Repurposing of Anti-infectives for the Management of Onchocerciasis using Machine Learning and Protein Docking Studies

Abstract

The discovery of new drugs for the treatment of neglected tropical diseases (NTDs) is hampered by the lack of financial reward associated with their development. A combination of exploratory data analysis, machine learning (ML) and molecular docking studies were used to evaluate 58 anti-infective agents to identify those with the potential to be repurposed for the management of onchocerciasis, an NTD. Out of the 58 test drugs, 14 were predicted by at least five ML models to be useful in managing onchocerciasis. Molecular docking studies using glycine receptor subunit α-3, gamma-aminobutyric acid receptor subunit β-3 and glutamate-gated chloride channel of the 14 drugs showed binding affinities comparable to that of doramectin and pyrvinium which are known onchocerciasis drugs. Diminazine, trimetinib, triclabendazole, cridanimod, vandetinib and trametinib were the top agents showing high binding affinities from the molecular docking studies. The binding affinities of diminazine and cridanimod are similar to doramectin and pyrvinium which have demonstrated activity against onchocerciasis. The outcome of this study shows the potential of using these 14 drugs to manage onchocerciasis.