Population pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus) in stable adult liver transplant recipients

Martial, Lisa C.; Biewenga, Maaike; Ruijter, Bastian N.; Keizer, Ron J.; Swen, Jesse J.; Hoek, Bart van; Moes, Dirk Jan A R

doi:10.1111/bcp.14842

Cited by 13 publications

(31 citation statements)

References 37 publications

(122 reference statements)

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“…This inhibits the activation of T cells and suppresses the secretion of the cytokine interleukin 2 (IL2). Similarly, the complex formed by TAC and FK-binding protein 12 (FKBP12) can block the production of IL2 ( Li and Li, 2015 ; Martial et al, 2021 ). Therefore, CTLA4 may act as an upstream regulator of IL2 secretion and indirectly influence the pharmacokinetic process of TAC by affecting IL2 production.…”

Section: Discussionmentioning

confidence: 99%

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

et al. 2022

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Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

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Section: Discussionmentioning

confidence: 99%

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

et al. 2022

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“…Initially, one-, two- and three-compartmental models were evaluated to identify the optimal starting model. In addition, a previously developed model, based on a dataset with liver transplant patients receiving meltdose tacrolimus, was used to fit the current dataset [ 10 ]. The best models were expanded with oral absorption lag time, oral absorption transit compartments and a mixture model for the absorption lag time.…”

Section: Methodsmentioning

confidence: 99%

“…AUC ref was calculated as (F po × D × 1000)/CL, where F po was the oral tacrolimus bioavailability fixed at 100%, D the meltdose tacrolimus dose in mg for the AUC ref interval and CL the elimination clearance in L/h. Finally, a prediction-corrected VPC (pcVPC) [ 17 ] was performed to evaluate the performance of the model by Martial et al [ 10 ] on our dataset.…”

Section: Methodsmentioning

confidence: 99%

“…LSSs have been developed and validated for most tacrolimus formulations [ 8 ]. However, because of the specific prolonged release profile of meltdose tacrolimus (Envarsus ® [ 9 ]), conventional tacrolimus limited sampling strategies (i.e., 0, 1, 3 and 0, 2, 3 h) are unsuitable for accurate AUC estimation [ 10 , 11 ]. Moreover, studies reporting LSSs for meltdose tacrolimus are scarce [ 10 , 11 ], and due to the need for late samples, these LSSs are not always practically feasible [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, because of the specific prolonged release profile of meltdose tacrolimus (Envarsus ® [ 9 ]), conventional tacrolimus limited sampling strategies (i.e., 0, 1, 3 and 0, 2, 3 h) are unsuitable for accurate AUC estimation [ 10 , 11 ]. Moreover, studies reporting LSSs for meltdose tacrolimus are scarce [ 10 , 11 ], and due to the need for late samples, these LSSs are not always practically feasible [ 11 ]. In addition, to our knowledge, no LSS validated in de novo elderly kidney transplant populations has currently been published, thus warranting further investigation.…”

Section: Introductionmentioning

confidence: 99%

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Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

Kamp,

Zwart,

Meziyerh

et al. 2023

Pharmaceutics

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Background: Meltdose tacrolimus (Envarsus®) has been marketed as a formulation achieving a more consistent tacrolimus exposure. Due to the narrow therapeutic window of tacrolimus, dose individualization is essential. Relaxation of the upper age limits for kidney transplantations has resulted in larger numbers of elderly patients receiving tacrolimus. However, due to the physiological changes caused by aging, the tacrolimus pharmacokinetics (PK) might be altered. The primary aim was to develop a population PK model in elderly kidney transplant recipients. Secondary aims were the development and evaluation of a limited sampling strategy (LSS) for AUC estimation. Methods: A total of 34 kidney transplant recipients aged ≥65 years, starting on meltdose tacrolimus directly after transplantation, were included. An eight-point whole blood AUC0–24h and an abbreviated dried blood spot (DBS) AUC0–24h were obtained. The PK data were analyzed using nonlinear mixed effect modeling methods. Results: The PK data were best described using a two-compartment model, including three transit compartments and a mixture model for oral absorption. The best three-sample LSS was T = 0, 2, 6 h. The best four-sample LSSs were T = 0, 2, 6, 8 h and T = 0, 1, 6, 8 h. Conclusions: The developed population PK model adequately described the tacrolimus PK data in a population of elderly kidney transplant recipients. In addition, the developed population PK model and LSS showed an adequate estimation of tacrolimus exposure, and may therefore be used to aid in tacrolimus dose individualization.

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Prediction of the Intra‐T Lymphocyte Tacrolimus Concentration after Kidney Transplantation with Population Pharmacokinetic Modeling

Udomkarnjananun,

Schagen,

Volarević

et al. 2024

Clin Pharma and Therapeutics

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The intracellular tacrolimus concentration in CD3+ T lymphocytes is proposed to be a better representative of the active component of tacrolimus than the whole blood concentration. However, intracellular measurements are complicated. Therefore, the aim of this study was to describe the relationship between intracellular and whole blood tacrolimus concentrations in a population pharmacokinetic model. Twenty‐eight de novo kidney transplant recipients, treated with a once‐daily oral extended‐release tacrolimus formulation, were followed during the first‐month post‐transplantation. Additional whole blood and intracellular tacrolimus concentrations were measured at day 6 ± 1 (pre‐dose, 4 and 8 hours post‐dose) and day 14 ± 3 (pre‐dose) post‐transplantation. Pharmacokinetic analysis was performed using nonlinear mixed effects modeling software (NONMEM). The ratio between intracellular (n = 109) and whole blood (n = 248) concentrations was best described by a two‐compartment whole blood model with an additional intracellular compartment without mass transfer from the central compartment. The ratio remained stable over time. Prednisolone dose influenced the absorption rate of tacrolimus, while hemoglobin, CYP3A4*22 allele carrier, and CYP3A5 expresser status were associated with the oral clearance of tacrolimus (P‐value < 0.001). Furthermore, the intracellular tacrolimus concentrations were correlated with the intracellular production of interleukin‐2 (P‐value 0.015). The intracellular tacrolimus concentration can be predicted from a measured whole blood concentration using this model, without the need for repeated intracellular measurements. This knowledge is particularly important when the intracellular concentration is ready to be implemented into clinical practice, to overcome the complexities of cell isolation and analytical methods.

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Population pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus) in stable adult liver transplant recipients

Abstract: Title: Population pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus ® ) in stable adult liver transplant recipients Lisa C Martial(1); Maaike Biewenga(2); Bastian N Ruijter(2); Ron Keizer (3); Jesse J Swen(1); Bart van Hoek(2); Dirk Jan AR Moes(1).

Cited by 13 publications

References 37 publications

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

Prediction of the Intra‐T Lymphocyte Tacrolimus Concentration after Kidney Transplantation with Population Pharmacokinetic Modeling

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Population pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus) in stable adult liver transplant recipients

Abstract: Title: Population pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus ® ) in stable adult liver transplant recipients Lisa C Martial(1*); Maaike Biewenga(2*); Bastian N Ruijter(2); Ron Keizer (3); Jesse J Swen(1); Bart van Hoek(2**); Dirk Jan AR Moes(1**).

Cited by 13 publications

References 37 publications

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

Prediction of the Intra‐T Lymphocyte Tacrolimus Concentration after Kidney Transplantation with Population Pharmacokinetic Modeling

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Abstract: Title: Population pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus ® ) in stable adult liver transplant recipients Lisa C Martial(1); Maaike Biewenga(2); Bastian N Ruijter(2); Ron Keizer (3); Jesse J Swen(1); Bart van Hoek(2); Dirk Jan AR Moes(1).