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Sholokhova AY, Matyushin DD, Shashkov MV. Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results. J Chromatogr A 2024; 1730:465144. [PMID: 38996513 DOI: 10.1016/j.chroma.2024.465144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 07/02/2024] [Accepted: 07/04/2024] [Indexed: 07/14/2024]
Abstract
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.
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Affiliation(s)
- Anastasia Yu Sholokhova
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, Moscow 119071, Russia
| | - Dmitriy D Matyushin
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, Moscow 119071, Russia.
| | - Mikhail V Shashkov
- Boreskov Institute of Catalysis, 5 Lavrentieva Prospect, Novosibirsk 630090, Russia
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Xu Z, Yu K, Zhang M, Ju Y, He J, Jiang Y, Li Y, Jiang J. Accurate Clinical Detection of Vitamin D by Mass Spectrometry: A Review. Crit Rev Anal Chem 2024:1-25. [PMID: 38376891 DOI: 10.1080/10408347.2024.2316237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Vitamin D deficiency is thought to be associated with a wide range of diseases, including diabetes, cancer, depression, neurodegenerative diseases, and cardiovascular and cerebrovascular diseases. This vitamin D deficiency is a global epidemic affecting both developing and developed countries and therefore qualitative and quantitative analysis of vitamin D in a clinical context is essential. Mass spectrometry has played an increasingly important role in the clinical analysis of vitamin D because of its accuracy, sensitivity, specificity, and the ability to detect multiple substances at the same time. Despite their many advantages, mass spectrometry-based methods are not without analytical challenges. Front-end and back-end challenges such as protein precipitation, analyte extraction, derivatization, mass spectrometer functionality, must be carefully considered to provide accurate and robust analysis of vitamin D through a well-designed approach with continuous control by internal and external quality control. Therefore, the aim of this review is to provide a comprehensive overview of the development of mass spectrometry methods for vitamin D accurate analysis, including emphasis on status markers, deleterious effects of biological matrices, derivatization reactions, effects of ionization sources, contribution of epimers, standardization of assays between laboratories.
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Affiliation(s)
- Zhilong Xu
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China
| | - Kai Yu
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
| | - Meng Zhang
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China
| | - Yun Ju
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China
| | - Jing He
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
| | - Yanxiao Jiang
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
| | - Yunuo Li
- College of Natural Resources and Environment, Northwest A&F University, Yangling, China
| | - Jie Jiang
- School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai, China
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, China
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Prediction of surface excess adsorption and retention factors in reversed-phase liquid chromatography from molecular dynamics simulations. J Chromatogr A 2022; 1685:463627. [DOI: 10.1016/j.chroma.2022.463627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/27/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
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Safa F, Manouchehri F. Unified Linear and Nonlinear Models for Retention Prediction of Aliphatic Aldehydes and Ketones in Different Columns and Temperatures: Application of Atom-Type-Based AI Topological Indices. CHEMISTRY AFRICA 2022. [DOI: 10.1007/s42250-022-00495-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Paritala J, Peraman R, Kondreddy VK, Subrahmanyam CVS, Ravichandiran V. Quantitative structure retention relationship (QSRR) approach for assessment of chromatographic behavior of antiviral drugs in the development of liquid chromatographic method. J LIQ CHROMATOGR R T 2022. [DOI: 10.1080/10826076.2022.2025827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Jagadeesh Paritala
- Department of Pharmaceutical Analysis, Raghavendra Institute of Pharmaceutical Education and Research (RIPER)-Autonomous, Anantapur, India
| | - Ramalingam Peraman
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India
| | - Vinod Kumar Kondreddy
- Department of Pharmaceutical Analysis, Raghavendra Institute of Pharmaceutical Education and Research (RIPER)-Autonomous, Anantapur, India
| | | | - V Ravichandiran
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India
- National Institute of Pharmaceutical Education & Research (NIPER), Kolkata, India
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Liapikos T, Zisi C, Kodra D, Kademoglou K, Diamantidou D, Begou O, Pappa-Louisi A, Theodoridis G. Quantitative Structure Retention Relationship (QSRR) Modelling for Analytes’ Retention Prediction in LC-HRMS by Applying Different Machine Learning Algorithms and Evaluating Their Performance. J Chromatogr B Analyt Technol Biomed Life Sci 2022; 1191:123132. [DOI: 10.1016/j.jchromb.2022.123132] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/12/2022] [Accepted: 01/16/2022] [Indexed: 12/26/2022]
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Gritti F. Perspective on the Future Approaches to Predict Retention in Liquid Chromatography. Anal Chem 2021; 93:5653-5664. [PMID: 33797872 DOI: 10.1021/acs.analchem.0c05078] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The demand for rapid column screening, computer-assisted method development and method transfer, and unambiguous compound identification by LC/MS analyses has pushed analysts to adopt experimental protocols and software for the accurate prediction of the retention time in liquid chromatography (LC). This Perspective discusses the classical approaches used to predict retention times in LC over the last three decades and proposes future requirements to increase their accuracy. First, inverse methods for retention prediction are essentially applied during screening and gradient method optimization: a minimum number of experiments or design of experiments (DoE) is run to train and calibrate a model (either purely statistical or based on the principles and fundamentals of liquid chromatography) by a mere fitting process. They do not require the accurate knowledge of the true column hold-up volume V0, system dwell volume Vdwell (in gradient elution), and the retention behavior (k versus the content of strong solvent φ, temperature T, pH, and ionic strength I) of the analytes. Their relative accuracy is often excellent below a few percent. Statistical methods are expected to be the most attractive to handle very complex retention behavior such as in mixed-mode chromatography (MMC). Fundamentally correct retention models accounting for the simultaneous impact of φ, I, pH, and T in MMC are needed for method development based on chromatography principles. Second, direct methods for retention prediction are ideally suited for accurate method transfer from one column/system configuration to another: these quality by design (QbD) methods are based on the fundamentals and principles of solid-liquid adsorption and gradient chromatography. No model calibration is necessary; however, they require universal conventions for the accurate determination of true retention factors (for 1 < k < 30) as a function of the experimental variables (φ, T, pH, and I) and of the true column/system parameters (V0, Vdwell, dispersion volume, σ, and relaxation volume, τ, of the programmed gradient profile at the column inlet and gradient distortion at the column outlet). Finally, when the molecular structure of the analytes is either known or assumed, retention prediction has essentially been made on the basis of statistical approaches such as the linear solvation energy relationships (LSERs) and the quantitative structure retention relationships (QSRRs): their ability to accurately predict the retention remains limited within 10-30%. They have been combined with molecular similarity approaches (where the retention model is calibrated with compounds having structures similar to that of the targeted analytes) and artificial intelligence algorithms to further improve their accuracy below 10%. In this Perspective, it is proposed to adopt a more rigorous and fundamental approach by considering the very details of the solid-liquid adsorption process: Monte Carlo (MC) or molecular dynamics (MD) simulations are promising tools to explain and interpret retention data that are too complex to be described by either empirical or statistical retention models.
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Affiliation(s)
- Fabrice Gritti
- Waters Corporation, 34 Maple Street, Milford, Massachusetts 01757, United States
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