Population pharmacokinetics of tacrolimus in children who receive cut-down or full liver transplants

Tacrolimus population pharmacokinetic model-informed precision dosing in adult liver transplant patients

We aimed to explore tacrolimus population pharmacokinetic (PPK) characteristics in adult liver transplantation patients and develop individualized dosing software for precision dosing. Data were retrospectively extracted from adult liver transplantation patients receiving tacrolimus at Chinese PLA General Hospital and Beijing Friendship Hospital. The PPK model was established using Phoenix, with the final model developed through forward inclusion-backward elimination. Bootstrap and visual predictive check (VPC) were used to validate the final model. External validation was conducted, and the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) were calculated. The software, developed in C# language, predicted drug concentrations three times for each of the 10 patients, calculating the predictive error (PE) and the absolute predictive error (APE). The application of this software for dosing regimen recommendations was also elucidated. Fifty-seven patients were included, with 633 blood drug concentrations collected. Data from 41 patients (Chinese PLA General Hospital) were used for modeling, and a one-compartment model with first-order absorption was built. Postoperative days and γ-glutamyl transferase affected clearance. The final model parameters were within Bootstrap's prediction range, and VPC prediction results aligned with the observations. Data from 16 patients (Beijing Friendship Hospital) were used for external validation, ME, MAE, and RMSE were - 0.32, 2.07, and 2.76 ng/mL, respectively, indicating robust predictive capability. PE and APE decreased with an increase in the number of blood drug concentrations. The developed software accurately predicts drug concentrations and the accuracy of these predictions increases with the number of drug concentrations used. A robust PPK model was established for liver transplant adults. The individualized dosing software not only predicts drug concentrations with increasing precision but also facilitates the practical application of model-informed precision dosing, offering customized dosing regimens that are poised to optimize therapeutic outcomes in liver transplant patients.

A retrospective analysis of tacrolimus pharmacokinetic in Saudi paediatric patients in early post-liver transplantation period

BACKGROUND

Tacrolimus is an essential immunosuppressive medication in paediatric patients' post-liver transplantation. Achieving tacrolimus target concentration in early post-transplantation is crucial to minimise the risk of acute rejection; however, this is challenging due to inter- and intra-patient variability in tacrolimus metabolism and clearance. Therefore, our study aims to describe tacrolimus trough concentration variability and pharmacokinetics in paediatric post-liver transplantation during the first two weeks post-transplantation.

METHOD

This retrospective multicentre observational study included paediatric patients post-liver transplantation. Post-operative data was collected within the initial 14 days using electronic health records, including daily tacrolimus doses, measured trough concentrations, graft data, surgical data, and documented acute rejection. Pharmacokinetic analysis was completed using the Monolix software. We used the empirical Bayesian estimates of clearance and volume of distribution for covariate testing to assess possible correlations. We performed a stepwise regression analysis (alpha = 0.05).

RESULTS

Ninety-one paediatric patients were included in the study, with a mean age of 4.1 years (SD = 4.6). The mean graft-to-recipient weight ratio (GRWR) was 3% (SD = 6). The vast majority of the patients received the liver from living donors (n = 84, 92.3%). The average time needed to reach therapeutic concentration was 4.6 (SD = 2.8) days. The initial clearance (Clini) was very low at baseline (0.012 L/h), then increased dramatically to 9.84 L/h at 14 days post-transplantation. The clearance appeared to be time-dependent, and the time needed to reach 50% of maximum clearance was five days post-transplantation. The covariates that significantly affected clearance included bodyweight and aspartate transaminase, while the only significant covariate for volume of distribution was bodyweight.

CONCLUSION

Tacrolimus is a drug with high intra- and interindividual variability, making dosing challenging in the paediatric liver transplantation population. Prospective studies with more intensive sampling are needed to address the time-dependent changes in clearance, which will aid in establishing the optimal dosing regimens in this population.

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.

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.

Pharmacokinetics of Dexmedetomidine in Infants and Children After Orthotopic Liver Transplantation

BACKGROUND

Dexmedetomidine (DEX) is a sedative and analgesic medication that is frequently used postoperatively in children after liver transplantation. Hepatic dysfunction, including alterations in drug clearance, is common immediately after liver transplantation. However, the pharmacokinetics (PK) of DEX in this population is unknown. The objective of this study was to determine the PK profile of DEX in children after liver transplantation.

METHODS

This was a single-center, open-label PK study of DEX administered as an intravenous loading dose of 0.5 μg/kg followed by a continuous infusion of 0.5 μg/kg/h. Twenty subjects, 1 month to 18 years of age, who were admitted to the pediatric intensive care unit after liver transplantation were enrolled. Whole blood was collected and analyzed for DEX concentration using a dried blood spot method. Nonlinear mixed-effects modeling was used to characterize the population PK of DEX.

RESULTS

DEX PK was best described by a 2-compartment model with first-order elimination. A typical child after liver transplantation with an international normalized ratio (INR) of 1.8 was found to have a whole blood DEX clearance of 52 L/h (95% confidence interval [CI], 31-73 L/h). In addition, intercompartmental clearance was 246 L/h (95% CI, 139-391 L/h), central volume of distribution was 186 L/70 kg (95% CI, 140-301 L/70 kg), and peripheral volume of distribution was 203 L (95% CI, 123-338 L). Interindividual variability ranged from 11% to 111% for all parameters. Clearance was not found to be associated with weight but was found to be inversely proportional to INR. An increase in INR to 3.2 resulted in a 50% decrease in DEX clearance. Weight was linearly correlated with central volume of distribution. All other covariates, including age, ischemic time, total bilirubin, and alanine aminotransferase, were not found to be significant predictors of DEX disposition.

CONCLUSIONS

Children who received DEX after liver transplantation have large variability in clearance, which was not found to be associated with weight but is influenced by underlying liver function, as reflected by INR. In this population, titration of DEX dosing to clinical effect may be important because weight-based dosing is poorly associated with blood concentrations. More attention to quality of DEX sedation may be warranted when INR values are changing.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Population Pharmacokinetics of Tacrolimus in 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.

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.

Paediatric models in motion: requirements for model-based decision support at the bedside

Optimal paediatric pharmacotherapy is reliant on a detailed understanding of the individual patient including their developmental status and disease state as well as the pharmaceutical agents he/she is receiving for treatment or management of side effects. Our appreciation for size and maturation effects on the pharmacokinetic/pharmacodynamic (PK/PD) phenomenon has improved to the point that we can develop predictive models that permit us to individualize therapy, especially in the situation where we are monitoring drug effects or therapeutic concentrations. The growth of efforts to guide paediatric pharmacotherapy via model-based decision support necessitates a coordinated and systematic approach to ensuring reliable and robust output to caregivers that represents the current standard of care and adheres to governance imposed by the host institution or coalition responsible. Model-based systems which guide caregivers on dosing paediatric patients in a more comprehensive manner are in development at several institutions. Care must be taken that these systems provide robust guidance with the current best practice. These systems must evolve as new information becomes available and ultimately are best constructed from diverse data representing global input on demographics, ethnic / racial diversity, diet and other lifestyle factors. Multidisciplinary involvement at the project team level is key to the ultimate clinical valuation. Likewise, early engagement of clinical champions is also critical for the success of model-based tools. Adherence to regulatory requirements as well as best practices with respect to software development and testing are essential if these tools are to be used as part of the routine standard of care.

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.

Determination of the most influential sources of variability in tacrolimus trough blood concentrations in adult liver transplant recipients: a bottom-up approach

Tacrolimus, an immunosuppressant drug, presents a narrow therapeutic window and a large pharmacokinetic variability with poor correlation between drug dosing regimen and blood concentration. The objective was to identify predictive factors influencing tacrolimus trough concentrations (C0) using a bottom-up approach. A physiologically based pharmacokinetic (PBPK) model of tacrolimus was proposed, taking into account the body weight, the proportion of fat (P(fat)), hematocrit, lipid fraction of organs, typical intrinsic clearance (CLi(typ)), CYP3A5 genotype of liver donor, plasma unbound fraction of tacrolimus (fu(p)), and concomitant drugs (CYP3A4 inhibitors). For the evaluation of the PBPK model, mean C0 and concentrations 2 h after oral dose of tacrolimus were compared with those from 66 liver transplant recipients included in a multicentric pharmacokinetic study and were found very close. Tacrolimus concentration profiles were simulated in a virtual population defined by a set of covariate values similar to those from the real population. The sensitivity of tacrolimus C0 with respect to each covariate has been tested to identify the most influential ones. With the range of covariate values tested, the impact of each covariate on tacrolimus C0 may be ranked as follows: fu(p), CLi(typ), bioavailability, body weight, hematocrit, CYP3A5 polymorphism, P(fat), and CYP3A4 inhibitory drug-drug interactions. Values for initial dosing regimen of tacrolimus in order to reach a C0 of 10 ng/ml at day 5 (assuming a constant dosing schedule) as a function of CYP3A5 donor genotype and patient's hematocrit and body weight are proposed.

The Population Pharmacokinetic Models of Tacrolimus in Chinese Adult Liver Transplantation Patients

Aim. The aim of this study was to establish population pharmacokinetic models of tacrolimus in Chinese adult liver transplantation patients. Methods. Tacrolimus dose and concentration data (n = 435) were obtained from 47 Chinese adult liver transplant recipients, and the data were analyzed using a nonlinear mixed-effect modeling (NONMEM) method. Results. The structural model was a two-compartment model with first-order absorption. The typical population values of tacrolimus for the pharmacokinetic parameters of apparent clearance (CL/F), apparent distribution volume of the central compartment (V₂/F), intercompartmental clearance (Q/F), apparent distribution volume of the peripheral compartment (V₃/F), and absorption rate (k_a) were 11.2 L/h, 406 L, 57.3 L/h, 503 L, and 0.723 h⁻¹, respectively. The interindividual variabilities of these parameters were 16.2%, 163%, 19.7%, 199%, and 74.3%, respectively, and the intraindividual variability of observed concentration was 26.54%. The covariates retained in the final models were postoperative days (POD) and dosage per day (DOSE) on CL/F. Conclusion. Population pharmacokinetic models of tacrolimus were developed in Chinese adult liver transplant patients. These results could provide the interpretation of the outcome of pharmacokinetics modeling and the impact of covariate tested on individualized tacrolimus therapy.

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.

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.

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.

An up-date review on individualized dosage adjustment of calcineurin inhibitors in organ transplant patients

Calcineurin inhibitors, tacrolimus (FK506) and cyclosporine (ciclosporin A), are the primary immunosuppressive agents used on recipients of organ transplantations. The hepatic metabolism of these drugs by cytochrome P450 IIIA (CYP3A) subfamilies is considered a major eliminating process. The intestinal efflux-pump P-glycoprotein (Pgp) (multidrug resistance 1 [MDR1], ATP-binding cassette B1 [ABCB1]) and CYP3A4 have been demonstrated as important for the bioavailability of drugs, so called "absorptive barriers". Recently, an important role for CYP3A5 in the intestine for the oral clearance of drugs has been identified. Both tacrolimus and cyclosporine are substrates of Pgp, CYP3A4 and CYP3A5, and therefore, these molecules are potential pharmacokinetic factors with which to establish personalized dosage regimens for these drugs. Although the effect of single nucleotide polymorphisms in the MDR1/ABCB1 and CYP3A5 genes on the pharmacokinetics of immunosuppressant has been widely examined, some contradictions have been emerged. In living-donor liver transplant (LDLT) patients, the intestinal mRNA expression level of MDR1 and CYP3A5 genotyping both in the native intestine and in the grafted liver are suggested to be potential pharmacokinetic factors for adjusting initial dosage and predicting post-operative variation in the pharmacokinetics of tacrolimus. We review the pharmacokinetic and pharmacodynamic characteristics of these drugs including the large pharmacokinetic variation and potential individualized dosage adjustments based on the genomic information of transporters and metabolic enzymes as well as classical pharmacokinetic analyses based on therapeutic drug monitoring (TDM).

FTY720, a novel immunomodulator in de novo kidney transplant patients: pharmacokinetics and exposure-response relationship

The pharmacokinetics, safety, and preliminary efficacy of FTY720, a novel immunomodulator, were examined in de novo renal transplant patients. Both noncompartmental and population methods were used to estimate pharmacokinetic estimates in the patients. The steady-state plasma concentrations of FTY720 increased in accordance with maintenance dose level, indicating linearity in clearance and volume of distribution over the 0.25- to 2.5-mg dose range. The pharmacokinetics of FTY720 in de novo renal transplant patients were characterized by the long terminal phase half-life of approximately 200 hours across doses, high volume of distribution (>3000 L), and low clearance (10.8 L/h). The intersubject variation of clearance was 55%, and the intrasubject variation of FTY720 concentrations was 28%. The population analysis revealed significant positive relationships between baseline alkaline phosphatase and clearance, as well as between baseline body weight on apparent volume of distribution. There was no relationship between FTY720 concentrations within a given FTY720 dose cohort and the rate of allograft rejection.

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.

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.

Time-related clinical determinants of long-term tacrolimus pharmacokinetics in combination therapy with mycophenolic acid and corticosteroids: a prospective study in one hundred de novo renal transplant recipients

BACKGROUND

Tacrolimus is an efficient primary immunosuppressive drug in renal transplantation but its long-term use is associated with calcineurin-inhibitor-related toxicity. The specific characteristics of the inter-relationship between dose, concentration and clinical (side-)effects for tacrolimus have not yet been identified and extensive long-term pharmacokinetic studies are presently lacking.

OBJECTIVE

To establish the characteristics of the long-term pharmacokinetics of tacrolimus, to determine the time-dependent factors that influence the pharmacokinetics within the first critical post-transplant year and to identify a more appropriate way of monitoring drug exposure in clinical practice.

STUDY DESIGN

A prospective pharmacokinetic study of tacrolimus was conducted in 100 de novo renal allograft recipients during the first year post-transplantation.

METHODS

Area under the concentration-time curve (AUC) blood samplings for tacrolimus were performed on days 7, 42, 90, 180 and 360 for all patients. Model-independent pharmacokinetic parameters for tacrolimus were calculated and dose-corrected when appropriate: AUC12, peak plasma concentration (Cmax), pre-dose trough concentration (C0), time to Cmax, average steady-state blood concentration, steady-state total body clearance, terminal half-life, volume of distribution and an estimate for tacrolimus bioavailability was derived from additional steady-state intravenous clearance data. The association between tacrolimus pharmacokinetic parameters and different clinical variables was evaluated on days 7, 42, 90, 180 and 360. The clinical variables were either donor-related (e.g. donor age), transplantation-related (e.g. delayed graft function), recipient-related (e.g. bodyweight), biochemical (e.g. serum albumin), therapeutic variables (e.g. corticosteroid dose) or disease variables (e.g. liver dysfunction).

RESULTS

Long-term tacrolimus dose-corrected exposure (AUC12, C0) is characterised by a late significant increase towards the end of the first year post-transplantation as the result of a significant increase in tacrolimus bioavailability (p < 0.05) and a slow decrease in tacrolimus steady-state clearance. Consequently, tacrolimus dose-requirements corrected for bodyweight decrease significantly in the first postoperative year (p < 0.05), in part because of the simultaneous tapering of the corticosteroid dose which significantly affects tacrolimus bioavailability (p < 0.05). Other clinical variables that significantly influenced tacrolimus administration, exposure and bioavailability in a time-related fashion were identified in this study (renal allograft function [p < 0.05], liver dysfunction [p < 0.05], diarrhoea [p < 0.05]), while the clinical relevance of other variables was considerably moderated by our findings (serum albumin, haematocrit). Time-unrelated variables proved to be of significant continuing clinical importance for tacrolimus dose-exposure pharmacokinetics throughout the first post-transplant year (recipient age [p < 0.05], gender [p < 0.01] and donor-receptor gender mismatch [p < 0.05]), while donor hypotension (p < 0.05) and cold ischaemia time (p < 0.05) also proved significant although at present the reasons for this are unknown. Finally, using multiple stepwise regression analysis we demonstrated that classical assessment of tacrolimus exposure by monitoring pre-dose trough blood concentration (or any other single concentration sampling timepoint) is not the most reliable method and that abbreviated AUC measurements may constitute a more accurate clinical tool for (therapeutic) monitoring of drug exposure.

CONCLUSION

Tacrolimus pharmacokinetics in the first year after renal transplantation are characterised by a specific time-dependent evolution. The identification of clinical variables that determine tacrolimus pharmacokinetics is an important aid in the development of reliable drug monitoring strategies using abbreviated AUC measurements.

Current status of liver transplantation in children

There are two critical issues on opposite ends of the timeline for patients who are eligible for liver transplantation. On the one hand, the crisis in the cadaveric organ supply makes surviving to transplant ever more risky. On the other hand, patients who receive successful transplants face the consequences of long-term immunosuppression and its potentially life-threatening complications. The donor shortage is forcing difficult decisions that affect all patients who await liver transplantation. It is important to scrutinize carefully the results of all policies that govern allocation and the ethics of the solutions we advocate to ensure that no patient subgroup is being at a disadvantage. Current immunosuppression practices are being challenged by an increasing understanding of the immunologic events triggered by the allograft and the goal to free patients from consequences of a lifetime of immunosuppression. Clinicians can expect, and perhaps require, that new immunosuppressive protocols will address how the planned intervention might be expected to advance the understanding of tolerance mechanisms. As knowledge increases, clinicians can anticipate innovative new immunosuppressive proposals. Calcineurin and steroid-free induction, the use of donor-derived bone marrow infusion, recipient pretreatment, costimulatory blockade, and new antibody induction approaches are all being proposed--often in combination--for clinical trials. Researchers face additional challenges in defining endpoints if the goal is not just the short-term reduction in rejection but the minimization, and eventual discontinuation, of immunosuppressive drugs while maintaining excellent long-term graft function. How much "failure" will be accepted and how will it be defined? How will clinicians interpret liver biopsies if they begin to accept that some lymphocytic infiltrates may be beneficial mediators of the ongoing immune activation necessary for the maintenance of tolerance? How will they adjust immunosuppression practices to the dynamic processes in the immune response that maintain tolerance? Remarkable short-term successes in providing transplants for thousands of children with liver failure have brought these challenges into sharp focus. Clinicians must seek to move the life-giving science of transplantation toward a new goal: providing long lifetimes of excellent graft function with minimal toxicity from immunosuppressive drugs and the hope of freedom from immunosuppression altogether. Pediatric liver recipients, whose grafts have inherent tolerogenic potential and for whom we can anticipate decades of life after transplant, may prove to be an ideal study population to further these goals.

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.

Comparison of an ELISA and an LC/MS/MS method for measuring tacrolimus concentrations and making dosage decisions in transplant recipients

This study compared an enzyme-linked immunosorbent assay (ELISA) to a liquid chromatography-tandem mass spectrometry (LC/MS/MS) technique for measurement of tacrolimus concentrations in adult kidney and liver transplant recipients, and investigated how assay choice influenced pharmacokinetic parameter estimates and drug dosage decisions. Tacrolimus concentrations measured by both ELISA and LC/MS/MS from 29 kidney (n = 98 samples) and 27 liver (n = 97 samples) transplant recipients were used to evaluate the performance of these methods in the clinical setting. Tacrolimus concentrations measured by the two techniques were compared via regression analysis. Population pharmacokinetic models were developed independently using ELISA and LC/MS/MS data from 76 kidney recipients. Derived kinetic parameters were used to formulate "typical dosing" regimens for concentration targeting. Dosage recommendations for the two assays were compared. The relation between LC/MS/MS and ELISA measurements was best described by the regression equation ELISA = 1.02. (LC/MS/MS) + 0.14 in kidney recipients, and ELISA = 1.12. (LC/MS/MS) - 0.87 in liver recipients. ELISA displayed less accuracy than LC/MS/MS at lower tacrolimus concentrations. Population pharmacokinetic models based on ELISA and LC/MS/MS data were similar with residual random errors of 4.1 ng/mL and 3.7 ng/mL, respectively. Assay choice gave rise to dosage prediction differences ranging from 0% to 30%. ELISA measurements of tacrolimus are not automatically interchangeable with LC/MS/MS values. Assay differences were greatest in adult liver recipients, probably reflecting periods of liver dysfunction and impaired biliary secretion of metabolites. While the majority of data collected in this study suggested assay differences in adult kidney recipients were minimal, findings of ELISA dosage underpredictions of up to 25% in the long term must be investigated further.