Mycophenolic Acid AUC Research Articles

Limited Sampling Strategy for Mycophenolic Acid in Chinese Kidney Transplant Recipients Receiving Enteric-Coated Mycophenolate Sodium and Tacrolimus During the Early Posttransplantation Phase.

Mycophenolic acid (MPA), a potent immunosuppressant, is widely used in solid organ transplantations. This study aimed to investigate the pharmacokinetics of enteric-coated mycophenolate sodium (EC-MPS) in Chinese adult renal allograft recipients and to generate optimal model equations for estimation of the MPA area under the concentration-time curve from 0 to 12 hours (AUC0-12 h), using a limited sampling strategy (LSS). Serial blood samples were collected over 12 hours from 38 recipients of a primary living-related donor kidney graft treated with EC-MPS, tacrolimus, and corticosteroid. MPA concentrations were evaluated using an enzyme-multiplied immunoassay technique. The LSSs were developed and validated by multiple regression analysis using a 2-group method (test group, n = 19; validation group, n = 19). The best algorithms obtained from the test group were the following: 15.09 + 1.05 × C1.5 + 1.8 × C4 + 4.18 × C6 (for 3 time points, r = 0.902) and 10.44 + 0.7 × C1 + 1.22 × C2 + 1.75 × C4 + 4.36 × C6 (for 4 time points, r = 0.941). When these algorithms were tested in the validation group, there were no significant differences in prediction errors. LSSs using time points of 1.5, 4, and 6 hours or 1, 2, 4, and 6 hours after dose provide effective and reliable estimations of the MPA AUC0-12 h in Chinese renal allograft recipients treated concomitantly with EC-MPS and tacrolimus during the early posttransplantation phase.

Risk Factors for BK Virus Replication During the First Three Months of Kidney Transplantation: Does It Matter to Consider a High AUC of Mycophenolic Acid?

Introduction: BK virus (BKv) infection is a threat to renal allograft survival. BKv nephropathy had largely been described since more potent immunosuppressive drugs appeared. An overimmunosuppression is often advocated for risk factor. Therapeutic drug monitoring may better assess individual immunosuppression. The aim of this study is to seek risk factors for significant BKviruria (> 10ˆ5 copies/ml) because there is no BK viremia under this threshold and to determine whether a high AUC of mycophenolic acid (MPA) is one of them. Method: we retrospectively observed 255 kidney recipients with a 1-year follow-up treated by mycophenolate mofetil between July 2006 and June 2011. BKv was regularly sought in blood and urine samples by quantitative PCR. Different risk factors were sought between group with significant BK viruria and without. Results: Fifty recipients (19.9%) had a BK viruria, 38 (14.9%) had a sustained BK viruria during the first 3 months and 25 (9.8%) had a BK viremia during the first year. Six (2.6%) patients underwent BKv nephropathy. Threshold for significant BK viruria was 56 h.mg/L determined by ROC equation. In univariate analysis, significant BK viruria occured more frequently in male recipients (81,6 vs 62,6%; p=0,02), older (54,6±14,3 vs 49,5±13,3 years; p=0,03) with a greater MPA AUC (54,1±21,1 vs 44,6±20,7 hr.mg/L; p=0,02). Nephroangiosclerosis and focal segmental glomerular sclerosis were more frequent when significant BK viruria occurred: 18,4 vs 7,1% (p=0,02) and 13,2 vs 4,3% (p=0,03) respectively. In multivariate analysis, male gender (relative risk (RR) 3,1; p<0,02) age above 58 years old (RR 2,6; p<0,02) and MPA AUC greater than 56 h.mg/l (RR 2,8; p<0,001) were risk factors for significant BK viruria. Conclusion: a high MPA AUC (>56 h.mg/l) seems to be a risk factor for significant BKviruria as male gender and age. It could be reasonable to not exceed MPA AUC > 55 h.mg/l to prevent significant BK viruria and BK viremia.

Impact of Omeprazole On Mycophenolic Acid Pharmacokinetics in Adult Renal Transplant Patients.

Background:Published studies have shown that proton pump inhibitors(PPIs) reduce the mycophenolic acid(MPA) area-under-the-curve(AUC0-12) in patients treated with mycophenolate mofetil(MMF), but not with enteric-coated mycophenolate sodium(EC-MPS). Aim:To analyse the impact of omeprazole(OME) coadministration on MPA pharmacokinetics in adult renal transplant patients treated with MMF(Cellcept®) or EC-MPS(Myfortic®). Patients and methods:Crossover study of 20 patients who underwent a cadaveric renal transplantation, in a stable clinical situation (6th month post-transplant), with tacrolimus, prednisone and MPA equivalent doses (500 mg MMF˜360 mg EC-MPS) and a 20-mg OME daily dose administered in the morning around 30 minutes before immunosuppressive therapy. For each patient, two pharmacokinetic profiles were obtained with OME treatment (1st) and without OME treatment (2nd), with a 7-10-day washout period between each. Samples were extracted before and at 1,2,3,4,6 and 8h after the oral MMF or EC-MPS morning dose. Plasma levels were measured using EMIT on Dimension™analyser. AUC0-12 was calculated using linear trapezoidal rule. MPA trough levels (Ctrough) and AUC0-12 valueswere compared during and after OME discontinuation. Statistical analysis: SPSS version 19.0. Wilcoxon Signed Rank Test was used for studying Ctrough and AUC0-12 variations. P<0.05: statistically significant. Results:The study included 20 patients (9:MMF, 11:EC-MPS), mean age: 50 years, mean weight: 69 kg.In patients treated with MMF, pharmacokinetic variables for those receiving OME or not were respectively: Ctrough (3.12±1.58 vs. 1.96±0.85 ng/mL,p=0.021, median 2.50 vs. 1.80) and AUC0-12 (49.34±10.47 vs. 37.37±7.68 ng.h/mL, p=0.008, median 50.25 vs. 38.85). When the patients were treated with EC-MPS, these parameters were:Ctrough (4.54±4.03 vs. 4.12±2.78 ng/mL,p=0.878, median 2.80 vs. 3.10) and AUC0-12 (52.04±39.78 vs. 71.87±43.03 ng.h/mL, p=0.003,median 41.55 vs.54.25). Conclusions: As omeprazole is an inhibitor, this study performed on renal transplant patients showed a significantly higher exposure (AUC0-12)with OME comedication during MMF and EC-MPS therapy. With respect to Ctrough, OME increased the obtained value for patients treated with MMF but not with EC-MPS.

Mycophenolate mofetil in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis: a prospective pharmacokinetics and clinical study

Anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) treatment strategy is based on immunosuppressive agents. Little information is available concerning mycophenolic acid (MPA) and the area under the curve (AUC) in patients treated for AAV. We evaluated the variations in pharmacokinetics for MPA in patients with AAV and the relationship between MPA-AUC and markers of the disease. MPA blood concentrations were measured through the enzyme-multiplied immunotechnique (C(0), C(30), C(1), C(2), C(3), C(4), C(6) and C(9)) to determine the AUC. Eighteen patients were included in the study. The median (range) MPA AUC(0-12) was 50·55 (30·9-105·4) mg/h/l. The highest coefficient of determination between MPA AUC and single concentrations was observed with C(3) (P < 0·0001) and C(2) (P < 0·0001) and with C(4) (P < 0·0005) or C(0) (P < 0·001). Using linear regression, the best estimation of MPA AUC was provided by a model including C(30), C(2) and C(4): AUC = 8·5 + 0·77 C(30) + 4·0 C(2) + 1·7 C(4) (P < 0·0001). Moreover, there was a significant relationship between MPA AUC(0-12) and lymphocyte count (P < 0·01), especially CD19 (P < 0·005), CD8 (P < 0·05) and CD56 (P < 0·05). Our results confirm the interindividual variability of MPA AUC in patients treated with MMF in AAV and support a personalized therapy according to blood levels of MPA.

Limited Sampling Model for Advanced Mycophenolic Acid Therapeutic Drug Monitoring After Liver Transplantation

The immunosuppressive drug mycophenolate mofetil (MMF), with mycophenolic acid (MPA) as active metabolite, is a nonnephrotoxic alternative to calcineurin inhibitors. Therapeutic drug monitoring (TDM) of MPA may improve clinical benefit from MMF therapy, especially in MMF monotherapy or with reduced dose of a calcineurin inhibitor. Limited data are available on TDM strategies for MPA in orthotopic liver transplantation (OLT). The authors here describe the pharmacokinetic (PK) behavior of MPA after OLT and developed a Bayesian limited sampling model for monitoring MMF after OLT. PK data were obtained from 57 stable patients, and trapezoidal area under the curve (AUC(0-12h)) was calculated. The effect of the covariates kidney function and serum albumin concentration was studied. A TDM strategy was developed based on individualized population PKs using Bayesian estimations and limited sampling models to predict the MPA AUC. A relationship between MMF dose and MPA AUC was found and a 8-fold apparent clearance range of MPA was observed at the same dose level. Significant relationships of albumin concentration and creatinine clearance with MPA plasma clearance were identified (respectively, r² = 0.12 and 0.24; P < 0.05). A model with limited sampling at 0, 0.5, 1, 2, and 3 hours after drug administration showed very good correlation with trapezoidal AUC(0-12h) with acceptable bias and precision (r² = 0.92, mean prediction error = 1, mean absolute prediction error = 13; P < 0.05). Remarkable variability of MPA clearance in stable OLT patients exists, which can be partially explained by the patients' albumin serum levels and creatinine clearance. Systemic exposure in these patients can be accurately assessed by the Bayesian limited sampling TDM strategy.

Tacrolimus Exposure and Mycophenolate Pharmacokinetics and Pharmacodynamics Early After Liver Transplantation

Mycophenolic acid (MPA) and tacrolimus play important roles in immunosuppressive therapy after solid organ transplantation (Tx) and show large intra- and interindividual pharmacokinetic (PK) variabilities. The purpose of this study was to describe the intra- and interindividual variabilities of MPA and tacrolimus PKs during the first 3 weeks after adult liver transplantation. Furthermore, inosine monophosphate dehydrogenase activity was investigated. This study describes PK and pharmacodynamic parameters of MPA and the PKs of tacrolimus in 16 liver transplant recipients, in 4 follow-up periods (I-IV). The area under the concentration-time curve (AUC(0-12 hours)) for tacrolimus was low early after Tx (eg, median 78.6 around day 4) and variable in all 4 periods ranging from 3.8 to 267 μg h/L, whereas the predose concentrations (C₀) were 0.0-17.9 μg/L. From periods I to IV, the tacrolimus dose was doubled and the median dose per body weight-adjusted AUC(0-12 hours) increased by 123% (P = 0.017). The AUC(0-12 hours) of MPA was in the range 8.6-57.4 mg h/L, with median values from 21.9 to 27.8 mg h/L, whereas C₀ was between 0.0 and 7.3 mg/L in the 4 periods (medians from 1.2 to 1.6 mg/L). The maximum inhibition of inosine monophosphate dehydrogenase within a dose interval ranged from 9.5% to 100%. This study confirmed the large variability in the PKs of tacrolimus and MPA in liver transplant recipients. In particular, the MPA AUC(0-12 hours) was consistently low in all 4 periods. We also observed a low tacrolimus exposure during the first days after transplant compared with the following weeks.

Use of limited sampling strategy for estimating area under concentration-versus-time curve of mycophenolate sodium in renal allograft recipients

To establish a formula for estimating area under the concentration-versus-time curve (AUC) of mycophenolate sodium in Chinese renal allograft recipients with a limited sampling model. A total of 35 renal allograft recipients were recruited from 2010 to 2013 to receive enteric-coated mycophenolate sodium (EC-MPS), calcineurin (CNI) and prednisone as immunosuppressive triple therapy. The serum concentration of mycophenolic acid (MPA) was assayed by enzyme multiplied immunoassay technique (EMIT) at pre-dose (C0), 0.5 (C0.5), 1.0 (C1), 1.5 (C1.5), 2.0 (C2), 3.0 (C3), 4.0 (C4), 6.0 (C6), 8.0 (C8) and 12.0 (C12) h post-dose respectively. Pharmacokinetic parameters of MPA (C0, C12, Cmax, Tmax, AUC0-12 h) were calculated by software WINNOLIN. Simplified formulae for estimation of MPA-AUC in tacrolimus (Tac) group or cyclosporin A (CsA) group were established by multiple stepwise regression analysis. There were variable MPA AUC0-12 h levels between 14 and 67 mg×h/L (mean: 37 ± 14). The MPA trough level (C0) had no correlations with MPA AUC0-12 h (r(2) = 0.090) . The simplified MPA AUC formula for Tac group was AUC = 5.678+1.718×C4+2.853×C6+1.812×C8+3.413×C12 with four sampling points (C4, C6, C8, C12). Estimated AUC with the formula had correlations with AUC0-12 h (r(2) = 0.890). The mean absolute predict error (APE) was 3.45% (0.41%-24.71%) and the proportion of APE above 15% stood at 11.1% (2/18) . In CsA group, the simplified MPA AUC formula was AUC = 7.072+1.525×C3+1.558×C4+ 1.573×C6+2.285×C8. The correlation was r(2) = 0.952, mean APE was 6.50% (0.02%-12.91%) and proportion of APE above 15% stood at 0. The above formulae were observed to have agreement with AUC0-12 h by Bland-Altman analysis. The simplified MPA AUC formulae with 4-point sampling provide an effective approach for estimating full MPA AUC0-12 h in Chinese renal recipients on EC-MPS plus tacrolimus or cyclosporin A.

Development and Validation of Limited Sampling Strategies for the Estimation of Mycophenolic Acid Area Under the Curve in Adult Kidney and Liver Transplant Recipients Receiving Concomitant Enteric-Coated Mycophenolate Sodium and Tacrolimus

Mycophenolic acid (MPA) is widely used in solid organ transplantation. MPA absorption from enteric-coated mycophenolate sodium (EC-MPS) is delayed, which results in a delayed enterohepatic recirculation and subsequently higher and more variable MPA 12-hour trough concentration and tmax values. Therefore, MPA trough level monitoring cannot be used to monitor MPA exposure in patients who are given EC-MPS. The aim of the study was to develop and validate a limited sampling strategy (LSS) for accurate prediction of the 12-hour area under the concentration-time curve (AUC0-12h) for MPA in patients who receive concomitant EC-MPS and Tacrolimus (Prograf or Advagraf) within 196 months posttransplantation. According to our knowledge, the LSS for MPA AUC estimation using high-performance liquid chromatography to determine MPA concentrations in plasma samples of kidney and liver transplant patients receiving EC-MPS and Tacrolimus (Advagraf) has not been previously evaluated. Seventy-four renal and liver transplant patients receiving EC-MPS and concomitant tacrolimus (either Prograf or Advagraf) provided a total of 74 pharmacokinetic profiles. MPA concentrations were measured using a validated high-performance liquid chromatography method for 9 plasma samples collected at predose and at 0.5, 1, 2, 3, 4, 6, 9, and 12 hours after the morning dose of EC-MPS after an overnight fast. LSS were developed and validated by stepwise multiple regression analysis with the use of a 2-group method (test, n = 37; and validation, n = 37). The 3 and 4 time point equations using C1h, C3h, C9h and C1h, C2h, C3h, C6h, respectively, were found to be superior to all other models tested. When these LSS models were tested in the validation group, the results were acceptable [for 3 time points equation: r = 0.824, percentage of prediction error: 6.32 ± 25.75, 95% confidence interval (CI): -40.71 to 79.76; percentage of absolute prediction error: 27.45 ± 29.89, 95% CI: 0.04-199.92, predictive performance, 71% of estimated AUCs comprised within 85%-115% of the measured full MPA AUC, natural logarithmic residuals (ln) mean ± SD: -0.03 ± 0.24; for 4 time points equation: r = 0.898, percentage of prediction error: 3.32 ± 18.26, 95% CI: -49.35 to 51.06; percentage of absolute prediction error: 14.05 ± 11.89, 95% CI 0.13-49.86, percentage of predictive performance, 83% of estimated AUCs comprised within 85%-115% of the measured full MPA AUC, natural logarithmic residuals (ln) mean ± SD: -0.01 ± 0.19]. LSS equations using concentrations at 1, 3, and 9 hours or 1, 2, 3, and 6 hours time points provided the most reliable and accurate estimations of the MPA AUC in stable renal and liver transplant recipients treated with EC-MPS and tacrolimus. Further studies on independent groups of patients are required to confirm clinical utility of the presented LSS models.

Statistical tools for dose individualization of mycophenolic acid and tacrolimus co‐administered during the first month after renal transplantation

To predict simultaneously the area under the concentration-time curve during one dosing interval [AUC(0,12 h)] for mycophenolic acid (MPA) and tacrolimus (TAC), when concomitantly used during the first month after transplantation, based on common blood samples. Data were from two different sources, real patient pharmacokinetic (PK) profiles from 65 renal transplant recipients and 9000 PK profiles simulated from previously published models on MPA or TAC in the first month after transplantation. Multiple linear regression (MLR) and Bayesian estimation using optimal samples were performed to predict MPA and TAC AUC(0,12 h) based on two concentrations. The following models were retained: AUC(0,12 h) = 16.5 + 4.9 × C1.5 + 6.7 × C3.5 (r(2) = 0.82, rRMSE = 9%, with simulations and r(2) = 0.66, rRMSE = 24%, with observed data) and AUC(0,12 h) = 24.3 + 5.9 × C1.5 + 12.2 × C3.5 (r(2) = 0.94, rRMSE = 12.3%, with simulations r(2) = 0.74, rRMSE = 15%, with observed data) for MPA and TAC, respectively. In addition, bayesian estimators were developed including parameter values from final models and values of concentrations at 1.5 and 3.5 h after dose. Good agreement was found between predicted and reference AUC(0,12 h) values: r(2) = 0.90, rRMSE = 13% and r(2) = 0.97, rRMSE = 5% with simulations for MPA and TAC, respectively and r(2) = 0.75, rRMSE = 11% and r(2) = 0.83, rRMSE = 7% with observed data for MPA and TAC, respectively. Statistical tools were developed for simultaneous MPA and TAC therapeutic drug monitoring. They can be incorporated in computer programs for patient dose individualization.

Population Pharmacokinetics and Dose Optimization of Mycophenolic Acid in HCT Recipients Receiving Oral Mycophenolate Mofetil

We sought to create a population pharmacokinetic model for total mycophenolic acid (MPA), to study the effects of different covariates on MPA pharmacokinetics, to create a limited sampling schedule (LSS) to characterize MPA exposure (i.e., area under the curve or AUC) with maximum a posteriori Bayesian estimation, and to simulate an optimized dosing scheme for allogeneic hematopoietic cell transplantation (HCT) recipients. Four thousand four hundred ninety-six MPA concentration-time points from 408 HCT recipients were analyzed retrospectively using a nonlinear mixed effects modeling approach. MPA pharmacokinetics was characterized with a two-compartment model with first-order elimination and a time-lagged first-order absorption process. Concomitant cyclosporine and serum albumin were significant covariates. The median MPA clearance (CL) and volume of the central compartment were 24.2 L/hour and 36.4 L, respectively, for a 70 kg patient receiving tacrolimus with a serum albumin of 3.4 g/dL. Dosing simulations indicated that higher oral MMF doses are needed with concomitant cyclosporine, which increases MPA CL by 33.8%. The optimal LSS was immediately before and at 0.25 hours, 1.25 hours, 2 hours, and 4 hours after oral mycophenolate mofetil administration. MPA AUC in an individual HCT recipient can be accurately estimated using a five-sample LSS and maximum a posteriori Bayesian estimation.

Impact of longitudinal exposure to mycophenolic acid on acute rejection in renal-transplant recipients using a joint modeling approach

This study aimed to investigate the association between longitudinal exposure to mycophenolic acid (MPA) and acute rejection (AR) risk in the first year after renal transplantation, and to propose MPA exposure targets conditionally to this association. A joint model, adjusted for monitoring strategy (fixed-dose versus concentration-controlled) and recipient age, was developed; it combined a mixed-effects model to describe the whole pattern of MPA exposure (i.e. area under the concentration–time curve (AUC)) and a survival model. MPA AUC thresholds were determined using time-dependent receiver-operating characteristics (ROC) curves. Data from 490 adult renal-transplant recipients, representative of the general population of adult renal-transplant patients (i.e. including patients considered at low immunological risk-enrolled in the OPERA trial as well as second renal transplant and patients co-treated by either cyclosporine or tacrolimus), were analyzed. A significant association was found between the longitudinal exposure to MPA (MPA AUCs=f(t)) and AR (p=0.0081), and validated by bootstrapping. A significant positive correlation was observed between time post-transplantation and ROC thresholds which increased in average from 35mgh/L in the first days to 41mgh/L beyond six months post-transplantation (p<0.001).Using a new modeling approach which recognizes the repeated measures in a same patient, this study supports the association between MPA exposure and AR.

Race and Drug Formulation Influence on Mycophenolic Acid Pharmacokinetics in Stable Renal Transplant Recipients

Limited mycophenolic acid (MPA) data are available comparing racial influence on mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS) pharmacokinetics. Intrapatient MPA pharmacokinetics of MMF versus EC-MPS were compared in 13 male African American (AA) and 14 Caucasian (C) renal transplant recipients (RTRs). RTRs were switched to equivalent doses of the alternate formulation for at least 10 days prior to the second study. Mycophenolic acid clearance and dose-normalized area under the concentration-time curve(0-12) (AUC*) were determined. Mixed model statistics evaluated the main effects of race, drug formulation, and interaction of race and drug formulation (R × D) with albumin, cyclosporine trough, renal function, and diabetes and enterhepatic recirculation. Significant R × D was identified for MPA AUC* for EC-MPS (AA, 0.056 ± 0.029 [mg·h/L]/mg; C, 0.080 ± 0.044 [mg·h/L]/mg) compared with MMF (AA, 0.053 ± 0.019 [mg·h/L]/mg; C, 0.060 ± 0.025 [mg·h/L]/mg), P = .022. Significant R × D was identified with albumin in the model for MPA clearance for MMF (AA, 21.7 ± 8.9 L/h; C, 20.5 ± 10.8 L/h) compared with EC-MPS (AA, 22.2 ± 10.1 L/h; C, 16.2 ± 9.1 L/h), P = .032. Race influences MPA exposure between MMF and EC-MPS and may warrant therapeutic monitoring during formulation conversion.

Population pharmacokinetics of unbound mycophenolic acid in adult allogeneic haematopoietic cell transplantation: effect of pharmacogenetic factors.

To evaluate pharmacogenetic factors as contributors to the variability of unbound mycophenolic acid (MPA) exposure in adult allogeneic haematopoietic cell transplantation (alloHCT) recipients. A population-based pharmacokinetic (PK) model of unbound MPA was developed using non-linear mixed-effects modelling (nonmem). Previously collected intensive unbound MPA PK data from 132 adult alloHCT recipients after oral and intravenous dosing of the prodrug mycophenolate mofetil (MMF) were used. In addition to clinical covariates, genetic polymorphisms in UGT1A8, UGT1A9, UGT2B7 and MRP2 were evaluated for their impact on unbound MPA PK. Unbound MPA concentration-time data were well described by a two compartment model with first order absorption and linear elimination. For the typical patient (52 years of age, creatinine clearance 86 ml min(-1)), the median estimated values [coefficient of variation, %, (CV)] of systemic clearance, intercompartmental clearance, central and peripheral volumes of MPA were 1610 l h(-1) (37.4%), 541 l h(-1) (75.6%), 1230 l (37.5%), and 6140 l (120%), respectively. After oral dosing, bioavailability was low (0.56) and highly variable (CV 46%). No genetic polymorphisms tested significantly explained the variability among individuals. Creatinine clearance was a small but significant predictor of unbound MPA CL. No other clinical covariates impacted unbound MPA PK. In adult alloHCT recipients, variability in unbound MPA AUC was large and remained largely unexplained even with the inclusion of pharmacogenetic information. Targeting unbound MPA AUC in a patient will require therapeutic drug monitoring.

Chronopharmacokinetics of Mycophenolic Acid and Its Glucuronide and Acyl Glucuronide Metabolites in Kidney Transplant Recipients Converted From Cyclosporine to Everolimus

The influence of the conversion from cyclosporine (CsA) to everolimus (EVR) on the chronopharmacokinetics of mycophenolic acid (MPA) and its glucuronide (MPAG) and acyl glucuronide (acyl-MPAG) metabolites in patients receiving enteric-coated mycophenolate sodium (EC-MPS) has not been studied. We evaluated daytime and nighttime steady-state MPA, MPAG, and acyl-MPAG pharmacokinetics in 24 stable kidney transplant recipients while receiving cyclosporine and 28 days after conversion from CsA to EVR. The effect of concomitant treatment and the circadian difference on AUC(t,ss) and C(max,ss) were assessed using a linear mixed model. After conversion from CsA to EVR, MPA AUC(t,ss) was 43% higher (29% daytime and 58% during nighttime), whereas MPAG AUC(t,ss) was 33% lower (35% daytime and 30% during nighttime) and acyl-MPAG AUC(t,ss) was 31% lower (36% during daytime and 26% nighttime). Compared with daytime, MPA AUC(t,ss) was 25% lower (32% with CsA and 17% with EVR), MPAG AUC(t,ss) was 24% lower (26% with CsA and 21% with EVR), and acyl-MPAG AUC(t,ss) was 26% lower (32% with CsA and 21% with EVR) during nighttime. After conversion from CsA to EVR, MPAG:MPA and acyl-MPAG:MPA AUC(t,ss) ratios were 50% lower but were not different during daytime compared with nighttime EC-MPS administration. There was no correlation between CsA or EVR concentrations with MPA, MPAG, and acyl-MPAG exposures during daytime and nighttime. At least 1 adverse event was reported in 70.8% of patients receiving EC-MPS and CsA and in 91.7% receiving EC-MPS and EVR. In stable kidney transplant recipients receiving EC-MPS and steroids, exposures to MPA, MPAG, and acyl-MPAG were lower during nighttime compared with daytime, both with CsA or EVR. This circadian effect on MPA exposure did not correlate with CsA or EVR concentrations or with altered MPAG and acyl-MPAG formation.

Effect of Time after Transplantation on Mycophenolic Acid Pharmacokinetic in Kidney Transplant Patients in Iran

Purpose: Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), exerts its immunosuppressive action by blocking T and B-cell proliferation via inhibition of inosine monophosphate dehydrogenase (IMPDH). Various factors affect MPA pharmacokinetic (PK) including renal function, concomitant immunosuppressant, serum protein level. Demographic data provide evidence for significant interpatient variability of MPA-PK parameters. Recent studies suggest that one of these affecting factors is uremic condition in early phase after transplantation. The aim of this study is to investigate the effect of time after transplantation on MPA-PK. Methods: In this study 19 patients in early phase post transplant period with normal graft function (GFR>70 ml/min), who fulfilled inclusion & exclusion criteria were selected. Blood sampling in different times were taken in early phase and six months after transplantation (stable phase). MPA plasma concentrations were measured by reverse phase high performance liquid chromatography (HPLC). MPA-PK parameters were calculated in both states. Multiple stepwise regression analysis was used to determine the minimal and convenient time points of MPA levels that could be used to drive model equation best fitted to MPA AUC0-12 in both states. Results and conclusion: Our results showed that the MPA-AUC (area under the curve) and clearance was not affected by time but MPA-tmax was significantly lower in stable phase compared with early phase (P=0.001). The best regression equation for AUC estimation in early phase was (AUC = 0.904 C1.5 + 1.955 C4 + 10.206 C10 + 8.32) and in stable phase was (AUC = 0.663C6+4.945C10+1.074C2+0.536C1+2.11 5C0+3.945). Hence, Pharmacokinetic of MPA could be affected by time after transplantation.

Population pharmacokinetics of mycophenolic acid and dose optimization with limited sampling strategy in liver transplant children

The aims were to estimate the mycophenolic acid (MPA) population pharmacokinetic parameters in paediatric liver transplant recipients, to identify the factors affecting MPA pharmacokinetics and to develop a limited sampling strategy to estimate individual MPA AUC(0,12 h). Twenty-eight children, 1.1 to 18.0 years old, received oral mycophenolate mofetil (MMF) therapy combined with either tacrolimus (n= 23) or ciclosporin (n= 5). The population parameters were estimated from a model-building set of 16 intensive pharmacokinetic datasets obtained from 16 children. The data were analyzed by nonlinear mixed effect modelling, using a one compartment model with first order absorption and first order elimination and random effects on the absorption rate (k(a)), the apparent volume of distribution (V/F) and apparent clearance (CL/F). Two covariates, time since transplantation (≤ and >6 months) and age affected MPA pharmacokinetics. k(a), estimated at 1.7 h(-1) at age 8.7 years, exhibited large interindividual variability (308%). V/F, estimated at 64.7 l, increased about 2.3 times in children during the immediate post transplantation period. This increase was due to the increase in the unbound MPA fraction caused by the low albumin concentration. CL/F was estimated at 12.7 l h(-1). To estimate individual AUC(0,12 h), the pharmacokinetic parameters obtained with the final model, including covariates, were coded in Adapt II(®) software, using the Bayesian approach. The AUC(0,12 h) estimated from concentrations measured 0, 1 and 4 h after administration of MMF did not differ from reference values. This study allowed the estimation of the population pharmacokinetic MPA parameters. A simple sampling procedure is suggested to help to optimize pediatric patient care.

Limited-Sampling Strategy for Mycophenolic Acid in Renal Transplant Recipients Reciving Enteric-Coated Mycophenolate Sodium and Tacrolimus

Substantial research has been conducted to develop limited-sampling strategies (LSS) to estimate the area under the curve (AUC(0-12h)) in patients treated with mycophenolate mofetil. However, no LSS has been validated for enteric-coated mycophenolate sodium (EC-MPS). Our aim was to develop an LSS to measure the AUC(0-12h) of mycophenolic acid in kidney recipients treated with tacrolimus and EC-MPS. Thirteen serial blood samples were collected over 12 hours from 71 patients treated with tacrolimus and EC-MPS. Mycophenolic acid was measured in plasma using the enzyme-multiplied immunoassay. LSSs were developed and validated by multiple regression analysis using a 2-group method (test, n = 47; validation, n = 24). The best LSS obtained in the test group were for 3 and 4 time points AUC(0-12h) (mg·h(-1)·L(-1)) = 15.99 + 0.87C1 h + 0.68C2 h + 7.85C4 h and AUC(0-12h) (mg·h(-1)·L(-1)) =11.15 + 0.68C1 h + 0.45C1.5 h + 0.57C2 h + 8.16C4 h, respectively. When these LSS were tested in the validation group, the results were acceptable (adjusted r(2) = 0.71, bias =-0.214 [95% confidence interval (CI): -7.91 to 7.48], precision = 7.48 (95% CI: 3.69-19.39) for 3 time points and adjusted r(2) = 0.76, bias = -1.48 (95% CI: -8.23 to 5.27), precision = 7.68 (95% CI: 4.23-13.50) for 4 time points). : An LSS using time points at C(1h)-C(2h)-C(4h) or C(1h)-C(1.5h)-C(2h)-C(4h) provides the most reliable and accurate estimation of the AUC(0-12h) of mycophenolic acid in stable renal transplant recipients treated with EC-MPS and tacrolimus.

Pharmacokinetics of mycophenolic acid and its phenyl glucuronide metabolite in kidney transplant recipients with renal impairment

IntroductionThe aim of the study was to analyse the influence of renal impairment on the pharmacokinetic parameters (PK) of mycophenolic acid (MPA) and its glucuronide metabolite (MPAG) in renal transplant recipients.Material and methodsThe study included 43 adult patients during the maintenance period (> 6 months) following renal transplantation, treated with mycophenolate mofetil (MMF), calcineurin inhibitors (CNI) (tacrolimus or cyclosporine) and steroids. The study compared patients with normal renal function (n = 17; creatinine clearance (Ccr) > 60 ml/min) and with renal impairment (n = 26; Ccr < 60 ml/min). Areas under the 4-h curve (AUC0-4 h) of MPA and MPAG were determined using a validated HPLC method.ResultsThe renal impairment group showed significantly increased AUC0-4 h and pre-dose (C0) for MPAG compared to patients with normal renal function and increased MPA C0. However, there was no significant difference in MPA AUC0-4 h between patients with renal impairment and patients with normal renal function. In multivariate analysis some MPA and MPAG PK parameters were correlated with sex, CNI co-administered and body weight.ConclusionsAlthough MPAG is an inactive metabolite, its accumulation in patients with renal impairment can be unfavourable. The results of our study indicate that solely MPA C0 determination in patients receiving MMF may be insufficient in clinical practice because of great inter-patient variability of this PK parameter caused mainly by enterohepatic recirculation.

The association of the UGT1A8, SLCO1B3 and ABCC2/ABCG2 genetic polymorphisms with the pharmacokinetics of mycophenolic acid and its phenolic glucuronide metabolite in Chinese individuals

BackgroundThis study aimed to evaluate the effect of UGT1A8*2, SLCO1B3 T334G, ABCC2 C-24T and ABCG2 C421A polymorphisms on the pharmacokinetics (PKs) of mycophenolic acid (MPA) and its phenolic glucuronide (MPAG) in healthy Chinese volunteers and in stable renal transplant patients. MethodsThe data were extracted from comparative bioavailability studies conducted in 42 healthy individuals and 37 renal transplant patients. A complete PK profile was obtained over 48h for healthy volunteers and over 12h for the transplant patients. The MPA/MPAG plasma concentrations were measured by HPLC. The genotypes were determined using either the Taqman probe technique or direct sequencing. A multivariate analysis was used to assess the effect of the genotypes (UGT1A8*2, SLCO1B3 T334G, ABCC2 C-24T and ABCG2 C421A) and other covariates (age, weight, height, calculated creatinine clearance, serum albumin, haemoglobin and drug comedication) on the AUC4–12 and AUC0–12 for MPA and MPAG in the healthy volunteers and patients. ResultsIn the healthy volunteers, the dose-adjusted geometric means (GM) of the MPA AUC4–12 in individuals with the SLCO1B3 334T allele were 30.4% lower than those values in the 334G homozygote carriers (P<0.05); in the transplant patients, the steroid dose was associated with a negative effect on the AUC of MPAG (P<0.03) and weight was associated with a negative effect on the AUC for MPA in the healthy volunteers and patients (P<0.03). No other significant effect of genotype or of the other studied variables on AUC4–12 or AUC0–12 of MPA/MPAG was found in the healthy volunteers or patients. ConclusionsThe PKs of MPA is affected by the SLCO1B3 polymorphism in healthy Chinese individuals. The absence of an effect of SLCO1B3 polymorphisms in transplant patients may be due to the co-administration of cyclosporine (CsA). Concomitant steroid dose and weight are two important covariates of the AUC of MPA and MPAG, which should be taken into account in clinical use. Further confirmatory in vivo studies are needed.

Maximum A Posteriori Bayesian Estimation of Mycophenolic Acid Area Under the Concentration-Time Curve: Is This Clinically Useful for Dosage Prediction Yet?

This review seeks to summarize the available data about Bayesian estimation of area under the plasma concentration-time curve (AUC) and dosage prediction for mycophenolic acid (MPA) and evaluate whether sufficient evidence is available for routine use of Bayesian dosage prediction in clinical practice. A literature search identified 14 studies that assessed the predictive performance of maximum a posteriori Bayesian estimation of MPA AUC and one report that retrospectively evaluated how closely dosage recommendations based on Bayesian forecasting achieved targeted MPA exposure. Studies to date have mostly been undertaken in renal transplant recipients, with limited investigation in patients treated with MPA for autoimmune disease or haematopoietic stem cell transplantation. All of these studies have involved use of the mycophenolate mofetil (MMF) formulation of MPA, rather than the enteric-coated mycophenolate sodium (EC-MPS) formulation. Bias associated with estimation of MPA AUC using Bayesian forecasting was generally less than 10%. However some difficulties with imprecision was evident, with values ranging from 4% to 34% (based on estimation involving two or more concentration measurements). Evaluation of whether MPA dosing decisions based on Bayesian forecasting (by the free website service https://pharmaco.chu-limoges.fr) achieved target drug exposure has only been undertaken once. When MMF dosage recommendations were applied by clinicians, a higher proportion (72-80%) of subsequent estimated MPA AUC values were within the 30-60 mg · h/L target range, compared with when dosage recommendations were not followed (only 39-57% within target range). Such findings provide evidence that Bayesian dosage prediction is clinically useful for achieving target MPA AUC. This study, however, was retrospective and focussed only on adult renal transplant recipients. Furthermore, in this study, Bayesian-generated AUC estimations and dosage predictions were not compared with a later full measured AUC but rather with a further AUC estimate based on a second Bayesian analysis. This study also provided some evidence that a useful monitoring schedule for MPA AUC following adult renal transplant would be every 2 weeks during the first month post-transplant, every 1-3 months between months 1 and 12, and each year thereafter. It will be interesting to see further validations in different patient groups using the free website service. In summary, the predictive performance of Bayesian estimation of MPA, comparing estimated with measured AUC values, has been reported in several studies. However, the next step of predicting dosages based on these Bayesian-estimated AUCs, and prospectively determining how closely these predicted dosages give drug exposure matching targeted AUCs, remains largely unaddressed. Further prospective studies are required, particularly in non-renal transplant patients and with the EC-MPS formulation. Other important questions remain to be answered, such as: do Bayesian forecasting methods devised to date use the best population pharmacokinetic models or most accurate algorithms; are the methods simple to use for routine clinical practice; do the algorithms actually improve dosage estimations beyond empirical recommendations in all groups that receive MPA therapy; and, importantly, do the dosage predictions, when followed, improve patient health outcomes?