Metabolite profiling and identification using LC-HRMS involves three steps: first, acquiring LC-MS and LC-MS/MS data with data-dependent acquisition (DDA) methods; second, processing the data to identify molecular ions of metabolites and retrieve their MS/MS spectra; and third, interpreting the data to determine metabolite structures. Commercial software tools for metabolite detection typically use targeted data mining techniques like extracted ion chromatography (EIC), mass defect filters (MDF), and product ion filters (PDF) or neutral loss filters (NLF), based on predicted molecular weights, mass defects, and fragmentations of common metabolites. XenoFinder offers a unique background subtraction filter (BSF) tool that can perform untargeted data processing to identify both common and rare metabolites present in a test sample but absent or at lower levels in a control sample. BSF can work alongside other time-mining tools for various metabolite profiling and identification tasks.

Fig. 1. XenoFinder LC-HRMS workflows for metabolite profiling and identification

of small molecule drugs and new therapeutic modalities

 o Case study (1): Detection and characterization of ranitidine metabolites in human plasma using LC-HRMS and background subtraction filter (BSF)