Population pharmacokinetics of flucloxacillin as intermittent bolus infusion in patients with Staphylococcus aureus bloodstream infection.

Hermann, Laura; Schöning, Verena; Dräger, Sarah; Rentsch, Katharina; Moser, Stephan; Gürtler, Nicolas; Sendi, Parham; Osthoff, Michael; Hammann, Felix (2024). Population pharmacokinetics of flucloxacillin as intermittent bolus infusion in patients with Staphylococcus aureus bloodstream infection. The journal of antimicrobial chemotherapy, 79(8), pp. 2031-2039. Oxford University Press 10.1093/jac/dkae207

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BACKGROUND

Optimal antibiotic dosing for Staphylococcus aureus bloodstream infections (BSI) is still controversial. One reason is inter-individual variation in pharmacokinetics, which may be influenced by various patient-related factors, particularly in critically ill patients.

OBJECTIVES

To describe the population pharmacokinetics (PopPK) of the antibiotic flucloxacillin in patients with S. aureus BSI. Subsequently, we sought to translate the model into a user-friendly app for generating a priori and a posteriori time-concentration curves and dose recommendations to optimize dosing regimens.

METHODS

Total and unbound flucloxacillin concentrations were included from 49 patients from a prospective cohort study conducted during clinical routine, including non-critically ill and critically ill individuals who received intermittent bolus applications. These data were analysed using non-linear mixed-effects modelling.

RESULTS

Most patients (98%) were treated with 2 g of flucloxacillin every 4 h. We developed a joint model that simultaneously described total and unbound concentrations. The model included an allometric effect of glomerular filtration rate on clearance and albumin on the albumin dissociation constant. The latter was especially important, as in our population the unbound fraction was higher at 11.5% (16.7% for critically ill patients) compared with reported values of approximately 5%. Based on our joint model, we developed a web-based app for optimizing dosing regimens of flucloxacillin.

CONCLUSIONS

By utilizing data from clinical routine, we were able to create a predictive PopPK model of flucloxacillin and identify influential covariates. The web-based app is currently being validated in a clinical trial.

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