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ABSTRACT

Title
Population pharmacokinetics of daptomycin in patients affected by Gram-positive infections
 
Authors
M. Polillo1, C. Tascini2, M. Lastella1, G. Gemignani2, L. Ciofi3, V. Sarli3, F. Menichetti2, R. Danesi1,3, A. Di Paolo1,3
 
1 Div. of Pharmacology, Dept. of Internal Medicine, University of Pisa
2 Unit of Infectious Diseases, University Hospital, Pisa
3 Unit of Clinical Pharmacology, University Hospital, Pisa
 
Abstract
Background. Gram-positive (G+) bacteria are often responsible for difficult-to-treat infections, but the lipopeptide daptomycin represents a step forward the treatment of those severe infections. Daptomycin has a concentration-dependent activity and its effectiveness is best predicted by the Cmax/MIC and AUC/MIC pharmacokinetic/pharmacodynamic parameters [Safdar et al, 2004]. The drug is not a substrate of liver metabolism and it is primarily excreted via the kidneys as unmodified molecule [Benvenuto et al, 2006]. In healthy volunteers, daily doses of 6 and 8 mg/kg were associated with Cmax values of approximately 80-90 and 110-120 mg/L, respectively [Ryback et al, 2006], but they were lower in neutropenic cancer patients [Bubalo et al, 2009], suggesting a high variability of plasma concentrations in some individuals.
Aim. To investigate the pharmacokinetics of daptomycin in patients with infections caused by G+ bacteria.
Patients and methods. Twenty-one patients, 16 men and 5 women (age, mean±SD 61.1±18.7 and 74.0±6.1 years, respectively) treated with daptomycin were enrolled. The drug was administered as a 30-min i.v. infusion every 24 h at doses of 6 mg/kg in 13 male patients and 8 mg/kg in 3 men and 5 women. After the fifth dose, when steady state was presumably achieved, blood samples were obtained immediately before (Cmin), 30 (Cmax) and 60 min after the beginning of drug administration. Drug concentrations in plasma were measured with an UV-HPLC method [Polillo et al, 2010]. Population pharmacokinetic (POP/PK) analysis was performed using the Monolix software. Selection of the best structural model was obtained by considering goodness of fit plots, precision of parameter estimates and the Akaike information criteria (AIC) value. A 1-compartment model with first-order elimination was chosen as the first model, while the residual error was initially described as an additive and proportional error model. The possible relationship among covariate (age, sex, plasma creatinine, body weight, height) and pharmacokinetic parameters was evaluated on the basis of covariate range in the dataset, scientific interest, and possible mechanistic involvement in drug disposition. The best model was chosen when the considered covariates led to a reduction in AIC value, and their effect on the model was assessed by the Wald test.
Results. Cmax and Cmin values (mean±SD) were 79.1±8.0 and 9.7±4.6 mg/L at dose of 8 mg/kg and 63.8±1.5 and 9.3±4.4 mg/L at 6 mg/kg, respectively. The best POP/PK model was a 2-compartment model with linear elimination (AIC value, 515). Results demonstrated a high interindividual variability (IIV) in pharmacokinetic parameters, especially intercompartmental clearance (67.8%) and volume of distribution of peripheral compartment (V2, 80.8%). The introduction of covariate within the model did not improve model performance, but when the effect of weight on volume of central compartment (V1) and/or V2 was considered, the AIC value was reduced. In fact, weight effect on V1 and V2, alone or in combination, led to AIC values of 503, 512 and 505, respectively, and explaining 4-6% of IIV of volume of distribution (p<0.05 at Wald test). Finally, the structural model demonstrated that the increase in body weight was associated with an exponential augmentation of V1 and V2.
Conclusions. The present results demonstrated that daptomycin could achieve lower Cmax plasma values with respect to those previously reported for the same doses in healthy volunteers. Moreover, in overweight or obese patients plasma concentrations of daptomycin could be lower than expected and possibly associated with a reduced probability of clinical and microbiological cure rate.
 
Benvenuto et al. Antimicrob Agents Chemother 2006;50:3245-9
Bubalo et al. Antimicrob Agents and Chemother 2009;53:1463–7
Polillo et al. Ther Drug Monit 2010;32:200-5
Ryback. Clin Microbiol Infect2006;12(Suppl 1):24-32
Safdar et al. Antimicrob Agents Chemother 2004;48:63-8