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

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

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. AIM To 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. METHODS The 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. RESULTS Twenty 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]± 5.2%) with an inter-individual variability of 21%. The central volume of distribution (V(c) ) was 18.3 l (RSD ± 8.7%) with an inter-individual variability of 29%. Bioavailability (F) was 0.71 (RSD ± 9.9%) with and inter-individual variability of 25% and lag time (t(lag) ) was 0.44 h (RSD 5.5%). Weight, body surface area, haematocrit, albumin, ALAT and ASAT had no significant influence on pharmacokinetic parameters. The best multiple point combination for posterior Bayesian fitting, in terms of estimating systemic CsA exposure, appeared to be C0 + C2 + C3. Two selected LSS two time point equations and all selected three and four time point equations predicted de all AUC(0,12 h) within 15% bias and prediction. CONCLUSIONS The i.v. and oralcurves were best described with a two compartment model with first-order absorption with lag time. With the Bayesian estimators from this model, the area under the concentration-time curve in HSCT patients taking fluconazole can be estimated with only three blood samples (0, 2, 3 h) with a bias of 1% and precision of 4%.

Optimal sampling strategy development methodology using maximum a posteriori Bayesian estimation

Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility.

Application of the optimal design approach to improve a pretransplant drug dose finding design for ciclosporin

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.

Flexible limited sampling model for monitoring tacrolimus in stable patients having undergone liver transplantation with samples 4 to 6 hours after dosing is superior to trough concentration

Trough (C0) monitoring is not optimal for therapeutic drug monitoring of tacrolimus. To better estimate systemic exposure of tacrolimus and achieve clinical benefit, an improved therapeutic drug monitoring strategy should be developed. The authors examined which single and combination of time points best estimated the empiric "gold standard" AUC0-12h and developed and validated a new, flexible, and accurate limited sampling model for monitoring tacrolimus in patients having undergone liver transplantation. Twenty-three stable patients with full AUC0-12h were divided into two groups based on area under the concentration-time curve/dose. With multiple regression analysis, limited sampling formulae were derived and population-pharmacokinetic-based limited sampling models were developed and validated. A regression analysis was performed between either area under the concentration-time curves calculated with formulae or models with the reference trapezoidal AUC0-12h. Both formulae and models based on single samples C4-C6 (r2 = 0.94 [MPE/MAPE 0/7]-0.90 [2/8] and 0.97 [0/7]-0.97 [1/5]) showed excellent performance. The calculated area under the concentration-time curve target range for tacrolimus was 90 to 130 h*microg/L. Multiple point sampling performed better, especially when using models (r2 > 0.94). C0 was a less precise predictor of AUC0-12h compared with both formulae and models (r2's 0.68 [5/17] and 0.87 [2/14]). In conclusion, trough concentration monitoring is not an accurate method for assessing systemic exposure to tacrolimus in stable patients having undergone liver transplantation. This new limited sampling model, based on single time points C4-C6, shows excellent performance in estimating the AUC0-12h.