Abstract
On the basis of the finding that various aminoalkyl-substituted chromene and chromane derivatives possess strong and highly selective in vitro bioactivity against Plasmodium falciparum, the pathogen responsible for tropical malaria, we performed a structure–activity relationship study for such compounds. With structures and activity data of 52 congeneric compounds from our recent studies, we performed a three-dimensional quantitative structure–activity relationship (3D-QSAR) study using the comparative molecular field analysis (CoMFA) approach as implemented in the Open3DQSAR software. The resulting model displayed excellent internal and good external predictive power as well as good robustness. Besides insights into the molecular interactions and structural features influencing the antiplasmodial activity, this model now provides the possibility to predict the activity of further untested compounds to guide our further synthetic efforts to develop even more potent antiplasmodial chromenes/chromanes.
Highlights
Malaria is one of the most life-threatening infectious diseases
229 million cases and 409,000 deaths by malaria in 2019 [1]. The decline in these figures observed in the last decade has been slowed down, at least partly because of the COVID19 pandemic, so that higher than expected malaria morbidity and mortality have been predicted for coming years by the World Health Organization (WHO) [1]
Increasing resistance of the parasites, in particular Plasmodium falciparum (Pf ) causing tropical malaria, against existing therapies leads to a rise in treatment failures
Summary
Malaria is one of the most life-threatening infectious diseases It is caused by eukaryotic blood parasites of the genus Plasmodium and spread by an insect vector, female. With the structures and activity data of 52 congeneric compounds of this series in hands, we performed a three-dimensional quantitative structure–activity relationship (3D-QSAR) study. For this purpose, we chose an approach based on comparative molecular field analysis (CoMFA), in which the compounds’ interaction energies with virtual probes in the surrounding space were calculated as molecular interaction fields (MIFs), which were analyzed by partial least squares (PLS) regression modeling for correlations with the activity data. We present the 3D-QSAR model resulting from this study
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