Annick Rousseau

The aims of the present study were (1) to investigate the impact of great age on pharmacokinetics of capecitabine and its metabolites and (2) to evaluate the exposure–effect relationship of capecitabine in elderly patients. Data collected from 20 elderly patients (75–92 years old) with breast or colorectal cancer who received oral capecitabine were analyzed. In order to study the old age effect on pharmacokinetics, data collected from two phase I studies involving 40 younger adults (<75 years old) with metastatic cancer who received oral capecitabine were added in the database. The population pharmacokinetic analysis was based on a four-compartment model describing the sequence of capecitabine and three of its metabolites. The absorption rate constant was found lower in the oldest patient group (≥75 years) compared with the youngest group, and the constant rate elimination of the 5-fluorouracil metabolite was found decreased over time (i.e., after 2 consecutive weeks of capecitabine administration). This time effect was not found different between the two age groups. In elderly patients, the exposure-safety analysis showed, from the second cycle of chemotherapy, significantly higher median exposures of capecitabine and its metabolites (5′-deoxy-5-fluorocytidine, 5′-deoxy-5-fluorouridine and 5-fluorouracil) in patients who experienced hand-foot syndrome compared with patients who did not. This study puts forward new arguments for the treatment of elderly cancer patients who could benefit from capecitabine chemotherapy with acceptable toxicity.

Therapeutic drug monitoring of tacrolimus is a major support to patient management and could help improve the outcome of lung transplant recipients, by minimizing the risk of rejections and infections. However, despite the wide use of tacrolimus as part of maintenance immunosuppressive regimens after lung transplantation, little is known about its pharmacokinetics in this population. Better knowledge of the pharmacokinetics of tacrolimus in lung transplant recipients, and the development of tools dedicated to its therapeutic drug monitoring, could thus help improve their outcome. The aims of this study were (i) to characterize the population pharmacokinetics of tacrolimus in lung transplant recipients, including the influence of biological and pharmacogenetic covariates; and (ii) to develop a Bayesian estimator of the tacrolimus area under the blood concentration-time curve from time zero to 12 hours (AUC₁₂) for its therapeutic drug monitoring in lung transplant recipients. A population pharmacokinetic model was developed by nonlinear mixed-effects modelling using NONMEM® version VI, from 182 tacrolimus full concentration-time profiles collected in 78 lung transplant recipients within the first year post-transplantation. Patient genotypes for the cytochrome P450 3A5 (CYP3A5) A6986G single nucleotide polymorphism (SNP) were characterized by TaqMan allelic discrimination. Patients were divided into an index dataset (n= 125 profiles) and a validation dataset (n = 57 profiles). A Bayesian estimator was derived from the final model using the index dataset, in order to determine the tacrolimus AUC₁₂ on the basis of a limited number of samples. The predictive performance of the Bayesian estimator was evaluated in the validation dataset by comparing the estimated AUC₁₂ with the trapezoidal AUC₁₂. Tacrolimus pharmacokinetics were described using a two-compartment model with Erlang absorption and first-order elimination. The model included cystic fibrosis (CF) and CYP3A5 polymorphism as covariates. The relative bioavailability in patients with CF was approximately 60% of the relative bioavailability observed in patients without CF, and the transfer rate constant between the transit compartments was 2-fold smaller in patients with CF than in those without CF (3.32 vs 7.06 h^-1). The apparent clearance was 40% faster in CYP3A5 expressers than in non-expressers (24.5 vs 17.5 L/h). Good predictive performance was obtained with the Bayesian estimator developed using the final model and concentrations measured at 40 minutes and at 2 and 4 hours post-dose, as shown by the mean bias (1.1%, 95% CI — 1.4, 3.7) and imprecision (9.8%) between the estimated and the trapezoidal AUC₁₂. The bias was >20% in 1.8% of patients. Population pharmacokinetic analysis showed that lung transplant patients with CF displayed lower bioavailability and a smaller transfer rate constant between transit compartments than those without CF, while the apparent clearance was faster in CYP3A5 expressers than in non-expressers. The Bayesian estimator developed in this study provides an accurate prediction of tacrolimus exposure in lung transplant patients, with and without CF, throughout the first year post-transplantation. This tool may allow routine tacrolimus dose individualization and may be used to conduct clinical trials on therapeutic drug monitoring of tacrolimus after lung transplantation.

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. 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. 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. 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.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Tacrolimus is an immunosuppressant agent, largely used in kidney transplantation, with a narrow therapeutic range.• Therapeutic drug monitoring of tacrolimus exposure improves the efficacy and toxicity of this drug.• Separated Bayesian estimators have been developed to estimate tacrolimus exposure following administration of Prograf® and the prolonged release formulation Advagraf®. WHAT THIS STUDY ADDS • A population model was developed to compare the pharmacokinetics of 32 patients treated with Prograf® and 41 treated with Advagraf®.• A mixture model and a model using formulation as covariates were developed to describe the bimodal distribution in the absorption rate following Advagraf® administration. Comparison of these two models showed that the nonmixture model was adequate.• A single Bayesian estimator was developed to estimate the exposure for both formulations, which is more suitable for clinical practice.AIM To investigate the differences in the pharmacokinetics of Prograf® and the prolonged release formulation Advagraf® and to develop a Bayesian estimator to estimate tacrolimus inter‐dose area under the curve (AUC) in renal transplant patients receiving either Prograf® or Advagraf®.METHODS Tacrolimus concentration–time profiles were collected, in adult renal transplant recipients, at weeks 1 and 2, and at months 1, 3 and 6 post‐transplantation from 32 Prograf® treated patients, and one profile was collected from 41 Advagraf® patients more than 12 months post‐transplantation. Population pharmacokinetic (popPK) parameters were estimated using nonmem®. In a second step, the popPK model was used to develop a single Bayesian estimator for the two tacrolimus formulations.RESULTS A two‐compartment model with Erlang absorption (n= 3) and first‐order elimination best described the data. In Advagraf® patients, a bimodal distribution was observed for the absorption rate constant (K_tr): one group with a K_tr similar to that of Prograf® treated patients and the other group with a slower absorption. A mixture model for K_tr was tested to describe this bimodal distribution. However, the data were best described by the nonmixture model including covariates (cytochrome P450 3A5, haematocrit and drug formulation). Using this model and tacrolimus concentrations measured at 0, 1 and 3 h post‐dose, the Bayesian estimator could estimate tacrolimus AUC accurately (bias = 0.1%) and with good precision (8.6%).CONCLUSIONS The single Bayesian estimator developed yields good predictive performance for estimation of individual tacrolimus inter‐dose AUC in Prograf® and Advagraf® treated patients and is suitable for clinical practice.

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 this study were (i) to...

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 (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.

Objectives: The objectives of the present study were: (i) to analyse the population pharmacokinetics of sirolimus in renal transplant recipients co-administered mycophenolate mofetil, but no calcineurin inhibitor over the first 3 months post-transplantation and study the influence of different potential covariates, including genetic polymorphisms of cytochrome P450 (CYP) metabolic enzymes and active transporters, on pharmacokinetic parameters; and (ii) to develop a Bayesian estimator able to reliably estimate the individual pharmacokinetic parameters and exposure indices in this population. Methods: Twenty-two adult renal transplant patients treated with sirolimus participated in this study. Ninety concentration-time profiles (938 sirolimus whole blood samples) were collected at days 7 and 14, and months 1 and 3 post-transplantation. The population pharmacokinetic study was conducted using the nonlinear mixed effects model software, NONMEM, and validated using both the bootstrap and the cross-validation approaches. Finally, a Bayesian estimator based on a limited sampling strategy was built using the post hoc option. Results: A two-compartment open model with first-order elimination and Erlang’s distribution (to describe the absorption phase) best fitted the data. The mean pharmacokinetic parameter estimates were 5.25 h^′1, 218L and 292L for the transfer rate constant, the apparent volume of the central and peripheral compartments, respectively. The CYP3A5*1/*3 polymorphism significantly influenced the apparent oral clearance: mean oral clearance = 14.1 L/h for CYP3A5 non expressers (CYP3A5*3/*3 genotype) versus 28.3 L/h for CYP3A5 expressers (CYP3A5 *l/*3 and *1/*1 genotypes). The standard errors of all the parameter estimates were a posteriori Bayesian forecasting allowed accurate prediction of sirolimus area under the concentration-time curve from 0 to 24 hours using a combination of only three sampling times (0, 1 and 3 hours post-dose), with a non-significant bias of−2.1% (range −22.2% to +25.9%), and a good precision (root mean square error = 10.3%). This combination is also easy to implement in clinical practice. Conclusion: This study presents an accurate population pharmacokinetic model showing the significant influence of the CYP3A5*1/*3 polymorphism on sirolimus apparent oral clearance, and a Bayesian estimator accurately predicting sirolimus pharmacokinetics in patients co-administered mycophenolate mofetil, but no calcineurin inhibitor.

Population pharmacokinetic studies of ciclosporin microemulsion are needed to identify the individual factors influencing ciclosporin pharmacokinetic variability in transplant patients and to design efficient tools for the accurate estimation of ciclosporin overall exposure (area under the plasma concentration-time curve from 0 to 12 hours [AUC₁₂]). In the present retrospective study, a large database of heart, lung (with or without cystic fibrosis) and kidney (both adult and paediatric) transplant patients receiving ciclosporin microemulsion was analysed with the aims of (i) building a population pharmacokinetic model and finding the main covariates linked with ciclosporin microemulsion pharmacokinetic parameters; and (ii) developing a maximum a posteriori probability Bayesian estimator (MAP-BE) to estimate ciclosporin microemulsion pharmacokinetic parameters using a limited-sampling strategy. 3072 concentration data from 147 patients (i.e. 309 full pharmacokinetic profiles) were analysed using the nonlinear mixed-effects model program NONMEM. The influence of numerous covariates was tested, and the final model was validated by data splitting. For Bayesian estimation, the best limited-sampling strategy was determined based on the D-optimality criterion, and validation performed in an independent group of 60 patients. The pharmacokinetics of ciclosporin microemulsion were accurately described by a two-compartment model with Erlang distribution for the absorption process. The type of graft and post-transplantation period were identified as significant sources of variability of the absorption parameter. Both apparent volume of the central compartment after oral administration (V₁/F) and apparent oral clearance (CL/F) increased with bodyweight. The best limited-sampling strategy for Bayesian estimation was 0 hour, 1 hour and 3 hour post-dose, providing accurate estimation of ciclosporin microemulsion AUC₁₂ in all patients of the test group, with a mean bias of 2.0 ± 10.5% (range: −19.1% to −21.4% and 95% CI −0.6, +4.7). Population pharmacokinetic analysis of ciclosporin microemulsion in allograft transplants resulted in the design of a new pharmacokinetic model for ciclosporin microemulsion, identification of significant covariates and the design of an accurate MAP-BE based on three blood concentrations and these covariates.