Repurposing of Anti-Infectives for the Management of Onchocerciasis Using Machine Learning and Protein Docking Studies.

Abstract

There is a need to improve the discovery of new drugs for neglected tropical diseases (NTDs), as the lack of financial incentives has slowed their development. Currently, ivermectin and moxidectin are used in the management of onchocerciasis. We present a proof-of-concept study based on computational methods to find anti-infectives that can be repurposed or serve as lead compounds for onchocerciasis. A combination of exploratory data analysis, machine learning (ML), and molecular docking studies was used to evaluate 58 anti-infective agents. Out of the 58 test drugs, 14 were predicted by at least 5 ML models to be potentially useful in managing onchocerciasis. Molecular docking studies with the 14 predicted drugs using glutamate-gated chloride channel, a known target of ivermectin, an onchocerciasis drug, yielded good results. Cridanimod, diminazene, and vandetanib were the top 3 agents showing the highest binding affinities of -7.8, -7.2, and 7.1 kcal/mol, respectively, higher than the native ligand glutamate, which has a value of -4.5 kcal/mol. The binding interactions of these agents also showed overlaps with that of doramectin and pyrvinium agents that have demonstrated activity against onchocerciasis and ivermectin, the gold standard for onchocerciasis management. This study highlights the potential of cridanimod, diminazene, and vandetanib as promising candidates for developing new treatments for onchocerciasis.