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

Aug 5, 2026

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Broad-spectrum anti-infective chemotherapy agents targeting Trypanosomes, Leishmania, and Mycobacterium tuberculosis were discovered through a high-throughput phenotypic screening campaign of 456 Ty-Box compounds. Machine learning–based compound characterization supported the identification and synthesis of 44 broad-spectrum antiparasitic candidates with minimal toxicity toward Trypanosoma brucei, Leishmania infantum, and Trypanosoma cruzi. In vitro validation confirmed predictive models, highlighting compound 40 as a new lead with an N-(5-pyrimidinyl)benzenesulfonamide scaffold, promising low micromolar efficacy, and reduced toxicity across parasites. Chemoinformatics and ML tools then selected candidates for further biological and toxicological evaluation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

High-throughput screening and the need for improved anti-infective therapies

Neglected infectious diseases and drug discovery challenges

This article is licensed under CC-BY 4.0 [pubs.acs.org/jmc](pubs.acs.org/jmc) Article High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents 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* Cite This: J. Med. Chem. 2023, 66, 15230−15255 Read Online | | | | | | | | --- | --- | --- | --- | --- | --- | | ACCESS | Metrics & More | | Article Recommendations | | *sı Supporting Information | 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. ■ INTRODUCTION Poverty-related infectious diseases such as tuberculosis, malaria, trypanosomiasis, and leishmaniasis afflict a massive global population. It has been estimated that, overall, over 200 costly and hence less accessible. Improved interventions could have a substantial effect on our ability to decrease the morbidity and mortality associated with the disease and to limit the further spread, as treatment of active TB is the major Downloaded via UNIV DEGLI STUDI DI MODENA on May 7, 2024 at 10:11:47 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles. million are affected or are at risk. The common problem among all these infectious diseases is the limited number of therapeutic drugs (Figure 1), their poor safety profile due to their toxicity, low compliance by patients, low accessibility, and drug resistance development.1,2 In the case of tuberculosis (TB) infections, the current standard treatment is effective, even though clinical practice suggests that patients with uncomplicated drug-susceptible TB are required to take multiple antibiotics for 6 months. Since compliance is low, WHO recommends that this must be directly supervised and possibly changed with a therapy that not only ensures higher compliance but is also shorter in duration and demonstrates effectiveness in the short term. This concept has generated a huge layer of infrastructure to the long treatment program. With the rise of drug resistance, treatment failure rates have also increased along with more toxic therapies that are far more modality for preventing transmission in most of the world. Recent analysis reports that 75% of all emerging human infectious diseases in the past three decades worldwide originated in animals.3 Poor and disadvantaged populations (subtropical regions), European Mediterranean cou

FAQs

How were candidate agents identified in this study?

A high-throughput phenotypic screening program screened 456 compounds from the Ty-Box database, followed by machine learning–driven compound characterization to select promising candidates.

What was the outcome of the machine learning workflow?

The workflow enabled the identification and synthesis of 44 broad-spectrum antiparasitic compounds with minimal toxicity, and highlighted compound 40 as a new lead.

Why is compound 40 considered promising?

Compound 40 emerged from predictive models and features an N-(5-pyrimidinyl)benzenesulfonamide scaffold, showing promising low micromolar activity against multiple parasites with low toxicity.

Aug 5, 2026

Upload by Hazel