Drug metabolism studies are important because the metabolites produced may have different pharmacological and toxicological activities from the administered drug. Therefore, for each drug in development it is necessary to understand what metabolic processes the drug undergoes after administration. Conducting metabolite identification studies in the early stages of drug candidate selection can screen out unsuitable compounds, saving time and money. Development of new technologies e.g. combinatorial chemistry and high throughput screening has increased the number of potential candidates and so the need for fast and reliable drug metabolite profiling has burgeoned.

Drug metabolites for candidate screening are generated either by in vitro or in vivo methods. There are many in vitro systems available differing in biological intricacy. One of the most commonly used methods is microsomal incubation of the administered drug. However microsome activity is primarily limited to Phase 1 reactions as the microsomes do not contain a full complement of drug metabolising enzymes. To date the best method to obtain a complete set of metabolites for a given species is through in vivo techniques.

Protocols utilising the hyphenation of liquid phase separation techniques with mass spectrometry such as LC-MS provide high throughput, fully automatable and rapid methods of analysis. The use of CE-MS techniques are still in the growth stages of development. They are less commonly used because they are less robust, more difficult to automate and have poor concentration limits of detection. However, their importance has been recognised as they provide an orthogonal method of analysis, as separation is based on size and charge of the analytes, whereas in LC it is most commonly based on interactions with a stationary phase. They also have the advantage of requiring minimal sample volume and offering high resolution.

We are developing and applying LC-MS and CE-MS methods for the analysis of pharmaceutical compounds and their metabolites. This poster considers the relative merits of each of these techniques. We present data obtained using these approaches for the analysis of biologically generated pharmaceutical metabolites.