Prediction of antifungal activity, cytotoxicity risks and molecular docking against <em>Malassezia furfur</em> of constituents of citronella essential oil (<em>Cymbopogon winterianus</em>)

Abstract

Malassezia furfur is a fungus classified as very common yeast, causing superficial infections and dandruff, its proliferation in the scalp can cause besides hair loss infection. The alopecia caused by this microorganism can be temporary or permanent, not only by M. furfur but also by M. globosa, reducing the quality of life of people, especially women who are affected. Malassézia can cause skin lesions. giving way to bacteria like Staphylococcus aureus. The aim of this study is an in silico analysis of citronella essential oil, aiming to identify possible constituents with fungicidal action against M. furfur. Initially the molecules were submitted to a biological activity prediction model developed in KNIME Analytics Platform 3.7, prediction of cytotoxicity risks by OSIRIS DataWarrior 5.0 software and molecular docking with Molegro Virtual Docker 6.0 (MVD). At the end of the research it was concluded that among the 15 components of the essential oil under study, only 1 constituent presented activity and no risk of cytotoxicity was verified, finally, presented better ligand-receptor interaction energy than the itraconazole and miconazole controls.