Quantitative Structure-retention Relationship Study Research Articles

In-Column Dehydration Benzyl Alcohols and Their Chromatographic Behavior on Pyridinium-Based Ionic Liquids as Gas Stationary Phases.

At present, stationary phases based on ionic liquids are a promising and widely used technique in gas chromatography, yet they remain poorly studied. Unfortunately, testing of "new" stationary phases is often carried out on a limited set of test compounds (about 10 compounds) of relatively simple structures. This study represents the first investigation into the physicochemical patterns of retention of substituted (including polysubstituted) aromatic alcohols on two stationary phases of different polarities: one based on pyridinium-based ionic liquids and the other on a standard polar phase. The retention order of the studied compounds on such stationary phases compared to the standard polar phase, polyethylene glycol (SH-Stabilwax), was compared and studied. It was shown that pyridinium-based ionic liquids stationary phase has a different selectivity compared to the SH-Stabilwax. Using a quantitative structure-retention relationships (QSRR) study, the differences in selectivity of the two stationary phases were interpreted. Using CHERESHNYA software, the importance of descriptors on different stationary phases was evaluated for the same data set. Different selectivity of the stationary phases correlates with different contributions of descriptors for the analytes under study. For the first time, we show that in-column dehydration is observed for some compounds (mostly substituted benzyl alcohols). This effect is worthy of further investigation and requires attention when analyzing complex mixtures. It suggests that when testing "new" stationary phases, it is necessary to conduct tests on a large set of different classes of compounds. This is because, in the case of using ionic liquids as an stationary phase, a reaction between the analyte and the stationary phase is possible.

Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results

Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no large enough retention data sets of structurally diverse compounds for them. Consequently, there are very few works devoted to quantitative structure-retention relationships (QSRR) for ionic liquid-based stationary phases. This work is aimed at closing this gap. Three ionic liquids with substituted pyridinium cations are considered. We provide large enough data sets (123–158 compounds) that can be used in further works devoted to QSRR and related methods. We provide a QSRR study using this data set and demonstrate the following. The retention index for a polyethylene glycol stationary phase (denoted as RI_PEG), predicted using another model, can be used as a molecular descriptor. This descriptor significantly improves the accuracy of the QSRR model. Both deep learning-based and linear models were considered for RI_PEG prediction. The ability to predict the retention indices for ionic liquid-based stationary phases with high accuracy is demonstrated. Particular attention is paid to the reproducibility and reliability of the QSRR study. It was demonstrated that adding/removing several compounds, small perturbations of the data set can considerably affect the results such as descriptor importance and model accuracy. These facts have to be considered in order to avoid misleading conclusions. For the QSRR research, we developed a software tool with a graphical user interface, which we called CHERESHNYA. It is intended to select molecular descriptors and construct linear equations connecting molecular descriptors with gas chromatographic retention indices for any stationary phase. The software allows the user to generate several hundred molecular descriptors (one-dimensional and two-dimensional). Among them, predicted retention indices for popular stationary phases such as polydimethylsiloxane and polyethylene glycol are used as molecular descriptors. Various methods for selecting (and assessing the importance of) molecular descriptors have been implemented, in particular the Boruta algorithm, partial least squares, genetic algorithms, L1-regularized regression (LASSO) and others. The software is free, open-source and available online: https://github.com/mtshn/chereshnya.

Quantitative Structure-Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography.

A comparative quantitative structure-retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features.

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure-Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.

Micellar Thin Layer Chromatography and Computer-Aided Analysis of Empagliflozin, Linagliptin and Metformin HCl Ternary Mixture.

The present work was performed in order to study the mechanism of micellar thin layer chromatography (MTLC) and to develop a new simple and sensitive simultaneous MTLC method for separation of empagliflozin, Linagliptin and metformin hydrochloride ternary mixture. The study was done using three different surfactants; sodium dodecyl sulphate (SDS), benzalkonium chloride (BAC) and polysorbate 80 (tween 80). Chromatographic procedure was performed using micellar mobile phase that composed of aqueous solution of each surfactant and methanol (6: 4v/v) and micellar TLC determination at λmax 237nm. Separation using SDS (anionic surfactant) and BAC (cationic surfactant) depends on ionization potential (AMI-IP), partition coefficient (logP (o/w)) and hydrogen bond donor atoms (a-don), whereas separation using tween 80 depends mainly on the lipophilicity (RM0), solvation energy (E-sol) and Van der Waals energy (E-vdw). Quantitative structure-retention relationships study was carried out, modeled, evaluated and validated using molecular operating environment software.

The evaluation of drug-plasma protein binding interaction on immobilized human serum albumin stationary phase, aided by different computational approaches

The drug-human serum albumin binding interaction was evaluated on a stationary phase immobilized with human serum albumin using a mixture of phosphate buffer (pH 7.0) and acetonitrile modifier as mobile phase. The 33 compounds that have a wide structural and therapeutic diversity were analyzed by performing a large number of experiments. The interaction mechanism was interpreted based on: i) retention characteristics of structurally related compounds, ii) retention modeling, iii) quantitative structure retention relationship (QSRR), and iv) molecular docking. Small structural differences of related compounds (e.g., reflected in different lipophilicity and polarity) have been found to affect their different binding to human serum albumin. It was found that drug retention in HSA column can be successfully described by using the quadratic function. The isocratic (logk(14%)) and extrapolated (b0(LSS)) retention factors showed the highest correlation (r > 0.76) with the constant that defines the binding affinity for human serum albumin (ACD/I-Lab). Therefore, selected chromatographic parameters can demonstrate reliable applicability for rapid screening of drug-plasma protein binding in drug discovery. In QSRR study, the resulting SVM/logk(14%) and MLR/b0(LSS) models display high internal and external predictive power. The constitutional properties (double bonds, aromatic rings, benzyl, allyl, -amino and -sulfur containing functional groups) supported by the charged parts of surface area had a significant impact on human serum albumin-binding affinity, which was also confirmed with molecular docking study. The high structural diversity of the data set provides wide applicability of tested chromatographic conditions and constructed models for defining the pharmacokinetic profile and possible structural modifications that can increase plasma protein binding of newly synthesized, pharmaceutically important compounds.

Application of Bayesian Multilevel Modeling in the Quantitative Structure-Retention Relationship Studies of Heterogeneous Compounds.

Quantitative structure-retention relationships (QSRRs) are used in the field of chromatography to model the relationship between an analyte structure and chromatographic retention. Such models are typically difficult to build and validate for heterogeneous compounds because of their many descriptors and relatively limited analyte-specific data. In this study, a Bayesian multilevel model is proposed to characterize the isocratic retention time data collected for 1026 heterogeneous analytes. The QSRR considers the effects of the molecular mass and 100 functional groups (substituents) on analyte-specific chromatographic parameters of the Neue model (i.e., the retention factor in water, the retention factor in acetonitrile, and the curvature coefficient). A Bayesian multilevel regression model was used to smooth noisy parameter estimates with too few data and to consider the uncertainties in the model parameters. We discuss the benefits of the Bayesian multilevel model (i) to understand chromatographic data, (ii) to quantify the effect of functional groups on chromatographic retention, and (iii) to predict analyte retention based on various types of preliminary data. The uncertainty of isocratic and gradient predictions was visualized using uncertainty chromatograms and discussed in terms of usefulness in decision making. We think that this method will provide the most benefit in providing a unified scheme for analyzing large chromatographic databases and assessing the impact of functional groups and other descriptors on analyte retention.

QSRR modelling aimed on the HPLC retention prediction of dimethylamino- and pyrrolidino-substitued esters of alkoxyphenylcarbamic acid

Two homologous series of alkoxyphenylcarbamic acids esters with biological activities were subjected to the QSRR (Quantitative Structure–Retention Relationships) study. Retention factors of studied derivatives were measured in four different HPLC (High Performance Liquid Chromatography) systems. Experimental data in combination with in silico calculated molecular descriptors were used for development complex QSRR model for prediction of retention factor and partially for elucidation of separation mechanisms. The best results in QSRR modelling were given by artificial neural networks in the terms of developing one single robust model with high accuracy of logk prediction ( $${Q}_{\mathrm{F}3}^{2}$$ = 0.998) for all studied chromatographic systems. Chemometrical techniques were also used to elucidate the separation mechanisms occurring in the HPLC systems. We found similarities between particular systems and we identified systems which were the most suitable for separation of positional isomers of studied compounds. We observed that solvent significantly influenced the chromatographic separation of positional isomers depending on the used column, whereas combination of acetonitrile and C18-phenyl column caused suppression of π − π interactions, so retention was wholly determined by the hydrophobic interactions.

Primena eksperimentalnog dizajna za razdvajanje lekova HPLC metodom

Design of Experiments (DoE) is an indispensable tool in contemporary drug analysis as it simultaneously balances a number of chromatographic parameters to ensure optimal separation in High Pressure Liquid Chromatography (HPLC). This manuscript briefly outlines the theoretical background of the DOE and provides step-by-step instruction for its implementation in HPLC pharmaceutical practice. It particularly discusses the classification of various design types and their possibilities to rationalize the different stages of HPLC method development workflow, such as the selection of the most influential factors, factors optimization and assessment of the method robustness. Additionally, the application of the DOE-based Analytical Quality by Design (AQbD) concept in the LC method development has been summarized. Recent achievements in the use of DOE in the development of stability-indicating LC and hyphenated LC-MS methods have also been briefly reported. Performing of Quantitative structure retention relationship (QSRR) study enhanced with DOE-based data collection was recomended as a future perspective in description of retention in HPLC system.

Fuzzy wavelet network based on extended Kalman filter training algorithm combined with least square weight estimation: Efficient and improved chromatographic QSRR/QSPR models

Inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts, a fuzzy wavelet network (FWN) is proposed for predicting the gas chromatographic retention time of phenol derivatives on fused-silica, wide-bore, and open-tubular columns with different polarities in terms of quantitative structure-retention relationship (QSRR) studies. The FWN consists of a set of fuzzy rules. Each rule corresponding to a sub-wavelet neural network (WNN) consists of single-scaling wavelets. The proposed FWN follows a two-stage efficient learning algorithm scheme, which undertakes the extended Kalman filter (EKF) training algorithm for adjusting all translation parameters, dilation parameters, and weight coefficients of the WNNs and then least square estimation for updating all the weights. 4-3-1 FWNs were developed using the descriptors selected by the multiple linear regression (MLR) models as inputs. By learning the translation parameters of the wavelets and adjusting the shape of membership functions, the model accuracy was remarkably improved. The accuracy and robustness of the generated models were illustrated using leave-one-out and leave-multiple-out cross-validations and also by Y-randomization. Sensitivity analysis by sequential zeroing of weights revealed that the molecular geometry and electronic interactions play a major role in the retention behavior of these compounds on non-polar and semi-polar columns, respectively.

Quantitative structure-retention relationships of the chromatographic retentions of phthalic acid ester contaminants in foods.

The harmful health effects caused by phthalic acid esters have been supported from the increasing scientific evidence, developing the efficient methodologies to monitor the levels of phthalic acid esters in various foods become especially important from the aspects of human exposure assessment and their migration mechanistic understanding. In this study, quantitative structure-retention relationship studies on both the gas and liquid chromatographic retention times of 23 phthalic acid esters were performed by genetic function approximation, and the optimal quantitative structure-retention relationship models (r2 >0.980, r2 CV >0.960, and r2 pred >0.865) passed the statistical tests of cross-validation, randomization, external prediction, Roy' rm 2 metrics, Golbraikh-Tropsha' criteria and applicability domain. The established predictive models elucidate the structural requirements for the retention of phthalic acid esters over different chromatographic columns, which were finally used to predict the retention times of 11 new phthalic acid esters. Hopefully, this work could provide useful guidelines for better understanding and accurate prediction of the retention behavior of undetermined phthalic acid esters when lacking standard samples or under poor experimental conditions, and make the simultaneous identification and quantification of numerous phthalic acid esters possible.

High performance liquid chromatography as a molecular probe in quantitative structure-retention relationships studies of selected lipid classes on polar-embedded stationary phases

Studies on the retention mechanism of lipid classes (phospholipids, sphingomyelin) were performed using three polar-embedded stationary phases for which diol, phosphate, amino, and amide moieties were incorporated into the alkyl chains of the stationary phases. Their structural descriptors were determined using the quantum-mechanical method. The retention behavior of the analytes was investigated as a function of different binary hydro-organic mobile phases containing (90%/10% acetonitrile (or methanol)/0.1% formic acid). It was found that the elution order on the tested stationary phases was governed chiefly by the hydrophilicity of the analyte and indicated the existence of a hydrophilic interaction liquid chromatography retention mechanism. Quantitative structure-retention relationships studies were performed to further elucidate the retention mechanism. These studies showed that the dominant analyte descriptor influencing retention on the alkyl-amine stationary phase was the logarithm of the octanol-water partition coefficient. For the phospho-diol and alkyl-amide stationary phases, the dominant analyte descriptor influencing retention was the molar volume and solvent accessible area of the analyte, respectively.

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics.

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Estimation of Retention Time in GC/MS of Volatile Metabolites in Fragrant Rice Using Principle Components of Molecular Descriptors.

A quantitative structure-retention relationship (QSRR) study was applied for an estimation of retention times of secondary volatile metabolites in Thai jasmine rice. In this study, chemical components in rice seed were extracted using solvent extraction, then separated and identified by gas chromatography-mass spectrometry (GC-MS). A set of molecular descriptors was generated for these substances obtained from GC-MS analysis to numerically represent the molecular structure of such compounds. Principal component analysis (PCA) and principal component regression analysis (PCR) were used to model the retention times of these compounds as a function of the theoretically derived descriptors. The best fitted regression model was obtained with R-squared of 0.900. The informative chemical properties related to retention time were elucidated. The results of this study demonstrate clearly that the combination of molecular weight and autocorrelation functions of two dimensional interatomic distance, which are molecular polarizability, atom identity, sigma charge, sigma electronegativity and polarizability, can be considered as comprehensive factors for predicting the retention times of volatile compounds in rice.

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies.

Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models. This study compares molecular modeling, semiempirical, and density functional methods (both B3LYP and M06) for structure optimization. Each of the calculations was performed in a vacuum, then repeated with solvent corrections for both acetonitrile and water. We also compared Natural Bond Orbital analysis with the Mulliken charge calculation method. The comparison of the examined computational methods for structure calculation shows that, possibly due to the error inherent in descriptor creation methods, a quick and inexpensive molecular modeling method of structure determination gives similar results to experiments where structures are optimized using an expensive and time-consuming level of computational theory. Also, for structures with low flexibility, vacuum or gas phase calculations are found to be as effective as those calculations with solvent corrections added.

Error measures in quantitative structure-retention relationships studies

An analysis and comparison of the use of four commonly used error measures (mean absolute error, percentage mean absolute error, root mean square error, and percentage root mean square error) for evaluating the predictive ability of quantitative structure-retention relationships (QSRR) models is reported. These error measures are used for reporting errors in the prediction of retention time of external test analytes, that is, analytes not employed during model development. The error-based validation metrics were compared using a simple descriptive statistic, the sum of squared residuals (SSR) of outliers to the edge of an error window. The comparisons demonstrate that Percentage Root Mean Squared Error of Prediction (RMSEP) provides the best estimate of the predictive ability of a QSRR model, having the lowest SSR value of 20.43.

A Comparative QSRR Study on Enantioseparation of Ethanol Ester Enantiomers in HPLC Using Multivariate Image Analysis, Quantum Mechanical and Structural Descriptors

Quantitative structure–retention relationship study was carried out for predicting the retention times of twenty‐six 1‐(2‐naphtyl)‐1 ethanol ester enantiomers in high‐pressure liquid chromatography by using original molecular, quantum mechanical, and multivariate image analysis (MIA) descriptors. Multiple linear regressions, partial least squares (PLS), and partial component regression (PCR) models combined with genetic algorithm (GA) were constructed as variable selection methods by using molecular descriptors to investigate the potential relationship between the selected descriptors and the retention times of the enantiomers. The molecular descriptors were generated from the molecular structure of enantiomers and calculated by the DRAGON software. Besides 504 DRAGON descriptors, additional 38 quantum mechanical descriptors were obtained using density functional theory/B3LYP/6‐31G method. Then, the results of the obtained models were compared with MIA descriptors that are pixels of two‐dimensional (2D) chemical structures. MIA descriptors were analyzed by correlation ranking‐PCR and PLS methods. The predictive ability of the models was evaluated using cross‐validation and an external test set. The results showed that GA is a good method for variable selection using structural descriptors combined with the PLS model. Since MIA descriptors are 2D pixels of the chemical structures, they can differentiate between the (R) and (S) isomers of enantiomers better than the quantum mechanical and structural descriptors. Comparison between the different models indicated that the GA‐PLS and MIA‐PLS models were the best methods.

Composition of the Essential Oils and Volatile Fractions of Artemisia absinthium by Three Different Extraction Methods: Hydrodistillation, Solvent-Free Microwave Extraction and Headspace Solid-Phase Microextraction Combined with a Novel QSRR Evaluation

The present work is mainly focused on relative abilities of three different extraction techniques, namely hydrodistillation (HD), solvent-free microwave extraction (SFME) and headspace solid-phase microextraction (HS-SPME) to separate the essential oils and volatile fractions from stems, leaves and fruits of Artemisia absinthium L., in combination with analysis by GC and GC-MS. By means of the HD method, 16, 21 and 31 components were identified in the stem, leaf and fruit oils, representing 99.9%, 98.5% and 98.0% of the total oil components, respectively; by the SFME method, 27, 23 and 20 components were identified, constituting 98.4%, 100.0% and 100.0% of the oils, respectively. The HS-SPME method revealed even more constituents, namely 37, 37 and 31 volatile fractions from the stem, leaf and fruit of A. absinthium, representing 99.5%, 98.5% and 95.4% of the total chemical profiles. β-Thujone, 1,8-cineole, cis-chrysanthenol, sabinene and α-phellandrene were identified as major compounds in all of the oils and volatiles. Furthermore, a quantitative structure-retention relationship (QSRR) study was performed using stepwise multiple linear regression (SW-MLR) method. A model, with high statistical significance (R2 = 0.952, Q2LOO =0.932, Q2LGO =0.930, F = 104.4), was found to be appropriate for predicting the retention indices of the essential oils and volatile components.

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio

Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE)<0.5min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE>1min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known).

QSRR and QSAR Studies of Antitumor Drugs in View of their Biological Activity Prediction.

The QSRRs and QSARs are relatively new approaches to relate internal chemical structure and particular biological activity. This methodology is based on theory that mechanisms which took place into chromatography column are similar to those that occur in a living organism at the molecular level, for example when compounds penetrate into cells. In this paper, we aim to describe different cytostatic activities of selected anticancer drugs as QSRR and QSAR models, and prove usefulness of connected QSRR and QSAR methodology in different types of studies. Chromatographic experiments using gradient RP-HPLC method and different C18 stationary phases were performed. As a result we obtained retention parameter log kw. Moreover, to calculate descriptors, which characterize lipophilicity of analyzed antitumor drugs, DryLab program was utilized. Molecular modeling studies were performed by using HyperChem program. Dragon software was used to calculate structural descriptors, and then selected descriptors were used to build QSRR and QSAR models. Obtained data were analyzed by multiple regression analysis (MLR). Experimental log kw and predicted log kw from QSRR models developed, were further used in QSAR analysis. The goodness of fit was in the range of R2= 0.75 - 0.95, and the predictive performance of the models was Q2 = 0.6 - 0.81. Both QSRR and QSAR strategies presented in this paper, allowed predicting HPLC retention parameters and cytotoxic activities of anticancer medicines without the necessity to carry out time-consuming and expensive experimental tests.