A limited sampling strategy for estimating mycophenolic acid area under the curve in adult heart transplant patients treated with concomitant cyclosporine

Mycophenolate mofetil (MMF) is increasingly used for the treatment of autoimmune diseases (AID). In renal transplant recipients, it has been demonstrated that adjustment of the MMF dose according to the area under the plasma concentration versus time curve (AUC) of mycophenolic acid (MPA), the active moiety of MMF, improves clinical outcome. The aim of this study was to develop a maximum a posteriori Bayesian estimator (MAP-BE) to estimate MPA AUC(0-12) in patients with AID using a limited number of samples. The predictive performance of the MAP-BE was compared with a multiple linear regression method. Full MPA concentration versus time curves were available from 38 patients with AID treated with MMF. Nonlinear mixed-effect modeling was used to develop a population pharmacokinetic model. Patients were divided in an index and a validation data set. The pharmacokinetic model derived from the index data set was used to develop several MAP-BEs. The Bayesian estimators were used to predict AUC(0-12) in the validation data set on the basis of a limited number of blood samples. The bias and precision of these predictions were compared with those of limited sampling strategies developed with multiple linear regression. The absorption of MPA was described with 2 first-order processes with a short and a long lag time and a subsequent first-order elimination. The 2-compartment model accounted for the enterohepatic recirculation of MPA as well. Using 1-4 samples, MPA AUC(0-12) was adequately estimated by the MAP-BE. Bias (-5.5%) was not significantly different from zero, and precision was below 27%. The predictive performance of the multiple linear regression method was comparable. In conclusion, MAP-BEs were developed for the estimation of MPA AUC(0-12) in patients with AID. The predictive performance was good and comparable to those of the multiple linear regression method. Due to its flexibility with respect to sample times, the MAP-BE may be preferred over the multiple linear regression method.

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Limited Sampling Strategies for Therapeutic Drug Monitoring of Mycophenolate Mofetil Therapy in Patients With Autoimmune Disease

Winter¹,

Neumann²,

Hest³

et al. 2009

“…Several studies agreed with the findings of Ting et al 21 and showed that an LSS using only plasma concentrations within 4 hours of MMF administration provided a good estimation of the AUC of MPA. [22][23][24][25] On the other hand, several reports showed that the plasma concentration at 6 hours (C 6h ) after MMF administration was necessary for a good estimation of the AUC of MPA. [26][27][28][29] Ting et al 21 showed that the predicted AUC should, ideally, be estimated by a few concentration measurements over a short period after administration.…”

Section: Discussionmentioning

confidence: 99%

Early Phase Limited Sampling Strategy Characterizing Tacrolimus and Mycophenolic Acid Pharmacokinetics Adapted to the Maintenance Phase of Renal Transplant Patients

Miura¹,

Satoh²,

Niioka³

et al. 2009

The aim of this study was to examine whether a limited sampling strategy (LSS) to allow the simultaneous estimation of the area under the concentration-time curves (AUCs) of tacrolimus and mycophenolic acid (MPA) calculated in the early stage after renal transplantation could be applied to maintenance phase pharmacokinetics. Seventy Japanese patients were enrolled. One year after transplantation, samples were collected just before and 1, 2, 3, 4, 6, 9, and 12 hours after tacrolimus and mycophenolate mofetil administration at 9:00 am and at 9:00 pm. The prediction formulas on day 28 (tacrolimus AUC 0-12 = 7.04 x C 0h + 1.71 x C 2h + 3.23 x C 4h + 15.19 and 2.25 x C 2h + 1.92 x C 4h + 7.27 x C 9h + 6.61, and MPA AUC 0-12 = 0.26 x C 0h + 2.06 x C 2h + 3.82 x C 4h + 20.38 and 1.77 x C 2h + 2.34 x C 4h + 4.76 x C 9h + 15.94) were applied to pharmacokinetic data obtained at 1 year. Three error indices [percent mean prediction error (ME), % mean absolute error, and percent root mean squared prediction error (RMSE)] were used to evaluate the predictive bias, accuracy, and precision. The predicted AUC 0-12 of tacrolimus and MPA at 3 time points, C 2h-C 4h-C 9h, showed higher correlation with the measured AUC 0-12 of tacrolimus and MPA (r2 = 0.817 and 0.789, respectively) in comparison with those at C 0h-C 2h-C 4h. The values for the prediction formulas for tacrolimus AUC at 1 year using the C 2h-C 4h-C 9h combination yielded less than 5% for %ME and 15% for %RMSE. The %ME and %RMSE values of the prediction formulas for tacrolimus AUC using the C 0h-C 2h-C 4h combination were 6.3% and 15.9%, respectively. The %ME and %RMSE values of the prediction formulas for MPA AUC at 1 year using the C 0h-C 2h-C 4h combination were 5.9% and 25.8%, respectively, and those for the C 2h-C 4h-C 9h combination were 4.9% and 21.2%, respectively. AUC 6-12/AUC 0-12 of MPA 1 year after transplantation was significantly lower than 28 days after transplantation. An LSS using C 2h-C 4h-C 9h seems to be applicable for predicting the AUC of tacrolimus and MPA at either posttransplantation stage. The enterohepatic circulation of MPA was significantly reduced 1 year after transplantation. Therefore, 1 year after transplantation, the estimation of the AUC 0-12 of MPA for the C 0h-C 2h-C 4h equations was imprecise. It is important that the LSS includes C 9h because it contains information on the secondary plasma peak of MPA.

“…Of these, nine articles presented 25 LSSs derived from patients cotreated with cyclosporine [29][30][31][32][33][34][35][36][37] and four articles presented 53 LSSs derived from patients cotreated with tacrolimus. [38][39][40][41] Patients and Data Collection Table 1 shows the baseline demographic and clinical characteristics of study participants. These were comparable to those of our overall transplant population (data not shown).…”

Section: Limited Sampling Strategiesmentioning

confidence: 99%

Evaluation of Limited Sampling Strategies for Mycophenolic Acid After Mycophenolate Mofetil Intake in Adult Kidney Transplant Recipients

Barraclough

Isbel²,

Franklin³

et al. 2010

Multiple limited sampling strategies (LSSs) have been proposed for estimation of mycophenolic acid (MPA) area under the concentration-time curve from 0 to 12 hours postdose (AUC 0-12) after mycophenolate mofetil intake. The aim of this study was to provide summary information on all published LSSs for MPA and to evaluate their predictive performance in an independent population of kidney transplant recipients. Seventy-eight LSSs for MPA were identified. Sixty-nine full AUC profiles were collected from 45 subjects (25 cotreated with cyclosporine and 20 with tacrolimus). Predicted MPA AUC 0-12, calculated by applying the relevant concentration measurements within the LSS equations, was compared with full AUC calculated by using all concentration measurements in the linear trapezoidal rule. Four error indices (median prediction error, median percentage prediction error [MPPE], root median squared prediction error, and median absolute percentage prediction error [MAPE]) were used to evaluate bias and imprecision. Twelve of the 25 LSSs for cyclosporine-cotreated recipients and one of the 53 LSSs for tacrolimus-cotreated recipients displayed acceptable (less than 15%) bias and imprecision. In the cyclosporine group, two equations demonstrated the highest predictive power, one that used four time points in the first 6 hours postdose (r2 = 0.84, MPPE 1.6%, MAPE 7.8%) and one that used four time points in the first 4 hours postdose (r2 = 0.76, MPPE -0.8%, MAPE 10.2%). In the tacrolimus group, an equation that used two time points in the first 4 hours postdose was superior (r2 = 0.80, MPPE -3.0%, MAPE 13.6%). Application of the LSSs most appropriate for cyclosporine-cotreated patients to the tacrolimus-cotreated group resulted in clinically unacceptable bias and imprecision and vice versa. High variability in performance of LSSs highlights the importance of validating any LSS before applying it to an alternative population. Attention to comedication use is of particular relevance when choosing a LSS.