A More Robust Assessment of Antibiotic Combinations by Dynamic Susceptibility Model




Altman, Rachel

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Background: Evolving bacterial resistance presents a rising challenge to public health. One mechanism of resistance, the production of drug degrading enzymes, is traditionally treated with a combination of active first line antibiotic and enhancing inhibitor. The conventional testing method currently used to guide treatment with these combinations has deficiencies. Objective: To develop a robust computational tool that will help identify the most effective treatment options. Methods: A clinical strain of Klebsiella pneumoniae expressing CTX-M15 was studied. Piperacillin (an antibiotic) susceptibility testing was performed at varying concentrations of avibactam (an enzyme inhibitor). The susceptibility results were modeled against the changing inhibitor concentrations to produce a dynamic model of susceptibility. Accounting for fluctuating antibiotic concentrations in the body, an integrated metric (% T > MICi) was used to assess the potential effectiveness of various drug combination dosing regimens. Results: Piperacillin susceptibility was well characterized as a function of avibactam (r2 = 98). A range of % T > MICi (from 67.5% to 83.1%) was simulated by the computational model using escalating doses of piperacillin every 8 hours. These simulations will be experimentally validated in a preclinical infection model in the future. Conclusions: Integrating changing drug concentration levels in the body with a dynamic measure of susceptibility will likely provide a more comprehensive picture of how a bacterial infection might respond to a given drug combination. If validated, this computational model in combination with a revised testing method would help doctors to choose the best available treatment for the patient.