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Updated: May 24, 2026
07:34
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
Published on: March 14, 2013
MS(M), an efficient workflow for metabolite identification using hybrid linear ion trap Orbitrap mass spectrometer
Robert Cho1, Yingying Huang, Jae C Schwartz
1Department of Pharmacokinetics and Drug Metabolism, Amgen Inc., South San Francisco, CA 94080, USA.
Journal of the American Society for Mass Spectrometry
|February 22, 2012
Summary
Identifying drug metabolites is crucial for drug development. A new MS(M) workflow using advanced mass spectrometry streamlines metabolite identification in various biological samples.
Area of Science:
- Analytical Chemistry
- Pharmacology
- Biochemistry
Background:
- Drug metabolite identification provides critical data on drug clearance, activity, and toxicity.
- Metabolite insights guide lead optimization for improved drug metabolic profiles.
- Detecting metabolites in complex biological matrices presents significant analytical challenges.
Purpose of the Study:
- To develop a streamlined experimental approach for metabolite identification.
- To leverage the unique capabilities of hybrid linear ion trap Orbitrap mass spectrometry.
- To enhance the speed and reliability of in vitro and in vivo metabolite identification.
Main Methods:
- Development of the MS(M) workflow utilizing multiple collision cells, dissociation methods, mass analyzers, and detectors.
- Integration of multiple scan types and dissociation modes into a single experimental method.
- Application of flexible post-acquisition data analysis options.
Main Results:
- The MS(M) workflow was successfully applied to metabolite identification.
- The approach demonstrated efficiency in analyzing both in vitro and in vivo samples.
- Verapamil metabolite identification in rat hepatocytes and bile samples was achieved.
Conclusions:
- The MS(M) workflow offers an attractive option for fast and reliable metabolite identification.
- This approach effectively addresses challenges in analyzing complex biological matrices.
- The method streamlines in vitro and in vivo metabolite identification experiments.
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