Unraveling Ceftriaxone Dosing: Free Drug Prediction, Threshold Optimization, and Model Validation

Area of Science:

• Pharmacokinetics and Pharmacodynamics
• Infectious Diseases
• Clinical Pharmacy

Background:

• Ceftriaxone (CEF_tot) is vital for severe infections, but predicting unbound concentrations (CEF_u) is challenging.
• Accurate CEF_u prediction is essential for optimizing therapeutic outcomes and minimizing resistance.

Purpose of the Study:

• To predict unbound ceftriaxone (CEF_u) from total ceftriaxone (CEF_tot) using existing models.
• To determine optimal CEF_tot trough concentrations for achieving target CEF_u levels.
• To validate published models and assess the sufficiency of current dosing regimens.

Main Methods:

• Evaluation of published models for CEF_u prediction from CEF_tot.
• Calculation of optimal CEF_tot targets to maintain CEF_u above 1x and 4x MIC.
• External validation using patient data (serum albumin, CEF_tot, CEF_u) and retrospective analysis of 408 samples from 222 patients.

Main Results:

• Optimal CEF_tot trough targets varied by model, ranging from 2.0–16.9 mg/L (1xMIC) and 7.9–56.2 mg/L (4xMIC).
• Some models demonstrated accurate CEF_u prediction during external validation.
• Retrospective analysis showed high probability of target attainment (PTA) for 1xMIC (94.4–98.7%) and variable PTA for 4xMIC (66.9–97.3%).

Conclusions:

• Accurate prediction or quantification of unbound ceftriaxone (CEF_u) can enhance patient outcomes.
• Standardized analytical methods and further research are needed to validate predictive models for CEF_u.
• Optimizing CEF_tot dosing based on predicted CEF_u may improve therapeutic efficacy in severe infections.