Tobramycin and Bicarbonate Synergise to Kill Planktonic Pseudomonas Aeruginosa, but Antagonise to Promote Biofilm Survival
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Increasing antibiotic resistance and the declining rate at which new antibiotics come into use create a need to increase the efficacy of existing antibiotics. The aminoglycoside tobramycin is standard-of-care for many types of Pseudomonas aeruginosa infections, including those in the lungs of cystic fibrosis (CF) patients. P. aeruginosa is a nosocomial and opportunistic pathogen that, in planktonic form, causes acute infections and, in biofilm form, causes chronic infections. Inhaled bicarbonate has recently been proposed as a therapy to improve antimicrobial properties of the CF airway surface liquid and viscosity of CF mucus. Here we measure the effect of combining tobramycin and bicarbonate against P. aeruginosa, both lab strains and CF clinical isolates. Bicarbonate synergises with tobramycin to enhance killing of planktonic bacteria. In contrast, bicarbonate antagonises with tobramycin to promote better biofilm growth. This suggests caution when evaluating bicarbonate as a therapy for CF lungs infected with P. aeruginosa biofilms. We analyse tobramycin and bicarbonate interactions using an interpolated surface methodology to measure the dose–response function. These surfaces allow more accurate estimation of combinations yielding synergy and antagonism than do standard isobolograms. By incorporating predictions based on Loewe additivity theory, we can consolidate information on a wide range of combinations that produce a complex dose–response surface, into a single number that measures the net effect. This tool thus allows rapid initial estimation of the potential benefit or harm of a therapeutic combination. Software code is freely made available as a resource for the community.
We thank Marvin Whiteley (The University of Texas at Austin) for his gift of CF clinical isolates and scientific discussions, Andreas Matouschek (The University of Texas at Austin) for use of his laboratory equipment and Kendra Rumbaugh (Texas Tech Health Sciences Center) for scientific input. We thank the Statistical Consulting Group (The University of Texas at Austin) and Biswanadham Sridhara and Dennis Wylie (bioinformatics consultants at The University of Texas at Austin) for discussion of response surface fitting and analysis, and Kanishk Jain for suggestions on fitting. This work was supported by start-up funds from The University of Texas at Austin to V.D.G., a gift from ExxonMobile to V.D.G., and a Microbiology Summer Merit Award from UT Austin to K.S.K. The funders had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.