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dc.contributor.author | Henley-Smith, Cynthia Joan![]() |
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dc.contributor.author | Steffens, Francois E.![]() |
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dc.contributor.author | Botha, Francien Susanna![]() |
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dc.contributor.author | Lall, Namrita![]() |
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dc.date.accessioned | 2014-09-09T07:51:12Z | |
dc.date.available | 2014-09-09T07:51:12Z | |
dc.date.issued | 2014-06-13 | |
dc.description | The University of Pretoria holds a provisional South African patent (ZA2013/ 06534) relating to the content of the manuscript. No financial benefits have been received by the authors. | en_US |
dc.description.abstract | 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. | en_US |
dc.description.librarian | am2014 | en_US |
dc.description.sponsorship | The University of Pretoria and the Gen Foundation. | en_US |
dc.description.uri | http://www.biomedcentral.com/bmccomplementalternmed | en_US |
dc.identifier.citation | Henley-Smith, CJ, Steffens, FE, Botha, FS & Lall, N 2014, 'Predicting the influence of multiple components on microbial inhibition using a logistic response model - a novel approach', BMC Complementary and Alternative Medicine, vol. 14, art. 90, pp. 1-10. | en_US |
dc.identifier.issn | 1472-6882 | |
dc.identifier.other | 10.1186/1472-6882-14-190 | |
dc.identifier.uri | http://hdl.handle.net/2263/41951 | |
dc.language.iso | en | en_US |
dc.publisher | BioMed Central | en_US |
dc.rights | © 2014 Henley-Smith et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. | en_US |
dc.subject | Synergism | en_US |
dc.subject | Oral pathogens | en_US |
dc.subject | Checkerboard method | en_US |
dc.subject | Heteropyxis natalensis | en_US |
dc.subject | Melaleuca alternifolia | en_US |
dc.subject | Mentha piperita | en_US |
dc.subject | TEAVIGO™ | en_US |
dc.title | Predicting the influence of multiple components on microbial inhibition using a logistic response model - a novel approach | en_US |
dc.type | Article | en_US |