Optimization of flucloxacillin dosing regimens in critically ill patients using population pharmacokinetic modelling of total and unbound concentrations

Abstract

Background: Initial appropriate anti-infective therapy is associated with improved outcomes in patients with severe infections. In critically ill patients, altered pharmacokinetic (PK) behaviour is common and known to influence the achievement of PK/pharmacodynamic targets.

Objectives: To describe population PK and optimized dosing regimens for flucloxacillin in critically ill patients.

Methods: First, we developed a population PK model, estimated between-patient variability (BPV) and identified covariates that could explain BPV through non-linear mixed-effects analysis, using total and unbound concentrations obtained from 35 adult critically ill patients treated with intermittent flucloxacillin. Second, we validated the model using external datasets from two different countries. Finally, frequently prescribed dosing regimens were evaluated using Monte Carlo simulations.

Results: A two-compartment model with non-linear protein binding was developed and validated. BPV of the maximum binding capacity decreased from 42.2% to 30.4% and BPV of unbound clearance decreased from 88.1% to 71.6% upon inclusion of serum albumin concentrations and estimated glomerular filtration rate (eGFR; by CKD-EPI equation), respectively. PTA (target of 100%fT>MIC) was 91% for patients with eGFR of 33 mL/min and 1 g q6h, 87% for patients with eGFR of 96 mL/min and 2 g q4h and 71% for patients with eGFR of 153 mL/min and 2 g q4h.

Conclusions: For patients with high creatinine clearance who are infected with moderately susceptible pathogens, therapeutic drug monitoring is advised since there is a risk of underexposure to flucloxacillin. Due to the non-linear protein binding of flucloxacillin and the high prevalence of hypoalbuminaemia in critically ill patients, dose adjustments should be based on unbound concentrations.

Figure 1.

Unbound versus total flucloxacillin concentrations, measured in 79 samples from 16 patients.

Figure 2.

Protein binding of flucloxacillin (%), calculated as (1 − unbound fraction) × 100, versus serum albumin concentrations, based on 79 patient samples.

Figure 3.

Covariate relationship between (a) serum albumin concentrations and B_max and (b) eGFR and CL of unbound flucloxacillin for the final model, for all patients for whom both total and unbound concentrations were measured (n = 16). The dots represent the individual estimates of (a) B_max and (b) unbound flucloxacillin CL. The line represents the model-predicted association between the parameter estimate and the covariate of interest.

Figure 4.

Illustration of the effect of the covariates eGFR (mL/min) and serum albumin concentration (g/L) on the concentration–time curve of flucloxacillin as assessed by Monte Carlo simulations of the first 24 h of treatment of a virtual critically ill patient, with all median characteristics of the population, but with two different eGFR values and two different serum albumin concentrations. Both total and unbound flucloxacillin concentrations were simulated for two different IV dosing regimens: (a) total flucloxacillin concentrations after 1 g q6h; (b) unbound flucloxacillin concentrations after 1 g q6h; (c) total flucloxacillin concentrations after 2 g q4h; and (d) unbound flucloxacillin concentrations after 2 g q4h. Alb, serum albumin concentration.

Figure 5.

Monte Carlo simulations (n = 1000) and PTA for achieving 100%fT_>MIC at t = 24 h for various eGFRs calculated by the CKD-EPI equation, for four different IV flucloxacillin dosing regimens administered to critically ill patients: (a) 1 g q6h; (b) 1 g q4h; (c) 2 g q6h; and (d) 2 g q4h. The ECOFF of cloxacillin (as a surrogate for flucloxacillin) for S. aureus is 0.5 mg/L, according to EUCAST.