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

BMC Complementary and Alternative Medicine

Research Article

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

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

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

DOI  :  10.1186/1472-6882-14-190

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

来源: Springer

【 摘 要 】

BackgroundThere 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.MethodsA 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.ResultsBased 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.ConclusionsUsing 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】

Unknown   
© Henley-Smith et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.