Identification of optimal renal dosage adjustments for high-dose extended-infusion cefepime dosing regimens in hospitalized patients.
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Issue Date
2015-03
Authors
Zasowski, Evan J
Bland, Christopher M
Tam, Vincent H
Lodise, Thomas P
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Abstract
OBJECTIVES: The objective of this study was to identify optimal renal dose adjustments for 2 g of cefepime every 8 h as a 3 h infusion where the probability of target attainment was optimized and drug accumulation was minimal.
METHODS: Embedded with a population pharmacokinetic model derived in a population of hospitalized patients with varying degrees of renal function, a series of 5000-subject Monte Carlo simulations using ADAPT 5 were performed for 3 h infusions of 2 g every 6, 8, 12 and 24 h. To assess exposure profiles across various levels of renal function, the estimated CLCR was fi xed at values between 20 and 150 mL/min. For each regimen examined, the fraction of simulated subjects who achieved free drug concentrations in excess of the MIC for >/=60% of the dosing interval (60% fT > MIC) at the various CLCR levels was determined and this information was used to identify optimal renal dose adjustments without profound drug exposure.
RESULTS: In the Monte Carlo simulations, modification of the parent regimen (2 g every 8 h) to 2 g every 6 h for CLCR >120 mL/min and extension of the dosing interval to every 12 and 24 h at CLCR of 60 and 20 mL/min, respectively, provided favourable probability of target attainment profiles without profound drug exposure.
CONCLUSIONS: The findings from this study identified renal dose alteration regimens that yielded favourable pharmacodynamic profiles without excessive drug exposure. As these findings were based on mathematical models, they should be validated in the clinical arena.
Citation
Zasowski E, Bland CM, Tam VH, Lodise TP. Identification of optimal renal dosage adjustments for high-dose extended-infusion cefepime dosing regimens in hospitalized patients. J Antimicrob Chemother. 2015 Mar;70(3):877-81. doi: 10.1093/jac/dku435. Epub 2014 Nov 6. PMID: 25381169.
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