High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents

Broad-spectrum anti-infective
chemotherapy agents with activity
against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening
program of the 456 compounds belonging to the Ty-Box, an in-house
industry database. Compound characterization using machine learning
approaches enabled the identification and synthesis of 44 compounds
with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies
confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar
activity against two parasites and low toxicity. Given the volume
and complexity of data generated by the diverse high-throughput screening
assays performed on the compounds of the Ty-Box library, the chemoinformatic
and machine learning tools enabled the selection of compounds eligible
for further evaluation of their biological and toxicological activities
and aided in the decision-making process toward the design and optimization
of the identified lead.