High-throughput screening and prediction models building for novel hemozoin inhibitors using physicochemical properties [PublishAheadOfPrint]

It is essential to continue the search for novel antimalarial drugs due to current spread of resistance against artemisinin by Plasmodium falciparum parasites. In this study, we developed in silico models to predict hemozoin inhibitors as a potential first-step screening for novel antimalarials. The in vitro colorimetric high throughput screening assay of hemozoin formation was used to identify hemozoin inhibitors from 9600 structurally diverse compounds. Physicochemical properties of positive hits and randomly selected compounds were extracted from ChemSpider database; they were used for developing prediction models to predict hemozoin inhibitors using two different approaches, i.e. traditional multivariate logistic regression, and Bayesian Modeling Average. Our results showed that a total of 224 positive hits exhibited the ability to inhibit the hemozoin formation with IC₅₀ ranging from 3.1 μM to 199.5 μM. The "best" model according to traditional multivariate logistic regression included three variables: octanol-water partition coefficient, number of hydrogen bond donors, and number of atoms of hydrogen. Whereas, the "best" model according to Bayesian Modeling Average was octanol-water partition coefficient, number of hydrogen bond donors, and index of refraction. Both models had a good discriminatory power with the area under curve values were 0.736, and 0.781 for the traditional multivariate model, and the Bayesian Modeling Average model respectively. In conclusion, the prediction models can be a new, useful and cost-effective approach for the first screen of hemozoin inhibition based antimalarial drug discovery.

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