Formulations employing synergistic combinations of antibiotics with Essential Oils (EOs) could help preserve the antibiotic repertoire by improving their activity against resistant bacteria. Antimicrobial interactions between double and triple combinations of EOs, EO components and antibiotics were determined using the checkerboard method. The most active triple combinations were then assessed by a time-kill assay. Two synergistic EO-antibiotic combinations and eight additive EO-antibiotic combinations reduced the antibiotic minimum inhibitory concentration below clinical sensitivity breakpoints according to the checkerboard method. However, all the tested combinations were additive according to the time-kill assay; while the combinations completely killed S. aureus, E. coli and P. aeruginosa cells in 2 h. At least one EO compound from the combination alone completely killed the cells of test species. Two synergistic EO-antibiotic combinations and eight additive EO-antibiotic combinations reduced the antibiotic minimum inhibitory concentration below clinical sensitivity breakpoints according to the checkerboard method. However, all the tested combinations were additive according to the time-kill assay; while the combinations completely killed S. aureus, E. coli and P. aeruginosa cells in 2 h. At least one EO compound from the combination alone completely killed the cells of test species. Positive interactions support the use of EOs or EO components to enhance antibiotic efficacy against antibiotic resistant bacteria. The EO-antibiotic combinations tested by the time kill assay were indifferent; therefore, the observed antimicrobial activity did not arise from synergistic mechanisms as indicated by the checkerboard method. Investigation of other synergistic combinations identified by the checkerboard method could reveal more promising candidates.
ABSTRACT
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- Publication year
2019
- Venue
Current Traditional Medicine
- Publication date
2019-05-20
- Fields of study
Biology, Medicine, Chemistry, Environmental Science
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Semantic Scholar
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