Abstract

A series of quinolon-4(1H)-imines have been recently discovered as antimalarials, targeting both the exoerythrocytic and erythrocytic stages of the parasite’s development stages, which correspond to the phase of clinical symptoms. Endowed with chemical and metabolic stability, the quinolon-4(1H)- imines are thus presented as promissory dual-stage antimalarials. Three versions of multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) methods, namely traditional MIA-QSAR, augmented MIA-QSAR (aug-MIA-QSAR) and color-encoded aug-MIA-QSAR (aug- MIA-QSAR_color), were applied to model the antimalarial activities in this series of compounds. The multiple linear regression models indicated that the aug-MIA-QSAR method is more predictive and reliable than the others (R² = 0.8079, R²cv = 0.6647 and R²pred = 0.9691) for this series of compounds. The selected aug- MIA-QSAR descriptors were used for pattern recognition using discriminant analysis by partial least squares (PLS-DA), in order to separate compounds with low, moderate and high bioactivities.

Keywords: Malaria, multivariate image analysis, multiple linear regression, QSAR, PLS-DA, quinolon-4(1H)-imines.