Population Pharmacokinetics of Tacrolimus in Pediatric Liver Transplantation: Early Posttransplantation Clearance

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.

Population pharmacokinetic models of tacrolimus in paediatric solid organ transplant recipients: A systematic review

AIMS

This study aimed to provide up-to-date information on paediatric population pharmacokinetic models of tacrolimus and to identify factors influencing tacrolimus pharmacokinetic variability.

METHODS

Systematic searches in the Web of Science, PubMed, Scopus, Science Direct, Cochrane, EMBASE databases and reference lists of articles were conducted from inception to March 2023. All population pharmacokinetic studies of tacrolimus using nonlinear mixed-effect modelling in paediatric solid organ transplant patients were included.

RESULTS

Of the 21 studies reviewed, 62% developed from liver transplant recipients and 33% from kidney transplant recipients. Most studies used a 1-compartment model to describe tacrolimus pharmacokinetics. Body weight was a significant predictor for tacrolimus volume of distribution (Vd/F). The estimated Vd/F for 1-compartment models ranged from 20 to 1890 L, whereas the peripheral volume of distribution (Vp/F) for 2-compartment models was between 290 and 1520 L. Body weight, days post-transplant, CYP3A5 genotype or haematocrit were frequently reported as significant predictors of tacrolimus clearance. The estimated apparent clearance values range between 0.12 and 2.18 L/h/kg, with inter-individual variability from 13.5 to 110.0%. Only 29% of the studies assessed the generalizability of the models with external validation.

CONCLUSION

This review highlights the potential factors, modelling approaches and validation methods that impact tacrolimus pharmacokinetics in a paediatric population. The clinician could predict tacrolimus clearance based on body weight, CYP3A5 genotype, days post-transplant or haematocrit. Further research is required to determine the relationship between pharmacogenetics and tacrolimus pharmacodynamics in paediatric patients and confirm the applicability of nonlinear kinetics in this population.

Comparing the Performance of Multiple Small-Data Personalized Tacrolimus Dosing Models for Pediatric Liver Transplant: A Retrospective Study

Tacrolimus is a potent immunosuppressant used after pediatric liver transplant. However, tacrolimus's narrow therapeutic window, reliance on physicians' experience for the dose titration, and intra- and inter-patient variability result in liver transplant patients falling out of the target tacrolimus trough levels frequently. Existing personalized dosing models based on the area-under-the-concentration over time curves require a higher frequency of blood draws than the current standard of care and may not be practically feasible. We present a small-data artificial intelligence-derived platform, CURATE.AI, that uses data from individual patients obtained once daily to model the dose and response relationship and identify suitable doses dynamically. Retrospective optimization using 6 models of CURATE.AI and data from 16 patients demonstrated good predictive performance and identified a suitable model for further investigations.Clinical Relevance- This study established and compared the predictive performance of 6 personalized tacrolimus dosing models for pediatric liver transplant patients and identified a suitable model with consistently good predictive performance based on data from pediatric liver transplant patients.

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 sublingually administered tacrolimus in infants and young children with liver transplantation

AIMS

Pharmacokinetics of tacrolimus after sublingual administration is not characterized in paediatric liver transplant patients. Therefore, we aimed to develop a population pharmacokinetic model of sublingually administered tacrolimus in patients who cannot swallow the capsules due to their age, sedation status and/or mechanical ventilation during the first weeks post-transplantation.

METHODS

Demographic, clinical and pharmacological variables, including tacrolimus whole blood concentrations obtained from therapeutic drug monitoring and data from dense-sampling pharmacokinetic profiles, were recorded in 26 paediatric patients with biliary atresia who underwent liver transplantation between 2016 and 2021. Population pharmacokinetic analysis was performed with NONMEM v7.4.

RESULTS

Disposition of tacrolimus was best characterized by a 2-compartment model with clearance achieving half of the maximum elimination capacity (CL_MAX  = 4.1 L/h) at 4.6 days post-transplantation (T₅₀ ). Compared to sedated patients, nonsedated status showed an increased first-order absorption rate constant (1.1 vs. 0.1 h^-1 ) and a 24% reduction in bioavailability (F_NS ) at 14 days post-transplant. The model was able to explain the oral absorption pattern in nonsedated patients as the result of gut bioavailability (0.9) and hepatic extraction ratio, with the latter being responsible for first-pass effects. Estimates of interindividual variability remained moderate (25.9% for the gut bioavailability) to high (79.8% for the apparent volume of distribution of the central compartment, and 101% for T₅₀ ).

CONCLUSION

A population pharmacokinetic model of sublingually administered tacrolimus in paediatric patients was developed to characterize different absorption mechanisms. Once the model is externally validated, the effect of post-transplant time on clearance and the sedation status may be considered in routine dosing management.

Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation

BACKGROUND

This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation.

METHODS

Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C₀ . PCR was used to determine the donors' and recipients' CYP3A5 genotypes. Multivariate stepwise regression analysis and stepwise elimination covariates were used for covariates selection. Thirteen machine learning algorithms were applied for the development of prediction models. APE, the ratio of the APE ≤3 ng ml^-1 and ideal rate (the proportion of the predicted value with a relative error of 30% or less) were used to evaluate the predictive performance of the model.

RESULTS

A total of 163 infant patients were included in this study. In the case of the optimal combination of covariates, the Ridge model had the lowest APE, 2.01 (0.85, 3.35 ng ml^-1 ). The highest ratio of the APE ≤3 ng ml^-1 was the LAR model (71.77%). And the Ridge model showed the highest ideal rate (55.05%). For the Ridge model, GRWR was the most important predictor.

CONCLUSIONS

Compared with other ML models, the Ridge model had good predictive performance and potential clinical application.

Network pharmacology-based analysis of the role of tacrolimus in liver transplantation

Background

Tacrolimus is a powerful immunosuppressant and has been widely used in organ transplantation. In order to further explore the role of tacrolimus in liver transplantation, we conducted network pharmacology analysis.

Methods

GSE100155 was obtained from the GEO database, and the DEGs of liver transplantation were analyzed. The 2D structure of tacrolimus was obtained from the National Library of Medicine, and the pharmacophore model of tacrolimus was predicted using the online tool pharmmapper. Then a network of tacrolimus and target genes was constructed through network pharmacology, and visualization and GO enrichment analysis was performed through Cytoscape. In addition, we also analyzed the correlation between key genes and immune infiltrating cells. The data of GSE84908 was used to verify the changes of key gene expression levels after tacrolimus treatment.

Results

The results of network pharmacological analysis showed that tacrolimus had 43 target genes, and the GO enrichment results showed many potential functions. Further analysis found that there were 5 key target genes in DEGs, and these 5 genes were significantly down-regulated in liver transplant patients. Another important finding was that 5 genes were significantly related to some immune infiltrating cells. The results of the GSE84908 data analysis showed that after tacrolimus treatment, the expression of DAAM1 was significantly increased (p = 0.015).

Conclusion

Tacrolimus may inhibit the human immune response by affecting the expression of DAAM1 in liver transplant patients.

Early impact of donor CYP3A5 genotype and Graft-to-Recipient Weight Ratio on tacrolimus pharmacokinetics in pediatric liver transplant patients

Tacrolimus (TAC) pharmacokinetics is influenced by the donor CYP3A5 genotype and the age of pediatric liver recipients. However, an optimization of a genotype-based algorithm for determining TAC starting is needed to earlier achieve stable target levels. As the graft itself is responsible for its metabolism, the Graft-to-Recipient Weight Ratio (GRWR) might play a role in TAC dose requirements. A single-center study was carried out in a cohort of 49 pediatric recipients to analyse the impact of patient and graft characteristics on TAC pharmacokinetics during the first 15 post-transplant days. Children < 2 years received grafts with a significantly higher GRWR (4.2%) than children between 2–8 (2.6%) and over 8 (2.7%). TAC concentration/weight-adjusted dose ratio was significantly lower in recipients from CYP3A5*1/*3 donors or with extra-large (GRWR > 5%) or large (GRWR 3–5%) grafts. The donor CYP3A5 genotype and GRWR were the only significant predictors of the TAC weight adjusted doses. Patients with a GRWR > 4% had a higher risk of acute rejection, observed in 20/49 (41%) patients. In conclusion, TAC starting dose could be guided according to the donor CYP3A5 genotype and GRWR, allowing for a quicker achievement of target concentrations and eventually reducing the risk of rejection.

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

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

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.

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

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

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

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

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

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

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.

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus (TAC) is widely used as part of immunosuppressive regimens. There is great interindividual variation on the disposition of TAC. The aim of this study was to develop a population pharmacokinetic (PPK) model for Chinese liver transplant patients and evaluate genetic polymorphism and other possible factors on the PK parameters. The exposure of TAC is to be estimated through Bayesian modelling.

METHODS

A total of 47 sets of rich-time PK and 1234 conventional therapeutic drug monitoring (TDM) data were collected from 125 Chinese liver transplant patients. The pathophysiological data of these patients were recorded. CYP3A5*3 and ABCB1 genotypes were determined for each patient. The PPK model for TAC was established by nonlinear mixed-effects modelling (nonmem). The impact of pathophysiology and genotype on PPK parameters was evaluated. Bayesian estimators for the area under concentration-time curve (AUC) of TAC were validated.

RESULTS

A two-compartment model with lag time was found to be the most suitable model for the pooled full PK and TDM data for Chinese liver transplant patients. The CL/F, V₂ /F, Q/F, V₃ /F, K_a and lag time were 17.4±0.81 L/h, 165±44.1 L, 54.9±25.8L/h, 594±87.5 L, 0.51±0.095 L/h and 1.57±0.34 h. Post-operative day (POD), creatinine clearance (CLcr) and ABCB1 C3435T genotypes were found to have significant influences on CL/F (P<.01). ABCB1 C3435T genotypes showed a significant correlation with V₂ /F (P<.01). C₀ -C₂ and C₀ -C₂ -C₄ were shown to be suitable for the estimation of AUC in Chinese liver transplant patients.

WHAT IS NEW AND CONCLUSION

A PPK model for TAC was established successfully in Chinese liver transplant patients. POD, CLcr and ABCB1 C3435T genotypes were shown to have significant effects on CL/F. The AUC of TAC in Chinese liver transplant patients could be estimated through Bayesian modelling, based on which individualized immunosuppressive regimens can be designed.

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.

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.

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.