Quantification of pharmaceutical compounds using matrix-assisted laser desorption/ ionization (MALDI) mass spectrometry (MS) is an alternative to traditional liquid chromatography (LC)-MS techniques. Benefits of MALDI-based approaches include rapid analysis times for liquid samples and imaging mass spectrometry capabilities for tissue samples. As in most quantification experiments, the use of internal standards can compensate for spot-to-spot and shot-to-shot variability associated with MALDI sampling. However, the lack of chromatographic separation in traditional MALDI analyses results in diminished peak capacity due to the chemical noise background, which can be detrimental to the dynamic range and limit of detection of these approaches. These issues can be mitigated by using a hybrid mass spectrometer equipped with a quadrupole mass filter (QMF) that can be used to fractionate ions based on their mass-to-charge ratios. When the masses of the analytes and internal standards are sufficiently disparate in mass, it can be beneficial to effect multiple narrow mass isolation windows using the QMF, as opposed to a single wide mass isolation window, to minimize chemical noise while allowing for internal standard normalization. Herein, we demonstrate a MALDI MS quantification workflow incorporating multiple sequential mass isolation windows enabled on a QMF, which divides the total number of MALDI laser shots into multiple segments (i.e., one segment for each mass isolation window). This approach is illustrated through the quantitative analysis of the pharmaceutical compound enalapril in human plasma samples as well as the simultaneous quantification of three pharmaceutical compounds (enalapril, ramipril, and verapamil). Results show a decrease in the limit of detection, relative standard deviations below 10%, and accuracy above 85% for drug quantification using multiple mass isolation windows. This approach has also been applied to the quantification of enalapril in brain tissue from a rat dosed in vitro. The average concentration of enalapril determined by imaging mass spectrometry is in agreement with the concentration determined by LC-MS, giving an accuracy of 104%.
Due to the high throughput and high sensitivity, the hyphenation of microchip-based high performance liquid chromatography with tandem mass spectrometry has been paid much attention. In our recent work, with poly (lauryl methacrylate-co-trimethylolpropane trimethacrylate) monolithic materials prepared in microchannels as trap and separation columns, conventional micro-liquid chromatography pumps and valves for fluidic control, and a small-bore open-tube capillary attached to the outlet channel as chip-mass spectrometer (MS) interface, the microchip-based reversed-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS) platform was established, and applied for the identification of proteins. By such platform, 100 ng digest of bovine serum albumin (BSA) was successfully analyzed with the sequence coverages as 39.37%, 37.89% and 34.10% (with the relative standard deviation (RSD) of 7.3%) in three runs, separately. To evaluate the chip-to-chip reproducibility, BSA was identified by such platform with the microchips from different batches containing trap column, separation column and chip-MS interface. The obtained sequence coverage and the number of peptides identified were comparable. All these results showed high sensitivity and good reproducibility of such platform, demonstrating the great potential for rapid protein analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.