Longitudinal Exposure to Tacrolimus and New-Onset Diabetes Mellitus in Renal Transplant Patients.

PURPOSE: Tacrolimus is an immunosuppressant widely used in transplantations requiring mandatory concentration-controlled dosing to prevent acute rejection or adverse effects, including new-onset diabetes mellitus (NODM). However, no relationship between NODM and tacrolimus exposure has been established. This study aimed to evaluate the relationship between cumulative tacrolimus exposure and NODM occurrence. METHODS: A total of 452 kidney transplant patients were included in this study. Sixteen patients developed NODM during the first 3 months after transplant. We considered all tacrolimus concentration (C0) values collected until the diagnosis of NODM in these patients and until 3 months after transplant in the others. New tacrolimus cumulative exposure metrics were derived from the time profile of the tacrolimus morning predose concentration, C0: the percentage of C0 values > cutoff, the average of C0 values above the cutoff, and the percentage of the area under C0 versus time curve, AUCC0, above the cutoff. The cutoff chosen was 15 ng/mL, corresponding to the higher end of the therapeutic range for the early post-transplant period. The influence of these metrics on NODM and other clinical and biological characteristics was investigated using the Cox models. RESULTS: The percentage of C0 > 15 mcg/L was statistically different between patients with and without NODM (P = 0.01). Only these tacrolimus C0-derived metrics were significantly associated with an increased risk of NODM [HR: 1.73 (1.43-2.10, P < 0.001)]. CONCLUSION: This study shows that tacrolimus concentrations >15 mcg/L affect the incidence of NODM.

A Hybrid Model Associating Population Pharmacokinetics with Machine Learning: A Case Study with Iohexol Clearance Estimation.

BACKGROUND: Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms. OBJECTIVE: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation. METHODS: The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients. RESULTS: The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset). CONCLUSIONS: In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone.

Population pharmacokinetics and Bayesian estimation of mycophenolate mofetil in patients with autoimmune hepatitis.

AIMS: Mycophenolate mofetil (MMF) is the most widely used second-line agent in autoimmune hepatitis (AIH). Individual dose adjustment of MMF may avoid adverse outcomes while maximizing efficacy. The aim of the present study was to develop population pharmacokinetic (popPK) models and maximum a posteriori Bayesian estimators (MAP-BEs) to estimate mycophenolic acid interdose area under the curve in AIH patients administered MMF using nonlinear mixed effect modelling. METHODS: We analysed 50 mycophenolic acid PK profiles from 34 different patients, together with some demographic, clinical, and laboratory test data. The median number of plasma samples per profile, immediately preceding and following the morning MMF dose, was 7. PopPK modelling was performed using parametric, top-down, nonlinear mixed effect modelling with NONMEM 7.3. MAP-BEs were developed based on the best popPK model and the best limited sampling strategy selected among several. RESULTS: The pharmacokinetic data were best described by a 2-compartment model, Erlang distribution to describe the absorption phase, and a proportional error. The mean (relative standard error) of popPK parameter estimates of clearance, intercompartmental clearance, central volume and absorption rate with the final model were: 21.6 L h-1 (11%), 22.7 L h-1 (19%), 35.9 L (21%) and 8.7 h-1 (9%), respectively. The peripheral volume was fixed to 300 L. The best MAP-BE relied on the limited sampling strategy at 0.33, 1 and 3 hours after MMF dose administration and was very accurate (bias = 5.6%) and precise (root mean squared prediction error <20%). CONCLUSION: The precise and accurate Bayesian estimator developed in this study for AIH patients on MMF can be used to improve the therapeutic management of these patients.

Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: The example of tacrolimus.

We previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Bayesian estimation (MAP-BE). However, the major limitation in the development of such ML algorithms is the limited availability of large databases of concentration vs. time profiles for such drugs. The objectives of this study were: (i) to develop a Xgboost model to estimate tacrolimus inter-dose AUC based on concentration-time profiles obtained from a literature population pharmacokinetic (POPPK) model using Monte Carlo simulation; and (ii) to compare its performance with that of MAP-BE in external datasets of rich concentration-time profiles. The population parameters of a previously published PK model were used in the mrgsolve R package to simulate 9000 rich interdose tacrolimus profiles (one concentration simulated every 30 min) at steady-state. Data splitting was performed to obtain a training set (75%) and a test set (25%). Xgboost algorithms able to estimate tacrolimus AUC based on 2 or 3 concentrations were developed in the training set and the model with the lowest RMSE in a ten-fold cross-validation experiment was evaluated in the test set, as well as in 4 independent, rich PK datasets from transplant patients. ML algorithms based on 2 or 3 concentrations and a few covariates yielded excellent AUC estimation in the external validation datasets (relative bias < 5% and relative RMSE < 10%), comparable to those obtained with MAP-BE. In conclusion, Xgboost machine learning models trained on concentration-time profiles simulated using literature POPPK models allow accurate tacrolimus AUC estimation based on sparse concentration data. This study paves the way to the development of artificial intelligence at the service of precision therapeutic drug monitoring in different therapeutic areas.

Evaluation of Longitudinal Exposure to Tacrolimus as a Risk Factor of Chronic Kidney Disease Occurrence Within the First-year Post-Liver Transplantation.

BACKGROUND: Renal failure is predictive of mortality in the early postliver-transplantation period and calcineurin inhibitors toxicity is a main challenge. Our aim is to assess the impact of longitudinal tacrolimus exposure (TLE) and other variables on chronic kidney disease (CKD)-free 1-year-survival. METHODS: Retrospective data of consecutive patients transplanted between 2011 and 2016 and treated with tacrolimus were collected. TLE and all relevant pre- and post-liver transplantation (LT) predictive factors of CKD were tested and included in a time-to-event model. CKD was defined by repeated estimated glomerular filtration rate (eGFR) values below 60 mL/min/1.73m2 at least for the last 3 months before M12 post-LT. RESULTS: Data from 180 patients were analyzed. CKD-free survival was 74.5% and was not associated with TLE. Pre-LT acute kidney injury (AKI) and eGFR at 1-month post-LT (eGFRM1) <60 mL/min/1.73m2 were significant predictors of CKD. By distinguishing 2 situations within AKI (ie, with or without hepatorenal syndrome [HRS]), only HRS-AKI remained associated to CKD. HRS-AKI and eGFRM1 <60 mL/min/1.73m2 increased the risk of CKD (hazard ratio, 2.5; 95% confidence interval, 1.2-4.9; hazard ratio, 4.8; 95% confidence interval, 2.6-8.8, respectively). CONCLUSIONS: In our study, TLE, unlike HRS-AKI and eGFRM1, was not predictive of CKD-free survival at 1-year post-LT. Our results once again question the reversibility of HRS-AKI.

A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation.

Identification of patients at risk of kidney graft loss relies on early individual prediction of graft failure. Data from 616 kidney transplant recipients with a follow-up of at least one year were retrospectively studied. A joint latent class model investigating the impact of serum creatinine (Scr) time-trajectories and onset of de novo donor-specific anti-HLA antibody (dnDSA) on graft survival was developed. The capacity of the model to calculate individual predicted probabilities of graft failure over time was evaluated in 80 independent patients. The model classified the patients in three latent classes with significantly different Scr time profiles and different graft survivals. Donor age contributed to explaining latent class membership. In addition to the SCr classes, the other variables retained in the survival model were proteinuria measured one-year after transplantation (HR=2.4, p=0.01), pretransplant non-donor-specific antibodies (HR=3.3, p<0.001), and dnDSA in patient who experienced acute rejection (HR=15.9, p=0.02). In the validation dataset, individual predictions of graft failure risk provided good predictive performances (sensitivity, specificity, and overall accuracy of graft failure prediction at ten years were 77.7%, 95.8%, and 85%, resp.) for the 60 patients who had not developed dnDSA. For patients with dnDSA individual risk of graft failure was not predicted with a so good 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).

Ciclosporin population pharmacokinetics and Bayesian estimation in thoracic transplant recipients.

BACKGROUND AND OBJECTIVES: Therapeutic drug monitoring of ciclosporin has been recognized as an essential tool in the management of allograft transplant recipients, as it could help improve their outcome. However, there is still no consensus about the optimal method for monitoring ciclosporin after thoracic transplantation. Better knowledge of the pharmacokinetics of ciclosporin in thoracic transplant patients and design of tools dedicated to ciclosporin monitoring could help its practice and its outcome in this population of patients. The aims of this study were to (i) investigate the population pharmacokinetics of ciclosporin in thoracic (heart or lung) transplant patients and study the influence of a range of potential covariates, including demographic, clinical and genetic factors, on pharmacokinetic parameters; and (ii) develop a Bayesian estimator able to predict the individual pharmacokinetic parameters and exposures indices in this population of patients. METHODS: The analysis was performed with 187 full pharmacokinetic profiles obtained in 57 lung and 19 heart transplant patients within the first year post-transplantation. A population pharmacokinetic model was developed by non-linear mixed-effects modelling using NONMEM(®) (version 7.1) from an index dataset (118 profiles). On the basis of this population model and a limited number of blood samples, a Bayesian estimator able to determine ciclosporin area under the blood concentration-time curve (AUC) during a dosage interval was built and evaluated in the validation dataset (69 profiles). RESULTS: Ciclosporin pharmacokinetics were described using a two-compartment model with time-lagged first order absorption and first-order elimination. The final population model included sex as a covariate: ciclosporin apparent oral clearance was on average 37 % faster in male than in female patients (34.8 vs. 25.4 L/h, p < 0.001). Good predictive performance of the Bayesian estimator was obtained using three blood concentrations measured at 40 min, 2 h and 4 h post-dose, with a non-significant bias of -5 % between the estimated and the reference trapezoidal AUC and a good precision (relative mean square error = 13 %). CONCLUSION: Ciclosporin population pharmacokinetic analysis in thoracic transplant patients (including patients with cystic fibrosis) showed a significant influence of sex on apparent clearance. The Bayesian estimator developed in this study yielded accurate prediction of ciclosporin exposure in this population throughout the first year post-transplantation. This tool may allow routine ciclosporin dose individualization.

Bayesian estimation of mycophenolate mofetil in lung transplantation, using a population pharmacokinetic model developed in kidney and lung transplant recipients.

BACKGROUND AND OBJECTIVES: The 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. METHODS: In 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. RESULTS: MPA 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. CONCLUSION: Lung 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.

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.

Inhibition of T-cell activation and proliferation by mycophenolic acid in patients awaiting liver transplantation: PK/PD relationships.

Mycophenolic acid (MPA) plasma concentrations were reported to be associated with a decrease in T-cell proliferation, and in both IL-2 α-chain (CD25) and transferin receptor (CD71) expression. The aim of this study was to confirm, quantify and model these PK/PD relationships. Full profiles of MPA plasma concentrations, T-cell proliferation, intracytoplasmic IL-2 and TNF-α expression, and both CD71 and CD25 expression were collected over the 12h after dosing in 10 patients on the waiting list for liver transplantation. Data were analyzed using NONMEM(®). Both CD25 and CD71 expression and T cell proliferation clearly decreased (median of decrease from baseline 62%, 68% and 94%, respectively) with increasing MPA concentrations, in contrast to IL-2 and TNF-α expression. The CD25 and CD71 baseline expression (E(0)) and maximum effect (E(max)) were correlated with the E(0) and E(max) values of T-cell proliferation (r(2)=0.509 and r(2)=0.622, respectively). The CD25, CD71 expression and T-cell proliferation profiles were adequately fitted using a sigmoid inhibitory E(max) model. Low estimated values (≤2 mg/L) for 50% inhibitory MPA concentrations were obtained. This study confirmed a transient MPA concentration-dependent decrease in T-cells expressing CD25 and CD71 and a strong reduction of T-cell proliferation and showed that CD25 and CD71 expression was correlated with T-cell proliferation.

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in renal transplant recipients on a new once-daily formulation.

BACKGROUND AND OBJECTIVES: Advagraf is a new extended-release once-daily formulation of tacrolimus, a potent immunosuppressant widely used in renal transplantation. The aims of his 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. METHODS: Full 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. 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. RESULTS: The trapezoidal area under the whole-blood concentration time curve from 0 to 24 hours (AUC(24)) 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 AUC(24) 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%). CONCLUSIONS: Population 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.

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

OBJECTIVES: 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 (AUC(12)) 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 AUC(12) 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 AUC(12).

Performance of the new mycophenolate assay based on IMPDH enzymatic activity for pharmacokinetic investigations and setup of Bayesian estimators in different populations of allograft recipients.

A new mycophenolate (MPA) assay based on the enzymatic activity of recombinant type II inosine monophosphate dehydrogenase (the pharmacological target of MPA) with excellent correlation with high-performance liquid chromatography has recently been released for the measurement of MPA plasma levels. This study aimed to (1) compare this new assay with liquid chromatography tandem mass spectrometry (LC-MS/MS) for MPA pharmacokinetic (PK) studies in different populations of allograft recipients given mycophenolate mofetil, (2) develop specific Bayesian estimators for this inhibition assay and test their accuracy, and (3) compare the resulting MPA area under the curve (AUC0-12h) estimates with those of Bayesian estimators developed based on the LC-MS/MS results. Sixty-four adult or pediatric, renal or lung transplant patients who were administered mycophenolate mofetil in association with cyclosporine, tacrolimus, or sirolimus at different post-transplant periods were enrolled as part of different PK studies. Eight hundred ninety-four patients' samples were analyzed in parallel with the enzymatic MPA assay and a reference LC-MS/MS method. Repeated analysis of quality control samples showed a mean difference of 6% between the 2 assays, whereas the results obtained in different populations of transplanted patients showed excellent correlation (r2 > 0.96) and small mean relative differences (2.0%-16.9%). The full profiles obtained with both assays were adequately fitted using either a 2-compartment model with 1 "gamma" absorption phase or a 1-compartment model with 2 gamma inputs. Several PK parameters were significantly affected by the analytical method used. Accurate Bayesian estimators could be specifically developed for the enzymatic MPA assay, using the same 3 concentration-time points (20 minutes, 1 hour, and 3 hours post dose) as with LC-MS/MS, with a median bias versus reference (trapezoidal) AUC0-12h values of -1.3% (range -45.2% to 40.4%), and 83% of the patients within +/-20% of the reference. These Bayesian estimates were significantly higher than those obtained with LC-MS/MS in patients on cyclosporine or sirolimus, but not in patients on tacrolimus.

A comparison of the effect of ciclosporin and sirolimus on the pharmokinetics of mycophenolate in renal transplant patients.

AIM: To compare the pharmacokinetics of mycophenolic acid when given with either ciclosporin or sirolimus, and investigate in vitro the potential effect of ciclosporin, sirolimus, tacrolimus and everolimus on mycophenolic acid metabolism. METHODS: In renal transplant patients given mycophenolate mofetil in combination with ciclosporin (n = 19) or sirolimus (n = 12), concentration-time profiles of mycophenolic acid, mycophenolic-acid-phenyl-glucuronide, mycophenolic-acid-acyl-glucuronide and mycophenolic-acid-phenyl-glucoside were determined at one month post-transplant. The effect of immunosuppressive drugs on mycophenolic acid glucuronidation and glycosylation was investigated in vitro using human liver microsomes. RESULTS: The mean mycophenolic acid AUC(0-9 h) in the sirolimus group was 44.9 mg h(-1) L(-1) (95% CI: 34.7-55.1), vs. 30.5 mg h(-1) L(-1) (95% CI: 25.4-35.6) in the ciclosporin group, corresponding to 1.5-fold dose-normalized difference (95% CI: 1.1-1.9; P < 0.05). In addition, the metabolite/mycophenolic acid AUC(0-9 h) ratios were significantly higher in patients cotreated with ciclosporin than with sirolimus, giving values of 1.8-fold (95% CI: 1.3-2.3; P = 0.0009), 2.6-fold (95% CI: 2.0-3.3; P < 0.0001) and 4.3-fold (95% CI: 2.6-6.0; P = 0.0016) for mycophenolic-acid-phenyl-glucuronide, mycophenolic-acid-acyl-glucuronide and mycophenolic-acid-phenyl-glucoside, respectively. In vitro, none of the immunosuppressive drugs tested inhibited mycophenolic acid metabolism. CONCLUSION: Patients taking mycophenolate mofetil and sirolimus experience a higher exposure to mycophenolic acid and a lower exposure to mycophenolic acid metabolites than those being treated with mycophenolate mofetil and ciclosporin. This interaction is probably not caused by inhibition of mycophenolic acid glucuronidation or glycosylation, but is more likely to be due to the influence of ciclosporin on the excretion of mycophenolic acid metabolites into bile.

Determination of mycophenolic acid plasma levels in renal transplant recipients co-administered sirolimus: comparison of an enzyme multiplied immunoassay technique (EMIT) and liquid chromatography-tandem mass spectrometry.

This study was designed to compare the enzyme multiplied immunoassay technique (EMIT) and a specific ;liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the determination of mycophenolic acid (MPA) concentrations in plasma samples collected from renal transplant patients receiving mycophenolate mofetil in association with sirolimus and corticosteroids. Ten-point blood concentration-time profiles, between pre-dose (C0) and 9-hour post-dose, were obtained on day 7, day 14, months 1, 2, and 3 posttransplantation in 8 patients. MPA plasma concentrations were measured simultaneously with both EMIT and LC-MS/MS. Higher concentrations were obtained with EMIT. The mean (+/-SD) absolute difference was +0.84 +/- 1.93 mg/L, which corresponds to a significant overestimation of 18.7 +/- 2 6.8% with EMIT, with variations depending on the time elapsed since transplantation. An EMIT overestimation of 16.7 +/- 22.5% also was obtained for the MPA areas under the curve calculated using the trapezoidal rule.

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

BACKGROUND: Mycophenolic acid (MPA) shows complex plasma concentration-time profiles, particularly in the immediate (first month) post-transplantation phase for which no relevant pharmacokinetic model has been proposed thus far. OBJECTIVE: The 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. METHOD: Full 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. RESULTS: Visual 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). CONCLUSION: The 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.

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 2Gamma-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 T20 min, T1 h, and T3 h 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.

Identification of the UDP-glucuronosyltransferase isoforms involved in mycophenolic acid phase II metabolism.

Mycophenolic acid (MPA), the active metabolite of the immunosuppressant mycophenolate mofetil is primarily metabolized by glucuronidation. The nature of UDP-glucuronosyltransferases (UGTs) involved in this pathway is still debated. The present study aimed at identifying unambiguously the UGT isoforms involved in the production of MPA-phenyl-glucuronide (MPAG) and MPA-acylglucuronide (AcMPAG). A liquid chromatography-tandem mass spectrometry method allowing the identification and determination of the metabolites of mycophenolic acid was developed. The metabolites were characterized in urine and plasma samples from renal transplant patients under mycophenolate mofetil therapy and in vitro after incubation of mycophenolic acid with human liver (HLM), kidney (HKM), or intestinal microsomes (HIM). The UGT isoforms involved in MPAG or AcMPAG production were investigated using induced rat liver microsomes, heterologously expressed UGT (Supersomes), and chemical-selective inhibition of HLM, HKM, and HIM. The three microsomal preparations produced MPAG, AcMPAG, and two mycophenolate glucosides. Among the 10 UGT isoforms tested, UGT 1A9 was the most efficient for MPAG synthesis with a K(m) of 0.16 mM, close to that observed for HLM (0.18 mM). According to the chemical inhibition experiments, UGT 1A9 is apparently responsible for 55%, 75%, and 50% of MPAG production by the liver, kidney, and intestinal mucosa, respectively. Although UGT 2B7 was the only isoform producing AcMPAG in a significant amount, the selective inhibitor azidothymidine only moderately reduced this production (approximately -25%). In conclusion, UGT 1A9 and 2B7 were clearly identified as the main UGT isoforms involved in mycophenolic acid glucuronidation, presumably due to their high hepatic and renal expression.

Characterization of a phase 1 metabolite of mycophenolic acid produced by CYP3A4/5.

To characterize a phase 1 metabolite of mycophenolic acid (MPA) and the human cytochrome P450 isoform(s) (CYP) involved in its formation. MPA metabolites were investigated in blood and urine samples from transplant patients under mycophenolate mofetil therapy (n = 5) as well as with in vitro incubation of MPA with human liver microsomes. The CYP isoforms involved in the oxidative metabolism were investigated in vitro on human liver microsomes with isoform-specific inhibitors as well as in human embryonic kidney cell lines expressing recombinant human CYPs. The analytic methods used were based on LC-MS/MS. A 6-O-desmethyl-MPA (DM-MPA) metabolite and 2 related glucuronides were identified in patients' blood and urine. Human liver microsomes produced DM-MPA with an apparent Km = 0.83 +/- 0.06 mmol/L and Vmax = 5.57 +/- 0.29 pmol/mg/min. The CYP3A inhibitor ketoconazole was found to inhibit DM-MPA formation by 50.3% with respect to the control, and trimethoprim (CYP2C8 inhibitor) reduced it by 30.1%. However, DM-MPA was produced only by the transfected cell lines expressing CYP3A4 and, to a lesser extent, CYP3A5. In vitro, MPA at concentrations above the plasma therapeutic range was found to decrease the metabolism of tacrolimus, suggesting a possible competition for CYP3A. No effect of MPAat therapeutic or higher level was found on cyclosporin metabolism. The phase 1 metabolite of MPA previously known as M-3 was identified as 6-O-desmethyl-MPA and is produced by CYP3A4/5 and probably CYP2C8. MPA might compete with other drugs on CYP3A because of its high therapeutic concentrations, although this was not the case for cyclosporin and to only a small extent for tacrolimus.