Quantitative Structure-retention Relationship Research Articles

Simulation of fatty acid peak profile on a 100-m CP-Sil 88 column and application for analysis of commercial trans fat alternatives in Thailand

A program to simulate fatty acid peak profiles on a gas chromatograph coupled with a 100-m CP-Sil 88 column has been developed. The concept of a quantitative structure-retention relationship (QSRR) with a Gibbs energy additivity approach and Gaussian function was used to create the program, which can be performed on Excel spreadsheets. The present method is applicable for optimizing multi-step temperature-programmed gas chromatography (GC) for determination of fatty acid profiles in samples. Strong correlation coefficients were obtained between experimental and simulated retention times (tR), with R2 = 0.9981. Based on simulation results, the AOAC2012.13 GC condition was recommended for the trans fat analysis. The proposed method was applied to analyze fatty acid compositions in 12 samples of trans fat alternatives, including shortenings, margarines, butter blends and non-dairy creamers, sold commercially in Thailand. The major trans fatty acid (TFA) isomer found after fractionation on Ag-Ion SPE was C18:1t11 (trans-vaccenic acid). The samples contained 0.0–6.3 g TFA/100 g fat, which is similar to the level found in nature. Contents of TFA in the samples determined by GC after fractionation on Ag-Ion SPE were comparable to those obtained by Fourier transform infrared analysis.

Modeling an in silico platform to predict chromatographic profiles of UV filters using ChromSimulator

The use of Analytical Quality by Design (AQbD) concepts is increasing for rational development of analytical methods of liquid chromatography. To facilitate the use of the AQbD approach, we developed an in silico tool capable of predicting the chromatographic profile of organic UV filters based on theoretical retention and separation predictions, in absence of experimentation, with sufficient assertiveness to support analytical development. For this purpose, we used Design of Experiments (DoE) in conjunction with chemical understanding offered by Quantitative Structure – Retention Relationship (QSRR), combined with Monte Carlo method (MCM). Seven analytes were used to establish the prediction model: benzophenone-3, butyl methoxydibenzoilmethane, ethylhexyl triazone, ethylhexyl dimetyl PABA, ethylhexyl methoxycinnamate, bis-ethylhexyloxyphenol methoxyphenyl triazine, and octocrylene. The model generated by multiple regression analysis, using Minitab 19 software, obtained a determination coefficient (R2) of 99.82% and an adjusted determination coefficient (R2 adj) of 99.80%, in addition, residual values had normal distribution, homoscedasticity, and independence. From the results obtained, the predictors considered in the model were the proportion of ethanol in the mobile phase, mobile phase pH, mobile phase flow rate, column temperature, and molecular descriptors (Wlambda3.unity, ATSc5, and geomShape). The model exhibited high performance in internal and external validation, obtaining coefficients of prediction (R2 pred) and determination (R2) of 99.71% and 99.79%, respectively. The in silico platform demonstrated great potential for predicting profile and chromatographic parameters of organic UV filters, allowing, without experimentation, an overview of the retention behavior of compounds under various chromatographic conditions.

QSRR Model for Predicting Retention Indices of Geraniol Chemotype of Thymus serpyllum Essential Oil

A total of 26 experimentally obtained retention indices on a logarithmic scale (log RI) from Thymus serpyllum essential oil were used to build a robust predictive model. The selected descriptors were used as inputs of an artificial neural network model to build a predictive quantitative structure-retention time relationship model. The coefficient of determination for the training cycle was 0.977, indicating that this model could be used for the prediction of retention indices for T. serpyllum essential oil compounds. These 26 compounds comprise about 99.8 % of the total oil, but among them only 6 compounds had the average relative concentration over 1 percent: geraniol (63.4 %), nerol (or cis-geraniol) (18.9 %), geranyl acetate (4.7 %), trans- caryophyllene (4.6 %), β-bisabolene (2.0 %) and geranial (1.2 %). According to these results, it can be concluded that T. serpyllum from village Sesalac (Serbia) belongs to geraniol chemotype, in total 82.3 % in both, trans and cis forms (63.4 % and 18.9 %, respectively).

Predicting retention indices of PAHs in reversed-phase liquid chromatography: A quantitative structure retention relationship approach

In this work, the liquid chromatography retention time in monomeric and polymeric stationary phases of PAHs was investigated. Quantitative structure retention relationship approach has been successfully performed. At first, 3224 molecular descriptors were calculated for the optimized PAHs structure using Dragon software. Afterwards, the modelled dataset was divided using the CADEX algorithm into two subsets for internal and external validation. The genetic algorithm-based on a multiple linear regression was used for feature selection of the most significant descriptors and the model development. The selected models with five descriptors: nCIR, GGI3, GGI4, JGT and DP14 were used for the monomeric column and nR10, EEig01x, L1m, H5v and HATS6v were introduced for the polymeric column. Robustness and predictive performance of the suggested models were verified by both internal and external statistical validation. The good quality of the statistical parameters indicates the stability and predictive power of the suggested models. This study demonstrated the suitability of the established models in the prediction of liquid chromatographic retention indices of PAHs.

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography

In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes’ retention in MLC mode becomes a challenging task. Mixed Quantitative Structure – Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes’ retention.This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN).A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q2) values.Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.

Development of a method of analysis for profiling of the impurities in phenoxymethylpenicillin potassium based on the analytical quality by design concept combined with the degradation mechanism of penicillins

Accurate analysis of all of the impurities present in a substance is critical for controlling the impurity profiles of drugs. Penicillins can easily yield a formidable array of degradation-related impurities (DRIs) with significantly different polarities and charge properties, which renders identifying each one a complicated matter. In this work, phenoxymethylpenicillin potassium (Pen V) was selected to find a way to quickly establish a robust analysis method for the impurity profiling of penicillin. Based on the analytical quality by design (AQbD) concept and the degradation mechanism of the drug, structures of all of the DRIs were first proposed. Then Pen V and its detected DRIs were separated and identified by liquid chromatography-tandem mass spectrometry method (LC–MS). Characteristic fragment ions and mass fragmentation process of Pen V and its detected DRIs were summarized. In addition, a quantitative structure-retention relationship (QSRR) model was constructed to predict the retention times of undetected impurities and to evaluate whether the chromatographic system can separate them. Finally, a stability-indicating high-performance liquid chromatography (HPLC) method was developed that can separate all of the DRIs of Pen V.

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

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.

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities.

Quantitative structure–chromatographic retention relationship of synthesized peptides (HGRFG, NPNPT) and their derivatives

Carapax Trionycis extract peptides (HGRFG, NPNPT) are able to protect against CCl4-induced liver fibrosis. Therefore, this study applies to deal with chromatographic lipophilicity determination of synthesized peptides (HGRFG, NPNPT) and their derivatives using reversed-phase high performance liquid chromatography (RP-HPLC) combined with methanol-water mobile phase and two reversed-phase chromatographic columns (COSMOISL 5C18-MS-II and SHIMADZU-C18). The chromatographic lipophilicity of the analyzed compounds was expressed as logkw constant and correlated with lipophilicity descriptors. Quantitative structure-retention relationships (QSRR) analysis was performed to imitate chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). The most influential molecular descriptors were lipophilicity descriptors, which are important for molecules ability to pass through biological membranes. All established QSRR models were statistically validated by standards, cross- and external validation parameters. According to these statistical validation parameters, MLR models (R2 > 0.856) were better for chromatographic lipophilicity prediction of peptide compounds. It can be concluded that chromatographic systems with COSMOISL 5C18-MS-II column were better for modeling of logkw than systems with SHIMADZU-C18 column. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates.

Estimation of passive gastrointestinal absorption and membrane retention using PAMPA test, quantitative structure-permeability and quantitative structure-retention relationship analyses of ethylenediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid and 1,3-propanediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid derivatives

Passive gastrointestinal absorption and membrane retention of twelve esters of (S,S)-ethylenediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid (EDCP) and (S,S)-1,3-propanediamine-N,N'-di-2-(3-cyclohexyl)propanoic acid (PDCP), as well as of these two non-esterified acids were estimated using PAMPA test. Artificial PAMPA membrane used in this study for the simulation of gastrointestinal barrier was solution of egg lecithin in dodecane (1 % w/v). All tested compounds belong to class III (high membrane retention and low permeation), whereas EDCP, dipentyl ester of PDCP (DPE-PDCP) and diisopentyl ester of PDCP (DIPE-PDCP) belong to class I (negligible membrane retention and low permeation). Finally, quantitative structure – permeability and structure – retention relationships models were created in order to find quantitative relationships between physico-chemical properties of tested compounds and PAMPA membrane permeability/membrane retention parameters. Statistically the most reliable models were analysed and used for the design of new compounds for which favourable membrane permeability and retention can be expected.

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.

Modeling of chromatographic retention of the selected antiarrhythmics and structurally related compounds in the hydrophilic interactions under the TLC and HPLC conditions

High-performance liquid chromatography (HPLC) plays an important role in testing the pharmaceutically active compounds. In despite of the advantages of HPLC, thin-layer chromatography (TLC) retains its applicability to the different experimental tasks. The experimental conditions which allow hydrophilic interactions in the chromatographic system were tested in the HPLC and TLC systems for ivabradine, its related compounds, diltiazem and verapamil. Under the TLC conditions, retention behavior of the investigated compounds was tested on silica gel modified with cyanopropyl ligands as stationary phase and acetonitrile + methanol containing 25% v/v formic acid. Under the HPLC conditions, we used silica gel modified with cyanopropyl ligands as a column packing and the acetonitrile + 0.25% aqueous solution of formic acid as mobile phase. Retention behavior of the investigated analytes depending on the changing volume fractions of the mobile phase modifier was characterized both for TLC and HPLC data sets by the Soczewiński–Wachtmeister equation. Linear relationships were established between the retention coefficients characterizing the retention mechanism (RM0/m, logk0/m) and molecular properties of the investigated compounds. The Quantitative Structure Retention Relationship (QSRR) modeling was performed with the use of the stepwise multiple linear regression, in order to select molecular properties which influence retention.

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

When cyclodextrins (CDs) are used in chromatography analytes’ retention time is decreased with an increase in concentration of CD in the mobile phase. Thus complex stability constants can be determined from the change in retention time of the ligand molecule upon complexation. Since the preceding approach implies extensive and time-consuming HPLC experiments, the goal of this research was to investigate the possibility of using in silico prediction tools instead. Quantitative structure–retention relationship (QSRR) model previously developed to explain the retention behavior of risperidone, olanzapine and their structurally related impurities in β-CD modified HPLC system was applied to predict retention factor under different chromatographic conditions within the examined domains. Predicted retention factors were further used for calculation of stability constants and important thermodynamic parameters, namely standard Gibbs free energy, standard molar enthalpy and entropy, contributing to inclusion phenomenon. Unexpected prolonged retention with an increase in β-CD concentration was observed, in contrast to the employed chromatographic theory used for the calculation of the stability constants. Consequently, it led to failure in stability constants and thermodynamic parameters calculation for almost all analytes when acetonitrile content was 20% (v/v) across the investigated pH range. Moreover, ionization of investigated analytes and free stationary phase silanol groups are pH dependent, leading to minimization of secondary interactions if free silanol groups are non-ionized at pH lower than 3. In order to prove accuracy of predicted retention factors, HPLC verification experiments were performed and good agreement between predicted and experimental values was obtained, confirming the applicability of proposed in-silico tool. However, the obtained results opened some novel questions and revealed that chromatographic method is not overall applicable in calculation of stability constants and thermodynamic parameters indicating the complexity of β-CD modified systems.

Essential oil profile of Origanum vulgare subsp. vulgare native population from Rtanj via chemometrics tools

The aim of this study was to predict the retention indices of chemical compounds found in the aerial parts of Origanum vulgare subsp. vulgare essential oil, obtained by hydrodistillation and analyzed by GC-MS. A total number of 28 compounds were detected in the essential oil. The compounds with the highest relative concentrations were germacrene D (21.5%), 1,8-cineole (14.2%), sabinene (14.0%) and trans-caryophyllene (13.4%). The retention time was predicted by using the quantitative structure–retention relationship, using seven molecular descriptors chosen by factor analysis and genetic algorithm. The chosen descriptors were mutually uncorrelated, and they were used to develop an artificial neural network model. A total number of 28 experimentally obtained retention indices (log RI) were used to set up a predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.998, indicating that this model could be used for predicting retention indices for O. vulgare subsp. vulgare essential oil compounds.

Streamlined MRM method transfer between instruments assisted with HRMS matching and retention-time prediction

Multiple reaction monitoring (MRM) mode using liquid-chromatography tandem mass spectrometry (e.g., LC-QqQ-MS/MS) has been extensively employed in the small molecule analysis with trace levels in complex samples owing to its high sensitivity. However, most of the reported MRM methods are developed using authentic standards, which are often costly yet not readily available. To address this question, a practical platform for the MRM method transfer between different LC-QqQ-MS/MS instruments, assisted by the high-resolution mass spectrometry (LC-HRMS) and retention time (RT) prediction, has been developed in this study. The reported platform can take advantage of both the high sensitivity of LC-MRM method and ion transition pairs from the previous publications. LC-HRMS can provide the accurate mass measurement of the compounds, though high-quality MS/MS fragments are usually difficult to obtain for chemicals at trace levels. Retention time matching and peaks matching between both instrumental platforms rule out isobaric candidates. With an additional retention time prediction filter from quantitative structure retention relationship (QSRR) model based on random forest feature selection (Pearson r2 = 0.63), identification of small molecules is achieved at a high confidence level without using authentic standards. The developed platform has been validated with robustness by examining spiked environmental chemicals in sludge water samples, biological urine, and cell extracts.

Lipophilicity Determination of Antifungal Isoxazolo[3,4-b]pyridin-3(1H)-ones and Their N1-Substituted Derivatives with Chromatographic and Computational Methods.

The lipophilicity of a molecule is a well-recognized as a crucial physicochemical factor that conditions the biological activity of a drug candidate. This study was aimed to evaluate the lipophilicity of isoxazolo[3,4-b]pyridine-3(1H)-ones and their N1-substituted derivatives, which demonstrated pronounced antifungal activities. Several methods, including reversed-phase thin layer chromatography (RP-TLC), reversed phase high-performance liquid chromatography (RP-HPLC), and micellar electrokinetic chromatography (MEKC), were employed. Furthermore, the calculated logP values were estimated using various freely and commercially available software packages and online platforms, as well as density functional theory computations (DFT). Similarities and dissimilarities between the determined lipophilicity indices were assessed using several chemometric approaches. Principal component analysis (PCA) indicated that other features beside lipophilicity affect antifungal activities of the investigated derivatives. Quantitative-structure-retention-relationship (QSRR) analysis by means of genetic algorithm—partial least squares (GA-PLS)—was implemented to rationalize the link between the physicochemical descriptors and lipophilicity. Among the studied compounds, structure 16 should be considered as the best starting structure for further studies, since it demonstrated the lowest lipophilic character within the series while retaining biological activity. Sum of ranking differences (SRD) analysis indicated that the chromatographic approach, regardless of the technique employed, should be considered as the best approach for lipophilicity assessment of isoxazolones.

Retention Time and Optimal Collision Energy Advance Structural Annotation Relied on LC-MS/MS: An Application in Metabolite Identification of an Antidementia Agent Namely Echinacoside.

The structural annotation of metabolites now relies heavily on HR-MS/MS information, resulting in ambiguous identities in most cases. More auxiliary evidence is therefore desired to achieve confirmative identification. Herein, we made an attempt to involve retention time (tR) along with optimal collision energy (OCE) as the additionally structural clues, and the applicability validation was conducted via confidence-enhanced metabolite characterization of echinacoside, an antidementia drug candidate within clinical trials. Quantitative structure-retention relationships (QSRR) were modeled via assaying 184 authentic compounds on RPLC, HILIC, and serially coupled RPLC and HILIC (RPLC-HILIC). Online energy-resolved MS was developed to yield breakdown graphs for selected ion transitions, and OCE was demonstrated to be superior to CE50 toward pointedly denoting the bonds-of-interest. Nineteen metabolites (M1-M19) were confidently identified in biological samples from echinacoside-treated rats by analyzing m/z values first to yield empirical formulas and substructures, and tR and OCE subsequently contributed to sift the candidate structures. Structural identification was validated by oral administration of three relevant compounds in parallel and chromatographic purification as well. Above all, the integration of retention and dissociation behaviors enabled promoting one step forward for structural annotation confidences merely relied on HR-MS/MS.

Localised quantitative structure–retention relationship modelling for rapid method development in reversed-phase high performance liquid chromatography

Quantitative structure–retention relationships (QSRR) predicting the values of solute “hydrophobicity” coefficient η′ in the approximate hydrophobic subtraction model (HSM) can be used to predict retention times of compounds on numerous reversed-phase (RP) columns, provided that column parameters on the corresponding stationary phases are available. In the present study, we propose a new dual clustering-based localised QSRR approach, combining P-ratio clustering (where P is the octanol–water partition coefficient) with second dominant interaction (SDI)-based clustering, to produce predictive models with an acceptable level of prediction accuracy for in silico column scoping in RP method development. QSRR models for η′ values were derived for 49 compounds out of 63 in a dataset extracted from the literature, where retention data were measured under one isocratic mobile phase condition (i.e., acetonitrile-water, 50:50 [v/v]). These models gave a predictive squared correlation coefficient Qext(F2)2 of 0.83 and a root mean square error of prediction (RMSEP) of 0.14. For the modelling, a genetic algorithm-partial least square regression (GA-PLS) approach was performed using the η′ values and their relevant molecular descriptors. The corresponding retention times were predicted by applying the predicted η′ values of the models and the stationary phase “hydrophobicity” parameter H values for the corresponding columns to the approximate HSM, resulting in excellent accuracy and predictability (Qext(F2)2 of 0.90 and RMSEP of 0.72 min). The established QSRR approach was experimentally verified for six Thermo Scientific columns (Acclaim™ 120 C18, Acclaim Polar Advantage, Acclaim Polar Advantage II, Accucore™ aQ, Accucore Phenyl-X, and Hypersil Gold C18 columns) using two types of datasets. The first dataset consisted of eight model compounds extracted from the original dataset and retention time predictions for those compounds were then evaluated on the above columns. The result showed good agreement between predicted and observed retention times with an acceptable error in retention time predictions (slope of 0.97, Qext(F2)2 of 0.95, a mean absolute error (MAE) of 0.43 min and RMSEP of 0.61 min). The second dataset included eight test compounds not included in the original dataset, which were all classified into the η′ cluster by applying a Tanimoto similarity (TS) threshold of 0.7. Similarly, predicted retention times of the test compounds were compared with their corresponding observed retention times, resulting in acceptable retention time predictions with the slope of 0.99, Qext(F2)2 of 0.93 and RMSEP of 0.52 min. Comparisons of resolution values between columns were utilised to select the most suitable columns for separations of the compounds in the respective test sets. Actual chromatograms obtained on the chosen columns showed the feasibility for effective column scoping without experimentation on numerous RP stationary phases available in the USP website, based on the predicted resolution values.

Application of reversed-phase thin layer chromatography and QSRR modelling for prediction of protein binding of selected β-blockers

Chromatographic properties of sixteen β-blockers were studied using planar chromatography. The aim of presented study was to investigate influence of different organic solvents (acetonitrile, methanol, dioxin and tetrahydrofurane) on β-blockers’ retention on C18 bonded silica gel stationary phase. Group of sixteen, diverse in terms of structure, beta blockers was used as a model set. The main goal of this study was to compare chromatographically estimated lipophilicity parameters with values obtained with the use of computational methods. Furthermore, in order to understand molecular mechanisms of retention better, quantitative structure-retention relationships (QSRR) analysis was performed. The next step was focused on application of chromatographically obtained lipophilicity parameters for prediction of protein binding (PB), based on quantitative retention–activity relationships (QRAR) approach. The obtained results showed that reversed-phase thin layer chromatography (RP-TLC), especially with tetrafydrofurane used as an organic modifier of mobile phase, is a useful tool for lipophilicity estimation, as well as for prediction of β-blockers’ biological properties. The QRAR model included C0 parameters for tetrahydrofuran-water as a mobile phase, as well as maximal projection area, and can be easily applied for prediction of systematically synthesized β-blockers structures’ PB.

Novel Molecular Descriptors for the Liquid- and the Gas-Chromatography Analysis of Amino Acids Analogues Derivatized with n-Propyl Chloroformate

The core idea behind this study was the utilization of the principal components (PCs) as a substitute of the original dataset of molecular descriptors for the derivatization products of amino acid analogues with n-propyl chloroformate. The derivatives were described through principal component analysis (PCA) with a total of over 1200 different molecular descriptors, split into groups according to their prime chemical characteristic. The form of the chemical space was modeled by PCA and optimized through a supervised procedure; whose quality was tested employing an internal cross-validation leave-more-out methodology supplying the Q2X metric (> 0.90 on most sets). The independent “spaces” formed contributed a total set of 63 variables (their PCs), the potential of which was evaluated through their application in two independent tests and more specifically in the formation of quantitative structure–retention relationships for two different chromatography systems (gas and liquid), based on published experimental data on those systems. The first model was developed through projection to latent structures methodology, while the second on multilinear regression (MLR). In both cases, the new derivatives’ descriptor set formed models of good quality (Q2Y ≥ 0.9), validated through both internal and external test set validation procedures. Their ability to be used along with other variables in simpler modelling methods, like MLR, attests to their potential to be used in models of multivariate system calibration without blowing the dataset out of proportion. Regardless of the selected modelling method, data support their use for the composition of predictive models for analytical purposes.