scholarly journals Limited Sampling Strategy for Determination of Ibrutinib Plasma Exposure: Joint Analyses with Metabolite Data

2021 ◽  
Vol 14 (2) ◽  
pp. 162
Author(s):  
Félicien Le Louedec ◽  
Fanny Gallais ◽  
Fabienne Thomas ◽  
Mélanie White-Koning ◽  
Ben Allal ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Sampling Strategy ◽  
Predictive Performance ◽  
Pharmacokinetic Profile ◽  
Limited Sampling Strategy ◽  
Joint Analysis ◽  
Therapeutic Drug ◽  
Post Dose ◽  
Limited Sampling ◽  
Three Samples

Therapeutic drug monitoring of ibrutinib is based on the area under the curve of concentration vs. time (AUCIBRU) instead of trough concentration (Cmin,ss) because of a limited accumulation in plasma. Our objective was to identify a limited sampling strategy (LSS) to estimate AUCIBRU associated with Bayesian estimation. The actual AUCIBRU of 85 patients was determined by the Bayesian analysis of the full pharmacokinetic profile of ibrutinib concentrations (pre-dose T0 and 0.5, 1, 2, 4 and 6 h post-dose) and experimental AUCIBRU were derived considering combinations of one to four sampling times. The T0–1–2–4 design was the most accurate LSS (root-mean-square error RMSE = 11.0%), and three-point strategies removing the 1 h or 2 h points (RMSE = 22.7% and 14.5%, respectively) also showed good accuracy. The correlation between the actual AUCIBRU and Cmin,ss was poor (r2 = 0.25). The joint analysis of dihydrodiol-ibrutinib metabolite concentrations did not improve the predictive performance of AUCIBRU. These results were confirmed in a prospective validation cohort (n = 27 patients). At least three samples, within the pre-dose and 4 h post-dose period, are necessary to estimate ibrutinib exposure accurately.

scholarly journals P30 Limited sampling strategies to predict valganciclovir exposure in kidney transplanted children

2019 ◽  
Vol 104 (6) ◽  
pp. e29.2-e29
Author(s):  
N Benyoub ◽  
A Facchin ◽  
S Magreault ◽  
E Jacqz-Aigrain ◽  
V Elie
Keyword(s):  
Model Development ◽  
Area Under The Curve ◽  
Sampling Strategy ◽  
Therapeutic Drug ◽  
Pharmacokinetic Data ◽  
Multilinear Regression ◽  
Post Dose ◽  
Blood Samples ◽  
Limited Sampling ◽  
Pharmacokinetic Profiles

BackgroundGanciclovir and its pro-drug, valganciclovir, are anti-viral drugs used in cytomegalovirus infections treatment in kidney transplanted children. Both present a high pharmacokinetic variability requiring dosage individualization and Therapeutic Drug Monitoring to ensure optimal therapeutic exposure. This retrospective monocentric study aimed to develop a Limited Sampling Strategy (LSS) predicting Area Under the Curve (AUC0-24h) reducing the number of blood samples to improve and facilitate kidney transplanted children medical care.MethodsPediatric kidney transplanted children treated with valganciclovir were included. Rich pharmacokinetic data from ganciclovir plasmatic dosages (sampling times at 0h, 1h, 2h, 4h, 8h, 12 h and 24 h) were collected between February 2005 and November 2018. Ganciclovir exposures at steady-state (AUC0-24h) were calculated using the trapezoidal method. The LSS was developed using a multilinear regression approach to predict AUC0-24h. The overall patients population was divided into two groups for model development and validation purposes.Results129 patients were included: 46 girls and 83 boys, mean age at transplantation was 11.3years ± 5.1. Multilinear regression models were developed on 85 pharmacokinetic profiles (85 patients, mean AUC0-24h=64µg.h/mL ± 27, creatinine clearance=72.4 mL/min per 1.73 m2) and validated on an independent group of 73 pharmacokinetic profiles (44 patients). Regressions based on samples collected at 0, 2, 4 h (R=0.946) or 0, 2, 8 h (R=0.968) presented the best AUC0-24h predictive performances (RMSE=7.5 and 6.6, MAE=5.7 and 4.8 respectively) with an average difference between reference and predicted AUC0-24h of -0.52 and 0.67µg.h/mL respectively.ConclusionsTo date, this is the largest cohort of valganciclovir treated pediatric transplanted children used to develop a LSS. This LSS allows to accurately predict ganciclovir AUC0-24h in pediatric transplanted patients using 3 pharmacokinetic blood samples at 0h, 2h, and 4 h post-dose. Beside other Bayesian estimators developed in the literature, this multilinear regression can be easily implemented into daily practice facilitating patients care.Disclosure(s)Nothing to disclose

Determination of mycophenolate area under the curve by limited sampling strategy

2001 ◽  
Vol 33 (1-2) ◽  
pp. 1052-1053 ◽  
Author(s):  
S Yeung ◽  
K.L Tong ◽  
W.K Tsang ◽  
H.L Tang ◽  
K.S Fung ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Sampling Strategy ◽  
Limited Sampling Strategy ◽  
Limited Sampling

Estimation of Abbreviated Mycophenolic Acid Area Under the Concentration-Time Curve during Stable Post-transplant Period by Limited Sampling Strategy

2020 ◽  
Vol 15 ◽  
Author(s):  
Asieh Karimani ◽  
Hasan Abedi ◽  
Fatemeh Nazemian ◽  
Atena Poortaji ◽  
Amir Hooshang Mohammad pour
Keyword(s):  
Renal Transplantation ◽  
Prediction Error ◽  
Sampling Strategy ◽  
Limited Sampling Strategy ◽  
Stable Phase ◽  
Therapeutic Drug ◽  
Time Curve ◽  
Limited Sampling ◽  
Concentration Time ◽  
Over Time

Background: The area under the concentration-time curve (AUC) of mycophenolic acid (MPA), is a valid prognosticator of the risk of rejection and the gold standard in its therapeutic drug monitoring (TDM), over time posttransplantation. Objective: This study aimed to investigate MPA pharmacokinetic parameters, as well as developing a limited sampling strategy (LSS) to estimate an abbreviated MPA AUC, in the stable phase post-renal transplantation. Methods: In this study 19 patients with normal graft function (glomerular filtration rate >70 ml/min) who fulfilled inclusion & exclusion criteria were involved. Blood samples at various times were taken in the stable phase after transplantation. MPA plasma concentration was measured by reverse-phase high-performance liquid chromatography. MPA AUC0–12h was calculated using the linear trapezoidal rule. Multiple stepwise regression analysis was used to determine the minimal time points of MPA levels that could be used to yield model equations best fitted to MPA AUC 0- 12h. The findings of this study were compared with the results of our previous study, which was done similarly in the early phase post-renal transplantation. Results: The results demonstrated that the MPA-AUC and clearance were not affected over time, but MPA-tmax was significantly lower in the stable phase in comparison with the early phase (P=0.001). The best regression equation for AUC estimation in the stable phase was AUC=9.57*C6+27.238 (r2=0.907). The validation of the method was performed using the jackknife method. The mean prediction error of these models was not different from zero (P > 0.05) and had a high root mean square prediction error (7.91). Conclusion: In conclusion, the pharmacokinetics of MPA could be affected by time after transplantation, make it essential to develop a limited sampling strategy granted an efficacious approach for therapeutic drug monitoring during the stable post-transplant period.

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