Anti-pseudomonad Activity of Manuka Honey and Antibiotics in a Specialized Model Simulating Cystic Fibrosis Lung Infection
Overview
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causes problematic chronic lung infections in those suffering from cystic fibrosis. This is due to its antimicrobial resistance mechanisms and its ability to form robust biofilm communities with increased antimicrobial tolerances. Using novel antimicrobials or repurposing current ones is required in order to overcome these problems. Manuka honey is a natural antimicrobial agent that has been used for many decades in the treatment of chronic surface wounds with great success, particularly those infected with . Here we aim to determine whether the antimicrobial activity of manuka honey could potentially be repurposed to inhibit pulmonary infections using two models. isolates ( = 28) from an international panel were tested for their susceptibility to manuka honey and clinically relevant antibiotics (ciprofloxacin, ceftazidime, and tobramycin), alone and in combination, using conventional antimicrobial susceptibility testing (AST). To increase clinical applicability, two porcine lung (EVPL) models (using alveolar and bronchiolar tissue) were used to determine the anti-biofilm effects of manuka honey alone and in combination with antibiotics. All isolates were susceptible to manuka honey, however, varying incidences of resistance were seen against antibiotics. The combination of sub-inhibitory manuka honey and antibiotics using conventional AST had no effect on activity against the majority of isolates tested. Using the two models, 64% (w/v) manuka honey inhibited many of the isolates where abnormally high concentrations of antibiotics could not. Typically, combinations of both manuka honey and antibiotics had increased antimicrobial activity. These results highlight the potential of manuka honey as a future antimicrobial for the treatment of pulmonary isolates, clearing potential infection reservoirs within the upper airway.
Recreating chronic respiratory infections using physiologically relevant models.
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