In silico Plasma Protein Binding Studies of Selected Group of Drugs Using TLC and HPLC Retention Data.

Plasma protein binding is an important determinant of the pharmacokinetic properties of chemical compounds in living organisms. The aim of the present study was to determine the index of protein binding affinity based on chromatographic experiments. The question is which chromatographic environment will best mimic the drug-protein binding conditions. Retention data from normal phase thin-layer liquid chromatography (NP TLC), reversed phase (RP) TLC and HPLC chromatography experiments with 129 active pharmaceutical ingredients (APIs) were collected. The stationary phase of the TLC plates was modified with protein and the HPLC column was filled with immobilized human serum albumin. In both chromatographic methods, the mobile phase was based on a buffer with a pH of 7.4 to mimic physiological conditions. Chemometric analyses were performed to compare multiple linear regression models (MLRs) with retention data, using protein binding values as the dependent variable. In the course of the analysis, APIs were divided into acidic, basic and neutral groups, and separate models were created for each group. The MLR models had a coefficient of determination between 0.73 and 0.91, with the highest values from NP TLC data.

Comparative (Q)SAR analysis of benzodiazepine derivatives with different biological activity.

211 compounds containing a benzodiazepine moiety (BZD) and belonging to 4 groups of different biological activity (H - inhibitors of reverse transcriptase of HIV-I virus, A - antiarrhythmic agents, G - ligands of benzodiazepine receptor in GABAergic system and C - cholecystokinin receptor antagonists) were subjected to structure-activity relationship (SAR) analysis. SAR investigations of all 211 BZD were based on Discriminant Function Analysis (DFA) of physicochemical data connected with BBB (blood-brain barrier) permeability of studied compounds. DFA was performed with STATISTICA 10.0 software by the stepwise method and resulted in 3 discriminant functions whose quality was assessed by Wilk's lambda parameter. Calculated discriminant functions (roots) were applied to draw the scatter diagram of canonical values that showed all 211 cases divided into 4 groups of different biological activity. The method was successfully validated with a set of 38 BZD derivatives expected to belong to groups H, A, G and C. The reliability of the obtained model was confirmed with a cross-validation test. Classification functions presented in this study may be used as a practical tool for predicting new BZD drugs activity.

Application of chromatographic data in QSAR Studies of 3-[ω-(4-Arylpiperazin-1-yl)alkyl]pyrimido[5,4-c]quinolin-4(3H)-one derivatives as 5-HT1A receptor ligands.

The activity of several 3-[ω-(4-arylpiperazin-1-yl)alkyl]pyrimido[5,4-c]quinolin-4(3H)-ones (LCAPs) with well-defined serotonin 1A (5-HT1A) receptor affinity was described by using chromatographic and calculated physicochemical parameters in quantitative structure-activity relationship analysis. Normal-phase thin-layer chromatography plates impregnated with solutions of L-aspartic acid, L-serine, L-phenylalanine, L-tryptophan, L-tyrosine, L-asparagine, L-threonine and their mixtures (denoted as S1-S11 biochromatographic models) were used with two mobile phases as a model of the interaction between LCAP and 5-HT1A receptors. Molecular descriptors for the investigated compounds were calculated by using HyperChem and ACD/Labs programs. The significant relationship explains that 82% of the variance was successfully validated by leave-one-out and leave-many-out tests. The results demonstrated that this model has significant predictive ability and can be used for the preliminary screening of newly synthesized potential 5-HT1A receptor ligands.

Development and validation of quantitative structure-activity relationship models for compounds acting on serotoninergic receptors.

A quantitative structure-activity relationship (QSAR) study has been made on 20 compounds with serotonin (5-HT) receptor affinity. Thin-layer chromatographic (TLC) data and physicochemical parameters were applied in this study. RP2 TLC 60F(254) plates (silanized) impregnated with solutions of propionic acid, ethylbenzene, 4-ethylphenol, and propionamide (used as analogues of the key receptor amino acids) and their mixtures (denoted as S1-S7 biochromatographic models) were used in two developing phases as a model of drug-5-HT receptor interaction. The semiempirical method AM1 (HyperChem v. 7.0 program) and ACD/Labs v. 8.0 program were employed to calculate a set of physicochemical parameters for the investigated compounds. Correlation and multiple linear regression analysis were used to search for the best QSAR equations. The correlations obtained for the compounds studied represent their interactions with the proposed biochromatographic models. The good multivariate relationships (R(2) = 0.78-0.84) obtained by means of regression analysis can be used for predicting the quantitative effect of biological activity of different compounds with 5-HT receptor affinity. "Leave-one-out" (LOO) and "leave-N-out" (LNO) cross-validation methods were used to judge the predictive power of final regression equations.

Normal and reversed phase thin layer chromatography data in quantitative structure-activity relationship study of compounds with affinity for serotonin (5-HT) receptors.

Quantitative structure-activity relationship (QSAR) analysis of 20 drugs with affinity for serotonin (5-HT) receptors was carried out. A set of physicochemical parameters calculated by HyperChem 7.0 and ACDLabs 8.0 programs and chromatographic data were applied in the analysis. Thin layer chromatography was performed on silica gel NP 60F(254) and silica gel RP2 60F(254) (silanized) plates impregnated with solutions of aspartic acid, serine, phenylalanine, tryptophan, tyrosine, asparagine, threonine and their mixtures (denoted as S1-S11 models), with two mobile phases - the systems were chosen as models of drug-5-HT-receptor interaction. Relationships between chromatographic data and molecular descriptors and biological activity data were found by means of regression analysis. The correlations obtained for the compounds with serotoninergic activity represent their interaction with the proposed biochromatographic models (S1-S11). The presented regression models based on biochromatographic studies can be an efficient tool in the QSAR analysis for initial prediction of compounds activity direction within 5-HT receptors.