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

Abstract:

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.

SEEK ID: https://publications.h-its.org/publications/1734

PubMed ID: 37921561

Research Groups: Scientific Databases and Visualisation

Publication type: Journal

Journal: J Med Chem

Citation: J Med Chem. 2023 Nov 3. doi: 10.1021/acs.jmedchem.3c01322.

Date Published: 3rd Nov 2023

Registered Mode: by PubMed ID

Authors: P. Linciano, A. Quotadamo, R. Luciani, M. Santucci, K. M. Zorn, D. H. Foil, T. R. Lane, A. Cordeiro da Silva, N. Santarem, C. B Moraes, L. Freitas-Junior, U. Wittig, W. Mueller, M. Tonelli, S. Ferrari, A. Venturelli, S. Gul, M. Kuzikov, B. Ellinger, J. Reinshagen, S. Ekins, M. P. Costi

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Created: 6th Nov 2023 at 10:06

Last updated: 5th Mar 2024 at 21:25

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