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Characterize teicoplanin PK in critically ill patients with a specific focus on alterations of exposure due to variability in renal function.
In a prospective, observational, open-label study the investigators aim to define PK of free drug concentrations of teicoplanine in ICU and heamatology patients and define a PK model for Bayesian dose individualization.
Bacterial infection with coagulase-negative staphylococci (CNS) or methicillin resistant and sensitive staphylococcus aureus(MRSA/MSSA) indicates poor prognosis and increased mortality in critically ill patients.
With the current emergence of glycopeptide-intermediate sensitive Staphylococcus aureus strains, personalized dosing of teicoplanin is of utmost importance to preserve the current therapeutic armamentarium.
Teicoplanin is considered equipotent to vancomycin, albeit safer with minimal nephrotoxicity. It is estimated that 50% of all critically ill patients treated with teicoplanin does not reach target exposure. This is the major driver for treatment failure and development of resistance and dose individualization will overcome this problem.
Our project is aimed at developing and implementing a personalized dosing strategy for teicoplanin, to prevent development of glycopeptide resistance and allow safe treatment of glycopeptide intermediate sensitive bacteria.
In a prospective clinical study, critically ill patients (defined as ICU and hematology patients) who receive teicoplanin as standard care for antimicrobial treatment will be eligible for inclusion. Minimally invasive blood sampling for pharmacokinetic analysis will be retrieved through an indwelling central venous catheter or an arterial line (9 samples per patient). Teicoplanin total and free drug concentrations will be measured using a validated analytical assay. A total of 30 patients will be included.
We will develop a population PK model using nonlinear mixed effects modelling for total and unbound teicoplanin to characterize the magnitude of inter-individual variability in PK parameters (clearance, distribution volume), and to identify patient-derived characteristics that can predict such variability in a critically ill patient population.
Radboud University Medical Center
Not yet recruiting
Published on BioPortfolio: 2019-09-25T06:13:36-0400
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