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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.

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.

Pharmacological classification of drugs by principal component analysis (PCA) based on molecular modeling and high-performance liquid chromatography (HPLC) retention data is proposed. First, a group of 20 drugs of recognized pharmacological classification are chromatographed in eight diversified HPLC systems, applying columns with octadecylsilanes, phosphatidylcholine, as well as α1-glycoprotein and albumin. Additionally, molecular modeling studies, based on the structural formula of the drugs considered, are performed. Sixteen structural descriptors are derived. A matrix of 20 × 24 HPLC data together with molecular parameters are subjected to principal component analysis, and this revealed five main factors with eigenvalues higher than 1. The first principal component (factor 1) accounted for 47.8% of the variance in the data, and the second principal component (factor 2) explained 21.0% of data variance. The total data variance was 82.6% and is explained by the first three factors. The clustering of drugs is in accordance with their pharmacological classification, which proved that the PCA of the HPLC retention data, together with their structural descriptors, allowed the drugs to be segregated accurately to their pharmacological properties. This may be of help in reducing the number of biological assays needed in the development of a new drug.

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