Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometry

• Published in: Journal of mass spectrometry. 2009-07, Vol.44 (7), p.999-1016
• Main Authors: Zhang, Haiying, Zhang, Donglu, Ray, Kenneth, Zhu, Mingshe
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animals; Biochemical Phenomena; Biological and medical sciences; Defects; drug metabolites; Drugs; Fragmentation; General pharmacology; high-resolution mass spectrometry; Humans; in vitro and in vivo metabolites; mass defect filter; Mass spectrometers; Mass spectrometry; Mass Spectrometry - methods; MDF; Medical sciences; Metabolites; neutral loss filter; Orbitrap; Pharmaceutical Preparations - analysis; Pharmaceutical Preparations - blood; Pharmaceutical Preparations - metabolism; Pharmaceutical Preparations - urine; Pharmacology. Drug treatments; product ion filter; reactive metabolites; Spectra; time-of-flight
• ISSN: 1076-5174; 1096-9888; 1096-9888
• DOI: 10.1002/jms.1610

Identification of drug metabolites by liquid chromatography/mass spectrometry (LC/MS) involves metabolite detection in biological matrixes and structural characterization based on product ion spectra. Traditionally, metabolite detection is accomplished primarily on the basis of predicted molecular masses or fragmentation patterns of metabolites using triple-quadrupole and ion trap mass spectrometers. Recently, a novel mass defect filter (MDF) technique has been developed, which enables high-resolution mass spectrometers to be utilized for detecting both predicted and unexpected drug metabolites based on narrow, well-defined mass defect ranges for these metabolites. This is a new approach that is completely different from, but complementary to, traditional molecular mass- or MS/MS fragmentation-based LC/MS approaches. This article reviews the mass defect patterns of various classes of drug metabolites and the basic principles of the MDF approach. Examples are given on the applications of the MDF technique to the detection of stable and chemically reactive metabolites in vitro and in vivo. Advantages, limitations, and future applications are also discussed on MDF and its combinations with other data mining techniques for the detection and identification of drug metabolites. Copyright © 2009 John Wiley & Sons, Ltd.