Population pharmacokinetics of tacrolimus in adult kidney transplant patients: impact of CYP3A5 genotype on starting dose

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.

Analytical modelling techniques for enhancing tacrolimus dosing in solid organ transplantation: a systematic review protocol

INTRODUCTION

Tacrolimus is an immunosuppressant commonly administered in transplant recipients. Given its narrow therapeutic range and susceptibility to various influencing variables, determining its optimal dosage is challenging. This systematic review seeks to identify effective analytical modelling techniques and methods for optimal tacrolimus dose prediction in solid transplant recipients.

METHODS AND ANALYSIS

This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The study will review the literature published from database inception to 11 March 2024, that assesses predictive models of tacrolimus dosing through analytical modelling techniques. We will include both randomised and non-randomised, as well as cross-sectional, qualitative and before-and-after studies and will perform our searches in four main databases-Ovid/MEDLINE, PubMed/MEDLINE, Scopus, Embase and Web of Science, and search engines including Centers for Disease Control (CDC) and Google Scholar. Papers that are not published in English or French are excluded from this study. A narrative synthesis and meta-analysis will be done if the extracted information permits such analysis. Conference abstracts will be ignored unless they are recent (published within 2 years of the search date).

ETHICS AND DISSEMINATION

Ethics clearance is not required for this study as no primary data will be collected. The completed manuscript will be published, and the results of the study will be presented at conferences.

STUDY REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO), CRD42024537212.

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.

Optimizing tacrolimus dosing in Hispanic renal transplant patients: insights from real-world data

Aim

Tacrolimus, an immunosuppressant used to prevent organ rejection in renal transplant patients, exhibits high inter-patient variability, necessitating therapeutic drug monitoring. Early post-transplant tacrolimus exposure in Hispanics is understudied. Although genotypic information is linked to pharmacokinetic differences, its clinical application remains limited. This study aimed to use a real-world data-driven, pharmacokinetic model-based approach for tacrolimus in Hispanics to determine a suitable initial dose and design an optimal dose titration strategy by simulations to achieve plasma trough concentration target levels of 10-12 ng/mL at the earliest.

Methods

Sparse concentration-time data of tacrolimus were obtained from electronic medical records for self-identified Hispanic subjects following renal transplant. Rich pharmacokinetic literature data was leveraged to estimate structural pharmacokinetic model parameters, which were then fixed in the current analysis. Only apparent clearance was estimated with the sparse tacrolimus data and potential covariates were identified. Simulations of various starting doses and different dose titration strategies were then evaluated.

Results

The analysis included 121 renal transplant patients with 2,215 trough tacrolimus concentrations. A two-compartment transit absorption model with allometrically scaled body weight and time-varying hematocrit on apparent clearance adequately described the data. The estimated apparent clearance was 13.7 L/h for a typical patient weighing 70 kg and at 30% hematocrit, demonstrating a 40% decrease in clearance compared to other patient populations. Model based simulations indicated the best initial dose for the Hispanic population is 0.1 mg/kg/day. The proposed titration strategy, with three dose adjustments based on trough levels of tacrolimus, increased the proportion of patients within the target range (10-12 ng/mL) more than 2.5-fold and decreased the proportion of patients outside the therapeutic window by 50% after the first week of treatment.

Conclusion

Hispanic renal transplant population showed an estimated 40% decrease of apparent clearance in the typical patient compared to other populations with similar characteristics. The proposed dose adjustment attained the target range rapidly and safely. This study advocates for tailored tacrolimus dosing regimens based on population pharmacokinetics to optimize therapy in Hispanic renal transplant recipients.

Guiding the starting dose of the once-daily formulation of tacrolimus in "de novo" adult renal transplant patients: a population approach

Aims

The once-daily extended-release tacrolimus formulation (ER-Tac) has demonstrated similar efficacy and safety to the twice-daily immediate-release formulation (IR-Tac), but few population-based pharmacokinetic models have been developed in de novo kidney transplant patients to optimize doses. Therefore, this study aimed i) at developing a population pharmacokinetic model for ER-Tac in de novo adult kidney transplant patients ii) and identifying genetic factors and time-varying covariates predictive of pharmacokinetic variability to guide tacrolimus dosage during the early post-transplant period.

Methods

A total of 1,067 blood tacrolimus concentrations from 138 kidney transplant patients were analyzed. A total of 29 out of 138 patients were intensively sampled for 24 h on the day 5 post-transplantation; meanwhile, for the remaining patients, concentrations were collected on days 5, 10, and 15 after transplantation. Tacrolimus daily doses and genetic and demographic characteristics were retrieved from the medical files. Biochemistry time-varying covariates were obtained on different days over the pharmacokinetic (PK) study. A simultaneous PK analysis of all concentrations was carried out using the non-linear mixed-effects approach with NONMEM 7.5.

Results

A two-compartment model with linear elimination and delayed absorption best described the tacrolimus pharmacokinetics. Between-patient variability was associated with oral blood clearance (CL/F) and the central compartment distribution volume (Vc/F). Tacrolimus concentrations standardized to a hematocrit value of 45% significantly improved the model (p < 0.001). This method outperformed the standard covariate modeling of the hematocrit-blood clearance relationship. The effect of the CYP3A5 genotype was statistically (p < 0.001) and clinically significant on CL/F. The CL/F of patients who were CYP3A5*1 carriers was 51% higher than that of CYP3A5*1 non-carriers. Age also influenced CL/F variability (p < 0.001). Specifically, CL/F declined by 0.0562 units per each increased year from the value estimated in patients who were 60 years and younger.

Conclusion

The 36% between-patient variability in CL/F was explained by CYP3A5 genotype, age, and hematocrit. Hematocrit standardization to 45% explained the variability of tacrolimus whole-blood concentrations, and this was of utmost importance in order to better interpret whole-blood tacrolimus concentrations during therapeutic drug monitoring. The dose requirements of CYP3A5*/1 carriers in patients aged 60 years or younger would be highest, while CYP3A5*/1 non-carriers older than 60 years would require the lowest doses.

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.

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

BACKGROUND

The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients.

METHODS

Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.

RESULTS

In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).

CONCLUSIONS

Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

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.

Artificial Neural Network Analysis of Determinants of Tacrolimus Pharmacokinetics in Liver Transplant Recipients

BACKGROUND

The efficacy and toxicity of tacrolimus are closely related to its trough blood concentrations. Identifying the influencing factors of pharmacokinetics of tacrolimus in the early postoperative period is conducive to the optimization of the individualized tacrolimus administration protocol and to help liver transplant (LT) recipients achieve the target blood concentrations.

OBJECTIVE

This study aimed to develop an artificial neural network (ANN) for predicting the blood concentration of tacrolimus soon after liver transplantation and for identifying determinants of the concentration based on Shapley additive explanation (SHAP).

METHODS

In this retrospective study, we enrolled 31 recipients who were first treated with liver transplantation from the Department of Liver Transplantation and Hepatic Surgery, the First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) from November 2020 to May 2021. The basic information, biochemical indexes, use of concomitant drugs, and genetic factors of organ donors and recipients were used for the ANN model inputs, and the output was the steady-state trough concentration (C0) of tacrolimus after oral administration in LT recipients. The ANN model was established to predict C0 of tacrolimus, SHAP was applied to the trained model, and the SHAP value of each input was calculated to analyze quantitatively the influencing factors for the output C0.

RESULTS

A back-propagation ANN model with 3 hidden layers was established using deep learning. The mean prediction error was 0.27 ± 0.75 ng/mL; mean absolute error, 0.60 ± 0.52 ng/mL; correlation coefficient between predicted and actual C0 values, 0.9677; and absolute prediction error of all blood concentrations obtained by the ANN model, ≤3.0 ng/mL. The results indicated that the following factors had the most significant effect on C0: age, daily drug dose, genotype at CYP3A5 polymorphism rs776746 in both recipient and donor, and concomitant use of caspofungin. The predicted C0 value of tacrolimus in LT recipients increased in a dose-dependent manner when the daily dose exceeded 3 mg, whereas it decreased with age when LT recipients were older than 48 years. The predicted C0 was higher when recipients and donors had the genotype CYP3A5*3*3 than when they had the genotype CYP3A5*1. The predicted C0 value also increased with the use of caspofungin or Wuzhi capsule.

CONCLUSION AND RELEVANCE

The established ANN model can be used to predict the C0 value of tacrolimus in LT recipients with high accuracy and good predictive ability, serving as a reference for personalized treatment in the early stage after liver transplantation.

Co-based Nanozymatic Profiling: Advances Spanning Chemistry, Biomedical, and Environmental Sciences

Nanozymes, next-generation enzyme-mimicking nanomaterials, have entered an era of rational design; among them, Co-based nanozymes have emerged as captivating players over times. Co-based nanozymes have been developed and have garnered significant attention over the past five years. Their extraordinary properties, including regulatable enzymatic activity, stability, and multifunctionality stemming from magnetic properties, photothermal conversion effects, cavitation effects, and relaxation efficiency, have made Co-based nanozymes a rising star. This review presents the first comprehensive profiling of the Co-based nanozymes in the chemistry, biology, and environmental sciences. The review begins by scrutinizing the various synthetic methods employed for Co-based nanozyme fabrication, such as template and sol-gel methods, highlighting their distinctive merits from a chemical standpoint. Furthermore, a detailed exploration of their wide-ranging applications in biosensing and biomedical therapeutics, as well as their contributions to environmental monitoring and remediation is provided. Notably, drawing inspiration from state-of-the-art techniques such as omics, a comprehensive analysis of Co-based nanozymes is undertaken, employing analogous statistical methodologies to provide valuable guidance. To conclude, a comprehensive outlook on the challenges and prospects for Co-based nanozymes is presented, spanning from microscopic physicochemical mechanisms to macroscopic clinical translational applications.

Population PK/PD model of tacrolimus for exploring the relationship between accumulated exposure and quantitative scores in myasthenia gravis patients

Tacrolimus is an important immunosuppressant used in the treatment of myasthenia gravis (MG). However, the population pharmacokinetic (PK) characteristics together with the exposure-response of tacrolimus in the treatment of MG remain largely unknown. In this study, we aimed to develop a population PK/pharmacodynamic (PK/PD) model of tacrolimus in patients with MG, in order to explore the relationships among tacrolimus dose, exposure, and its therapeutic efficacy. The genotype of CYP3A5, Osserman's classification, and status of thymus, as well as demographic characteristics and other biomarkers from laboratory testing were tested as covariate, and simulations were performed based on the final model. The population PK model was described using a one-compartment model with first-order elimination and fixed absorption parameters. CYP3A5 genotype significantly influenced the apparent clearance, and total protein (TP) influenced the apparent volume of distribution as covariates. The quantitative MG scores were characterized by the cumulated area under curve of tacrolimus in a maximum effect function. Osserman's classification was a significant covariate on the initial score of patients with MG. The simulations demonstrated that tacrolimus showed an unsatisfying effect possibly due to insufficient exposure in some patients with MG. A starting dose of 2 mg/d and even higher dose for patients with CYP3A5 *1/*1 and *1/*3 and lower TP level were required for the rapid action of tacrolimus. The population PK/PD model quantitatively described the relationships among tacrolimus dose, exposure, and therapeutic efficacy in patients with MG, which could provide reference for the optimization of tacrolimus dosing regimen at the individual patient level.

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 (AUC0-12h).

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 AUC0-12h (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.

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.

Model-Based Tacrolimus Follow-up Dosing in Adult Renal Transplant Recipients: A Simulation Trial

BACKGROUND

Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.

METHODS

This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C 0,obs ), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C 0 , age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C 0,pred ) were predicted.

RESULTS

Of 190 tacrolimus C 0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C 0,obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C 0,pred ( P = 0.89). The median absolute difference between the tacrolimus C 0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C 0,obs versus 1.6 ng/mL for C 0,pred . In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C 0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL).

CONCLUSIONS

The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.

Important lack of difference in tacrolimus and mycophenolic acid pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients

AIM

To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians.

METHODS

Concentration-time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each individual were derived from concentration-time profiles combined with population pharmacokinetic priors, with subsequent assessment for between-group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit.

RESULTS

No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between-subject variability in tacrolimus CL/F (SD = 5.00 compared with 3.25 L/h/70 kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance (p = .07 and p = .08).

CONCLUSION

There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between-subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.

A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques

Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R² (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.

Effect of drug combination on tacrolimus target dose in renal transplant patients with different CYP3A5 genotypes

Various factors, including genetic polymorphisms, drug-drug interactions, and patient characteristics influence the blood concentrations of tacrolimus in renal transplant patients. In the present study, we established a population pharmacokinetic model to explore the effect of combined use of Wuzhi capsules/echinocandins and the patients' biochemical parameters such as hematocrit on blood concentrations and target doses of tacrolimus in renal transplant patients with different CYP3A5 genotypes. The aim of the study was to propose an individualized tacrolimus administration regimen for early renal transplant recipients.In this retrospective cohort study, we included 240 renal transplant recipients within 21 days of surgery (174 males and 66 females, mean age 39.4 years), who received tacrolimus alone (n = 54), in combination with Wuzhi capsules (99) or caspofungin (57) or micafungin (30). We collected demographic characteristics, clinical indicators, CYP3A5 genotypes, and 1950 steady-state trough concentrations of tacrolimus and included them in population pharmacokinetic model. An additional 110 renal transplant recipients and 625 steady-state trough concentrations of tacrolimus were included for external validation of the model. The population pharmacokinetic model was established and Monte Carlo was used to simulate probabilities for achieving the target concentration for individual tacrolimus administration.A two-compartment model of first-order absorption and elimination was developed to describe the population pharmacokinetics of tacrolimus. CYP3A5 genotypes and co-administration of Wuzhi capsules, as well as time after renal transplantation and hematocrit, were important factors affecting the clearance of tacrolimus. We found no obvious change in trend in the scatter plot of tacrolimus clearance rate vs. hematocrit. The Monte Carlo simulation indicated the following recommended doses of tacrolimus alone: 0.14 mg·kg^-1·d^-1 for genotype CYP3A5*1*1, 0.12 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.10 mg·kg^-1·d^-1 for CYP3A5*3*3. For patients receiving the combination with Wuzhi capsules, the recommended doses of tacrolimus were 0.10 mg·kg^-1·d^-1 for CYP3A5*1*1, 0.08 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.06 mg·kg^-1·d^-1 for CYP3A5*3*3 genotypes. Caspofungin or micafungin had no effect on the clearance of tacrolimus in renal transplant recipients.The population pharmacokinetics of tacrolimus in renal transplant patients was evaluated and the individual administration regimen of tacrolimus was simulated. For early kidney transplant recipients receiving tacrolimus treatment, not only body weight, but also CYP3A5 genotypes and drugs used in combination should be considered when determining the target dose of tacrolimus.

Population pharmacokinetics of tacrolimus in Chinese adult liver transplant patients

Tacrolimus is widely used in organ transplantation to prevent rejection. However, the narrow therapeutic window and the large inter-and intra-individual variability in the pharmacokinetics (PK) of tacrolimus make it difficult for individualization of dosing. This study aimed at developing a population pharmacokinetic model for estimating the oral clearance of tacrolimus in Chinese liver transplant patients, and identifying factors that contribute to the PK variability of tacrolimus. Data of 151 liver transplant patients who received tacrolimus were analyzed in this study. The population PK model was analyzed and the covariates including population demographic and biochemical characteristics, drug combination, and genetic polymorphism were explored using non-linear mixed-effects modeling approach. A single-compartment population PK model was developed, and the final model was CL/F = (14.6-2.38 × cytochrome P450 (CYP) 3A5-3.72 × WZC+1.04 × (POD/9)+2.48 × COR) × Exp(η_i ), where CYP3A5 was 1 for CYP3A5*3/*3, Wuzhi Capsule (WZC) was 1 when patients took tacrolimus combined with WZC, otherwise it was 0, corticosteroids (COR) was 1 when patients take tacrolimus combined with COR, otherwise, it was 0, POD was the post-operative day. Visual inspection and bootstrap indicated that the final model was stable and robust. In this study, we developed the first tacrolimus population PK model in Chinese adult liver transplant patients. We first determined the influence of WZC on tacrolimus in these people, which could provide useful PK information for the drug combination of tacrolimus and WZC. We also revealed the influence of genetic polymorphism of CYP3A5, POD, and a combination of COR on tacrolimus PK. Therefore, these significant factors should be taken into consideration in optimizing dosage regimens.

Predicting model‐informed precision dosing: A test‐case in tacrolimus dose adaptation for kidney transplant recipients

Before investing resources into the development of a precision dosing (model‐informed precision dosing [MIPD]) tool for tacrolimus, the performance of the tool was evaluated in silico. A retrospective dataset of 315 de novo kidney transplant recipients was first used to identify a one‐compartment pharmacokinetic (PK) model with time‐dependent clearance. MIPD performance was subsequently evaluated by calculating errors to predict future concentrations, which is directly related to dosing precision and probability of target attainment (PTA). Based on the identified model residual error, the theoretical upper limit was 45% PTA for a target of 13.5 ng/ml and an acceptable range of 12–15 ng/ml. Using empirical Bayesian estimation, this limit was reached on day 5 post‐transplant and beyond. By incorporating correlated within‐patient variability when predicting future individual concentrations, PTA improved beyond the theoretical upper limit. This yielded a Bayesian feedback dosing algorithm accurately predicting future trough concentrations and adapting each dose to reach a target concentration. Simulated concentration‐time profiles were then used to quantify MIPD‐based improvement on three end points: average PTA increased from 28% to 39%, median time to three concentrations in target decreased from 10 to 7 days, and mean log‐squared distance to target decreased from 0.080 to 0.055. A study of 200 patients was predicted to have sufficient power to demonstrate these nuanced PK end points reliably. These simulations supported our decision to develop a precision dosing tool for tacrolimus and test it in a prospective trial.

Tacrolimus Dose-Conversion Ratios Based on Switching of Formulations for Patients with Solid Organ Transplants

Background

Tacrolimus may be administered during hospitalization as an IV formulation or oral suspension. However, literature suggesting appropriate ratios for conversion from these formulations to capsules is limited.

Objective

To evaluate conversion ratios after a switch in formulation of tacrolimus for solid-organ transplant recipients.

Methods

This single-centre observational longitudinal study involved hospitalized patients who underwent a switch in formulation of tacrolimus according to 1 of 3 possible scenarios: IV to oral suspension, IV to capsule, or oral suspension to capsule. Data were collected from the earliest accessible electronic file (January 2009) to January 1, 2019. Conversion ratios were calculated for each of the 3 groups using data for blood concentrations and doses before and after the switch. The calculated ratios were then compared with recommended conversion ratios: 1:5 (i.e., 1 mg of IV tacrolimus is converted to 5 mg of oral tacrolimus, expressed as "5") for either of the switches involving an IV formulation and 1:1 (i.e., same amount, expressed as "1") for the switch from oral formulation to capsules.

Results

For the group who underwent switching from the IV formulation to oral suspension, the mean calculated conversion ratio was 3.04, which was significantly different from the recommended ratio of 5. For the group who underwent switching from the IV formulation to capsules, the calculated conversion ratio was 5.18, which was not significantly different from the recommended ratio of 5. For the group who underwent switching from oral suspension to capsules, the calculated conversion ratio was 1.17, which was not significantly different from the recommended ratio of 1.

Conclusion

In this small retrospective study of tacrolimus therapy, the calculated conversion ratio was significantly different from the recommended ratio for patients who were switched from IV administration to oral suspension, but not for those switched from IV administration or oral suspension to capsules. Therapeutic drug monitoring therefore appears indispensable, regardless of conversion ratios.

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.

Precision Dosing for Tacrolimus Using Genotypes and Clinical Factors in Kidney Transplant Recipients of European Ancestry

Genetic variation in the CYP3A4 and CYP3A5 (CYP3A4/5) genes, which encode the key enzymes in tacrolimus metabolism, is associated with tacrolimus clearance and dose requirements. Tacrolimus has a narrow therapeutic index with high intra- and intersubject variability, in part because of genetic variation. High tacrolimus clearance and low trough concentration are associated with a greater risk for rejection, whereas high troughs are associated with calcineurin-induced toxicity. The objective of this study was to develop a model of tacrolimus clearance with a dosing equation accounting for genotypes and clinical factors in adult kidney transplant recipients of European ancestry that could preemptively guide dosing. Recipients receiving immediate-release tacrolimus for maintenance immunosuppression from 2 multicenter studies were included. Participants in the GEN03 study were used for tacrolimus model development (n = 608 recipients) and was validated by prediction performance in the DeKAF Genomics study (n = 1361 recipients). Nonlinear mixed-effects modeling was used to develop the apparent oral tacrolimus clearance (CL/F) model. CYP3A4/5 genotypes and clinical covariates were tested for their influence on CL/F. The predictive performance of the model was determined by assessing the bias (median prediction error [ME] and median percentage error [MPE]) and the precision (root median squared error [RMSE]) of the model. CYP3A5*3, CYP3A4*22, corticosteroids, calcium channel blocker and antiviral drug use, age, and diabetes significantly contributed to the interindividual variability of oral tacrolimus apparent clearance. The bias (ME, MPE) and precision (RMSE) of the final model was good, 0.49 ng/mL, 6.5%, and 3.09 ng/mL, respectively. Prospective testing of this equation is warranted.

CYP3A5 Genotype-Dependent Drug-Drug Interaction Between Tacrolimus and Nifedipine in Chinese Renal Transplant Patients

Purpose: The drug-drug interactions (DDIs) of tacrolimus greatly contributed to pharmacokinetic variability. Nifedipine, frequently prescribed for hypertension, is a competitive CYP3A5 inhibitor which can inhibit tacrolimus metabolism. The objective of this study was to investigate whether CYP3A5 genotype could influence tacrolimus-nifedipine DDI in Chinese renal transplant patients.

Method: All renal transplant patients were divided into CYP3A5*3/*3 homozygotes (group I) and CYP3A5*1 allele carriers (CYP3A5*1/*1 + CYP3A5*1/*3) (group II). Each group was subdivided into patients taking tacrolimus co-administered with nifedipine (CONF) and that administrated with tacrolimus alone (Controls). Tacrolimus trough concentrations (C₀) were measured using high performance liquid chromatography. A retrospective analysis compared tacrolimus dose (D)-corrected trough concentrations (C₀) (C₀/D) between CONF and Controls in group I and II, respectively. At the same time, a multivariate line regression analysis was made to evaluate the effect of variates on C₀/D.

Results: In this study, a significant DDI between tacrolimus and nifedipine with respect to the CYP3A5*3 polymorphism was confirmed. In group I (n = 43), the C₀/D of CONF was significantly higher than in Controls [225.2 ± 66.3 vs. 155.1 ± 34.6 ng/ml/(mg/kg); p = 0.002]. However, this difference was not detected in group II (n = 27) (p = 0.216). The co-administrated nifedipine and CYP3A5*3/*3 homozygotes significantly increased tacrolimus concentrations in multivariate line regression analysis.

Discussion: A CYP3A5 genotype-dependent DDI was found between tacrolimus and nifedipine. Therefore, personalized therapy accounting for CYP3A5 genotype detection as well as therapeutic drug monitoring are necessary for renal transplant patients when treating with tacrolimus and nifedipine.

Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation

Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.

Tacrolimus Exposure in Obese Patients: and A Case-Control Study in Kidney Transplantation

BACKGROUND

Tacrolimus pharmacokinetics in obese (Ob) patients has been poorly studied. In this article, the authors explored the impact of obesity on tacrolimus exposure in kidney transplant recipients (KTRs) and estimated a more suitable initial dosage in this population.

METHODS

A retrospective, observational, monocentric case-control study was performed in obese KTRs (BMI > 30 kg/m2) who received tacrolimus between 2013 and 2017 (initial dose: 0.15 mg/kg/d) (actual weight). Nonobese (Nob) controls (BMI <30 kg/m2) were matched for age and sex. Weekly centralized monitoring of tacrolimus trough levels was performed by liquid chromatography/mass spectrometry until the third month (M3). Target trough levels were set between 8 and 10 ng/mL. All patients received antilymphocyte globulin, corticosteroids, and mycophenolate mofetil.

RESULTS

Of the 541 KTRs, 28 tacrolimus-treated Ob patients were included and compared with 28 NOb-matched controls. With a mean of 22 assays/patient, tacrolimus trough levels were higher in Ob patients (mean 9.9 versus 8.7 ng/mL; P = 0.008); the weight-related dose of Tac was lower at M3 (mean 0.10 versus 0.13 mg/kg/d, P < 0.0001). The tacrolimus concentration to dose (C0/D) was higher in the Ob cohort [mean 116 versus 76 (ng/mL)/(mg/kg/d); P = 0.001]. In Ob patients, a mean decrease of -4.6 mg/d in the 3 months after tacrolimus initiation was required (versus -1.12 in NOb; P = 0.001) to remain within the therapeutic range. Obesity, high mycophenolate mofetil daily dose at M3, and CYP3A5 expression were independently associated with higher tacrolimus exposure. Four dose-adaptation strategies were simulated and compared with the study results.

CONCLUSIONS

An initial dose calculation based on either ideal or lean body weight may allow for faster achievement of tacrolimus trough level targets in Ob KTRs, who are at risk of overexposure when tacrolimus is initiated at 0.15 mg/kg/d. A prospective study is required to validate alternative dose calculation strategies in these patients.

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

Bodyweight‐based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady‐state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C₀). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug‐related toxicity, respectively. To minimize subtherapeutic and supratherapeutic 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, bodyweight‐based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C₀, measured for the first time at day 3, was 7.5–12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post‐transplantation, 34 of 59 patients (58%, 90% CI 47–68%) had a tacrolimus C₀ within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 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 C₀ in as many as 58% of the patients on day 3 after kidney transplantation.

Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients

Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.

Tacrolimus Starting Dose Prediction Based on Genetic Polymorphisms and Clinical Factors in Chinese Renal Transplant Recipients

Aims: Tacrolimus has extensive pharmacokinetic variability among patients and a narrow therapeutic window. The U.S. Clinical Pharmacogenetics Implementation Consortium recommends a starting dose for tacrolimus of 0.15-0.3 mg/kg/day, which is much higher compared with 0.05-0.15 mg/kg/day used in China. The purpose of this study was to investigate the influence of clinical factors and single nucleotide polymorphisms (SNPs) on tacrolimus concentrations in Chinese renal transplant recipients. Methods: This study enrolled 406 tacrolimus-treated patients. After renal transplantation, the first tacrolimus trough concentration and corresponding clinical information were collected from all patients. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to genotype 15 SNPs. The relationship between the genetic and clinical factors and dose-adjusted tacrolimus trough concentration was examined. The tacrolimus starting dose was predicted using a classification and regression tree analysis. Results: Examination of the 15 SNPs and several clinical factors identified the CYP3A5 genotype (p = 5.6 × 10^-11) and hemoglobin (p = 8.4 × 10^-10) as the most significant determinants of tacrolimus C₀/D. Accordingly, a concise tacrolimus recommendation dosage model, a classification scheme, and a regression tree were developed. Conclusion: A new classification and regression tree model was developed for establishing the starting dose of tacrolimus based on the CYP3A5 genotype and hemoglobin values. This result may help clinicians prescribe an appropriate initial tacrolimus dose. ClinicalTrials.gov ID: 2020-KY-147.

Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives

INTRODUCTION

In kidney transplantation, tacrolimus (TAC) is at the cornerstone of current immunosuppressive strategies. Though because of its narrow therapeutic index, it is critical to ensure that TAC levels are maintained within this sharp window through reactive adjustments. This would allow maximizing efficiency while limiting drug-associated toxicity. However, TAC high intra- and inter-patient pharmacokinetic (PK) variability makes it more laborious to accurately predict the appropriate dosage required for a given patient.

AREAS COVERED

This review summarizes the state-of-the-art knowledge regarding drug interactions, demographic and pharmacogenetics factors as predictors of TAC PK. We provide a scoring index for each association to grade its relevance and we present practical recommendations, when possible for clinical practice.

EXPERT OPINION

The management of TAC concentration in transplanted kidney patients is as critical as it is challenging. Recommendations based on rigorous scientific evidences are lacking as knowledge of potential predictors remains limited outside of DDIs. Awareness of these limitations should pave the way for studies looking at demographic and pharmacogenetic factors as well as gut microbiota composition in order to promote tailored treatment plans. Therapeutic approaches considering patients' clinical singularities may help allowing to maintain appropriate concentration of TAC.

Population pharmacokinetics model and initial dose optimization of tacrolimus in children and adolescents with lupus nephritis based on real-world data

The present study aimed to establish a population pharmacokinetics model of tacrolimus and further optimize the initial dosing regimen of tacrolimus in pediatric and adolescent patients with lupus nephritis (LN). Pediatric and adolescent patients with LN were recruited between August 2014 and September 2019 at the Children's Hospital of Fudan University (Shanghai, China). Relevant information was used to set up a population pharmacokinetics model with a Nonlinear Mixed Effect Model and the initial dosage regimen was simulated with the Monte Carlo method. Body weight and co-administration of wuzhi capsule were indicated to influence tacrolimus clearance in pediatric and adolescent patients with LN, and at the same body weight, the rate of tacrolimus clearance in patients without vs. with co-administration of wuzhi capsule was 1:0.71. In addition, in patients who were not administered wuzhi capsule, an initial dosage regimen of 0.15 mg/kg/day was recommended for a body weight of 10-23 kg and 0.10 mg/kg/day for 23-60 kg; in patients who were administered wuzhi capsule, an initial dosage regimen of 0.10 mg/kg/day was recommended for a body weight of 10-23 kg and 0.05 mg/kg/day for 23-60 kg. To the best of our knowledge, the present study was the first to establish a population pharmacokinetics model of tacrolimus in order to determine the optimal initial dosage regimen of tacrolimus in pediatric and adolescent patients with LN.

Impact of Donor and Recipient CYP3A5*3 Genotype on Tacrolimus Population Pharmacokinetics in Chinese Adult Liver Transplant Recipients

Background: Tacrolimus (TAC) is widely used after liver transplantation, but the therapeutic window is narrow. Objective: The purpose was to study both donor and recipient CYP3A5*3 genotypes affecting TAC apparent clearance rate (CL/F) and investigate a TAC population pharmacokinetic (PPK) model in Chinese liver transplant recipients for potential starting-dose individualized medication. Methods: A data set of 721 TAC concentrations was obtained from 43 adult liver transplant recipients. The TAC PPK model was analyzed using nonlinear mixed-effects modeling. Potential covariates, including demographic characteristics, physiological and pathological data, concomitant medications, and CYP3A5*3 genotype, were evaluated. The final model was validated using normalized prediction distribution errors and bootstrapping. Results: A 2-compartment model with first-order absorption and elimination was used to describe TAC disposition. Population estimates of TAC, CL/F, apparent central distribution volume (V2/F), rate of absorption (Ka), and apparent peripheral distribution volume (V3/F) were 18.1 L/h (12%), 72.7 L (34%), 0.163 h−1 (17%), and 412 L (21%), respectively. The model and estimated parameters were found to be stable. Other covariates did not influence TAC CL/F. Both donor and recipient CYP3A5*1 genotypes were significantly correlated with TAC clearance, and CL/F was 1.70-fold higher in both donor and recipient CYP3A5*1 carriers than in noncarriers among Chinese liver transplant recipients. Conclusion and Relevance: A PPK model of TAC was established in Chinese adult liver transplantation recipients for starting-dose individualized medication, which can be expanded to optimize clinical efficacy and minimize toxicity with therapeutic drug monitoring.

Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach

AIMS

The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications.

METHODS

A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies.

RESULTS

A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation.

CONCLUSION

A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

Population pharmacokinetics of tacrolimus in pediatric patients with systemic-onset juvenile idiopathic arthritis: Initial dosage recommendations

Pediatric patients with systemic-onset juvenile idiopathic arthritis (SOJIA) may be treated with tacrolimus. However, the therapeutic range for tacrolimus is narrow with considerable inter- and intra-individual variability, making it difficult to formulate an ideal dosage regimen for personalized treatment. The purpose of the present study was to set up a population pharmacokinetics (PPK) model of tacrolimus treatment for SOJIA to determine the optimal initial dosage. Patients with SOJIA were analyzed using non-linear mixed-effects modeling. Different regimens were analyzed using Monte Carlo simulation with concentration profiles. A first-order absorption and elimination one-compartment model was selected as the most appropriate model for SOJIA. Based on initial dosage recommendations, the regimen of 0.5 mg every 24 h (q24h) appeared to be most suitable for subjects with a body weight of 5 kg, while the 0.5 mg q12h regimen was most suitable for subjects with a body weight of 15–25 kg, the 1/0.5 mg q24h regimen was appropriate for the 26–35 kg group and the 1 mg q12h regimen was suitable for the subjects with a body weight of 36–50 kg. To the best of our knowledge, the present study established the first PPK model of tacrolimus treatment that may be used for the selection of the initial dose based on body weight of pediatric patients with SOJIA.

Population Pharmacokinetic Analysis of Immediate-Release Oral Tacrolimus Co-administered with Mycophenolate Mofetil in Corticosteroid-Free Adult Kidney Transplant Recipients

BACKGROUND AND OBJECTIVE

Tacrolimus is the mainstay calcineurin inhibitor frequently administered with mycophenolic acid with or without corticosteroids to prevent graft rejection in adult kidney transplant recipients. The primary objective of this study was to develop and evaluate a population pharmacokinetic model characterizing immediate-release oral tacrolimus co-administered with mycophenolate mofetil (a pro-drug of mycophenolic acid) in adult kidney transplant recipients on corticosteroid-free regimens. The secondary objective was to investigate the effects of clinical covariates on the pharmacokinetics of tacrolimus, emphasizing the interacting effects of mycophenolic acid.

METHODS

Population modeling and evaluation were conducted with Monolix (Suite-2018R1) using the stochastic approximation expectation-maximization algorithm in 49 adult subjects (a total of 320 tacrolimus whole-blood concentrations). Effects of clinical variables on tacrolimus pharmacokinetics were determined by population covariate modeling, regression modeling, and categorical analyses.

RESULTS

A two-compartment, first-order absorption with a lag-time, linear elimination, and constant error model best represented the population pharmacokinetics of tacrolimus. The apparent clearance value for tacrolimus was 17.9 l/h (6.95% relative standard error) in our model, which is lower compared with similar subjects on corticosteroid-based therapy. The glomerular filtration rate had significant effects on the apparent clearance and central compartment volume of distribution. Conversely, mycophenolic acid did not affect the apparent clearance of tacrolimus.

CONCLUSION

We have developed and internally evaluated a novel population pharmacokinetic model for tacrolimus co-administered with mycophenolate mofetil in corticosteroid-free adult kidney transplant patients. These findings are clinically important and provide further reasons for conducting therapeutic drug monitoring in this specific population.

A 6 Second Analytical Method for Quantitation of Tacrolimus in Whole Blood by Use of Laser Diode Thermal Desorption Tandem Mass Spectrometry

BACKGROUND

Therapeutic drug monitoring of immunosuppressive drugs is imperative for organ transplant recipients. High-performance LC-MS/MS is considered gold standard; however, immunoassays provide rapid turnaround time. New technology was developed to reduce mass spectrometry analytical run-time. The laser diode thermal desorption source coupled with tandem mass spectrometry (LDTD-MS/MS) eliminates chromatographic separation to increase analytical throughput.

METHODS

A rapid, 6 second, LDTD-MS/MS analytical method was developed for the quantification tacrolimus in whole blood. Whole blood samples were lysed, followed by protein precipitation and solid-phase extraction. Extracted samples with desorption solution were spotted onto a LazWell plate then dried and loaded into the LDTD source for analysis with an AB SCIEX 5500 mass spectrometer in positive multiple reaction monitoring mode. The LDTD laser profile ramps from 0% to 65% of full power over 3 s and is held at 65% for 1 s before returning to initial conditions for 2 s.

RESULTS

Data presented include tacrolimus by LDTD-MS/MS comparison to LC-MS/MS, sensitivity, imprecision, interference, linearity, and stability. Method comparison between LDTD-MS/MS and a validated in-house LC-MS/MS assay yielded the following: (LDTD-MS/MS) = 1.119 (LC-MS/MS) + 0.23 ng/mL, S_y/x = 1.26, r = 0.9871 (n = 122). The limit of quantification by LDTD-MS/MS for tacrolimus was <0.3 ng/mL and total imprecision was <10%.

CONCLUSIONS

Laser diode thermal desorption tandem mass spectrometry technology can provide rapid turnaround time to result for tacrolimus. The analytical time for LDTD-MS/MS was 6 s compared to 135 s by LC-MS/MS, a >95% decrease in analytical time.

Tacrolimus increases the expression level of the chemokine receptor CXCR2 to promote renal fibrosis progression

Tacrolimus is one of the most used and effective immunosuppressive agents currently available in the clinic; however, its use is limited by nephrotoxicity, which is the main secondary effect of this drug. The mechanisms underlying tacrolimus-induced nephrotoxicity remain unknown. The present study aimed to investigate the mechanism underlying tacrolimus-induced nephrotoxicity and to identify novel potential targets. Masson staining, Sirius red staining and periodic acid-silver methenamine staining were used to observe kidney pathological changes. Immunohistochemical and immunofluorescent analyses were performed to examine the expression levels of vimentin, E-cadherin and α-smooth muscle actin (α-SMA). Transcriptomics and bioinformatics analyses were performed to investigate the nephrotoxicity mechanism induced by tacrolimus using RNA-sequencing, differentially expressed genes identification and annotation, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The present results demonstrated that compared with the normal control group, the tacrolimus nephrotoxicity group exhibited severe renal fibrosis (P<0.05), upregulated vimentin (P<0.01), downregulated E-cadherin (P<0.05) and upregulated α-SMA (P<0.01). Transcriptomics and bioinformatics analyses identified the pathway 'cytokine-cytokine receptor interaction' as the most significantly enriched (P<0.05). Moreover, KEGG pathway enrichment analysis identified that tacrolimus increased the expression levels of chemokine (C-X-C) motif ligand (CXCL)1, CXCL2 and CXCL3 and the chemokine receptor C-X-C chemokine receptor type 2 (CXCR2). Collectively, the present study suggested that tacrolimus increases the level of chemokine receptor CXCR2 to promote renal fibrosis progression, which is one of the potential mechanisms underlying tacrolimus-induced nephrotoxicity.

Immunomonitoring of Tacrolimus in Healthy Volunteers: The First Step from PK- to PD-Based Therapeutic Drug Monitoring?

Therapeutic drug monitoring is routinely performed to maintain optimal tacrolimus concentrations in kidney transplant recipients. Nonetheless, toxicity and rejection still occur within an acceptable concentration-range. To have a better understanding of the relationship between tacrolimus dose, tacrolimus concentration, and its effect on the target cell, we developed functional immune tests for the quantification of the tacrolimus effect. Twelve healthy volunteers received a single dose of tacrolimus, after which intracellular and whole blood tacrolimus concentrations were measured and were related to T cell functionality. A significant correlation was found between tacrolimus concentrations in T cells and whole blood concentrations (r = 0.71, p = 0.009), while no correlation was found between tacrolimus concentrations in peripheral blood mononuclear cells (PBMCs) and whole blood (r = 0.35, p = 0.27). Phytohemagglutinin (PHA) induced the production of IL-2 and IFNγ, as well as the inhibition of CD71 and CD154 expression on T cells at 1.5 h post-dose, when maximum tacrolimus levels were observed. Moreover, the in vitro tacrolimus effect of the mentioned markers corresponded with the ex vivo effect after dosing. In conclusion, our results showed that intracellular tacrolimus concentrations mimic whole blood concentrations, and that PHA-induced cytokine production (IL-2 and IFNγ) and activation marker expression (CD71 and CD154) are suitable readout measures to measure the immunosuppressive effect of tacrolimus on the T cell.

Model based development of tacrolimus dosing algorithm considering CYP3A5 genotypes and mycophenolate mofetil drug interaction in stable kidney transplant recipients

This study quantifies the interaction between tacrolimus (TAC) and mycophenolate mofetil (MMF) in kidney transplant recipients. Concentrations of TAC, mycophenolic acid (MPA), and metabolites were analyzed and relevant genotypes were determined from 32 patients. A population model was developed to estimate the effect of interaction. Concentrations of TAC were simulated in clinical scenarios and dose-adjusted trough concentrations per dose (C/D) were compared. Effect of interaction was described as the inverse exponential relationship. Major determinants of trough levels of TAC were CYP3A5 genotype and interaction with MPA. The absolute difference in C/D of TAC according to co-administered MMF was higher in CYP3A5 non-expressers (0.55 ng/mL) than in CYP3A5 expressers (0.35 ng/mL). The effect of MMF in determining the TAC exposure is more pronounced in CYP3A5 non-expressers. Based on population pharmacokinetic model, we suggest the TAC dosing algorithm considering the effects of CYP3A5 and MMF drug interaction in stable kidney transplant recipients.

A Minimal Physiologically-Based Pharmacokinetic Model for Tacrolimus in Living-Donor Liver Transplantation: Perspectives Related to Liver Regeneration and the cytochrome P450 3A5 (CYP3A5) Genotype

In adult patients after living‐donor liver transplantation, postoperative days and the cytochrome P450 3A5 (CYP3A5) genotype are known to affect tacrolimus pharmacokinetics. In this study, we constructed a physiologically‐based pharmacokinetic model adapted to the clinical data and evaluated the contribution of liver regeneration as well as hepatic and intestine CYP3A5 genotypes on tacrolimus pharmacokinetics. As a result, liver function recovered immediately and affected the total body clearance of tacrolimus only during a limited period after living‐donor liver transplantation. The clearance was about 1.35‐fold higher in the recipients who had a liver with the CYP3A5*1 allele than in those with the CYP3A5*3/*3 genotype, whereas bioavailability was ~0.7‐fold higher in the recipients who had intestines with the CYP3A5*1 allele than those with CYP3A5*3/*3. In conclusion, the constructed physiologically‐based pharmacokinetic model clarified that the oral clearance of tacrolimus was affected by the CYP3A5 genotypes in both the liver and intestine to the same extent.

Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese pediatric hematopoietic stem cell transplantation patients

1. Several tacrolimus population pharmacokinetic (PPK) models in hematopoietic stem cell transplantation (HSCT) patients have been set up to recommend an optimal dosage schedule. However, the PPK model of Chinese pediatric HSCT patients has not been reported. The study is to investigate whether published PPK models of HSCT patients can be used to simulate Chinese pediatric HSCT patients and establish the tacrolimus PPK model of Chinese pediatric HSCT patients.2. Published PPK models were collected from the literature and assessed using Chinese pediatric HSCT patients via the individual prediction error method. The establishment of tacrolimus PPK model in Chinese pediatric HSCT patients was characterized with nonlinear mixed-effects modeling (NONMEM).3. Three published HSCT PPK models were identified, two of which could be applied to our external dataset. However, these models were dissatisfactory in terms of individual prediction error and, hence, inadequate for extrapolation. Finally, a new tacrolimus PPK model in Chinese pediatric HSCT patients was established. Based on the simulation results of our model, new initial dosage suggestions were recommended. In conclusion, the tacrolimus PPK model in Chinese pediatric HSCT patients was presented and the model could be used to predict individualized dosing regimens in children with HSCT.

Population pharmacokinetics of tacrolimus in pediatric refractory nephrotic syndrome and a summary of other pediatric disease models

Different tacrolimus (TAC) population pharmacokinetic (PPK) models have been established in various pediatric disease populations. However, a TAC PPK model for pediatric refractory nephrotic syndrome (PRNS) has not been well characterized. The current study aimed to establish a TAC PPK model in Chinese PRNS and provide a summary of previous literature concerning TAC PPK models in different pediatric diseases. A total of 147 TAC conventional therapeutic drug monitoring (TDM) data from multiple blood samples obtained from 65 Chinese patients with PRNS were characterized using nonlinear mixed-effects modeling. The impacts of demographic features, biological characteristics and drug combination were evaluated. Model validation was assessed using the bootstrap method. A one-compartment model with first-order absorption and elimination was determined to be the most suitable model for TDM data in PRNS. The absorption rate constant (Ka) was set at 4.48 h⁻¹. The typical values of apparent oral clearance (CL/F) and apparent volume of distribution (V/F) in the final model were 5.46 l/h and 57.1 l, respectively. The inter-individual variability of CL/F and V/F were 22.2 and 0.2%, respectively. The PPK equation for TAC was: CL/F = 5.46 × exponential function (EXP)(0.0323 × age) × EXP(−0.359 × cystatin-C) × EXP(0.148 × daily dose of TAC). No significant effects of covariates on V/F were observed. In conclusion, the current study developed and validated the first TAC PPK model for patients with PRNS. The study also provided a summary of previous literature concerning other TAC PPK models in different pediatric diseases.