Influence of HPLC Retention Data and Molecular Modeling Descriptors on Prediction of Pharmacological Classification of Drugs Using Principal Component Analysis Method

Marcin      Koba; Leszek      Bober; Urszula      Judycka-Proma; Tomasz      Baczek

doi:10.2174/138620710792927411

Abstract

The usage of principal component analysis (PCA) method in prediction of pharmacological classification of the drugs based on high-performance liquid chromatography (HPLC) retention data and on non-empirical structural parameters was studied. A group of 36 drugs of established pharmacological classification were chromatographed in ten carefully designed HPLC systems. Additionally, twelve structural descriptors were derived by molecular modeling studies based on the structural formula of considered drugs. A matrix of 36 x 22 HPLC data together with molecular properties parameters was subjected to chemometric analysis by PCA. Although that size of the training set could be sometimes disputable, the work remains as a demonstration of the basic methodology without the straight focus primarily intended as a report on a comprehensive predictive model. Nevertheless, the obtained clustering of drugs was in accordance with their pharmacological classification as well as chemical structures classification. The PCA method of the HPLC retention data and structural descriptors allowed to segregate drugs and drug candidates according to their pharmacological properties, and may be of potential help to limit the number of biological assays in the search for new drugs.

Keywords: Pharmacological classification, principal component analysis (PCA), molecular modeling, high-performance liquid chromatography (HPLC), stationary phases for HPLC.