4.5 Article

Antiprotozoal QSAR modelling for trypanosomiasis (Chagas disease) based on thiosemicarbazone and thiazole derivatives

Journal

JOURNAL OF MOLECULAR GRAPHICS & MODELLING
Volume 103, Issue -, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jmgm.2020.107821

Keywords

QSAR modelling; Chagas disease; Trypanosoma cruzi; Thiosemicarbazones; Thiazoles; Antiprotozoal agents

Funding

  1. Direccion de Investigaciones (DIN) of the Universidad Pedagogica y Tecnologica de Colombia (UPTC) [SGI 1798]
  2. National Scientific and Technical Research Council of Argentina [Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Argentina] [PIP11220130100311]

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Chagas disease, caused by Trypanosoma cruzi, affects around 8 million people globally and causes 12,000 deaths annually. Traditional chemotherapy with Benznidazole and Nifurtimox has limitations. Recent research focuses on finding new chemical structures with better efficacy and tolerance. Thiosemicarbazone and thiazole derivatives show promising in vitro activity against T. cruzi.
Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, remains a neglected endemic infection that affects around 8 million people worldwide and causes 12,000 premature deaths per year. Traditional chemotherapy is limited to the nitro-antiparasitic drugs Benznidazole and Nifurtimox, which present serious side effects and low long-term efficacy. Several research efforts have been made over the last decade to find new chemical structures with better effectiveness and tolerance than standard anti-Chagas drugs. Among these, new sets of thiosemicarbazone and thiazole derivatives have exhibited potent in vitro activity against T. cruzi, especially for its extracellular forms (epimastigote and trypomastigote). In this work, we have developed three antiprotozoal quantitative structure-relationship (QSAR) models for Chagas disease based on the in vitro activity data reported as IC50 (mu M) and CC50 (mu M) over the last decade, particularly by Lima-Leite's group in Brazil. The models were developed using the replacement method (RM), a technique based on Multivariable Linear Regression (MLR), and external and internal validation methodologies, like the use of a test set, Leave-one-Out (LOO) cross-validation and Y-Randomization. Two of these QSAR models were developed for trypomastigotes form of the parasite Trypanosoma cruzi, one based on IC50 and the other on CC50 data; while the third QSAR model was developed for its epimastigotes form based on CC50 activity. Our models presented sound statistical parameters that endorses their prediction capability. Such capability was tested for a set of 13 hitherto-unknown structurally related aromatic cyclohexanone derivatives. (C) 2020 Elsevier Inc. All rights reserved.

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