Quantitative Structure-retention Relationships Equations Research Articles

Study of chromatographic behavior of antibiotic drugs and their metabolites based on quantitative structure-retention relationships with the use of HPLC-DAD

The separation of eleven antibiotics and ten metabolites were studied using high performance liquid chromatography. The C18-PFP octadecyl with integral PFP, C18-AR octadecyl with integral phenyl, C18-HL octadecyl and phenyl phase were used as stationary phases. Mixtures of acetontrile–0.1 % formic acid in water were investigated as mobile phases. The elution order of the target compounds was similar for all four HPLC columns applied. The best separation was obtained using the column with the pentafluorophenylpropyl chain. In addition, in order to optimize the parameters of retention elution for the column and to predict the conditions for the best separation of the active compounds studied biologically the ChromSword software was used. To obtain reliable information of the physicochemical properties and to estimate the relative biological activity of a group of the studied analytes, the QSRR approach was applied. Molecular descriptors were calculated using the HyperChem software. The study was based on multiple linear regression and the results were presented as quantitative structure–retention relationships equations. The QSRR models were determined using 16 molecular descriptors mainly related to the dipole moment (μ), the solvent accessible surface area (SAS), the van der Waals surface area (VWS), the minimum charge (δmin) as well as the polar surface area (PSA). Moreover, structural descriptors of the target compounds were used to describe their chromatographic retention behavior under the optimized HPLC conditions.

Study of the chromatographic behavior of selected antipsychotic drugs on RP-TLC based on quantitative structure\u2013retention relationships

The chromatographic properties for nine antipsychotic drugs: chlorpromazine, chlorprothixene, clozapine, quetiapine, perazine, perphenazine, prochlorperazine, promazine and trifluoperazine were studied using planar chromatography. Both C18-bonded silica gel and diol-bonded phase were used as stationary phases. Mixtures of methanol–water and tetrahydrofuran–water were investigated as mobile phases. Molecular descriptors were calculated using HyperChem, ChemAxon and ACD/labs software. The study was based on multiple linear regression and the results were presented as quantitative structure–retention relationships equations. Furthermore, both principal component and cluster analysis were performed. The classification methods showed clear differences between calculated and chromatographic established lipophilicity. Chromatographic parameters were similar to bulkiness-related, geometrical descriptors and topological indexes. On the other hand, the electronic descriptors which were strongly connected with the computational log P values had lower impact on chromatographic parameters.

Quantitative structure—retention relationships of some steroid and phenanthrene derivatives on cyano-, reversed-phase, and normal-phase thin-layer chromatography stationary phases

In the presented study, quantitative structure—retention relationships (QSRR) methodology was used to investigate the relationship between the chemical structures of steroids and phenanthrene derivatives, their physicochemical properties, and the chromatographic retention. Normal-, reversed-phase and cyano-bonded silica stationary phases were tested with five binary mobile phases (acetonitrile—water, acetonitrile—dimethyl sulfoxide (DMSO), acetonitrile— methanol, acetone—petroleum ether, and acetone—water). The study was based on multiple linear regression, and the results are presented as QSRR equations. Additionally, principal component analysis and cluster analysis were performed. They have been carried out based on retention parameters and molecular descriptors, which were calculated from the optimized structures by use of HyperChem and ChemAxon software. The high correlation coefficients (r), F-values, and low values of the standard deviation indicate that the obtained QSRR equations present well the...

The study of salting-out thin-layer chromatography and their application on QSRR/QSAR of some macrolide antibiotics

The object of this study was to investigate the lipophilicity and hydrophobicity parameters of selected macrolide antibiotics obtained in the salting-out thin-layer chromatography. Statistically significant correlations between chromatographic data and calculated lipophilicity were found and presented as quantitative structure–retention relationships equations. The value of calculated distribution coefficient (clog D pH7.4) in most cases significantly affects the retention of tested macrolide antibiotics. Another aspect of the study was the comparison of the usefulness of chromatographic parameters and calculated lipophilicity values for prediction of antimicrobial properties of studied compounds. The quantitative structure–activity relationships equations were proposed, which can be used for predictions of antimicrobial activities against Streptococcus pyogenes, Streptococcus pneumoniae, and Listeria monocytogenes. The results showed that chromatographic parameters obtained in the salting-out thin-layer chromatography can be successfully used to predict antibacterial activity for this group of antibiotics. The results were also confirmed by principal component analysis.

Thermodynamic and QSRR Modeling of HPLC Retention on Modern Stationary Phases

This paper investigates correlations between the standard chromatographic parameter, log kw, obtained experimentally (classical thermodynamic) and obtained through the use of quantitative structure-retention relationships (QSRR) models (extrathermodynamic approach). QSRR models were created using descriptors from calculation chemistry, selected a priori on the basis of chemical intuition. Four modern analytical columns (packed with C18), often used in pharmaceutical analysis, were selected for the study. The simple extrathermodynamic model was demonstrated to be superior in terms of retention description with relation to rather complex models based on thermodynamic hermeneutics. However, all the QSRR equations derived are of limited predictive value as regards prediction of retention on the basis of the structure of the analyte. Nonetheless, they allow the differentiation of mechanism of separation operating on individual tested stationary phases. The descriptors, which are most significant in QSRR equations, appear to characterize better the net effect of interactions of the analytes with the stationary phases and the mobile phase containing acetonitrile as compared to the HPLC systems comprising methanol. These observations suggest that retention properties of new stationary phases studied in this work differ.

Quantitative Structure-Retention Relationships Studies of Selected Groups of Compounds Characterized by Different Pharmacological Activity Using Multiple Linear Regression Procedure

In this paper the quantitative structure-retention relationships (QSRR) studies for three different groups of drugs (cardiovascular system drugs, analgesic drugs and some compounds characterized by divergent pharmacological activity) and for chromatographic parameters (log k' or log k' w retention factors) determined with the use of the HPLC methods were performed. Molecular descriptors (over 4900 molecular modeling parameters) obtained using the HyperChem and the Dragon computer programs, and the Virtual Computational Chemistry Laboratory (VCCLAB) website were applied to derive the QSRR equations by means of multiple linear regression method with stepwise procedure. Several statistically significant QSRR equations as two-parameters for both cardiovascular and analgesic drugs, and sevenparameters for group of “other” drugs were built. Six QSRR equations with correlations R ranged from 0.9822 to 0.9957 were obtained for cardiovascular drugs, twenty-six QSRRs with R equal to 0.9120-0.9776 were derived for analgesics, and ten QSRRs with R from 0.9529 to 0.9924 were calculated for “other“ drugs. Moreover, the proposed QSRR models give important information about physico-chemical properties of analyzed drugs, and indicated that descriptors characterized topology, geometry and lipophilicity of molecular structures of analyzed compounds are crucial for prediction of their retention parameters. Additionally, derived QSRR models can be helpful to search (to prediction) HPLC retention factor for the new drug candidates.

Application of lipophilicity parameters in QSRR analysis of newly synthesized s-triazine derivatives: Prediction of the retention behavior

Considerable attention has been paid to the analysis of chemicals in the s-triazine group, due to their widespread use in agricultural chemistry and their subsequent impact on biological systems. For initial chemical screening of the activity of newly synthesized compounds, it is recommended to determine their lipophilicity and physico-chemical property in relation to biological activity. Lipophilicity is difficult to quantify. The most widely accepted measure of lipophilicity is the octanol-water partition coefficient. Measurement of the octanol-water partition coefficients is achieved by an alternative method, i.e. reversed-phase liquid chromatography. Reversed-phase thin-layer chromatography (RP TLC) is a rapid method for the analysis of large number of s-triazine type compounds. Certain relationship between the structure of s-triazine compounds and their mobility on silica gel impregnated with paraffin oil have recently been demonstrated. The retention behavior of compounds in various chromatographic systems strongly depends on their physico-chemical properties. Recently, much effort was given in finding adequate mathematical model relating the retention of the given analyte to its physico-chemical and structural parameters (descriptors). These correlations are known as quantitative structure-retention relationships (QSRR), which offer a powerful tool for the prediction of separation behavior. The QSRR equations describing retention constants RM0, determined for different modifiers in mobile phase in terms of logarithms of n-octanol-water partition coefficients, were derived. The partition coefficients (AlogPs, AClogP, AB/logP, milogP, AlogP, MlogP, logPKowin, XlogP2, XlogP3, ACDlogP i ClogP) were calculated by application of different software packages. The goal of this paper was to select the logP data and TLC system that best characterize octanol/water partitioning and thus the lipophilicity of the investigated molecules.

Comparative evaluation of high-performance liquid chromatography stationary phases used for the separation of peptides in terms of quantitative structure–retention relationships

Chromatographic measurements were made on 17 physicochemically diversified high-performance liquid chromatography (HPLC) columns which were further analyzed in terms of their similarities and dissimilarities for 25 carefully designed, structurally diverse peptides showing distinctly distinguished groups. The goal of the study was to investigate the molecular mechanism of retention and to find an objective manner of quantitative comparison of retention properties and classification of modern stationary phases for reverse phase HPLC (RP-HPLC). We utilized the structural descriptors of peptides obtained from molecular modeling to describe their chromatographic retention behavior under given HPLC conditions. Quantitative structure–retention relationships (QSRR) with the following descriptors were employed: logarithm of the sum of amino acid retention contributions in a given peptide, log Sum AA, logarithm of the Van der Waals volume of the peptide, log VDW vol, and logarithm of its calculated n-octanol/water coefficient, clog P. The best QSRR equations were obtained in the case of monolithic and regular octadecylsilica columns. On the other hand, the combination of QSRR and principal component analysis (PCA) can be considered as the efficient tool allowing column classification and searching for orthogonal HPLC conditions required to separate peptides.

Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models

To quantitatively characterize the structure of a peptide and to predict its gradient retention time at given HPLC conditions three structural descriptors are used: (i) logarithm of the sum of retention times of the amino acids composing the peptide, log SumAA, (ii) logarithm of the van der Waals volume of the peptide, log VDW(Vol), (iii) and the logarithm of the peptide's calculated n-octanol-water partition coefficient, clog P. The log SumAA descriptor is obtained from empirical data for 20 natural amino acids, determined in a given HPLC system. The two other descriptors are calculated from the peptides' structural formulas using molecular modeling methods. The quantitative structure-retention relationships (QSRR), build by multiple linear regression, describe HPLC retention of peptide on a given chromatographic system on which the retention of the 20 amino acids was predetermined. A structurally diversified series of 98 peptides was employed. The predicted gradient retention times on several chromatographic systems were in good agreement with the experimental data. The QSRR equations, derived for a given system operated at variable gradient times and temperatures allowed for the prediction of peptide retention in that system. Matching the experimental HPLC retention to the theoretically predicted for a presumed peptide could facilitate original protein identification in proteomics. In conjunction with MS data, prediction of the retention time for a given peptide might be used to improve the confidence of peptide identifications and to increase the number of correctly identified peptides.

Prediction of gradient retention from the linear solvent strength (LSS) model, quantitative structure‐retention relationships (QSRR), and artificial neural networks (ANN)

AbstractThe linear solvent strength (LSS) model combined with quantitative structure‐retention relationships (QSRR) and artificial neural network (ANN) analysis has been shown to permit approximate prediction of the gradient high‐performance liquid chromatography (HPLC) retention time for any analyte on a once‐characterized column. The approach applies well to the reversed‐phase HPLC mode with a methanol‐water (buffer) eluent of linearly changing composition. Its suitability was tested for a representative series of structurally diverse analytes. In this approach the determination of retention times, t R, in two gradient runs for a predesigned model series of 15 analytes is first needed. Next, model QSRR equations describing t R in terms of analyte structure are derived to characterize the HPLC systems of interest. To quantitatively characterize the structure of the analytes the following three structural descriptors from molecular modeling are employed: total dipole moment; electron excess charge of the most negatively charged atom; and water‐accessible molecular surface area. Using these data a general QSRR equation is derived which is valid for a given column/eluent system. Next, having the structural descriptors for any analyte to be chromatographed in such a characterized HPLC system, one employs the previously derived general QSRR equation to calculate the analyte's retention time. The expected gradient retention time for any gradient conditions can be calculated by means of appropriate LSS equations. Independent of the standard QSRR calculation procedure based on multiple regression analysis (MRA), predictions of gradient retention times were performed by means of artificial neural networks (ANN). It has been found that the predictive power of ANN is similar to that of MRA. The combined LSS/QSRR approach has been demonstrated to provide approximate, yet otherwise unattainable, a priori predictions of gradient retention of analytes based solely on their chemical formulae. That way a rational chemometric basis for a systematic optimization of chromatographic separations has been elaborated as an alternative to the trial‐and‐error method normally applied at present.

Combination of linear solvent strength model and quantitative structure–retention relationships as a comprehensive procedure of approximate prediction of retention in gradient liquid chromatography

Quantitative structure–retention relationships (QSRR) combined with the linear solvent strength (LSS) model are demonstrated to provide approximate predictions of gradient reversed-phase high-performance liquid chromatography (HPLC) retention time for any structurally defined analyte on a once characterized column. The approach requires at first the determination of retention times for a predesigned model series of 15 analytes in two gradient runs. Then by employing the LSS theory a given HPLC system of interest is quantitatively characterized. Structure of the model analytes is next described quantitatively by means of three structural descriptors from standard molecular modeling: total dipole moment, electron excess charge of the most negatively charged atom and water-accessible molecular surface area. With those data the general QSRR equations are derived which describe gradient retention times of the model analytes in the specific column/eluent system. Having now the structural descriptors for any analyte to be chromatographed in such a characterized HPLC system, one employs respective general QSRR equations to calculate its expected gradient retention time at given gradient conditions by means of appropriate LSS equations. Additionally, the chromatographic parameters log k w and S can be calculated and retention coefficients corresponding to chosen isocratic conditions evaluated. The approach provides retention predictions which can be treated as a first approximation of actual data. Predictions are not yet precise enough for practical separation purposes but can be of use in rational modification of analytical conditions aimed at optimization of separations.

Quantitative structure/retention relationships in affinity chromatography

Affinity chromatography (AC) followed by quantitative structure/retention relationships (QSRR) analysis provides information on both the analytes and the macromolecules forming the stationary phases. QSRR equations derived for test series of analytes (often drugs) are interpreted in terms of structural requirements of the specific binding sites on macromolecules. Chromatographically demonstrated differences in analyte/macromolecule interactions may be relevant to molecular pharmacology and rational drug design. Multiple regression analysis of appropriately designed sets of affinity-chromatographic data may help increase the speed and efficiency of search as for new drugs and reduce the need for in vivo screening. Specific high-performance affinity-chromatographic separations can be optimized by rational selection of chiral columns, the characteristics of which are provided by QSRR.

Quantitative structure-retention relationships with model analytes as a means of an objective evaluation of chromatographic columns.

The performance of several previously designed model series of test analytes has been tested to characterize in an objective, quantitative manner modern stationary phases for reversed-phase high-performance liquid chromatography (RP-HPLC) using quantitative structure-retention relationships (QSRRs). Three QSRR approaches and three respective series of test analytes recommended for studies of the molecular mechanism of chromatographic retention are employed: the reduced linear solvation energy relationship (LSER)-based model of Abraham, a model employing structural descriptors from molecular modeling, and a model relating retention to the n-octanol-water partition coefficient log P. All of the models and test analytes proposed provide reliable QSRR equations. Those equations discriminate in quantitative terms individual columns and chromatographic systems and can be interpreted in straightforward rational chemical categories. In view of QSRRs, the differences in the intermolecular interactions between a given stationary phase and a structurally defined analyte rationalize the observed differences in retention. The QSRR models (previously derived retrospectively) are demonstrated to work well on new sets of RP-HPLC data. At the same time, it has been confirmed that the three test series of analytes have properly been designed and can be recommended for comparative studies of analytical columns. QSRRs once derived on a given column for model analytes can be used to predict the retention of other analytes of a defined structure. That in turn can facilitate the procedure of the rational optimization of chromatographic separations.

Test Analytes for Studies of the Molecular Mechanism of Chromatographic Separations by Quantitative Structure−Retention Relationships

Three model series of nonionized in water analytes are proposed for objective interlaboratory comparisons of effects on chromatographic separations of the stationary and the mobile phases by means of the analysis of quantitative structure-retention relationships (QSRR). Each series was designed specifically for a given general QSRR model by selecting the analytes whose properties were well reflected by the respective structural descriptors. Rules of a meaningful chemometric analysis were observed, and the structural information content was compromised with the length of analyte series. Three QSRR models were verified and are recommended for studies of molecular mechanism of chromatographic retention: the reduced linear solvation energy relationship-based model of Abraham, a model employing structural descriptors from molecular modeling, and a model correlating retention to the 1-octanol-water partition coefficient, log P. All the models were demonstrated to provide reliable QSRR equations for five sets of diverse retention data. These equations discriminate quantitatively individual chromatographic systems and are interpretable in straightforward chemical categories. In view of QSRR analysis, the retention processes clearly emerge as the net effects of fundamental intermolecular interactions involving the analyte and the components of chromatographic systems.

Mechanism of separation on cholesterol–silica stationary phase for high-performance liquid chromatography as revealed by analysis of quantitative structure–retention relationships

The retention characteristics of a newly synthesized stationary phase were determined for reversed-phase high-performance liquid chromatography obtained by chemical immobilization of cholesterol on spherical silica gel. For a designed series of analytes the retention factors, log k, were determined at several compositions of the methanol-water mobile phase. Logarithms of retention factor corresponding to a hypothetical pure water eluent, log k w, were calculated by extrapolation of the linear relationships of individual log k data versus volume percent of methanol. The series of 24 test analytes were characterized structurally by means of the logarithms of n-octanol–water partition coefficients, log P, by a set of the linear solvation energy relationship (LSER)-based descriptors of the polarity and bulkiness of the analytes and by structural descriptors of analyte size and polarity acquired by molecular modelling. Quantitative structure–retention relationships (QSRR) were derived by multiple regression analysis using the three groups of structural descriptors of analytes and the log k w data determined on the new stationary phase. For the sake of comparison the corresponding QSRR equations were also derived for retention parameters determined on a standard octadecylsilica and on the so-called immobilized artificial membrane (IAM) stationary phase. The QSRR analysis clearly proved distinctive retention properties of the new cholesterol–silica stationary phase. It has been concluded that the new phase may possess valuable analytical specificity. Its application for modelling penetration of xenobiotics through biological membranes appears rather unlikely.

Polyethylene-coated silica and zirconia stationary phases in view of quantitative structure-retention relationships

Retention properties of two new high-performance liquid chromatographic (HPLC) stationary phases, polyethylene-coated silica (PECSiO2) and polyethylene-coated zirconia (PECZrO2), were compared chemometrically. Reversed phase HPLC capacity factors were determined isocratically for a carefully designed series of 25 structurally diverse solutes. Linear free energy relationship (LFER)-based solute parameters (excess molar refraction, dipolarity-polarizability, hydrogen-bond acidity and basicity, McGowan volume) and solute structural descriptors determined by physico-chemical methods (water accessible van der Waals volume, dipole moment, atomic electron excess charge) were regressed as independent variables against log capacity factors. The quantitative structure-retention relationships (QSRR) obtained show the predominating partition mechanism of separation on both phases. Both the LFER parameters and the structural parameters obtained by molecular modelling produced QSRR equations of high and practically identical retention prediction ability. It has been concluded that intermolecular interactions of the dipolarity-polarizability type manifest themselves more strongly on the silica-based phase than on the zirconia-based one. This can be due to a role of active free silanols on the silica support.

Quantum chemical parameters in correlation analysis of gas-liquid chromatographic retention indices of amines

Quantitative structure-retention relationships (QSRRs) have been studied for a diverse group of aliphatic and heterocyclic amines subjected to gas-liquid chromatography on three stationary phases of different polarities. As a numerical descriptor of the ability of the solutes to take part in non-specific interactions with stationary phase, a quantum-chemically calculated (by the CNDO/2 method) total energy, E T, is employed. To characterize quantitatively the differences in specific or polar properties of the solutes, a new topological electronic index, T E, is proposed. This index is calculated by summation of the absolute differences in electronic excess charges on all atomic pairs in a given molecule, divided by the squares of the respective interatomic distances. The resulting two-parameter QSRR equations involving E T and T E are highly statistically significant for retention data determined on either non-polar or polar stationary phases. The index T E is a better descriptor of molecular polarity than other measures previously reported.