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

Abstract

緒 言 Despite significant advances in the prevention and treatment of malaria, it remains to be one of the calamitous global health and socioeconomic concern. Several drugs are currently available for malaria treatment, however parasites began to develop resistance against most of these drugs including the first line drug, Artemisinin. A newly approved vaccine, RTS, S, has raised hope for preventive therapy, yet suffered with limited efficacy (shorter and stage-specific immunity). As a result, look for new small molecule drug candidates with novel target/mechanism of action has become pivotal. There are various phenotypic screening methods to identify novel antimalarials, however, most of them are time-consuming, costly and laborious. In contrary, in silico approach found to be effective to screen millions of compounds comparatively at shorter time and less expensive way than conventional screening. Therefore, in this study, we developed new prediction models for in silico antimalarial compound screening based on the physicochemical properties of small chemical compounds (hemozoin inhibitors) identified from our previous study. 対象と方法In this study, 224 positive hemozoin inhibitors (obtained from our previous study), were tested for in vitro erythrocytic antimalarial activity against chloroquine -mefloquine sensitive Plasmodium falciparum strain, 3D7A and their antihemozoin activity. The physicochemical properties of the active compounds (antimalarials and hemozoin inhibitors) were extracted from ChemSpider and SciFinder databases. To develop the model, univariable logistic regression was performed to examine the association between physicochemical properties (variables) and antimalarial activity of the compounds (outcome). Subsequently, to find independent predictors, variables P<0.1 and/or significant variables in previous study, were subjected to multivariable analysis using Bayesian model averaging (BMA) based on the Bayesian information criterion (BIC), where the smaller BIC value indicates the better model. The data were randomly divided into two sets -training and testing, with a ratio of 70:30. The BMA models were developed using training set and validated by testing data set. The data were analysed by using RStudio 1.0.44

Virtual screening as a tool to discover new β-haematin inhibitors with activity against malaria parasites

“…Recently, ligand based virtual screening applying Bayesian classifiers based on Bayes' theorem has been used to build in silico activity models trained with HTS data for predicting β-haematin inhibition and in vitro antimalarial activity 21,31,40,41 . However, to our knowledge, this is the first study applying SBVS for identification of new actives against β-haematin.…”

“…Here, the structure of the β-haematin crystal previously published was used as the macromolecule (receptor) 9 . The search space encompassed the whole of the modelled crystal (made up of 27 unit cells) with the following dimensions in Å: centre (x, y, z) = (18, 20, -14), dimensions (x, y, z) = (41,53,36). The docking simulation was then run at an exhaustiveness of 8 and set to only output the lowest energy pose.…”

Antimalarial activity of traditional Kampo medicine Coptis Rhizome extract and its major active compounds

“…A SYBR Green based micro uorometric method was used to quantify parasite level as previously described [37]. Brie y, after 48 hrs of incubation with herbal extracts or compounds, we added 100 µL of lysis buffer to RBCs by using 20 mM Tris, 10 mM EDTA, 0.01% saponin (wt/vol), and then we added 0.1% Triton X-100 (vol/vol), in pH 7.5 as well as 1× the nal concentration of SYBR Green -I (Lonza, Rockland, ME) into each well.…”

Antimalarial activity of traditional Kampo medicine Coptis Rhizome extract and its major active compounds

“…A SYBR Green based microfluorometric method was used to quantify parasite level as previously described [43]. Briefly, after 48 hrs of incubation with crude drug extracts or compounds, RBCs were lysed by adding 100 µL of lysis buffer (20 mM Tris, 10 mM EDTA, 0.01% saponin (wt/vol), and 0.1% Triton X-100 (vol/vol), pH 7.5) and 1× final concentration of SYBR Green -I (Lonza, Rockland, ME) into each well.…”