Role of 2-Dimensional Autocorrelation Descriptors in Predicting Antimalarial Activity of Artemisinin and its Aanalogues: A QSAR Study

Abstract

Background: Malaria, one of the World's biggest billers' is on the schedule for biomedical research and public health policies. The introduction of the artemisinin, a Chinese traditional drug from Artemisia annua is a revolution in the treatment of malaria. Artemisinin-based combination treatment (ACT) is considered to be the best strategy for uncomplicated Falciparum malaria. The presence of 1,2,4-trioxane system in artemisinin is responsible for its antimalarial activity.

Methods: In this study, twenty-nine analogues of artemisinin were taken into account for QSAR studies along with artemisinin. The most active analogue of artemisinin 21 was energy minimized. All the structures were prepared from the active conformer 21 and energy was minimized to the stable state using MMFF94 force field using ChemBioDraw-12. Genetic Algorithm is used to decide the descriptors best required for the model generation. The test set and training set division were done by using hierarchal clustering module available with NCSS statistical software.

Results and conclusion: The antimalarial activity of the artemisinin and its substituted analogues has been analyzed through the multiple linear regression (MLR) using various physiochemical and structural descriptors obtained from PADEL software. The models were prepared using the Sigma Plot version 11. The calculated 2D autocorrelation descriptors and the MLR model suggest that artemisinin and its analogues hold the scope in the optimization of antimalarial activity.

Keywords: Antimalarial; Artemisinin; Descriptors; IC50; MLR; Padel..