Individualized population pharmacokinetic model with limited sampling for cyclosporine monitoring after liver transplantation in clinical practice

Langers, Pieter; Cremers, Serge; Hartigh, Jan den; Rijnbeek, E. M. T.; Ringers, J.; Lamers, C. B. H. W.; Hommes, Daan W.; Hoek, Bart van

doi:10.1111/j.1365-2036.2007.03514.x

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…Concerning the three point approaches with the population pharmacokinetic model, we support the strong correlation between predicted and measured AUC applying C0 + C2 + C3 as recently published by Langers et al in liver transplant recipients [56] and Cremers et al in kidney and simultaneous pancreas-kidney transplant recipients [57]. They reported r 2 = 0.92 [MPE/MAPE 2/5], r 2 = 0.96 [-3/11] and r 2 = 0.93 [-1/6] respectively vs. r 2 = 0.95 [3/5] in our study.…”

Section: Discussionsupporting

confidence: 86%

“…Intestinal mucosal damage due to the conditioning therapy could explain the slow absorption in a subset of our patients. As we demonstrated in our results (and has been shown by others [56]), more sampling time points are needed to overcome this variability in calculating an accurate AUC. At least two samples were needed for an adequate prediction of the AUC(0,12 h) by limited sampling.…”

Section: Discussionmentioning

confidence: 55%

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Population pharmacokinetics of ciclosporin in haematopoietic allogeneic stem cell transplantation with emphasis on limited sampling strategy

Wilhelm¹,

Graaf²,

Veldkamp³

et al. 2012

Brit J Clinical Pharma

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WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The population pharmacokinetics and limited sampling strategies for ciclosporin monitoring have been extensively studied in renal and liver transplant recipients. Little is known about the pharmacokinetics of ciclosporin in patients undergoing haematopoietic allogeneic stem cell transplantation (HSCT).• It is anticipated that there is a difference in pharmacokinetics in patients after kidney or liver transplantation compared with patients undergoing stem cell transplantation, because of mucositis and interacting drugs (e.g. fluconazole).• Data on the pharmacokinetics of ciclosporin and the relationship between its systemic exposure, as reflected by the area under the curve (AUC), and the biological effect as graft vs. host-disease (GVHD) prophylaxis and graft vs. tumour (GVT) response are scarce in patients after HSCT. WHAT THIS STUDY ADDS• A pharmacokinetic model was developed for orally and intravenously administered ciclosporin, enabling an adequate estimate of the systemic exposure of ciclosporin in patients after HSCT. A limited sampling strategy was tested that may serve as a tool to study the optimum systemic exposure (AUC) of ciclosporin in HSCT to prevent GVHD but establish adequate GVT response and to guide therapeutic drug monitoring. AIMTo develop a population pharmacokinetic model of ciclosporin (CsA) in haematopoietic allogeneic stem cell transplantation to facilitate a limited sampling strategy to determine systemic exposure (area under the curve [AUC]), in order to optimize CsA therapy in this patient population. METHODSThe pharmacokinetics of CsA were investigated prospectively in 20 patients following allogeneic haematopoietic stem cell transplantation (HSCT). CsA was given twice daily, as a 3 h i.v. infusion starting at day 1 of the conditioning scheme, and orally later on, when oral intake was well tolerated. Fluconazole was given as antimycotic prophylaxis. Pharmacokinetic parameter estimation was performed using nonlinear mixed effect modelling as implemented in the NONMEM program. A first order absorption model with lag time was compared with Erlang frequency distribution and Weibull distribution models. The influence of demographic variables on the individual empirical Bayesian estimates of clearance and distribution volume was tested. Subsequently two limited sampling strategies (LSS) were evaluated: posterior Bayesian fitting and limited sampling equations. RESULTSTwenty patients were included and 435 samples were collected after i.v. and oral administration of CsA. A two compartment model with first order absorption best described the data. Clearance (CL) was 21.9 l h -1 (relative standard deviation [RSD] Ϯ 5.2%) with an inter-individual variability of 21%. The central volume of distribution (Vc) was 18.3 l (RSD Ϯ 8.7%) with an inter-individual variability of 29%. Bioavailability (F) was 0.71 (RSD Ϯ 9.9%) with and inter-individual variability of 25% and lag time (tlag) was 0.44 h (RSD 5.5%). Weight, body surface area, haematocrit, albumin, ALAT...

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 55%

Population pharmacokinetics of ciclosporin in haematopoietic allogeneic stem cell transplantation with emphasis on limited sampling strategy

Wilhelm¹,

Graaf²,

Veldkamp³

et al. 2012

Brit J Clinical Pharma

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“…48 It only requires a simple computer program the like of which is available in most hospital pharmacies. 48 It only requires a simple computer program the like of which is available in most hospital pharmacies.…”

Section: Discussionmentioning

confidence: 99%

Limited Sampling Model for Advanced Mycophenolic Acid Therapeutic Drug Monitoring After Liver Transplantation

Langers¹,

Press²,

Inderson³

et al. 2014

Therapeutic Drug Monitoring

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“…A second way to improve the information content of sparse individual PK data is to change the design parameters of the study to improve the information content. Other publications have previously tried to improve monitoring designs for immunosuppressive agents; however, all of these studies have only adjusted the sampling times and/or the number of samples 4 – 6 . In addition to the sampling times after the IV (xt IV ) and the PO dose (xt PO ) and the number of sampling times (n IV , n PO ), other design parameters could also be considered for optimization (and in this example must be considered to achieve the clinically desired simplifications) including the dosing interval (t 2nd dose ), the amount of IV (Dose IV ) and PO ciclosporin given (Dose PO ), the duration of infusion for the IV dose (D inf ), and the order of the given doses (Dose order ).…”

mentioning

confidence: 99%

“…Other publications have previously tried to improve monitoring designs for immunosuppressive agents; however, all of these studies have only adjusted the sampling times and/or the number of samples. [4][5][6] In addition to the sampling times after the IV (xt IV ) and the PO dose (xt PO ) and the number of sampling times (n IV , n PO ), other design parameters could also be considered for optimization (and in this example must be considered to achieve the clinically desired simplifications) including the dosing interval (t 2nd dose ), the amount of IV (Dose IV ) and PO ciclosporin given (Dose PO ), the duration of infusion for the IV dose (D inf ), and the order of the given doses (Dose order ). In this study we use the optimal design approach to optimize the pretransplant dose finding procedure for ciclosporin for all of the above mentioned design parameters (xt IV , xt PO , n IV , n PO , t 2nd dose , Dose IV , Dose PO , D inf , Dose order ).…”

mentioning

confidence: 99%

Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin

Hennig¹,

Nyberg²,

Fanta³

et al. 2012

The Journal of Clinical Pharma

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A time and sampling intensive pretransplant test dose design was to be reduced, but at the same time optimized so that there was no loss in the precision of predicting the individual pharmacokinetic (PK) estimates of posttransplant dosing. The following variables were optimized simultaneously: sampling times, ciclosporin dose, time of second dose, infusion duration, and administration order, using a published ciclosporin population PK model as prior information. The original design was reduced from 22 samples to 6 samples/patient and both doses (intravenous oral) were administered within 8 hours. Compared with the prior information given by the published ciclosporin population PK model, the expected standard deviations (SDs) of the individual parameters for clearance and bioavailability could be reduced by, on average, 40% under the optimized sparse designs. The gain of performing the original rich design compared with the optimal reduced design, considering the standard errors of the parameter estimates, was found to be minimal. This application demonstrates, in a practical clinical scenario, how optimal design techniques may be used to improve diagnostic procedures given available software and methods.

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Individualized population pharmacokinetic model with limited sampling for cyclosporine monitoring after liver transplantation in clinical practice

Cited by 8 publications

References 16 publications

Population pharmacokinetics of ciclosporin in haematopoietic allogeneic stem cell transplantation with emphasis on limited sampling strategy

Population pharmacokinetics of ciclosporin in haematopoietic allogeneic stem cell transplantation with emphasis on limited sampling strategy

Limited Sampling Model for Advanced Mycophenolic Acid Therapeutic Drug Monitoring After Liver Transplantation

Application of the Optimal Design Approach to Improve a Pretransplant Drug Dose Finding Design for Ciclosporin

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