Drug Repurposing for Trypanosomiasis: Using Machine Learning Models and Polypharmacology to Identify Multitarget Candidates

Chagas disease and African sleeping sickness are neglected tropical diseases (NTD) caused by Trypanosoma parasites, with current treatments facing challenges like toxicity and resistance. This study integrates machine learning and Quantitative Structure-Activity Relationship (QSAR) models to repurpose Food and Drug Administration (FDA)-approved drugs as potential treatments for these diseases. A dataset of 21,608 compounds with inhibitory activity against Trypanosoma cruzi and Trypanosoma brucei was analyzed using PubChem fingerprints. Random Forest and Extreme Gradient Boosting models were trained and applied to screen the ZINC-22 database for new therapeutic options. Posaconazole was predicted as the top candidate for multitarget activity against both Trypanosoma species, followed by pentamidine, a drug already approved for sleeping sickness. Additionally, 40 other drug candidates were identified by the models (pIC50 > 6 and coefficient of variation < 0.05), mainly antineoplastics (32%) and antifungals (19%). This approach demonstrates the potential of computational techniques in accelerating the discovery of drug candidates for neglected infectious diseases.