Green Sample Preparation for Liquid Chromatography and Capillary Electrophoresis of Anionic and Cationic Analytes

A sample preparation device for the simultaneous enrichment and separation of cationic and anionic analytes was designed and implemented in an eight-channel configuration. The device is based on the use of an electric field to transfer the analytes from a large volume of sample into small volumes of electrolyte that was suspended into two glass micropipettes using a conductive hydrogel. This simple, economical, fast, and green (no organic solvent required) sample preparation scheme was evaluated using cationic and anionic herbicides as test analytes in water. The analytical figures of merit and ecological aspects were evaluated against the state-of-the-art sample preparation, solid-phase extraction. A drastic reduction in both sample preparation time (94% faster) and resources (99% less consumables used) was observed. Finally, the technique in combination with high-performance liquid chromatography and capillary electrophoresis was applied to analysis of quaternary ammonium and phenoxypropionic acid herbicides in fortified river water as well as drinking water (at levels relevant to Australian guidelines). The presented sustainable sample preparation approach could easily be applied to other charged analytes or adopted by other laboratories

Electrokinetic Removal of Dodecyl Sulfate Micelles from Digested Protein Samples Prior to Electrospray-Ionization Mass Spectrometry

In proteomics, dodecyl sulfate (DS^–) as sodium salt is commonly used in protein solubilization prior to tryptic digestion, but the presence of the DS^– hampers the electrospray ionization mass spectrometric (ESI-MS) analysis. The development of DS^– depletion techniques is therefore important especially when dealing with small samples where there could be poor sensitivity due to sample loss or dilution during sample preparation. Here, we present a simple and fast electrokinetic removal method of DS^– from small volumes of peptide and digested protein samples prior to ESI-MS. The selective removal was accomplished using an acidic extraction solution (ES) containing acetonitrile (ACN) inside a fused-silica capillary that was dipped into the sample. The use of acidic ES suppressed the electroosmotic flow; allowing the electrokinetic movement of DS^– monomers and micelles into the capillary. The high amount of ACN present at the tip of the capillary served to collapse the micelles migrating into the capillary, thereby releasing the peptides that were bound to these micelles, facilitating peptide retention in the sample and efficient DS^– removal. Increased % MS signal intensity (SI) restoration of the peptide was observed, while DS^– removal was unaffected when the amount of ACN in the ES was increased. This is because of the micelle to solvent stacking mechanism (effective electrophoretic mobility reversal) working at high concentration of ACN for the improved recovery of the peptides. % MS SI restoration for the Z-Gly-Gly-Val and bradykinin peptides were 75–83% while % MS SI reduction of DS^– was up to 99% under optimal conditions, that is, 40% ACN in the ES. Higher % peptide recoveries from digested protein samples were obtained using the proposed method compared to the conventional cold acetone precipitation method

Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure–Retention Relationships and Design of Experiments

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with <i>R</i>² > 0.95. Further, a quantitative structure–retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model’s prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure–Retention Relationships Studies

Quantitative structure–retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models. This study compares molecular modeling, semiempirical, and density functional methods (both B3LYP and M06) for structure optimization. Each of the calculations was performed in a vacuum, then repeated with solvent corrections for both acetonitrile and water. We also compared Natural Bond Orbital analysis with the Mulliken charge calculation method. The comparison of the examined computational methods for structure calculation shows that, possibly due to the error inherent in descriptor creation methods, a quick and inexpensive molecular modeling method of structure determination gives similar results to experiments where structures are optimized using an expensive and time-consuming level of computational theory. Also, for structures with low flexibility, vacuum or gas phase calculations are found to be as effective as those calculations with solvent corrections added

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure–retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding <i>R</i>² of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics

In Silico Screening of Two-Dimensional Separation Selectivity for Ion Chromatography × Capillary Electrophoresis Separation of Low-Molecular-Mass Organic Acids

A prerequisite for ordered two-dimensional (2D) separations and full utilization of the enhanced 2D peak capacity is selective exploitation of the sample attributes, described as sample dimensionality. In order to take sample dimensionality into account prior to optimization of a 2D separation, a new concept based on construction of 2D separation selectivity maps is proposed and demonstrated for ion chromatography × capillary electrophoresis (IC×CE) separation of low-molecular-mass organic acids as test analytes. For this purpose, 1D separation selectivity maps were constructed based on calculation of pairwise separation factors and identification of critical pairs for four IC stationary phases and 28 levels of background electrolyte pH in CE. The derived IC and CE maps were then superimposed and the effectiveness of the respective 2D separations assessed using an in silico approach, followed by testing examples of one successful and one unsuccessful 2D combination experimentally. The results confirmed the efficacy of the predictions, which require a minimal number of experiments compared to the traditional one-at-a-time approach. Following the same principles, the proposed framework can also be adapted for optimization of separation selectivity in various 2D combinations and for other applications