Population Pharmacokinetic–Pharmacogenetic Model of Tacrolimus in the Early Period after Kidney Transplantation

Abstract

As tacrolimus has a rather narrow therapeutic range and high individual variability in its pharmacokinetics, it is important to determine the cause of the variation in tacrolimus pharmacokinetics. The purpose of this study was to establish a population pharmacokinetic-pharmacogenetic model of tacrolimus and identify covariates that affect pharmacokinetic parameters to prevent fluctuations in the tacrolimus trough concentration during the early period after transplantation. Data from 1501 trough concentrations and 417 densely collected concentrations were compiled from 122 patients who were on post-operative days 10-20 and analysed with a nonlinear mixed-effect model. The first-order conditional estimation (FOCE) with interaction method was used to fit the model using the NONMEM program. Clinical/laboratory data were also collected for the same period, and CYP3A5 and ABCB1 genotypes were analysed for use in modelling from all included patients. An empirical Bayesian approach was used to estimate individual pharmacokinetic profiles. A one-compartment model with first absorption and elimination and lag time best described the data. The estimated population mean of clearance (CL/F), volume of distribution (V/F) and absorption rate (K a ) were 21.9 L/hr, 205 L, and 3.43/hr, respectively, and the lag time was fixed at 0.25 hr. Clearance increased with days after transplantation and decreased with CYP3A5*3/*3 about 18.4% compared with CYP3A5*1 carriers (p < 0.001). A population pharmacokinetic model was developed for tacrolimus in early post-kidney transplantation recipients to identify covariates that affect tacrolimus pharmacokinetics. Post-operative days and CYP3A5 genotype were confirmed as critical factors of tacrolimus pharmacokinetics.

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients

“…However, the effect is different among various studies. Some studies suggested that CL/F increased with POD in the early stage . Other studies found that CL/F of TAC decreased with time for a longer duration after transplantation, which is attributed to the elevation of haematocrit and albumin levels.…”

“…The CL/F, V 2 /F, Q/F, V 3 /F, K a and t lag of various models were 10.3‐29 L/h, 31.3‐463 L, 19.5‐101 L/h, 202‐3707 L, 0.34‐3.57 1/h and 0.25‐1.0 hours, respectively. On the other hand, many other PPK studies were carried out using conventional TDM data, in which the single‐compartment model was most frequently used . The CL/F, Vd/F and K a were 13.9‐30.6 L/h, 205‐1100 L and 0.44‐4.5 1/h, respectively.…”

“…Clearance of TAC varied at various stages post‐transplantation. POD is considered as an important covariate in renal or liver transplant recipients . However, the effect is different among various studies.…”

“…MAP has the following advantages: patient characteristics can be incorporated into the model to improve the prediction of individualized AUC; the predictive performance can be improved with additional data; the sampling time can be chosen flexibly; and different PK parameters can be predicted simultaneously. Different TAC PPK models have been established in various populations including renal transplant patients, liver transplant patients, hematopoietic stem cell transplant patients and lung transplant patients . Some studies have tried to estimate TAC AUC using MAP.…”

Population pharmacokinetics of tacrolimus in paediatric systemic lupus erythematosus based on real-world study

“…[6][7][8][9][10][11][12][13][14][15] However, tacrolimus has a narrow therapeutic window and displays considerable interindividual and intra-individual variabilities in its pharmacokinetics, with poor correlation between drug dosage and blood concentrations, making it difficult to define an optimal dose schedule. 18 Currently, different tacrolimus PPK models have been set up for various populations including renal transplant patients, [19][20][21][22][23][24] liver transplant patients, [25][26][27][28][29][30] haematopoietic stem cell transplant patients 31 and lung transplant patients. Moreover, the population analysis methodology differentiates between interindividual variability and residual unexplained variability.…”