Investigation and prediction of retention characteristics of imidazoline and serotonin receptor ligands and their related compounds on mixed-mode stationary phase

Obradović, Darija; Oljačić, Slavica; Nikolić, Katarina; Agbaba, Danica

doi:10.1016/j.chroma.2018.11.051

Cited by 17 publications

(5 citation statements)

References 41 publications

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“…Obradovic et al. investigated the retention of imidazoline and serotonin receptor ligands on a mixed‐mode column and were able to fit the retention data at different mobile phase concentrations to an MM model, thereby confirming the retention mechanism [140]. Balkatzopoulou et al.…”

Section: Methodsmentioning

confidence: 99%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

J of Separation Science

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Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

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Section: Methodsmentioning

confidence: 99%

Recent applications of retention modelling in liquid chromatography

Uijl

Schoenmakers

Pirok

et al. 2020

J of Separation Science

View full text Add to dashboard Cite

show abstract

“…Adsorption, on the other hand, was driven by molecular geometry, electronegativity, polarizability, van der Waals volume, and atomic mass of the tested analytes. For the turning point and modality expressions, distribution of ionic forms, hydrogen bonding properties, and electronic properties, as well as atomic mass, were significant [91].…”

Section: Mixed-mode Liquid Chromatographymentioning

confidence: 99%

QSRR Approach: Application to Retention Mechanism in Liquid Chromatography

Krmar¹,

Svrkota²,

Đajić³

et al. 2023

Novel Aspects of Gas Chromatography and Chemometrics

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One-factor-at-a-time experimentation was used for a long time as gold-standard optimization for liquid chromatographic (LC) method development. This approach has two downsides as it requires a needlessly great number of experimental runs and it is unable to identify possible factor interactions. At the end of the last century, however, this problem could be solved with the introduction of new chemometric strategies. This chapter aims at presenting quantitative structure–retention relationship (QSRR) models with structuring possibilities, from the point of feature selection through various machine learning algorithms that can be used in model building, for internal and external validation of the proposed models. The presented strategies of QSRR model can be a good starting point for analysts to use and adopt them as a good practice for their applications. QSRR models can be used in predicting the retention behavior of compounds, to point out the molecular features governing the retention, and consequently to gain insight into the retention mechanisms. In terms of these applications, special attention was drawn to modified chromatographic systems, characterized by mobile or stationary phase modifications. Although chromatographic methods are applied in a wide variety of fields, the greatest attention has been devoted to the analysis of pharmaceuticals.

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“…Furthermore, the predictive ability of QSRRs was internally and externally validated using a training set of 15 and a test set of 6 amino acids, giving RMSEP values of 0.75 and 0.23, respectively. Obradovićet al 166 have demonstrated the application of QSRR on a mixed-mode column in the combined RPLC and HILIC modes. Based on a complex set of pharmaceuticals and using the calculated constitutional, geometrical, electronic, and physicochemical properties as the molecular descriptors and stepwise MLR and the SVM method based on polynomial and the radial basis functions as the machine learning methods, accurate models were generated which enabled the prediction of retention times for their experimental setup.…”

Section: ■ Prediction Of Retention In Hilicmentioning

confidence: 99%