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Quantitative Structure–Retention Relationships and Related Methods in Separation Science: In Memory of Prof. Roman Kaliszan

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Biochemistry".

Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 16664

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Dr. Petar Žuvela

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Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore
Interests: Chromatography; chemometrics; QSRRs; QSARs; drug design; molecular dynamics

Dr. Mariusz Belka

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Department of Pharmaceutical Chemistry, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416 Gdańsk, Poland
Interests: Chromatography; bioanalysis; chemometrics; QSRRs; QSARs; drug design; 3D printing

Prof. Dr. Tomasz Bączek

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Department of Pharmaceutical Chemistry, Medical University of Gdańsk, Hallera 107, 80-416 Gdańsk, Poland
Interests: Pharmaceutical and biomedical analysis; bioanalytics; proteomics; chemometrics and medicinal chemistry

Prof. Dr. Bogusław Buszewski

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Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7, 87-100 Toruń, Poland
Interests: analytical chemistry; physical chemistry; separation sciences; chromatography and related techniques (HPLC, SPE, SFC, GC, CZE, adsorption, sample preparation); spectrometry; bioanalytics; metabolomics; nanotechnology; environmental chemistry; modeling and chemometrics
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Dear Colleagues,

RP-HPLC accounts for >90% of separations in modern analytical laboratories, and prediction of retention time in RP-HPLC has become routine for chromatographic method development. Quantitative structure–(chromatographic) retention–relationships (QSRRs) modelling and related methods can be used for that purpose and have found a variety of applications: (i) Identification of most useful structural descriptors related to the retention mechanism; (ii) prediction of retention for new analytes and identification of unknown ones; (iii) quantitative comparison of separation properties of different chromatographic columns; and (iv) evaluation of physical properties, such as lipophilicity, dissociation constants, or relative bioactivities of xenobiotics; among others.

This Special Issue aims to honor the memory of Prof. Dr. Roman Kaliszan, a pioneer of QSRRs. Prof. Kaliszan published two seminal books and several highly cited reviews on the subject. He was a (co-)author of ~300 full length articles published in globally recognized journals on general chemistry (e.g., Chemical Reviews), medicinal chemistry (e.g., Journal of Medicinal Chemistry), analytical chemistry (e.g., Analytical Chemistry; Biosensors and Bioelectronics), bioinformatics (e.g., Proteomics; Journal of Proteome Research), and many others.

In memoriam Prof. Dr. Roman Kaliszan
(1945/12/23–2019/05/09)

With kind regards,

Dr. Petar Žuvela
Dr. Mariusz Belka
Prof. Dr. Tomasz Bączek
Prof. Dr. Bogusław Buszewski

Guest Editors

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Published Papers (4 papers)

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Research

15 pages, 3843 KiB

Open AccessArticle

A Novel Approach to Optimize Hot Melt Impregnation in Terms of Amorphization Efficiency

by Kamil Garbera, Krzesimir Ciura and Wiesław Sawicki

Int. J. Mol. Sci. 2020, 21(11), 4032; https://doi.org/10.3390/ijms21114032 - 4 Jun 2020

Cited by 1 | Viewed by 2459

Abstract

In this study, an innovative methodology to optimize amorphization during the hot melt impregnation (HMI) process was proposed. The novelty of this report revolves around the use of thermal analysis in combination with design of experiments (DoEs) to reduce residual crystallinity during the HMI process. As a model formulation, a mixture of ibuprofen (IBU) and Neusilin was used. The main aim of the study was to identify the critical process parameters of HMI and determine their optimal values to assure a robust impregnation process and possibly the highest possible amorphization rate of IBU. In order to realize this, a DoE approach was proposed based on a face-centered composite design involving three factors. The IBU/Neusilin ratio, the feeding rate, and the screw speed were considered as variables, while the residual crystallinity level of IBU, determined using differential scanning calorimetry (DSC), was measured as the response. Additionally, the stability of IBU under HMI was analyzed using high-performance liquid chromatography to estimate the extent of potential degradation. In order to verify the correctness of the DoE model, tested extrudates were manufactured by HMI and the obtained extrudates were thoroughly examined using scanning electron micrography, X-ray powder diffraction, and DSC. Full article

(This article belongs to the Special Issue Quantitative Structure–Retention Relationships and Related Methods in Separation Science: In Memory of Prof. Roman Kaliszan)

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21 pages, 3250 KiB

Open AccessArticle

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

by Bogusław Buszewski, Petar Žuvela, Gulyaim Sagandykova, Justyna Walczak-Skierska, Paweł Pomastowski, Jonathan David and Ming Wah Wong

Int. J. Mol. Sci. 2020, 21(6), 2053; https://doi.org/10.3390/ijms21062053 - 17 Mar 2020

Cited by 13 | Viewed by 4283

Abstract

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical ‘black box’ approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations. Full article

(This article belongs to the Special Issue Quantitative Structure–Retention Relationships and Related Methods in Separation Science: In Memory of Prof. Roman Kaliszan)

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15 pages, 1361 KiB

Open AccessArticle

Lipophilicity Determination of Quaternary (Fluoro)Quinolones by Chromatographic and Theoretical Approaches

by Krzesimir Ciura, Joanna Fedorowicz, Filip Andrić, Katarzyna Ewa Greber, Alina Gurgielewicz, Wiesław Sawicki and Jarosław Sączewski

Int. J. Mol. Sci. 2019, 20(21), 5288; https://doi.org/10.3390/ijms20215288 - 24 Oct 2019

Cited by 33 | Viewed by 3184

Abstract

Lipophilicity is a vital physicochemical parameter of a molecule, which affects several biological processes such as absorption, tissue distribution, and pharmacokinetic properties. In this study, evaluation of lipophilicities of a series of novel fluoroquinolone-Safirinium dye hybrids using chromatographic and computational methods is presented. Fluoroquinolone-Safirinium dye hybrids have been synthesized as new dual-acting hydrophilic antibacterial agents. Reversed phase thin-layer chromatography and micellar electrokinetic chromatography experiments were carried out. Furthermore, logP values of the target structures were predicted by means of different software platforms and algorithms. In order to assess similarities and dissimilarities of the obtained lipophilicity indexes, cluster analysis and sum of ranking differences were performed. The significant differences of calculated logP values (α = 0.05, p < 0.001) indicated that an experimental approach is necessary for lipophilicity prediction of this class of antibiotics. Chromatographic data indicated that the newly synthesized hybrid (fluoro)quinolone-based quaternary ammonium derivatives show less lipophilic character than the parent (fluoro)quinolones. Additionally, the chromatographically obtained lipophilicity indexes were evaluated for possible application in quantitative retention–activity relationships. The established lipophilicity models have the potential to predict antimicrobial activities of a series of quaternary (fluoro)quinolones against Bacillus subtilis, Escherichia coli, and Proteus vulgaris. Full article

(This article belongs to the Special Issue Quantitative Structure–Retention Relationships and Related Methods in Separation Science: In Memory of Prof. Roman Kaliszan)

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11 pages, 1648 KiB

Open AccessArticle

Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order

by J. Jay Liu, Alham Alipuly, Tomasz Bączek, Ming Wah Wong and Petar Žuvela

Int. J. Mol. Sci. 2019, 20(14), 3443; https://doi.org/10.3390/ijms20143443 - 12 Jul 2019

Cited by 9 | Viewed by 3705

Abstract

In this work, we employed a non-linear programming (NLP) approach via quantitative structure–retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(t_R) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures. Full article

(This article belongs to the Special Issue Quantitative Structure–Retention Relationships and Related Methods in Separation Science: In Memory of Prof. Roman Kaliszan)

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Quantitative Structure–Retention Relationships and Related Methods in Separation Science: In Memory of Prof. Roman Kaliszan

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