Drug repurposing for paracoccidioidomycosis through a computational chemogenomics framework
Nenhuma Miniatura disponível
Data
2019
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Malaria is an infectious disease that affects over 216 million people worldwide, killing over
445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed
binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested
compounds. Then, we applied the best models for a virtual screening of a large database of
chemical compounds. The top computational predictions were evaluated experimentally
against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent
antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity
in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial
agents, which are in compliance with the guidelines and criteria for antimalarial target
candidates.
Descrição
Palavras-chave
Citação
OLIVEIRA, Amanda Alves de et al. Drug repurposing for paracoccidioidomycosis through a computational chemogenomics framework. Frontiers in Microbiology, Lausanne, v. 10, e1301, 2019. DOI: 10.3389/fmicb.2019.01301. Disponível em: https://pubmed.ncbi.nlm.nih.gov/31244810/. Acesso em: 10 set. 2024.