The 8th Mass Spectrometry CVG Annual Symposium - Full Day Event!!!

"Analytical, Bioanalytical, Environmental, Proteomics, Forensic and Instrumental Topics "

September 23, 2009, Montreal, Canada

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Presentation No. 10 - Development of a Metabolite Identification Workflow using MALDI-QTof and Multivariate Statistical Analysis
Andrew Baker1; Stephen Mcdonald1; Henry Y. Shion2
1Waters Corporation, Beverly, MA; 2Waters Corporation, Milford , MA

Novel Aspect: Implementation of MALDI-Q-Tof Mass Spectrometry with Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) for in-vitro metabolite identification .

Introduction: In pharmaceutical drug discovery it is essential to identify all metabolites of a new chemical entity to assess the possible toxic effects; typically this work is performed with LC-MS/MS analysis. We present a method of determining presence and relative abundance of metabolic biotransformations using solid phase extraction, accurate mass MALDI Q-Tof MS analysis and OPLS-DA multivariate statistical analysis. This approach provides complimentary information and allows users to unambiguously target and identify these metabolites, in a quick and simple fashion.

Methods: Verapamil, trimipramine, and propranolol were incubated at 10 �M with rabbit liver microsomes for thirty minutes and extracted using a 96 well HLB �Elution plate. The extracts were mixed with ?-cyano-4-hydroxycinnamic matrix and analyzed using a Waters MALDI SYNAPT mass spectrometer. Data was acquired in triplicate for control (t=0) and incubated samples (t=30). Spectra were filtered using a chemically intelligent mass defect filter to remove unrelated matrix ions from the analysis prior to statistical analysis using OPLS-DA. An S-Plot identified ions that differ as a function of their covariance and contribution across the sample groups (Control and Incubation). These results were submitted for elemental composition analysis to confirm their identity. Identified metabolites were analyzed by MALDI MS/MS for further characterization.

Results: The application of chemically intelligent mass defect filters were effective in removing interfering mass spectral peaks from the data set and resulted in reduced spectral complexity. Advanced statistical methods such as orthogonal partial least squares (OPLS-DA) and the visualization techniques provided in MarkerLynx XS allow facile identification of components that lead to class separation between metabolized and control samples. MassFragment was used interpret MS/MS spectra for characterization and localization of the observed biotransformations. The results obtained using the MALDI-TOF approach correlate well with results obtained using LC/MS based metabolic profiling techniques and reports in the literature, however at a substantial saving of instrument time (1-2 minutes per sample by MALDI analysis as opposed to 10 minute standard LC/MS methods) and laboratory resources.