Abstract
This study is concerned with identifying features of 4-aminoquinoline scaffolds that can help pinpoint characteristics that enhance activity against chloroquine-resistant parasites. Statistically valid predictive models are reported for a series of 4-aminoquinoline analogues that are active against chloroquine-sensitive (NF54) and chloroquine-resistant (K1) strains of Plasmodium falciparum. Quantitative structure activity relationship techniques, based on statistical and machine learning methods such as multiple linear regression and partial least squares, were used with a novel pruning method for the selection of descriptors to develop robust models for both strains. Inspection of the dominant descriptors supports the hypothesis that chemical features that enable accumulation in the food vacuole of the parasite are key determinants of activity against both strains. The hydrophilic properties of the compounds were found to be crucial in predicting activity against the chloroquine-sensitive NF54 parasite strain, but not in the case of the chloroquine-resistant K1 strain, in line with previous studies. Additionally, the models suggest that ‘softer’ compounds tend to have improved activity for both strains than do ‘harder’ ones. The internally and externally validated models reported here should also prove useful in the future screening of potential antimalarial compounds for targeting chloroquine-resistant strains.Graphical Predictive models reveal linear relationships for activity of 4-aminoquinoline analogues active against chloroquine-sensitive strains of Plasmodium falciparum
Highlights
Malaria is a life-threatening disease, which, according to the World Health Organization (WHO), resulted in almost half a million deaths in 2016, with 91% of those occurring in the WHO African Region [1]
In order to identify the most significant independent variables, a descriptor selection method was adopted in which quantitative structure–activity relationship (QSAR) models were developed independently using either the DRAGON [21] or ADMEWORKS Modelbuilder [22] descriptor sets
Significant QSAR models have been developed for both the chloroquine-sensitive NF54 and chloroquineresistant K1 strains using multiple linear regression (MLR) and Partial least squares (PLS) methods
Summary
Malaria is a life-threatening disease, which, according to the World Health Organization (WHO), resulted in almost half a million deaths in 2016, with 91% of those occurring in the WHO African Region [1]. The eventual rupture of the hepatocytes releases merozoites into the blood, which go on to enter the red blood cells and undergo further maturation and multiplication. The rupture of these blood cells results in the characteristic cyclical fever associated with malaria. Free heme is generated during this process and is ordinarily toxic to the parasite, but it is readily converted to hematin and subsequently undergoes dimerization to form β-hematin. The majority of this β-hematin is rendered harmless to the parasite through biocrystallization to form insoluble hemozoin [4]
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