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The present study was designed to apply the mass defect filter (MDF) approach to the screening and identification of reactive metabolites using high-resolution mass spectrometry. Glutathione (GSH)-trapped reactive metabolites of acetaminophen, diclofenac, carbamazepine, clozapine, p-cresol, 4-ethylphenol, and 3-methylindole in human liver microsomes (HLM) were analyzed by HPLC coupled with Orbitrap or Fourier transform ion cyclotron resonance mass spectrometry. Through the selective removal of all ions that fall outside of the GSH adduct MDF template windows, the processed full scan MS chromatograms displayed GSH adducts as major components with no or a few interference peaks. The accurate mass LC-MS data sets were also utilized for the elimination of false positive peaks, detection of stable oxidative metabolites with other MDF templates, and determination of metabolite molecular formulas. Compared to the neutral loss scan by a triple quadrupole instrument, the MDF approach was more sensitive and selective in screening for GSH-trapped reactive metabolites in HLM and rat bile and far more effective in detecting GSH adducts that do not afford the neutral loss of 129 Da as a significant fragmentation pathway. The GSH adduct screening capability of the MDF approach, together with the utility of accurate mass MS/MS information in structural elucidation, makes high-resolution LC-MS a useful tool for analyzing reactive metabolites.

Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MSbased targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed. High-resolution (HR)2 MS has made a huge impact in a number of analytical fields. Most applications utilize the robust accuracy of modern instruments to do unsupervised searches or cataloging of ions present in a given sample. Examples of the impact of HR-MS on these types of applications are identification of unknown proteins (1) and protein modification (2-4), peptide mapping (5, 6), metabonomics (7), and biomarker discovery (8). Drug metabolism research is slightly different in that the ions of interest all arise from a known starting mass, the administered drug, which can be used as a starting point for searches. Although the design of specific search techniques that take advantage of properties of the drug makes finding drugrelated material easier, the fact that these components must be found in a very sensitive fashion from among a variety of very complex background matrices still presents many challenges. The application of HR-MS technology to drug metabolism shares many similarities with applications in areas such as forensic science and doping control (9 -11).Targeted searches for metabolites take advantage of the fact that the majority of drug metabolites can be categorized as predictable, i.e. those formed via common biotransformation reactions. However, there are many examples of important metabolites that arise from uncommon reactions and are thus not easily predicted a priori. Molecular masses of predicted metabolites (m/z values) can be readily calculated based on mass shifts from the parent drug (e.g. the protonated molecular mass of M2 and M5 of nefazodone is that of the parent drug plus 15.9949 Da) (Fig. 1). Detection of expected metabolites by LC/MS can be accomplished by acquisition of full-scan MS data sets using various MS instruments, followed by extracted ion chromatography (EIC) of the ions (e.g. ion at m/z 486.2272 for M2 and M5 in Fig. 1) (12, 13). The most challenging task in metabolite identification by LC/MS is the detection and structural elucidation of trace levels of unexpected metabolites in the presence of large amounts of complex interference ions from endogenous components (14 -16).Since electrospray instruments were introduced in the 1...

A control sample background-subtraction algorithm was developed for thorough subtraction of background and matrix-related signals in high-resolution, accurate mass liquid chromatography/mass spectrometry (LC/MS) data to reveal ions of interest in an analyte sample. This algorithm checked all ions in the control scans within a specified time window around the analyte scan for potential subtraction of ions found in that analyte scan. Applying this method, chromatographic fluctuations between runs were dealt with and background and matrix-related signals in the sample could be thoroughly subtracted. The effectiveness of this algorithm was demonstrated using four test compounds, clozapine, diclofenac, imipramine, and tacrine, to reveal glutathione (GSH)-trapped reactive metabolites after incubation with human liver microsomes supplemented with GSH (30 microM compound, 45-min incubation). Using this algorithm with a+/-1.0 min control scan time window, a+/-5 ppm mass error tolerance, and appropriate control samples, the GSH-trapped metabolites were revealed as the major peaks in the processed LC/MS profiles. Such profiles allowed for comprehensive and reliable identification of these metabolites without the need for any presumptions regarding their behavior or properties with respect to mass spectrometric detection. The algorithm was shown to provide superior results when compared to several commercially available background-subtraction algorithms. Many of the metabolites detected were doubly charged species which would be difficult to detect with traditional GSH adduct screening techniques, and thus, some of the adducts have not previously been reported in the literature.

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