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Abstract
β-lactam antibiotics have been prescribed for most bacterial infections since their discovery. However, resistance to β-lactams, mediated by β-lactamase (Bla) enzymes such as extended spectrum β-lactamases (ESBLs), has become widespread. Bla inhibitors can restore the efficacy of β-lactams against resistant bacteria, an approach which preserves existing antibiotics despite declining industry investment. However, the effects of combination treatment on selection for β-lactam resistance are not well understood. Bla production confers both private benefits for resistant cells and public benefits which faster-growing sensitive cells can also exploit. These benefits may be differentially impacted by Bla inhibitors, leading to non-intuitive selection dynamics. In this study, we demonstrate strain-to-strain variation in effective combination doses, with complex growth dynamics in mixed populations. Using modeling, we derive a criterion for the selection outcome of combination treatment, dependent on the burden and effective private benefit of Bla production. We then use engineered strains and natural isolates to show that strong private benefits of Bla are associated with increased selection for resistance. Finally, we demonstrate that this parameter can be coarsely estimated using high-throughput phenotyping of clonal populations. Our analysis shows that quantifying the phenotypic responses of bacteria to combination treatment can facilitate resistance-minimizing optimization of treatment.
The authors derive a criterion for when β-lactam/β-lactamase inhibitor combinations can select against a β-lactam-resistant bacterial strain. In particular, the private benefit of resistance is shown to be estimable from clonal growth curves and predictive of selection outcomes.
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1 Duke University, Department of Biomedical Engineering, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Center for Quantitative Biodesign, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)
2 Duke University, Department of Biology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Department of Computer Science, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)
3 Duke University School of Medicine, Division of Infectious Diseases, Department of Medicine, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University School of Medicine, Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)
4 Duke University, Department of Biomedical Engineering, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University, Center for Quantitative Biodesign, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961); Duke University School of Medicine, Department of Molecular Genetics and Microbiology, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)