Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant patients

Development and validation of individualized tacrolimus dosing software for Chinese pediatric liver transplantation patients: a population pharmacokinetic approach

OBJECTIVE

We aim to describe the population pharmacokinetics (PPK) of tacrolimus in Chinese pediatric patients under 4 years old after liver transplantation and to develop individualized tacrolimus dosing software.

METHODS

A total of 663 blood concentrations from 85 patients aged 4.57 months to 3.97 years were collected in this study. PPK analysis was performed using a nonlinear mixed effects modeling approach with the software, Phoenix. Using C#, an individualized tacrolimus dosing software was created. The software was then used to predict the concentrations of another ten pediatric liver transplantation patients to verify the accuracy of said software. The predictive error (PE) and the absolute predictive error (APE) for each predicted time point were computed.

RESULTS

A one-compartment model with first-order elimination best fitted the data. The apparent volume of distribution (V/F) and apparent clearance (CL/F) were 198.65 L and 2.41 L/h. Postoperative days (POD), total bilirubin (TBIL), and the use of voriconazole significantly influenced tacrolimus apparent clearance. The incorporation of an increasing number of actual blood drug concentrations into the prediction resulted in a decrease in both PE (72%, 17%, 7%) and APE (87%, 53%, 26%).

CONCLUSIONS

A qualified PPK model of tacrolimus was developed in Chinese pediatric patients. The individualized tacrolimus dosing software could be used as a suitable tool for the personalization of tacrolimus dosing for pediatric patients after liver transplantation.

Guidance on Selecting Optimal Steady-State Tacrolimus Concentrations for Continuous IV Perfusion: Insights from Physiologically Based Pharmacokinetic Modeling

Introduction: The dose-response relationships of tacrolimus have been primarily assessed through trough concentrations during intermittent administrations. In scenarios where oral administration (PO) is unfeasible, continuous intravenous (IV) administration is advised. Under these circumstances, only steady-state (Css) plasma or blood concentrations are measured, with the absence of distinct trough levels (Cmin). Consequently, the measured concentrations are frequently misinterpreted as trough concentrations, potentially resulting in sub-therapeutic true tacrolimus blood levels. This study employs physiologically based pharmacokinetic modeling (PBPK) to establish the Css/Cmin ratio for tacrolimus across various clinical scenarios. Method: Using a validated PBPK model, the tacrolimus dose (both PO and IV) and the Css/Cmin ratios corresponding to matching area under the blood concentration-time curve during a dosage interval (AUCτ) values were estimated under different conditions, including healthy subjects and individuals exhibiting cytochrome P450 3A (CYP3A) interactions or CYP3A5 polymorphisms, along with a demonstration of a real-life clinical application. Result: In healthy volunteers, the oral/intravenous (PO/IV) dose ratio was found to be 4.25, and the Css/Cmin ratio was 1.40. A specific clinical case substantiated the practical applicability of the Css/Cmin ratio as simulated by PBPK, demonstrating no immediate clinical complications related to the transplant. When considering liver donors versus recipients expressing CYP3A5, the tacrolimus AUCτ was notably affected, yielding a PO/IV dose ratio of 4.00 and a Css/Cmin ratio of 1.75. Furthermore, the concomitant administration of the CYP3A inhibitor itraconazole given PO resulted in a PO/IV ratio of 1.75 with and a Css/Cmin ratio of 1.28. Notably, the inhibitory effect of itraconazole was diminished when administered IV. Conclusions: Through the application of PBPK methodologies, this study estimates the PO/IV dose ratios and Css/Cmin ratios that can enhance dose adjustment and therapeutic drug monitoring during the switch between IV and PO administration of tacrolimus in transplant patients, ultimately guiding clinicians in real-time decision-making. Further validation with in vivo data is recommended to support these findings.

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.

Initial dosage optimisation of cyclosporine in Chinese paediatric patients undergoing allogeneic haematopoietic stem cell transplantation based on population pharmacokinetics: a retrospective study

OBJECTIVE

Improved understanding of cyclosporine A (CsA) pharmacokinetics in children undergoing allogeneic haematopoietic stem cell transplantation (allo-HSCT) is crucial for effective prevention of acute graft-versus-host disease and medication safety. The aim of this study was to establish a population pharmacokinetic (Pop-PK) model that could be used for individualised therapy to paediatric patients undergoing allo-HSCT in China.

DESIGN, SETTING AND PARTICIPANTS

A retrospective analysis of 251 paediatric HSCT patients who received CsA intravenously in the early post transplantation period at Women and Children's Medical Center in Guangzhou was conducted.

ANALYSIS MEASURES

The model building dataset from 176 children was used to develop and analyse the CsA Pop-Pk model by using the nonlinear mixed effect model method. The basic information was collected by the electronic medical record system. Genotype was analysed by matrix-assisted time-of-flight mass spectrometry. The stability and predictability of the final model were verified internally, and a validation dataset of 75 children was used for external validation. Monte Carlo simulation is used to adjust and optimise the initial dose of CsA in paediatric allo-HSCT patients.

RESULTS

The typical values for clearance (CL) and volume of distribution ([Formula: see text]) were 14.47 L/hour and 2033.53 L, respectively. The body weight and haematocrit were identified as significant variables for V, while only body weight had an impact on CL. The simulation based on the final model suggests that paediatrics with HSCT required an appropriate intravenous dose of 5 mg/kg/day to reach the therapeutic trough concentration.

CONCLUSIONS

The CsA Pop-PK model established in this study can quantitatively describe the factors influencing pharmacokinetic parameters and precisely predict the intrinsic exposure to CsA in children. In addition, our dosage simulation results can provide evidence for the personalised medications TRIAL REGISTRATION NUMBER: ChiCTR2000040561.

Population Pharmacokinetics and Initial Dosage Optimization of Tacrolimus in Pediatric Hematopoietic Stem Cell Transplant Patients

Background: There is a substantial lack of tacrolimus pharmacokinetic information in pediatric hematopoietic stem cell transplant (HSCT) patients. This study aimed to develop population pharmacokinetics (PopPK) of tacrolimus in pediatric HSCT patients and to devise model-guided dosage regimens. Methods: A retrospective analysis was performed on 86 pediatric HSCT patients who received tacrolimus intravenously or orally. A total of 578 tacrolimus trough concentrations (C₀) were available for pharmacokinetic analysis using a non-linear mixed-effects modeling method. Demographic and clinical data were included and assessed as covariates via the stepwise method. Bayesian estimators were used to devise pediatric dosage regimens that targeted C₀ of 5-15 ng mL^-1. Results: A one-compartment model with first-order absorption adequately described the tacrolimus pharmacokinetics. Clearance (CL), volume of distribution (V), and typical bioavailability (F) in this study were estimated to be 2.42 L h^-1 (10.84%), 79.6 L (16.51%), and 19% (13.01%), respectively. Body weight, hematocrit, post-transplantation days, and caspofungin and azoles concomitant therapy were considered significant covariates for tacrolimus CL. Hematocrit had a significant impact on the V of tacrolimus. In the subgroup cohort of children (n = 24) with CYP3A5 genotype, the clearance was 1.38-fold higher in CYP3A5 expressers than in non-expressers. Simulation indicated that the initial dosage optimation of tacrolimus for intravenous and oral administration was recommended as 0.025 and 0.1 mg kg^-1 d^-1 (q12h), respectively. Conclusion: A PopPK model for tacrolimus in pediatric HSCT patients was developed, showing good predictive performance. Model-devised dosage regimens with trough tacrolimus concentrations provide a practical strategy for achieving the therapeutic range.

Predicting Blood Concentration of Tacrolimus in Patients With Autoimmune Diseases Using Machine Learning Techniques Based on Real-World Evidence

Tacrolimus is a widely used immunosuppressive drug in patients with autoimmune diseases. It has a narrow therapeutic window, thus requiring therapeutic drug monitoring (TDM) to guide the clinical regimen. This study included 193 cases of tacrolimus TDM data in patients with autoimmune diseases at Southern Medical University Nanfang Hospital from June 7, 2018, to December 31, 2020. The study identified nine important variables for tacrolimus concentration using sequential forward selection, including height, tacrolimus daily dose, other immunosuppressants, low-density lipoprotein cholesterol, mean corpuscular volume, mean corpuscular hemoglobin, white blood cell count, direct bilirubin, and hematocrit. The prediction abilities of 14 models based on regression analysis or machine learning algorithms were compared. Ultimately, a prediction model of tacrolimus concentration was established through eXtreme Gradient Boosting (XGBoost) algorithm with the best predictive ability (R² = 0.54, mean absolute error = 0.25, and root mean square error = 0.33). Then, SHapley Additive exPlanations was used to visually interpret the variable’s impacts on tacrolimus concentration. In conclusion, the XGBoost model for predicting blood concentration of tacrolimus on the basis of real-world evidence has good predictive performance, providing guidance for the adjustment of regimen in clinical practice.

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.

Identification of Factors Affecting Tacrolimus Trough Levels in Latin American Pediatric Liver Transplant Patients

Tacrolimus is the cornerstone in pediatric liver transplant immunosuppression. Despite close monitoring, fluctuations in tacrolimus blood levels affect safety and efficacy of immunosuppressive treatments. Identifying the factors related to the variability in tacrolimus exposure may be helpful in tailoring the dose. The aim of the present study was to characterize the clinical, pharmacological, and genetic variables associated with systemic tacrolimus exposure in pediatric liver transplant patients. De novo transplant patients with a survival of more than 1 month were considered for inclusion and were genotyped for cytochrome P450 3A5 (CYP3A5). Peritransplant clinical factors and laboratory covariates were recorded retrospectively between 1 month and 2 years after transplant, including alanine aminotransferase (ALT), aspartate aminotransferase, hematocrit, and tacrolimus predose steady-state blood concentrations collected 12 hours after tacrolimus dosing. A linear mixed effect (LME) model was used to assess the association of these factors and the log-transformed tacrolimus dose-normalized trough concentration (logC0/D) levels. Bootstrapping was used to internally validate the final model. External validation was performed in an independent group of patients who matched the original population. The developed LME model described that logC0/D increases with increases in time after transplant (β = 0.019, 95% confidence interval [CI], 0.010-0.028) and ALT values (β = 0.00030, 95% CI, 0.00002-0.00056), whereas logC0/D is significantly lower in graft CYP3A5 expressers compared with nonexpressers (β = -0.349, 95% CI, -0.631 to -0.062). In conclusion, donor CYP3A5 genotype, time after transplant, and ALT values are associated with tacrolimus disposition between 1 month and 2 years after transplant. A better understanding of tacrolimus exposure is essential to minimize the occurrence of an out-of-range therapeutic window that may lead to adverse drug reactions or acute rejection.

Model extrapolation to a real-world dataset: evaluation of tacrolimus population pharmacokinetics and drug interaction in pediatric liver transplantation patients

1. Numerous tacrolimus population pharmacokinetic (PPK) models in pediatric liver transplantation patients have been established to define an optimal dose schedule. However, the applicability of extrapolating these PPK models to our clinical center remains unknown. The goals of the present study was to evaluate model external predictiveness and establish a new model applicable to traditional therapeutic drug monitoring data.2. Published PPK models were collected from the literature and assessed using our real-world dataset including 41 pediatric liver transplantation patients via the individual prediction error method. The establishment of a new model was characterized using non-linear mixed-effects modeling.3. Nine published pediatric liver transplantation PPK models were identified, three of which could be applied to our real-world dataset. However, these models were dissatisfactory in terms of individual prediction error and hence, inadequate for extrapolation. Finally, a new model applicable to our real-world dataset was established as follows: CL/F = 22.9 × (WT/70)^0.75 × (1 - WZ × 0.264) × (1 - FCZ × 0.338) × (1 + ASPI × 0.281) × (POD/41)^0.0486 L/h; V/F = 906 × (WT/70) L. Where WT, WZ, FCZ, ASPI and POD were weight, Wuzhi capsule, fluconazole, aspirin and post-transplantation day, respectively. In conclusion, published models were inadequate for application to our real-world dataset. The present study produced a new model applicable to our real-world study data.

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.

Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Tacrolimus, a calcineurin inhibitor, is a common immunosuppressant prescribed after organ transplantation and has notable inter- and intrapatient pharmacokinetic variability. The sources of variability have been investigated using population pharmacokinetic modeling over the last 2 decades. This article provides an updated synopsis on published nonlinear mixed-effects analyses developed for tacrolimus in transplant recipients. The objectives were to establish a detailed overview of the current data and to investigate covariate relationships determined by the models. Sixty-three published analyses were reviewed, and data regarding the study design, modeling approach, and resulting findings were extracted and summarized. Most of the studies investigated tacrolimus pharmacokinetics in adult and pediatric renal and liver transplants after administration of the immediate-release formulation. Model structures largely depended on the study sampling strategy, with ∼50% of studies developing a 1-compartment model using trough concentrations and a 2-compartment model with delayed absorption from intensive sampling. The CYP3A5 genotype, as a covariate, consistently impacted tacrolimus clearance, and dosing adjustments were required to achieve similar drug exposure among patients. Numerous covariates were identified as sources of interindividual variability on tacrolimus pharmacokinetics with limited consistency across these studies, which may be the result of the study designs. Additional analyses are required to further evaluate the potential impact of these covariates and the clinical implementation of these models to guide tacrolimus dosing recommendations. This article may be useful for guiding the design of future population pharmacokinetic studies and provides recommendations for the selection of an existing optimal model to individualize tacrolimus therapy.

Pharmacogenetics of post-transplant diabetes mellitus in children with renal transplantation treated with tacrolimus

BACKGROUND

Post-transplant diabetes mellitus (PTDM) is a major complication of immunosuppressive therapy, with many risk factors reported in adults with renal transplantation. The objective of this study was to investigate potential non-genetic and genetic risk factors of PTDM in children with renal transplantation treated with tacrolimus.

METHODS

A national database was screened for patients developing PTDM within 4 years following tacrolimus introduction. PTDM was defined as glucose disorder requiring anti-diabetic treatment. PTDM patients were matched to "non-PTDM" control transplanted children according to age, gender, and duration of post-transplant follow-up. Patients were genotyped for six selected genetic variants in POR*28 (rs1057868), PPARa (rs4253728), CYP3A5 (rs776746), VDR (rs2228570 and rs731236), and ABCB1 (rs1045642) genes, implicated in glucose homeostasis and tacrolimus disposition.

RESULTS

Among the 98 children with renal transplantation enrolled in this multicentre study, 18 developed PTDM. None of the clinical and biological parameters was significant between PTDM and control patients. Homozygous carriers of POR*28 or wild-type ABCB1 (rs1045642) gene variants were more frequent in PTDM than in control patients with differences close to significance (p = 0.114 and p = 0.066 respectively). A genetic score based on these variants demonstrated that POR*28/*28 and ABCB1 CC or CT genotype carriers were at a significantly higher risk of developing PTDM after renal transplantation.

CONCLUSION

Identification of PTDM risk factors should allow clinicians to allocate the best immunosuppressant for each patient with renal transplantation, and improve care for patients who are at a higher risk.

Tacrolimus Population Pharmacokinetics and Multiple CYP3A5 Genotypes in Black and White Renal Transplant Recipients

Tacrolimus exhibits inter-patient pharmacokinetic variability attributed to CYP3A5 isoenzymes and the efflux transporter, P-glycoprotein. Most black renal transplant recipients require higher tacrolimus doses compared to whites to achieve similar troughs when race-adjusted recommendations are used. An established guideline provides tacrolimus genotype dosing recommendations based on CYP3A5*1(W/T) and loss of protein function variants: CYP3A5*3 (rs776746), CYP3A5*6 (rs10264272), CYP3A5*7 (rs41303343) and may provide more comprehensive race-adjusted dosing recommendations. Our objective was to develop a tacrolimus population pharmacokinetic model evaluating demographic, clinical, and genomic factors in stable black and white renal transplant recipients. A secondary objective investigated race-based tacrolimus regimens and genotype-specific dosing. Sixty-seven recipients receiving oral tacrolimus and mycophenolic acid ≥6 months completed a 12-hour pharmacokinetic study. CYP3A5*3,*6,*7 and ABCB1 1236C>T, 2677G>T/A, 3435C>T polymorphisms were characterized. Patients were classified as extensive, intermediate, and poor metabolizers using a novel CYP3A5*3*6*7 metabolic composite. Modeling and simulation was performed with computer software (NONMEM 7.3, ICON Development Solutions; Ellicott City, Maryland). A 2-compartment model with first-order elimination and absorption with lag time best described the data. The CYP3A5*3*6*7 metabolic composite was significantly associated with tacrolimus clearance (P value < .05), which was faster in extensive (mean: 45.0 L/hr) and intermediate (29.5 L/hr) metabolizers than poor metabolizers (19.8 L/hr). Simulations support CYP3A5*3*6*7 genotype-based tacrolimus dosing to enhance general race-adjusted regimens, with dose increases of 1.5-fold and 2-fold, respectively, in intermediate and extensive metabolizers for comparable exposures to poor metabolizers. This model offers a novel approach to determine tacrolimus dosing adjustments that maintain comparable therapeutic exposure between black and white recipients with different CYP3A5 genotypes.

Choosing the right dose of tacrolimus

Choosing the right dose of tacrolimus 'adapted to each individual patient' is a central question after transplantation. The pharmacokinetic behaviour of tacrolimus in paediatric patients is significantly influenced by clinical factors growth and maturation, as well as genetic factors. Large interindividual variability and narrow therapeutic index make dosage individualisation mandatory in children. CYP3A5 expressers require a 1.8-fold higher tacrolimus dose than non-expressers. A visual patient-tailored dosing chart, taking into consideration the child's weight, recent haematocrit level and CYP3A5 genotype, was developed based on a population pharmacokinetic-pharmacogenetic model, and can be used routinely to individualise tacrolimus starting dose. Area under the concentration-time curve-based dosage adaptation through limited sampling strategy and Bayesian estimation is more reliable than trough concentration. Therapeutic drug monitoring and dosage adaptation can be included in routine post-transplantation consultation and should be considered in the urgent situations (eg, rejection, adverse event, lack of compliance, change of coadministration drug with potential drug-drug interaction and other situations).

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in paediatric liver transplant recipients

AIMS

The objectives of this study were to develop a population pharmacokinetic (PopPK) model for tacrolimus in paediatric liver transplant patients and determine optimal sampling strategies to estimate tacrolimus exposure accurately.

METHODS

Twelve hour intensive pharmacokinetic profiles from 30 patients (age 0.4-18.4 years) receiving tacrolimus orally were analysed. The PopPK model explored the following covariates: weight, age, sex, type of transplant, age of liver donor, liver function tests, albumin, haematocrit, drug interactions, drug formulation and time post-transplantation. Optimal sampling strategies were developed and validated with jackknife.

RESULTS

A two-compartment model with first-order absorption and elimination and lag time described the data. Weight was included on all pharmacokinetic parameters. Typical apparent clearance and central volume of distribution were 12.1 l h(-1) and 31.3 l, respectively. The PopPK approach led to the development of optimal sampling strategies, which allowed estimation of tacrolimus pharmacokinetics and area under the concentration–time curve (AUC) on the basis of practical sampling schedules (three or four sampling times within 4 h) with clinically acceptable prediction error limit. The mean bias and precision of the Bayesian vs. reference (trapezoidal) AUCs ranged from -2.8 to -1.9% and from 7.4 to 12.5%, respectively.

CONCLUSIONS

The PopPK of tacrolimus and empirical Bayesian estimates represent an accurate and convenient method to predict tacrolimus AUC(0-12) in paediatric liver transplant recipients, despite high between-subject variability in pharmacokinetics and patient demographics. The developed optimal sampling strategies will allow the undertaking of prospective trials to define the tacrolimus AUC-based therapeutic window and dosing guidelines in this population.

Population pharmacokinetic and pharmacogenetic analysis of tacrolimus in paediatric liver transplant patients

AIMS

To build a population pharmacokinetic model that describes the apparent clearance of tacrolimus and the potential demographic, clinical and genetically controlled factors that could lead to inter-patient pharmacokinetic variability within children following liver transplantation.

METHODS

The present study retrospectively examined tacrolimus whole blood pre-dose concentrations (n = 628) of 43 children during their first year post-liver transplantation. Population pharmacokinetic analysis was performed using the non-linear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance and influential covariates.

RESULTS

The final model identified time post-transplantation and CYP3A5*1 allele as influential covariates on tacrolimus apparent clearance according to the following equation: TVCL = 12.9 x (Weight/13.2)(0.75) x EXP(-0.00158 x TPT) x EXP(0.428 x CYP3A5) where TVCL is the typical value for apparent clearance, TPT is time post-transplantation in days and the CYP3A5 is 1 where *1 allele is present and 0 otherwise. The population estimate and inter-individual variability (%CV) of tacrolimus apparent clearance were found to be 0.977 l  h(-1)  kg(-1) (95% CI 0.958, 0.996) and 40.0%, respectively, while the residual variability between the observed and predicted concentrations was 35.4%.

CONCLUSION

Tacrolimus apparent clearance was influenced by time post-transplantation and CYP3A5 genotypes. The results of this study, once confirmed by a large scale prospective study, can be used in conjunction with therapeutic drug monitoring to recommend tacrolimus dose adjustments that take into account not only body weight but also genetic and time-related changes in tacrolimus clearance.

Bottom-up modeling and simulation of tacrolimus clearance: prospective investigation of blood cell distribution, sex and CYP3A5 expression as covariates and assessment of study power

The objectives were to investigate the ability of population-based in vitro-in vivo extrapolation (IVIVE) to reproduce the influence of haematocrit on the clearance of tacrolimus, observed previously, and to assess the power of clinical studies to detect the effects of covariates on the clearance of tacrolimus. A population-based pharmacokinetic simulator (Simcyp) was used to simulate tacrolimus clearance from in vitro metabolism data and demographic characteristics of Japanese liver transplant patients (JLTs). The relationship between haematocrit and dose-to-concentration (D/C) ratio was validated using seven JLTs, whose highly variable haematocrit and D/C ratio were previously analysed. This validation was used as a surrogate for establishing 'interindividual' variability and to assess the power of clinical studies to discern the effect of haematocrit, sex and CYP3A5 genotype on tacrolimus clearance in a virtual JLT population. The relationship between haematocrit and D/C ratio was reproducible by Simcyp and corresponded well to those observed in seven JLTs. The number of JLTs required to detect the influence of CYP3A5 genotype and sex were estimated to be about 50 and > 600, respectively, which was consistent with the results of previous population pharmacokinetic studies for tacrolimus. In conclusion, population-based IVIVE is considered to be a useful approach to assess the influence of covariates a priori before conducting clinical studies. This is also helpful with study design and assessment of the statistical power of clinical studies involving population-based pharmacokinetics to detect the effects of covariates.

Prediction tacrolimus blood levels based on the Bayesian method in adult kidney transplant patients

The use of tacrolimus is complicated by its narrow therapeutic index and wide intra- and interpatient variability. We have previously described a tacrolimus population pharmacokinetics model obtained in an adult kidney transplant cohort. The aims of the present study were (1) to validate that model using an external dataset and (2) to evaluate the prediction using a Bayesian method. Data were retrospectively collected from 34 adult patients receiving kidney transplantation. Trough blood concentrations of tacrolimus were predicted using the empirical Bayesian method with sparse samples obtained during the previous week. The system performance was evaluated by the mean prediction error (ME), mean absolute prediction error (MAE). and root mean square error (RMSE). Patients were administrated oral or intravenous tacrolimus as part of a triple immunosuppressive regimen with mycophenolate mofetil and corticosteroids. Subsequent doses were adjusted on the basis of clinical evidence of efficacy and toxicity and by routine therapeutic drug monitoring. In our previous model, clearance increased with post transplantation days and with prednisone dosage. Concentrations predicted by the population mean pharmacokinetic parameter values match well with observed concentrations during oral therapy. Bayesian prediction using trough concentrations obtained after 21 days of treatment significantly decreased ME, MAE, and RMSE compared with predictions from data including this period. After 21 days of treatment, there was an insignificant bias ME (0.22 ± 2.59 ng/ml), a reasonable precision MAE (1.97 ± 1.69 ng/ml) and RMSE (1.28 ± 0.58 ng/ml). The present study demonstrates the suitability of the Bayesian method for the prediction of trough blood concentrations of tacrolimus using only few samples in adult kidney transplantation recipients.

Impact of MDR1 and CYP3A5 on the oral clearance of tacrolimus and tacrolimus-related renal dysfunction in adult living-donor liver transplant patients

OBJECTIVE

The potential influence of the multidrug resistance 1 (MDR1) gene and the cytochrome P450 (CYP) genes, CYP3A4 and CYP3A5, on the oral clearance (CL/F) of tacrolimus in adult living-donor liver transplant patients was examined. Furthermore, the development of renal dysfunction was analyzed in relation to the CYP3A5 genotype.

METHODS

Sixty de novo adult liver transplant patients receiving tacrolimus were enrolled in this study. The effects of various covariates (including intestinal and hepatic mRNA levels of MDR1 and CYP3A4, measured in each tissue taken at the time of transplantation, and the CYP3A5*3 polymorphism) on CL/F during the first 50 days after surgery were investigated with the nonlinear mixed-effects modeling program.

RESULTS

CL/F increased linearly until postoperative day 14, and thereafter reached a steady state. The initial CL/F immediately after liver transplantation was significantly affected by the intestinal MDR1 mRNA level (P<0.005). Furthermore, patients carrying the CYP3A5*1 allele in the native intestine, but not in the graft liver, showed a 1.47 times higher (95% confidence interval, 1.17-1.77 times, P<0.005) recovery of CL/F with time than patients having the intestinal CYP3A5*3/*3 genotype. The cumulative incidence of renal dysfunction within 1 year after transplantation, evaluated by the Kaplan-Meier method, was significantly associated with the recipient's but not donor's CYP3A5 genotype (*1/*1 and *1/*3 vs. *3/*3: recipient, 17 vs. 46%, P<0.05; donor, 35 vs. 38%, P=0.81).

CONCLUSION

These findings suggest that the CYP3A5*1 genotype as well as the MDR1 mRNA level in enterocytes contributes to interindividual variation in the CL/F of tacrolimus in adult recipients early after living-donor liver transplantation. Furthermore, CYP3A5 in the kidney may play a protective role in the development of tacrolimus-related nephrotoxicity.

[Therapeutic monitoring of immunosuppressive drugs: interest of calcineurin activity assessment in liver transplantation]

Therapeutic monitoring of calcineurin inhibitors (ciclosporin and tacrolimus) consists in pharmacokinetic monitoring. Pharmacodynamics based on calcineurin activity may be particularly interesting in liver transplantation due to the large intra- and interindividual variability of pharmacokinetics of ciclosporin and tacrolimus. A recent investigation on the pharmacokinetic-pharmacodynamic relationship of tacrolimus showed that monitoring of calcineurin activity in PBMC may be particularly relevant within the first three post-transplantation months. Thereafter, the monitoring of trough blood concentrations of tacrolimus remains adequate. Moreover, two clinical investigations carried out within the early and late post-transplantation periods reported a promising result which is a positive correlation between calcineurin activity and incidence of graft rejection, whatever graft type and calcineurin inhibitors. In each study, transplanted recipients with a graft rejection exhibited a greater trough calcineurin activity compared to patients without graft rejection. However, prospective investigations are required because of the small cohorts of patients enrolled in both studies. The aim of these investigations will be to confirm the interest of calcineurin activity monitoring as a marker of cellular immunity and its positive link with pharmacokinetic monitoring.

Factors affecting the apparent clearance of tacrolimus in Korean adult liver transplant recipients

STUDY OBJECTIVE

To identify the factors affecting tacrolimus apparent total body clearance (Cl/F [F = bioavailability]) in adult liver transplant recipients.

DESIGN

Population pharmacokinetic analysis using data from a retrospective chart review.

SETTING

University-affiliated hospital in Seoul, South Korea.

PATIENTS

Fifty-one adult liver transplant recipients who had received tacrolimus after transplantation.

MEASUREMENTS AND MAIN RESULTS

Data on 35 adult liver transplant recipients for model building and 16 patients for model validation were obtained retrospectively. Population average parameter estimates of Cl/F and apparent volume of distribution (V/F) were sought by using the nonlinear mixed-effect model (NONMEM) program. A number of clinical covariates were screened for their influence on these pharmacokinetic parameters. The final optimal population model related Cl/F to total bilirubin, early (< or = 3 days) and late (> 35 days) postoperative days, international normalized ratio (INR), and graft:recipient weight ratio (GRWR). The NONMEM estimates indicated that the Cl/F of tacrolimus was decreased in patients with a small graft, hyperbilirubinemia, and a high INR. In addition, the Cl/F of tacrolimus almost doubled 4 days after transplantation, but decreased with an increase in duration of therapy after day 35. Mean prediction error and mean absolute prediction error were 0.26 and 3.78 ng/ml, respectively, for the validation sample. A final analysis in all 51 patients, which consisted of 1775 blood samples for concentration measurements, identified the following regression model: Cl/F (L/hr) = (0.36 + 2.01/POD * L) * TBIL(-0.23 (TBIL = 1 if TBIL level < or = 1.2 mg/dl, otherwise TBIL = TBIL level)) *49((if POD < or = 3 days)) * 0.75((if INR > 1.4)) * 0.86((if GRWR < or = 1.25%)) * WT, where L was 1 if postoperative day (POD) was greater than 35 days, otherwise L was 0; V/F was 568 L, TBIL was total bilirubin, and WT was body weight. The interindividual variabilities (coefficients of variation) in Cl/F and V/F were 35.35% and 68.12%, respectively. The residual variability was 3.14 ng/ml.

CONCLUSION

These findings could be useful to the health care provider for adjustment of tacrolimus dosage in adult liver transplant recipients with various clinical factors.

Population pharmacokinetic estimation of tacrolimus apparent clearance in adult liver transplant recipients

The goal was to study the factors affecting tacrolimus apparent clearance (CL/F) in adult liver transplant recipients. Tacrolimus dose and concentration data (n = 694) were obtained from 67 liver transplant recipients (22 female and 45 male), and the data were analyzed using a nonlinear mixed-effect modeling (NONMEM) method. A 1-compartment pharmacokinetic model with first-order elimination, an absorption rate constant fixed at 4.5 hours, and first-order conditional estimation was used to describe tacrolimus disposition. The predictive performance of the final model was evaluated using data splitting and assessing bias and precision of the estimates. The population estimate of tacrolimus CL/F and apparent volume of distribution (V/F) were found to be 21.3 L/h (95% confidence interval, CI, 18.0-24.6 L/h) and 316.1 L (95% CI 133-495 L), respectively. Neither patient's age, weight, gender, nor markers of liver function influenced tacrolimus CL/F. The final model was TVCL = 21.3 + 9.8 x (1 - HEM) + 3.4 x (1 - ALB) - 2.1 x (1 - DIL) - 7.4 x (1 - FLU), where TVCL, typical estimate of apparent clearance, HEM = 0 if hematocrit <35%, otherwise 1; ALB = 0 if albumin <3.5 g/dL, otherwise 1; DIL = 0 if diltiazem is coadministered, otherwise 1; FLU = 0 if fluconazole is coadministered, otherwise 1. This study identified the factors that significantly affect tacrolimus disposition in adult liver transplant recipients during the early posttransplantation period. This information will be helpful to clinicians for dose individualization of tacrolimus in liver transplant recipients with different clinical conditions including anemia or hypoalbuminemia or in those patients receiving diltiazem or fluconazole.

Population Pharmacokinetics of Tacrolimus in Whole Blood and Plasma in Asian Liver Transplant Patients

OBJECTIVES

The objectives of this study were to develop population pharmacokinetic models of tacrolimus in an Asian population with whole blood and plasma drug concentration data, to compare the variability of the pharmacokinetic parameters in these two matrices and to search for the main patient characteristics that explain the variability in pharmacokinetic parameters.

STUDY DESIGN

Prospective pharmacokinetic assessment followed by model fitting.

PATIENTS

Whole blood samples from 31 liver transplant patients in a local hospital receiving oral tacrolimus as part of their immunosuppressive therapy were assessed. Plasma samples from 29 of the 31 patients were also evaluated. Concentrations of tacrolimus in whole blood and plasma were determined by an electrospray high-performance liquid chromatography with tandem mass spectrometry. Two hundred and thirteen whole blood and 157 plasma tacrolimus concentrations were used for building two nonlinear mixed-effects population models to describe the disposition of tacrolimus in whole blood and plasma, respectively. Covariates that were investigated included demographic characteristics, biological markers of liver and renal functions, corticosteroid dose and haematological parameter.

RESULTS

A one-compartment model was used to describe the whole blood and plasma concentration-time data of tacrolimus after oral administration. For the whole blood population model, the population estimates of the first-order absorption rate constant (k(a)), apparent clearance based on whole blood concentration after oral administration (CL(B)/F) and apparent volume of distribution based on whole blood concentrations after oral administration (V(d,B)/F) were 2.08h(-1), 14.1 L/h and 217L, respectively. The coefficient of variations (CVs) of interpatient variabilities in CL(B)/F and V(d,B)/F were 65.7% and 63.8%, respectively. Bodyweight, liver and renal function influenced CL(B)/F, while height and haematocrit influenced V(d,B)/F. The residual (unexplained) variability was 34.8%. For the plasma population model, the population estimates of the k(a), apparent clearance based on plasma concentrations after oral administration (CL(P)/F) and apparent volume of distribution based on plasma concentrations after oral administration (V(d,P)/F) were 5.21h(-1), 537 L/h and 563L, respectively. The CVs of interpatient variabilities in CL(P)/F and V(d,P)/F were 96.0% and 105.4%, respectively. Bodyweight was found to influence CL(P)/F, while the erythrocyte-to-plasma concentration ratio influenced V(d,P)/F. The residual (unexplained) variability was 49.8% at the mean plasma concentration of 1.1 ng/mL.

CONCLUSIONS

Whole blood and plasma population pharmacokinetic models of tacrolimus in Asian adult and paediatric liver transplant patients were developed using prospective data in a clinical setting. This has identified and quantified sources of interindividual variability in CL(B)/F, V(d,B)/F, CL(P)/F and V(d,P)/F of tacrolimus in Asian liver transplant patients. Information derived from the whole blood population model may subsequently be used by clinicians for dosage individualisation through Bayesian forecasting.

Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation

The aim of this review is to analyse critically the recent literature on the clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplant recipients. Dosage and target concentration recommendations for tacrolimus vary from centre to centre, and large pharmacokinetic variability makes it difficult to predict what concentration will be achieved with a particular dose or dosage change. Therapeutic ranges have not been based on statistical approaches. The majority of pharmacokinetic studies have involved intense blood sampling in small homogeneous groups in the immediate post-transplant period. Most have used nonspecific immunoassays and provide little information on pharmacokinetic variability. Demographic investigations seeking correlations between pharmacokinetic parameters and patient factors have generally looked at one covariate at a time and have involved small patient numbers. Factors reported to influence the pharmacokinetics of tacrolimus include the patient group studied, hepatic dysfunction, hepatitis C status, time after transplantation, patient age, donor liver characteristics, recipient race, haematocrit and albumin concentrations, diurnal rhythm, food administration, corticosteroid dosage, diarrhoea and cytochrome P450 (CYP) isoenzyme and P-glycoprotein expression. Population analyses are adding to our understanding of the pharmacokinetics of tacrolimus, but such investigations are still in their infancy. A significant proportion of model variability remains unexplained. Population modelling and Bayesian forecasting may be improved if CYP isoenzymes and/or P-glycoprotein expression could be considered as covariates. Reports have been conflicting as to whether low tacrolimus trough concentrations are related to rejection. Several studies have demonstrated a correlation between high trough concentrations and toxicity, particularly nephrotoxicity. The best predictor of pharmacological effect may be drug concentrations in the transplanted organ itself. Researchers have started to question current reliance on trough measurement during therapeutic drug monitoring, with instances of toxicity and rejection occurring when trough concentrations are within 'acceptable' ranges. The correlation between blood concentration and drug exposure can be improved by use of non-trough timepoints. However, controversy exists as to whether this will provide any great benefit, given the added complexity in monitoring. Investigators are now attempting to quantify the pharmacological effects of tacrolimus on immune cells through assays that measure in vivo calcineurin inhibition and markers of immunosuppression such as cytokine concentration. To date, no studies have correlated pharmacodynamic marker assay results with immunosuppressive efficacy, as determined by allograft outcome, or investigated the relationship between calcineurin inhibition and drug adverse effects. Little is known about the magnitude of the pharmacodynamic variability of tacrolimus.

Bayesian forecasting and prediction of tacrolimus concentrations in pediatric liver and adult renal transplant recipients

AIM

To test the predictive capacity of two recently derived population pharmacokinetic models and the usefulness of Bayesian forecasting to predict tacrolimus blood concentrations in pediatric liver and adult kidney transplant recipients.

MATERIALS AND METHODS

New databases were added to the Abbottbase PKS (Bayesian dosage prediction) program to incorporate the population pharmacokinetic models developed for tacrolimus. Two independent populations of transplant recipients were used to predict tacrolimus trough concentrations. Pharmacokinetic, demographic, and covariate data were collected from patient records. Different time weighting factors were tested (1, 1.005, 1.01) and the influence of excluding data collected in the first 5 days post-transplant examined. Concentrations were predicted until the 10th tacrolimus measurement. Actual tacrolimus concentrations were compared with those predicted by the PKS program and bias and precision determined.

RESULTS

Tacrolimus concentrations predicted by the PKS program were, on average, unbiased for the pediatric liver population, but were over-predicted (9%) for the adult renal population. In both populations predictions were not precise (imprecision ranged from 39 to 50%).

CONCLUSIONS

Due to the imprecision seen in this study, these models could not be used in clinical practice in the immediate post-transplant period. Poor precision may be due to reliance on routine drug monitoring data alone, difficulties with expression of covariates in continuous modeling relationships in the PKS program, lack of accurate quantitative measures of liver function, or large, random intraindividual variability in the bioavailability of tacrolimus.

Toward better outcomes with tacrolimus therapy: population pharmacokinetics and individualized dosage prediction in adult liver transplantation

Patient outcomes in transplantation would improve if dosing of immunosuppressive agents was individualized. The aim of this study is to develop a population pharmacokinetic model of tacrolimus in adult liver transplant recipients and test this model in individualizing therapy. Population analysis was performed on data from 68 patients. Estimates were sought for apparent clearance (CL/F) and apparent volume of distribution (V/F) using the nonlinear mixed effects model program (NONMEM). Factors screened for influence on these parameters were weight, age, sex, transplant type, biliary reconstructive procedure, postoperative day, days of therapy, liver function test results, creatinine clearance, hematocrit, corticosteroid dose, and interacting drugs. The predictive performance of the developed model was evaluated through Bayesian forecasting in an independent cohort of 36 patients. No linear correlation existed between tacrolimus dosage and trough concentration (r(2) = 0.005). Mean individual Bayesian estimates for CL/F and V/F were 26.5 +/- 8.2 (SD) L/hr and 399 +/- 185 L, respectively. CL/F was greater in patients with normal liver function. V/F increased with patient weight. CL/F decreased with increasing hematocrit. Based on the derived model, a 70-kg patient with an aspartate aminotransferase (AST) level less than 70 U/L would require a tacrolimus dose of 4.7 mg twice daily to achieve a steady-state trough concentration of 10 ng/mL. A 50-kg patient with an AST level greater than 70 U/L would require a dose of 2.6 mg. Marked interindividual variability (43% to 93%) and residual random error (3.3 ng/mL) were observed. Predictions made using the final model were reasonably nonbiased (0.56 ng/mL), but imprecise (4.8 ng/mL). Pharmacokinetic information obtained will assist in tacrolimus dosing; however, further investigation into reasons for the pharmacokinetic variability of tacrolimus is required.

Forecasting of Blood Tacrolimus Concentrations Based on the Bayesian Method in Adult Patients Receiving Living-Donor Liver Transplantation

OBJECTIVE

To evaluate Bayesian prediction of blood tacrolimus concentrations in adult patients receiving living-donor liver transplantation (LDLT) using previously obtained population pharmacokinetic parameters.

PATIENTS AND METHODS

Data were retrospectively collected from 47 adult patients receiving LDLT who were not included in the estimation of population pharmacokinetic parameters. Blood tacrolimus concentrations were predicted without or with the empirical Bayesian method using sparse samples obtained in the previous week. Predictive performance of the concentrations was evaluated by the mean prediction error (ME), mean absolute prediction error (MAE) and root mean square error (RMSE) as well as the percentage of successful predictions (percentage of absolute prediction error less than 3 microg/L, %PRED3).

RESULTS

Concentrations predicted by the population mean pharmacokinetic parameter values coincided well with observed concentrations during the period of tacrolimus infusion immediately after the operation. For concentrations during subsequent oral therapy with tacrolimus, predictability by the population mean pharmacokinetic parameter values alone was not satisfactory. Bayesian forecasting using one or two blood concentrations obtained in the previous week significantly decreased (p<0.05) MAE and RMSE compared with predictions based on the population mean pharmacokinetic parameters on postoperative days 21 and 28, but not on day 14. During postoperative days 15-21, %PRED3 was increased to 68.6% or 71.2% with the Bayesian method using one or two blood concentrations, respectively, from 44.9% with the population mean pharmacokinetic parameter values.

CONCLUSION

The present study demonstrated the applicability of the Bayesian method with use of one or two samples for prediction of blood tacrolimus concentrations in adult patients receiving LDLT.