Dosing Optimization of Posaconazole in Lung-Transplant Recipients Based on Population Pharmacokinetic Model.
Eliška DvořáčkováMartin SimaAndrea ZajacováKristýna VyskočilováTereza KotowskiKateřina DunovskáEva KlapkováJan HavlínRobert LischkeOndrej SlanarPublished in: Antibiotics (Basel, Switzerland) (2023)
Although posaconazole tablets show relatively low variability in pharmacokinetics (PK), the proportion of patients achieving the PK/PD target at the approved uniform dose for both prophylaxis and therapy is not satisfactory. The aim of this study was to develop a posaconazole population PK model in lung-transplant recipients and to propose a covariate-based dosing optimization for both prophylaxis and therapy. In this prospective study, 80 posaconazole concentrations obtained from 32 lung-transplant patients during therapeutic drug monitoring were analyzed using nonlinear mixed-effects modelling, and a Monte Carlo simulation was used to describe the theoretical distribution of posaconazole PK profiles at various dosing regimens. A one-compartment model with both linear absorption and elimination best fit the concentration-time data. The population apparent volume of distribution was 386.4 L, while an apparent clearance of 8.8 L/h decreased by 0.009 L/h with each year of the patient's age. Based on the covariate model, a dosing regimen of 200 mg/day for prophylaxis in patients ˃60 years, 300 mg/day for prophylaxis in patients ˂60 years and for therapy in patients ˃60 years, and 400 mg/day for therapy in patients ˂60 years has been proposed. At this dosing regimen, the PK/PD target for prophylaxis and therapy is reached in 95% and 90% of population, respectively, representing significantly improved outcomes in comparison with the uniform dose.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- computed tomography
- magnetic resonance imaging
- stem cells
- skeletal muscle
- magnetic resonance
- deep learning
- mesenchymal stem cells
- metabolic syndrome
- artificial intelligence
- monte carlo
- smoking cessation