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Levofloxacin in Bone and Joint Infections: Development of an Application for Model-Informed Precision Dosing
Léo Mimram1, Sophie Magreault1,2, Florian Lemaitre3
1Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, Paris, France.
Bayesian estimation accurately predicts levofloxacin exposure (AUC0-24) for bone infections using limited sampling. A free web app aids precision dosing, though Cmax estimation remains challenging.
Area of Science:
- Pharmacology
- Infectious Diseases
- Computational Biology
Background:
- Levofloxacin is crucial for bone and joint infections.
- Treatment failure and resistance necessitate precision dosing strategies.
- Pharmacokinetic/pharmacodynamic (PK/PD) monitoring tools are needed.
Purpose of the Study:
- To assess Bayesian estimation for predicting levofloxacin AUC0-24 and Cmax.
- To develop a web application for model-informed precision dosing.
- To compare population PK models for levofloxacin in bone infections.
Main Methods:
- Systematic review of published PK models.
- Bayesian estimation with simulated data under various sampling scenarios.
- Validation using a real-life data cohort.
- Development and validation of a free web-based application.
Main Results:
- One selected levofloxacin PK model demonstrated good predictive performance.
- A 2-sample limited sampling strategy (T0h-T3h) accurately estimated AUC0-24.
- Cmax estimation was not satisfactory across tested scenarios.
- A validated web application (https://levoshiny.iame-research.center/) was developed.
Conclusions:
- Bayesian estimation with limited sampling is effective for AUC0-24 prediction in levofloxacin therapy.
- The developed web application facilitates precision dosing for bone and joint infections.
- Further optimization may be needed for Cmax estimation.
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