Quantitative Structure-retention Relationship Research Articles

Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples.

Over the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation, and retention time plausibility, should be met to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. Two extensive data sets were built for two chromatographic systems, one for positive and one for negative electrospray ionization mode, containing information for the retention time of 528 and 298 compounds, respectively, to expand the applicability domain of the developed models. Then, the data sets were split into training and test set, employing k-nearest neighborhood c...

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...

Prediction of Acridinones’ Ability to Interstrand DNA Crosslinks Formation Using Connected QSRR and QSAR Analysis

QSAR studies to predict acridinones’ ability to interstrand DNA crosslinks formation were performed. The study is based on experimental, as well as predicted retention data (log k) and was conducted by connected QSRR and QSAR strategy. For this purpose, chromatography analysis of acridinone derivatives was utilized. Moreover, computer modeling of the above-mentioned compounds was performed. Afterwards, statistical analysis of the obtained results was performed by two different HPLC stationary phases: phosphatidylcholine (IAM) and α1-glycoprotein (AGP). This approach allowed determining retention parameter log k, which characterizes binding affinity of acridinones to phospholipids or proteins. Moreover, molecular modeling was performed based on the chemical structure of considered acridinones using HyperChem 8.0 program (HyperCube Inc., Gainesville, FL, USA). Structural descriptors were obtained from Dragon 6.0 software (Talete, Italy). Those data were used to create general QSAR equations. Derived QSAR models described acridinones’ ability to interstrand DNA crosslinks formation (C0) depending on HPLC retention parameters, whereas log k parameter obtained by HPLC analysis was mostly dependent on molecular descriptors calculated. Additionally, the predictive performance of obtained QSARs and QSRRs models allowed us to predict the ability to interstrand DNA crosslinks formation by acridinones derivatives. It also enabled us to predict their chromatographic retention parameters. Proposed connected QSRR and QSAR approach could be a useful tool for in silico experiments, which verifies acridinones’ activity, without any in vivo biological test. Keywords: Quantitative structure-activity relationships (QSAR), quantitative structure-retention relationships (QSRR), multiple linear regression (MLR), high-Performance Liquid Chromatography (HPLC), acridinones, interstrand DNA crosslinks, molecular modeling descriptors.

Study of the chromatographic behavior of selected steroid hormones on aluminum oxide plates based on quantitative structure-retention relationships

The chromatographic properties of eight steroids: trans-andros-terone, methyltestosterone, testosterone, progesterone, cortisone, hydrocortisone, estrone, and β-estradiol were studied by planar chromatography by use of aluminum oxide plates as well as of four different binary mobile phases (acetonitrile-water, acetonitrile-DMSO, acetone-petroleum ether, and acetone-water). Correlations between chromatographic data and physicochemical parameters were presented as results of established quantitative structure-retention relationships (QSRRs). Principal component analysis (PCA) and multiple linear regression (MLR) showed the possible relationships between the retentions and the physicochemical parameters of the studied compounds. The evaluation of thin-layer chromatographic (TLC) separation power, based on ΔhRF, was also discussed. Finally, two chromatographic systems for the effective separation of glucocorticoids and other analyzed steroid hormones were recommended.

Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column

The retention behaviour of 22 selected imidazoline drugs and derivatives was investigated on α1-acid glycoprotein (AGP) column using Sørensen phosphate buffer (pH 7.0) and 2-propanol as organic modifier. Quantitative Structure-Retention Relationships (QSRR) models were built using extrapolated logkw values as well as isocratic retention factors (logk5, logk8, logk10, logk12, logk15 obtained for 5%, 8%, 10%, 12%, and 15%, of 2-propanol in mobile phase, respectively) as dependant variables and calculated physicochemical parameters as independant variables. The established QSRR models were built by stepwise multiple linear regression (MLR) and partial least squares regression (PLS). The performance of the stepwise and PLS models was tested by cross-validation and the external test set prediction. The validated QSRR models were compared and the optimal PLS-QSRR model for logkw and each isocratic retention factors (PLS-QSRR(logk5), PLS-QSRR(logk8), PLS-QSRR(logk10), MLR-QSRR(logk12), MLR-QSRR(logk15)) were selected. The QSRR results were further confirmed by Linear Solvation Energy Relationships (LSER). LSER analysis indicated on hydrogen bond basicity, McGowan volume and excess molar refraction as the most significant parameters for all AGP chromatographic retention factors and logkw values of 22 selected imidazoline drugs and derivatives.

Quantitative structure–retention relationships of flavonoids unraveled by immobilized artificial membrane chromatography

The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure–retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R2=0.96 and Q2=0.85 with four latent variables).

Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification

The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q2) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification.

Hydro-distilled Volatile Oil Constituents from the Aerial Parts of Satureja mutica and QSRR Simulation by Multiple Linear Regression

The volatile oil obtained by hydro-distillation (HD) from the aerial parts of Satureja mutica Fisch. & C.A. Mey. has been analyzed by means of GC and GC-MS. The major components of the oil were found to be carvacrol (34.1%), thymol (22%), ρ-cymene (16.2%) and γ-terpinene (12.6%). Furthermore, a reliable quantitative structure-retention relationship (QSRR) model has been constructed to predict the retention indices from theoretical point of view and to compare them with experimental ones. Accordingly, a stepwise multiple linear regression (MLR) was performed to build an accurate model which is only composed of three molecular descriptors with VEA1, R1e+ and DP19 acronyms. The statistical characteristics provided by the model (R2=0.963, REP %=7.816, Q2LOO=0.946, Q2LGO=0.944) confirm its satisfactory stability and substantial predictivecapability.

Application of a quantitative structure retention relationship approach for the prediction of the two-dimensional gas chromatography retention times of polycyclic aromatic sulfur heterocycle compounds

Information on the sulfur classes present in petroleum is a key factor in determining the value of refined products and processing behavior in the refinery. A large part of the sulfur present is included in polycyclic aromatic sulfur heterocycles (PASHs), which in turn are difficult to desulfurize. Furthermore, some PASHs are potentially more mutagenic and carcinogenic than polycyclic aromatic hydrocarbons, PAHs. All of this calls for improved methods for the identification and quantification of individual sulfur species. Recent advances in analytical techniques such as comprehensive two-dimensional gas chromatography (GC×GC) have enabled the identification of many individual sulfur species. However, full identification of individual components, particularly in virgin oil fractions, is still out of reach as standards for numerous compounds are unavailable. In this work, a method for accurately predicting retention times in GC×GC using a QSRR (quantitative structure retention relationship) method was very helpful for the identification of individual sulfur compounds. Retention times for 89 saturated, aromatic, and polyaromatic sulfur-containing heterocyclic compounds were determined using two-dimensional gas chromatography. These retention data were correlated with molecular descriptors generated with CODESSA software. Two independent QSRR relationships were derived for the primary as well as the secondary retention characteristics. The predictive ability of the relationships was tested by using both independent sets of compounds and a cross-validation technique. When the corresponding chemical standards are unavailable, the equations developed for predicting retention times can be used to identify unknown chromatographic peaks by matching their retention times with those of sulfur compounds of known molecular structure.

Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches

Peptides’ retention time prediction is gaining increasing popularity in liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics. This is a promising approach for improving successful proteome mapping, useful both in identification and quantification workflows. In this work, a quantitative structure-retention relationships (QSRR) model for its direct prediction from the molecular structure of 185 peptides originating from 8 well-characterized proteins and two Bacillus subtilis proteomes has been developed. Genetic Algorithm (GA) was used for selection of a subset of molecular descriptors coupled with three machine learning methods: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and kernel Partial Least Squares (kPLS) for regression. Final GA-SVR, GA-ANN, and GA-kPLS models were validated through an external validation set of 95 peptides originating from the human epithelial HeLa cells proteomes. Robustness and stability was ensured by defining their applicability domain. The descriptors of the developed models were interpreted confirming a causal relationship between parameters of molecular structure and retention time. GA-SVR model has shown to be superior over the others in terms of both predictive ability, and interpretation of the selected descriptors.

Computational Model for Chromatographic Relative Retention Time of Polychlorinated Biphenyls Using Sub-structural Molecular Fragments

Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q 2) of 0.9913 and root mean square error (RMSE) of 0.0169.

Structural characterization and prediction of kovats retention indices (RI) for alkylbenzene compounds

A new molecular structural characterization (MSC) method called the molecular vertex eigenvalue correlative index (MVECI) is constructed and used to describe the structures of 122 alkylbenzene compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with correlation coefficient (R) of 0.995 is obtained. Through partial least-square regression (PLS), another QSRR model with correlation coefficient (R) of 0.991 is obtained. The estimation stability and prediction ability of the two models are strictly analyzed by both internal and external validations. For the internal validation, the cross-validation (CV) correlation coefficients (RCV) of the two models are 0.993 and 0.988. For the external validation, the correlation coefficients (Rtest) of the two models are 0.996 and 0.995, respectively. The results show that the stability and predictability of the models are good, and the molecular vertex eigenvalue correlative index can successfully describe the structures of alkylbenzene compounds.

A Pharmacovigilance Study on Linear and Non–Linear Quantitative Structure (Chromatographic) Retention Relationships (QSRR) Models for the Prediction of Retention Time of Anti–Cancer Nano Drugs under Synchrotron Radiations

A Pharmacovigilance Study on Linear and Non–Linear Quantitative Structure (Chromatographic) Retention Relationships (QSRR) Models for the Prediction of Retention Time of Anti–Cancer Nano Drugs under Synchrotron Radiations

A Gastrointestinal Study on Linear and Non-Linear Quantitative Structure (Chromatographic) Retention Relationships (QSRR) Models for Analysis 5- Aminosalicylates Nano Particles as Digestive System Nano Drugs under Synchrotron Radiations

A Gastrointestinal Study on Linear and Non-Linear Quantitative Structure (Chromatographic) Retention Relationships (QSRR) Models for Analysis 5- Aminosalicylates Nano Particles as Digestive System Nano Drugs under Synchrotron Radiations

Quantitative structure–retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5min under following conditions: gradient time of 12.5min, buffer pH of 3.95 and buffer molarity of 25mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans.

QSRR Analysis in Characterization of Some Benzimidazole Derivatives

In this paper, quantitative structure-retention relationship study has been applied in order to correlate obtained retention parameter R(M)(0) and two groups of molecular descriptors, for eleven investigated benzimidazole derivatives. Principal component analysis (PCA), followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR), was applied in order to identify the most important molecular descriptors. Mathematical models were established and the best models were further validated by leave-on-out (LOO) technique as well as by the calculation of the statistical parameters. Statistically significant models were established.

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.

Predicting gas chromatography relative retention times for polychlorinated biphenyls using chlorine substitution pattern contribution method

Various quantitative structure retention relationships have been published in an effort to understand and predict chromatographic retention times. This work presents a chlorine substitution pattern contribution (Cl-SPC) model for relative retention times (RRT) of polychlorinated biphenyls (PCBs), using 27 sets of previously published gas chromatography RRT data. The Cl-SPC model calculates the contribution factors (βk) for each of 19 chlorine substitution “patterns” (such as 2-, 2,4-, 2,3,6-, 2,3,4,5,6-, etc.) using multiple linear regression (MLR). The 27 separate MLRs had R2 values ranging from 0.961 to 1.000; the average absolute errors were 0.55% for the training sets and 0.95% for the test sets. Cross-validation of the model was carried out by splitting each data set into training and test sets for groupings based on nine PCB congener mixes commercialized by AccuStandard. No weakening of the model performance was observed when the size of data set used to develop the model was decreased from 209 to 39 congeners. In addition to the separate models, a single mixed model was fit combining all 27 data sets. The estimated random effects, which reflect the impact of GC configuration and operational conditions on RRTs, are minor compared with the fixed effects estimated for the βk values. The major advantages of the Cl-SPC model are its unmatched simplicity and equally excellent robustness when compared with other quantitative structure retention relationship models.

UPLC–MS retention time prediction: a machine learning approach to metabolite identification in untargeted profiling

Metabolic profiling focuses on the analysis of a wide range of small endogenous molecules in order to understand the response of a living system to perturbations. Ultra high performance liquid chromatography–mass spectrometry is a widely employed profiling tool, but its application is limited by difficulties in identification of detected metabolites. Herein, we demonstrate how the prediction of retention time can help resolve this major issue. We describe a general approach that enables the generation of reliable quantitative structure retention relationship models tailored to specific chromatographic protocols. This methodology, applied to 442 experimentally characterised standards, employs a combination of random forest and support vector regression models with molecular interaction descriptors. In this unusual application, the Volsurf + molecular descriptors demonstrated a high ability to describe chromatographic retention. On external validation sets, and for a wide range of chemical classes, predicted values were in average within 13 % of the experimentally observed retention time. More importantly, the presented procedure reduced by more than 80 % the number of false putative identification, greatly improving metabolite identification. Furthermore, in 95 % of cases, the correct identification was promoted within the top three metabolite suggestions. This retention time prediction framework can be replicated by different laboratories to suit their profiling platforms and enhance the value of standard library by providing a new tool for compound identification.

Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modelling of retention times in reversed-phase liquid chromatography

The relative performance of six multivariate data analysis methods derived from or combined with partial least squares (PLS) has been compared in the context of quantitative structure–retention relationships (QSRR). These methods include, GA (genetic algorithm)-PLS, Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), iteratively retaining informative variables (IRIV), variable iterative space shrinkage approach (VISSA) and PLS with automated backward selection of predictors (autoPLS). A set of 825 molecular descriptors was computed for 86 suspected sports doping compounds and used for predicting their gradient retention times in reversed-phase liquid chromatography (RPLC). The correlation between molecular descriptors selected by each technique and the retention time was established using the PLS method. All models derived from a selected subset of descriptors outperformed the reference PLS model derived from all descriptors, with very small demands of computational time and effort. A performance comparison indicated great diversity of these methods in selecting the most relevant molecular descriptors, ranging from 28 for CARS to 263 for MC-UVE. While VISSA provided the lowest degree of over-fitting for the training set, CARS demonstrated the best compromise between the prediction accuracy and the number of selected descriptors, with the prediction error of as low as 46s for the external test set. Only ten descriptors were found to be common for all models, with the characteristics of these descriptors being representative of the retention mechanism in RPLC.