Modeling of Anticancer Sulfonamide Derivatives Lipophilicity by Chemometric and Quantitative Structure-Retention Relationships Approaches.

Sulfonamides are a classic group of chemotherapeutic drugs with a broad spectrum of pharmacological action, including anticancer activity. In this work, reversed-phase high-performance liquid chromatography and biomimetic chromatography were applied to characterize the lipophilicity of sulfonamide derivatives with proven anticancer activities against human colon cancer. Chromatographically determined lipophilicity parameters were compared with obtained logP, employing various computational approaches. Similarities and dissimilarities between experimental and computational logP were studied using principal component analysis, cluster analysis, and the sum of ranking differences. Furthermore, quantitative structure-retention relationship modeling was applied to understand the influences of sulfonamide's molecular properties on lipophilicity and affinity to phospholipids.

Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis.

Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization.

Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.

Retention prediction using quantitative structure-retention relationships combined with the hydrophobic subtraction model in reversed-phase liquid chromatography.

The hydrophobic subtraction model (HSM) combined with quantitative structure-retention relationships (QSRR) methodology was utilized to predict retention times in reversed-phase liquid chromatography (RPLC). A selection of new analytes and new RPLC columns that had never been used in the QSRR modeling process were used to verify the proposed approach. This work is designed to facilitate early prediction of co-elution of analytes in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR models were constructed through partial least squares regression combined with a genetic algorithm (GA-PLS) which was employed as a feature selection method to choose the most informative molecular descriptors calculated using VolSurf+ software. The analyte hydrophobicity coefficient of the HSM was predicted for subsequent calculation of retention. Clustering approaches based on the local compound type and the local second dominant interaction were investigated to select the most appropriate training set of analytes from a larger database. Predicted retention times of five new compounds on five new RPLC C18 columns were compared with their measured retention times with percentage root-mean-square errors of 15.4 and 24.7 for the local compound type and local second dominant interaction clustering methods, 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.

Comparative study and multiple linear regression analysis for assessment of chromatographic behavior of structurally related ß-blockers on different stationary phases.

A quantitative structure-retention relationship study was performed by thin layer chromatography on a number of ß-blockers using 315 molecular descriptors of which nine were selected to be having the most important physicochemical properties. These descriptors provide good correlations with chromatographic behavior of the studied structurally related drugs. This research was completed on three pretreated silica gel plates via impregnation in urea, sodium dodecyl sulfate, and dimethylformamide, hence it possesses varying interplay mechanisms and polarities. The retention parameters were obtained by utilizing four solvent systems of two additives of variable ratios, consequently specific polarities in addition to imparted different pH values using either glacial acetic acid or liquid ammonia. Calculated theoretical approaches prove good correlations between investigated descriptors and retention factors. Some correlations show excellent predicting models, which might be critical for toning better know-how relationships between chemical structures and retention of ß-blockers.

Quantitative Structure-Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order.

In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.

Different approaches to quantitative structure-retention relationships in the prediction of oligonucleotide retention.

Quantitative structure-retention relationships studies were performed for cholesterol and alkylamide stationary phases, which were previously applied in the analysis of nucleotides and oligonucleotides. An octadecyl column was also tested. Twenty-four oligonucleotides of various sequences and length were chosen; next, their structural descriptors were determined with the use of quantum-mechanics method. The sequence features were related mainly to their surface area, hydrophobicity, and the nature of nucleobases. Moreover, for the first time models employing experimentally derived descriptors (the sum of retention factor for individual nucleotides) were developed in the quantitative structure-retention relationship studies of these compounds. The retention of oligonucleotides for alkylamide and cholesterol stationary phases may be effectively predicted with the use of quantitative structure-retention relationship models based only on molecularly modeled descriptors, as well as with models employing experimentally derived descriptors. Therefore, we recommend the first approach, since descriptors may be easily and quickly calculated. However, oligonucleotide retention prediction for octadecyl phases gives better results, when individual nucleotide retention factors are known and utilized for the creation of a mathematical model.

Construction and application of a QSRR approach for identifying flavonoids.

A quantitative structure retention relationship (QSRR) method was developed to identify flavonoid isomers auxiliary using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method based on the linear relationships between the Ln(k') values of flavonoids and their hydrogen bonding energy (XAH) and dissolution energy (ES). Chromatographic separation was achieved with a Hypersil GOLD C18 (100 mm × 2.1 mm, 1.9 µm) column and Agilent SB-C18 (2.1 ×50 mm, 1.8 µm) column on a Dionex Ultimate 3000 RSLC chromatograph. Compounds were eluted isocratically using a mobile phase containing 0.1% formic acid/water solution and methanol at a ratio of 55:45 (v/v). Mass spectrometry was performed in the negative and positive ionization modes on a Thermo Fisher Q Exactive Orbitrap mass spectrometer equipped with an electrospray ionization interface. The established QSRR model was Ln(k') = 5.6163 + 0.0469ES - 0.0984XAH, with a determination coefficient (R2) of 0.9981, adjusted determination coefficient (adjR2) of 0.9976, and corrected root mean square error of 0.0682. The determination coefficient of the leave-one-out (LOO) cross-validation (Q2LOO) was 0.9976, and the cross-verification root mean square error was 0.0754. Simulated samples containing 7 flavonoids were used to validate the feasibility of the method. The classical method (UHPLC-MS/MS combined the CD software and the mzCloud, mzVault and Chemspider databases) was used to identify the seven flavonoids in the simulated samples. This classic identification strategy cannot provide accurate identification results, which provided multiple identification results for each compound in the simulated samples. On the basis of the results, the 7 flavonoids were accurately identified by the established QSRR model, and the reference standards were used to validate it. The relative error of retention time(RE(tR)) between the model calculation and experimental results was less than 10%. This method effectively complements and improves the classical methods, that UHPLC-MS/MS combined the CD software and the mass spectra databases were used to identify flavonoids identification.

Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms.

Immobilized artificial membrane chromatography (IAM) has been proposed as a more biosimilar alternative to classical lipophilicity measurement. Determination of small molecule's affinity to phospholipids can be supported for predicting their behavior in the human body. Therefore, a better understanding of the molecular interaction mechanism between small xenobiotics and phospholipids can accelerate drug discovery. Here, the quantitative structure-retention relationships (QSRR) approach was integrated with mechanistic descriptors calculated using Chemicalize software to propose an easy-to-interpretation QSRR model. Considering the heterogeneous character of the data set, locally weighted least squares kernel regression belonging to similarity-based machine learning methods have been applied. The results showed that lipophilicity, charge, and maximum projection area determine molecule binding to phospholipids. Full validation of the obtained model based on OECD recommendations has been performed and the applicability domain was defined using the probability-oriented distance-based approach. The high values of predictive squared correlation coefficient (Q2), and small root mean square error of prediction (RMSEP), 0.812 and 6.739, respectively, confirmed that the obtained QSRR model is not well-fitted to the training data but also showed prediction power. Additionally, only 1.5% of molecules from the training set and 2.8% from the validation test are outside the applicability domain, confirming great predictive abilities.

Enhancing analytical development in the pharmaceutical industry: A DoE-QSRR model for virtual Method Operable Design Region assessment.

Recently, the pharmaceutical industry has increasingly adopted the Analytical Quality by Design (AQbD) approach for analytical development. To facilitate AQbD approach implementation in the development of chromatographic methods for determining cephalosporin antibiotics, an in silico tool capable of performing virtual DoEs was developed enabling to obtain virtual operable regions of method. To this end, the drugs cephalexin, cefazolin, cefotaxime and ceftriaxone were analyzed using four experimental designs, deriving a DoE-QSRR model and employing Monte Carlo method. The DoE-QSRR model and virtual DoEs were validated using data not used in model's construction, obtaining coefficients of determination of 84.72 % for DoE-QSRR model and over 77 % for virtual DoEs. Virtual MODRs were constructed using data from the virtual DoEs. The virtual MODRs were validated by comparing them with experimental MODRs under various scenarios, with overlap areas reaching values exceeding 84 %. Therefore, the in silico tool was considered suitable for indicating analyte trends under different analytical conditions, being capable of performing virtual DoEs for cephalosporin drugs with sufficient assertiveness to guide analytical development and allow obtaining a MODR capable of providing results of adequate quality.

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.

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine.

Retention indices for frequently reported compounds of plant essential oils on three different stationary phases were investigated. Multivariate linear regression, partial least squares, and support vector machine combined with a new variable selection approach called random-frog recently proposed by our group, were employed to model quantitative structure-retention relationships. Internal and external validations were performed to ensure the stability and predictive ability. All the three methods could obtain an acceptable model, and the optimal results by support vector machine based on a small number of informative descriptors with the square of correlation coefficient for cross validation, values of 0.9726, 0.9759, and 0.9331 on the dimethylsilicone stationary phase, the dimethylsilicone phase with 5% phenyl groups, and the PEG stationary phase, respectively. The performances of two variable selection approaches, random-frog and genetic algorithm, are compared. The importance of the variables was found to be consistent when estimated from correlation coefficients in multivariate linear regression equations and selection probability in model spaces.

Development of the HPLC Method for Simultaneous Determination of Lidocaine Hydrochloride and Tribenoside Along with Their Impurities Supported by the QSRR Approach.

A new liquid chromatographic (LC) method for simultaneous determination of lidocaine hydrochloride (LH) and tribenoside (TR) along with their related compounds in pharmaceutical preparations is described. Satisfactory LC separation of all analytes after the liquid-liquid extraction (LLE) procedure with ethanol was performed on a C18 column using a gradient elution of a mixture of acetonitrile and 0.1 % orthophosphoric acid as the mobile phase. The procedure was validated according to the ICH guidelines. The limits of detection (LOD) and quantification (LOQ) were 4.36 and 13.21 µg mL-1 for LH, 7.60 and 23.04 µg mL-1 for TR, and below 0.11 and 0.33 µg mL-1 for their impurities, respectively. Intra- and inter-day precision was below 1.97 %, whereas accuracy for all analytes ranged from 98.17 to 101.94 %. The proposed method was sensitive, robust, and specific allowing reliable simultaneous quantification of all mentioned compounds. Moreover, a comparative study of the RP-LC column classification based on the quantitative structure-retention relationships (QSRR) and column selectivity obtained in real pharmaceutical analysis was innovatively applied using factor analysis (FA). In the column performance test, the analysis of LH and TR in the presence of their impurities was carried out according to the developed method with the use of 12 RP-LC stationary phases previously tested under the QSRR conditions. The obtained results confirmed that the classes of the stationary phases selected in accordance with the QSRR models provided comparable separation for LH, TR, and their impurities. Hence, it was concluded that the proposed QSRR approach could be considered a supportive tool in the selection of the suitable column for the pharmaceutical analysis.

Impact of structural similarity on the accuracy of retention time prediction.

Quantitative Structure-Retention Relationships offer a valuable tool for de-risking chromatographic methods in relation to newly formed or hypothetical compounds, arising from synthetic processes or formulation activities. They can also be used to identify optimal separation conditions, or in support of structural elucidation. In this contribution, we provide a systematic study of the relationship between the accuracy of the retention model, the size of the training set and its structural similarity to the predicted compound. We compare structural similarity expressed either on a fingerprint basis (e.g., Tanimoto index), or by Euclidean distance calculated from of subset of molecular descriptors. The results presented indicate that accurate and predictive models can be built from a small dataset containing as few as 25 compounds, provided that the training set is structurally similar to the test compound. When the training set contains compounds selected by minimizing the Euclidean distance calculated from 3 descriptors most correlated with the retention time, root mean square error of 0.48 min and correlation coefficient of 0.9464 were observed for the test sets of 104 compounds. Moreover, these models meet the Tropsha predictivity criteria. These findings potentially bring the prediction of retention times within the practical reach of pharmaceutical analysts involved in chromatographic method development. We also present an optimisation approach to select algorithm settings in order to minimize the prediction error and ensure model predictivity.

Development and optimization of stability-indicating method of ethinylestradiol, levonorgestrel, and their main impurities using quality by design approach.

The association of Ethinylestradiol 0.03 mg and Levonorgestrel 0.15 mg is a hormonal contraceptive that combines estrogen and progestogen. According to a bibliographic survey, these combined drugs present at least 18 known degradation products, which are required to control the potential impurities harmful to human health. The high number of impurities and the low concentrations of the active pharmaceutical ingredients (APIs) and their respective degradation products increase the complexity of the stability-indicating method development for this medicine. Thus, this work aimed to develop and optimize the stability-indicating method using the quality by design (QbD) approach and in-silico tools for application in samples of oral contraceptives sold in Brazil. The analysis samples were initially subjected to a forced degradation study through 7 days of exposure under acid and alkali hydrolysis, oxidative condition, and oxidation by metal ions. In addition to the chemical exposure, the sample was subjected to physical stress through 10 days of exposure under dry heat, moisture, and photolytic degradation. These exposure samples were analyzed in the development and optimization of chromatographic conditions. As a result, the developed method was able to separate 20 known substances, including the two APIs and their respective 18 degradation products, as well as unknown degradation products obtained by the forced degradation study. Finally, this stability-indicating method was successfully applied for comparative analysis of contraceptive drugs marketed in Brazil, newly purchased and subjected to accelerated stability condition at 40 °C and 75% RH over the 6-month period.

Investigation of supercritical fluid chromatography retention behaviors using quantitative structure-retention relationships.

Supercritical Fluid Chromatography (SFC), a high-throughput separation technique, has been widely applied as a promising routine method in pharmaceutical, pesticides, and metabolome analysis in the same way as conventional liquid chromatography and gas chromatography. However, the retention behaviors of many compounds in SFC are not fully investigated. In this study, more than 500 pesticides were analyzed on several polar and nonpolar columns using SFC/MS/MS. Then, partial least squares regression (PLS) was used to explore the retention behaviors of pesticides and construct the quantitative structure-retention relationships under practical gradient elution. The optimized relationships between pesticide structures and pesticide retention were established and validated for predicting power using both internal- and external-validations; hence, several important factors affecting retention of the compounds were identified. In the best case, approximately almost all pesticides in the training set and nearly 80% of pesticides in the external validation set could be predicted with the prediction error of less than 0.5 min. Moreover, the proposed workflow successfully established the local interaction profiles, describing the possible interactions in the 8 studied chromatographic systems, and can be further applied for any groups of compounds under any system conditions.

Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases.

In the present study, computational molecular descriptors of 90 saturated esters and seven poly(siloxane) stationary phases with different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP) were combined into quantitative structure-retention relationship (QSRR) models aimed at predicting the Kováts retention indices (RIs) of the solutes. The molecular descriptors (174) of the stationary phases included in the models were computed using Dragon software from poly(siloxane) oligomers made of 20 siloxane units reflecting the nominal composition of the stationary phase, whereas 439 molecular descriptors were adopted to represent the esters. Different QSRR models were generated by means of Partial Least Squares (PLS) regression to assess the accuracy of this approach in predicting the RIs of unexplored solutes both in known and external stationary phases. After calibration of each PLS model, the descriptors were selected/discarded according to their relevance, evaluated by Covariance Selection (CovSel), and the PLS models were re-built, which resulted in a noticeable improvement of their predictive ability. Firstly, all the available data were equally divided into a training and a test set; the model built on the calibration set was used to predict the RIs of the validation observations. Successively, seven diverse PLS models were created following a "leave-one-column-out" fashion procedure, each one finalized to the estimation of the RIs of the 90 esters associated with a single stationary phase, whereas the calibration model was calculated on the remaining data. All the estimated models provided successful results on the external stationary phase, and predictive performance further increased after variable selection based on CovSel analysis. The final models provided a Root Mean Square Error in Cross Validation (RMSECV) in the range 12-20, a Root Mean Square Error in Prediction (RMSEP) in the range 11-26, and Mean Absolute Percentage Errors in Prediction (MAMEPs) in the range 0.7-1.5, revealing accurate cross-column prediction. Eventually, to test the robustness of the proposed approach, the 90 solutes were equally partitioned into a calibration and a test set and two further QSSR strategies were applied. The first PLS model was calibrated on all the seven stationary phases and the RIs of the 45 external solutes in the same seven columns were simultaneously predicted. The last QSRR approach followed a "leave-one-column-out" scheme and RI of 45 test solutes on an external stationary phase was predicted by a PLS model calibrated with the data of the 45 remaining solutes and the six left stationary phases. After selection of the significant molecular descriptors, PLS regression provided RMSECV values in the range 6-19, RMSEPs in the range 10-14, and MAPEPs in the range 0.9-2.4, revealing the suitability of the approach to deduce the RI of unknown solutes in uncharted stationary phases.

Amalgamation of comparative protein modeling with quantitative structure-retention relationship for prediction of the chromatographic behavior of peptides.

Peptide therapeutics plays a prominent role in medical practice. Both peptides and proteins have been used in several disease conditions like diabetes, cancer, bacterial infections etc. The optimization of a peptide library is a time consuming and expensive chore. The tools of computational chemistry offer a way to optimize the properties of peptides. Quantitative Structure Retention (Chromatographic) Relationships (QSRR) is a powerful tool which statistically derives relationships between chromatographic parameters and descriptors that characterize the molecular structure of analytes. In this paper, we show how Comparative Protein ModelingQuantitative Structure Retention Relationship (acronym ComProM-QSRR) can be used to predict the retention time of peptide sequences. This formalism is founded on our earlier published QSAR methodology HomoSAR. ComProM-QSRR can recognize and distinguish the contribution of amino acids at specific positions in the peptide sequences to the retention phenomena through their related physicochemical properties. This study firmly establishes the fact that this approach can be pragmatically used to predict the retention time to all classes of peptides regardless of size or sequence.

Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks.

Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating interactions between a molecule and a biological membrane. This study developed a quantitative structure-retention relationships model that would predict the chromatographically determined affinity of xenobiotics to phospholipids, expressed as a chromatographic hydrophobicity index determined using immobilized artificial membrane chromatography. A heterogeneous set of 261 molecules, mostly showing pharmacological activity or toxicity, was analyzed chromatographically to realize this goal. The chromatographic analysis was performed using the fast gradient protocol proposed by Valko, where acetonitrile was applied as an organic modifier. Next, quantitative structure-retention relationships modeling was performed using multiple linear regression (MLR) methods and artificial neural networks (ANNs) coupled with genetic algorithm (GA)-inspired selection. Subsequently, the selection of the best ANN was supported by statistical parameters, the sum of ranking differences approach with the comparison of rank by random numbers and hierarchical cluster analysis.