Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients

External evaluation of tacrolimus population pharmacokinetic models in adult lung transplant patients: How to enhance the predictive ability of the model?

PURPOSE

Tacrolimus is the cornerstone of current immunosuppressive strategies after lung transplantation. However, its narrow therapeutic range and considerable pharmacokinetic variability pose challenges for individualized treatment. Several tacrolimus population pharmacokinetic (popPK) models have been developed for precision dosing in adult lung transplant patients. However, their applicability across different clinical settings remains uncertain. The aim of this study was to evaluate the external predictability of these models and identify influential factors.

METHODS

Published models were systematically retrieved and assessed based on an external dataset of 39 patients (1240 tacrolimus trough concentrations) using three approaches: (1) prediction-based diagnosis using dosing records and patient characteristics; (2) simulation-based diagnosis, with prediction- and variability-corrected visual predictive checks (pvcVPC) and normalized prediction distribution error tests (NPDE); and (3) Bayesian forecasting using one to four observations for posterior predictions. We also investigated the impact of model structure and covariates on predictability.

RESULTS

The predictive performance of six published models was externally evaluated, but none demonstrated satisfactory accuracy in prediction- and simulation-based diagnosis. Bayesian forecasting yielded satisfactory results with only one prior observation and optimal predictive performance with 2-3 priors for all included models. The structural model parameterized on plasma tacrolimus concentration outperformed others. Significant correlations were observed between prediction-error and daily tacrolimus dose, postoperative day, and voriconazole co-administration.

CONCLUSIONS

The overall predictive performance of all published models was unsatisfactory, making direct extrapolation inappropriate. However, Bayesian forecasting significantly improves predictive performance. Utilizing plasma tacrolimus concentration for parameter estimation can improve the predictive ability of tacrolimus popPK models.

Model-Informed Precision Dosing of Tacrolimus: A Systematic Review of Population Pharmacokinetic Models and a Benchmark Study of Software Tools

BACKGROUND AND OBJECTIVE

Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment.

METHODS

We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration.

RESULTS

We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/ , allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey.

CONCLUSIONS

Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment.

Individualized dosing parameters for tacrolimus in the presence of voriconazole: a real-world PopPK study

Objectives

Significant increase in tacrolimus exposure was observed during co-administration with voriconazole, and no population pharmacokinetic model exists for tacrolimus in renal transplant recipients receiving voriconazole. To achieve target tacrolimus concentrations, an optimal dosage regimen is required. This study aims to develop individualized dosing parameters through population pharmacokinetic analysis and simulate tacrolimus concentrations under different dosage regimens.

Methods

We conducted a retrospective study of renal transplant recipients who were hospitalized at the Second Xiangya Hospital of Central South University between January 2016 and March 2021. Subsequently, pharmacokinetic analysis and Monte Carlo simulation were employed for further analysis.

Results

Nineteen eligible patients receiving tacrolimus and voriconazole co-therapy were included in the study. We collected 167 blood samples and developed a one-compartment model with first-order absorption and elimination to describe the pharmacokinetic properties of tacrolimus. The final typical values for tacrolimus elimination rate constant (Ka), apparent volume of distribution (V/F), and apparent oral clearance (CL/F) were 8.39 h-1, 2690 L, and 42.87 L/h, respectively. Key covariates in the final model included voriconazole concentration and serum creatinine. Patients with higher voriconazole concentration had lower tacrolimus CL/F and V/F. In addition, higher serum creatinine levels were associated with lower tacrolimus CL/F.

Conclusion

Our findings suggest that clinicians can predict tacrolimus concentration and estimate optimal tacrolimus dosage based on voriconazole concentration and serum creatinine. The effect of voriconazole concentration on tacrolimus concentration was more significant than serum creatinine. These findings may inform clinical decision-making in the management of tacrolimus and voriconazole therapy in solid organ transplant recipients.

External validation of population pharmacokinetic models of tacrolimus in Thai adult liver transplant recipients

OBJECTIVE

Several population pharmacokinetic models of tacrolimus in liver transplant patients were built, and their predictability was evaluated in their settings. However, the extrapolation in the prediction was unclear. This study aimed to evaluate the predictive performance of published tacrolimus models in adult liver transplant recipients using data from the Thai population as an external dataset.

METHODS

The selected published models were systematically searched and evaluated for their quality. The external dataset of patients who underwent the first liver transplant and received immediate-release tacrolimus was used to assess the predictive performance of each selected model. Trough concentrations between 3 and 6 months were retrospectively collected to evaluate the predictability of each model using prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting.

RESULTS

Sixty-seven patients with 360 trough concentrations and eight selected published models were included in this study. None of the models met the predictive precision criteria in prediction-based diagnostics. Meanwhile, four published population pharmacokinetic models showed a normal distribution in NPDE testing. Regarding Bayesian forecasting, all models improved their forecasts with at least one prior information data point.

CONCLUSION

Bayesian forecasting is more accurate and precise than other testing methods for predicting drug concentrations. However, none of the evaluated models provides satisfactory predictive performance for generalization to Thai liver transplant patients. This underscores the need for future research to develop population PK models tailored to the Thai population. Such efforts should consider the inclusion of nonlinear pharmacokinetics and region-specific factors, including genetic variability, to improve model accuracy and applicability.

Genotype-Guided Model for Prediction of Tacrolimus Initial Dosing After Lung Transplantation

The determination of the appropriate initial dose for tacrolimus is crucial in achieving the target concentration promptly and avoiding adverse effects and poor prognosis. However, the trial-and-error approach is still common practice. This study aimed to establish a prediction model for an initial dosing algorithm of tacrolimus in patients receiving a lung transplant. A total of 210 lung transplant recipients were enrolled, and 26 single nucleotide polymorphisms (SNP) from 18 genes that could potentially affect tacrolimus pharmacokinetics were genotyped. Associations between SNPs and tacrolimus concentration/dose ratio were analyzed. SNPs that remained significant in pharmacogenomic analysis were further combined with clinical factors to construct a prediction model for tacrolimus initial dose. The dose needed to reach steady state tacrolimus concentrations and achieve the target range was used to validate model prediction efficiency. Our final model consisted of 7 predictors-CYP3A5 rs776746, SLCO1B3 rs4149117, SLC2A2 rs1499821, NFATc4 rs1955915, alanine aminotransferase, direct bilirubin, and hematocrit-and explained 41.4% variance in the tacrolimus concentration/dose ratio. It achieved an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.746-0.861). The Hosmer-Lemeshow test yielded a nonsignificant P value of .790, suggesting good fit of the model. The predicted dose exhibited good correlation with the observed dose in the early postoperative period (r = 0.748, P less than .001). Our study provided a genotype-guided prediction model for tacrolimus initial dose, which may help to guide individualized dosing of tacrolimus in the lung transplant population in clinical practice.

Optimizing the initial tacrolimus dosage in Chinese children with lung transplantation within normal hematocrit levels

Background

The appropriate initial dosage of tacrolimus is undefined in Chinese pediatric lung transplant patients with normal hematocrit values. The purpose of this study is to optimize the initial dose of tacrolimus in Chinese children who are undergoing lung transplantation and have normal hematocrit levels.

Methods

The present study is based on a published population pharmacokinetic model of tacrolimus in lung transplant patients and uses the Monte Carlo simulation to optimize the initial tacrolimus dosage in Chinese children with lung transplantation within normal hematocrit levels.

Results

Within normal hematocrit levels, for children with lung transplantation who do not carry the CYP3A5*1 gene and have no coadministration with voriconazole, it is recommended to administer tacrolimus at a dosage of 0.02 mg/kg/day, divided into two doses, for children weighing 10-32 kg, and a dosage of 0.03 mg/kg/day, also divided into two doses, for children weighing 32-40 kg. For children with lung transplantation who carry the CYP3A5*1 gene and have no coadministration with voriconazole, tacrolimus dosages of 0.02, 0.03, and 0.04 mg/kg/day split into two doses are recommended for children weighing 10-15, 15-32, and 32-40 kg, respectively. For children with lung transplantation who do not carry the CYP3A5*1 gene and have coadministration with voriconazole, tacrolimus dosages of 0.01 and 0.02 mg/kg/day split into two doses are recommended for children weighing 10-17 and 17-40 kg, respectively. For children with lung transplantation who carry the CYP3A5*1 gene and have coadministration with voriconazole, a tacrolimus dosage of 0.02 mg/kg/day split into two doses is recommended for children weighing 10-40 kg.

Conclusions

It is the first time to optimize the initial dosage of tacrolimus in Chinese children undergoing lung transplantation within normal hematocrit.

Individual dose recommendations for drug interaction between tacrolimus and voriconazole in adult liver transplant recipients: A semiphysiologically based population pharmacokinetic modeling approach

The magnitude of drug-drug interaction between tacrolimus and voriconazole is highly variable, and individually tailoring the tacrolimus dose when concomitantly administered with voriconazole remains difficult. This study aimed to develop a semiphysiologically based population pharmacokinetic (semi-PBPK) model and a web-based dashboard to identify the dynamic inhibition of tacrolimus metabolism caused by voriconazole and provide individual tacrolimus regimens for Chinese adult liver transplant recipients. A total of 264 tacrolimus concentrations and 146 voriconazole concentrations were prospectively collected from 32 transplant recipients. A semi-PBPK model with physiological compartments including the gut wall, portal vein, and liver was developed using the nonlinear mixed-effects modeling software NONMEM (version 7.4). A web-based dashboard was established in R software (version 3.6.1) to recommend the individual tacrolimus regimens when concomitantly administered with voriconazole. The reversible inhibition of tacrolimus metabolism caused by voriconazole was investigated in both the liver and the gut wall. Moreover, voriconazole could highly inhibit the CYP3A activity in the gut wall more than in the liver. BMI and postoperative days were identified as significant covariates on intrinsic intestinal and hepatic clearance of tacrolimus, respectively. Age and postoperative days were identified as significant covariates on the volume of distribution of voriconazole. The individual tacrolimus regimens when concomitantly administered with voriconazole could be recommended in the dashboard (https://tac-vor-ddi.shinyapps.io/shinyapp3/). In conclusion, the semi-PBPK model successfully described the dynamic inhibition process between tacrolimus and voriconazole, and the web-based dashboard could provide individual tacrolimus regimens when concomitantly administered with voriconazole.

Pharmacokinetic Evaluation of Tacrolimus in Chinese Adult Patients during the Early Stages Post-Lung Transplantation

BACKGROUND

Although tacrolimus has been widely used in patients undergoing lung transplantation, few studies have reported the pharmacokinetics of tacrolimus in Chinese patients after lung transplantation. Thus, we aimed to investigate the pharmacokinetics and influential factors in this patient cohort in the early stage after lung transplantation.

METHODS

We enrolled 14 adult lung transplant recipients who were treated with tacrolimus and then intensively collected blood samples within a 12-h dosing interval. The pharmacokinetic parameters of tacrolimus were calculated using non-compartmental analysis, and the influence of pathophysiological characteristics and CYP3A5*3 and CYP3A4*1G genotypes on the pharmacokinetics of tacrolimus was assessed. Using linear regression analysis, we investigated the correlation between tacrolimus concentration at different sampling points and measured the area under the time-concentration curve (AUC_0-12h).

RESULTS

Geometric mean of apparent clearance (CL/F) was 18.13 ± 1.65 L/h in non-CYP3A5*3/*3 carriers, five times higher than that in CYP3A5*3/*3 carriers (p < 0.001). Furthermore, the tacrolimus concentration 4 h after administration had the strongest correlation with AUC_0-12h (R² = 0.979).

CONCLUSION

The pharmacokinetics of tacrolimus varied largely between patients during the early stage post-transplantation, which could be partially explained by CYP3A5*3 genetic polymorphisms.

Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

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 Ka , V2 /F, Q/F and V3 /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, V2 /F, V3 /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.

Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison

Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined.

Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison.

Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: −0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable.

Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.

Initial sirolimus dosage recommendations for pediatric patients with PIK3CD mutation-related immunodeficiency disease

Sirolimus is used to treat pediatric patients with PIK3CD mutation-related immunodeficiency disease. However, the initial dosages of sirolimus remain undecided. The present study aims to explore initial dosages in pediatric patients with PIK3CD mutation-related immunodeficiency disease. Pediatric patients with this disease were analyzed using the population pharmacokinetic (PPK) model and the Monte Carlo simulation. Body weight and concomitant use of posaconazole were included in the final PPK model, where, under the same weight, clearances of sirolimus were 1 : 0.238 between children without and children with posaconazole. Without posaconazole, the initial dosages of sirolimus were 0.07, 0.06, 0.05, and 0.04 mg/kg/day for body weights of 10–14, 14–25, 25–50, and 50–60 kg, respectively. With posaconazole, the initial dosages of sirolimus were 0.02 mg/kg/day for body weights of 10–60 kg. This is the first attempt to build a sirolimus PPK model for recommending initial dosages in children with PIK3CD mutation-related immunodeficiency disease, thereby providing a reference for individualized clinical drug administration.

Optimization of initial dose regimen of tacrolimus in paediatric lung transplant recipients based on Monte Carlo simulation

WHAT IS KNOWN AND OBJECTIVES

The initial tacrolimus dose regimen in paediatric lung transplant recipients is unknown. The present study optimized the initial tacrolimus dose regimen for paediatric lung transplant recipients.

METHODS

This study was based on a published population pharmacokinetic model of tacrolimus in lung transplant recipients and used Monte Carlo simulations to recommend an initial dose regimen of tacrolimus in paediatric lung transplant recipients.

RESULTS

Without voriconazole, the tacrolimus doses recommended for paediatric lung transplant recipients who were not CYP3A5*1 carriers were 0.02, 0.03, and 0.04 mg/kg/day, split into two doses, for weights of 10-16, 16-30, and 30-40 kg, respectively. For paediatric lung transplant recipients who were CYP3A5*1 carriers, the tacrolimus doses of 0.03, 0.04, 0.05, and 0.06 mg/kg/day, split into two doses, were recommended for weights of 10-16, 16-25, 25-30, and 30-40 kg, respectively. With voriconazole, the tacrolimus dose recommended for paediatric lung transplant recipients who were not CYP3A5*1 carriers was 0.02 mg/kg/day, split into two doses, for weights of 10-40 kg. For paediatric lung transplant recipients who were CYP3A5*1 carriers, tacrolimus doses of 0.02 and 0.03 mg/kg/day, split and two doses, were recommended for weights of 10-24 and 24-40 kg, respectively.

WHAT IS NEW AND CONCLUSIONS

This study developed tacrolimus dose regimens for the first time for paediatric lung transplant recipients using Monte Carlo simulation and optimized initial dosage in paediatric lung transplant recipients.

Population Pharmacokinetic Evaluation with External Validation of Tacrolimus in Chinese Primary Nephrotic Syndrome Patients

PURPOSE

The generalizability of numerous tacrolimus population pharmacokinetic (popPK) models constructed to promote optimal tacrolimus dosing in patients with primary nephrotic syndrome (PNS) is unclear. This study aimed to evaluate the predictive performance of published tacrolimus popPK models for PNS patients with an external data set.

METHODS

We prospectively collected 223 concentrations from 50 Chinese adult patients with PNS who were undergoing tacrolimus treatment. Data on published tacrolimus popPK models for adults and children with PNS were extracted from the literature. Model predictability was evaluated with prediction-based and simulation-based diagnostics and Bayesian forecasting.

RESULTS

In prediction-based evaluation, none of the 11 identified published popPK models of tacrolimus had met a predefined criteria of a mean prediction error ≤  ± 20%, and the prediction error within ± 30% of the identified models didn't exceed 50%. Simulation-based diagnostics also indicated unsatisfactory predictability. Bayesian forecasting demonstrated amelioration in the model predictability with the inclusion of 2-3 prior observations. Moreover, the predictive performance of nonlinear models was not better than that of one-compartment models.

CONCLUSIONS

The prediction of tacrolimus concentrations for patients with PNS remains challenging; published models are not applicable for extrapolation to other hospitals. Bayesian forecasting significantly improved model predictability and thereby helped to individualize tacrolimus dosing.

The importance of CYP2C19 genotype in tacrolimus dose optimization when concomitant with voriconazole in heart transplant recipients

AIMS

Voriconazole remains the mainstay for the treatment of invasive fungal infections in the heart transplant patients and can significantly increase tacrolimus exposure because of drug-drug interaction (DDI). However, the magnitude of this DDI is highly variable and difficult to predict. The purpose of this study was to present the characteristics of DDI between tacrolimus and voriconazole, and further identify the various predictors of tacrolimus dose modification.

METHODS

We retrospectively enrolled 69 heart transplant recipients without using voriconazole as the control and 68 patients received voriconazole treatment in voriconazole group. CYP3A4*1G, CYP3A5*3 and CYP2C19*2 or *3 were thereafter genotyped by Sanger sequencing. The requirement of tacrolimus dose to achieve the therapeutic concentrations and tacrolimus dose-corrected trough concentration (C₀ /D) before and after VRC administration were evaluated.

RESULTS

The DDI between tacrolimus and voriconazole displayed a large inter-individual variability with more than ten-fold changes in tacrolimus dose (range 1.28-13.00) and C₀ /D (range 1.43-13.75). Besides, the fold changes of tacrolimus dose were associated with CYP2C19 genotype, which was found to be significantly lower in CYP2C19 extensive metabolizers than that in CYP2C19 intermediate metabolizers or poor metabolizers (4.06±1.85 vs 5.49±2.47, p=0.0031). However, no significant difference was found in both CYP3A4 and CYP3A5 genotypes. Moreover, CYP2C19 genotype and hematocrit acted as independent predicting factors for tacrolimus dose modification after voriconazole co-therapy.

CONCLUSIONS

The findings of this study have identified the various important factors to adjust tacrolimus dosage when co-administrated with voriconazole in individual patients. CYP2C19 genotype and hematocrit should be considered in tailoring tacrolimus dose.

Effects Of Voriconazole Exposure on The Pharmacokinetics of Tacrolimus in Lung Transplantation Patients: Based on Therapeutic Drug Monitoring Data

Tacrolimus and voriconazole are usually used simultaneously in lung transplantations. Voriconazole can increase tacrolimus concentrations by inhibiting the CYP enzyme, which poses a great challenge for dose adjustment. The aim of this study is to clarify the correlation between voriconazole exposure and tacrolimus trough concentrations (C₀ ), and to establish a population pharmacokinetic model including voriconazole trough concentrations (VOZ) as a covariate for dose optimization. All data were retrospectively collected from lung transplantation patients who were subjected to the therapeutic drug monitoring of tacrolimus and voriconazole. The correlation between C₀ and VOZ or voriconazole daily doses was analyzed by Spearman's correlation. A total of 52 patients accounting for 351 pairs of tacrolimus and voriconazole trough concentrations were included. C₀ and C₀ /DD had a significant correlation with VOZ (P<0.01) rather than voriconazole daily doses. A linear one-compartment model with first-order absorption and elimination was used as basic model in population pharmacokinetic analysis. The body weight (WT), daily dose of tacrolimus (DD), VOZ, and hematocrit (HCT) were included as covariates in the final model. With the increase in voriconazole concentrations, the apparent total clearance (CL/F) of tacrolimus decreased significantly. The simulation results showed that the highest proportion of C₀ within the target range can only reach lower than 50% when optimal initial drug regimen was given. Therefore, both tacrolimus and voriconazole concentrations need to be continuously monitored during treatments in lung transplantation patients, and the tacrolimus dose can be optimized according to VOZ based on the established pharmacokinetic model. This article is protected by copyright. All rights reserved.

Effects of Posaconazole on Tacrolimus Population Pharmacokinetics and Initial Dose in Children With Crohn’s Disease Undergoing Hematopoietic Stem Cell Transplantation

The present study explored the effects of posaconazole on tacrolimus population pharmacokinetics (PPK) in children with Crohn’s disease (CD) undergoing hematopoietic stem cell transplantation (HSCT). Tacrolimus concentrations, physiological and biochemical factors, and concomitant medications from 51 CD children undergoing HSCT were used to establish a PPK model based on a nonlinear mixed-effect model. Steady-state concentrations of tacrolimus for children weighing less than 20 kg treated with different dose regimens were simulated by the Monte Carlo method. Weight and concomitant medications were included as covariates. At the same weight, the relative tacrolimus clearance was 1:0.43 in children without or with posaconazole. Compared to children not receiving posaconazole, the simulated tacrolimus steady-state concentrations at different doses for different body weights were all higher in children receiving posaconazole (p &lt; 0.01). Furthermore, in children not receiving posaconazole, the dosage regimen with the best probability of achieving the target concentration was 0.6 mg/kg/day for children weighing 5–8.2 kg and 0.5 mg/kg/day for children weighing 8.2–20 kg, while for children receiving posaconazole, the best probability of reaching the target concentration of tacrolimus was a dosage regimen of 0.5 mg/kg/day for children weighing 5–20 kg. In conclusion, the PPK for tacrolimus was determined in children with CD undergoing HSCT for the first time. Co-treatment with posaconazole significantly increased tacrolimus concentrations, and we recommend a specific initial dose regimen for tacrolimus.

Effects of cimetidine on ciclosporin population pharmacokinetics and initial dose optimization in aplastic anemia patients

The present study aimed to explore the effects of cimetidine on ciclosporin population pharmacokinetics and initial dose optimization in aplastic anemia patients. Aplastic anemia patients were used to establish a population pharmacokinetic model by the nonlinear mixed effect (NONMEM), and concentrations of ciclosporin were simulated by Monte Carlo method. With the same weight, the ciclosporin clearance rates were 0.387:1 in patients with or without cimetidine, respectively. In the measured ciclosporin concentrations, compared to aplastic anemia patients without cimetidine, ciclosporin concentrations were higher in patients with cimetidine (P < 0.01). Further research found that at the same body weight and same dose, ciclosporin concentrations in aplastic anemia patients with cimetidine were indeed higher than those in patients without cimetidine (P < 0.01). The initial recommended ciclosporin dose for patients without cimetidine were 7mg/kg splited into two doses for weight of 40-60kg, and 6mg/kg splited into two doses for weight of 60-100kg. The patients with cimetidine were recommended to take 3mg/kg ciclosporin splited into two doses for weight of 40-100kg. It was the first time to explore the effects of cimetidine on ciclosporin population pharmacokinetics and initial dose optimization in aplastic anemia patients. Patients coadministration of cimetidine, may need low ciclosporin dose.

Effect of MMF Immunosuppression Based on CNI Reduction on CNI-Related Renal Damage after Lung Transplantation

In this paper, numerous effects of immunosuppressive regimen of mycophenolate mofetil (MMF) on CNI-related renal damage after lung transplantation are evaluated thoroughly. For this purpose, 110 lung transplant recipients who were treated in our hospital from March 2016 to January 2018 were randomly selected. All patients took prednisone acetate tablets or rapamycin at the same time or not at the same time. MMF is 1 g every time, twice a day, and adjusted according to the re-examination. According to the different drugs taken by 110 patients, they were divided into cyclosporine A group and tacrolimus group. Among them, 92 patients in cyclosporine A group took cyclosporine A; 18 patients in tacrolimus group took tacrolimus. The clinical data of age and gender of the two groups were collected, To observe and compare the occurrence of CNI-related renal damage in lung transplant recipients and different immunosuppressants. The CNI dosage of tacrolimus group and cyclosporine A group was compared before and after MMF. The changes of serum creatinine level and serum creatinine clearance rate were measured before MMF administration and 30, 60, and 90 days after MMF administration, to observe the complications of CNI-related renal damage after lung transplantation. Experimental results showed that there were 16 cases (14.55%) of CNI-related renal damage in lung transplant recipients and different immunosuppressants, including 10 cases (11.36%) in males, 6 cases (27.27%) in females, 11 cases (12.09%) in tacrolimus group, and 5 cases (26.32%) in cyclosporine A group. There was no significant difference between the two groups (P > 0.05). Compared with MMF before and after administration, CNI dosage of cyclosporine A group and tacrolimus group decreased significantly (P < 0.01). Compared with MMF before administration, serum creatinine level decreased and serum creatinine clearance rate increased significantly (P < 0.05). In the follow-up, 16 patients with CNI-related renal damage were found to be immune rejection before the adjustment of immunosuppression program, no complications such as immune rejection, myelosuppression, and infection occurred within 15 months after the adjustment of immunosuppression program, blood glucose increased in 3 patients within 2 years after operation, blood lipid increased in 1 patient, urea increased in 1 patient, and uric acid increased in 1 patient. MMF immunosuppressive therapy based on CNI reduction is a safe and effective immunosuppressive therapy, which can significantly reduce immune rejection, improve renal function, and play an important role in improving CNI-related renal damage after lung transplantation.

The impact of cytochrome P450 3A5 genotype on early tacrolimus metabolism and clinical outcomes in lung transplant recipients

Background Tacrolimus (Tac) is the cornerstone of immunosuppressant therapy after lung transplantation (LTx). It shows great inter-individual variability in pharmacokinetics, which could partly be explained by pharmacogenetic factors. Aim We aim to investigate the influence of cytochrome P450 3A5 (CYP3A5) genotypes on early post-LTx Tac metabolism and whether it is affected by concomitant use of azole antifungals. Also, we explored the association between CYP3A5 genotype and clinical outcomes. Method 90 recipients who underwent LTx from 2017 to 2019 were enrolled in the study. The effect of CYP3A5 genotype on Tac metabolism and interaction with azole antifungals were assessed during week 1-4 after transplantation. Associations between CYP3A5 genotype and the incidence of acute kidney injury (AKI), length of hospital stay and mortality were analyzed. ResultsCYP3A5*1 carriers had lower dose adjusted concentration (C/D) than CYP3A5*3/*3 group at all time points (p < 0.05). The dose ratio of CYP3A5*1 carriers to CYP3A5*3/*3 was between 1.3 and 2.4 when comparable concentrations were reached. Use of azole antifungals did not blunt the effect of CYP3A5 genotypes on Tac metabolism. Logistic regression showed Tac concentration ≥ 7.5 ng/mL at week 1 was associated with higher incidence of AKI. No statistically significant difference was found between CYP3A5 genotypes and the length of hospital stay. Kaplan-Meier analysis showed no statistically significant difference between 30-day or 1-year mortality and CYP3A5 genotype. Conclusion CYP3A5 genotype could affect Tac metabolism early after LTx. However, it had no influence on the incidence of AKI, length of hospital stay and mortality.

Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software

BACKGROUND

Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations.

METHODS

A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.

RESULTS

The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.

CONCLUSIONS

Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

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

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.

Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis

Purpose

In this study, we aimed to establish a tacrolimus population pharmacokinetic model and better understand the drug-drug interaction between Wuzhi capsule and tacrolimus in Chinese renal transplant recipients.

Patients and Methods

We performed a population pharmacokinetic analysis using a non-linear mixed-effects model to determine the suitable Wuzhi capsule dose in combination with tacrolimus. Data on 1378 tacrolimus steady-state concentrations were obtained from 142 patients who received kidney transplant in Changhai Hospital and Huashan Hospital. Demographic characteristics, laboratory tests, genetic polymorphisms, and co-medications were evaluated.

Results

The one-compartment model best described data. Our final model identified creatinine clearance rate, hematocrit, Wuzhi capsule dose, CYP3A5*3 genetic polymorphisms, and tacrolimus daily dose as significant covariates for tacrolimus clearance, with the value of 14.4 L h^-1, and the between-subject variability (BSV) was 25.4%. The Wuzhi capsule showed a dose-dependent effect on tacrolimus pharmacokinetics, demonstrating a stronger inhibitory effect than inductive effect.

Conclusion

Our model can accurately describe population pharmacokinetics of tacrolimus when combined with different doses of Wuzhi capsule. Additionally, this model can be used for individualizing tacrolimus dose following kidney transplantation.