Population pharmacokinetic analysis of tacrolimus in the first year after pediatric liver transplantation

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.

External assessment and refinement of a population pharmacokinetic model to guide tacrolimus dosing in pediatric heart transplant

STUDY OBJECTIVE

The immunosuppressant tacrolimus is a first-line agent to prevent graft rejection following pediatric heart transplant; however, it suffers from extensive inter-patient variability and a narrow therapeutic window. Personalized tacrolimus dosing may improve transplant outcomes by more efficiently achieving and maintaining therapeutic tacrolimus concentrations. We sought to externally validate a previously published population pharmacokinetic (PK) model that was constructed with data from a single site.

DATA SOURCE

Data were collected from Seattle, Texas, and Boston Children's Hospitals, and assessed using standard population PK modeling techniques in NONMEMv7.2.

MAIN RESULTS

While the model was not successfully validated for use with external data, further covariate searching identified weight (p < 0.0001 on both volume and elimination rate) as a model-significant covariate. This refined model acceptably predicted future tacrolimus concentrations when guided by as few as three concentrations (median prediction error = 7%; median absolute prediction error = 27%).

CONCLUSION

These findings support the potential clinical utility of a population PK model to provide personalized tacrolimus dosing guidance.

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

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

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Population pharmacokinetic analysis and dosing guidelines for tacrolimus co-administration with Wuzhi capsule in Chinese renal transplant recipients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus (TAC) is a first-line immunosuppressant which is used to prevent transplant rejection after solid organ transplantation (SOT). However, it has a narrow therapeutic index and high individual variability in pharmacokinetics (PK) and pharmacogenomics (PG). It has been reported that the metabolism of TAC can be affected by genetic factors, leading to different rates of metabolism in different subjects. Wuzhi Capsule (WZC) is a commonly used TAC-sparing agent in Chinese SOT to reduce TAC dosing due to its inhibitory effect on TAC metabolism by enzymes of the CYP3A subfamily. The aims of this study were to assess the effect of TAC+WZC co-administration and genetic polymorphism on the pharmacokinetics of TAC, by using a population pharmacokinetic (PPK) model. A dosing guideline for individualized TAC dosing is proposed based on the PPK study.

METHODS

The medical records of 165 adult patients with kidney transplant and their 824 TAC concentrations from two kidney transplantation centres were reviewed. The genotypes of four single-nucleotide polymorphisms (SNPs) in CYP3A5*3 and ABCB1 (rs1128503, rs2032582 and rs1045642) were tested by MASSARRAY. A PPK model was constructed by nonlinear mixed effect model (NONMEM^® , Version 7.3). Finally, Monte Carlo simulations were employed to design initial dosing regimens based on the final model.

RESULTS AND DISCUSSION

The one-compartmental PPK model with first-order absorption and elimination of TAC was established in kidney transplant recipients (KTRs). CYP3A5*3 had significant impact on the PPK model. The haematocrit (HCT), postoperative time (POD) and CYP3A5*3 genotypes had a significant influence on TAC clearance when combined with WZC. The model was expressed as 23.4 × (HCT/0.3)^-0.729  × 0.837 (combination with WZC) × e^{-0.0875(POD/12.6)} ×1.18 (CYP3A5 expressors). For patients carrying the CYP3A5*3/*3 allele and with 30% HCT, the required TAC dose to achieve target trough concentrations of 10-15 ng/ml was 4 mg twice daily (q12h). For patients with the CYP3A5*3/*3 allele, the required dose was 3 mg TAC q12h when combined with WZC, and for patients with the CYP3A5*1/*1 or *1/*3 allele, the required dose was 4 mg of TAC q12h when co-administered with WZC.

WHAT IS NEW AND CONCLUSION

Wuzhi Capsule co-administration and CYP3A5 variants affect the PK of TAC Dosing guidelines are made based on the PPK model to allow individualized administration of TAC, especially when co-administered with WZC.

Tacrolimus treatment in childhood refractory nephrotic syndrome: A retrospective study on efficacy, therapeutic drug monitoring, and contributing factors to variable blood tacrolimus levels

Tacrolimus, an immunosuppressive drug, was recommended by the 2012 KDIGO guidelines to treat nephrotic syndrome (NS) in children and adults. However, it has high interpatient pharmacokinetic variability and exposure levels should be monitored, although there are no specified target concentrations. This retrospective study aimed to review efficacy and safety after concomitant treatment with tacrolimus and prednisone, and to identify factors that contribute to the variable blood-trough-concentration-to-dose (C₀/Dose) ratio in children with refractory NS (RNS). A 6-month therapy induced complete or partial remission in 95% of patients. One-year follow-up indicated a high remission rate and low nephrotoxicity. Under maintenance dosages, approximately 95% of the C₀ values were 2-7 ng/mL. Body weight (BW), age, CYP3A5 polymorphisms were the factors affecting the C₀/Dose ratio. The C₀/Dose ratio in patients with a BW of <20 kg was 1.5-fold than that in patients with BW of ≥40 kg. Moreover, the C₀/Dose ratio in patients aged 1-≤6 and 6-≤12 years was significantly lower than that in patients aged 12-≤18 years, by 25% and 48%, respectively. There were no significant association between CYP3A5 genotyping and C₀/Dose ratio in younger children (1-≤6 years), rather than older children (6-≤18 years). In conclusion, routine CYP3A5 genotyping should be considered in children aged over 6 years and exposure levels (C₀) of 2-7 ng/mL may be feasible when tacrolimus is combined with low-dose prednisone to treat childhood RNS.

Impact of EBV infection and immune function assay for lymphoproliferative disorder in pediatric patients after liver transplantation: A single-center experience

BACKGROUND

Post-transplant lymphoproliferative disorder (PTLD) is a lethal complication after pediatric liver transplantation, but information regarding risk factors for the development of PTLD remains unclear. This study was to identify characteristics and risk factors of PTLD.

METHODS

A total of 705 pediatric patients who underwent liver transplantation between January 2017 and October 2018 were studied. Impact of clinical characteristics and Epstein-Barr virus (EBV) infection on the development of PTLD was evaluated. In addition, ImmuKnow assay was adopted in partial patients to analyze the immune status.

RESULTS

Twenty-five (3.5%) patients suffered from PLTD with a median time of 6 months (3-14 months) after transplantation. Extremely high tacrolimus (TAC) level was found in 2 fatal cases at PTLD onset. EBV infection was found in 468 (66.4%) patients. A higher peak EBV DNA loads (>9590 copies/mL) within 3 months was a significant indicator for the onset of PTLD. In addition, the ImmuKnow assay demonstrated that overall immune response was significantly lower in patients with EBV infection and PTLD (P<0.0001). The cumulative incidence of PTLD was also higher in patients with lower ATP value (≤187 ng/mL, P<0.05).

CONCLUSIONS

A careful monitoring of EBV DNA loads and tacrolimus concentration might be supportive in prevention of PTLD in pediatric patients after liver transplantation. In addition, application of the ImmuKnow assay may provide guidance in reducing immunosuppressive agents in treatment of PTLD.

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.

Graft-to-Recipient Weight Ratio Associated With Tacrolimus Metabolism Following Pediatric Living Donor Liver Transplantations

OBJECTIVES

Tacrolimus (TAC) is an important immunosuppressant in liver transplantation. Since TAC is mainly metabolized by the liver enzymes CYP3A4 and 5, liver function is crucial for its pharmacokinetics (PK). Liver function is dynamic after liver transplantation; hence the PK of TAC metabolism after pediatric liver transplantation is not well understood. We aimed to investigate the time-dependent changes in TAC metabolism and to find factors influencing TAC PK after pediatric liver transplantation.

METHODS

We retrospectively reviewed the characteristics of the donors and recipients in pediatric living donor liver transplantation and used the TAC concentration-dose (CD) ratio as a surrogate marker of TAC metabolism.

RESULTS

Included were 326 patients with a median age of 13 months. After the liver transplantation, the CD ratio gradually decreased, then plateaued around day 21 to 28. A linear regression analysis demonstrated that a lower graft-to-recipient weight ratio (GRWR) and higher prothrombin time-international normalized ratio (PT-INR) were independently associated with a higher CD ratio in the early period after liver transplantation. However, association between GRWR and TAC CD ratio disappeared around 6 to 12 months after a liver transplantation possibly owing to graft regeneration.

CONCLUSIONS

Tacrolimus metabolism improved within the first month after liver transplantation, and the small graft size was associated with lower TAC metabolism in the early period after pediatric living donor liver transplantation.

Population pharmacokinetic analysis of tacrolimus in Chinese cardiac transplant recipients

Objective

Usage of tacrolimus is complicated by its narrow therapeutic index and wide between- and within-subject pharmacokinetic variability. We aimed to obtain more information regarding the influence of various covariates on the disposition of tacrolimus in the early phase after cardiac transplantation using a population pharmacokinetic method, and provide information for the individualisation of drug dosing in the clinical setting.

Methods

Routine therapeutic drug monitoring concentrations (897 observations) were retrospectively collected from 146 hospitalised patients. One compartment model with first-order absorption (absorption rate constant K_a was fixed as 4.48/hour) was employed to establish the population pharmacokinetic model using a non-linear mixed-effects modelling approach. Various demographic parameters, postoperative day and concomitant medications influencing drug clearance and distribution volume were investigated in this study. Bootstrap and prediction-corrected visual predictive check were employed to validate the final model. With the goal of tacrolimus trough concentrations within the therapeutic window, simulation was performed.

Results

Pharmacokinetic parameter population typical estimates for clearance (CL/F) and apparent distribution volume (V/F) were 14.23 L/hour and 760.80 L, respectively. Postoperative day and co-administration of Wuzhi capsules were identified as important factors affecting CL/F. Total body weight was significantly associated with the V/F. Results of model evaluation indicated a good stable and precise performance of the final model. Based on the simulation results, a simple-touse dosage regimen table to guide clinicians with drug dosing was created.

Conclusion

The final population model could provide information for the individualised dosing of tacrolimus for cardiac transplant recipients.

Survival Time to Biopsy-Proven Acute Rejection and Tacrolimus Adverse Drug Reactions in Pediatric Liver Transplantation

BACKGROUND

Despite advances in surgical procedures and the optimization of immunosuppressive therapies in pediatric liver transplantation, acute rejection (AR) and serious adverse drug reaction (ADR) to tacrolimus still contribute to morbidity and mortality. Identifying risk factors of safety and efficacy parameters may help in optimizing individual immunosuppressive therapies. This study aimed to identify peritransplant predictors of AR and factors related to the risk of ADR to tacrolimus in a large Latin American cohort of pediatric liver transplant patients.

METHODS

We performed a retrospective cohort study in a pediatric liver transplant population (n = 72). Peritransplant variables were collected retrospectively including demographic, clinical, laboratory parameters, genomic (CYP3A5 donor and recipients polymorphism), and tacrolimus trough concentrations (C0) over a 2-year follow-up period. Variability in tacrolimus C0 was calculated using percent coefficient of variation and tortuosity. ADR- and AR-free survival rates were calculated using the Kaplan-Meier method, and risk factors were identified by multivariate Cox regression models.

RESULTS

Cox-proportional hazard models identified that high tortuosity in tacrolimus C0 was associated with an 80% increased risk of AR [hazard ratio (HR), 1.80; 95% confidence interval (CI), 1.01-3.22; P < 0.05], whereas steroid in maintenance doses decreased this risk (HR, 0.56; 95% CI, 0.31-0.99; P < 0.05). Forty-six patients experienced at least one ADR including hypomagnesemia, nephrotoxicity, hypertension, malignancies, and tremor as a first event. Multivariate analysis showed that C0 values 10 days before the event (HR, 1.25; 95% CI, 1.21-1.39; P < 0.0001) and CYP3A5 expresser recipients (HR, 2.05; 95% CI, 1.03-4.06; P < 0.05) were independent predictors of ADR.

CONCLUSIONS

Tacrolimus C0 values, its variability, and CYP3A5 polymorphisms were identified as risk factors of AR and tacrolimus ADR. This knowledge may help to control and reduce their incidence in pediatric liver transplant patients. Prospective studies are important to validate these results.

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.

The Effect of Weight and CYP3A5 Genotype on the Population Pharmacokinetics of Tacrolimus in Stable Paediatric Renal Transplant Recipients

BACKGROUND

The aim of this study was to develop a population pharmacokinetic model of tacrolimus in paediatric patients at least 1 year after renal transplantation and simulate individualised dosage regimens.

PATIENTS AND METHODS

We included 54 children with median age of 11.1 years (range 3.8-18.4 years) with 120 pharmacokinetic profiles performed over 2 to 4 h. The pharmacokinetic analysis was performed using the non-linear mixed-effects modelling software (NONMEM(®)). The impact of covariates including concomitant medications, age, the cytochrome P450 (CYP) CYP3A5*3 gene and the adenosine triphosphate binding cassette protein B1 (ABCB1) 3435 C→T gene polymorphism on tacrolimus pharmacokinetics was analysed. The final model was externally validated on an independent dataset and dosing regimens were simulated.

RESULTS

A two-compartment model adequately described tacrolimus pharmacokinetics. Apparent oral clearance (CL/F) was associated with weight (allometric scaling) but not age. Children with lower weight and CYP3A5 expressers required higher weight-normalised tacrolimus doses. CL/F was inversely associated with haematocrit (P < 0.05) and γ-glutamyl transpeptidase (γGT) (P < 0.001) and was increased by 45 % in carriers of the CYP3A5*1 allele (P < 0.001). CL/F was not associated with concomitant medications. Dose simulations show that a daily tacrolimus dose of 0.2 mg/kg generates a pre-dose concentration (C 0) ranging from 5 to 10 µg/L depending on the weight and CYP3A5 polymorphism. The median area under the plasma concentration-time curve (AUC) corresponding with a tacrolimus C 0 of 4-8 µg/L was 97 h·µg/L (interquartile range 80-120).

CONCLUSIONS

In patients beyond the first year after transplantation, there is a cumulative effect of CYP3A5*1 polymorphism and weight on the tacrolimus C 0. Children with lower weight and carriers of the CYP3A5*1 allele have higher weight-normalised tacrolimus dose requirements.

Pharmacoepidemiology of tacrolimus in pediatric liver transplantation

AEs during immunosuppressive treatment with tacrolimus are very common. We retrospectively evaluated FK safety and efficacy in a large pediatric liver transplant cohort in Latin America. During 2-year follow-up, we analyzed data from patients who underwent liver transplantation over the period 2010-2012 and recorded FK exposure, AEs, and AR episodes. AEs were classified according causality and severity. Tacrolimus exposure before and during AE was compared using Wilcoxon matched-pairs test. Kaplan-Meier curves were used for survival analysis. In total, 46 patients (out of 72 patients) experienced 69 AEs, such as hypomagnesemia (49%), PTLD (6%), hypertension (6%), and/or nephrotoxicity (22%). 43% of AEs were classified as moderate or serious, and 89% were assigned as probable or definitive. Patients who had one or more AR episodes accounted for 65%. The 12-month acute rejection-free survival was 41% (95% CI, 30.1%-53.1%). A significant difference was observed in FK trough concentrations before and during hypomagnesemia and nephrotoxicity (P<.05). This study is the first report of FK safety in a large group of pediatric liver transplant patients in Latin America. Children experience AEs, even in protocols with low FK doses. Therapeutic monitoring is an important tool to manage immunosuppressive schemes containing tacrolimus in vulnerable populations.

Predicting tacrolimus concentrations in children receiving a heart transplant using a population pharmacokinetic model

OBJECTIVE

Immunosuppressant therapy plays a pivotal role in transplant success and longevity. Tacrolimus, a primary immunosuppressive agent, is well known to exhibit significant pharmacological interpatient and intrapatient variability. This variability necessitates the collection of serial trough concentrations to ensure that the drug remains within therapeutic range. The objective of this study was to build a population pharmacokinetic (PK) model and use it to determine the minimum number of trough samples needed to guide the prediction of an individual's future concentrations.

DESIGN SETTING AND PATIENTS

Retrospective data from 48 children who received tacrolimus as inpatients at Primary Children's Hospital in Salt Lake City, Utah were included in the study. Data were collected within the first 6 weeks after heart transplant.

OUTCOME MEASURES

Data analysis used population PK modelling techniques in NONMEM. Predictive ability of the model was determined using median prediction error (MPE, a measure of bias) and median absolute prediction error (MAPE, a measure of accuracy). Of the 48 children in the study, 30 were used in the model building dataset, and 18 in the model validation dataset.

RESULTS

Concentrations ranged between 1.5 and 37.7 μg/L across all collected data, with only 40% of those concentrations falling within the targeted concentration range (12 to 16 μg/L). The final population PK model contained the impact of age (on volume), creatinine clearance (on elimination rate) and fluconazole use (on elimination rate) as covariates. Our analysis demonstrated that as few as three concentrations could be used to predict future concentrations, with negligible bias (MPE (95% CI)=0.10% (-2.9% to 3.7%)) and good accuracy (MAPE (95% CI)=24.1% (19.7% to 27.7%)).

CONCLUSIONS

The use of PK in dose guidance has the potential to provide significant benefits to clinical care, including dose optimisation during the early stages of therapy, and the potential to limit the need for frequent drug monitoring.

Influence of donor-recipient CYP3A4/5 genotypes, age and fluconazole on tacrolimus pharmacokinetics in pediatric liver transplantation: a population approach

AIM

To characterize the effect of donor and recipient CYP3A4, CYP3A5 and ABCB1 genotypes as well as relevant patient characteristics on tacrolimus pharmacokinetics in pediatric liver transplantation.

PATIENTS & METHODS

Data from 114 pediatric liver transplant recipients were retrospectively collected during the first 3 months following transplantation. Population pharmacokinetic analysis was performed using nonlinear mixed effects modeling, including characterization of influential covariates.

RESULTS

A two-compartment model with first order elimination best fitted the data. Estimates of apparent volume of the central compartment, intestinal clearance, hepatic clearance and intercompartmental clearance were 79 l, 0.01 l/h, 10.9 l/h and 105 l/h, respectively. Time post-transplantation, recipient age, donor CYP3A5 and CYP3A4 genotypes and fluconazole administration significantly influenced tacrolimus apparent clearance while bodyweight influenced volume of distribution.

CONCLUSION

The proposed model displayed acceptable fitting performances and enabled identification of statistically significant and clinically relevant covariates on tacrolimus pharmacokinetics in the early pediatric post liver transplantation period.

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.

A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients

AIM

To develop a population pharmacokinetic (PopPK) model of tacrolimus in healthy Chinese volunteers and liver transplant recipients for investigating the difference between the populations, and for potential individualized medication.

METHODS

A set of 1100 sparse trough concentration data points from 112 orthotopic liver transplant recipients, as well as 851 dense data points from 40 healthy volunteers receiving a single dose of tacrolimus (2 mg, p.o.) were collected. PopPK model of tacrolimus was constructed using the program NONMEM. Related covariates such as age, hepatic and renal functions that were potentially associated with tacrolimus disposition were evaluated. The final model was validated using bootstrapping and a visual predictive check.

RESULTS

A two-compartment model of tacrolimus could best describe the data from the two populations. The final model including two covariates, population (liver transplant recipients or volunteers) and serum ALT (alanine aminotransferase) level, was verified and adequately described the pharmacokinetic characteristics of tacrolimus. The estimates of V2/F, Q/F and V3/F were 22.7 L, 76.3 L/h and 916 L, respectively. The estimated CL/F in the volunteers and liver transplant recipients was 32.8 and 18.4 L/h, respectively. Serum ALT level was inversely related to CL/F, whereas age did not influence CL/F. Thus, the elderly (≥65 years) and adult (<65 years) groups in the liver transplant recipients showed no significant difference in the clearance of tacrolimus.

CONCLUSION

Compared with using the sparse data only, the integrating modeling technique combining sparse data from the patients and dense data from the healthy volunteers improved the PopPK analysis of tacrolimus.

Effect of CYP3A5 genotype, steroids, and azoles on tacrolimus in a pediatric renal transplant population

BACKGROUND

Numerous studies have described the impact of cytochrome P450 3A5 (CYP3A5) genotype on Tacrolimus (TAC) exposure. The purpose of this study was to conduct a comprehensive analysis of genetic and non-genetic factors affecting the TAC dose-exposure relationship over the first year post pediatric renal transplant.

METHODS

Data were collected retrospectively for the first year post-transplant in pediatric renal transplant patients receiving TAC maintenance immunosuppression. The effect of CYP3A5 genotype (CYP3A5*3 and *6 alleles), age, azoles, and corticosteroids on TAC trough concentration normalized for dose (TAC Co/D ng/ml/mg/kg/day) was assessed using a linear mixed model.

RESULTS

Over time, TAC Co/D was lower in recipients with CYP3A5*1/*3 genotype compared to those with CYP3A5*3/*3 genotype (44.5 ± 14.4 vs. 107.6 ± 6.4, p = 0.03), increased in patients >12 years of age compared to < 12 years (93.9 ± 8.7 vs. 53.1 ± 12.9, p = 0.007), and decreased by concomitant corticosteroids (69.5 ± 12.7 vs. 89.9 ± 20.0, p = 0.04). The observed increased TAC Co/D in the presence of azoles (271 ± 41 vs. 111 ± 91, p = 0.016) could be attributed to clotrimazole.

CONCLUSIONS

Multiple factors, including CYP3A5 genotype, and age, influence TAC Co/D in pediatric kidney transplant recipients. Clotrimazole administered as troches also contribute to TAC Co/D variability.

Pharmacogenetics in clinical pediatrics: challenges and strategies

The use of genetic information to guide medication decisions holds great promise to improve therapeutic outcomes through increased efficacy and reduced adverse events. As in many areas of medicine, pediatric research and clinical implementation in pharmacogenetics lag behind corresponding adult discovery and clinical applications. In adults, genotype-guided clinical decision support for medications such as clopidogrel, warfarin and simvastatin are in use in some medical centers. However, research conducted in pediatric populations demonstrates that the models and practices developed in adults may be inaccurate in children, and some applications lack any pediatric research to guide clinical decisions. To account for additional factors introduced by developmental considerations in pediatric populations and provide pediatric patients with maximal benefit from genotype-guided therapy, the field will need to develop and employ creative solutions. In this article, we detail some concerns about research and clinical implementation of pharmacogenetics in pediatrics, and present potential mechanisms for addressing them.

Pharmacogenetics in clinical pediatrics: challenges and strategies

The use of genetic information to guide medication decisions holds great promise to improve therapeutic outcomes through increased efficacy and reduced adverse events. As in many areas of medicine, pediatric research and clinical implementation in pharmacogenetics lag behind corresponding adult discovery and clinical applications. In adults, genotype-guided clinical decision support for medications such as clopidogrel, warfarin and simvastatin are in use in some medical centers. However, research conducted in pediatric populations demonstrates that the models and practices developed in adults may be inaccurate in children, and some applications lack any pediatric research to guide clinical decisions. To account for additional factors introduced by developmental considerations in pediatric populations and provide pediatric patients with maximal benefit from genotype-guided therapy, the field will need to develop and employ creative solutions. In this article, we detail some concerns about research and clinical implementation of pharmacogenetics in pediatrics, and present potential mechanisms for addressing them.