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

Title: High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents Authors: Pasquale Linciano*, Antonio Quotadamo, Rosaria Luciani, Matteo Santucci, Kimberley M. Zorn, Daniel H. Foil, Thomas R. Lane, Anabela Cordeiro da Silva, Nuno Santarem, Carolina B Moraes, Lucio Freitas-Junior, Ulrike Wittig, Wolfgang Mueller, Michele Tonelli, Stefania Ferrari, Alberto Venturelli, Sheraz Gul, Maria Kuzikov, Bernhard Ellinger, Jeanette Reinshagen, Sean Ekins*, and Maria Paola Costi* Journal, date, issue, pages:J. Med. Chem. 2023, 66, 22, 15230–15255

DOI:doi.org/10.1021/acs.jmedchem.3c01322 

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.