Aurélie Prémaud

Tacrolimus population pharmacokinetic-pharmacogenetic analysis and Bayesian estimation in renal transplant recipients.

AbstractObjectives: The aims of this study were (i) to investigate the population pharmacokinetics of tacrolimus in renal transplant recipients, including the influence of biological and pharmacogenetic covariates; and (ii) to develop a Bayesian estimator able to reliably estimate the individual pharmacokinetic parameters and inter-dose area under the blood concentration-time curve (AUC) from 0 to 12 hours (AUC12) in renal transplant patients. Methods: Full pharmacokinetic profiles were obtained from 32 renal transplant patients at weeks 1 and 2, and at months 1, 3 and 6 post-transplantation. The population pharmacokinetic analysis was performed using the nonlinear mixed-effect modelling software NONMEM® version VI. Patients’ genotypes were characterized by allelic discrimination for PXR −25385C>T genes. Results: Tacrolimus pharmacokinetics were well described by a two-compartment model combined with an Erlang distribution to describe the absorption phase, with low additive and proportional residual errors of 1.6 ng/mL and 9%, respectively. Both the haematocrit and PXR −25385C>T single nucleotide polymorphism (SNP) were identified as significant covariates for apparent oral clearance (CL/F) of tacrolimus, which allowed improvement of prediction accuracy. Specifically, CL/F decreased gradually with the number of mutated alleles for the PXR −25385C>T SNP and was inversely proportional to the haematocrit value. However, clinical criteria of relevance, mainly the decrease in interindividual variability and residual error, led us to retain only the haematocrit in the final model. Maximum a posteriori Bayesian forecasting allowed accurate prediction of the tacrolimus AUC12 using only three sampling times (at 0 hour [predose] and at 1 and 3 hours postdose) in addition to the haematocrit value, with a nonsignificant mean AUC bias of 2% and good precision (relative mean square error = 11%). Conclusion: Population pharmacokinetic analysis of tacrolimus in renal transplant recipients showed a significant influence of the haematocrit on its CL/F and led to the development of a Bayesian estimator compatible with clinical practice and able to accurately predict tacrolimus individual pharmacokinetic parameters and the AUC12.

Maximum a posteriori bayesian estimation of mycophenolic acid pharmacokinetics in renal transplant recipients at different postgrafting periods.

The aim of this study was to develop maximum a posteriori probability (MAP) Bayesian estimators of mycophenolic acid (MPA) pharmacokinetics (PK) capable of accurately estimating the MPA interdose AUC in renal transplant patients using a limited number of blood samples. The individual MPA plasma concentration-time profiles of 44 adult kidney transplant recipients were retrospectively studied: in 24 de novo transplant patients, 2 profiles were obtained on day 7 and day 30 after transplantation, and in 20 stable transplant patients, 1 profile was obtained in the stable period (>3 months). MPA was assayed by liquid chromatography-mass spectrometry. Concentration data were fitted using previously designed PK models, including 1 or 2Γ-distribution to describe the absorption rate. MAP-Bayesian estimations were performed using an in-house program. For each posttransplantation period, the limited sampling strategies (LSS) providing either the best determination coefficient or the lowest bias for AUC estimates with respect to trapezoidal AUCs were selected and compared with respect to the percentage of “clinically acceptable” AUC estimates (ie, within −20% to +20% of the true value) they yielded. A common LSS (blood samples collected at T20min, T1h, and T3h postdosing), convenient for all 3 periods, was also selected and validated: bias (RMSE%) values were −5.7% (20.5%), −8.2% (14.4%), and +0.4% (12.0%) on D7, D30, and for >M3 with respect to the reference values obtained using the trapezoidal rule, respectively. For the first time, MAP-Bayesian estimators of MPA systemic exposure at different posttransplantation periods (early as well as later periods) could be designed. They have since been used for MPA dose adaptation in concentration-controlled studies as well as for MPA therapeutic drug monitoring in clinical practice.

A double absorption-phase model adequately describes mycophenolic acid plasma profiles in de novo renal transplant recipients given oral mycophenolate mofetil.

BackgroundMycophenolic acid (MPA) shows complex plasma concentrationtime profiles, particularly in the immediate (first month) post-transplantation phase for which no relevant pharmacokinetic model has been proposed thus far.ObjectiveThe aim of this study was to develop a model to accurately describe the time profile of plasma MPA concentrations after oral administration of mycophenolate mofetil in adult kidney transplant patients, in any post-transplantation period.MethodFull interdose pharmacokinetic profiles were collected in 45 adult renal transplant patients who were orally administered mycophenolate mofetil and ciclosporin; 25 patients were de novo transplant patients for whom individual pharmacokinetics were assessed at three post-transplantation periods (days 3, 7 and 30) and 20 patients were stable transplant patients (>3 months post-transplantation). MPA was determined in plasma by liquid chromatography-mass spectrometry. Models combining a single- or double-input (described as single or double gamma distributions) with one- or two-compartments were developed using in-house software and fitted to the individual profiles by nonlinear regression.ResultsVisual inspection of the pharmacokinetic profiles showed highly variable absorption profiles and secondary peaks of various intensity. The pharmacokinetic models including a double gamma distribution best fitted these various profiles in the immediate post-transplantation period (mean bias and precision of −0.92% and 20.19%; −1.5% and 18.02%, on day 7 and day 30, respectively), while in the stable post-grafting phase (beyond 3 months), the single- and double-absorption models performed similarly (mean bias and precision of −3.37% and 17.64%; −3.12% and 18.44%, on day 7 and day 30, respectively).ConclusionThe proposed pharmacokinetic models adequately describe the concentration-time profiles of MPA in renal transplant patients and could be helpful in the development of tools for MPA monitoring.

Population Pharmacokinetics and Bayesian Estimation of Tacrolimus Exposure in Renal Transplant Recipients on a New Once-Daily Formulation

The population pharmacokinetic model was further refined by taking into account all of the data from the 41 patients, and the final model was validated using a bootstrap and a visual predictive check. For Bayesian estimation, the best limited-sampling strategy was determined on the basis of the D-optimality criterion and validation performed in the validation group.Background and ObjectivesAdvagraf® is a new extended-release once-daily formulation of tacrolimus, a potent immunosuppressant widely used in renal transplantation. The aims of this study were (i) to develop a population pharmacokinetic model for once-daily tacrolimus in adult renal transplant patients; and (ii) to develop a Bayesian estimator able to reliably estimate individual pharmacokinetic parameters and exposure indices.MethodsFull pharmacokinetic profiles obtained from 41 adult renal transplant patients who had been switched from ciclosporin to a single daily dose of the new once-daily tacrolimus formulation for more than 6 months were analysed. Tacrolimus concentrations were measured using validated turbulent flow chromatography-tandem mass spectrometry methods. Population parameters were computed using nonlinear mixed-effect modelling software (NONMEM® Version VI). The patients were randomly divided into (i) a model-building test group (n = 29); and (ii) a validation group (n= 12). Population pharmacokinetic analysis was performed to estimate the effects on tacrolimus pharmacokinetics of demographic characteristics (sex, bodyweight, age), drug interaction with prednisolone, laboratory test results (the haematocrit, haemaglobin level and serum creatinine level) and cytochrome P450 (CYP) 3A5 (CYP3A5) genetic polymorphism.ResultsThe trapezoidal area under the whole-blood concentration time curve from 0 to 24 hours (AUC24) of tacrolimus varied by up to 50% for the same trough concentration value. The pharmacokinetics of once-daily tacrolimus were well described by a two-compartment model combined with an Erlang distribution to describe the absorption phase. The CYP3A5 genotype was the only covariate retained in the final model. The apparent clearance of tacrolimus was 2-fold higher in expressers (with the CYP3A5*1/*1 and CYP3A5*1/*3 genotypes) than in non-expressers (with the CYP3A5*3/*3 genotype). This factor explained around 25% of the interindividual variability in the apparent clearance. A posteriori Bayesian estimation allowed accurate prediction of the AUC24 of once-daily tacrolimus, using just three sampling times (0, 1 and 3 hours post-dose) with a nonsignificant mean bias of 0.7% (range 16–20%) and good precision (root mean square error 9%).ConclusionsPopulation pharmacokinetic analysis of once-daily tacrolimus in renal transplant recipients resulted in identification of the CYP3A5*1/*3 genotype as a significant covariate on the apparent clearance of tacrolimus, and the design of an accurate maximum a posteriori Bayesian estimator based on three blood concentration measurements and this covariate. Such a tool could be helpful for comparing different exposure indices or different target levels. It could contribute to improvement of the efficacy and tolerability of once-daily tacrolimus in some patients.

Large scale analysis of routine dose adjustments of mycophenolate mofetil based on global exposure in renal transplant patients.

Background: We report a feasibility study based on our large-scale experience with mycophenolate mofetil dose adjustment based on mycophenolic acid interdose area under the curve (AUC) in renal transplant patients. Methods: Between 2005 and 2010, 13,930 requests for 7090 different patients (outside any clinical trial) were posted by more than 30 different transplantation centers on a free, secure web site for mycophenolate mofetil dose recommendations using three plasma concentrations and Bayesian estimation. Results: This retrospective study showed that 1) according to a consensually recommended 30- to 60-mg·h/L target, dose adjustment was needed for approximately 35% of the patients, 25% being underexposed with the highest proportion observed in the first weeks after transplantation; 2) when dose adjustment had been previously proposed, the subsequent AUC was significantly more often in the recommended range if the dose was applied than not at all posttransplantation periods (72-80% vs. 43-54%); and 3) the interindividual AUC variability in the “respected-dose” group was systematically lower than that in the “not respected-dose” group (depending on the posttransplantation periods; coefficient of variation %, 31-41% vs 49-70%, respectively). Further analysis suggested that mycophenolic acid AUC should best be monitored at least every 2 weeks during the first month, every 1 to 3 months between months 1 and 12, whereas in the stable phase, the odds to be still in the 30- to 60-mg·h/L range on the following visit was still 75% up to 1 year after the previous dose adjustment. Conclusion: This study showed that the monitoring of mycophenolate mofetil on the basis of AUC measurements is a clinically feasible approach, apparently acceptable by the patients, the nurses, and the physicians owing to its large use in routine clinics.

Bayesian Estimation of Mycophenolate Mofetil in Lung Transplantation, Using a Population Pharmacokinetic Model Developed in Kidney and Lung Transplant Recipients

Background and ObjectivesThe immunosuppressive drug mycophenolate mofetil is used to prevent rejection after organ transplantation. In kidney transplant recipients, it has been demonstrated that adjustment of the mycophenolate mofetil dose on the basis of the area under the concentration-time curve (AUC) of mycophenolic acid (MPA), the active moiety of mycophenolate mofetil, improves the clinical outcome. Because of the high risks of rejections and infections in lung transplant recipients, therapeutic drug monitoring of the MPA AUC might be even more useful in these patients. The aims of this study were to characterize the pharmacokinetics of MPA in lung and kidney transplant recipients, describe the differences between the two populations and develop a Bayesian estimator of the MPA AUC in lung transplant recipients.MethodsIn total, 460 MPA concentration-time profiles from 41 lung transplant recipients and 116 kidney transplant recipients were included. Nonlinear mixed-effects modelling was used to develop a population pharmacokinetic model. Patients were divided into an index dataset and a validation dataset. The pharmacokinetic model derived from the index dataset was used to develop a Bayesian estimator, which was validated using the 35 lung transplant recipients’ profiles from the validation dataset.ResultsMPA pharmacokinetics were described using a two-compartment model with lag time, first-order absorption and first-order elimination. The influence of ciclosporin co-treatment and the changes over time post-transplantation were included in the model. Lung transplant recipients had, on average, a 53% slower absorption rate and 50% faster MPA apparent oral clearance than kidney transplant recipients (p<0.001). In lung transplant recipients, the bioavailability was, on average, 31% lower in patients with cystic fibrosis than in patients without cystic fibrosis (p<0.001). The Bayesian estimator developed using the population pharmacokinetic model — and taking into account ciclosporin co-treatment, cystic fibrosis and time post-transplantation, with concentrations measured at 0, 1 and 4 hours after mycophenolate mofetil dose administration — resulted in a non-significant bias and mean imprecision of 5.8 mg • h/L. This higher imprecision compared with those of similar estimators that have previously been developed in kidney transplantation might have been caused by the high MPA pharmacokinetic variability seen in the lung transplant recipients and by the fact that a large proportion of the patients did not receive ciclosporin, which reduces variability in the elimination phase of MPA by blocking its enterohepatic cycling.ConclusionLung transplant recipients have a slower MPA absorption rate and faster apparent oral clearance than kidney transplant recipients, while cystic fibrosis results in lower MPA bioavailability. A Bayesian estimator using MPA concentration-time samples at 0, 1 and 4 hours post-dose had the best predictive performance.

An adjustable predictive score of graft survival in kidney transplant patients and the levels of risk linked to de novo donor-specific anti-HLA antibodies.

Most predictive models and scores of graft survival in renal transplantation include factors known before transplant or at the end of the first year. They cannot be updated thereafter, even in patients developing donor-specific anti-HLA antibodies and acute rejection.We developed a conditional and adjustable score for prediction of graft failure (AdGFS) up to 10 years post-transplantation in 664 kidney transplant patients. AdGFS was externally validated and calibrated in 896 kidney transplant patients.The final model included five baseline factors (pretransplant non donor-specific anti-HLA antibodies, donor age, serum creatinine measured at 1 year, longitudinal serum creatinine clusters during the first year, proteinuria measured at 1 year), and two predictors updated over time (de novo donor-specific anti-HLA antibodies and first acute rejection). AdGFS was able to stratify patients into four risk-groups, at different post-transplantation times. It showed good discrimination (time-dependent ROC curve at ten years: 0.83 (CI95% 0.76–0.89).