ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
Derick Erl P. Sumalapao1 , Nelson R. Villarante2, Josephine D. Agapito3, Abubakar S. Asaad1 and Nina G. Gloriani4
1Department of Epidemiology and Biostatistics, College of Public Health, University of the Philippines Manila, Philippines.
2Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, Philippines.
3Department of Biology, College of Arts and Sciences, University of the Philippines Manila, Philippines.
4Department of Medical Microbiology, College of Public Health, University of the Philippines Manila, Philippines.
J. Pure Appl. Microbiol., 2020, 14 (1): 247-254 | Article Number: 6097
https://doi.org/10.22207/JPAM.14.1.25 | © The Author(s). 2020
Received: 19/02/2020 | Accepted: 17/03/2020 | Published: 28/03/2020
Abstract

Multivariate statistical models were utilized to identify the interaction between the inhibitory activity and the molecular properties of the different antimycotics against Microsporum canis. Information on the inhibitory potency against M. canis and molecular properties of antifungal agents were obtained from literature. The relationship between the inhibitory potency and the molecular properties of the different antimycotics against M. canis was established using multiple linear regression analysis (MLRA) and principal component analysis (PCA). Three major descriptors: topological polar surface area, molecular weight, and rotatable bond count of the antimycotics were identified to confer inhibitory action against M. canis using MLRA (r2=0.8968, p<0.0001) and PCA (95.86% total contribution rate). Both MLRA and PCA as statistical approaches demonstrate their potential as tools in computational structure design and for possible synthesis of next generation antimycotics as more effective treatments of fungal infections.

Keywords

dermatophytes, fungal infections, molecular descriptors, multivariate data analysis, regression equation.

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