| Literature DB >> 30496872 |
Isis Regina Grenier Capoci1, Daniella Renata Faria2, Karina Mayumi Sakita2, Franciele Abigail Vilugron Rodrigues-Vendramini2, Patricia de Souza Bonfim-Mendonça2, Tania Cristina Alexandrino Becker2, Érika Seki Kioshima2, Terezinha Inez Estivalet Svidzinski2, Bernard Maigret3.
Abstract
Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.Entities:
Keywords: Antifungal activity; Drug repositioning; Ligand-based; Paracoccidioidomycosis; Raltegravir; Structure-based
Year: 2018 PMID: 30496872 DOI: 10.1016/j.bioorg.2018.11.019
Source DB: PubMed Journal: Bioorg Chem ISSN: 0045-2068 Impact factor: 5.275