Identifying exposure targets for treatment of staphylococcal pneumonia with ceftobiprole.
Academic Article
Overview
abstract
Ceftobiprole is a cephalosporin with potent activity against methicillin (meticillin)-resistant Staphylococcus aureus (MRSA). In order to treat patients with severe staphylococcal pneumonia, it is important to understand the drug exposure required to mediate the killing of multiple log(10) cells in a preclinical-infection model. We measured drug exposure in terms of the percentage of penetration of the drug into epithelial lining fluid (ELF) and in terms of the time for which the drug concentration was above the MIC (time>MIC) in plasma and ELF. In a murine model of staphylococcal pneumonia, we demonstrated that ceftobiprole penetrated into ELF from the plasma at a median level of nearly 69% (25th to 75th percentile range, 25 to 187%), as indexed to the ratio of values for the area under the concentration-time curve in ELF and plasma. The total-drug times>MIC in ELF that were required to kill 1 log(10) and 2 log(10) CFU/g of lung tissue were 15% and 25% of the dosing interval. We also examined the penetration of ELF by ceftobiprole in volunteers, demonstrating mean and median penetration percentages of 25.5% and 15.3%, respectively (25th to 75th percentile range, 8 to 30%). Attainment rates were calculated for kill targets of 1 log(10) and 2 log(10) CFU/g, taken from the murine model, but using the volunteer ceftobiprole ELF penetration data. The standard dose for ceftobiprole is 0.5 g every 8 h as a 2-h infusion. The attainment rates remained above 90% for 1-log(10) and 2-log(10) CFU/g kill targets at MICs of 1 and 0.5 mg/liter, respectively. Taking the expectation over the distribution of ceftobiprole MICs for 4,958 MRSA isolates showed an overall target attainment of 85.6% for a 1-log(10) CFU/g kill and 79.7% for a 2-log(10) CFU/g kill. It is important to derive exposure targets in preclinical-infection models of the infection site so that these targets can be explored in clinical trials in order to optimize the probability of a good clinical outcome.
