An integrated method for metabolite detection and identification using a linear ion trap/Orbitrap mass spectrometer and multiple data processing techniques: application to indinavir metabolite detection

A new strategy using a hybrid linear ion trap/Orbitrap mass spectrometer and multiple post-acquisition data mining techniques was evaluated and applied to the detection and characterization of in vitro metabolites of indinavir. Accurate-mass, full-scan MS and MS/MS data sets were acquired with a generic data-dependent method and processed with extracted-ion chromatography (EIC), mass-defect filter (MDF), product-ion filter (PIF), and neutral-loss filter (NLF) techniques. The high-resolution EIC process was shown to be highly effective in the detection of common metabolites with predicted molecular weights. The MDF process, which searched for metabolites based on the similarity of mass defects of metabolites to those of indinavir and its core substructures, was able to find uncommon metabolites not detected by the EIC processing. The high-resolution PIF and NLF processes selectively detected metabolites that underwent fragmentation pathways similar to those of indinavir or its known metabolites. As a result, a total of 15 metabolites including two new indinavir metabolites were detected and characterized in a rat liver S9 incubation sample. Overall, these data mining techniques, which employed distinct metabolite search mechanisms, were complementary and effective in detecting both common and uncommon metabolites. In summary, the results demonstrated that this analytical strategy enables the high-throughput acquisition of accurate-mass LC/MS data sets, comprehensive search of a variety of metabolites through the post-acquisition processes, and effective structural characterization based on elemental compositions of metabolite molecules and their product ions.