Teresa Garcia-Martinez (1,2), Mª Dolores Belles-Medall(1), Raul Ferrando Piqueres (1,2), Victor Mangas-Sanjuan (2,3), Matilde Merino-Sanjuan (2,3)
(1) Department of Pharmacy, General University Hospital of Castellón. Castellón de La Plana, Spain. (2) Department of Pharmacy and Pharmaceutical Technology and Parasitology, Faculty of Pharmacy, University of Valencia, Valencia, Spain. (3) Interuniversity Institute of Recognition Research Molecular and Technological Development, Burjassot, Valencia, Spain.
Introduction: Daptomycin is authorized for the treatment of complicated skin and soft tissue infections (IPPBc), right-sided infective endocarditis (EID) due to Staphylococcus aureus, and bacteraemia caused by Staphylococcus aureus when is associated with EID or IPPBc. The authorized dose is 4 mg/kg in a single daily dose in IPPBc without bacteraemia and 6 mg / kg in a single daily dose in EID and IPPBc associated with bacteraemia. The use of larger doses is currently being extended, in a range between 8 and 12 mg / kg / day. The results provided by the post-marketing trials show that the higher clinical response rate is obtained when a dose of 10mg / kg is used, which is justified by the daptomycin-dependent concentration effect [1-2].
Objectives: The aim of this study is to develop a population pharmacokinetic model of daptomycin in patients with normal and impaired renal function using limited sampling strategies.
Methods: Prospective study of patients without renal replacement therapy treated with intravenous daptomycin at any indication for one year in the General University Hospital of Castellon from March 2019 to August 2020. Blood samples were obtained at the fourth day of treatment at different sampling times within a 24-hour interval and the number of blood samples varied from 2 to 5 per individual. Daptomycin concentrations were determined by a validated high-performance liquid chromatography method [3]. Demographic and biochemical variables (serum creatinine and serum albumin) from each patient were collected. Several compartmental pharmacokinetic models were implemented assuming linear and non-linear PK processes. Inter-individual variability (IIV) associated to the PK parameters was modeled exponentially and residual unexplained variability was described with an additive model on the logarithmic scale. The population PK parameters were estimated using Stochastic Approximation Expectation Maximization + Monte Carlo importance sampling. Model selection was based on the statistically decrease of the objective function value (OFV) and the goodness-of-fit (GOF) plots. Model evaluation was performed through Visual predictive check and bootstrap analysis (n=1000). Experimental data were logarithmically transformed. All data analyses were performed based on the population approach with the software Nonmem v7.4.
Results: 50 patients (9 women) with a median age of 67 years (range: 23-89) and a mean dose of daptomycin of 8.4 mg/kg (range: 5-13.3) were included. The mean serum creatinine values were 0,93 mg/dl and the serum albumin was 2.9 mg/dl. A total of 178 concentration values (5,1% < lower limit of quantification) were available with the following distribution: 67 pre-dose samples, 42 at 30 minutes after the end of the infusion and 69 between infusions. The two-compartment model parametrized in terms of central (CL=7.18 L/h) and inter-compartmental clearances (Q=2.08 L/h), apparent volume of distribution of the central (V1=0.986 L) and peripheral compartments (V2=22.6 L) adequately characterized the time-course of daptomycin in the population and are in agreement with previous articles [4]. A non-linear plasmatic protein binding relationship [5,6] was established through the maximal binding capacity (Bmax=167 mg) and the dissociation constant (Kd=3.78 mg/L) to predict the interaction of total daptomycin with proteins in the central compartment. The large V2 could explain a large diffusion of daptomycin into deep perfused tissues due to its high lipophilicity. Inter-individual random effects were associated to CL (32%) and V2 (46%). The covariate analyses identified a statistical relationship of creatinine clearance on CL. Model evaluation techniques showed the adequacy of the population PK model to characterize the median and the variability observed.
Conclusions: Total daptomycin concentrations were adequately captured through a two-compartment pharmacokinetic model assuming linear disposition and non-linear plasmatic protein binding kinetics. Creatinine clearance was concluded as a predictive covariate of the renal function of patients on CL. Prospective analysis are encouraged to understand the clinical relevance of therapeutic drug monitoring of daptomycin for its optimal dose selection.
References:
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[3] C. M. Tobin, J. M. Darville, A. M. Lovering and A. P. MacGowan. An HPLC assay for daptomycin in serum. Journal of Antimicrobial Chemotherapy 62, 1462–1476.
[4] Dvorchik B, Arbeit RD, Chung J, Liu S, Knebel W, Kastrissios H. Population pharmacokinetics of daptomycin. Antimicrob Agents Chemother. 2004;48(8):2799-2807.
[5] ter Heine R, Kane SP, Huitema ADR, Krasowski MD, van Maarseveen EM. Nonlinear protein binding of phenytoin in clinical practice: development and validation of a mechanistic prediction model. Br J Clin Pharmacol. 2019;85:2360–8.
[6] Toutain PL, Bousquet-Melou A. Free drug fraction vs free drug concentration: a matter of frequent confusion. J Vet Pharmacol Ther. 2002;25:460–3.
Reference: PAGE 29 (2021) Abstr 9620 [www.page-meeting.org/?abstract=9620]
Poster: Drug/Disease Modelling - Infection