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.

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 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.

scholarly journals P99 Clinical validation of published vancomycin population PK models in critically ill neonates

2019 ◽  
Vol 104 (6) ◽  
pp. e58.2-e59
Author(s):  
A van der Veen ◽  
RJ Keizer ◽  
W de Boode ◽  
A Somers ◽  
R Brüggemann ◽  
...  
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Drug Monitoring ◽  
A Priori ◽  
Predictive Performance ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Suitable Model ◽  
List Type ◽  
A Posteriori ◽  
Target Attainment

BackgroundVancomycin is commonly used for treatment of severe Gram+ neonatal infections. Currently, even with the use of optimized dosing regimens and therapeutic drug monitoring (TDM), target attainment rates are abominable, leaving patients at risk for therapeutic failure and toxicity. Model-informed precision dosing (MIPD) offers a large potential to improve therapy in the individual patient.The aim of this study was to identify a suitable model for bedside MIPD by assessing the predictive performance of published population pharmacokinetic (popPK) models.MethodsA literature search was conducted to identify parametric popPK models. PK vancomycin data were retrospectively collected from NICU patients at the Radboud University Hospital, Nijmegen, The Netherlands. The model predictive performance was assessed by comparison of predictions to observations, calculation of bias (Mean Percentage Errors, MPE) and imprecision (Normalized Root Mean Squared Errors, NRMSE). Evaluations included both a priori (model covariate input) and a posteriori (model covariate and TDM concentration input) scenarios.Results265 TDM measurements from 65 neonates (median postmenstrual age:32 weeks [range:25–45 weeks]; median weight:1281g [range:597–5360g]; median serum creatinine:0,48 mg/dL [range:0,15–1,28 mg/dL]) were used for model evaluation. Six popPK models were evaluated1–6. A posteriori predictions of all models were consistently more accurate and precise compared to the a priori (starting dose) predictions. PopPK models of Frymoyer et al. and Capparelli et al. consistently performed best through all evaluations in both the a priori and a posteriori scenario (MPE ranging from -18 to 6,4% in a priori scenario and -6,5 to -3,8% in a posteriori scenario; NRMSE ranging from 34 to 40% in a priori scenario and 23 to 24% in a posteriori scenario).ConclusionLarge differences in predictive performance of popPK models were observed. Repeated therapeutic drug monitoring remains necessary to increase target attainment rate. Best performing models for bedside MIPD were identified in our patient population.ReferencesZhao W, Lopez E, Biran V, et al. ( 2013). Vancomycin continuous infusion in neonates: Dosing optimisation and therapeutic drug monitoring. Arch Dis Child;98(6):449–453.Capparelli EV, Lane JR, Romanowski GL, et al. ( 2001). The influences of renal function and maturation on vancomycin elimination in newborns and infants. J Clin Pharmacol, 41:927–934.De Cock RFW, Allegaert K, Brussee JM, et al. ( 2014). Simultaneous pharmacokinetic modeling of gentamicin, tobramycin and vancomycin clearance from neonates to adults: towards a semi-physiological function for maturation in glomerular filtration. Pharm Res;31(10):2642–2654.Frymoyer A, Hersh AL, El-Komy MH, et al. ( 2014). Association between vancomycin trough concentration and area under the concentration-time curve in neonates. Antimicrob Agents Chemother, 58(11):6454–6461.Anderson BJ, Allegaert K, Van Den Anker JN, Cossey V, Holford NHG. ( 2006). Vancomycin pharmacokinetics in preterm neonates and the prediction of adult clearance. Br J Clin Pharmacol;63(1):75–84.Germovsek E, Osborne L, Gunaratnam F, Lounis SA, Busquets FB, Sinha AK. ( 2019). Development and external evaluation of a population pharmacokinetic model for continuous and intermittent administration of vancomycin in neonates and infants using prospectively collected data. J Antimicrob Chemother, 1–9.Disclosure(s)R. Keizer is an employee and stockholder of InsightRX.

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