Population pharmacokinetic analysis of tacrolimus in Chinese cardiac 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.

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.

A Physiologically Based Pharmacokinetic Approach to Recommend an Individual Dose of Tacrolimus in Adult Heart Transplant Recipients

Tacrolimus is the principal immunosuppressive drug which is administered after heart transplantation. Managing tacrolimus therapy is challenging due to a narrow therapeutic index and wide pharmacokinetic (PK) variability. We aimed to establish a physiologically based pharmacokinetic (PBPK) model of tacrolimus in adult heart transplant recipients to optimize dose regimens in clinical practice. A 15-compartment full-PBPK model (Simbiology® Simulator, version 5.8.2) was developed using clinical observations from 115 heart transplant recipients. This study detected 20 genotypes associated with tacrolimus metabolism. CYP3A5*3 (rs776746), CYP3A4*18B (rs2242480), and IL-10 G-1082A (rs1800896) were identified as significant genetic covariates in tacrolimus pharmacokinetics. The PBPK model was evaluated using goodness-of-fit (GOF) and external evaluation. The predicted peak blood concentration (Cmax) and area under the drug concentration-time curve (AUC) were all within a two-fold value of the observations (fold error of 0.68-1.22 for Cmax and 0.72-1.16 for AUC). The patients with the CYP3A5*3/*3 genotype had a 1.60-fold increase in predicted AUC compared to the patients with the CYP3A5*1 allele, and the ratio of the AUC with voriconazole to alone was 5.80 when using the PBPK model. Based on the simulation results, the tacrolimus dosing regimen after heart transplantation was optimized. This is the first PBPK model used to predict the PK of tacrolimus in adult heart transplant recipients, and it can serve as a starting point for research on immunosuppressive drug therapy in heart transplant patients.

Population Pharmacokinetics of Tacrolimus in Pediatric Patients With Umbilical Cord Blood Transplant

Tacrolimus was frequently used in pediatric patients with umbilical cord blood transplant for the prevention of graft-versus-host disease. The aim of the present study was to evaluate the population pharmacokinetics of tacrolimus among pediatric patients with umbilical cord blood transplant and find potential influenced factors. A total of 275 concentrations from 13 pediatric patients were used to build a polulation pharmacokinetic model using a nonlinear mixed-effects modeling approach. The impact of demographic features, biological characteristics, and concomitant medications, including sex, age, body weight, postoperative day, white blood cell count, red blood cell count, hemoglobin, platelets, hematocrit, blood urea nitrogen, creatinine, aspartate transaminase, alanine transaminase, total bilirubin, albumin, and total protein were investigated. The pharmacokinetics of tacrolimus were best described by a 1-compartment model with first- and zero-order mixed absorption and first-order elimination. The clearance and volume of distribution of tacrolimus were 1.93 L/h and 75.1 L, respectively. A covariate analysis identified that postoperative day and co-administration with trimethoprim-sulfamethoxazole were significant covariates influencing clearance of tacrolimus. Frequent blood monitoring and dose adjustment might be needed with the prolongation of postoperative day and coadministration with trimethoprim-sulfamethoxazole.

Population Pharmacokinetic Analysis for Model-Based Therapeutic Drug Monitoring of Tacrolimus in Chinese Han Heart Transplant Patients

BACKGROUND AND OBJECTIVE

Tacrolimus has become the first-line immunosuppressant for preventing rejection after heart transplantation. The present study aimed to investigate genetic variants and clinical factors affecting the variability of tacrolimus in Chinese Han heart transplant patients using a population pharmacokinetic approach.

METHODS

The retrospective study included 53 hospitalized patients with 547 tacrolimus concentrations for analysis. Nonlinear mixed-effects modeling was used to develop the population pharmacokinetics model for tacrolimus in patients with heart transplants, followed by Monte Carlo simulations to design initial dosing regimens.

RESULTS

In our study, the mutation rate of CYP3A4*18B (C>T) was 27.36%. An oral one-compartment model with first-order absorption and elimination was used to describe the pharmacokinetics of tacrolimus in heart transplant patients. In the final model, the estimated apparent clearance (CL/F) and volume of distribution (V/F) were 532.5 L/h [12.20% interindividual variability, IIV] and 16.87 L (23.16% IIV), respectively. Albumin, postoperative time, and rs2242480 (CYP3A4*18B) gene polymorphisms were the significant covariates affecting CL/F, and creatinine clearance had significant effects on the V/F.

CONCLUSION

The population pharmacokinetic model of tacrolimus in heart transplant patients can better estimate the population and individual pharmacokinetic parameters of patients and can provide a reference for the design of individualized dosing regimens.

Controversial Interactions of Tacrolimus with Dietary Supplements, Herbs and Food

Tacrolimus is an immunosuppressive calcineurin inhibitor used to prevent rejection in allogeneic organ transplant recipients, such as kidney, liver, heart or lung. It is metabolized in the liver, involving the cytochrome P450 (CYP3A4) isoform CYP3A4, and is characterized by a narrow therapeutic window, dose-dependent toxicity and high inter-individual and intra-individual variability. In view of the abovementioned facts, the aim of the study is to present selected interactions between tacrolimus and the commonly used dietary supplements, herbs and food. The review was based on the available scientific literature found in the PubMed, Scopus and Cochrane databases. An increase in the serum concentration of tacrolimus can be caused by CYP3A4 inhibitors, such as grapefruit, pomelo, clementine, pomegranate, ginger and turmeric, revealing the side effects of this drug, particularly nephrotoxicity. In contrast, CYP3A4 inducers, such as St. John’s Wort, may result in a lack of therapeutic effect by reducing the drug concentration. Additionally, the use of Panax ginseng, green tea, Schisandra sphenanthera and melatonin in patients receiving tacrolimus is highly controversial. Therefore, since alternative medicine constitutes an attractive treatment option for patients, modern healthcare should emphasize the potential interactions between herbal medicines and synthetic drugs. In fact, each drug or herbal supplement should be reported by the patient to the physician (concordance) if it is taken in the course of immunosuppressive therapy, since it may affect the pharmacokinetic and pharmacodynamic parameters of other preparations.

Population pharmacokinetics and dosage optimization of tacrolimus coadministration with Wuzhi capsule in adult liver transplant patients

1. This study aimed to establish a population pharmacokinetic model of tacrolimus coadministration with Wuzhi capsule and optimize the dosage regimen in adult liver transplant patients.2. Totally 1327 tacrolimus trough concentrations from 116 adult liver transplant patients were obtained for model development. A one-compartment model with first-order absorption and elimination was used to analyse the data, and the final model was internally verified using a goodness-of-fit diagnostic plot, bootstrap methods, and visual prediction test. A total of 29 patients with 250 tacrolimus trough concentrations was used for external validation via prediction-based diagnostics. Additionally, the simulation was used to optimize the recommended dose of tacrolimus and Wuzhi capsules.3. The estimated apparent clearance and volume of the distribution of tacrolimus were 15.4 L/h and 1210 L, respectively. The tacrolimus daily dose, Wuzhi capsule daily dose, postoperative time, alanine transaminase, haemoglobin, total bilirubin, direct bilirubin, estimated glomerular filtration rate, and urea, concomitant with voriconazole and fluconazole, were identified as significant covariates affecting the pharmacokinetic parameters. Internal and external validation showed that the final model was stable and reliable for predicting performance.4. The final model could provide guidance for dosage optimization of tacrolimus coadministered with Wuzhi capsules in adult liver transplantation patients.

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.

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

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.

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

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.

The Impact of Smoking, Sex, Infection, and Comedication Administration on Oral Olanzapine: A Population Pharmacokinetic Model in Chinese Psychiatric Patients

BACKGROUND AND OBJECTIVE: Prior olanzapine population pharmacokinetic (PPK) models have focused on the effects of sex and smoking on olanzapine clearance. This PPK model in Chinese adult psychiatric patients also investigated the influence of comedications and co-occurrence of infections on olanzapine clearance, and explored how to personalize oral olanzapine dosage in the clinical setting.

METHODS

A total of 1546 serum concentrations from 354 patients were collected in this study. A one-compartment model with first-order absorption was employed to develop the PPK model using a nonlinear mixed-effects modeling approach. Covariates included demographic parameters, co-occurrence of infection and concomitant medications (including dangguilonghui tablets, a Chinese herbal medicine for constipation). Bootstrap validation (1000 runs) and external validation of 50 patients were employed to evaluate the final model. Simulations were performed to explore the personalization of olanzapine dosing after stratification by sex, smoking, and comedication with valproate.

RESULTS

Typical estimates for the absorption rate constant (K_a), apparent clearance (CL/F), and apparent distribution volume (V/F) were 0.30 h^-1, 12.88 L/h, and 754.41 L, respectively. Olanzapine clearance was increased by the following variables: 1.23-fold by male sex, 1.23-fold by smoking, 1.23-fold by comedication with valproate, 1.16-fold by sertraline, and 2.01-fold by dangguilonghui tablets. Olanzapine clearance was decreased by the following variables: 0.75-fold by co-occurrence of infection, 0.70-fold by fluvoxamine, and 0.78-fold by perphenazine. The model evaluation indicated that the final model's performance was good, stable, and precise.

CONCLUSION

This study contributes to the personalization of oral olanzapine dosing, but further studies should be performed to verify the effects of infection and comedications, including valproate and dangguilonghui.