Levofloxacin in Bone and Joint Infections: Development of an Application for Model-Informed Precision Dosing.

Abstract

Levofloxacin is a valuable antibiotic in the treatment of bone and joint infections. Due to the known risk of treatment failure and bacterial resistance, the development of tools facilitating pharmacokinetic/pharmacodynamic parameter monitoring to inform precision dosing is needed. Therefore, we assessed the use of Bayesian estimation to predict AUC_0-24 and C_max of levofloxacin under various sampling scenarios realistic in clinical routine. Furthermore, we developed a free web-based application allowing model-informed precision dosing. All published population pharmacokinetic models of ofloxacin and levofloxacin in bone and joint infections were researched and their predictive performance was compared using a real-life data cohort. We used simulated data to validate the robustness of various scenarios for Bayesian estimation of AUC_0-24 and C_max with up to three samples, including the potential impact of an incorrectly reported sampling time at peak concentration. Relative bias and relative root mean square error were estimated to assess accuracy and precision, respectively. One of the three published levofloxacin models showed negligible mean relative prediction error (0.02 ± 0.09). We modified it to a closed form approximation to facilitate the implementation in the application. The 2-sample scenario (T0h-T3h) allowed accurate and precise AUC_0-24 estimations for 500 mg q12h and 750 mg q24h dosing regimens. None of the tested scenarios allowed a satisfactory estimation of C_max. We finally developed and validated a free web-based application (https://levoshiny.iame-research.center/) using the selected model. The Shiny application will be useful in clinical practice to individualize dose regimens based on the AUC_0-24 obtained using the proposed limited sampling strategy.