An alternative approach to the identification and quantification of patient samples is through the use of mass spectrometry. This approach offers excellent precision and sensitivity in the identification, characterization and quantification of metabolites in multiple patient sample types, such as blood and urine. The mass spectrometry (MS) approach is typically coupled to gas chromatography (GC), in GC-MS  or liquid chromatography (LC), in LC-MS, which aid in initially separating out the metabolite components within complex sample mixtures, and can allow for the isolation of particular metabolite subsets for analysis. GC-MS can provide relatively precise quantification of metabolites, as well as chemical structural information that can be compared to pre-existing chemical libraries. GC-MS can be conducted in a relatively high-throughput manner (greater than 100 samples per day) with greater detection sensitivity than NMR analysis. A limitation of GC-MS for this application, however, is that processed metabolite components must be readily volatilized for sample processing.
LC-MS initially separates out the components of a sample mixture based on properties such as hydrophobicity, before processing them for identification and quantification by mass spectrometry (MS). Overall, LC-MS is an extremely flexible method for processing most compound types in a somewhat high-throughput manner (20-100 samples a day), also with greater sensitivity than NMR analysis. For both GC-MS and LC-MS there are limitations in the reproducibility of metabolite quantification. Furthermore, sample processing for downstream mass spectrometry (MS) analysis is much more intensive than in NMR application, and results in the destruction of the original sample (via trypsin digestion).
Following identification and quantification of metabolites in individual patient samples, NMR and mass spectrometry (MS) output is compiled into a dataset. These datasets include information on the identity and levels of individual metabolites detected within processed samples, as well as characteristics of each metabolite during the detection process (e.g. mass-to-charge ratios for mass spectrometry (MS)-based analysis). Multiple datasets can be created and compiled into large databases for individual patients in order to monitor varying metabolic profiles over a treatment course (i.e. pre- and post-treatment profiles). Each database is then processed through a type of informatics platform with software designed to characterize and analyze the data to generate an overall metabolic profile for the patient. To generate this overall profile, computational programs are designed to:
* identify metabolic disease signatures
* assess treatment class (pre- or post-treatment)
* identify compounds present in a patient sample that may alter drug response, or be caused by a therapy
* identify metabolite variables and interactions among these variables
* map identified variables to known metabolic and biochemical pathways