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

  • J Med Chem. 2023 Nov 3. doi: 10.1021/acs.jmedchem.3c01322.
Pasquale Linciano  1 ,  Antonio Quotadamo  1 ,  Rosaria Luciani  1 ,  Matteo Santucci  1 ,  Kimberley M Zorn  2 ,  Daniel H Foil  2 ,  Thomas R Lane  2 ,  Anabela Cordeiro da Silva  3  4 ,  Nuno Santarem  3  4 ,  Carolina B Moraes  5 ,  Lucio Freitas-Junior  5 ,  Ulrike Wittig  6 ,  Wolfgang Mueller  6 ,  Michele Tonelli  7 ,  Stefania Ferrari  1 ,  Alberto Venturelli  1  8 ,  Sheraz Gul  9  10 ,  Maria Kuzikov  9  10 ,  Bernhard Ellinger  9  10 ,  Jeanette Reinshagen  9  10 ,  Sean Ekins  2 ,  Maria Paola Costi  1
Affiliations
  • 1. Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.
  • 2. Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • 3. Institute for Molecular and Cell Biology, 4150-180 Porto, Portugal.
  • 4. Instituto de Investigaçao e Inovaçao em Saúde, Universidade do Porto and Institute for Molecular and Cell Biology, 4150-180 Porto, Portugal.
  • 5. Brazilian Biosciences National Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), 13083-970 Campinas, São Paulo, Brazil.
  • 6. Scientific Databases and Visualization Group and Molecular and Cellular Modelling Group, Heidelberg Institute for Theoretical Studies (HITS), D-69118 Heidelberg, Germany.
  • 7. Department of Pharmacy, University of Genoa, Viale Benedetto XV n.3, 16132 Genoa, Italy.
  • 8. TYDOCK PHARMA S.r.l., Strada Gherbella 294/b, 41126 Modena, Italy.
  • 9. Fraunhofer Translational Medicine and Pharmacology, Schnackenburgallee 114, D-22525 Hamburg, Germany.
  • 10. Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD, Schnackenburgallee 114, D-22525 Hamburg, Germany.
PMID: 37921561 DOI: 10.1021/acs.jmedchem.3c01322
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.

Products