Prediction Model for Antimalarial Activities of Hemozoin Inhibitors by Using Physicochemical Properties

Abstract

The rapid spread of strains of malaria parasites that are resistant to several drugs has threatened global malaria control. Hence, the aim of this study was to predict the antimalarial activity of chemical compounds that possess anti-hemozoin-formation activity as a new means of antimalarial drug discovery. After the initial in vitro anti-hemozoin-formation high-throughput screening (HTS) of 9,600 compounds, a total of 224 hit compounds were identified as hemozoin inhibitors. These 224 compounds were tested for in vitro erythrocytic antimalarial activity at 10 μM by using chloroquine-mefloquine-sensitive Plasmodium falciparum strain 3D7A. Two independent experiments were conducted. The physicochemical properties of the active compounds were extracted from the ChemSpider and SciFinder databases. We analyzed the extracted data by using Bayesian model averaging (BMA). Our findings revealed that lower numbers of S atoms; lower distribution coefficient (log D) values at pH 3, 4, and 5; and higher predicted distribution coefficient (ACD log D) values at pH 7.4 had significant associations with antimalarial activity among compounds that possess anti-hemozoin-formation activity. The BMA model revealed an accuracy of 91.23%. We report new prediction models containing physicochemical properties that shed light on effective chemical groups for synthetic antimalarial compounds and help with in silico screening for novel antimalarial drugs.

Keywords: antimalarial; drug; hemozoin; malaria; prediction.

FIG 1

Workflow of this study. First, 224 compounds that showed antihemozoin activity in our previous study (22) underwent in vitro antimalarial assays at 10 μM. Second, 30 compounds with ≥45% parasite-inhibitory efficacy underwent an in vitro dose-response assay at concentrations of between 0.5 nM and 10 μM. Next, 22 compounds with IC₅₀s of ≤10 μM were identified. Subsequently, the main structures and physicochemical properties of these compounds were searched by using SciFinder. Finally, the prediction models were generated by the traditional approach versus the Bayesian approach, using their physical and chemical properties.

FIG 2

Initial in vitro antimalarial screening of 224 compounds using the chloroquine-mefloquine-sensitive P. falciparum 3D7A strain. The circle dots represent the percentages of parasite inhibition of 224 compounds, including 30 compounds with parasite inhibition values of ≥45%.

FIG 3

Models selected by Bayesian model averaging. Blue and red represent positive and negative variable estimates, respectively, while uncolored variables were not included in the model. On the x axis, models were listed in the order of the declines in the posterior probability.