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Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework

Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requir...

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Autores principales: de Oliveira, Amanda Alves, Neves, Bruno Junior, Silva, Lívia do Carmo, Soares, Célia Maria de Almeida, Andrade, Carolina Horta, Pereira, Maristela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581699/
https://www.ncbi.nlm.nih.gov/pubmed/31244810
http://dx.doi.org/10.3389/fmicb.2019.01301
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author de Oliveira, Amanda Alves
Neves, Bruno Junior
Silva, Lívia do Carmo
Soares, Célia Maria de Almeida
Andrade, Carolina Horta
Pereira, Maristela
author_facet de Oliveira, Amanda Alves
Neves, Bruno Junior
Silva, Lívia do Carmo
Soares, Célia Maria de Almeida
Andrade, Carolina Horta
Pereira, Maristela
author_sort de Oliveira, Amanda Alves
collection PubMed
description Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requires long-term administration protocols. In addition, chemotherapy is related to a significantly increased frequency of disease relapse, high toxicity, and incomplete elimination of the fungus. Due to the limitations of current anti-PCM drugs, we developed a computational drug repurposing-chemogenomics approach to identify approved drugs or drug candidates in clinical trials with anti-PCM activity. In contrast to the one-drug-one-target paradigm, our chemogenomics approach attempts to predict interactions between drugs, and Paracoccidioides protein targets. To achieve this goal, we designed a workflow with the following steps: (a) compilation and preparation of Paracoccidioides spp. genome data; (b) identification of orthologous proteins among the isolates; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Paracoccidioides essential targets using validated genes from Saccharomyces cerevisiae; (e) homology modeling and molecular docking studies; and (f) experimental validation of selected candidates. We prioritized 14 compounds. Two antineoplastic drug candidates (vistusertib and BGT-226) predicted to be inhibitors of phosphatidylinositol 3-kinase TOR2 showed antifungal activity at low micromolar concentrations (<10 μM). Four antifungal azole drugs (bifonazole, luliconazole, butoconazole, and sertaconazole) showed antifungal activity at low nanomolar concentrations, validating our methodology. The results suggest our strategy for predicting new anti-PCM drugs is useful. Finally, we could recommend hit-to-lead optimization studies to improve potency and selectivity, as well as pharmaceutical formulations to improve oral bioavailability of the antifungal azoles identified.
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spelling pubmed-65816992019-06-26 Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework de Oliveira, Amanda Alves Neves, Bruno Junior Silva, Lívia do Carmo Soares, Célia Maria de Almeida Andrade, Carolina Horta Pereira, Maristela Front Microbiol Microbiology Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requires long-term administration protocols. In addition, chemotherapy is related to a significantly increased frequency of disease relapse, high toxicity, and incomplete elimination of the fungus. Due to the limitations of current anti-PCM drugs, we developed a computational drug repurposing-chemogenomics approach to identify approved drugs or drug candidates in clinical trials with anti-PCM activity. In contrast to the one-drug-one-target paradigm, our chemogenomics approach attempts to predict interactions between drugs, and Paracoccidioides protein targets. To achieve this goal, we designed a workflow with the following steps: (a) compilation and preparation of Paracoccidioides spp. genome data; (b) identification of orthologous proteins among the isolates; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Paracoccidioides essential targets using validated genes from Saccharomyces cerevisiae; (e) homology modeling and molecular docking studies; and (f) experimental validation of selected candidates. We prioritized 14 compounds. Two antineoplastic drug candidates (vistusertib and BGT-226) predicted to be inhibitors of phosphatidylinositol 3-kinase TOR2 showed antifungal activity at low micromolar concentrations (<10 μM). Four antifungal azole drugs (bifonazole, luliconazole, butoconazole, and sertaconazole) showed antifungal activity at low nanomolar concentrations, validating our methodology. The results suggest our strategy for predicting new anti-PCM drugs is useful. Finally, we could recommend hit-to-lead optimization studies to improve potency and selectivity, as well as pharmaceutical formulations to improve oral bioavailability of the antifungal azoles identified. Frontiers Media S.A. 2019-06-12 /pmc/articles/PMC6581699/ /pubmed/31244810 http://dx.doi.org/10.3389/fmicb.2019.01301 Text en Copyright © 2019 de Oliveira, Neves, Silva, Soares, Andrade and Pereira. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
de Oliveira, Amanda Alves
Neves, Bruno Junior
Silva, Lívia do Carmo
Soares, Célia Maria de Almeida
Andrade, Carolina Horta
Pereira, Maristela
Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework
title Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework
title_full Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework
title_fullStr Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework
title_full_unstemmed Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework
title_short Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework
title_sort drug repurposing for paracoccidioidomycosis through a computational chemogenomics framework
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581699/
https://www.ncbi.nlm.nih.gov/pubmed/31244810
http://dx.doi.org/10.3389/fmicb.2019.01301
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