Pharmacokinetic study of mycophenolate mofetil in patients with systemic lupus erythematosus and design of Bayesian estimator using limited sampling strategies

BACKGROUND: Monitoring of the area under the plasma concentration-time curve (AUC) of mycophenolic acid (MPA) has been developed for individual dose adjustment of mycophenolate mofetil (MMF) in renal allograft recipients. MMF is currently used as an off-label drug in the treatment of systemic lupus erythematosus (SLE), but factors of its exposition may be different in these patients and need to be determined for therapeutic drug monitoring (TDM) purposes. OBJECTIVE: The aim of the study was to develop a maximum a posteriori probability (MAP) Bayesian estimator of MPA exposition in patients with SLE, with the objective of TDM based on a limited sample strategy. METHODS: Twenty adult patients with SLE given a stable 1 g/day, 2 g/day or 3 g/day dose of MMF orally for at least 10 weeks were included in the study. MPA was measured by high-performance liquid chromatography (HPLC) coupled to a photodiode array detector (11 plasma measurements over 12 hours post-dose per patient). Free MPA concentrations were measured by HPLC with fluorescence detection. Two different one-compartment models with first-order elimination were tested to fit the data: one convoluted with a double gamma distribution to describe secondary concentrations peaks, and one convoluted with a triple gamma distribution to model a third, later peak. RESULTS: A large interindividual variability in MPA concentration-time profiles was observed. The mean maximum plasma concentration, trough plasma concentration, time to reach the maximum plasma concentration and AUC from 0 to 12 hours (AUC(12)) were 13.6 +/- 8.4 microg/mL, 1.4 +/- 1.2 microg/mL, 1.1 +/- 1.2 hours and 32.2 +/- 17.1microg . h/mL, respectively. The mean free fraction of MPA was 1.7%. The one-compartment model with first-order elimination convoluted with a triple gamma distribution best fitted the data. Accurate Bayesian estimates of the AUC(12) were obtained using three blood samples collected at 40 minutes, 2 hours and 3 hours, with a coefficient of correlation (R) = 0.95 between the observed and predicted AUC(12) and with a difference of <20% in 16 of the 20 patients. CONCLUSION: A specific pharmacokinetic model was built to accurately fit MPA blood concentration-time profiles after MMF oral dosing in SLE patients, which allowed development of an accurate Bayesian estimator of MPA exposure that should allow MMF monitoring based on the AUC(12) in these patients. The predictive value of targeting one specific or different AUC values on patients' outcome using this estimator in SLE will need to be evaluated.     

Patient characteristics influencing ciclosporin pharmacokinetics and accurate Bayesian estimation of ciclosporin exposure in heart, lung and kidney transplant patients

BACKGROUND AND OBJECTIVES: 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. METHODS: 3,072 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. RESULTS: 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). CONCLUSION: 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.     

Maximum a posteriori Bayesian estimation of oral cyclosporin pharmacokinetics in patients with stable renal transplants

OBJECTIVE: To develop a maximum a posteriori probability (MAP) Bayesian estimator for the pharmacokinetics of oral cyclosporin, based on only three timepoints, and evaluate its performance with respect to a full-profile nonlinear regression approach. PATIENTS: 20 adult patients with stable renal transplants given orally administered microemulsified cyclosporin and mycophenolate. METHODS: Cyclosporin was assayed by liquid chromatography-mass spectrometry. Nonlinear regression and MAP Bayesian estimation were performed using a home-made program and a previously designed pharmacokinetic model including an S-shaped absorption profile described by a gamma distribution. OUTCOME MEASURES AND RESULTS: MAP Bayesian estimation using the best limited sampling strategy (before administration, and 1 and 3 hours after administration) was compared with nonlinear regression (taken as the reference method) for the prediction of the different pharmacokinetic parameters and exposure indices. Median relative prediction error was -0.49 and -3.42% for area under the concentration-time curve over the administration interval of 12 hours (AUC12) and estimated peak drug concentration (Cmax), respectively (nonsignificant). Relative precision was 2.00 and 4.32%, and correlation coefficient (r) was 0.985 and 0.955, for AUC12 and Cmax, respectively. CONCLUSION: This paper reports preliminary results in a stable renal transplant patient population, showing that MAP Bayesian estimation can allow accurate prediction of AUC12 and Cmax with only three samples (0, 1 and 3 hours). Although these results require confirmation by further studies in other clinical settings, using other drug combinations, other analytical methods and commercially available pharmacokinetic software, the method seems promising as a tool for the therapeutic drug monitoring of cyclosporin in clinical practice or for exposure-controlled studies.     

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 (lambda(1) = 4.14 +/- 3.01 vs. 2.16 +/- 1.75 h(-1); P < 0.01; lambda(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 AUC(0-12), AUC(0-4), C(max), and T(max) 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.     

Evaluation of a limited sampling strategy to estimate area under the curve of tacrolimus in adult renal transplant patients

Limited sampling strategies have been developed to predict full AUCs. The goal of this study was to develop a limited sampling strategy to estimate the AUC of tacrolimus in adult renal transplant patients and to evaluate its predictive performance in an independent patient population. A total of 27 tacrolimus pharmacokinetic profiles were studied. Blood samples were collected before the dose (0) and at 0.5, 1, 2, 4, 6, 8, and 12 hours postdose. The study was divided into 2 phases. In phase 1, the goal was to obtain a sampling strategy from 14 pharmacokinetic profiles. In phase 2, the bias and precision of the model were evaluated in another 13 pharmacokinetic profiles. The best correlation was achieved at 4 hours after dose (r(2) = 0.790). Stepwise multiple regression analysis determined that the abbreviated AUC at 0, 1, and 4 hours could accurately predict total AUC (r(2) = 0.965). The following formula was developed: AUC = 8.90 + 4.0C0h+ 1.77C1h + 5.47C4h. No significant differences were found between calculated and estimated AUC (165.6 +/- 41.1 and 166.7 +/- 43.2 ng.h/mL, respectively). The mean prediction error (MPE), the relative prediction error (PE), and the mean squared error (MSE) were 0.48 ng.h/mL, 0.16%, and 40.0 ng.h/mL, respectively. The limited sampling with use of the 3 levels at 0, 1, and 4 hours postdose provides accurate, reliable determination of tacrolimus AUC in renal transplant patients.     

Population pharmacokinetics of tacrolimus in whole blood and plasma in asian liver transplant patients

OBJECTIVES: The objectives of this study were to develop population pharmacokinetic models of tacrolimus in an Asian population with whole blood and plasma drug concentration data, to compare the variability of the pharmacokinetic parameters in these two matrices and to search for the main patient characteristics that explain the variability in pharmacokinetic parameters. STUDY DESIGN: Prospective pharmacokinetic assessment followed by model fitting. PATIENTS: Whole blood samples from 31 liver transplant patients in a local hospital receiving oral tacrolimus as part of their immunosuppressive therapy were assessed. Plasma samples from 29 of the 31 patients were also evaluated. Concentrations of tacrolimus in whole blood and plasma were determined by an electrospray high-performance liquid chromatography with tandem mass spectrometry. Two hundred and thirteen whole blood and 157 plasma tacrolimus concentrations were used for building two nonlinear mixed-effects population models to describe the disposition of tacrolimus in whole blood and plasma, respectively. Covariates that were investigated included demographic characteristics, biological markers of liver and renal functions, corticosteroid dose and haematological parameter. RESULTS: A one-compartment model was used to describe the whole blood and plasma concentration-time data of tacrolimus after oral administration. For the whole blood population model, the population estimates of the first-order absorption rate constant (k(a)), apparent clearance based on whole blood concentration after oral administration (CL(B)/F) and apparent volume of distribution based on whole blood concentrations after oral administration (V(d,B)/F) were 2.08h(-1), 14.1 L/h and 217L, respectively. The coefficient of variations (CVs) of interpatient variabilities in CL(B)/F and V(d,B)/F were 65.7% and 63.8%, respectively. Bodyweight, liver and renal function influenced CL(B)/F, while height and haematocrit influenced V(d,B)/F. The residual (unexplained) variability was 34.8%. For the plasma population model, the population estimates of the k(a), apparent clearance based on plasma concentrations after oral administration (CL(P)/F) and apparent volume of distribution based on plasma concentrations after oral administration (V(d,P)/F) were 5.21h(-1), 537 L/h and 563L, respectively. The CVs of interpatient variabilities in CL(P)/F and V(d,P)/F were 96.0% and 105.4%, respectively. Bodyweight was found to influence CL(P)/F, while the erythrocyte-to-plasma concentration ratio influenced V(d,P)/F. The residual (unexplained) variability was 49.8% at the mean plasma concentration of 1.1 ng/mL. CONCLUSIONS: Whole blood and plasma population pharmacokinetic models of tacrolimus in Asian adult and paediatric liver transplant patients were developed using prospective data in a clinical setting. This has identified and quantified sources of interindividual variability in CL(B)/F, V(d,B)/F, CL(P)/F and V(d,P)/F of tacrolimus in Asian liver transplant patients. Information derived from the whole blood population model may subsequently be used by clinicians for dosage individualisation through Bayesian forecasting.     

Chronopharmacokinetics of tacrolimus in kidney transplant recipients: occurrence of acute rejection

The circadian variation of clinical pharmacokinetics of tacrolimus was studied using 16 adult renal transplant recipients 1 month after the operation. The recipients were administered tacrolimus twice a day (9 a.m. and 9 p.m.), and whole-blood samples were obtained just prior to and 1, 2, 3, 6, 9, and 12 hours after oral administration. Histological specimens of transplant kidney were collected by an allograft core biopsy on day 28 after the transplantation. There were no circadian changes in the area under the concentration-time curve (AUC0-12) (214 ng.h/mL during daytime vs. 223 ng.h/mL during nighttime) resulting from morning and night doses. A slight delay in mean residence time (MRT0-12) and time to the peak concentration (tmax) was found after night doses, but there was no statistical significance. Three patients (18.8%) had a clinical acute rejection (AR) episode 4 to 6 weeks after transplantation, and AUC0-12 at nighttime was significantly lower (18.4% on average) in patients with AR in comparison to those without AR. There was no statistical significance in maximum concentration (Cmax) or morning/night trough levels between patients with and without AR. In regard to the correlation between tacrolimus concentrations in each sampling time and AUC0-12, the morning trough concentrations were less predictable for daytime AUC0-12 (r2 = 0.125), but there was a weak correlation to nighttime AUC0-12 (r2 = 0.424). Tacrolimus concentrations at 2, 3, and 6 hours after the morning dose (C2, C3, and C6) had a good correlation against daytime AUC. The results of this study indicate that the variance on the clinical pharmacokinetics of tacrolimus between daytime and nighttime in renal transplant patients is not significant, while the lower nighttime AUC corresponded to the occurrence of AR.     

Population pharmacokinetics and bayesian estimator of cyclosporine in pediatric renal transplant patients

Cyclosporine A (CsA) is an immunosuppressive drug widely used in pediatric renal graft recipients. Its large interindividual pharmacokinetic variability and narrow therapeutic index render therapeutic drug monitoring necessary. However, information about CsA pharmacokinetics is scarce and no population pharmacokinetic (popPK) studies in these populations have been reported so far. to the objectives of this study were 1) to develop a PKpop model and identify the individual factors influencing the variability of CsA pharmacokinetics in pediatric kidney recipients; and 2) to build a Bayesian estimator allowing the estimation of the main PK parameters and exposure indices to CsA on the basis of a limited sampling strategy (LSS). The popPK analysis was performed using the NONMEM program. A total of 256 PK profiles of CsA collected in 98 pediatric renal transplant patients (mean age 9.7 +/- 4.5 years old) within the first year posttransplantation were studied. A 2-compartment model with first-order elimination, and Erlang distribution to describe the absorption phase, fitted the data adequately. For Bayesian estimation, the best LSS was determined based on its performance in estimating area under the concentration-time curve (AUC0-12h) and validated in an independent group of 20 patients. The popPK analysis identified body weight and posttransplant delay as individual factors influencing the apparent central volume of distribution and the apparent clearance, respectively. Bayesian estimation allowed accurate prediction of AUC0-12h using predose, C1h, and C3h blood samples with a mean bias between observed and estimated AUC of 0.5% +/- 11% and good precision (root mean square error = 10.9%). This article reports the first popPK study of CsA in pediatric renal transplant patients. It confirms the reliability and feasibility of CsA AUC estimation in this population. The body weight and the posttransplantation delay were identified to influence PK interindividual variability of CsA and were included in the Bayesian estimator developed, which could be helpful in further clinical trials.     

Pharmacokinetic interaction between tacrolimus and diltiazem: dose-response relationship in kidney and liver transplant recipients

OBJECTIVE: To study the dose-response relationship of the pharmacokinetic interaction between diltiazem and tacrolimus in kidney and liver transplant recipients. DESIGN: Nonrandomised seven-period stepwise pharmacokinetic study. PATIENTS: Stable kidney (n = 2) and liver (n = 2) transplant recipients maintained on oral tacrolimus twice daily but not taking diltiazem. METHODS: Patients were treated with seven incremental dosages of diltiazem (0 to 180 mg/day) at > or = 2-weekly intervals. At the end of each interval, 13 blood samples were taken over a 24-hour period to allow determination of morning (AUC(12)), evening (AUC(12-24)) and 24-hour (AUC(24)) areas under the concentration-time curve for tacrolimus, as well as AUC(24) for diltiazem and three of its metabolites. RESULTS: There was considerable interpatient variability in tacrolimus-sparing effect. In the two kidney transplant recipients, an increase in tacrolimus AUC(24) occurred following the 20 mg/day dosage of diltiazem (26 and 67%). The maximum increase in tacrolimus AUC(24) occurred at the maximum diltiazem dosage used (180 mg/day), when the increase was 48 and 177%. In the two liver transplant recipients, an increase in tacrolimus AUC(24) did not occur until a higher diltiazem dosage (60 to 120 mg/day) was given. The increase at the maximum diltiazem dosages used (120 mg/day in one and 180 mg/day in the other) was also lower (18 and 22%) than that exhibited by the kidney transplant recipients. The increase in tacrolimus AUC(12) was similar to the increase in AUC(12-24) when diltiazem was given in the morning only (dosages < or = 60 mg/day). Hence, diltiazem affects blood tacrolimus concentrations for longer than would be predicted from the half-life of diltiazem in plasma. CONCLUSIONS: The mean tacrolimus-sparing effect of diltiazem was similar in magnitude to the cyclosporin-sparing effect previously reported. Whether the lesser tacrolimus-sparing effect with diltiazem seen in the liver transplant recipients was due to functional differences in the transplanted liver is not known, but it was not due to lower plasma diltiazem concentrations. Diltiazem makes a logical tacrolimus-sparing agent because of the potential financial savings and therapeutic benefits. Because of interpatient variability, the sparing effect should be demonstrated in each patient and not merely assumed.     

Population pharmacokinetic modeling of oral cyclosporin using NONMEM: comparison of absorption pharmacokinetic models and design of a Bayesian estimator

There have been very few population pharmacokinetic (PopPK) studies and Bayesian forecasting methods dealing with cyclosporin (CsA) so far, probably because of the difficulty of modeling the particular absorption profiles of CsA. The present study was conducted in stable renal transplant patients treated with Neoral and employed the NONMEM program. Its goals were (1) to develop a population pharmacokinetic model for CsA based on an Erlang frequency distribution (which describes asymmetric S-shaped absorption profiles) combined with a 2-compartment model; (2) to compare this model with models combining a time-lag parameter and either a zero-order or first-order rate constant and with a model based on a Weibull distribution; and (3) to develop a PK Bayesian estimator for full AUC estimation based on that "Erlang model." The PopPK model was developed in an index set of 70 patients, and then individual PK parameters and AUC were estimated in 10 other patients using Bayesian estimation. The "Erlang" model best described the data, with mean absorption time (MAT), apparent clearance (CL/F), and apparent volume of the central compartment (Vc/F) of 0.78 hours, 26.3 L/h, and 76 L, respectively (interindividual variability CV = 33, 30, and 48%). Bayesian estimation allowed accurate prediction of systemic exposure using only 3 samples collected at 0, 1, and 3 hours. Regression analysis found no significant difference between the predicted and observed concentrations (10 per patient), and AUC(0-12) were estimated with a nonsignificant bias (0.6 to 8.7%) and good precision (RMSE = 5.3%). In conclusion, the Erlang distribution best described CsA absorption profiles, and a Bayesian estimator developed using this model and a mixed-effect PK modeling program provided accurate estimates of CsA systemic exposure using only 3 blood samples.     

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in paediatric liver transplant recipients

Aims

The objectives of this study were to develop a population pharmacokinetic (PopPK) model for tacrolimus in paediatric liver transplant patients and determine optimal sampling strategies to estimate tacrolimus exposure accurately.

Methods

Twelve hour intensive pharmacokinetic profiles from 30 patients (age 0.4–18.4 years) receiving tacrolimus orally were analysed. The PopPK model explored the following covariates: weight, age, sex, type of transplant, age of liver donor, liver function tests, albumin, haematocrit, drug interactions, drug formulation and time post-transplantation. Optimal sampling strategies were developed and validated with jackknife.

Results

A two-compartment model with first-order absorption and elimination and lag time described the data. Weight was included on all pharmacokinetic parameters. Typical apparent clearance and central volume of distribution were 12.1 l h⁻¹ and 31.3 l, respectively. The PopPK approach led to the development of optimal sampling strategies, which allowed estimation of tacrolimus pharmacokinetics and area under the concentration–time curve (AUC) on the basis of practical sampling schedules (three or four sampling times within 4 h) with clinically acceptable prediction error limit. The mean bias and precision of the Bayesian vs. reference (trapezoidal) AUCs ranged from −2.8 to −1.9% and from 7.4 to 12.5%, respectively.

Conclusions

The PopPK of tacrolimus and empirical Bayesian estimates represent an accurate and convenient method to predict tacrolimus AUC_(0–12) in paediatric liver transplant recipients, despite high between-subject variability in pharmacokinetics and patient demographics. The developed optimal sampling strategies will allow the undertaking of prospective trials to define the tacrolimus AUC-based therapeutic window and dosing guidelines in this population.     

Population pharmacokinetics of mycophenolic acid in kidney transplant pediatric and adolescent patients

Current data on mycophenolate mofetil (MMF) suggest that there is a pharmacokinetic/pharmacodynamic relationship between the mycophenolic acid (MPA) area under the curve (AUC) during treatment and both the risk of acute rejection and the occurrence of side effects. The aim of this study was to characterize the population pharmacokinetics of MPA in kidney transplant patients between the ages of 2 and 21 years and to propose a limited sampling strategy to estimate individual MPA AUCs. Forty-one patients received long-term oral MMF continuous therapy as part of a triple immunosuppressive regimen, which also included cyclosporine or tacrolimus (n=3) and corticosteroids. Therapy was initiated at a dose of 600 mg/m twice daily. The population parameters were calculated from an initial group of 32 patients. The data were analyzed by nonlinear mixed-effect modeling using a 2-compartment structural model with first-order absorption and a lag time. The interindividual variability in the initial volume of distribution was partially explained by the fact that this parameter was weight-dependent. Fifteen concentration-time profiles from 13 patients were used to evaluate the predictive performance of the Bayesian approach and to devise a limited sampling strategy. The protocol, involving two sampling times, 1 and 4 hours after oral administration, allows the precise and accurate determination of MPA AUCs (bias -0.9 microg.h/mL; precision 6.02 microg.h/mL). The results of this study combine the relationships between the pharmacokinetic parameters of MPA and patient covariates, which may be useful for dose adjustment, with a convenient sampling procedure that may aid in optimizing pediatric patient care.     

Concomitant clotrimazole therapy more than doubles the relative oral bioavailability of tacrolimus

The purpose of this pharmacokinetic study was to determine whether the relative oral bioavailability of tacrolimus is increased with concomitant administration of clotrimazole. Pharmacokinetic studies were conducted in 6 adult kidney transplant patients receiving tacrolimus therapy. Pharmacokinetic profiling was performed by blood sampling over 12 hours before and after the administration of a 5-day course of clotrimazole. Tacrolimus whole-blood concentrations were determined by microparticle enzyme immunoassay. Noncompartmental pharmacokinetic analysis was conducted using WinNonLin, Standard Edition, Version 1.1. Concomitant administration of clotrimazole more than doubled the relative oral bioavailability of tacrolimus. The mean AUC0-12 of tacrolimus was increased 250% with clotrimazole (467.0 +/- 170.0 ng.h/mL versus 188.7 +/- 50.2 ng.h/mL; P = 0.002). Tacrolimus blood trough concentrations also more than doubled with coadministration of clotrimazole (27.7 +/- 10.4 ng/mL versus 11.6 +/- 4.0 ng/mL; P = 0.003). Mean Cmax was significantly increased with clotrimazole (70.7 +/- 34.7 ng/mL versus 27.4 +/- 11.1 ng/mL, P = 0.01). Tmax decreased from 3.2 +/- 1.6 hours to 1.9 +/- 1.0 hours (P = NS). In addition, the apparent oral clearance decreased 60% with coadministration of clotrimazole (median oral clearance 0.16 L/h/kg versus 0.40 L/h/kg; P = 0.03). Thus, clotrimazole causes a significant increase in the relative oral bioavailability, Tmax, and trough concentration of tacrolimus. Tacrolimus levels should be monitored following initiation or discontinuation of clotrimazole to minimize toxicity or precipitation of an acute rejection episode due to subtherapeutic levels.     

塞克硝唑片剂健康人体生物等效性研究

目的:研究塞克硝唑试验片与参比片的生物利用度,并进行生物等效性评价。方法:20名健康男性志愿者单剂量口服塞克硝唑试验或参比制剂各2 g;采用反相高效液相色谱法测定其血药浓度。结果:人体药动学研究表明,口服塞克硝唑片的药-时曲线符合一级吸收的单房室模型。试验片与参比片的主要药代动力学参数:tmax分别为(2.30±1.06)和(2.28±1.10)h;Cmax分别为(49.63±6.35)和(46.17±4.24)mg/L;t1/2分别为(28.84±3.41)和(29.05±4.01)h;AUC0-96分别为(1832.06±180.15)和(1847.14±204.14)mg.h-1.L-1;相对生物利用度为(99.99±11.92)%。结论:塞克硝唑片两种制剂具有生物等效性。     

Pharmacokinetics of mycophenolic acid and determination of area under the curve by abbreviated sampling strategy in Chinese liver transplant recipients

OBJECTIVES: This study aimed to: (i) define the clinical pharmacokinetics of mycophenolic acid (MPA) in Chinese liver transplant recipients; and (ii) develop a regression model best fitted for the prediction of MPA area under the plasma concentration-time curve from 0 to 12 hours (AUC(12)) by abbreviated sampling strategy. METHODS: Forty liver transplant patients received mycophenolate mofetil 1g as a single dose twice daily in combination with tacrolimus. MPA concentrations were determined by high-performance liquid chromatography before dose (C(0)) and at 0.5 (C(0.5)), 1 (C(1)), 1.5 (C(1.5)), 2 (C(2)), 4 (C(4)), 6 (C(6)), 8 (C(8)), 10 (C(10)) and 12 (C(12)) hours after administration on days 7 and 14. A total of 72 pharmacokinetic profiles were obtained. MPA AUC(12) was calculated with 3P97 software. The trough concentrations (C(0)) of tacrolimus and hepatic function were also measured simultaneously. Multiple linear regression analysis was used to establish the models for estimated MPA AUC(12). The agreement between predicted MPA AUC(12) and observed MPA AUC(12) was investigated by Bland-Altman analysis. RESULTS: The pattern of MPA concentrations during the 12-hour interval on day 7 was very similar to that on day 14. In the total of 72 profiles, the mean maximum plasma concentration (C(max)) and time to reach C(max) (t(max)) were 9.79 +/- 5.26 mg/L and 1.43 +/- 0.78 hours, respectively. The mean MPA AUC(12) was 46.50 +/- 17.42 mg . h/L (range 17.99-98.73 mg . h/L). Correlation between MPA C(0) and MPA AUC(12) was poor (r(2) = 0.300, p = 0.0001). The best model for prediction of MPA AUC(12) was by using 1, 2, 6 and 8 hour timepoint MPA concentrations (r(2) = 0.921, p = 0.0001). The regression equation for estimated MPA AUC(12) was 5.503 + 0.919 . C(1) + 1.871 . C(2) + 3.176 . C(6) + 3.664 . C(8).This model had minimal mean prediction error (1.24 +/- 11.19%) and minimal mean absolute prediction error (8.24 +/- 7.61%). Sixty-three of 72 (88%) estimated MPA AUC(12) were within 15% of MPA AUC(12). Bland-Altman analysis also revealed the best agreement of this model compared with the others and a mean error of +/-9.89 mg . h/mL. CONCLUSION: This study showed the wide variability in MPA AUC(12) in Chinese liver transplant recipients. Single timepoint MPA concentration during the 12-hour dosing interval cannot reflect MPA AUC(12). MPA AUC(12) could be predicted accurately using 1, 2, 6 and 8 hour timepoint MPA concentrations by abbreviated sampling strategy.     

Tacrolimus in diabetic kidney transplant recipients: pharmacokinetics and application of a limited sampling strategy

The effect of diabetes mellitus on the pharmacokinetics of tacrolimus is not well characterized. We have compared tacrolimus 12-hour steady-state concentration-time profiles in diabetic (n = 11) and demographically matched nondiabetic (n = 9) stable kidney transplant recipients and derived a limited sampling strategy for the estimation of tacrolimus area under the concentration-time curve (AUC(0-12)). Tacrolimus concentration was measured by liquid chromatography tandem mass spectrometry and acetaminophen absorption method was used to characterize gastric emptying time.Demographic and biochemical characteristics were comparable between the two groups with the exception of significantly higher glycated hemoglobin levels in patients with diabetes (P = 0.02). Time to maximum concentration (T(max)) of acetaminophen was significantly longer in diabetics [D: 74.1 minute versus nondiabetics (ND): 29.3 minutes, P = 0.02]; however, tacrolimus T(max) was not significantly different (D: 121 minutes versus ND: 87 minutes, P = 0.15). Median (interquartile range) of tacrolimus AUC(0-12) was 114 (101-161) microg*hr/L in patients with diabetes and 113 (87-189) microg*hr/L in nondiabetics (P = 0.62). The following limited sampling equation [AUC(pred) (microg*hr/L) = 18.70 - 1.72 C(1 hr) - 4.09 C(2 hr) + 14.40 C(3 hr)] was derived from a training data set that included 10 patients. The correlation coefficient between model-predicted and observed AUC0-12 values was 0.999. Mean prediction error and root mean square error of the model-predicted values derived from the patients in validation data set were 0.04 and 17.48 microg*hr/L, respectively.In conclusion, it appears that diabetes has a modest effect on the rate but not the extent of tacrolimus absorption, and a three-point abbreviated sampling strategy common to both groups may prove useful for the estimation of tacrolimus exposure in kidney transplant recipients.     

Comparison of the Bayesian approach and a limited sampling model for the estimation of AUC and Cmax: a computer simulation analysis.

OBJECTIVES: To compare two limited sampling methods (Bayesian and the limited sampling model) for the estimation of AUC and Cmax following a single oral dose of a hypothetical drug. METHODS: The plasma concentration vs time data sets for 50 subjects using a linear one- or two-compartment pharmacokinetic model were generated by simulation. The limited sampling model (LSM) was developed using samples from 10 subjects using one or two time points. The simulated plasma concentrations were also used for Bayesian evaluation. Bayesian analysis was performed on Non-Mem and mean pharmacokinetic parameters used for simulation were assumed as population pharmacokinetic parameters. In addition a test drug was also used to compare the predicted AUC and Cmax for the two approaches. RESULTS: Both methods were validated in 40 subjects for the hypothetical drug and in 12 subjects for the test drug. Both methods provided good estimates of AUC and Cmax. CONCLUSION: The results indicate that the LSM is similar to the Bayesian method and may be used in lieu of the Bayesian approach in estimating AUC and Cmax using one or two samples in clinical settings without detailed pharmacokinetic studies.     

Limited sampling strategy for predicting area under the concentration-time curve of mycophenolic acid in adult lung transplant recipients

STUDY OBJECTIVE: To develop limited sampling strategies for estimation of mycophenolic acid exposure (by determining area under the concentration-time curve [AUC]) in lung transplant recipients by using sampling times within 2 hours after drug administration and a maximum of three plasma samples. DESIGN: Prospective, open-label clinical study. SETTING: Lung transplant clinic in Vancouver, British Columbia, Canada. PATIENTS: Nineteen adult (mean age 48.3 yrs) lung transplant recipients who were receiving mycophenolate mofetil therapy along with cyclosporine (9 patients) or tacrolimus (10 patients). INTERVENTION: Eleven blood samples were collected from each of the 19 patients over 12 hours: immediately before (0 hr) and 0.3, 0.6, 1, 1.5, 2, 4, 6, 8, 10, and 12 hours after administration of mycophenolate mofetil. MEASUREMENTS AND MAIN RESULTS: Mycophenolic acid levels in plasma were determined by a high-performance liquid chromatography-ultraviolet detection method. The 19 patients were randomly divided into index (10 patients) and validation (9 patients) groups. Limited sampling strategies were developed with multiple regression analysis by using data from the index group. Data from the validation group were used to test each strategy. Bias and precision of each limited sampling strategy were determined by calculating the mean prediction error and the root mean square error, respectively. The correlation between AUC and single concentrations was generally poor (r2= 0.18-0.73). Two single-concentration strategies, eight two-concentration strategies, and eight three-concentration strategies matched our criteria. However, the best overall limited sampling strategies (and their predictive performance) were the following: log AUC = 0.241 log C0 + 0.406 log C2 + 1.140 (bias -5.82%, precision 5.97%, r2= 0.828) and log AUC = 0.202 log C0 + 0.411 log C1.5 + 1.09 (bias -5.71%, precision 6.94%, r2= 0.791), where Cx is mycophenolic acid concentration at time x hours. CONCLUSION: Two-concentration limited sampling strategies provided minimally biased and highly precise estimation of mycophenolic acid AUC in lung transplant recipients. These optimal and most clinically feasible limited sampling strategies are based collectively on the number of blood samples required, r2 value, bias, and precision.     

国产与进口美洛昔康片单次给药后人体药动学(英文)

目的 :比较健康志愿者国产与进口美洛昔康片的人体药动学和相对生物利用度。方法 :采用单次给药 2周期交叉设计 ,HPLC法测定 12名健康男性志愿者口服 15mg美洛昔康后血药浓度 ,计算药动学参数和国产片的相对生物利用度。结果 :2种制剂的药时曲线符合一级吸收的一室开放模型。国产与进口片的药动学参数分别是AUC0 - 96 为 (6 7±s 14 )mg·h·L- 1和 (6 4± 15 )mg·h·L- 1;AUC0 -∞ 为 (73± 19)mg·h·L- 1和 (70± 18)mg·h·L- 1;Cmax为 (2 .3± 0 .5 )mg·L- 1和 (1.6± 0 .3)mg·L- 1;Tmax为 (2 .0± 1.6 )h和 (6± 3)h ;T12 ke为 (2 5± 6 )h和 (2 4± 5 )h ;MRT为 (4 0± 13)hand (39±8)h。方差分析表明两者AUC之间无显著差异 (P>0 .0 5 ) ,但两者的Cmax和Tmax之间有显著差异 (P<0 .0 5 )。结论 :国产片的释药速率比进口片快 ,其相对生物利用度为 (10 5± 13) %。     

Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations--twice daily Prograf and once daily Advagraf

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.