Impact of unbound fraction variations on piperacillin clearance
and dosing recommendations
Within the dataset, patients were separated into four renal function
categories based on their creatinine clearance (CL): (1) below 30
mL/min, (2) between 30 and 80 mL/min, (3) between 80 and 130 mL/min, and
(4) above 130 mL/min. Afterwards, for each category, one patient with
median renal function was selected for this evaluation. Thus, four
patients with different profiles were retained. To simulate the effect
of various unbound fractions of piperacillin in cases of
hypoalbuminemia, we applied various factors to the total concentrations
of piperacillin for all four subjects. As piperacillin’s unbound
fraction is estimated at 70%, we started our evaluation with this
value. A similar evaluation was performed for unbound fractions of 75,
80 and 85%. This resulted in four different unbound fraction scenarios,
each containing four subjects for the assessment of target attainment.
A previously externally validated
model was used to estimate piperacillin total CL for each patient within
each data set through Bayesian estimation [15]. Individual CL
estimation was realized by omitting the estimation step (MAXEVAL =0) and
by fixing model parameters to the mean population estimates reported by
the authors.
Once individual CL was obtained,
dosing simulations were performed to determine whether unbound fraction
variations influenced target attainment of piperacillin. Patient PK
parameters were inputted on NONMEM, and the simulated piperacillin dose
was based on the patient’s renal function; i.e., a loading dose of 4 g
followed by a maintenance dose of 8, 12 or 16 g if CLCr was below 30,
between 30 and 80, or above 80 mL/min, respectively, to reflect the
practices reported by Klastrup et al. [11]. Simulations were
repeated for the same subject with unbound fractions ranging from 70 to
85%. Target attainment was defined as 100% f T >
MIC, for a MIC value of 16 mg/L, corresponding to the EUCAST clinical
breakpoint of Pseudomonas aeruginosa . Concentration-time plots
were generated to compare the various profiles for each subject.