Predicting the influence of multiple components on microbial inhibition using a logistic response model - a novel approach

BMC Complementary and Alternative Medicine

Namrita Lall²  Francien S Botha¹  Francois E Steffens³  Cynthia J Henley-Smith² 

[1] Department of Paraclinical Sciences, Phytomedicine Programme, Faculty of Veterinary Sciences, University of Pretoria, Pretoria 0002, South Africa;Department of Plant Science, Faculty of Natural and Agriculture Sciences, University of Pretoria, Pretoria 0002, South Africa;Department of Statistics, Faculty of Natural and Agriculture Sciences, University of Pretoria, Pretoria 0002, South Africa

关键词: TEAVIGO™;    Mentha piperita;    Melaleuca alternifolia;    Heteropyxis natalensis;    Checkerboard method;    Oral pathogens;    Synergism;   

Others  :  1220093
DOI  :  10.1186/1472-6882-14-190

 received in 2014-02-04, accepted in 2014-05-21,  发布年份 2014

【 摘 要 】

Background

There are several synergistic methods available. However, there is a vast discrepancy in the interpretation of the synergistic results. Also, these synergistic methods do not assess the influence the tested components (drugs, plant and natural extracts), have upon one another, when more than two components are combined.

Methods

A modified checkerboard method was used to evaluate the synergistic potential of Heteropyxis natalensis, Melaleuca alternifolia, Mentha piperita and the green tea extract known as TEAVIGO™. The synergistic combination was tested against the oral pathogens, Streptococcus mutans, Prevotella intermedia and Candida albicans. Inhibition data obtained from the checkerboard method, in the form of binary code, was used to compute a logistic response model with statistically significant results (p < 0.05). This information was used to construct a novel predictive inhibition model.

Results

Based on the predictive inhibition model for each microorganism, the oral pathogens tested were successfully inhibited (at 100% probability) with their respective synergistic combinations. The predictive inhibition model also provided information on the influence that different components have upon one another, and on the overall probability of inhibition.

Conclusions

Using the logistic response model negates the need to ‘calculate’ synergism as the results are statistically significant. In successfully determining the influence multiple components have upon one another and their effect on microbial inhibition, a novel predictive model was established. This ability to screen multiple components may have far reaching effects in ethnopharmacology, agriculture and pharmaceuticals.

【 授权许可】

   
2014 Henley-Smith et al.; licensee BioMed Central Ltd.

【 图 表 】

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