Metabolite identification using an ion mobility enhanced data‐independent acquisition strategy and automated data processing

Abstract

Rationale: Liquid chromatography/mass spectrometry is an essential tool for efficient and reliable quantitative and qualitative analysis and underpins much of contemporary drug metabolism and pharmacokinetics. Data-independent acquisition methods such as MS E have reduced the potential to miss metabolites, but do not formally generate quadrupole-resolved product ion spectra. The addition of ion mobility separation to these approaches, for example, in High-Definition MS E (HDMS E ) has the potential to reduce the time needed to set up an experiment and maximize the chance that all metabolites present can be resolved and characterized.We compared High-Definition Data-Dependent Acquisition (HD-DDA), MS E and HDMS E approaches using automated software processing with Mass-MetaSite and WebMetabase. Methods:Metabolite identification was performed on incubations of glucagon-like peptide-1 (7-37) (GLP-1) and verapamil hydrochloride. The HD-DDA, MS E and HDMS E experiments were conducted on a Waters ACQUITY UPLC I-Class LC system with a VION IMS quadrupole time-of-flight (QTOF) mass spectrometer operating under UNIFI control. All acquired data were processed using MassMetaSite able to read data from UNIFI 1.9.4. WebMetabase was used to review the detected chromatographic peaks and the spectral data interpretations. Results:A comparison of outcomes obtained for MS E and HDMS E data demonstrated that the same structures were proposed for metabolites of both verapamil and GLP-1. The ratio of structurally matched to mismatched product ions found by MassMetaSite was slightly greater for HDMS E than for MS E , and HD-DDA, thus improving confidence in the structures proposed through the addition of ion mobility based data acquisitions . Conclusions: HDMS E data acquisition is an effective approach for the elucidation of metabolite structures for both small molecules and peptides, with excellent accuracy and quality, requiring minimal tailoring for the compound under investigation.

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

“…Monitoring metabolic stability by a kinetic approach allows not only the half-life calculation for the substrate but also reduction in false positives in the characterization of metabolites. Samples for each time point were analyzed by LC–MS/MS, and raw data were analyzed using Mass-MetaSite software , in the WebMetabase platform. − Figure C shows that the PBS/ACN-DMSO protocol significantly increases the stability of 1 ( dBet1 ) during analysis. Therefore, the final method for the metabolic stability assay used in this study included the PBS/ACN-DMSO protocol (see the Methods section for the whole procedure), to reduce the risk of further degradation of the substrate in the autosampler during analysis.…”

“…The crude residue was purified by flash column chromatography on SiO 2 (DCM/MeOH, 96:4) to afford the title compound (0.112 g, 66% yield) as a colorless oil. 1 (69). Under a nitrogen atmosphere, a solution of 42 (0.150 g, 0.372 mmol), 5-azidopentanoic acid (0.053 g, 0.372 mmol), HOBt (0.011 g, 0.074 mmol), EDC hydrochloride (0.142 g, 0.744 mmol), and NMM (0.40 mL, 3.700 mmol) in dry DMF (2.0 mL) was stirred at room temperature for 5 h. Then, the reaction mixture was poured in ice-water and extracted with EA (×3).…”

Comprehensive multicomponent characterization and fingerprinting analysis of Lanqin Oral Liquid by ultra‐high‐performance liquid chromatography coupled with ion mobility‐quadrupole time‐of‐flight mass spectrometry

“…Streamlined data processing workflows can be established by using the multifunctional bioinformatics platform of UNIFI (Waters). Efficient peak annotation workflows have been well established in our lab, which rely on the UNIFI software and the in‐house chemical library [2,15,16]. We established a chemical library of LOL consisting of 650 compounds, including 131 ones from IR, 147 from SR, 165 from PCC, 162 from GF, and 45 from SLS (Table S4).…”

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

“…To help identify complex mixtures of metabolites, the fragmentation of precursor ions can be performed following the IMS separation. Some instrument configurations of the TWIM‐MS mass spectrometers allow IMS separation of coeluting precursor ions before fragmentation, yielding the acquisition of cleaner product ion spectra, which facilitates lipid and metabolite annotation and reduces false‐positive assignments (Pacini et al, 2015; Paglia & Astarita, 2017; Radchenko et al, 2020; Shah et al, 2013; Zhang et al, 2019). We recently demonstrated the utility of this approach, showing how inclusion of TWIMS in a LC‐MS‐DIA analysis separates co‐eluting phosphatidylethanolamine (PE) species (PE plasmalogen 18:0/20:4 and PE plasmalogen 18:0/18:1) from human brain in negative electrospray ionization mode.…”