N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry–based metabolomics

Chromatographic retention time information is valuable, orthogonal information to MS and MS/MS data that can be used in metabolite identification. However, while comparison of MS data between different instruments is possible to a certain degree, retention times (RTs) can vary extensively, even when nominally the same phase system is used. Different factors such as column dead volumes, system extra column volume, and gradient dwell volume can influence absolute retention times. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for deviations in experimental conditions. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics to the same extent as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinium sulfonates (NAPS). These reference standards ionize in both positive and negative ionization modes and are UV-active. We demonstrate the NAPS can be used for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry (RP-LC–MS)–based metabolomics. Having measured >500 metabolite standards and varying flow rate and column dimension, we show that conversion of RT to retention indices (RI) substantially improves comparability of retention information and enables to use of RI for metabolite annotation and identification.

Interaction of Surface Active Drug Promethazine Hydrochloride with Surfactants: Drug Release from Microemulsions

The interaction of surface-active drugs with surfactants, used in the simulation of artificial membranes by direct and reversed micelles, mainly determines the transport of drugs in the body and the complex process of the binding to receptors. Besides, the delivery of drugs into the body via microemulsions has been successfully used to reduce the first-pass metabolism. The structure of mixed reverse microemulsions based on the ionic surfactant sodium bis(2-ethylhexyl)sulfosuccinate (AOT) and the cationic surface active drug promethazine hydrochloride (PMT) was studied spectroscopically in the infrared and UV-visible regions, as well as using electrical conductivity and dynamic light scattering. The release profile of PMT from AOT-based microemulsions was studied using cellulose dialysis bags. The introduction of PMT additive into the water pockets of reverse AOT micelles leads to: a) an increase in free water fraction and a decrease in bound water fraction; b) changing the chromatographic retention factors of the model compounds; c) insignificant influence on the values of the binding constant of optical probe o-nitroaniline with the head groups of AOT; d) quenching of water-induced percolation in electrical conductance of reverse AOT microemulsions; e) a slight decrease in the size of water droplets at the same values of the molar ratio of water/surfactant. The release of PMT from the aqueous system obeys Fick’s law of diffusion (n = 0.4852), and the release of PMT from microemulsions is based on non-Fickian or anomalous diffusion.

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.