The secret of reversed-phase/weak cation exchange retention mechanisms in mixed-mode liquid chromatography applied for small drug molecule analysis

Sustainable Analysis of Diclofenac Salts: A Chemometric Approach to Mixed-Mode Liquid Chromatography With Charged Aerosol Detection

Active pharmaceutical ingredients (APIs) are often used in salt form because of enhanced bioavailability. This study aims to propose a new environmentally friendly method for the analysis of raw diclofenac substance, achieving simultaneous analysis of diclofenac and its counterions (Na+ and K+), utilizing mixed-mode liquid chromatography (MMLC) and charged aerosol detector (CAD). To optimize the critical method characteristic-the mobile phase composition-a 32 full factorial design of experiments and multiobjective decision making using Derringer's desirability function were employed. Two optimized methods with acceptable run times and satisfactory peak separation were developed. The methods compared the use of acetonitrile (ACN) and acetone (ACE) in terms of method sustainability. The mobile phase composition in the first method (MMLC-ACN) was 40% ACN and 60% ammonium acetate buffer (48.00 mM, pH 4.82), whereas in the second, improved method (MMLC-ACE), it was 50% ACE and 50% ammonium acetate buffer (40.00 mM, pH 4.62). The eco-friendliness of the developed methods was assessed using the GAPI, the Analytical GREEnness (AGREE) score, and the Greenness Index. The method with ACE as the mobile phase modifier demonstrated a better environmental profile, achieving an AGREE score of 0.69, compared to the ACN-based method, which scored 0.60. Method performance characteristics of the MMLC-ACE method used for the quantitative analysis of diclofenac salt raw materials were evaluated according to ICH Q2(R2) guidelines: precision-repeatability (RSD from 1.07% to 2.41% and recovery >97%), selectivity between critical peak pair (αNa/K > 1) and obtained linear response within concentration range of 50%-150% (r > 0.99).

Application of artificial intelligence to quantitative structure-retention relationship calculations in chromatography

Quantitative structure-retention relationship (QSRR) is an important tool in chromatography. QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation. This approach involves developing models for predicting the retention time (RT) of analytes, thereby accelerating method development and facilitating compound identification. In addition, QSRR can be used to study compound retention mechanisms and support drug screening efforts. This review provides a comprehensive analysis of QSRR workflows and applications, with a special focus on the role of artificial intelligence-an area not thoroughly explored in previous reviews. Moreover, we discuss current limitations in RT prediction and propose promising solutions. Overall, this review offers a fresh perspective on future QSRR research, encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.

Preparation of ionic gel-modified stationary phase for RPLC/HILIC/IC separation and its application in per aqueous liquid chromatography

In this study, we synthesized and employed an ionic gel-functionalized silica stationary phase for high-performance liquid chromatography. The successful fabrication of the stationary phase was confirmed through attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS), zeta-potential measurements, and elemental analysis (EA). Comparative performance evaluation against a commercial column demonstrated the prepared column's effectiveness in the mixed mode of reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), and ion chromatography (IC). Moreover, the stationary phase exhibited exceptional retention repeatability in per aqueous liquid chromatography, showcasing its potential as an environmentally friendly analytical method. Mechanistic investigations unveiled multiple solute-stationary phase interactions, including π-π interactions, hydrogen bonding, and ion exchange. Finally, we applied the developed stationary phase for the precise detection of preservatives in carbonated beverages and jelly, achieving high levels of accuracy and recovery rates.

Transfer Learning Approach to Multitarget QSRR Modeling in RPLC

QSRR is a valuable technique for the retention time predictions of small molecules. This aims to bridge the gap between molecular structure and chromatographic behavior, offering invaluable insights for analytical chemistry. Given the challenge of simultaneous target prediction with variable experimental conditions and the scarcity of comprehensive data sets for such predictive modelings in chromatography, this study introduces a transfer learning-based multitarget QSRR approach to enhance retention time prediction. Through a comparative study of four models, both with and without the transfer learning approach, the performance of both single and multitarget QSRR was evaluated based on Mean Squared Error (MSE) and R2 metrics. Individual models were also tested for their performance against benchmark studies in this field. The findings suggest that transfer learning based multitarget models exhibit potential for enhanced accuracy in predicting retention times of small molecules, presenting a promising avenue for QSRR modeling. These models will be highly beneficial for optimizing experimental conditions in method development by better retention time predictions in Reversed-Phase Liquid Chromatography (RPLC). The reliable and effective predictive capabilities of these models make them valuable tools for pharmaceutical research and development endeavors.

Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results

Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no large enough retention data sets of structurally diverse compounds for them. Consequently, there are very few works devoted to quantitative structure-retention relationships (QSRR) for ionic liquid-based stationary phases. This work is aimed at closing this gap. Three ionic liquids with substituted pyridinium cations are considered. We provide large enough data sets (123-158 compounds) that can be used in further works devoted to QSRR and related methods. We provide a QSRR study using this data set and demonstrate the following. The retention index for a polyethylene glycol stationary phase (denoted as RI_PEG), predicted using another model, can be used as a molecular descriptor. This descriptor significantly improves the accuracy of the QSRR model. Both deep learning-based and linear models were considered for RI_PEG prediction. The ability to predict the retention indices for ionic liquid-based stationary phases with high accuracy is demonstrated. Particular attention is paid to the reproducibility and reliability of the QSRR study. It was demonstrated that adding/removing several compounds, small perturbations of the data set can considerably affect the results such as descriptor importance and model accuracy. These facts have to be considered in order to avoid misleading conclusions. For the QSRR research, we developed a software tool with a graphical user interface, which we called CHERESHNYA. It is intended to select molecular descriptors and construct linear equations connecting molecular descriptors with gas chromatographic retention indices for any stationary phase. The software allows the user to generate several hundred molecular descriptors (one-dimensional and two-dimensional). Among them, predicted retention indices for popular stationary phases such as polydimethylsiloxane and polyethylene glycol are used as molecular descriptors. Various methods for selecting (and assessing the importance of) molecular descriptors have been implemented, in particular the Boruta algorithm, partial least squares, genetic algorithms, L1-regularized regression (LASSO) and others. The software is free, open-source and available online: https://github.com/mtshn/chereshnya.

Exploring the potential applications of amphiphilic carbon dots based nanocomposite hydrogel in liquid chromatographic separations

BACKGROUND

Due to their excellent stability, low toxicity, flexible modification and adjustable functionality, carbon dots (CDs) have a promising application prospect in the field of chromatographic stationary phases. Hydrogels are new functional polymer materials with three-dimensional network structure that have excellent hydrophilicity, high porosity and unique mechanical properties, which are also good candidate materials for liquid chromatography. Nevertheless, a review of the literature reveals that CDs based nanocomposite hydrogels have not yet been reported as HPLC stationary phases.

RESULTS

In this work, amphiphilic CDs with multiple functional groups and polyacrylic acid hydrogel were grafted to the surface of silica gel by an in-situ polymerization method, and a CDs/polyacrylic acid nanocomposite hydrogel stationary phase (CDs/hydrogel@SiO2) was prepared. CDs act as the macroscopic cross-linking agents to form a cross-linked network with polyacrylic acid chains through physical cross-linking by hydrogen bonding and chemical cross-linking by amidation and esterification reactions, which not only improve the swelling property of the hydrogel but also increase its stability. Additionally, the introduction of CDs with multifunctional groups modulates the hydrophilic-hydrophobic balance of the hydrogel that also imparts good hydrophobicity to the composite hydrogel. Through the study of retention mechanism and influencing factors, it is certificate that the CDs/hydrogel@SiO2 has mixed-mode chromatographic performance. Furthermore, the CDs/hydrogel@SiO2 column shows great potential for the determination of organic contaminants in environmental water samples.

SIGNIFICANCE

This work confirms the potential application of CDs/hydrogel composite for the separation of various samples and provides the possibility of developing CDs based nanocomposite hydrogel in the field of liquid chromatography. Introducing CDs into hydrogel can open up a new way for nanocomposite hydrogels to be used in HPLC, which expands the advance of hydrogel and CDs in separation field.

Fabrication of ionic liquid functionalized silica with different anions and the application in mixed-mode and chiral chromatography

To realize the potential of ionic liquid functionalized silica to prepare mixed-mode and chiral stationary phases, two ionic liquid silane reagents with different anions were synthesized via a high-efficiency click reaction. Then they were decorated onto the surface of silica by a one-step bonding reaction. The functionalized silica was characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), and elemental analysis (EA). Two stationary phases provided satisfactory performance when compared with a commercial mixed-mode column. Notably, Sil-C10Im-D-BCS with D-3-bromocamphor-8-sulfonate (D-BCS) as anion presented chiral separation capacity towards 1,2,3,4-Tetrahydro-1-naphthol. The separation mechanism was investigated through multiple pathways, and the results revealed that the prepared stationary phases can retain and separate solutes through multiple interactions, like hydrophobic effect, ion exchange, hydrogen-bond interaction, etc. Quantum chemical calculation (QC) was employed to obtain the optimized structures and the binding energy of anions to cations. The results provided some insights into the retention mechanism from a molecular perspective. This work demonstrated the superiority of ionic liquid functionalized silica as mixed-mode stationary phases and the potential of chiral ionic liquid as chiral selectors.

A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions

Hydrated electrons (eaq-) exhibit rapid degradation of diverse persistent organic contaminants (OCs) and hold great promise as a formidable reducing agent in water treatment. However, the diverse structures of compounds exert different influences on the second-order rate constant of hydrated electron reactions (keaq-), while the same OCs demonstrate notable discrepancies in keaq- values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between eaq- and OCs. Furthermore, the introduction of the pH variable enables a comprehensive investigation into the impact of ambient conditions on this process, thereby improving the practicality of the model. A dataset encompassing 701 keaq- values derived from 351 peer-reviewed publications was compiled. To comprehensively investigate compound properties, this study introduced molecular descriptor (MD), molecular fingerprint (MF), and the integration of both (MD + MF) as model variables. Furthermore, 60 sets of predictive models were established utilizing two variable screening methodologies (MLR and RF) and ten prominent algorithms. Through statistical parameter analysis, it was determined that descriptors combined with MD and MF, the RF screening method, and the symbolism algorithm exhibited the best predictive efficacy. Importantly, the combination of descriptor models exhibited significantly superior performance compared to individual MF and MD models. Notably, the optimal model, denoted as RF - (MF + MD) - LGB, exhibited highly satisfactory predictive results (R2tra = 0.967, Q2tra = 0.840, R2ext = 0.761). The mechanistic explanation study based on Shapley Additive Explanations (SHAP) values further elucidated the crucial influences of polarity, pH, molecular weight, electronegativity, carbon-carbon double bonds, and molecular topology on the degradation of OCs by eaq-. The proposed modeling approach, particularly the integration of MF and MD, alongside the introduction of pH, may furnish innovative ideas for advanced reduction or oxidation processes (ARPs/AOPs) and machine learning applications in other domains.

A multi-target QSRR approach to model retention times of small molecules in RPLC

Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).

Predicting liquid chromatography-electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities

A priori estimation of analyte response is crucial for the efficient development of liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure-property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC-ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC-ESI/MS that provided a holistic overview of the analyte's response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA-GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.