Quantitative structure retention relationship modeling as potential tool in chromatographic determination of stability constants and thermodynamic parameters of β-cyclodextrin complexation process

A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins

Cyclodextrins are macrocyclic rings composed of glucose residues. Due to their remarkable structural properties, they can form host-guest inclusion complexes, which is why they are frequently used in the pharmaceutical, cosmetic, and food industries, as well as in environmental and analytical chemistry. This review presents the reports from 2011 to 2023 on the quantitative structure-activity/property relationship (QSAR/QSPR) approach, which is primarily employed to predict the thermodynamic stability of inclusion complexes. This article extensively discusses the significant developments related to the size of available experimental data, the available sets of descriptors, and the machine learning (ML) algorithms used, such as support vector machines, random forests, artificial neural networks, and gradient boosting. As QSAR/QPR analysis only requires molecular structures of guests and experimental values of stability constants, this approach may be particularly useful for predicting these values for complexes with randomly substituted cyclodextrins, as well as for estimating their dependence on pH. This work proposes solutions on how to effectively use this knowledge, which is especially important for researchers who will deal with this topic in the future. This review also presents other applications of ML in relation to CD complexes, including the prediction of physicochemical properties of CD complexes, the development of analytical methods based on complexation with CDs, and the optimisation of experimental conditions for the preparation of the complexes.

A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method

A novel model is presented for reliable estimation of the stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions (log β₁₁) in an aqueous solution, which has wide usage in environmental safety and ecotoxicology applications. The biggest reported data of log β₁₁ for 120 metalthiosemicarbazone complexes are used for deriving and testing the novel model. In contrast to available methods where they need the two-dimensional (2D) and three-dimensional (3D) complex molecular descriptors as well as expert users and computer codes, the novel correlation uses four additive and two non-additive structural parameters of thiosemicarbazone ligands. The calculated results of the novel correlation are compared with the outputs of the genetic algorithm with multivariate linear regression method (GA-MLR) as one of the best existing methods, which requires seven complex descriptors. The estimated results for 78 of training as well as 42 of two different test sets were established by external and internal validations. The values of statistical parameters comprising average deviation, average absolute deviation, average absolute relative deviation, absolute maximum deviation, and the coefficient of determination for 73 data of training set of New model/GA-MLR are 0.04/ - 0.25, 1.06/1.31, 14.4/18.7, 3.18/7.92, and 0.830/0.652, respectively. Thus, the predicted results of the new model are worthy as compared to the complex GA-MLR model. Moreover, assessments of various statistical parameters confirm that the new model provides great reliability, goodness-of-fit, accuracy, and precision.

A comprehensive study on retention of selected model substances in β-cyclodextrin-modified high performance liquid chromatography

The quantitative structure-retention relationship (QSRR) models are not only employed in retention behaviour prediction, but also in an in-depth understanding of complex chromatographic systems. The goal of the present research is to enable the comprehensive understanding of retention underlying the separation in β-cyclodextrin (CD) modified reversed-phase high performance liquid chromatography (RP-HPLC) systems, through the development of mixed QSRR models. Moreover, the amount of β-CD adsorbed on the stationary phase surface (β-CD_A) is added as the model's input in order to evaluate its contribution to both model performances and retention. Nuclear magnetic resonance (NMR) experiments were conducted to confirm the predicted inclusion complex structures and support the application of in silico tools. The most significant descriptors revealed that retention is governed by the steric factors 7.5 Å distant from the geometrical centre of a molecule, 3D arrangement of atoms determining the molecular size and shape, lipophilicity indicated by topological distances, as well as the unbound system's energy, related to the inclusion complex formation. In addition, a notable effect of the pH of the aqueous phase on the retention of ionizable analytes was shown. In the case of pH of the aqueous phase and β-CD_A the change in retention behaviour of the studied analytes was observed only at the highest β-CD_A value (5.17 μM/m²), but it was not related to the ionization state of analytes. When the analytes did not change the ionization form across the investigated studied pH range, and the acetonitrile content in the mobile phase was 25% (v/v), the retention factor had low values regardless of the β-CD_A; under these circumstances the retention is probably acetonitrile driven.

Corona Charged Aerosol Detector in studying retention and β-cyclodextrin complex stability using RP-HPLC

Binding between cyclodextrin (CD) cavity and guest molecule in Reversed Phase High-Performance Liquid Chromatography (RP-HPLC) is dynamic process. In general, increasing CD concentration is inducing inclusion complex formation, leading to reduction of analyte's retention time. Consequently, the shortness in retention time is a measure of complex stability in HPLC. However, under certain experimental conditions, the retention of some analytes could be prolonged even when concentration of CD in the mobile phase is increased. In order to reveal the cause of this unexpected retention behavior, the present study was carried on. The model mixture consisted of risperidone, olanzapine and their related impurities, while β-CD was selected among CDs, as in the previous study. In order to achieve fast equilibrium between free analyte and β-CD-analyte complex, β-CD was not added to the mobile phase, but only to the sample. Detection was performed with Corona Charged Aerosol Detector (CAD), suitable for non-chromophoric β-CD. When analyzing olanzapine impurity B-β-CD sample, three peaks were detected, namely free β-CD, β-CD-analyte complex and free analyte. The complex stability constant was calculated employing a modification of the Benesi-Hildebrandt equation and CAD has proven to be useful in complex stability constants assessment if retention of free analyte and β-CD-analyte complex is distinguished. For all other analytes only two peaks could be detected, because free analyte and formed complex are eluting at the same retention time. Under such circumstances, the authors proposed the methodology for calculating stability constants and confirmed its applicability to studied model mixture.