Chemometric exploration of the dependencies between molecular modeling descriptors and analytical chemistry data of antihistaminic drugs.

The relationships between experimental and computational descriptors of antihistamine drugs were studied using principal component analysis (PCA). Empirical data came from UV and IR spectroscopic measurements. Nonempirical data, such as structural molecular descriptors and chromatographic data, were obtained from HyperChem software. Another objective was to test whether the parameters used as independent variables (nonempirical and empirical-spectroscopic) could lead to attaining classification similar to that developed on the basis of the chromatographic parameters. To arrive at the answer to the question, a matrix of 18x49 data, including HPLC and UV and IR spectroscopic data, together with molecular modeling studies, was evaluated by the PCA method. The obtained clusters of drugs were consistent with the drugs' chemical structure classification. Moreover, the PCA method applied to the HPLC retention data and structural descriptors allowed for classification of the drugs according to their pharmacological properties; hence it may potentially help limit the number of biological assays in the search for new drugs.