Novel Real-Time Library Search Driven Data Acquisition Strategy for Identification and Characterization of Metabolites

Structural characterization of novel metabolites in drug discovery or metabolomics is one of the most challenging tasks. Multilevel fragmentation (MSn) based approaches combined with various dissociation modes are frequently utilized for facilitating structure assignment of unknown compounds. As each of the MS precursors undergoes MSn, the instrument cycle time can limit the total number of precursors analyzed in a single LC run for complex samples. This necessitates splitting data acquisition into several analyses to target lower concentration analytes in successive experiments. Here we present a new LC/MS data acquisition strategy, termed Met-IQ, where the decision to perform an MSn acquisition is automatically made in real time based on the similarity between the experimental MS2 spectrum and a spectrum in a reference spectral library for the known compounds of interest. If similarity to a spectrum in the library is found, the instrument performs a decision-dependent event, such as an MS3 spectrum. Compared to an intensity-based, data-dependent MSn experiment, only a limited number of MS3 are triggered using Met-IQ, increasing the overall MS2 instrument sampling rate. We applied this strategy to an Amprenavir sample incubated with human liver microsomes. The number of MS2 spectra increased 2-fold compared to a data dependent experiment where MS3 was triggered for each precursor, resulting in identification of 14-34% more unique potential metabolites. Furthermore, the MS2 fragments were selected to focus likely sources of useful structural information, specifically higher mass fragments to maximize acquisition of MS3 data relevant for structure assignment. The described Met-IQ strategy is not limited to metabolism experiments and can be applied to analytical samples where the detection of unknown compounds structurally related to a known compound(s) is sought.

Automated summary

Structural characterization of novel metabolites in drug discovery or metabolomics is challenging. We present a new LC/MS data acquisition strategy, Met-IQ, which automatically decides whether to perform an MSn acquisition based on the similarity between the experimental MS2 spectrum and a spectrum in a reference spectral library. This strategy significantly increases the MS2 instrument sampling rate and the number of MS2 spectra.