Limited Sampling Strategies to Estimate the Exposition of Different Lipid Nanocaspules.

Cyclosporine (CSA; Neoral) is one of the most common immunosuppressants used in pediatric renal transplantation. Research in adult renal transplant recipients has shown that 2-h post-dose concentration (C2) monitoring and limited sampling strategies (LSSs) are better at predicting drug exposure and outcome than trough concentrations (C0). While C0 monitoring is the usual practice in pediatric renal transplant patients, area under the curve (AUC) monitoring has been shown to be superior in terms of predictive ability and outcomes. However, AUC monitoring is impractical and inconvenient in a clinic setting because it involves many blood samples. An LSS provides a reliable alternative. The purpose of this study was to prospectively define an LSS (AUC(0-12)) for CSA monitoring and to test its predictive performance. As well, an LSS (AUC(0-4)) for CSA was developed and its predictive performance tested. Blood samples for CSA concentrations were collected in 29 stable pediatric renal transplant patients prior to (t = 0) and at 0.5, 1, 2, 4, 6, and 8 h following a steady-state morning CSA dose. AUC was calculated by the trapezoidal method; LSSs for AUC(0-12) and AUC(0-4) were determined using multiple regression analysis in 14 patients; and the LSSs' predictive performance was tested in 15 additional patients. Both LSSs require two blood samples. For the LSS (AUC(0-12)), blood samples are required immediately before the dose and 2 h post-dose: AUC(0-12) = 12.45 C0 + 2.17 C2 + 723.16 (r2 = 0.909). For the LSS (AUC(0-4)), blood samples are required at one and 2 h post-dose, AUC(0-4) = 1.17 C1 + 1.85 C2 - 41.00 (r2 = 0.971). The LSSs demonstrated low bias and high precision for both AUC(0-12) and AUC(0-4). Our two-concentration LSSs are accurate and precise predictors that are more clinically useful for our patient population than other LSSs that have been developed for pediatric renal transplant patients. Our study template provides a guide for other centers to develop accurate and precise LSSs specific to their own patient population.

Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0). Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0. The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2h postdose) yielded an AUC bias ± SD =2.7 ±10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor). The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

DiI and DiD, two fluorophores able to interact by FRET (Förster resonance energy transfer), were coencapsulated in the core of lipid nanocapsules (LNCs) and nanoemulsions (LNEs), lipophilic reservoirs for the delivery of drugs. The ability of FRET imaging to provide information on the kinetics of dissociation of the nanoparticles in the presence of bovine serum albumin (BSA) or whole serum, or after incubation with cancer cells, and after systemic administration in tumor-bearing mice, was studied. Both microscopic and macroscopic imaging was performed to determine the behavior of the nanostructures in a biological environment. When 2 mg/mL FRET LNEs or LNCs were dispersed in buffer, in the presence of unloaded nanoparticles, BSA, or in whole serum, the presence of serum was the most active in destroying the particles. This occurred immediately with a diminution of 20% of FRET, then slowly, ending up with still 30% intact nanoparticles at 24 h. LNCs were internalized rapidly in cultured cells with the FRET signal decreasing within the first minutes of incubation, and then a plateau was reached and LNCs remained intact during 3 h. In contrast, LNEs were poorly internalized and were rapidly dissociated after internalization. Following their iv injection, LNCs appeared very stable in subcutaneous tumors implanted in mice. Intact particles were found using microscopic FRET determination on tumor sections 24 h after injection, that correlated well with the 8% calculated noninvasively on live animals. FRET investigations showed the potential to determine valid and reliable information about in vitro and in vivo behavior of nanoparticles.

Mycophenolic acid (MPA) is a potent immunosuppressant agent, which is increasingly being used in the treatment of patients with various autoimmune diseases. Dosing to achieve a specific target MPA area under the concentration-time curve from 0 to 12 h post-dose (AUC12) is likely to lead to better treatment outcomes in patients with autoimmune disease than a standard fixed-dose strategy. This review summarizes the available published data around concentration monitoring strategies for MPA in patients with autoimmune disease and examines the accuracy and precision of methods reported to date using limited concentration-time points to estimate MPA AUC12. A total of 13 studies were identified that assessed the correlation between single time points and MPA AUC12 and/or examined the predictive performance of limited sampling strategies in estimating MPA AUC12. The majority of studies investigated mycophenolate mofetil (MMF) rather than the enteric-coated mycophenolate sodium (EC-MPS) formulation of MPA. Correlations between MPA trough concentrations and MPA AUC12 estimated by full concentration-time profiling ranged from 0.13 to 0.94 across ten studies, with the highest associations (r (2) = 0.90-0.94) observed in lupus nephritis patients. Correlations were generally higher in autoimmune disease patients compared with renal allograft recipients and higher after MMF compared with EC-MPS intake. Four studies investigated use of a limited sampling strategy to predict MPA AUC12 determined by full concentration-time profiling. Three studies used a limited sampling strategy consisting of a maximum combination of three sampling time points with the latest sample drawn 3-6 h after MMF intake, whereas the remaining study tested all combinations of sampling times. MPA AUC12 was best predicted when three samples were taken at pre-dose and at 1 and 3 h post-dose with a mean bias and imprecision of 0.8 and 22.6 % for multiple linear regression analysis and of -5.5 and 23.0 % for maximum a posteriori (MAP) Bayesian analysis. Although mean bias was less when data were analysed using multiple linear regression, MAP Bayesian analysis is preferable because of its flexibility with respect to sample timing. Estimation of MPA AUC12 following EC-MPS administration using a limited sampling strategy with samples drawn within 3 h post-dose resulted in biased and imprecise results, likely due to a longer time to reach a peak MPA concentration (t max) with this formulation and more variable pharmacokinetic profiles. Inclusion of later sampling time points that capture enterohepatic recirculation and t max improved the predictive performance of strategies to predict EC-MPS exposure. Given the considerable pharmacokinetic variability associated with mycophenolate therapy, limited sampling strategies may potentially help in individualizing patient dosing. However, a compromise needs to be made between the predictive performance of the strategy and its clinical feasibility. An opportunity exists to combine research efforts globally to create an open-source database for MPA (AUC, concentrations and outcomes) that can be used and prospectively evaluated for AUC target-controlled dosing of MPA in autoimmune diseases.

Background and ObjectiveThis study aimed to develop and evaluate a population pharmacokinetic model and limited sampling strategy for isoniazid to be used in model-based therapeutic drug monitoring.MethodsA population pharmacokinetic model was developed based on isoniazid and acetyl-isoniazid pharmacokinetic data from seven studies with in total 466 patients from three continents. Three limited sampling strategies were tested based on the available sampling times in the dataset and practical considerations. The tested limited sampling strategies sampled at 2, 4, and 6 h, 2 and 4 h, and 2 h after dosing. The model-predicted area under the concentration–time curve from 0 to 24 h (AUC24) and the peak concentration from the limited sampling strategies were compared to predictions using the full pharmacokinetic curve. Bias and precision were assessed using the mean error (ME) and the root mean square error (RMSE), both expressed as a percentage of the mean model-predicted AUC24 or peak concentration on the full pharmacokinetic curve.ResultsPerformance of the developed model was acceptable and the uncertainty in parameter estimations was generally low (the highest relative standard error was 39% coefficient of variation). The limited sampling strategy with sampling at 2 and 4 h was determined as most suitable with an ME of 1.1% and RMSE of 23.4% for AUC24 prediction, and ME of 2.7% and RMSE of 23.8% for peak concentration prediction. For the performance of this strategy, it is important that data on both isoniazid and acetyl-isoniazid are used. If only data on isoniazid are available, a limited sampling strategy using 2, 4, and 6 h can be employed with an ME of 1.7% and RMSE of 20.9% for AUC24 prediction, and ME of 1.2% and RMSE of 23.8% for peak concentration prediction.ConclusionsA model-based therapeutic drug monitoring strategy for personalized dosing of isoniazid using sampling at 2 and 4 h after dosing was successfully developed. Prospective evaluation of this strategy will show how it performs in a clinical therapeutic drug monitoring setting.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40262-020-00971-2.

To develop limited sampling strategies for estimation of mycophenolic acid exposure (by determining area under the concentration-time curve [AUC]) in lung transplant recipients by using sampling times within 2 hours after drug administration and a maximum of three plasma samples. Prospective, open-label clinical study. Lung transplant clinic in Vancouver, British Columbia, Canada. Nineteen adult (mean age 48.3 yrs) lung transplant recipients who were receiving mycophenolate mofetil therapy along with cyclosporine (9 patients) or tacrolimus (10 patients). Eleven blood samples were collected from each of the 19 patients over 12 hours: immediately before (0 hr) and 0.3, 0.6, 1, 1.5, 2, 4, 6, 8, 10, and 12 hours after administration of mycophenolate mofetil. Mycophenolic acid levels in plasma were determined by a high-performance liquid chromatography-ultraviolet detection method. The 19 patients were randomly divided into index (10 patients) and validation (9 patients) groups. Limited sampling strategies were developed with multiple regression analysis by using data from the index group. Data from the validation group were used to test each strategy. Bias and precision of each limited sampling strategy were determined by calculating the mean prediction error and the root mean square error, respectively. The correlation between AUC and single concentrations was generally poor (r2= 0.18-0.73). Two single-concentration strategies, eight two-concentration strategies, and eight three-concentration strategies matched our criteria. However, the best overall limited sampling strategies (and their predictive performance) were the following: log AUC = 0.241 log C0 + 0.406 log C2 + 1.140 (bias -5.82%, precision 5.97%, r2= 0.828) and log AUC = 0.202 log C0 + 0.411 log C1.5 + 1.09 (bias -5.71%, precision 6.94%, r2= 0.791), where Cx is mycophenolic acid concentration at time x hours. Two-concentration limited sampling strategies provided minimally biased and highly precise estimation of mycophenolic acid AUC in lung transplant recipients. These optimal and most clinically feasible limited sampling strategies are based collectively on the number of blood samples required, r2 value, bias, and precision.

Mycophenolic acid is widely used to treat lupus nephritis (LN). However, it exhibits complex pharmacokinetics with large interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model and a 3-sample limited sampling strategy (LSS) to optimize therapeutic drug monitoring in Indian patients with LN. Five blood samples from each LN patient treated with mycophenolic acid were collected at steady-state predose and 1, 2, 4, and 6 hours postdose. Demographic parameters were tested as covariates to explain interindividual variability. PopPK analysis was performed using Monolix and the stochastic approximation expectation-maximization algorithm. An LSS was derived from 500 simulated pharmacokinetic (PK) profiles using maximum a posteriori Bayesian estimation to estimate individual PK parameters and area under the curve (AUC). The LSS-calculated AUC was compared with the AUC calculated using the trapezoidal rule and all the simulated samples. A total of 51 patients were included in this study. Based on the 245 mycophenolic acid concentrations, a 1-compartmental model with double absorption using gamma distributions best fitted the data. None of the covariates improved the model significantly. The model was internally validated using diagnostic plots, prediction-corrected visual predictive checks, and bootstrapping. The best LSS included samples at 1, 2, and 4 hours postdose and exhibited good performances in an external dataset (root mean squared error, 21.9%; mean bias, -4.20%). The popPK model developed in this study adequately estimated the PK of mycophenolic acid in adult Indian patients with LN. This simple LSS can optimize TDM based on the AUC in routine practice.

The aim of this study was to establish a limited sample strategy (LSS) to predict the mycophenolic acid (MPA) area under the curve (AUC)(0-12) in children with systemic lupus erythematosus (SLE). Three months after initiation of mycophenolate mofetil (MMF) 26 children with SLE presented for therapeutic drug monitoring of MPA. On the day of the test, 10 specimens were collected, analyzed, and MPA AUC(0-12) was calculated. Using step-wise regression analysis, LSS equations were developed. Using bootstrap validation, the predictive performance was calculated. The measured mean (standard deviation) for the trough concentration and AUC(0-12) were 2.55 (1.57) μg/ml and 62.6 (21.67) mg.h/L, respectively. The range of trough concentrations and AUC(0-12) were 0.7–5.54 μg/ml and 22.1–104.8 mg.h/L, respectively. The interindividual variability (%CV) for dose normalized AUC(0-12) and dose normalized Ctrough was 46.5% and 61.1%, respectively. The correlation between the concentrations at the different time points and MPA AUC(0-12) ranged from 0.05 (1.5 h) to 0.56 (4 h). Two LSS equations that included 4 or 5 time points up to 3 h were developed and validated. The 4 point LSS had a correlation (R2) of 0.88 and the 5 point LSS an R2 of 0.87. With respect to the 4 point and 5 point MPA LSS AUC(0-12), the bias was 1.92% and 1.96%, respectively, and the imprecision was 11.24% and 11.28%, respectively. A 4 point LSS which concludes within 3 h after the administration of the MMF dose was developed and validated, to determine the MPA AUC(0-12) in children with SLE.

Area under the curve (AUC)-based monitoring of cyclosporin (CsA) could help to optimise therapeutic drug monitoring in certain transplant patients in addition to trough concentration monitoring. It is the method of choice for mycophenolic acid (MPA). The objective of this study was to develop a limited sampling strategy for simultaneous estimation of CsA and MPA AUCs in long-term renal transplant patients. Twenty kidney transplant patients treated with CsA and mycophenolate mofetil were included in a pharmacokinetic study more than 6 months after transplantation. Multilinear regression analyses were performed to develop a model enabling the estimation of both drugs' AUCs using a limited number of samples. Dose-normalised data were used throughout the analysis. Trough concentrations of MPA were poorly correlated with AUC, either used alone (r2 = 0.232) or together with other concentrations. Several models for CsA AUC estimation met the predefined criteria (r2>0.9, P<0.05). The AUC of MPA was best estimated by a three-concentration model (AUC=0.58 C20 min+ 0.97 C1 h + 6.64 C3 h + 3.48; r2 = 0.946). These sampling times also applied to CsA AUC (AUC = 1.17 C20 min + 0.68 C1 h + 5.36 C3 h + 4.24; r = 0.985). AUCs estimated using these models in our patients using the jack-knife procedure were found to be precise and unbiased as compared with reference trapezoidal AUCs. We were able to develop a multilinear regression model for simultaneous estimation of both CsA and MPA AUCs using only three blood samples taken up to 3 h post-dosing.

1) To develop and validate limited sampling strategies (LSSs) for tacrolimus (TAC) and mycophenolic acid (MPA) in renal transplant recipients not receiving corticosteroids; and 2) to evaluate predictive performance of published LSSs (for steroid-based regimens) in a steroid-free population. On administration of steady-state morning TAC and mycophenolate mofetil doses, 12-hour serial blood samples from 28 stable renal transplant recipients were collected and measured by validated high-performance liquid chromatography methods and area under the curve (AUC) by trapezoidal rule. TAC LSSs were developed and validated by multiple regression analysis by a two-group method (index n = 18; validation n = 10) and MPA LSSs by the jackknife method (n = 28). Potential LSSs were those with r ≥ .8 (TAC) or r ≥ 0.7 (MPA) and < 3 time points within 2 hours (TAC) or 4 hours (MPA) postdose. Predictive performance was calculated and other published TAC and MPA LSSs tested using preset criteria for bias and precision of within ± 15%. For TAC, three three-concentration, one two-concentration, and one one-concentration model met preset criteria. The best equations were: TAC AUC = 10.338 + 7.739C0 + 3.589C2 (r = 0.956, bias = -3.4%, precision = 4.7%) and TAC AUC = 29.479 + 5.016C2 (r = 0.862, bias = 3.2%, precision = 9.7%). For MPA, only one model was identified: MPA AUC = 9.328 + 1.311C1 + 1.455C2 + 2.901C4 (r = 0.838, bias = -3.8%, precision = 14.9%). One published TAC (and no MPA) LSS in renal transplant recipients on steroid-based regimens met criteria. To the authors' knowledge, these LSSs are the first to be developed and validated in steroid-free renal transplant recipients and can be used to accurately predict TAC and MPA AUCs for steroid-free regimens. Because the commonly used MPA LSS is based on a steroid regimen and not predictive for steroid-free patients, the newly derived MPA LSS is being applied at the authors' institution. Other renal transplant centers may also wish to validate this equation in their own patients.

Mycophenolate mofetil (MMF), the oral prodrug of mycophenolic acid (MPA), is increasingly used in liver transplantation and plays a central role in the immunosuppressive regimen in liver transplantation. To study pharmacokinetic-pharmacodynamic relationships and therapeutic drug monitoring of MPA in the clinical setting, limited sampling strategies have been investigated for the estimation of MPA areas under the curves (AUCs). Thirty-eight adult patients undergoing liver transplant (31 males, seven females) receiving 1.0 g MMF twice daily and concomitant tacrolimus provided a total of 72 pharmacokinetic profiles. Multiple stepwise regression analysis was used to determine the algorithms for limited sampling strategies. Twenty-eight one-, two-, three-, and four-sampling estimation models were fitted (r = 0.288-0.964) to all the profiles using linear regression and were used to estimate MPA AUC0-12h comparing those estimates with the corresponding AUC0-12h values calculated with the linear trapezoidal rule, including all 10 timed MPA concentrations. The four-point estimates at C1h, C2h, C6h, and C8h resulted in the best correlation between estimated AUC and true AUC when using the formula AUC = 6.03 + 0.89C1h + 1.94C2h + 2.24C6h + 4.64 C8h (r = 0.911). Bland and Altman analysis revealed good agreement between estimated AUC and AUC from the full profile. This limited sampling strategy provides an effective approach for estimation of full MPA AUC0-12h in patients undergoing liver transplant receiving concomitant tacrolimus therapy.

Previous studies predicted that limited sampling strategies (LSS) for estimation of mycophenolic acid (MPA) area-under-the-curve (AUC(0-12)) after ingestion of enteric-coated mycophenolate sodium (EC-MPS) using a clinically feasible sampling scheme may have poor predictive performance. Failure of LSS was thought to be due to the slow absorption of MPA causing late and variable times of maximum MPA concentration and variable predose concentrations. The aim of this study was to formally test the performance of LSS by developing and validating LSS for estimation of MPA AUC(0-12) after EC-MPS administration. Pharmacokinetic data from 109 renal transplant recipients collected during the maintenance period after transplantation were analysed retrospectively. LSS were developed separately for renal transplant patients who concurrently used cyclosporine (n = 79) and for patients not concurrently treated with cyclosporine (n = 30). Data were split into an index and a validation data set. For clinical feasibility reasons, a LSS could consist of a maximum of 3 sampling time points with the latest sample drawn 2 hours after drug administration. LSS with the latest sample drawn 3 hours after drug administration or even later were also tested. The validation of the developed LSS showed that MPA AUC(0-12) for patients concurrently treated with cyclosporine was best estimated by AUC(0-12) (mg x h x L(-1)) = 36.536 + 1.642 x C0.5 + 0.569 x C1.5 + 0.905 x C2 (r2 = 0.33, bias = -1.0 mg x h x L(-1), precision = 24 mg x h x L(-1)), whereas AUC(0-12) [mg x h x L(-1)] = 19.801 + 1.827 x C0.5 + 1.111 x C1 + 1.429 x C2 was the best AUC(0-12) estimator for patients not cotreated with cyclosporine (r2 = 0.31, bias = 0.4 mg x h x L(-1), precision = 14.5 mg x h x L(-1)). Both LSS showed poor precision and overestimation of AUC(0-12) values below the therapeutic window and underestimation of AUC(0-12) values above the therapeutic window of MPA. Using C3 as latest sampling time point improved the fit slightly, but not satisfactory, with r2 still <0.40 and precision still >14.0 mg x h x L(-1). Estimation of MPA AUC(0-12) with LSS for EC-MPS drawn within 2 or 3 hours postdose in renal transplant recipients in the maintenance period is likely to result in biased and imprecise results.

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Single-Chain Lanthanide Luminescence Biosensors for Cell-Based Imaging and Screening of Protein-Protein Interactions.

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%.