Population pharmacokinetics of tacrolimus in Chinese adult liver transplant patients

Exploring the comprehensive factors influencing tacrolimus pharmacokinetics in early renal transplant recipients: A population pharmacokinetic analysis

PURPOSE

To establish a population pharmacokinetic (PopPK) model of tacrolimus in the early stages after renal transplantation and evaluate the model's predictive performance with external data.

METHODS

Intravenous and oral tacrolimus were administered to 302 renal transplant recipients in the early posttransplantation stages. Related data were obtained from the electronic medical records. Single nucleotide polymorphisms in genes associated with tacrolimus pharmacokinetics were tested. The data were analyzed by NONMEM. The external data from 153 patients were subsequently used to evaluate model extrapolation.

RESULTS

A one-compartment model was used to determine tacrolimus pharmacokinetics. The estimated clearance (CL), volume of distribution (V) and bioavailability (F) of tacrolimus were 4.91 L/h, 77 L and 26.5%, respectively. CL and V decreased with increasing hematocrit. CL and F decreased with increasing operation time. Diltiazem and Wuzhi capsule resulted in 28.4% and 43.9% decreases in the CL, respectively. Omeprazole or esomeprazole resulted in a 9% increase in F. The value of F for patients expressing CYP3A5 was 36.6% lower than that for the patients who did not express CYP3A5. The evaluation of external data revealed that the proportion of individual prediction error within 20% of the observed tacrolimus concentration was greater than 77.3%.

CONCLUSIONS

A PopPK model for tacrolimus was established for early renal transplantation. CYP3A5 was a significant covariate for F. Fat-free mass was the best predictor of the influence of body size on CL and V. The model could be extrapolated to stable renal transplant recipients.

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.

Population pharmacokinetics study of tacrolimus in liver transplant recipients: a comparison between patients with or without liver cancer before surgery

Background and Objective

The main challenge for immunosuppressive therapy using tacrolimus in liver transplantation is the considerable variability in its oral bioavailability and the narrow treatment range. Many population pharmacokinetic (PopPK) models have been established to precisely estimate the PK variability of tacrolimus in liver transplant recipients. However, it remains unclear whether there is a significant difference in the PK behavior of tacrolimus between patients with or without liver cancer before surgery. Therefore, we aimed to compare the differences of PK parameters and simulate exposures of tacrolimus between populations preoperatively diagnosed with liver cancer or not by PopPK modeling.

Methods

In total, 802 blood concentrations of tacrolimus from 196 patients (118 liver cancer and 78 non-liver-cancer samples) were included in this study. Demographic data and clinical parameters were integrated to perform a PopPK analysis using the nonlinear mixed-effects modeling approach. Potential covariates were evaluated by using a stepwise method. Goodness-of-fit plot and bootstrap were performed to assess the model stability and predictive performance. Simulations were introduced to optimize dosing regimens of both the liver cancer and non-liver-cancer groups according to the guidance.

Results

The PK of tacrolimus was best described by a one-compartment model with first-order absorption and linear elimination, with weight and direct bilirubin as the significant covariates. In the process of constructing the basic model, we tried to separately estimate the PK parameters in liver cancer and non-liver-cancer populations. The results showed that the PK parameters in the two populations were similar, and the individual variation in Ka in non-liver-cancer subjects was large. Hence, the final model did not distinguish between the two populations. Moreover, a minor increase of less than 10% was observed in the simulated exposure in the patients preoperatively diagnosed with liver cancer compared with that in non-liver-cancer groups.

Conclusion

The established PopPK model was capable of optimizing tacrolimus dosing in whole populations who underwent liver transplantation. Although a minimal difference was found in tacrolimus exposure between the liver cancer and non-liver-cancer groups, more research is warranted to explore the differences between the two populations in the future, given the potential limitations of this study.

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Aims

The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.

Methods

Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.

Results

The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.

Conclusion

The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

Numerical Optimization of Prednisolone-Tacrolimus Loaded Ultraflexible Transethosomes for Transdermal Delivery Enhancement; Box-Behnken Design, Evaluation, Optimization, and Pharmacokinetic Study

The aim of the present study is to formulate highly permeable carriers (i.e., transethosomes) for enhancing the delivery of prednisolone combined with tacrolimus for both topical and systemic pathological conditions. A Box-Behnken experimental design was implemented in this research. Three independent variables: surfactant concentration (X1), ethanol concentration (X2), and tacrolimus concentration (X3) were adopted in the design while three responses: entrapment efficiency (Y1), vesicle size (Y2), and zeta potential (Y3) were investigated. By applying design analysis, one optimum formulation was chosen to be incorporated into topical gel formulation. The optimized transethosomal gel formula was characterized in terms of pH, drug content, and spreadability. The gel formula was challenged in terms of its anti-inflammatory effect and pharmacokinetics against oral prednisolone suspension and topical prednisolone-tacrolimus gel. The optimized transethosomal gel achieved the highest rate of rat hind paw edema reduction (98.34%) and highest pharmacokinetics parameters (Cmax 133.266 ± 6.469 µg/mL; AUC_0-∞ 538.922 ± 49.052 µg·h/mL), which indicated better performance of the formulated gel.

Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of Michaelis-Menten and theory-based pharmacokinetic models

Background and Objective: Tacrolimus, a calcineurin inhibitor widely used as a potent immunosuppressant to prevent graft rejection, exhibits nonlinear kinetics in patients with kidney transplantation and nephrotic syndrome. However, whether nonlinear drug metabolism occurs in adult patients undergoing liver transplantation remains unclear, as do the main underlying mechanisms. Therefore, here we aimed to further confirm the characteristics of nonlinearity through a large sample size, and determine the potential influence of nonlinearity and its possible mechanisms.

Methods: In total, 906 trough concentrations from 176 adult patients (150 men/26 women; average age: 50.68 ± 9.71 years, average weight: 64.54 ± 11.85 kg after first liver transplantation) were included in this study. Population pharmacokinetic analysis was performed using NONMEM®. Two modeling strategies, theory-based linear compartmental and nonlinear Michaelis–Menten (MM) models, were evaluated and compared. Potential covariates were screened using a stepwise approach. Bootstrap, prediction-, and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine model stability and predictive performance. Finally, Monte Carlo simulations based on the superior model were conducted to design dosing regimens.

Results: Postoperative days (POD), Aspartate aminotransferase (AST), daily tacrolimus dose, triazole antifungal agent (TAF) co-therapy, and recipient CYP3A5*3 genotype constituted the main factors in the theory-based compartmental final model, whereas POD, Total serum bilirubin (TBIL), Haematocrit (HCT), TAF co-therapy, and recipient CYP3A5*3 genotype were important in the nonlinear MM model. The theory-based final model exhibited 234 L h−1 apparent plasma clearance and 11,000 L plasma distribution volume. The maximum dose rate (Vmax) of the nonlinear MM model was 6.62 mg day−1; the average concentration at steady state at half-Vmax (Km) was 6.46 ng ml−1. The nonlinear MM final model was superior to the theory-based final model and used to propose dosing regimens based on simulations.

Conclusion: Our findings demonstrate that saturated tacrolimus concentration-dependent binding to erythrocytes and the influence of daily tacrolimus dose on metabolism may partly contribute to nonlinearity. Further investigation is needed is need to explore the causes of nonlinear pharmacokinetic of tacrolimus. The nonlinear MM model can provide reliable support for tacrolimus dosing optimization and adjustment in adult patients undergoing liver transplantation.

Population pharmacokinetics and initial dose optimization of tacrolimus in children with severe combined immunodeficiency undergoing hematopoietic stem cell transplantation

The present study aimed to explore the population pharmacokinetics and initial dose optimization of tacrolimus in children with severe combined immunodeficiency (SCID) undergoing hematopoietic stem cell transplantation (HSCT). Children with SCID undergoing HSCT treated with tacrolimus were enrolled for analysis. Population pharmacokinetics of tacrolimus was built up by a nonlinear mixed-effects model (NONMEM), and initial dose optimization of tacrolimus was simulated with the Monte Carlo method in children weighing <20 kg at different doses. A total of 18 children with SCID undergoing HSCT were included for analysis, with 130 tacrolimus concentrations. Body weight was included as a covariable in the final model. Tacrolimus CL/F was 0.36–0.26 L/h/kg from body weights of 5–20 kg. Meanwhile, we simulated the tacrolimus concentrations using different body weights (5–20 kg) and different dose regimens (0.1–0.8 mg/kg/day). Finally, the initial dose regimen of 0.6 mg/kg/day tacrolimus was recommended for children with SCID undergoing HSCT whose body weights were 5–20 kg. It was the first time to establish tacrolimus population pharmacokinetics in children with SCID undergoing HSCT; in addition, the initial dose optimization of tacrolimus was recommended.