A novel antiplasmodial compound: integration of in silico and in vitro assays

Malaria is a disease caused by Plasmodium genus. which P. falciparum is responsible for the most severe form of the disease, cerebral malaria. In 2018, 405,000 people died of malaria. Antimalarial drugs have serious adverse effects and limited efficacy due to multidrug-resistant strains. One way to overcome these limitations is the use of computational approaches for prioritizing candidates to phenotypic assays and/or in vitro assays against validated targets. Plasmodium falciparum Enoyl-ACP reductase (PfENR) is noteworthy because it catalyzes the rate-limiting step of the biosynthetic pathway of fatty acid. Thus, the study aimed to identify potential PfENR inhibitors by ligand (2D molecular similarity and pharmacophore models) and structure-based virtual screening (molecular docking). 2D similarity-based virtual screening using Tanimoto Index (> 0.45) selected 29,236 molecules from natural products subset available in ZINC database (n = 181,603). Next, 10 pharmacophore models for PfENR inhibitors were generated and evaluated based on the internal statistical parameters from GALAHAD (TM) and ROC/AUC curve. These parameters selected a suitable pharmacophore model with one hydrophobic center and two hydrogen bond acceptors. The alignment of the filtered molecules on best pharmacophore model resulted in the selection of 10,977 molecules. These molecules were directed to the docking-based virtual screening by AutoDock Vina 1.1.2 program. These strategies selected one compound to phenotypic assays against parasite. ZINC630259 showed EC50 = 0.12 +/- 0.018 mu M in antiplasmodial assays and selective index similar to other antimalarial drugs. Finally, MM/PBSA method showed stability of molecule within PfENR binding site (Delta G(binding)=-57.337 kJ/mol). Communicated by Ramaswamy H. Sarma

Automated summary

Malaria is caused by Plasmodium genus, with P. falciparum responsible for the most severe form of the disease. Antimalarial drugs have limited efficacy due to multidrug-resistant strains. Computational approaches were used to identify potential PfENR inhibitors for malaria treatment.