BACKGROUND: Combination antimicrobial therapy is clinically used as a last-resort strategy to control multidrug-resistant bacterial infections. However, selection of antibiotics is often empirical, and conventional assessment of combined drug effect has not been correlated to clinical outcomes. Here, we report a quantitative method to assess combined killing of antimicrobial agents against 2 multidrug-resistant bacteria. METHODS: Combined time-kill studies were performed using clinically achievable concentrations for each 2-agent combination against clinical isolates of Acinetobacter baumannii and Pseudomonas aeruginosa. Bacterial burden observed at 24 h was mathematically modeled using a 3-dimensional response surface. Subsequently, a neutropenic murine pneumonia model with simulated clinical dosing exposures was used to validate our quantitative assessment of combined killing. RESULTS: Different antimicrobial combinations were found to have varying efficacy against the multidrug-resistant bacteria. As predicted by our quantitative method, cefepime plus amikacin was found to be the most superior combination, which was evidenced by a reduction in tissue bacterial burden and prolonged survival of infected animals. CONCLUSIONS: The consistency between the predictions of the mathematical model and in vivo observations substantiated the robustness of our quantitative method. These data highlighted a novel and promising method to guide rational selection of antimicrobial combination in the clinical setting.
BACKGROUND: Combination antimicrobial therapy is clinically used as a last-resort strategy to control multidrug-resistant bacterial infections. However, selection of antibiotics is often empirical, and conventional assessment of combined drug effect has not been correlated to clinical outcomes. Here, we report a quantitative method to assess combined killing of antimicrobial agents against 2 multidrug-resistant bacteria. METHODS: Combined time-kill studies were performed using clinically achievable concentrations for each 2-agent combination against clinical isolates of Acinetobacter baumannii and Pseudomonas aeruginosa. Bacterial burden observed at 24 h was mathematically modeled using a 3-dimensional response surface. Subsequently, a neutropenic murine pneumonia model with simulated clinical dosing exposures was used to validate our quantitative assessment of combined killing. RESULTS: Different antimicrobial combinations were found to have varying efficacy against the multidrug-resistant bacteria. As predicted by our quantitative method, cefepime plus amikacin was found to be the most superior combination, which was evidenced by a reduction in tissue bacterial burden and prolonged survival of infected animals. CONCLUSIONS: The consistency between the predictions of the mathematical model and in vivo observations substantiated the robustness of our quantitative method. These data highlighted a novel and promising method to guide rational selection of antimicrobial combination in the clinical setting.
Authors: Jürgen B Bulitta; Neang S Ly; Cornelia B Landersdorfer; Nicholin A Wanigaratne; Tony Velkov; Rajbharan Yadav; Antonio Oliver; Lisandra Martin; Beom Soo Shin; Alan Forrest; Brian T Tsuji Journal: Antimicrob Agents Chemother Date: 2015-02-02 Impact factor: 5.191
Authors: Rajbharan Yadav; Cornelia B Landersdorfer; Roger L Nation; John D Boyce; Jürgen B Bulitta Journal: Antimicrob Agents Chemother Date: 2015-02-02 Impact factor: 5.191
Authors: Jie He; Kamilia Abdelraouf; Kimberly R Ledesma; Diana S-L Chow; Vincent H Tam Journal: Int J Antimicrob Agents Date: 2013-08-22 Impact factor: 5.283
Authors: Dana R Bowers; Henry Cao; Jian Zhou; Kimberly R Ledesma; Dongxu Sun; Olga Lomovskaya; Vincent H Tam Journal: Antimicrob Agents Chemother Date: 2015-02-23 Impact factor: 5.191
Authors: Cornelia B Landersdorfer; Rajbharan Yadav; Jürgen B Bulitta; Kate E Rogers; Tae Hwan Kim; Beom Soo Shin; John D Boyce; Roger L Nation Journal: Antimicrob Agents Chemother Date: 2018-03-27 Impact factor: 5.191