In Vivo Pharmacokinetic/Pharmacodynamic Profiles of Danofloxacin in Rabbits Infected With Salmonella typhimurium After Oral Administration.

Salmonella typhimurium is a highly transmissible pathogen in rabbits that causes significant losses. Danofloxacin shows excellent efficacy against S. typhimurium infections. However, there are few reports of the pharmacokinetic/pharmacodynamic (PK/PD) modeling of danofloxacin against this pathogen. The aim of this study was to evaluate the in vivo PK/PD relationship of danofloxacin in rabbits infected with S. typhimurium. We used the reduction of bacterial burden in the blood, liver, spleen, and lung as the target PD endpoints, and determined the PK/PD indexes that best correlated with the efficacy and its corresponding magnitude. Danofloxacin was administrated orally to experimentally S. typhimurium -infected rabbits once daily for three successive days. The concentrations of danofloxacin in the serum and the bacterial burden in the blood, liver, spleen, and lung were determined. The PK/PD relationships of danofloxacin against S. typhimurium were evaluated using a Sigmoid Emax model. The results showed that the area under the concentration-time curve from 0 to 24 h/minimum inhibitory concentration (AUC24 h /MIC) ratio correlated well with the in vivo antibacterial effectiveness in different organs, with an r2 of 0.8971, 0.9186, 0.9581, and 0.8708 in the blood, liver, spleen, and lung, respectively. The AUC24 h /MIC ratios for the bactericidal effect (3 × Log10 colony forming units/mL reductions) were 121.30, 354.28, 216.64, and 228.66 in the blood, liver, spleen, and lung, respectively, indicating that the in vivo effectiveness of danofloxacin against S. typhimurium using bacterial reduction in different organs as PD endpoints was not identical. This study illustrated that the selection of the target organ for bacterial reduction determination had little effect on best PK/PD parameter determination, but is critical for parameter magnitude calculation in antimicrobial PK/PD modeling, and furthermore, has an impact on the rational dosage optimization process.