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

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

Volume: 11 Issue: 5

Author(s): Jolanta Stasiak, Marcin Koba, Tomasz Baczek and Adam Bucinski

Affiliation:

Keywords: HPLC retention parameters, structural descriptors, CA, PCA, FA.

Abstract: 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 logkw) 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.

Cite this article as:

Stasiak Jolanta, Koba Marcin, Baczek Tomasz and Bucinski Adam, Chemometric Analysis of Some Biologically Active Groups of Drugs on the Basis Chromatographic and Molecular Modeling Data, Medicinal Chemistry 2015; 11 (5) . https://dx.doi.org/10.2174/1573406411666150114102926