Abstract Drug discovery is an intricate and costly process. Repurposing existing drugs and active compounds offers a viable pathway to develop new therapies for various diseases. By leveraging publicly available biomedical information, it is possible to predict a compound's activity and identify their potential targets across diverse organisms. In this study, we aimed to assess the antiplasmodial activity of compounds from The Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library using in vitro and bioinformatics approaches. We evaluated the in vitro antiplasmodial activity of the compounds using blood-stage and liver-stage drug susceptibility assays. We used protein sequences of known targets of the ReFRAME compounds with high antiplasmodial activity (EC₅₀ < 10 uM) to conduct a protein-pairwise search to identify similar Pf strain 3D7 proteins (from PlasmoDB) using NCBI protein BLAST. We further assessed the association between the compounds' in vitro antiplasmodial activity and the target similarity parameters between their known targets and similar Pf proteins using linear regression analyses. BLAST analyses identified 735 Pf proteins that were similar to the 226 known protein targets associated with the compounds showing antiplasmodial antimalarial potency. Antiplasmodial activity of the compounds was positively associated with the degree of similarity between the compound’s known targets and predicted Pf protein targets (percentage identity, E value, and bit score), the number of the predicted Pf targets, and their respective mutagenesis index and fitness scores (R² ranged from 0.066 to 0.92, P < 0.05). Compounds predicted to target essential Pf proteins or those with a druggability index of 1 showed highest antiplasmodial activity. This study is the first to demonstrate a positive correlation between in vitro antipathogenic activity of compounds and target similarity across species. Our findings indicate that leveraging protein-target similarity may accelerate the drug repurposing process for many diseases by predicting compounds’ activity and their prospective targets in different organisms.
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Mogire et al. (2024) studied this question.
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