A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients

Abstract

Aim: To develop a population pharmacokinetic (PopPK) model of tacrolimus in healthy Chinese volunteers and liver transplant recipients for investigating the difference between the populations, and for potential individualized medication. Methods: A set of 1100 sparse trough concentration data points from 112 orthotopic liver transplant recipients, as well as 851 dense data points from 40 healthy volunteers receiving a single dose of tacrolimus (2 mg, po) were collected. PopPK model of tacrolimus was constructed using the program NONMEM. Related covariates such as age, hepatic and renal functions that were potentially associated with tacrolimus disposition were evaluated. The final model was validated using bootstrapping and a visual predictive check. Results: A two-compartment model of tacrolimus could best describe the data from the two populations. The final model including two covariates, population (liver transplant recipients or volunteers) and serum ALT (alanine aminotransferase) level, was verified and adequately described the pharmacokinetic characteristics of tacrolimus. The estimates of V2/F, Q/F and V3/F were 22.7 L, 76.3 L/h and 916 L, respectively. The estimated CL/F in the volunteers and liver transplant recipients was 32.8 and 18.4 L/h, respectively. Serum ALT level was inversely related to CL/F, whereas age did not influence CL/F. Thus, the elderly (≥65 years) and adult (<65 years) groups in the liver transplant recipients showed no significant difference in the clearance of tacrolimus. Conclusion: Compared with using the sparse data only, the integrating modeling technique combining sparse data from the patients and dense data from the healthy volunteers improved the PopPK analysis of tacrolimus.

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

“…studied a group of healthy subjects from various ethnic backgrounds after intravenous and oral administration of TAC and found that TAC PK possessed biexponential characteristics. Similarly, the two‐compartment model with first‐order absorption is frequently used as the structure model for PPK study in adult and paediatric patients after an intense sampling strategy . PPK parameters are variable among various populations.…”

“…Although PK parameters obtained are limited as only C 0 is available, these parameters are valuable in the estimation of TAC exposure. Lu et al . established a two‐compartment model of TAC using sparse trough concentration data points from 112 orthotopic liver transplant recipients and 851 rich‐time data points from 40 healthy volunteers.…”

“…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.…”

“…Some studies have tried to estimate TAC AUC using MAP. Some other TAC PPK studies focused on Chinese liver transplant patients . However, they only used conventional TDM data for modelling and assaying the individualized PPK parameters assay rather through Bayesian estimation.…”

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.…”