Bacteriophages as potential antibiotic potentiators in cystic fibrosis: A new model to study the combination of antibiotics with a bacteriophage cocktail targeting dual species biofilms of Staphylococcus aureus and Pseudomonas aeruginosa

Objectives: Staphylococcus aureus and Pseudomonas aeruginosa co-infections in patients with cystic fibrosis (CF) are associated with disease severity. Their treatment is complicated by biofilm formation in the sticky mucus obstructing the airways. We investigated the activity of phages-antibiotics combinations using a dual species biofilm ( P. aeruginosa/S. aureus ) formed in artificial sputum medium. Methods: Biofilmswere incubated with broad-spectrum antibiotics (meropenem, ceftazidime, ciprofloxacin, tobramycin) combined with a cocktail of two (bacterio)phages (PSP3 and ISP) proven active via spot tests and double agar on P. aeruginosa PAO1 and S. aureus ATCC 25923. Results: At the highest tested concentrations (100 x MIC), antibiotics alone caused a 20-50% reduction in biomass and reduced S. aureus and P. aeruginosa CFU of 2.3 to 2.8 and 2.1 to 3.6 log10 , respectively. Phages alone reduced biofilm biomass by 23% and reduced P. aeruginosa CFU of 2.1 log10 , but did not affect S. aureus viability. Phages enhanced antibiotic effects on biomass and exhibited additive effects with antibiotics against P. aeruginosa , but not against S. aureus. Following inhibition of bacterial respiration by phages in planktonic cultures rationalised these observations by demonstrating that PSP3 was effective at multiplicities of infection (MOI) as low as 10(-4) plaque forming units (PFU)/CFU on P. aeruginosa , but ISP, at higher MOI ( > 0.1) against S. aureus. Conclusion: Pre-screening inhibition of bacterial respiration by phages may assist in selecting those showing activity at sufficiently low titers to showcase anti-biofilm activity in this complex but clinicallyrelevant in vitro model of biofilm. (c) 2024 Elsevier Ltd and International Society of Antimicrobial Chemotherapy. All rights are reserved, including those for text and data mining, AI training, and similar technologies.