A meta-analysis of protein binding of flucloxacillin in healthy volunteers and hospitalized patients

Objectives: The aim of this study was to develop a mechanistic protein-binding model to predict the unbound flucloxacillin concentrations in different patient populations. Methods: A mechanistic protein-binding model was fitted to the data using non-linear mixed-effects modelling. Data were obtained from four datasets, containing 710 paired total and unbound flucloxacillin concentrations from healthy volunteers, non-critically ill and critically ill patients. A fifth dataset with data from hospitalized patients was used for evaluation of our model. The predictive performance of the mechanistic model was evaluated and compared with the calculation of the unbound concentration with a fixed unbound fraction of 5%. Finally, we performed a fit-for-use evaluation, verifying whether the model-predicted unbound flucloxacillin concentrations would lead to clinically incorrect dose adjustments. Results: The mechanistic protein-binding model predicted the unbound flucloxacillin concentrations more accurately than assuming an unbound fraction of 5%. The mean prediction error varied between -26.2% to 27.8% for the mechanistic model and between -30.8% to 83% for calculation with a fixed factor of 5%. The normalized root mean squared error varied between 36.8% and 69% respectively between 57.1% and 134%. Predicting the unbound concentration with the use of the mechanistic model resulted in 6.1% incorrect dose adjustments versus 19.4% if calculated with a fixed unbound fraction of 5%. Conclusions: Estimating the unbound concentration with a mechanistic protein-binding model outperforms the calculation with the use of a fixed protein binding factor of 5%, but neither demonstrates acceptable performance. When performing dose individualization of flucloxacillin, this should be done based on measured unbound concentrations rather than on estimated unbound concentrations from the measured total concentrations. In the absence of an assay for unbound concentrations, the mechanistic binding model should be preferred over assuming a fixed unbound fraction of 5%

Precision dosing software to optimize antimicrobial dosing: a systematic search and follow-up survey of available programs

Background: Precision dosing programs are promising tools for optimising antimicrobial dosing. Selecting the ideal program for local application may be challenging due to the large variety of available programs with differing characteristics. Objectives: The objectives of this study were to systematically identify available precision dosing software programs to optimize antimicrobial dosing and describe the characteristics of each program. Details on the ability of programs to provide beta-lactam dosing support was also gathered. Sources: A systematic review search strategy was used to identify candidate software programs described in the literature in Embase and PubMed. A detailed survey was then developed to identify characteristics of programs, including details on the underlying methodology driving dosing software recommendations, interface characteristics, costs and regulatory affairs. Software developers from all identified programs were invited to participate in the survey. Content: The systematic search results identified 18 programs. Fifteen developers responded to the survey (83%) and 11 programs provide dosing support for at least one beta-lactam. Fourteen programs can utilize measured drug concentrations to generate dosing recommendations, with 13 able to generate empiric dosing recommendations. Six programs integrate with local electronic health records and four are registered with at least one regulatory agency. Pharmacokinetic models in combination with Bayesian statistics is the most common methodology used to generate dosing recommendations, with 14 programs utilizing this method. Implications: There was significant variability in the available antimicrobial profiles and characteristics among dosing software programs. As healthcare providers will differ in their requirements within their local settings, clinicians should use these findings to identify potential candidate programs and, if feasible, trial these to ensure they meet their specific requirements