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.
Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework.
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作者:de Oliveira Amanda Alves, Neves Bruno Junior, Silva LÃvia do Carmo, Soares Célia Maria de Almeida, Andrade Carolina Horta, Pereira Maristela
| 期刊: | Frontiers in Microbiology | 影响因子: | 4.500 |
| 时间: | 2019 | 起止号: | 2019 Jun 12; 10:1301 |
| doi: | 10.3389/fmicb.2019.01301 | ||
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