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Aim: Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction., Methods: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of K a , V 2 /F, Q/F and V 3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V 2 /F, V 3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions., Results: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P < .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing., Conclusion: A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes., (© 2022 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

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 prediction-corrected 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., (© 2021 British Pharmacological Society.)

Background and Objectives: Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions., Methods: The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020., Results: Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies)., Conclusion: The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

Aims: To (i) describe tacrolimus (TAC) pre-dose concentrations (C 0 ), (ii) calculate apparent oral TAC clearance (CL/F HCT ) adjusted for measured haematocrit (HCTi) and standardised to a HCT of 45%, across three observation time points and (iii) explore if low TAC C 0 or high mean CL/F HCT are associated with an increased risk of rejection episodes early after lung transplantation., Methods: TAC whole blood concentration-time profiles and transbronchial biopsies were performed prospectively at weeks 3, 6 and 12 after lung transplantation. The TAC pre-dose concentration (C 0 ) was measured, and CL/F HCT was determined using non-compartmental analysis. The associations between TAC C 0 and CL/F HCT and rejection status were explored using repeated measures logistic regression., Results: Eighteen patients provided 377 TAC whole blood concentrations. Considerable variability around the median (IQR) CL/F HCT 6.8 (4.2-15.9) L h -1 , and the median C 0 12.7 (9.9-16.6) μg L -1 was noted. Despite adjustment for haematocrit, a significant decrease was observed in CL/F HCT in all patients over time: CL/F HCT 14 (5.4-23) at week 3, CL/F HCT 7.7 (4.5-12) at week 6 and CL/F HCT 3.9 (2.4-11) L h -1 at week 12 (p < 0.01). Seven (38.9%) patients experienced a single grade 2 rejection, whilst 11 (61.1%) patients experienced no rejection. Higher TAC C 0 were associated with a reduced risk of rejection OR 0.68 (95% CI 0.51-0.91, p = 0.02), and greater mean CL/F HCT was associated with an increased risk of rejection OR 1.34 (95% CI 1.01-1.81 p = 0.04)., Conclusion: Monitoring TAC C 0 , HCT and CL/F HCT in patients after lung transplantation may assist clinicians in detecting patients at risk of acute rejection and may guide future research into TAC and HCT monitoring after lung transplantation.

Aims: The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models., Methods: Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting., Results: Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range., Conclusion: A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

Objectives: The aims of this study were to develop a population pharmacokinetic model of tacrolimus in adult kidney transplant recipients, to use this model to compare cytochrome P450 3A5 (CYP3A5) genotype-based initial dosing of tacrolimus with standard per-kilogram-based dosing, and to predict the best starting dose of tacrolimus based on patient genotype to achieve a trough concentration between 6 and 10 µg/L by day 5 posttransplantation., Methods: Population analysis was performed using the software program NONMEM. Tacrolimus dosing regimens were compared by predicting tacrolimus trough concentrations in a simulated data set by running NONMEM with population parameters fixed at the final model estimates. Data from 173 patients with 1554 tacrolimus concentration-time measurements were modeled., Results: Tacrolimus disposition was well described by a 2-compartment model with first-order elimination and first-order absorption after a lag time. Patient CYP3A5 genotype (rs776746), weight, hematocrit, and postoperative day were identified as significant covariates effecting tacrolimus apparent oral clearance (CL/F), with higher CL/F in CYP3A5*1 allele carriers, heavier patients, patients with low hematocrit, and in the immediate posttransplantation period. Typical population estimates for tacrolimus CL/F in CYP3A5*1 allele carriers and noncarriers were 40.8 and 25.5 L/h, respectively., Conclusions: In patients carrying the CYP3A5*1 allele, a per-kilogram dose of 0.075 mg/kg twice daily seemed too much low with approximately 65% of simulated subjects predicted to achieve a trough below 6 µg/L at day 5 posttransplantation. To reduce the risk of under immunosuppression in the immediate posttransplantation period, carriers of a CYP3A5*1 allele are likely to benefit from a tacrolimus starting dose of either 10 mg or 0.115 mg/kg twice daily.