Development of a Population PK Model of Tacrolimus for Adaptive Dosage Control in Stable Kidney Transplant Patients

Model-Informed Precision Dosing of Tacrolimus: A Systematic Review of Population Pharmacokinetic Models and a Benchmark Study of Software Tools

BACKGROUND AND OBJECTIVE

Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment.

METHODS

We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration.

RESULTS

We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/ , allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey.

CONCLUSIONS

Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment.

Weight, CYP3A5 Genotype, and Voriconazole Co-administration Influence Tacrolimus Initial Dosage in Pediatric Lung Transplantation Recipients with Low Hematocrit based on a Simulation Model

OBJECTIVE

The method of administering the initial doses of tacrolimus in recipients of pediatric lung transplantation, especially in patients with low hematocrit, is not clear. The present study aims to explore whether weight, CYP3A5 genotype, and voriconazole co-administration influence tacrolimus initial dosage in recipients of pediatric lung transplantation with low hematocrit based on safety and efficacy using a simulation model.

METHODS

The present study utilized the tacrolimus population pharmacokinetic model, which was employed in lung transplantation recipients with low hematocrit.

RESULTS

For pediatric lung transplantation recipients not carrying CYP3A5*1 and without voriconazole, the recommended tacrolimus doses for weights of 10-13, 13-19, 19-22, 22-35, 35-38, and 38-40 kg are 0.03, 0.04, 0.05, 0.06, 0.07, and 0.08 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients carrying CYP3A5*1 and without voriconazole, the recommended tacrolimus doses for weights of 10-18, 18-30, and 30-40 kg are 0.06, 0.08, 0.11 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients not carrying CYP3A5*1 and with voriconazole, the recommended tacrolimus doses for weights of 10-20 and 20-40 kg are 0.02 and 0.03 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients carrying CYP3A5*1 and with voriconazole, the recommended tacrolimus doses for weights of 10-20, 20-33, and 33-40 kg are 0.03, 0.04, and 0.05 mg/kg/day, which are split into two doses, respectively.

CONCLUSION

The present study is the first to recommend the initial dosages of tacrolimus in recipients of pediatric lung transplantation with low hematocrit using a simulation model.

Tacrolimus population pharmacokinetics in adult heart transplant patients

INTRODUCTION

Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0).

MATERIAL AND METHODS

Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0.

RESULTS

The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor).

CONCLUSION

The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

CYP3A5*3 and CYP3A4*22 Cluster Polymorphism Effects on LCP-Tac Tacrolimus Exposure: Population Pharmacokinetic Approach

The aim of the study is to develop a population pharmacokinetic (PopPK) model and to investigate the influence of CYP3A5/CYP3A4 and ABCB1 single nucleotide polymorphisms (SNPs) on the Tacrolimus PK parameters after LCP-Tac formulation in stable adult renal transplant patients. The model was developed, using NONMEM v7.5, from full PK profiles from a clinical study (n = 30) and trough concentrations (C0) from patient follow-up (n = 68). The PK profile of the LCP-Tac formulation was best described by a two-compartment model with linear elimination, parameterized in elimination (CL/F) and distributional (CLD/F) clearances and central compartment (Vc/F) and peripheral compartment (Vp/F) distribution volumes. A time-lagged first-order absorption process was characterized using transit compartment models. According to the structural part of the base model, the LCP-Tac showed an absorption profile characterized by two transit compartments and a mean transit time of 3.02 h. Inter-individual variability was associated with CL/F, Vc/F, and Vp/F. Adding inter-occasion variability (IOV) on CL/F caused a statistically significant reduction in the model minimum objective function MOFV (p < 0.001). Genetic polymorphism of CYP3A5 and a cluster of CYP3A4/A5 SNPs statistically significantly influenced Tac CL/F. In conclusion, a PopPK model was successfully developed for LCP-Tac formulation in stable renal transplant patients. CYP3A4/A5 SNPs as a combined cluster including three different phenotypes (high, intermediate, and poor metabolizers) was the most powerful covariate to describe part of the inter-individual variability associated with apparent elimination clearance. Considering this covariate in the initial dose estimation and during the therapeutic drug monitoring (TDM) would probably optimize Tac exposure attainments.

A prospective controlled, randomized clinical trial of kidney transplant recipients developed personalized tacrolimus dosing using model-based Bayesian Prediction

For three decades, tacrolimus (Tac) dose adjustment in clinical practice has been calculated empirically according to the manufacturer's labeling based on a patient's body weight. Here, we developed and validated a Population pharmacokinetic (PPK) model including pharmacogenetics (cluster CYP3A4/CYP3A5), age, and hematocrit. Our study aimed to assess the clinical applicability of this PPK model in the achievement of Tac Co (therapeutic trough Tac concentration) compared to the manufacturer's labelling dosage. A prospective two-arm, randomized, clinical trial was conducted to determine Tac starting and subsequent dose adjustments in 90 kidney transplant recipients. Patients were randomized to a control group with Tac adjustment according to the manufacturer's labeling or the PPK group adjusted to reach target Co (6-10 ng/ml) after the first steady state (primary endpoint) using a Bayesian prediction model (NONMEM). A significantly higher percentage of patients from the PPK group (54.8%) compared with the control group (20.8%) achieved the therapeutic target fulfilling 30% of the established superiority margin defined. Patients receiving PPK showed significantly less intra-patient variability compared to the control group, reached the Tac Co target sooner (5 days vs 10 days), and required significantly fewer Tac dose modifications compared to the control group within 90 days following kidney transplant. No statistically significant differences occurred in clinical outcomes. Thus, PPK-based Tac dosing offers significant superiority for starting Tac prescription over classical labeling-based dosing according to the body weight, which may optimize Tac-based therapy in the first days following transplantation.

Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

BACKGROUND AND OBJECTIVES

Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions.

METHODS

The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020.

RESULTS

Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies).

CONCLUSION

The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

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

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

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

WHAT IS KNOWN AND OBJECTIVES

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

METHODS

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

RESULTS AND DISCUSSION

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

WHAT IS NEW AND CONCLUSION

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

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

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

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.

Predictive engines based on pharmacokinetics modelling for tacrolimus personalized dosage in paediatric renal transplant patients

The development of predictive engines based on pharmacokinetic-physiological mathematical models for personalised dosage recommendations is an immature field. Nevertheless, these models are extensively applied during the design of new drugs. This study presents new advances in this subject, through a stable population of patients who underwent kidney transplantation and were prescribed tacrolimus. We developed 2 new population pharmacokinetic models based on a compartmental approach, with one following the physiologically based pharmacokinetic approach and both including circadian modulation of absorption and clearance variables. One of the major findings was an improved predictive capability for both models thanks to the consideration of circadian rhythms, both in estimating the population and in Bayesian individual customisation. This outcome confirms a plausible mechanism suggested by other authors to explain circadian patterns of tacrolimus concentrations. We also discovered significant intrapatient variability in tacrolimus levels a week after the conversion from a fast-release (Prograf) to a sustained-release formulation (Advagraf) using adaptive optimisation techniques, despite high adherence and controlled conditions. We calculated the intrapatient variability through parametric intrapatient variations, which provides a method for quantifying the mechanisms involved. We present a first application for the analysis of bioavailability changes in formulation conversion. The 2 pharmacokinetic models have demonstrated their capability as predictive engines for personalised dosage recommendations, although the physiologically based pharmacokinetic model showed better predictive behaviour.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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.

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes

PURPOSE

Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus.

METHODS

A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting.

RESULTS

Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting.

CONCLUSIONS

Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

A New CYP3A5*3 and CYP3A4*22 Cluster Influencing Tacrolimus Target Concentrations: A Population Approach

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients.

METHODS

Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM^® version 7.2.

RESULTS

The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers.

CONCLUSIONS

Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.

Longitudinal Pharmacokinetics of Tacrolimus in Elderly Compared With Younger Recipients in the First 6 Months After Renal Transplantation

BACKGROUND

Elderly (Eld) (≥60 years) recipients are receiving renal transplants more frequently. The pharmacokinetics (PK) studies of immunosuppressive drugs in healthy volunteers, rarely, include old patients.

METHODS

We studied 208 12-hour tacrolimus (TAC) PK (0, 20, 40, 60, 90, 120, 180, 240, 360, 480, 600, 720 min) in 44 Eld (65 ± 3 years) and compared the results with 31 younger controls (Ctrl) (35 ± 6 years) recipients, taking oral TAC/mycophenolate sodium (MPS)/prednisone, at 4 different timepoints: PK1 (8 ± 2 days; n = 72), PK2 (31 ± 4 days; n = 61), PK3 (63 ± 6 days; n = 44), and PK4 (185 ± 10 days; n = 31). Tacrolimus PK was measured by ultraperformance liquid chromatography coupled to a mass spectrometer repetition and noncompartmental PKs were analyzed using Phoenix WinNonlin.

RESULTS

Mean TAC dose was lower in the Eld group than in Ctrl ones throughout timepoints either by total daily dose or adjusted (Adj) per body weight. Mean TAC trough level (Cmin), used to adjust daily dose, was not different between the 2 groups in all timepoints. AdjCmax and AdjTAC-area under the curve at dosing interval were both higher in the Eld compared to the Ctrl group in PKs1, 3, and 4. Estimated total body clearance normalized by dose and weight was lower in the Eld group compared with the Ctrl in all PKs and statistically lower at PKs 1 and 3. Similar to younger recipients TAC trough level has also a high correlation (R = 0.76) with area under the curve at dosing interval.

CONCLUSIONS

These data indicate that Eld recipients have a lower TAC clearance and therefore need a lower TAC dose than younger recipients.

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.

Clinical Pharmacokinetics and Pharmacodynamics of Monoclonal Antibodies Approved to Treat Rheumatoid Arthritis

Monoclonal antibodies (mAbs) are increasingly used to treat rheumatoid arthritis (RA). At present, anti-tumor necrosis factor-α drugs (infliximab, adalimumab, certolizumab pegol, and golimumab), rituximab, and tocilizumab are approved for RA treatment. This review focuses on the pharmacokinetics and pharmacodynamics of mAbs approved in RA. Being large proteins, mAbs exhibit complex pharmacokinetic and pharmacodynamic properties. In particular, owing to the interactions of mAbs with their antigenic targets, the pharmacokinetics of mAbs depends on target turnover and exhibits non-specific (linear) and target-mediated (often nonlinear) clearances. Their volume of distribution is low (3-4 L) and their elimination half-life usually ranges from 2 to 3 weeks. The inter-individual pharmacokinetic variability of mAbs is usually large and is partly explained by differences in antigenic burden or by anti-drug antibodies, which accelerate mAb elimination. The inter-individual variability of clinical response is large and influenced by the pharmacokinetics. The analysis of mAbs concentration-effect relationship relies more and more often on pharmacokinetic-pharmacodynamic modeling; these models being suitable for dosing optimization. Even if adverse effects of mAbs used in RA are well known, the relationship between mAb concentration and adverse effects is poorly documented, especially for anti-tumor necrosis factor-α mAbs. Overall, RA patients treated with mAbs should benefit from individualized dosing strategies. Because of the complexity of their pharmacokinetics and mechanisms of action, the current dosing strategy of mAbs is not based on sound knowledge. New studies are needed to assess individual dosing regimen, adjusted notably to disease activity.

External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients

AIM

Several tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors.

METHODS

Published models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction- and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated.

RESULTS

Sixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used.

CONCLUSIONS

The published models were unsatisfactory in prediction- and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability.