Rapid screening and characterization of drug metabolites using a multiple ion monitoring-dependent MS/MS acquisition method on a hybrid triple quadrupole-linear ion trap mass spectrometer

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

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.

Rapid screening and characterization of drug metabolites using multiple ion monitoring dependent product ion scan and postacquisition data mining on a hybrid triple quadrupole-linear ion trap mass spectrometer

Multiple ion monitoring (MIM)-dependent acquisition with a triple quadrupole-linear ion trap mass spectrometer (Q-trap) was previously developed for drug metabolite profiling. In the analysis, multiple predicted metabolite ions are monitored in both Q1 and Q3 regardless of their fragmentations. The collision energy in Q2 is set to a low value to minimize fragmentation. Once an expected metabolite is detected by MIM, enhanced product ion (EPI) spectral acquisition of the metabolite is triggered. To analyze in vitro metabolites, MIM-EPI retains the sensitivity and selectivity similar to that of multiple reaction monitoring (MRM)-EPI in the analysis of in vitro metabolites. Here we present an improved approach utilizing MIM-EPI for data acquisition and multiple data mining techniques for detection of metabolite ions and recovery of their MS/MS spectra. The postacquisition data processing tools included extracted ion chromatographic analysis, product ion filtering and neutral loss filtering. The effectiveness of this approach was evaluated by analyzing oxidative metabolites of indinavir and glutathione (GSH) conjugates of clozapine and 4-ethylphenol in liver microsome incubations. Results showed that the MIM-EPI-based data mining approach allowed for comprehensive detection of metabolites based on predicted protonated molecules, product ions or neutral losses without predetermination of the parent drug MS/MS spectra. Additionally, it enabled metabolite detection and MS/MS acquisition in a single injection. This approach is potentially useful in high-throughout screening of metabolic soft spots and reactive metabolites at the drug discovery stage.