A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients

Abstract

Aims The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient. Methods Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. Results A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h−1, clearance was 23.0 l h–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: Dose0.25em()mg=2220.25emng0.25emnormalh0.25emml–1*0.5em22.50.25emnormall0.25emnormalh–1*[](),1.0if0.25emCYP3normalA5*3/*30.25emor0.25em(),1.62if0.25emCYP3normalA5*1/*30.25emor0.25emCYP3normalA5*1/*1*[](),1.0if0.25emCYP3normalA4*10.25emor unknown0.25emor0.25em(),0.814if0.25emCYP3normalA4*22*Age56−0.50*BSA1.930.72/1000 Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model. Conclusions For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

A Population Pharmacokinetic Model Does Not Predict the Optimal Starting Dose of Tacrolimus in Pediatric Renal Transplant Recipients in a Prospective Study: Lessons Learned and Model Improvement

“…In this study, we concluded that patients with a higher hematocrit had a lower CL/F. Previous research substantiates these findings [17,19,31,35,36,[42][43][44][45]. Hematocrit did not influence the starting dose and was therefore not included in the dosing algorithm.…”

“…There is compelling evidence that CYP3A5 expressers require a 1.5-to 2-fold higher tacrolimus dose than nonexpressers [15,[17][18][19][30][31][32][33][34][35][36]. A randomized clinical trial of age-and genotype-guided tacrolimus dosing in children concluded that CYP3A5 genotype-guided dosing stratified by age resulted in earlier attainment of therapeutic tacrolimus concentrations and fewer out-of-range concentrations [37].…”

“…Tacrolimus undergoes hepatic elimination and almost no renal elimination, thus the explanation for this observation remains unclear. Some studies have reported a correlation between creatinine and tacrolimus CL [31,46,47], whereas others found no such effect [48][49][50]. Four decades previously, Sheiner et al concluded that forecasting a concentration based on covariates does not improve accuracy and precision as much as one previous concentration [51].…”

Fasting Status and Circadian Variation Must be Considered When Performing AUC‐based Therapeutic Drug Monitoring of Tacrolimus in Renal Transplant Recipients

“…5,6 There is still room for improving long-term outcomes following renal transplantation, 7,8 and improved tailoring of the Tac dosing may be an important contributor. 9 The area under the concentration vs. time curve (AUC), reflecting total systemic Tac exposure, should theoretically be a more relevant measure for both efficacy and side effects compared with trough concentrations. 10 A recent consensus report also recommended AUC thresholds and advocates the need for prospective AUC-dosed studies.…”

Comparison of the Impact of Pharmacogenetic Variability on the PK of Slow Release and Immediate Release Tacrolimus Formulations

“…The Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working group have provided dosing recommendations for tacrolimus based on the CYP3A5 genotype [ 3 , 4 ]. Furthermore, besides covariates such as age, gender and body weight, the CYP3A5 genotype has been included in dosing algorithms to select the best tacrolimus starting dose for each individual patient following kidney transplantation [ 5 ].…”