The use of Ultra Performance Liquid Chromatography (UPLC), with a rapid 1.5 minute reversed-phase gradient separation on a 1.7 µm reversed-phase packing material to provide rapid “high throughput” support for metabonomic screening is demonstrated. The peak capacity and the number of marker ions detected using these fast UPLC separations and oa-TOF MS was found to be similar to that generated by conventional HPLC-MS methods with a 10 minute separation. The UPLC-MS methodology was applied to the analysis of urine samples from rodents, including normal and Zucker obese rats and three strains of mice (of both sexes), and was found to provide rapid discrimination between age, strain, gender and diurnal variation.

The process of metabolite identification is essential to the drug discovery and development process; this is usually achieved by liquid chromatography/tandem mass spectrometry (LC/MS/MS) or a combination of liquid chromatography/mass spectrometry (LC/MS) and nuclear magnetic resonance (NMR) spectroscopy. Metabolite identification is, however, a time‐consuming process requiring an experienced skilled scientist. Multivariate statistical analysis has been used in the field of metabonomics to elucidate differences in endogenous biological profiling due to a toxic effect or a disease state. In this paper we show how a combination of liquid chromatography/time‐of‐flight mass spectrometry (LC/TOFMS) and multivariate statistical analysis can be used to detect drug metabolites in a biological fluid with no prior knowledge of the compound administered. Copyright © 2003 John Wiley & Sons, Ltd.

Metabolite identification represents a major bottleneck in contemporary metabolomics research and a step where critical errors may occur and pass unnoticed. This is especially the case for studies employing liquid chromatography-mass spectrometry technology, where there is increased concern on the validity of the proposed identities. In the present perspective article, we describe the issue and categorize the errors into two types: identities that show poor biological plausibility and identities that do not comply with chromatographic data and thus to physicochemical properties (usually hydrophobicity/hydrophilicity) of the proposed molecule. We discuss the problem, present characteristic examples, and propose measures to improve the situation.