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Original Article

Use of Monte Carlo simulation to evaluate the efficacy of tigecycline and minocycline for the treatment of pneumonia due to carbapenemase-producing Klebsiella pneumoniae

, , , , , & show all
Pages 507-513 | Received 01 Oct 2017, Accepted 28 Dec 2017, Published online: 09 Jan 2018
 

Abstract

Background: Pneumonia caused by carbapenemase-producing Klebsiella pneumoniae (CP-KP) are increasingly encountered in hospitals worldwide, causing high mortality due to lack of treatment options. The goal of this study was to assess the efficacy of tigecycline and minocycline for CP-KP hospital-acquired pneumonia (HAP) by using Monte Carlo simulation.

Methods: A total of 164 non-duplicated CP-KP strains were collected from sputum or blood in patients with HAP. The MICs for antimicrobials were determined by the agar dilution method. A 10,000-patient Monte Carlo Simulation based on a PK/PD model incorporating the MICs and population pharmacokinetic parameters were conducted to calculate probability of target attainment (PTA) at each MIC value and total cumulative fraction of response (CFR).

Results: The susceptibility rate of tigecycline and minocycline were 79.9% and 41.5%, respectively. At recommended doses, an optimal PTA of 90% was obtained for treating HAP caused by CP-KP with MICs of tigecycline ≤0.5 mg/L or minocycline ≤4 mg/L. The CFR of tigecycline at the recommended dose and double dose (100 mg q12h) were 71.2% and 90.2%, respectively. The CFR of minocycline at recommended dose and double dose (200 mg q12h) was 53.4% and 77.2%, respectively.

Conclusions: The findings of this study suggest that the recommended dose of tigecycline was not effective in HAP caused by CP-KP, and a higher CFR indicating a better clinical efficacy can be gained by doubling the dose (100 mg q12h). minocycline (200 mg q12h) might be a potential alternative of tigecycline to against strains with MICs≤ 8 mg/L.

Disclosure statement

The authors report no conflicts of interest.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China under Grant no. 81371855 and the Shenzhen's Sanming Project.

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