An in silico Approach to Detect Efficient Malaria Drug Targets to Combat the Malaria Resistance Problem

Abstract

Resistance to malaria drugs is a major challenging problem in most parts of the world especially in the African continent where about ninety per cent of malaria cases occur. As a response to this alarming problem, the World Health Organisation (W.H.O) recommends that all countries experiencing resistance to conventional monotherapies, such as chloroquine, amodiaquine or sulfadoxine–pyrimethamine, should use combination therapies [1]. Therefore there is a need to discover new drug targets that are able to target the malarial parasite at distinct pathways for an efficient malaria drug. In this paper, we presented a machine-learning tool which is able to identify novel drug targets from the metabolic network of Plasmodium falciparum. With our tool we identified among others 19 drug targets confirmed from literature which we analyzed further with a sophisticated gene expression analysis tool. Our data was clustered using common distance similarity measurements and hierarchical clustering to propose a profound combination of drug targets. Our result suggests that two or more enzymatic reactions from the list of our drug targets which span across about ten pathways (Table 2) could be combined to target at distinct time points in the parasite's intraerythrocytic developmental cycle to detect efficient malaria drug target combinations.