In silico prediction of potential antimalarial drug candidates through genomic analysis

Introduction: Plasmodium falciparum malaria, an infectious  disease, kills lot of people especially in Africa. The parasites have developed resistance to artemisinin, the most active component of artemisinin combination therapies(ACTs) in Southeast Asia. Researching into new drugs, particularly, population-specific, is critical even as measures are instituted to control resistance. Objectives: The objective  is to identify protein targets that could facilitate predicting potential drug candidates specific to the African populations through a comparative host and Plasmodium falciparum genomic analysis. Method: We leveraged malaria-specific genome-wide association study summary statistics data  from African populations and malaria-associated genes and functional datasets to construct biological network for human and pathogen. Bioinformatics tools and approaches were used to analyze the networks to identify connected subnetworks. Pharmaceutical datasets and natural products would be screened against potential targets  to identify repurposable and novel drug molecules. Preliminary results: Significant SNPs from the summary statistics data were mapped to genes. Falciparum PPIs dataset, human-falciparum dataset, human PPIs dataset and micro-array data were obtained from literature and databases. Pairwise interactions obtained through sequence BLAST together with the other datasets were integrated into a unified network. Conclusions/Next steps: Pathways enrichment analysis would be performed  to identify key genes and biological processes  that could be potential drug targets for drug discovery.