Chemometric Analysis for the Classification of some Groups of Drugs with Divergent Pharmacological Activity on the Basis of some Chromatographic and Molecular Modeling Parameters.

AIM AND OBJECTIVE: In this study, chemometric methods as correlation analysis, cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D) structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11 cardiovascular drugs and 36 "other" compounds and especially to choose the most important data of them. MATERIAL AND METHODS: All chemometric analyses have been carried out, graphically presented and also discussed for each group of drugs. At first, compounds' structural and chromatographic parameters were correlated. The best results of correlation analysis were as follows: correlation coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36 "other" compounds, respectively. Next, part of molecular and HPLC experimental data from each group of drugs were submitted to FA/PCA and CA techniques. RESULTS: Almost all results obtained by FA or PCA, and total data variance, from all analyzed parameters (experimental and calculated) were explained by first two/three factors: 84.28%, 76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 "other" compounds, respectively. Compounds clustering by CA method had similar characteristic as those obtained by FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely characterized and discussed in case of their molecular structure and pharmacological activity. CONCLUSION: Proposed QSAR strategy of reduced number of parameters could be useful starting point for further statistical analysis as well as support for designing new drugs and predicting their possible activity.

Chemometric Analysis of Some Biologically Active Groups of Drugs on the Basis Chromatographic and Molecular Modeling Data.

In this work, three different groups of drugs such as 12 analgesic drugs, 11 cardiovascular system drugs and 36 "other" compounds, respectively, were analyzed with cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) methods. All chemometric analysis were based on the chromatographic parameters (logk and logk(w)) determined by means of high-performance liquid chromatography (HPLC) and also by molecular modeling descriptors calculated using various computer programs (HyperChem, Dragon, and the VCCLAB). The clustering of compounds were obtained by CA (using various algorithm as e.g. Ward method or unweighted pair-group method using arithmetic averages as well as Euclidean or Manhattan distance), and allowed to build dendrograms linked drugs with similar physicochemical and pharmacological properties were discussed. Moreover, the analysis performed for analyzed groups of compounds with the use of FA or PCA methods indicated that almost all information reached in input chromatographic parameters as well as in molecular modeling descriptors can be explained by first two factors. Additionally, all analyzed drugs were clustered according to their chemical structure and pharmacological activity. Summarized, the performed classification analysis of studied drugs was focused on similarities and differences in methods being used for chemometric analysis as well as focused abilities to drugs classification (clustering) according to their molecular structures and pharmacological activity performed on the basis of chromatographic experimental and molecular modeling data. Thus, the most important application of statistically important molecular descriptors taken from QSRR models to classification analysis allow detailed biological (pharmacological) classification of analyzed drugs.

The comparison between the calculated and HPLC-predicted lipophilicity parameters for selected groups of drugs.

The parameters of lipophilicity for three different groups of drugs (twelve analgesics drugs, eleven cardiovascular system drugs, and thirty six compounds characterized by divergent pharmacological activity) were experimentally determined by HPLC methods as well as calculated using various computer programs (HyperChem, ACD/Labs, ChemAxon, Dragon and VCCLab). The relationships between experimental (chromatographic) parameters of lipophilicity (log k and log kw) and the chemical structure of the studied compounds, and their comparison due to their lipophilic and hydrophilic character were presented. Moreover, the experimental and calculated values of parameters of lipophilicity were correlated and compared. Finally, both these groups of parameters of lipophilicity were analyzed using PCA or FA methods for the classification of studied compounds according to their chemical structures and pharmacological activity.

Principal component analysis of HPLC retention data and molecular modeling structural parameters of cardiovascular system drugs in view of their pharmacological activity.

Evaluation of relationships between molecular modeling structural parameters and high-performance liquid chromatography (HPLC) retention data of 11 cardiovascular system drugs by principal component analysis (PCA) in relation to their pharmacological activity was performed. The six retention data parameters were determined on three different HPLC columns (Nucleosil C18 AB with octadecylsilica stationary phase, IAM PC C10/C3 with chemically bounded phosphatidylcholine, and Nucleosil 100-5 OH with chemically bounded propanodiole), and using isocratically acetonitrile: Britton-Robinson buffer as the mobile phase. Additionally, molecular modeling studies were performed with the use of HyperChem software and MM+ molecular mechanics with the semi-empirical AM1 method deriving 20 structural descriptors. Factor analysis obtained with the use of various sets of parameters: structural parameters, HPLC retention data, and all 26 considered parameters, led to the extraction of two main factors. The first principal component (factor 1) accounted for 44-57% of the variance in the data. The second principal component (factor 2) explained 29-33% of data variance. Moreover, the total data variance explained by the first two factors was at the level of 73-90%. More importantly, the PCA analysis of the HPLC retention data and structural parameters allows the segregation of circulatory system drugs according to their pharmacological (cardiovascular) properties as shown by the distribution of the individual drugs on the plane determined by the two principal components (factors 1 and 2).

Evaluation of molecular descriptors and HPLC retention data of analgesic and anti-inflammatory drugs by factor analysis in relation to their pharmacological activity.

Factor analysis (FA) was performed for some analgesic, anti-inflammatory and antipyretic drugs to model relationships between molecular descriptors and HPLC retention parameters. FA performed using 26 sets of structural parameters, 26 sets of HPLC retention data, and considering all parameters together (52 parameters) led to the extraction of two main factors. The first principal component (factor 1) accounted for 65-73% of the variance in the data. The second principal component (factor 2) explained 27-35% of data variance. Moreover, of the 52 parameters tested, the highest influence on factor value was found with chromatographic parameters and selected structural parameters (i.e., energy quantum-chemical parameters and electron affinity specifying parameters). Additionally, the pattern of distribution of individual drugs within the plane determined by the two principal components (factors 1 and 2) was in good agreement with their pharmacological (analgesic, anti-inflammatory and antipyretic) properties. The findings are discussed from the point of view of structure-activity relationships.

Factor analysis of microbiological activity data and structural parameters of antibacterial quinolones.

Factor analysis (FA) was performed on quinolone derivatives with antibacterial activity to model relationships between molecular descriptors and microbiological activities determined on five bacterial cell lines (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus pneumoniae). Molecular modeling studies were performed with the use of HyperChem software and MM+ molecular mechanics with the semi-empirical AM1 method. Factor analysis led to the extraction of two main factors, with the share of factor 1 amounting to about 76% and factor 2 to about 24% for all the parameters used in the statistical analysis. Moreover, FA results indicated that energy of orbitals lowest unoccupied molecular orbital, energy of ionization, electron affinity, electronegativity, maximum electron density, refraction and polarizability appeared to be descriptors important for the antibacterial activity of quinolones.