Abstract
Malaria is among the leading causes of death worldwide. The emergence of Plasmodium falciparum resistant strains with reduced sensitivity to the first line combination therapy and suboptimal responses to insecticides used for Anopheles vector management have led to renewed interest in novel therapeutic options. Here, we report the development and validation of an ensemble of ligand-based computational models capable of identifying falcipain-2 inhibitors, and their subsequent application in the virtual screening of DrugBank and Sweetlead libraries. Among four hits submitted to enzymatic assays, two (odanacatib, an abandoned investigational treatment for osteoporosis and bone metastasis, and the antibiotic methacycline) confirmed inhibitory effects on falcipain-2, with Ki of 98.2 nM and 84.4 μM. Interestingly, Methacycline proved to be a non-competitive inhibitor (α = 1.42) of falcipain-2. The effects of both hits on falcipain-2 hemoglobinase activity and on the development of P. falciparum were also studied.
Highlights
Despite decades of successful interventions aimed at reducing its incidence and mortality, malaria continues being one of global leading causes of death, being the main global cause globally in the 5- to 14-year-old population and the third cause among children below five (World Health Organization (WHO), 2017; Ritchie and Roser, 2018)
There are some previous reports on the development of ligand-based models to predict the activity of P. falciparum cysteine proteases
The approaches used in such studies show considerable differences with the one reported here: they used conformation-dependent descriptors (3D QSAR) to infer regression models; in almost all cases, congeneric series of comparatively narrow chemical diversity have been used to train the models, limiting their applicability domain and; the reported models have mostly been used for explanatory rather than predictive purposes
Summary
Despite decades of successful interventions aimed at reducing its incidence and mortality, malaria continues being one of global leading causes of death, being the main global cause globally in the 5- to 14-year-old population and the third cause among children below five (World Health Organization (WHO), 2017; Ritchie and Roser, 2018). Drug repurposing involves finding novel medical uses for existing drugs, including approved, investigational, discontinued, and shelved therapeutics. When none of the previous conditions were met, the compound was labeled as INACTIVE Considering such criteria, the dataset includes 122 active compounds and 393 inactive compounds. A representative sampling procedure was used to divide the datasets into: (a) a training set, that was used to calibrate the models and; (b) a test set, that was used to independently assess model predictivity. Such representative partition of the dataset resulted from a serial combination of two clustering procedures. Hierarchical clustering allowed deciding on an initial partition of n molecules into k groups, and this
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