MicroRNA Guided In Silico Drug Repositioning for Malaria.

Background: The rise in Plasmodium resistant strains, decreasing susceptibility to first-line combination therapies, and inadequate efficacy shown by vaccines developed to date necessitate innovative approaches to combat malaria. Drug repurposing refers to finding newer indications for existing medications that provide significant advantages over de novo drug discovery, leading to rapid treatment options. Growing evidence suggests that drugs could regulate the expression of disease-associated microRNAs (miRNAs), implying the potential of miRNAs as attractive targets of therapy for several diseases.

Methods: We aimed to computationally predict drug-disease relationships through miRNAs for the potential repurposing of the drugs as antimalarials. To achieve this, we created a model that combines experimentally validated miRNA-drug interactions and miRNA-disease correlations, assuming that drugs will be linked to disease if they share significant miRNAs. The first step involved constructing a network of drug-drug interactions using curated drug-miRNA relations from the Pharmaco-miR and SM2miR databases. Additionally, the drug-disease relations were acquired from the comparative toxicogenomics database (CTD), and the random walk with restart (RWR) algorithm was applied to the interaction network to anticipate newer drug indications. Further, experimentally verified miRNA-disease associations were procured from the human microRNA disease database (HMDD), followed by an evaluation of the model's performance by examining case studies retrieved from the literature.

Results: Topological network analysis revealed that beta-adrenergic drugs in the network that are closely linked may have a tendency to be used as antimalarials. Case studies retrieved from the literature demonstrated acceptable model performance. A few of the predicted drugs, namely, propranolol, metoprolol, epinephrine, and atenolol, have been evaluated for their association with malaria, thereby indicating the adequacy of our model and offering experimental leads for alternative drugs.

Conclusion: The study puts forth a computational model for forecasting potential connections between beta-adrenergic receptor targeting drugs and malaria to suggest potential for future drug repurposing. This takes into account the concept of commonly associated miRNA partners and providing a mechanistic basis for targeting diseases, elucidating the implication of miRNAs in novel drug-disease relations.