Quantitative structure – retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography

The series of L-lysine-derived gelators-modified multifunctional chromatography stationary phase for separation of chiral and achiral compounds

In this study, using chiral L-lysine as the molecular skeleton, three kinds of L-lysine-derived gelators (GBLB, GBLF and GFLF) were synthesized and then bonded to the surface of silica matrix (5 μm) by amide condensation to prepare a series of multifunctional chromatography stationary phases (GBLB-SiO2, GBLF-SiO2, and GFLF-SiO2) were prepared. The L-lysine-derived gelators not only possess chiral recognition ability, but also can spontaneously form oriented and ordered network structures in liquid medium through the interaction of non-covalent bonding forces such as hydrogen bonding, π-π stacking, and van der Waals forces. The comprehensive effect of multiple weak interaction sites enhances the molecular recognition ability and further improves the separation diversity of different types of compounds on stationary phases. The separation and evaluation of chiral compounds showed that benzoin, 1-phenyl-ethanol, 1-phenyl-propanol and 6-hydroxyflavanone could be separated in normal phase mode (NPLC). The separation of different types of non-chiral compounds, such as sulfonamides, nucleosides, nucleobases, polycyclic aromatic hydrocarbons (PAHs), anilines, and aromatic acids, were achieved in hydrophilic interaction/reversed-phase/ion-exchange mode (HILIC/RPLC/IEC), and the separation of polarized compounds could be performed under the condition of ultrapure water as the mobile phase, which has the typical retention characteristics of per aqueous liquid chromatography (PALC). The effects of organic solvent content, temperature, pH value, and buffer salt concentration on the retention and separation performance of the column were investigated. Comparison of the three prepared columns showed that the separation performance (such as aromatic selectivity) could be improved by increasing the types of functional groups on the surface of the stationary phase and the number of aromatic groups. In a word, the prepared stationary phase have multiple retention properties, can simultaneously separate chiral compounds and various types of achiral compounds. This work provides an idea for developing multifunctional liquid chromatography stationary phase materials, and further expands the application of gelators in separation science.

A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction

To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Analytical method for the determination of curcumin entrapped in polymeric micellar powder using HPLC

OBJECTIVES

Curcumin belongs to the family of curcuminoids, natural polyphenolic compounds that possesses neuroprotective properties, anti inflammatory and anticancer. Its entrapment in the developed casein-based micellar powder (CMP) and poloxamer-based micellar powder (PMP) was to enhance the solubility and improve the bioavailability. Henceforth, the present study aimed to acquire an efficient analytical method for the curcumin analysis in polymeric micellar formulations.

METHODS

A fast and specific HPLC method was developed for analyzing curcumin in two different micellar matrices using casein and poloxamer. The HPLC was equipped with a C18 column (250 × 4 mm, 5 µm) and diode array detector. A designated isocratic elution of curcumin was employed using mobile phase with a composition of water (1%, v/v acetic acid) and acetonitrile in a ratio of 50:50 v/v. The employed flow rate was 1.0 mL/min and the analyte was examined at 421 nm.

RESULTS

An effective analysis in HPLC was successfully achieved by the predetermined HPLC condition. A good resolution of peaks at the employed flow rate was achieved. The linearity was excellent in two different range of concentrations, 2-20 and 10-50 μg/mL. The selectivity, accuracy and precision fulfilled the acceptable requirements.

CONCLUSIONS

The developed method was practically effective to qualitatively identified curcumin. In addition, the assay also effectively quantified the amount of curcumin in the polymeric entrapping matrices which demonstrates that it has great potential to be used in natural compound analysis.