A new population pharmacokinetic model for vancomycin in patients with variable renal function: Therapeutic drug monitoring based on extended covariate model using CKD‐EPI estimation

2019 ◽  
Vol 44 (5) ◽  
pp. 750-759
Author(s):  
Dong‐Jin Kim ◽  
Dong‐Hwan Lee ◽  
Sangzin Ahn ◽  
Jinah Jung ◽  
Sungmin Kiem ◽  
...  
Keyword(s):  
Renal Function ◽  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Covariate Model

P342 A population pharmacokinetic model to support therapeutic drug monitoring during vedolizumab therapy

2019 ◽  
Vol 13 (Supplement_1) ◽  
pp. S273-S274 ◽  
Author(s):  
E Dreesen ◽  
B Verstockt ◽  
S Vermeire ◽  
M Ferrante ◽  
A Gils
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic

Population pharmacokinetic model of Vancomycin based on therapeutic drug monitoring data in critically ill septic patients

2020 ◽  
Vol 55 ◽  
pp. 116-121 ◽  
Author(s):  
Tijana Kovacevic ◽  
Branislava Miljkovic ◽  
Pedja Kovacevic ◽  
Sasa Dragic ◽  
Danica Momcicevic ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Critically Ill ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Monitoring Data ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic

Population Pharmacokinetic Model of Carbamazepine Derived from Routine Therapeutic Drug Monitoring Data

2007 ◽  
Vol 29 (6) ◽  
pp. 781-788 ◽  
Author(s):  
Katarina Vučićević ◽  
Branislava Miljković ◽  
Ružica Veličković ◽  
Milena Pokrajac ◽  
Aleš Mrhar ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Monitoring Data ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Routine Therapeutic Drug Monitoring

scholarly journals Validation of a Population Pharmacokinetic Model of Vortioxetine Using Therapeutic Drug Monitoring Data

2021 ◽  
Author(s):  
Trine Frederiksen ◽  
Robert L. Smith ◽  
Birgit M. Wollmann ◽  
Johan Areberg ◽  
Espen Molden
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Monitoring Data ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic

scholarly journals Isavuconazole therapeutic drug monitoring associated to a pharmacogenetic exploration is far from being a pharmacologists’ whim

2018 ◽  
Author(s):  
L Darnaud ◽  
F Lamoureux ◽  
C Godet ◽  
S Pontier ◽  
A Debard ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Pharmacokinetic Profile ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Collect Blood Sample ◽  
High Concentrations ◽  
Toxic Concentrations

ABSTRACTIsavuconazole is a new antifungal prodrug to treat invasive aspergillosis and mucormycosis, theoretically not requiring drug monitoring. However, we reported 4 clinical cases with toxic concentrations. Based on Desai’s population pharmacokinetic model, we estimated patients’ kinetic profile. Clearance was abnormally low, likely related to CYP3A4/5 polymorphisms. Thus, we recommend to collect blood sample just before the first maintenance dose to estimate pharmacokinetic profile and individualized dose. For patients presenting high concentrations, pharmacogenetics can be done.

β-Lactam therapeutic drug monitoring in the critically ill: optimising drug exposure in patients with fluctuating renal function and hypoalbuminaemia

2013 ◽  
Vol 41 (2) ◽  
pp. 162-166 ◽  
Author(s):  
Yoshiro Hayashi ◽  
Jeffrey Lipman ◽  
Andrew A. Udy ◽  
Mandy Ng ◽  
Brett McWhinney ◽  
...  
Keyword(s):  
Renal Function ◽  
Therapeutic Drug Monitoring ◽  
Critically Ill ◽  
Drug Monitoring ◽  
Drug Exposure ◽  
Therapeutic Drug

scholarly journals Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis

2015 ◽  
Vol 59 (8) ◽  
pp. 4907-4913 ◽  
Author(s):  
Marieke G. G. Sturkenboom ◽  
Leonie W. Mulder ◽  
Arthur de Jager ◽  
Richard van Altena ◽  
Rob E. Aarnoutse ◽  
...  
Keyword(s):  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Mean Squared Error ◽  
Geometric Mean ◽  
Plasma Concentrations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Optimal Sampling ◽  
Sampling Strategies ◽  
Wide Range

ABSTRACTRifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC0–24) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC0–24values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n= 1,000). The geometric mean AUC0–24value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with anr2value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of −0.4%. This study showed that the rifampin AUC0–24in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.

Population Pharmacokinetics of Meropenem in Preterm Infants

2021 ◽  
Vol 76 (5) ◽  
pp. 497-505
Author(s):  
Irina B. Bondareva ◽  
Sergey K. Zyryanov ◽  
Aleksandra M. Kazanova
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Pharmacokinetic Parameter ◽  
Gestational Age ◽  
Drug Monitoring ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Variability ◽  
Older Children ◽  
Term Neonates

Background. Meropenem, a broad spectrum carbapenem antibiotic, is often used for newborns despite of limited data available on neonatal pharmacokinetics. Due to pharmacokinetic and pharmacodynamic differences as well as to significant changes in the human body related to growth and maturation of organs and systems, direct scaling and dosing extrapolation from adults or older children with adjustment on patients weight can result in increased risk of toxicity or treatment failures. Aims to evaluate the pharmacokinetics of meropenem in premature neonates based on therapeutic drug monitoring data in real clinical settings. Materials. Of 53 pre-term neonates included in the pharmacokinetic/pharmacodynamic analysis, in 39 (73.6%) patients, gestational age ranged from 23 to 30 weeks. Population and individual pharmacokinetic parameter values were estimated by the NPAG program from the Pmetrics package based on peak-trough therapeutic drug monitoring. Samples were assayed by high-performance liquid chromatography. One-compartment pharmacokinetic model with zero-order input and first-order elimination was used to fit concentration data and to predict pharmacokinetic parameter (%T MIC of free drug) for virtual patients with simulated fast, moderate and slow meropenem elimination received different dosage by minimum inhibitory concentration (MIC) level. Univariate and multivariate regression analysis was used to evaluate the influence of patients covariates (gestational age, postnatal age, postconceptual age, body weight, creatinine clearance calculated by Schwartz formula, etc) on estimated meropenem pharmacokinetic parameters. Results. The identified population pharmacokinetic parameters of meropenem in pre-term newborns (elimination half-lives T1/2 = 1.93 0.341 h; clearance CL = 0.26 0.085 L/h/ kg; volume of distribution V = 0.71 0.22 L/h) were in good agreement with those published in the literature for adults, neonates and older children. Pharmacokinetic/pharmacodynamic modeling demonstrated that a meropenem dosage regimen of 90 mg/kg/day administered using prolonged 3-hour infusion every 8 hours should be considered as potentially effective therapy if nosocomial infections with resistant organisms (MIC 8 mg/L) are treated. Conclusions. Neonates and especially pre-term neonates have a great pharmacokinetic variability. Meropenem dosing in premature newborns derived from population pharmacokinetic/pharmacodynamic model can partly overcome the variability, but not all pharmacokinetic variability can be explained by covariates in a model. Further personalizing based on Bayesian forecasting approach and a patients therapeutic drug monitoring data can help to achieve desired pharmacodynamic target.

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