QSRR models for potential local anaesthetic drugs using high performance liquid chromatography

Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm

The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.

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.

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.

Investigation of lipophilicity and pharmacokinetic properties of 2-(methoxy)phenylpiperazine dopamine D2 ligands

Reversed-phase thin-layer chromatography and micellar thin-layer chromatography were used in order to investigate retention behaviour and to determine lipophilicity of series of 2-(methoxy)phenylpiperazine dopamine D2 ligands with different size, shape and rigidity. The retention mechanism was discussed. The lipophilicity parameters obtained in conventional reversed-phase systems expressed as R_M⁰ and C⁰, as well as R_M values determined in microemulsion reversed-phase systems were correlated with in silico determined lipophilicity values. In silico pharmacokinetic properties of 2-(methoxy)phenylpiperazine dopamine D2 ligands revealed the importance of experimentally determined lipophilicity values besides the molecular weight, on the blood-brain barrier permeability process. Also, the experimentally determined lipophilicity was found as a very important factor in plasma protein binding process of 2-(methoxy)phenylpiperazine dopamine D2 ligands. Besides, the Lipinski's rule of five indicates that examined ligands satisfy the criterion of drug-like molecules. The principal component analysis was performed on the experimentally determined and calculated lipophilicity values as well on the molecular descriptors which describe the pharmacokinetic properties in order to provide basic insights into similarities among the studied ligands.

Atom-Type-Based AI Topological Indices for Artificial Neural Network Modeling of Retention Indices of Monomethylalkanes

In this work, a combination of Xu and atom-type-based AI topological indices (TIs) were employed for quantitative structure-retention relationship (QSRR) study of monomethylalkanes (MMAs). A total of 196 temperature-programmed gas chromatographic retention indices corresponding to all C4-C30 MMAs on OV-1 stationary phase have been used in QSRR modeling. Results of the study showed that an artificial neural network (ANN) with 4-9-1 topology and Levenberg-Marquardt training algorithm can predict the retention indices with high degree of accuracy. The statistics of root-mean-square error for the training, validation and test sets were 0.200, 0.316 and 0.215, respectively. The proposed model resulted in a maximum relative error of 0.24% suggesting the TIs as excellent alternative for estimating retention indices of MMAs. According to the obtained results, relative importance of the TIs decreased in the order of AI(-CH3)> AI(-CH2-)> AI(>CH-)> Xu showing significant role of molecular branching, steric factor and molecular size as effective structural features on retention indices of MMAs.

A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach

A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.

Identification of impurities in macrolides by liquid chromatography-mass spectrometric detection and prediction of retention times of impurities by constructing quantitative structure-retention relationship (QSRR)

Macrolides are multicomponent drugs whose impurity control is always a challenge demanding analysis method with good sensitivity and selectivity. Three separate, sensitive, accurate liquid chromatography tandem mass spectrometry methods (LC-MS) were developed for the measurement of three 16-membered ring macrolides (josamycin, josamycin propionate and midecamycin acetate) and related substances in commercial samples. The characteristics of impurities in macrolides were summarized as useful guidance for the impurity analysis of this class of drugs. For each drug, a large number of unknown components have been detected with the high-sensitive MS detector and possible structures of the majority of them were postulated based on the summarized fragmentation rules of 16-membered ring macrolides. A QSRR model was constructed by multilinear regression to predict the retention times of identified impurities which were not detected by the LC-MS methods, without obtaining their reference standards. Satisfactory performance was obtained during leave-one-out cross-validation with a predictive ability (Q²) of 0.95. The generalisation ability of the model was further confirmed by an average error of 2.3% in external prediction. The best QSRR model, based on eight molecular descriptors, exhibited a promising predictive performance and robustness.

Study of the Relationships between the Structure, Lipophilicity and Biological Activity of Some Thiazolyl-carbonyl-thiosemicarbazides and Thiazolyl-azoles

Lipophilicity, as one of the most important physicochemical parameters of bioactive molecules, was investigated for twenty-two thiazolyl-carbonyl-thiosemicarbazides and thiazolyl-azoles. The determination was carried out by reversed-phase thin-layer chromatography, using a binary isopropanol-water mobile phase. Chromatographically obtained lipophilicity parameters were correlated with calculated log P and log D and with some biological parameters, determined in order to evaluate the anti-inflammatory and antioxidant potential of the investigated compounds, by using principal component analysis (PCA). The PCA grouped the compounds based on the nature of their substituents (X, R and Y), indicating that their nature, electronic effects and molar volumes influence the lipophilicity parameters and their anti-inflammatory and antioxidant effects. Also, the results of the PCA analysis applied on all the experimental and computed parameters show that the best anti-inflammatory and antioxidant compounds were correlated with medium values of the lipophilicity parameters. On the other hand, the knowledge of the grouping patterns of the tested variables allows the reduction of the number of parameters, determined in order to establish the biological activity.

Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents

The toxic substances, pesticides, and organic contaminants in effluents can potentially be causing damage that includes increased cancer risk; liver, kidney, stomach, nervous system, and immune system problems; reproductive difficulties; cataracts; and anemia. A quantitative structure-retention relationship (QSRR) was developed using the partial least square (PLS), kernel PLS (KPLS), and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data which contained retention time (RT) of the 47 hazardous compounds in effluents were obtained by reverse-phase high-performance liquid chromatography. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, GA-PLS descriptors are selected for L-M ANN. Finally, a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The described model does not require experimental parameters and potentially provides useful prediction for RT of new compounds. This is the first research on the QSRR of hazardous compounds in effluents using the chemometrics models.

Chemometrics in Medicine and Pharmacy

This minireview summarizes the basic ways of application of chemometrics in medicine and pharmacy. It brings a collection of applications of chemometric used for the solution of diverse practical problems, e.g. exploitation of biologically active species, effective use of biomarkers, advancement of clinical diagnosis, monitoring of the patient's state and prediction of its perspectives, drug design or classification of toxic chemical substances. The aim of this contribution is a brief presentation of versatile potentialities of contemporary chemometrical techniques and relevant software. They are exemplified by typical cases from literature as well as by own research results of the Chemometrics group at Department of Chemistry, the University of Ss. Cyril & Methodius in Trnava.