Sampling Times for Monitoring Tacrolimus in Stable Adult Liver Transplant Recipients

Abstract

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Limited Sampling Strategy for the Estimation of Tacrolimus Area Under the Concentration-Time Curve in Chinese Adult Liver Transplant Patients

“…Models from previous studies were tested by using the Chinese liver transplant patients data in the present study, we found there was good correlation between AUC 0-12Pred and AUC 0-12Obs (r 2 = 0.880-0.928) [19][20][21][23][24][25] . The percentage of profiles for which PE fell within ±15% was similar compared with our equations (31.9-70.2%).…”

“…LSS studies were carried on in various transplant patients and different equations were obtained [15][16][17][18][19][20][21][22][23][24][25] . A useful LSS model should be accurate and precise in prediction and convenient for the TDM of patients.…”

“…However, some studies found that C 0 could not reflect the inter-individual difference of total TAC exposure reliably. Poor correlation between the TAC C 0 and AUC 0-12 was found in kidney [8,9] and liver [10,11] transplant patients.…”

“…Besides, models from previous studies including 2-4 time points were used to test the prediction of concentration data from liver transplant patients in the present study. The models selected for testing were established based on data from pediatric [24] and adult [25] liver transplant recipients, renal transplant recipients [19][20][21] , heart transplant recipients [22] and lung transplant recipients [23] . The immunosuppressive regimens are similar, including TAC and mycophenolate mofetil (MMF) [21,23,24] ; TAC, MMF and steroid [19,20] ; TAC, MMF and azathioprine [22] .…”

“…It is the most frequently used LSS method. Most of the studies on LSS of TAC through MRA assay were carried out in stable renal transplant patients [15][16][17][18][19][20][21] , the studies on heart [22] , lung [23] and liver [24,25] transplant patients are relatively minor. It has been suggested that patient demographics including patients' genetic background and transplant type, concomitant medications, biopharmaceutical drug formulation may influence the prediction performance of LSS model [26] .…”