Contemporary issues in toxicology - The role of metabonomics in toxicology and its evaluation by the COMET project

The role that metabonomics has in the evaluation of xenobiotic toxicity studies is presented here together with a brief summary of published studies. To provide a comprehensive assessment of this approach, the Consortium for Metabonomic Toxicology (COMET) has been formed between six pharmaceutical companies and Imperial College of Science, Technology and Medicine (IC), London, UK. The objective of this group is to define methodologies and to apply metabonomic data generated using H-1 NMR spectroscopy of urine and blood serum for preclinical toxicological screening of candidate drugs. This is being achieved by generating databases of results for a wide range of model toxins which serve as the raw material for computer-based expert systems for toxicity prediction. The project progress on the generation of comprehensive metabonomic databases and multivariate statistical models for prediction of toxicity, initially for Ever and kidney toxicity in the rat and mouse, is reported. Additionally, both the analytical and biological variation which might arise through the use of metabonomics has been evaluated. An evaluation of intersite NMR analytical reproducibility has revealed a high degree of robustness. Second, a detailed comparison has been made of the ability of the six companies to provide consistent urine and serum samples using a study of the toxicity of hydrazine at two doses in the male rat, this study showing a high degree of consistency between samples from the various companies in terms of spectral patterns and biochemical composition. Differences between samples from the various companies were small compared to the biochemical effects of the toxin. A metabonomic model has been constructed for urine from control rats, enabling identification of outlier samples and the metabolic reasons for the deviation. Building on this success, and with the completion of studies on approximately 80 model toxins, first expert systems for prediction of liver and kidney toxicity have been generated. (C) 2003 Elsevier Science (USA). All rights reserved.