Study of physicochemical interaction of aryloxyaminopropanol derivatives with teicoplanin and vancomycin phases in view of quantitative structure-property relationship studies.

The aim of this work was to study the physicochemical interactions between chiral stationary phases and chiral molecules and to elucidate which of the specific interactions are more or less important. The HPLC separation of 58 aryloxyaminopropanols was performed on two chiral stationary phases containing the macrocyclic antibiotics teicoplanin or vancomycin and using a methanol/acetonitrile/acetic acid/triethylamine mobile phase (volume ratios 45/55/0.3/0.2). The resolution of enantiomers (Rij) as the target variable was predicted for the mentioned kind of compounds by means of thoroughly selected descriptors provided by the applied Dragon software. The created QSPR models can be considered as a way to explore and discover new relationships or interactions between the quantitative structure and resolution of enantiomers. For calculation and validation of the QSPR models, different modelling methodologies were applied based on MLR (multiple linear regression) and ANN (artificial neural network) techniques. Both methods exhibit an ability for successful prediction of the enantioresolution characteristics of the studied molecules. The results seem to demonstrate that it is possible to predict resolution values of enantiomeric separations of related compounds on given chromatographic systems.

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

Quantitative structure-retention relationships (QSRR) were proposed for Separon SGX C18 and Separon SGX Phenyl columns using physico-chemical molecular descriptors for the compounds, which are potential local anaesthetic drugs. Chemometrical methods were used for the QSRR studies of the HPLC retention factor k of 59 esters of alkoxyphenylcarbamic acid, which exhibit surface and/or infiltration anaesthetic activity. Four separation systems were used: phenyl column and acetonitrile/water mobile phase, phenyl column and methanol/water mobile phase, C18 column and acetonitrile/water mobile phase, and C18 column and methanol/water mobile phase. The values of logP and logS and ¹³C and ¹H NMR chemical shifts were simulated and utilized in calculating the corresponding QSRR models and predicting the retention factors by artificial neural networks (ANN). In addition, principal component analysis and cluster analysis were used for a closer characterization of alkoxyphenylcarbamic acid esters. The proposed ANN models, based on optimally selected species descriptors, showed a high degree of correlation between k predicted and k measured. The intercepts and the slopes of the obtained dependences were close to the theoretically expected values of 0 and 1, respectively.

Effect of the statin therapy on biochemical laboratory tests--a chemometrics study.

Statins are the first-line choice for lowering total and LDL cholesterol levels and very important medicaments for reducing the risk of coronary artery disease. The aim of this study is therefore assessment of the results of biochemical tests characterizing the condition of 172 patients before and after administration of statins. For this purpose, several chemometric tools, namely principal component analysis, cluster analysis, discriminant analysis, logistic regression, KNN classification, ROC analysis, descriptive statistics and ANOVA were used. Mutual relations of 11 biochemical laboratory tests, the patient's age and gender were investigated in detail. Achieved results enable to evaluate the extent of the statin treatment in each individual case. They may also help in monitoring the dynamic progression of the disease.