Pierre Marquet

Buprenorphine withdrawal syndrome in a newborn

A pregnant woman who was addicted to heroin rapidly withdrew from illicit drugs after the onset of a 4 mg/day buprenorphine treatment. In the newborns blood, urine, and meconium 20 hours after birth, high concentrations of buprenorphine and its metabolite norbuprenorphine were detected, with a higher buprenorphine/norbuprenorphine ratio than in adults, possibly as a consequence of immature hepatic function; no illicit drugs were found. The child had a weak withdrawal syndrome on the second day of life and recovered rapidly. The measured buprenorphine daily dose ingested by the newborn through mothers milk was very low (3.28 μg) and probably had little pharmacologic effect because no withdrawal signs could be noted when maternal feeding was later abruptly interrupted. Further investigations are required to determine whether buprenorphine can be considered to be a good alternative to methadone in the treatment of pregnant heroin addicts to prevent marked withdrawal syndromes in newborns.

Mycophenolic acid area under the curve correlates with disease activity in lupus patients treated with mycophenolate mofetil

OBJECTIVE Mycophenolic acid (MPA) is the active metabolite of mycophenolate mofetil (MMF), which is widely used to treat systemic lupus erythematosus (SLE). In transplantation, MPA area under the plasma concentration-time curve from 0 to 12 hours (MPA AUC(0-12)) is correlated with clinical outcome. We undertook the present study to assess possible relationships between SLE activity and MPA AUC(0-12). METHODS Using a Bayesian estimator, MPA AUC(0-12) was determined in 71 consecutive SLE patients (61 women and 10 men; mean +/- SD age 34 +/- 10 years) receiving a stable MMF dose. On the same day, SLE activity was assessed using the SLE Disease Activity Index (SLEDAI; active disease defined as a SLEDAI score > or = 6) and the British Isles Lupus Assessment Group (BILAG) index (active disease defined as BILAG A or B). RESULTS Two groups were studied: patients with active SLE (mean +/- SD SLEDAI score 11.6 +/- 4.4; n = 26) and patients with inactive SLE (mean +/- SD SLEDAI score 1.9 +/- 1.6; n = 45). MPA AUC(0-12) correlated weakly with the dose of MMF (r = 0.33, P = 0.005). Mean +/- SD MPA AUC(0-12) in the group with active SLE was significantly lower than that in the group with inactive SLE (26.8 +/- 13.6 microg.hour/ml versus 46.5 +/- 16.3 microg.hour/ml; P < 0.0001). MPA AUC(0-12) was negatively correlated with the SLEDAI (r = -0.64, P < 0.0001). In multivariate analysis, MPA AUC(0-12) was the sole parameter associated with SLE activity (odds ratio 0.89 [95% confidence interval 0.83-0.96], P = 0.002). The MPA AUC(0-12) threshold value of 35 microg.hour/ml was associated with the lowest risk of active SLE. CONCLUSION Our data show that SLE activity is strongly correlated with MPA AUC(0-12). An individualized dosing regimen of MMF, with a target AUC(0-12) of 35 microg.hour/ml, should be considered for SLE patients.

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.

Limited sampling models and Bayesian estimation for mycophenolic acid area under the curve prediction in stable renal transplant patients co-medicated with ciclosporin or sirolimus.

Background and ObjectiveMycophenolate mofetil is a prodrug of mycophenolic acid (MPA), an immunosuppressive agent used in combination with corticosteroids and calcineurin inhibitors or sirolimus for the prevention of acute rejection after solid organ transplantation. Although MPA has a rather narrow therapeutic window and its pharmacokinetics show considerable intra- and interindividual variability, dosing guidelines recommend a standard dosage regimen of 0.5–1.0g twice daily in adult renal, liver and cardiac transplant recipients. The main objective ofthe present study was to develop a method to predict the MPA area under the plasma concentration-time curve during one 12-hour dosing interval (AUC12) by using multiple linear regression models and maximum aposteriori (MAP) Bayesian estimation methods in patients co-medicated with ciclosporin or sirolimus, aiming to individualize the dosage regimen of mycophenolate mofetil.Patients and MethodsPharmacokinetic profiles of MPA and mycophenolic acid glucuronide (MPAG), the main metabolite of MPA, were obtained from 40 stable adult renal allograft recipients on three different occasions: the day before switching from ciclosporin to sirolimus co-medication (±7.4 months posttransplantation; period I) and at 60 days and 270 days after the switch (periods II and III). Blood samples for determination of MPA and MPAG concentrations in plasma were taken at 0 hours (pre-dose) and at 0.33, 0.66, 1.25, 2, 4, 6, 8 and 12 hours after oral intake ofmycophenolate mofetil. The MPA AUC12 was calculated by the trapezoidal method (the observed AUC12). Patients were randomly divided into (i) a model-building test group (n = 27); and (ii) a model-validation group (n = 13). Multiple linear regression models were developed, based on three sampling times after drug administration. Subsequently, a population pharmacokinetic model describing MPA and MPAG plasma concentrations was developed using nonlinear mixed-effects modelling and a Bayesian estimator based on the population pharmacokinetic model was used to predict the MPA AUC12 based on three sampling times taken within 2 hours following dosing.ResultsFifty-two percent ofthe observed AUC12 values (three periods) in the 40 patients receiving a fixed dose of mycophenolate mofetil 750mg twice daily were outside the recommended therapeutic range (30–60 mg • h/mL). The failure of the standard dose to yield an AUC12 value within the therapeutic range was especially pronounced during the first study period. Of the multiple linear regression models that were tested, the equation based on the 0-hour (pre-dose), 0.66- and 2-hour sampling times showed the best predictive performance in the validation group: r2 = 0.79, relative root mean square error (rRMSE)= 14% and mean relative prediction error (MRPE) = 0.9%. The pharmacokinetics of MPA and MPAG were best described by a two-compartment model with first-order absorption and elimination for MPA, plus a compartment for MPAG, also including a gastrointestinal compartment and enterohepatic cycling in the case of sirolimus co-medication. The ratio of aminotransferase liver enzymes (AST and ALT) and the glomerular filtration rate significantly influenced MPA glucuronidation and MPAG renal excretion, respectively. Bayesian estimation ofthe MPA AUC12 based on 0-, 1.25- and 2-hour sampling times predicted the observed AUC12 values ofthe patients in the validation group, with the following predictive performance characteristics: r2 = 0.93, rRMSE= 12.4% and MRPE = -0.4%.ConclusionUse ofthe developed multiple linear regression equation and Bayesian estimator, both based on only three blood sampling times within 2 hours following a dose of mycophenolate mofetil, allowed an accurate prediction ofa patient’s MPA AUC12 for therapeutic drug monitoring and dose individualization. These findings should be validated in a randomized prospective trial.

Pharmacokinetic study of tacrolimus in cystic fibrosis and non-cystic fibrosis lung transplant patients and design of bayesian estimators using limited sampling strategies

ObjectivesTo: (i) test different pharmacokinetic models to fit full tacrolimus concentration-time profiles; (ii) estimate the tacrolimus pharmacokinetic characteristics in stable lung transplant patients with or without cystic fibrosis (CF); (iii) compare the pharmacokinetic parameters between these two patient groups; and (iv) design maximum a posteriori Bayesian estimators (MAP-BE) for pharmacokinetic forecasting in these patients using a limited sampling strategy.MethodsTacrolimus blood concentration-time profiles obtained on three occasions within a 5-day period in 22 adult lung transplant recipients (11 with CF and 11 without CF) were retrospectively studied. Three different one-compartment models with first-order elimination were tested to fit the data: one with first-order absorption, one convoluted with a gamma distribution to describe the absorption phase, and one convoluted with a double gamma distribution able to describe secondary concentration peaks. Finally, Bayesian estimation using the best model and a limited sampling strategy was tested in the two groups of patients for its ability to provide accurate estimates of the main tacrolimus pharmacokinetic parameters and exposure indices.ResultsThe one-compartment model with first-order elimination convoluted with a double gamma distribution gave the best results in both CF and non-CF lung transplant recipients. The patients with CF required higher doses of tacrolimus than those without CF to achieve similar drug exposure, and population modelling had to be performed in CF and non-CF patients separately. Accurate Bayesian estimates of area under the blood concentration-time curve from 0 to 12 hours (AUC12), AUC from 0 to 4 hours, peak blood concentration (Cmax) and time to reach Cmax were obtained using three blood samples collected at 0, 1 and 3 hours in non-CF patients (correlation coefficient between observed and estimated AUC12, R2 = 0.96), and at 0, 1.5 and 4 hours in CF patients (R2 = 0.91).ConclusionA particular pharmacokinetic model was designed to fit the complex and highly variable tacrolimus blood concentration-time profiles. Moreover, MAP-BE allowing tacrolimus therapeutic drug monitoring based on AUC12 were developed.

Cyclosporine pharmacokinetics and dose monitoring after lung transplantation: comparison between cystic fibrosis and other conditions.

Background. In cystic fibrosis (CF), absorption of cyclosporine A (CsA) through the gastrointestinal tract is often impaired because of fat malabsorption. The aim of this study was to compare the steady-state pharmacokinetics of CsA and the inter- and intrasubject variability of CsA exposure in stable lung transplant recipients with and without CF and to determine the best single-time predictors of the area under the curve (AUC). Methods. Ten lung transplant recipients without CF and 10 lung transplant recipients with CF were studied. All patients received Neoral twice daily. Blood samples were obtained predose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, and 12 h postdose on three separate days within a 5-day period. Results. CsA exposure and pharmacokinetic variables were similar in the two groups, although exposure-per-milligram-per-dose was ∼25% lower in CF patients. Coefficients of intersubject variability were numerically higher in CF patients, but the difference between groups did not reach significance. On the other hand, the maximum concentration (Cmax), the concentration 2 hours after administration (C2), AUC0–12, and AUC0–4 showed a twofold greater intrasubject variability in CF patients. CsA trough concentration did not predict accurately the AUC, but C2 was a good predictor of the AUC0–4 in both CF (r2=0.90) and non-CF (r2=0.78) patients. Conclusion. Compared to patients without CF, patients with CF show a lower bioavailability of CsA and a greater intrasubject variability of Cmax, C2, and AUC. C2 is the best single-point predictor of the AUC0–4 in lung transplant recipients with and without CF.

Assuring the proper analytical performance of measurement procedures for immunosuppressive drug concentrations in clinical practice: Recommendations of the international association of therapeutic drug monitoring and clinical toxicology immunosuppressive drug scientific committee

Abstract: Monitoring immunosuppressive drugs (ISDs) in blood or plasma is still a key therapeutic drug monitoring (TDM) application in clinical settings. Narrow target ranges and severe side effects at drug underexposure or overexposure make accurate and precise measurements a must. This overview prepared by the Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology is intended to serve as a summary and guidance document describing the current state-of-the-art in the TDM of ISDs.

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.

Falsely elevated whole-blood tacrolimus concentrations in a kidney-transplant patient: potential hazards.

Tacrolimus‐based immunosuppression is the most frequently prescribed immunosuppression for kidney‐transplant (KT) patients. Because tacrolimus has a narrow therapeutic window, drug monitoring is mandatory. Of the many methods used to assess whole‐blood trough levels, antibody‐conjugated magnetic immunoassay (ACMIA) is popular because, compared with microparticle enzyme‐linked immunoassays (MEIA), there is no need to pretreat samples, thus reducing time taken by the laboratory technician. Herein, we report on a KT tacrolimus‐treated patient who experienced falsely elevated whole‐blood tacrolimus concentrations after using the ACMIA method. ACMIA gave trough levels of 24 ng/ml, whereas the actual trough level, when measured by enzyme‐multiplied immunoassay technique (EMIT) and high‐performance liquid chromatography coupled with mass spectrometry (LC‐MS/MS), was nil. After a workup we only found one factor that might have caused the elevated concentration: positive anti‐double stranded DNA autoantibodies. We conclude that, when ACMIA produces surprisingly high tacrolimus concentrations in organ‐transplant patients, these should be reassessed immediately using either LC‐MS/MS or another immunoassay in order to eliminate falsely elevated results.

Tacrolimus Pharmacokinetics and Dose Monitoring After Lung Transplantation for Cystic Fibrosis and Other Conditions

In cystic fibrosis (CF), absorption of tacrolimus through the gastrointestinal tract may be impaired due to fat malabsorption. The aim of this pilot study was to compare tacrolimus pharmacokinetics and inter‐ and intrasubject variability of exposure in stable lung transplant recipients with and without CF, and to determine the best single‐time predictors of exposure. The study included 11 lung transplant recipients with CF and 11 without CF who received tacrolimus twice daily. Blood samples were obtained predose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8 and 12 h postdose on 3 separate days within 1 week. Tacrolimus pharmacokinetics and inter‐ and intrasubject variability of exposure were similar in the two groups, though exposure‐per‐milligram‐dose was ∼50% lower in CF patients. Tacrolimus trough concentration did not accurately predict the area under the concentration curve (AUC0–12), but the concentration measured 3 h postdose (C3) was tightly correlated with the AUC0–12 in both CF (r2= 0.86) and non‐CF (r2= 0.92) patients. In summary, patients with CF have a higher tacrolimus oral clearance, but nonsignificant differences in short‐term inter‐ and intrasubject variability of exposure compared to patients without CF. C3 is tightly correlated with AUC0–12 in lung transplant recipients with and without CF.