Information is power and never more so than in the world of drug development. For a pharmaceutical company, having detailed knowledge of how a drug candidate is metabolized as early as possible in the drug development process is crucial. It can save time, precious resources, and maximize return-on-investment. The final selection of a successful drug candidate relies enormously on the metabolism studies that are performed in vitro and in vivo.
Mass spectrometry is a key analytical tool used for metabolism studies because it can identify the metabolites and the sites at which metabolism occurs. Liquid chromatography coupled with mass spectrometry (LC-MS/MS) has become the technique of choice for drug metabolite identification because of its sensitivity and ability to analyze complex mixtures. But while analytical sensitivity has improved over the past decade, detecting and identifying metabolites in the presence of complex biological matrices remains a challenge.
Recent advances in both mass spectrometry hardware and software have helped in this ongoing challenge. Coupling a Thermo Scientific Orbitrap mass analyzer (www. thermo.com) to a linear ion trap mass spectrometer, for example, simplifies metabolite identification, making it faster, more sensitive, more precise (with resolution higher than 100K and mass accuracies better than three ppm) and capable of revealing rich structural information.
With high-resolution and accurate mass data, researchers can resolve and identify metabolite peaks from background matrix ions. New data-processing techniques, such as mass defect filtering, can be used to remove the vast majority of matrix-related background ions and reduce the number of false-positives, providing a more confident result.
This tutorial describes how the combination of innovative mass spectrometry with mass defect filtering software leads to fast and accurate metabolite identification.
High-resolution mass spectrometry enables post-acquisition data-refinement techniques that are otherwise not possible with nominal-resolution instruments. One of them is mass defect filtering (MDF), which is remarkably successful in improving the signal-to-noise ratio by cleaning up metabolite spectra and filtering out the vast majority of background ions.
Mass defect refers to the difference between the exact mass of an element (or a compound) and its closest integer value (Table). The mass defect of metabolites usually lies within a relatively narrow range. Based on the molecular weight of the parent drug, researchers can estimate the range of molecular weight and the range of mass defect in which these metabolites will fall. An MDF is then used to exclude all ions that fall outside the expected range.
This data-reduction technique allows users to focus solely on the analysis of species that are potential drug metabolite candidates. It is also a powerful and sophisticated way of using high-resolution mass data to obtain a smaller, more refined data set for review. The method can also be extended to include multiple mass defect filters (MMDFs), in which several filters are used to identify metabolites of interest over a wide range of mass defects.