Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

Kirubakaran, Ranita; Stocker, Sophie L.; Hennig, Stefanie; Day, Richard O.; Carland, Jane E.

doi:10.1007/s40262-020-00922-x

Cited by 48 publications

(56 citation statements)

References 93 publications

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“…were administered an oral dose of 20 mg prednisolone once daily (days 4-14 post-transplantation), after which the dose was lowered to 15 mg once daily (day [15][16][17][18][19][20][21][22][23][24][25][26][27][28] and then tapered to 5 mg at month three post-transplantation.…”

Section: Accepted Articlementioning

confidence: 99%

“…Age, albumin, body surface area (BSA), co-medication, cytochrome P450 (CYP) 3A genotype, ethnicity and hematocrit influence tacrolimus pharmacokinetics (10-13). Population pharmacokinetic (popPK) models and dosing equations incorporating these variables have been developed to predict an individual transplant recipient's tacrolimus dose requirement (1,6,11,(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). Clinical application of a popPK model to guide the tacrolimus (starting) dose may shorten the time to reach the target tacrolimus C 0 , minimize under-and overexposure, and improve clinical outcomes (26,28).…”

Section: Introductionmentioning

confidence: 99%

“…10 , 11 , 12 , 13 Population pharmacokinetic (popPK) models and dosing equations incorporating these variables have been developed to predict an individual transplant recipient’s tacrolimus dose requirement. 1 , 27 Clinical application of a popPK model to guide the tacrolimus (starting) dose may shorten the time to reach the target tacrolimus C 0 , minimize underexposure and overexposure, and improve clinical outcomes. 26 , 28 …”

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confidence: 99%

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Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Francke

Andrews

et al. 2021

Clin Pharma and Therapeutics

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Bodyweight-based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meagre 38% of patients is on target at first steady state and it can take up to three weeks to reach the target tacrolimus pre-dose concentration (C 0). Tacrolimus under-and overexposure is associated with an increased risk of rejection and drug-related toxicity, respectively. To minimize sub-and supra-therapeutic tacrolimus exposure in the immediate post-transplant phase, a previously-developed dosing algorithm to predict an individual's tacrolimus starting dose was tested prospectively. In this single-arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweightbased dose. The algorithm included cytochrome P450 (CYP) 3A4 and 3A5 genotype, body surface area and age as covariates. The target tacrolimus C 0 , measured for the first time at day 3, was 7.5-12.5 ng/mL. Between 23 February 2019 and 07 July 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day three post-transplantation, 34 out of 59 patients (58%; 90%-CI 47% to 68%) had a tacrolimus C 0 within the therapeutic range. Markedly sub-therapeutic (<5.0 ng/mL) and supra-therapeutic (>20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy-proven acute rejection occurred in three patients (5%). In conclusion, algorithm-based tacrolimus dosing leads to the achievement of the tacrolimus target C 0 in as many as 58% of the patients on day three after kidney transplantation.

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Section: Accepted Articlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Francke

Andrews

et al. 2021

Clin Pharma and Therapeutics

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“…18 At least 70 population pharmacokinetic models of tacrolimus in adult transplant recipients have been published to characterise tacrolimus pharmacokinetics. [19][20][21] However, very few models reflect current clinical practice as only 3 tacrolimus models 20,22,23 accounted for concomitant azole antifungal use, despite the clinically significant drug-drug interaction. [24][25][26][27][28][29][30] Azole antifungals are commonly prescribed in thoracic transplant recipients as a prophylaxis, or for treatment, against invasive fungal infection.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

Kirubakaran

Hennig

Maslen

et al. 2021

Brit J Clinical Pharma

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Background and Aim: Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments. Methods: Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use. Results: Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The predictioncorrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations. Conclusion: All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

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“…[6][7][8] To date, tacrolimus pharmacokinetic variability has been attributed to multiple covariates, such as postoperative day (POD), cytochrome P450 (CYP) 3A5, and ABCB1 polymorphisms, hematocrit, age, sex, ethnicity, hepatic dysfunction, and drug-drug interactions. [9][10][11][12][13][14] There are far more studies of tacrolimus population models in kidney transplant patients than in liver transplant patients. Genetic polymorphism research also focused on the liver of the donor rather than the receiver.…”

mentioning

confidence: 99%

Effects of Postoperative Day and NR1I2 on Tacrolimus Clearance in Chinese Liver Transplant Recipients—A Population Model Approach

Cui³

et al. 2021

Clinical Pharm in Drug Dev

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We aimed to explore the new biomarkers influencing tacrolimus in vivo behavior in Chinese liver transplant recipients. A total of 418 drug concentration samples of 41 liver transplant patients were collected for modeling. A population pharmacokinetic model was developed using the nonlinear mixed-effects modeling approach. The potential covariates, such as postoperative day (POD), age, body weight, hepatic and renal function, and recipient genetic polymorphisms (ABCB1, CYP3A4, CYP3A5, NR1I2) were evaluated using forward-inclusion and backward-elimination methods. A 1-compartment model was used describing the in vivo behavior of tacrolimus in liver transplant patients. The estimates of CL/F and V/F were 8.88 L/h and 495.82 L, respectively. Two covariates, POD and NR1I2 rs2276707 genotypes, were incorporated into the final population pharmacokinetic model, and they could significantly impact the CL/F: CL/F (L/h) = 8.88 × (POD/16) 0.18 × e 0.91 × NR1I2 × e ηCL . The model evaluation and validation indicated a stable and precise performance of the final model. The functional annotation using ENCODE data indicated that rs2276707 was located on the higher peak of the H3K4Me1 and H3K4Me3 histone marker. To our knowledge, this is the first report indicating NR1I2 rs2276707 genotypes is another biomarker impacting tacrolimus clearance in liver transplant recipients. The NR1I2 gene polymorphism may affect the in vivo behavior of tacrolimus by regulating gene expression.

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Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

Cited by 48 publications

References 93 publications

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

Effects of Postoperative Day and NR1I2 on Tacrolimus Clearance in Chinese Liver Transplant Recipients—A Population Model Approach

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