On the performance of multivariate curve resolution to resolve highly complex liquid chromatography–full scan mass spectrometry data for quantification of selected immunosuppressants in blood and water samples

Abstract

Liquid chromatography triple-quadrupole mass spectrometry (LC–MS/MS) in multiple reaction monitoring (MRM) mode is a commonly used approach for quantification of target compounds in complex samples. Regarding the multivariate advantages that can be obtained in the light of full spectra recording, the potential and performance of a smart strategy which is a post-processing of LC–MS full scan data sets with multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for determination of cyclosporine-A (CsA) and tacrolimus (TAC) as typical immunosuppressants in human blood and organic micropollutants in surface water samples was investigated in the current study. Before peak resolution and quantification, LC–MS data compression was performed on each individual and augmented data matrices based on searching the regions of interest (ROIs) in the m/z domain. Then, due to presence of matrix effect, MCR-ALS modeling was performed using a matrix-matched calibration strategy. Here, the serious LC–MS issues such as background drift, chromatographic shifts and co-elution of non-calibrated sample constituents were resolved by implementation of MCR-ALS method on ROI based compressed data. It was shown that the strategy is a powerful tool for the quantitation of CsA and TAC in complex matrices and in the presence of uncalibrated interferences with highly overlapped peaks. However, the overall results indicated that performance of the multivariate modeling of LC–MS in full scan mode for drug quantitation strongly depends on the selected drug, as in the current case superiority of the method for analysis of CsA over TAC, with different therapeutic concentration ranges, was observed.