination of data-rich analytical chemical measurements and chemometrics for profiling metabolism in complex systems, is described and its applications are reviewed. Metabonomics is typically carried out using biofluids or tissue samples. The relevance of the technique is reviewed in relation to other '-omics', and it is shown how the methods can be applied to physiological evaluation, drug safety assessment, characterization of genetically modified animal models of disease, diagnosis of human disease, and drug therapy monitoring. The different types of analytical data, mainly from nuclear magnetic resonance spectroscopy and mass spectrometry, are summarized. The outputs from a metabonomics study allow sample classification, for example according to phenotype, drug safety or disease diagnosis, and interpretation of the reasons for classification yields information on combination biomarkers of effect. Transcriptomic and metabonomic data is currently being further integrated into a holistic understanding of systems biology. An assessment of the possible future role and impact of metabonomics is presented.

Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug-induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, 1 H NMR spectroscopic-based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter-animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time-course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information-rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues.

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Hydrazine is a model toxin that induces both hepatotoxic and neurotoxic effects in experimental animals. The direct biochemical effects of hydrazine in kidney, liver, and brain tissue were assessed in male Sprague-Dawley rats using magic angle spinning nuclear magnetic resonance (NMR) spectroscopy. A single dose of hydrazine (90 mg/kg) resulted in changes to the biochemical composition of the liver after 24 h including an increase in triglycerides and beta-alanine, together with a decrease in hepatic glycogen, glucose, choline, taurine, and trimethylamine-N-oxide (TMAO). From histopathology measurements of liver tissue, minimal to mild hepatocyte alteration was observed in all animals at 24 h. The NMR spectra of the renal cortex at 24 h after dosing were dominated by a marked increase in the tissue concentration of 2-aminoadipate (2-AA) and beta-alanine, concomitant with depletions in TMAO, myo-inositol, choline, taurine, glutamate, and lysine. No alteration to the NMR spectral profile of the substantia nigra was observed after hydrazine administration, but perturbations to the relative concentrations of creatine, aspartate, myo-inositol, and N-acetyl aspartate were apparent in the hippocampus of hydrazine-treated animals at 24 h postdose. No overt signs of histopathological toxicity were observed in either the kidney or the brain regions examined. Elevated alanine levels were observed in all tissues indicative of a general inhibition of alanine transaminase activity. By 168 h postdose, NMR spectral profiles of treated rats appeared similar to those of matched controls for all tissue types indicative of recovery from toxic insult.

Metabonomic analysis of biofluids and tissues utilizing high-resolution NMR spectroscopy and chemometric techniques has proven valuable in characterizing the biochemical response to toxicity for many xenobiotics. To assess the analytical reproducibility of metabonomic protocols, sample preparation and NMR data acquisition were performed at two sites (one using a 500 MHz and the other using a 600 MHz system) using two identical (split) sets of urine samples from an 8-day acute study of hydrazine toxicity in the rat. Despite the difference in spectrometer operating frequency, both datasets were extremely similar when analyzed using principal components analysis (PCA) and gave near-identical descriptions of the metabolic responses to hydrazine treatment. The main consistent difference between the datasets was related to the efficiency of water resonance suppression in the spectra. In a 4-PC model of both datasets combined, describing all systematic dose- and time-related variation (88% of the total variation), differences between the two datasets accounted for only 3% of the total modeled variance compared to ca. 15% for normal physiological (pre-dose) variation. Furthermore, <3% of spectra displayed distinct inter-site differences, and these were clearly identified as outliers in their respective dose-group PCA models. No samples produced clear outliers in both datasets, suggesting that the outliers observed did not reflect an unusual sample composition, but rather sporadic differences in sample preparation leading to, for example, very dilute samples. Estimations of the relative concentrations of citrate, hippurate, and taurine were in >95% correlation (r(2)) between sites, with an analytical error comparable to normal physiological variation in concentration (4-8%). The excellent analytical reproducibility and robustness of metabonomic techniques demonstrated here are highly competitive compared to the best proteomic analyses and are in significant contrast to genomic microarray platforms, both of which are complementary techniques for predictive and mechanistic toxicology. These results have implications for the quantitative interpretation of metabonomic data, and the establishment of quality control criteria for both regulatory agencies and for integrating data obtained at different sites.