Caroline Monchaud

Pharmacokinetic optimization of immunosuppressive therapy in thoracic transplantation: part I

Although immunosuppressive treatments and therapeutic drug monitoring (TDM) have significantly contributed to the increased success of thoracic transplantation, there is currently no consensus on the best immunosuppressive strategies. Maintenance therapy typically consists of a triple-drug regimen including corticosteroids, a calcineurin inhibitor (ciclosporin or tacrolimus) and either a purine synthesis antagonist (mycophenolate mofetil or azathioprine) or a mammalian target of rapamycin inhibitor (sirolimus or everolimus). The incidence of acute and chronic rejection and of mortality after thoracic transplantation is still high compared with other types of solid organ transplantation. The high allogenicity and immunogenicity of the lungs justify the use of higher doses of immunosuppressants, putting lung transplant recipients at a higher risk of drug-induced toxicities. All immunosuppressants are characterized by large intra- and interindividual variability of their pharmacokinetics and by a narrow therapeutic index. It is essential to know their pharmacokinetic properties and to use them for treatment individualization through TDM in order to improve the treatment outcome. Unlike the kidneys and the liver, the heart and the lungs are not directly involved in drug metabolism and elimination, which may be the cause of pharmacokinetic differences between patients from all of these transplant groups.TDM is mandatory for most immunosuppressants and has become an integral part of immunosuppressive drug therapy. It is usually based on trough concentration (C0) monitoring, but other TDM tools include the area under the concentration-time curve (AUC) over the (12-hour) dosage interval or the AUC over the first 4 hours post-dose, as well as other single concentration-time points such as the concentration at 2 hours. Given the peculiarities of thoracic transplantation, a review of the pharmacokinetics and TDM of the main immunosuppressants used in thoracic transplantation is presented in this article. Even more so than in other solid organ transplant populations, their pharmacokinetics are characterized by wide intra- and interindividual variability in thoracic transplant recipients. The pharmacokinetics of ciclosporin in heart and lung transplant recipients have been explored in a number of studies, but less is known about the pharmacokinetics of mycophenolate mofetil and tacrolimus in these populations, and there are hardly any studies on the pharmacokinetics of sirolimus and everolimus. Given the increased use of these molecules in thoracic transplant recipients, their pharmacokinetics deserve to be explored in depth. There are very few data, some of which are conflicting, on the practices and outcomes of TDM of immunosuppressants after thoracic transplantation. The development of sophisticated TDM tools dedicated to thoracic transplantation are awaited in order to accurately evaluate the patients’ exposure to drugs in general and, in particular, to immunosuppressants. Finally, large cohort TDM studies need to be conducted in thoracic transplant patients in order to identify the most predictive exposure indices and their target values, and to validate the clinical usefulness of improved TDM in these conditions.In part I of the article, we review the pharmacokinetics and TDM of calcineurin inhibitors. In part II, we will review the pharmacokinetics and TDM of mycophenolate and mammalian target of rapamycin inhibitors, and provide an overall discussion along with perspectives.

Bayesian forecasting of oral cyclosporin pharmacokinetics in stable lung transplant recipients with and without cystic fibrosis.

The aims of the current study were (1) to study Neoral pharmacokinetics (PK) in stable lung recipients with or without cystic fibrosis (CF), (2) to compare Neoral PK between these two groups, and (3) to design Bayesian estimators for PK forecasting and dose adjustment in these patients using a limited number of blood samples. The individual PK of 19 adult lung transplant recipients, 9 subjects with CF and 10 subjects without CF, were retrospectively studied. Three profiles obtained within 5 days were available for each patient. A PK model combining a gamma distribution to describe the absorption profile and a two-compartment model were applied. Different exposure indices were estimated using nonlinear regression and Bayesian estimation. The PK model developed reliably described the individual PK of Neoral in lung transplant patients with and without CF, and the values of the first and second half-lives were different in these two populations (&lgr;1 = 4.14 ± 3.01 vs. 2.16 ± 1.75 h−1;P < 0.01; &lgr;2 = 0.36 ± 0.11 vs. 0.49 ± 0.12 h−1;P < 0.01), while the mean absorption time and standard deviation of absorption time tended to be less in patients with cystic fibrosis (P < 0.1). Also, the patients with CF required higher doses than those without CF to achieve similar drug exposure. Consequently, population modeling was performed in CF and non-CF patients separately. Bayesian estimation allowed accurate prediction of AUC0–12, AUC0–4, Cmax, and Tmax using three blood samples collected at T0h, T1h, and T3h in both groups. This study demonstrated the applicability and good performance of the PK model previously developed for oral cyclosporin and of the MAP Bayesian estimation of cyclosporin systemic exposure in CF and non-CF patients. Moreover, it is the first to propose a monitoring tool specifically designed for cyclosporin monitoring in patients with CF.

Population pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation.

BackgroundTherapeutic 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.ObjectivesThe 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.MethodsA 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.ResultsTacrolimus 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.ConclusionPopulation 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.

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.

Towards therapeutic drug monitoring of everolimus in cancer? Results of an exploratory study of exposure-effect relationship

Introduction: Therapeutic drug monitoring (TDM) of everolimus is not performed in oncology and no trough level (C0) target has been yet defined. The aim of this study was to determine everolimus C0 target for toxicity and efficacy. Materials and methods: Clinical, biological and radiologic data from 54 patients were collected. Toxicity event was defined by termination, temporary interruption and/or dose reduction of everolimus while efficacy was defined as progression‐free survival. C0 values were dichotomized by ROC curve analysis and the association between exposure and outcome was determined using Cox models for repeated events (toxicity) or Cox model censured at the first event (progression free survival). Results: Among the 42 patients (77.8%) with breast cancer, 10 (18.5%) kidney cancer and 2 (3.7%) neuroendocrine cancer, adverse events were reported in 75.9% of the patients (everolimus termination in 25.9% patients). C0 everolimus higher than 26.3 ng/mL (Sen = 0.38,Spe = 0.88) were associated with a 4‐fold increased risk of toxicity (HR = 4.12, IC95% = [1.48–11.5], p = 0.0067) whereas C0 lower than 11.9 ng/mL were associated with a 3‐fold increased risk of progression (HR = 3.2, IC95% = [1.33–7.81],p = 0.001). Discussion: Further studies are required to evaluate the everolimus C0 threshold proposed for toxicity (26.3 ng/mL) and for progression (11.9 ng/mL) especially with a large number of patients and more homogeneous types of cancer. However, these results are in favour of TDM for everolimus in oncology.

A candidate gene approach of the calcineurin pathway to identify variants associated with clinical outcomes in renal transplantation

AIM To investigate the potential influence of variants in genes involved in the calcineurin pathway on the efficacy and toxicity of calcineurin inhibitors in renal transplantation. MATERIALS & METHODS Twenty-three polymorphisms in thirteen genes were tested in 381 renal transplant recipients receiving ciclosporin (n = 221) or tacrolimus (n = 160) and mycophenolate mofetil. Data were collected prospectively over the first year post-transplantation. RESULTS Multivariate survival analyses revealed no genetic associations with biopsy proven acute graft rejection and serious infections. Donor-recipient Cytomegalovirus mismatch was the only variable associated with serious infection. CONCLUSION This large exploratory study casts doubts on the potential interest of genetic biomarkers related to CNI pharmacodynamics but associations with other phenotypes in transplantation deserve further studies.

Mycophenolic mofetil optimized pharmacokinetic modelling, and exposure-effect associations in adult heart transplant recipients.

UNLABELLED Mycophenolic acid (MPA) area under the curve (AUC) has been associated with graft outcome. THE AIMS OF OUR STUDY WERE (1) to develop pharmacokinetic tools to optimize MPA inter-dose AUC estimation in heart transplant patients; and (2) to investigate the relationships between acute allograft rejection and MPA AUC, trough level (C0) or mycophenolate mofetil (MMF) dose. Two independent modeling approaches (parametric and non parametric) were used to fit 56 rich MPA pharmacokinetic (PK) profiles collected from 40 adult heart transplant recipients enrolled in the PIGREC study, receiving MMF and a calcineurin inhibitor (CNI), in the first year post-transplantation. In addition, associations between drug exposure (MPA C0, AUC and MMF dose) and acute rejection or MMF adverse events were investigated using time-dependent Cox models with stratification on the type of calcineurin inhibitor. Exposure threshold values were investigated using ROC curve analysis. The 2 models developed fit adequately the data and the use of their combination yielded 100% consistency with the measured AUC in terms of strategy of dose adjustment (maintain, increase or decrease). MPA measured AUC adjusted on CNI exposure was significantly associated with rejection (per unit increase: HR [95% CI]=0.97 [0.95-0.99], p=0.0122), while no effect was shown for adverse events attributable to MMF. An AUC threshold of 50 mg×h/L was proposed (sensitivity=77%, specificity=25%) beyond which the risk of rejection was significantly increased (low vs. high: HR=3.48 [1.21-10.0], p=0.0204). The tools developed have already been made available to the heart transplant community on our ISBA website (https://pharmaco.chu-limoges.fr).

Consensus conference on a composite endpoint for clinical trials on immunosuppressive drugs in lung transplantation.

Background In lung transplantation, diverse clinical events may impact patient outcome. In clinical trials comparing intervention strategies, single primary endpoints require large populations, or long study durations, whereas composite endpoints (CEPs) do not take into account the respective impact of their components on patient survival. The objective of this study was to propose consensus recommendations on endpoints for clinical trials on immunosuppressants in lung transplantation. Methods The consensus process was managed through the Internet using the Delphi method. Forty experts were invited by the pilot group with the help of the International Society for Heart and Lung Transplantation and The Transplantation Society. In the first round, a questionnaire was made available to the experts to complete, and the responses were analyzed. In each next round, a new questionnaire was developed from the previous responses and sent to the panel members. Results Consensus between 17 experts was achieved after five rounds. Two score-type CEPs were defined for immunosuppressive drug efficacy (7 items) and for toxicity (15 items). Death related to graft loss or immunosuppressive drug toxicity was attributed a maximum weight of 100. The weights of the items included in the efficacy and toxicity CEPs ranged between 10 and 80 and between 25 and 70, respectively. The CEP scores are calculated by adding the weights of all the items composing them, without exceeding 90 as long as the patient is alive. Conclusion This consensus conference proposed two score-type CEPs including relevant endpoints. After validation, they should allow clinical trials with higher statistical power, improving the evaluation of the interventions tested.