Optimization of Inhibitory Effects of Thymus daenensis Celak. and Zataria multifera Boiss. Essential Oils on Candida albicans Using Response Surface Methodology and Artificial Neural Network

Document Type: Research Paper

Authors

1 Department of Plant Protection, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Department of Biology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia

10.22034/ijps.2019.43113

Abstract

Fungus Candida albicans has received much attention due to its oral, vaginal and/or systemic candidiasis. This study was undertaken to find out the optimum antifungal effect of concentration of two essential oils namely Thymus daenensis Celak. and Zataria multifera Boiss. either alone or in combination ratio and their time of action against C. albicans. The essential oils (EOs) were obtained by hydrodistillation method from T. daenensis and Z. multifera and Time-dependent killing potential of essential oils were assessed in phosphate buffered saline solution containing the desired concentrations of the test agents and of yeast suspension. Time of action, concentration of individual or EO mixture and T. daenensis:Z. multifera mass ratio predicted using response surface methodology (RSM) and artificial neural network (ANN). The results showed that that T. daenensis and Z. multifera shows potent antifungal effects against clinical C. albicans isolates. RSM and ANN techniques predicted the 0.8% as an optimum percentage concentration of EO mixture in oils ratio T. daenensis:Z. multifera 1:1, ensuring the highest antifungal effect of 95.8% and 96.4% after 20 h. Appraisal of the models through the coefficient of determination (R2) and mean-square error (MSE) show that the ANN was superior (R2= 0.994) to the RSM model (R2= 0.957) in predicting the percentage of reduced cells. Our data confirmed that proper EOs mixtures may reduce the minimum effective dose of individual EOs.

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