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Prediction of antifungal activity, cytotoxicity risks and molecular docking against Malassezia furfur of constituents of citronella essential oil (Cymbopogon winterianus)
* 1 , 1 , 1 , 2 , 1, 3 , 1 , 1, 4
1  Postgraduate Program in Bioactive Natural and Synthetic Products, Federal University of Paraíba, Castelo Branco - João Pessoa - Brazil
2  Specialization Course in Aesthetics and Cosmetics - Integrated Center for Technology and Research - CINTEP, Rua Deputado Geraldo Mariz, 849, Tambauzinho, João Pessoa - PB, CEP 58042-060
3  Molecular Modeling Laboratory, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
4  Teaching and Research Management - University Hospital, Federal University of Paraíba, João Pessoa, PB, Brazil

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.

Keywords: Malassezia furfur; molecular docking; KNIME; in silico
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