Predicting the influence of multiple components on microbial inhibition using a logistic response model - a novel approach
Predicting the influence of multiple components on microbial inhibition using a logistic response model - a novel approach
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DOI：
10.1186/1472-6882-14-190
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发表时间：
2014-06-13
影响因子：
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通讯作者：
Lall, Namrita
中科院分区：
文献类型：
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作者：
Henley-Smith, Cynthia J.;Steffens, Francois E.;Lall, Namrita
背景：有几种协同作用的方法可用。然而，在对协同结果的解释上存在巨大的差异。此外，当两种以上的成分组合时，这些协同作用方法不评估被测成分(药物、植物和天然提取物)之间的相互影响。方法：采用改进的棋盘法评价了纳豆、互叶白千层、胡椒薄荷和绿茶提取物TEAVIGO(TM)的协同潜力。对口腔病原菌、变形链球菌、中间普氏杆菌和白色念珠菌进行了抑菌试验。从棋盘法获得的二进制代码形式的抑制数据被用于计算Logistic反应模型，结果具有统计学意义(p<；0.05)。结果：基于每种微生物的预测抑制模型，所测试的口腔病原菌在其各自的协同作用下被成功地抑制(以100%的概率)。预测抑制模型还提供了不同成分之间相互影响的信息，以及总体抑制概率的信息。结论：使用Logistic反应模型不需要‘计算’协同作用，因为结果具有统计学意义。为了成功地确定多个组分之间的影响以及它们对微生物抑制的影响，建立了一个新的预测模型。这种筛选多种成分的能力可能会对民族药物学、农业和制药产生深远影响。
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(TM). 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.