Population Pharmacokinetics and Bayesian Estimators for Refined Dose Adjustment of a New Tacrolimus Formulation in Kidney and Liver Transplant Patients

Optimizing the initial tacrolimus dosage in Chinese children with lung transplantation within normal hematocrit levels

Background

The appropriate initial dosage of tacrolimus is undefined in Chinese pediatric lung transplant patients with normal hematocrit values. The purpose of this study is to optimize the initial dose of tacrolimus in Chinese children who are undergoing lung transplantation and have normal hematocrit levels.

Methods

The present study is based on a published population pharmacokinetic model of tacrolimus in lung transplant patients and uses the Monte Carlo simulation to optimize the initial tacrolimus dosage in Chinese children with lung transplantation within normal hematocrit levels.

Results

Within normal hematocrit levels, for children with lung transplantation who do not carry the CYP3A5*1 gene and have no coadministration with voriconazole, it is recommended to administer tacrolimus at a dosage of 0.02 mg/kg/day, divided into two doses, for children weighing 10-32 kg, and a dosage of 0.03 mg/kg/day, also divided into two doses, for children weighing 32-40 kg. For children with lung transplantation who carry the CYP3A5*1 gene and have no coadministration with voriconazole, tacrolimus dosages of 0.02, 0.03, and 0.04 mg/kg/day split into two doses are recommended for children weighing 10-15, 15-32, and 32-40 kg, respectively. For children with lung transplantation who do not carry the CYP3A5*1 gene and have coadministration with voriconazole, tacrolimus dosages of 0.01 and 0.02 mg/kg/day split into two doses are recommended for children weighing 10-17 and 17-40 kg, respectively. For children with lung transplantation who carry the CYP3A5*1 gene and have coadministration with voriconazole, a tacrolimus dosage of 0.02 mg/kg/day split into two doses is recommended for children weighing 10-40 kg.

Conclusions

It is the first time to optimize the initial dosage of tacrolimus in Chinese children undergoing lung transplantation within normal hematocrit.

Tacrolimus versus mycophenolate for AutoImmune hepatitis patients with incompLete response On first-line therapy (TAILOR study): a study protocol for a phase III, open-label, multicentre, randomised controlled trial

BACKGROUND

Autoimmune hepatitis (AIH) is a rare, chronic inflammatory disease of the liver. The treatment goal is reaching complete biochemical response (CR), defined as the normalisation of aspartate and alanine aminotransferases and immunoglobulin gamma. Ongoing AIH activity can lead to fibrosis and (decompensated) cirrhosis. Incomplete biochemical response is the most important risk factor for liver transplantation or liver-related mortality. First-line treatment consists of a combination of azathioprine and prednisolone. If CR is not reached, tacrolimus (TAC) or mycophenolate mofetil (MMF) can be used as second-line therapy. Both products are registered for the prevention of graft rejection in solid organ transplant recipients. The aim of this study is to compare the effectiveness and safety of TAC and MMF as second-line treatment for AIH.

METHODS

The TAILOR study is a phase IIIB, multicentre, open-label, parallel-group, randomised (1:1) controlled trial performed in large teaching and university hospitals in the Netherlands. We will enrol 86 patients with AIH who have not reached CR after at least 6 months of treatment with first-line therapy. Patients are randomised to TAC (0.07 mg/kg/day initially and adjusted by trough levels) or MMF (max 2000 mg/day), stratified by the presence of cirrhosis at inclusion. The primary endpoint is the difference in the proportion of patients reaching CR after 12 months. Secondary endpoints include the difference in the proportion of patients reaching CR after 6 months, adverse effects, difference in fibrogenesis, quality of life and cost-effectiveness.

DISCUSSION

This is the first randomised controlled trial comparing two second-line therapies for AIH. Currently, second-line treatment is based on retrospective cohort studies. The rarity of AIH is the main issue in clinical research for alternative treatment options. The results of this trial can be implemented in existing international clinical guidelines.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05221411 . Retrospectively registered on 3 February 2022; EudraCT number 2021-003420-33. Prospectively registered on 16 June 2021.

Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

BACKGROUND

Meltdose tacrolimus (Envarsus®) has been marketed as a formulation achieving a more consistent tacrolimus exposure. Due to the narrow therapeutic window of tacrolimus, dose individualization is essential. Relaxation of the upper age limits for kidney transplantations has resulted in larger numbers of elderly patients receiving tacrolimus. However, due to the physiological changes caused by aging, the tacrolimus pharmacokinetics (PK) might be altered. The primary aim was to develop a population PK model in elderly kidney transplant recipients. Secondary aims were the development and evaluation of a limited sampling strategy (LSS) for AUC estimation.

METHODS

A total of 34 kidney transplant recipients aged ≥65 years, starting on meltdose tacrolimus directly after transplantation, were included. An eight-point whole blood AUC0-24h and an abbreviated dried blood spot (DBS) AUC0-24h were obtained. The PK data were analyzed using nonlinear mixed effect modeling methods.

RESULTS

The PK data were best described using a two-compartment model, including three transit compartments and a mixture model for oral absorption. The best three-sample LSS was T = 0, 2, 6 h. The best four-sample LSSs were T = 0, 2, 6, 8 h and T = 0, 1, 6, 8 h.

CONCLUSIONS

The developed population PK model adequately described the tacrolimus PK data in a population of elderly kidney transplant recipients. In addition, the developed population PK model and LSS showed an adequate estimation of tacrolimus exposure, and may therefore be used to aid in tacrolimus dose individualization.

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.

Tacrolimus Exposure Before and After a Switch From Twice-Daily Immediate-Release to Once-Daily Prolonged Release Tacrolimus: The ENVARSWITCH Study

LCP-tacrolimus displays enhanced oral bioavailability compared to immediate-release (IR-) tacrolimus. The ENVARSWITCH study aimed to compare tacrolimus AUC0-24 h in stable kidney (KTR) and liver transplant recipients (LTR) on IR-tacrolimus converted to LCP-tacrolimus, in order to re-evaluate the 1:0.7 dose ratio recommended in the context of a switch and the efficiency of the subsequent dose adjustment. Tacrolimus AUC0-24 h was obtained by Bayesian estimation based on three concentrations measured in dried blood spots before (V2), after the switch (V3), and after LCP-tacrolimus dose adjustment intended to reach the pre-switch AUC0-24 h (V4). AUC0-24 h estimates and distributions were compared using the bioequivalence rule for narrow therapeutic range drugs (Westlake 90% CI within 0.90-1.11). Fifty-three KTR and 48 LTR completed the study with no major deviation. AUC0-24 h bioequivalence was met in the entire population and in KTR between V2 and V4 and between V2 and V3. In LTR, the Westlake 90% CI was close to the acceptance limits between V2 and V4 (90% CI = [0.96-1.14]) and between V2 and V3 (90% CI = [0.96-1.15]). The 1:0.7 dose ratio is convenient for KTR but may be adjusted individually for LTR. The combination of DBS and Bayesian estimation for tacrolimus dose adjustment may help with reaching appropriate exposure to tacrolimus rapidly after a switch.

Envarsus XR® pharmacokinetics in adolescents post-kidney transplantation - A pilot study

INTRODUCTION

Envarsus XR® (LCPT), a once daily dosage formulation of tacrolimus, is an FDA-approved medication in adult renal transplant recipients (RTRs). There are limited data on its pharmacokinetics (PK) in adolescent RTRs. We report here the PK profile of LCPT in adolescent RTRs.

METHODS

The dose of LCPT was determined using a dose conversion ratio targeting 0.7 relative to the total daily immediate-release tacrolimus (IR-Tac) dose. On day 7 after converting to LCPT, patients had an abbreviated PK assessment with sampling at: 0 h (pre-dose), 8-, and 12-h post-dose. The PK data analysis was performed using Bayesian estimators. Our results were compared to those of published adult PK data for LCPT and pediatric PK data for IR-Tac and extended release tacrolimus (ER-Tac) formulation (Advagraf).

RESULTS

PK data from three adolescent patients on LCPT were evaluated. The mean (±SD) area under the time-concentration curve (AUC) was 240 (±20.22) h*ng/mL. The mean T_max was 9.01 ± 2.12 h, and the % fluctuation was 77.71 ± 3.96%. The AUC, T_max , and % fluctuation were similar to reported results in adult patients taking LCPT. The AUC was higher and the T_max was longer than what has been reported in pediatric patients taking IR-Tac and ER-Tac. In addition, the LCPT group showed a lower % fluctuation than patients receiving ER-Tac.

CONCLUSION

The PK evaluation of LCPT in adolescent RTRs showed similar results to adults. Adolescents taking LCPT had a higher AUC, a more attenuated T_max , and a lower fluctuation than that seen with ER-Tac in pediatrics.

Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation

PURPOSE

Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (= "true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose^®-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE.

METHODS

Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients.

RESULTS

Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = - 1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = - 1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = - 3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = - 3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points.

CONCLUSION

The MARS ML models developed using "true" MeltDose^®-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations.

Can the Area Under the Curve/Trough Level Ratio Be Used to Optimize Tacrolimus Individual Dose Adjustment?

BACKGROUND

The aim of this work was to evaluate, in a large data set of renal transplant recipients, the intraindividual variability of the area under the curve (AUC)/predose concentration (C0) ratio in comparison with that of AUC, C0, AUC/dose, and C0/dose.

METHODS

Patients with at least 2 tacrolimus AUC estimation requests were extracted from the Immunosuppressant Bayesian dose Adjustment website, and relative variations between 2 consecutive visits for the different metrics were calculated and compared.

RESULTS

Data from 1325 patients on tacrolimus (3827 measured C0 and estimated AUC) showed that the lowest mean relative variation between 2 consecutives visits was for the AUC/C0 ratio (95% confidence interval [CI] relative fold change = -43% to 44% for AUC/C0; 95% CI, -77% to 72% for AUC; 95% CI, -82% to 98% for AUC/dose; 95% CI, -81% to 80% for C0 and 95% CI, -94% to 117% for C0/dose. The correlation between 2 consecutive requests, whether close or far apart, was also best for the AUC/C0 ratio ( r  = 0.33 and r  = 0.34, respectively) in comparison with C0 ( r  = 0.21 and r  = 0.22, respectively) and AUC ( r  = 0.19 and 0.28, respectively). Regression analysis between AUC0-24 and C0 showed that for some patients, the usual C0 targets translated into some very unusual AUC values. As the AUC/C0 ratio is quite stable during large periods, individualized C0 targets can be derived from the AUC targets, and an algorithm that estimates the individualized C0 was developed for situations in which prior AUC estimates are available or not.

CONCLUSIONS

In this study, we confirmed in a large data set that the AUC/C0 ratio yields low intraindividual variability, whereas C0 shows the largest, and we propose to calculate individualized C0 targets based on this ratio.

The Effect of Voriconazole on Tacrolimus in Kidney Transplantation Recipients: A Real-World Study

Tacrolimus is an immunosuppressant with a narrow therapeutic window. Tacrolimus exposure increased significantly during voriconazole co-therapy. The magnitude of this interaction is highly variable, but it is hard to predict quantitatively. We conducted a study on 91 kidney transplantation recipients with voriconazole co-therapy. Furthermore, 1701 tacrolimus concentration data were collected. Standard concentration adjusted by tacrolimus daily dose (C/D) and weight-adjusted standard concentration (CDW) increased to 6 times higher during voriconazole co-therapy. C/D and CDW increased with voriconazole concentration. Patients with the genotype of CYP3A5 *3/*3 and CYP2C19 *2/*2 or *2/*3 were more variable at the same voriconazole concentration level. The final prediction model could explain 54.27% of the variation in C/D and 51.11% of the variation in CDW. In conclusion, voriconazole was the main factor causing C/D and CDW variation, and the effect intensity should be quantitative by its concentration. Kidney transplant recipients with CYP3A5 genotype of *3/*3 and CYP2C19 genotype of *2/*2 and *2/*3 should be given more attention during voriconazole co-therapy. The prediction model established in this study may help to reduce the occurrence of rejection.

Optimization of initial dose regimen of tacrolimus in paediatric lung transplant recipients based on Monte Carlo simulation

WHAT IS KNOWN AND OBJECTIVES

The initial tacrolimus dose regimen in paediatric lung transplant recipients is unknown. The present study optimized the initial tacrolimus dose regimen for paediatric lung transplant recipients.

METHODS

This study was based on a published population pharmacokinetic model of tacrolimus in lung transplant recipients and used Monte Carlo simulations to recommend an initial dose regimen of tacrolimus in paediatric lung transplant recipients.

RESULTS

Without voriconazole, the tacrolimus doses recommended for paediatric lung transplant recipients who were not CYP3A5*1 carriers were 0.02, 0.03, and 0.04 mg/kg/day, split into two doses, for weights of 10-16, 16-30, and 30-40 kg, respectively. For paediatric lung transplant recipients who were CYP3A5*1 carriers, the tacrolimus doses of 0.03, 0.04, 0.05, and 0.06 mg/kg/day, split into two doses, were recommended for weights of 10-16, 16-25, 25-30, and 30-40 kg, respectively. With voriconazole, the tacrolimus dose recommended for paediatric lung transplant recipients who were not CYP3A5*1 carriers was 0.02 mg/kg/day, split into two doses, for weights of 10-40 kg. For paediatric lung transplant recipients who were CYP3A5*1 carriers, tacrolimus doses of 0.02 and 0.03 mg/kg/day, split and two doses, were recommended for weights of 10-24 and 24-40 kg, respectively.

WHAT IS NEW AND CONCLUSIONS

This study developed tacrolimus dose regimens for the first time for paediatric lung transplant recipients using Monte Carlo simulation and optimized initial dosage in paediatric lung transplant recipients.

Predicting model‐informed precision dosing: A test‐case in tacrolimus dose adaptation for kidney transplant recipients

Before investing resources into the development of a precision dosing (model‐informed precision dosing [MIPD]) tool for tacrolimus, the performance of the tool was evaluated in silico. A retrospective dataset of 315 de novo kidney transplant recipients was first used to identify a one‐compartment pharmacokinetic (PK) model with time‐dependent clearance. MIPD performance was subsequently evaluated by calculating errors to predict future concentrations, which is directly related to dosing precision and probability of target attainment (PTA). Based on the identified model residual error, the theoretical upper limit was 45% PTA for a target of 13.5 ng/ml and an acceptable range of 12–15 ng/ml. Using empirical Bayesian estimation, this limit was reached on day 5 post‐transplant and beyond. By incorporating correlated within‐patient variability when predicting future individual concentrations, PTA improved beyond the theoretical upper limit. This yielded a Bayesian feedback dosing algorithm accurately predicting future trough concentrations and adapting each dose to reach a target concentration. Simulated concentration‐time profiles were then used to quantify MIPD‐based improvement on three end points: average PTA increased from 28% to 39%, median time to three concentrations in target decreased from 10 to 7 days, and mean log‐squared distance to target decreased from 0.080 to 0.055. A study of 200 patients was predicted to have sufficient power to demonstrate these nuanced PK end points reliably. These simulations supported our decision to develop a precision dosing tool for tacrolimus and test it in a prospective trial.

Parametric and Nonparametric Methods in Population Pharmacokinetics: Experts' Discussion on Use, Strengths, and Limitations

Population pharmacokinetics consists of analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring and model-informed precision dosing. There are 2 main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians, and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion article may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area.

Therapeutic Drug Monitoring Strategies for Envarsus in De Novo Kidney Transplant Patients Using Population Modelling and Simulations

INTRODUCTION

Tacrolimus, the cornerstone of transplantation immunosuppression, is a narrow therapeutic index drug with a low and highly variable bioavailability. Therapeutic drug monitoring based on trough level assessment is mandatory in order to target a personalised exposure and avoid both rejection and toxicity. Population pharmacokinetic (POPPK) models might be a useful tool for improving early attainment of target range by guiding initial doses until steady state is reached and trough levels can be reliably used as surrogate marker of exposure. Here we present the first POPPK for predicting the initial doses of the once-daily prolonged release tacrolimus Envarsus (LCPT) in adult kidney recipients.

METHODS

The model was developed exploiting the data from a recent pharmacokinetic randomised clinical study, in which 69 de novo kidney recipients, 33 of whom treated with LCPT, underwent an intensive blood sampling strategy for tacrolimus including four complete pharmacokinetic profiles.

RESULTS

The complex and prolonged absorption of LCPT is well described by the three-phase model that incorporates body weight and CYP3A5 genotype as significant covariates accounting for a great proportion of the inter-patient variability: in particular, CYP3A5*1/*3 expressors had a 66% higher LCPT clearance. We have then generated by simulation a personalised dosing strategy based on the model that could improve the early attainment of therapeutic trough levels by almost doubling the proportion of patients within target range (69.3% compared to 36.1% with the standard body weight-based approach) on post-transplantation day 4 and significantly reduce the proportion of overexposed patients at risk of toxicity.

CONCLUSIONS

A POPPK model was successfully developed for LCPT in de novo kidney recipients. The model could guide a personalised dosing strategy early after transplantation. For the model to be translated into clinical practice, its beneficial impact of earlier attainment of therapeutic trough levels should be demonstrated on hard clinical outcomes in further studies.

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.

Overview of therapeutic drug monitoring of immunosuppressive drugs: Analytical and clinical practices

Immunosuppressant drugs (ISDs) play a key role in short-term patient survival together with very low acute allograft rejection rates in transplant recipients. Due to the narrow therapeutic index and large inter-patient pharmacokinetic variability of ISDs, therapeutic drug monitoring (TDM) is needed to dose adjustment for each patient (personalized medicine approach) to avoid treatment failure or side effects of the therapy. To achieve this, TDM needs to be done effectively. However, it would not be possible without the proper clinical practice and analytical tools. The purpose of this review is to provide a guide to establish reliable TDM, followed by a critical overview of the current analytical methods and clinical practices for the TDM of ISDs, and to discuss some of the main practical aspects of the TDM.

Tacrolimus Bayesian Dose Adjustment in Pediatric Renal Transplant Recipients

BACKGROUND

Immunosuppressant Bayesian Dose Adjustment (ISBA) is an online expert system that estimates the area under the curve (AUC) of immunosuppressive drugs through pharmacokinetic modelling and Bayesian estimation to propose dose adjustments to reach predefined exposure targets. The ISBA database was retrospectively analyzed to describe tacrolimus pharmacokinetics and exposure, evaluate the efficiency of ISBA dose recommendations, and propose tacrolimus AUC0-12h target ranges for pediatric renal allograft recipients treated with immediate release tacrolimus.

METHODS

The database included 1935 tacrolimus dose adjustment requests from 419 patients <19 years old who were treated with immediate-release tacrolimus and followed in 21 French hospitals. The tacrolimus exposure evolution with patient age and posttransplantation time, the correlation between trough tacrolimus concentration (C0) and AUC0-12h at different periods posttransplantation, and the efficiency of dose recommendations to avoid underexposure and overexposure and to decrease between-patient AUC variability were investigated.

RESULTS

Tacrolimus AUC showed large between-patient variability (CV% = 40%) but moderate within-patient variability (median = 24.3% over a 3-month period). Dose-standardized exposure but not the AUC/C0 ratio significantly decreased with time posttransplantation and patient age. We derived AUC0-12h ranges from the consensual C0 ranges using linear regression equations. When the ISBA recommended dose was applied, the AUC distribution was narrower and a significantly higher proportion was within the targets (P < 0.0001).

CONCLUSIONS

ISBA efficiently reduced tacrolimus underexposure and overexposure. The AUC0-12h target ranges for pediatric patients derived from the database were similar to those previously reported for adults. Estimating the AUC/C0 ratio could help determine personalized C0 targets.

Point-of-Care Therapeutic Drug Monitoring for Precision Dosing of Immunosuppressive Drugs

BACKGROUND

Immunosuppressive drugs (ISD) are an essential tool in the treatment of transplant rejection and immune-mediated diseases. Therapeutic drug monitoring (TDM) for determination of ISD concentrations in biological samples is an important instrument for dose personalization for improving efficacy while reducing side effects. While currently ISD concentration measurements are performed at specialized, centralized facilities, making the process complex and laborious for the patient, various innovative technical solutions have recently been proposed for bringing TDM to the point-of-care (POC).

CONTENT

In this review, we evaluate current ISD-TDM and its value, limitations, and proposed implementations. Then, we discuss the potential of POC-TDM in the era of personalized medicine, and provide an updated review on the unmet needs and available technological solutions for the development of POC-TDM devices for ISD monitoring. Finally, we provide concrete suggestions for the generation of a meaningful and more patient-centric process for ISD monitoring.

SUMMARY

POC-based ISD monitoring may improve clinical care by reducing turnaround time, by enabling more frequent measurements in order to obtain meaningful pharmacokinetic data (i.e., area under the curve) faster reaction in case of problems and by increasing patient convenience and compliance. The analysis of the ISD-TDM field prompts the evolution of POC testing toward the development of fully integrated platforms able to support clinical decision-making. We identify 4 major areas requiring careful combined implementation: patient usability, data meaningfulness, clinicians' acceptance, and cost-effectiveness.

Dose tailoring of tacrolimus based on a non-linear pharmacokinetic model in children with refractory nephrotic syndrome

The population pharmacokinetics (PPK) of tacrolimus (TAC) in children with refractory nephrotic syndrome (RNS) have not been well-characterized. This study aimed to investigate the significant factors affecting the TAC PPK characteristics of children with RNS and to optimize the dosing regimen. A total of 494 concentrations from 108 children were obtained from routine therapeutic drug monitoring between 2016 and 2018. Information regarding the demographic features, laboratory test results, genetic polymorphisms of CYP3A5 (rs776746) and co-therapy medications were collected. PPK analysis was performed using the nonlinear mixed-effects modelling (NONMEM) software and two modelling strategies (the linear one-compartment model and nonlinear Michaelis-Menten model) were evaluated and compared. CYP3A5 genotype, weight, daily dose of TAC and daily dose of diltiazem were retained in the final linear model. The absorption rate constant (Ka) was set at 4.48 h^-1 in the linear model, and the apparent clearance (CL/F) and volume of distribution (V/F) in the final linear model were 14.2 L/h and 172 L, respectively. CYP3A5 genotype, weight and daily dose of diltiazem were the significant factors retained in the final nonlinear model. The maximal dose rate (V_max) and the average steady-state concentration at half-Vmax (K_m) in the final nonlinear model were 2.15 mg/day and 0.845 ng/ml, respectively. The nonlinear model described the pharmacokinetic data of TAC better than the linear model in children with RNS. A dosing regimen was proposed based on weight, CYP3A5 genotype and daily dose of diltiazem according to the final nonlinear PK model, which may facilitate individualized drug therapy with TAC.

Understanding the Role of Pharmacometrics-Based Clinical Decision Support Systems in Pediatric Patient Management: A Case Study Using Lyv Software

Pharmacometrics could play a key role in shifting pediatric pharmacotherapy from dosing for an average patient to individualizing dosing. Clinicians can have these quantitative tools at their disposal without requiring significant training through the development of clinical decision support systems with easy-to-use interfaces that have a back-end analysis engine or pharmacometric model that uses extensive electronic health record data to predict an individualized dose for each patient. There has been increased development of these clinical decision support systems recently, and for these tools to make the proper breakthrough into clinical practice, it is of utmost importance to perform rigorous testing to ensure adequate predictive performance. In this article, we walk through the components of a decision support tool and the testing required to determine its robustness using an example of a decision support tool we developed for vancomycin dosing in pediatrics.

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 pharmacokinetics and genetics of oral meltdose tacrolimus (Envarsus) in stable adult liver transplant recipients

AIMS

Meltdose tacrolimus (Envarsus) is marketed as a formulation with a more consistent exposure. Due to the narrow therapeutic window, therapeutic drug monitoring is essential to maintain adequate exposure. The primary objective of this study was to develop a population pharmacokinetic (PK) model of Envarsus among liver transplant patients and select a limited sampling strategy (LSS) for AUC estimation. The secondary objective was to investigate potential covariates including CYP3A/IL genotype suitable for initial dose optimization when converting to Envarsus.

METHODS

Adult liver transplant patients were converted from prolonged release tacrolimus (Advagraf) to Envarsus and blood samples were obtained using whole blood and dried blood spot sampling. Subsequently the population PK parameters were estimated using nonlinear-mixed effect modelling. Demographic factors, and recipient and donor CYP3A4, CYP3A5, IL-6, -10 and -18 genotype were tested as potential covariates to explain interindividual variability.

RESULTS

Fifty-five patients were included. A 2-compartment model with delayed absorption was the most suitable to describe population PK parameters. The population PK parameters were as follows: clearance, 3.27 L/h; intercompartmental clearance, 9.6 L/h; volume of distribution of compartments 1 and 2, 95 and 500 L, respectively. No covariates were found to significantly decrease interindividual variability. The best 3-point LSS was t = 0,4,8 with a median bias of 1.8% (-12.5-12.5).

CONCLUSIONS

The LSS can be used to adequately predict the AUC. No clinically relevant covariates known to influence the PK of Envarsus, including CYP3A status, were identified and therefore do not seem useful for initial dose optimization.

Population pharmacokinetics and individual analysis of daptomycin in kidney transplant recipients

BACKGROUND

Little is known about the population pharmacokinetics (PPK) of daptomycin in kidney transplant patients. The present study established a pharmacokinetic model for daptomycin in kidney transplant patients in China and examinee the important factors affecting the pharmacokinetic parameters of daptomycin.

METHODS

The study population included 49 kidney transplant patients with 537 daptomycin concentrations. The PPK model of daptomycin was developed using a nonlinear mixed-effects model, a two-compartment structural model, and a mixed residual error model. The stability and predictive ability of the final model were evaluated based on bootstrapping, visual prediction checks and normalized prediction distribution errors.

RESULTS

Glomerular filtration rate (GFR) and total body weight significantly affected clearance, and body weight influenced the central volume of distribution. The average clearance of the population was 0.316 L/h, the central volume of distribution was 6.04 L, the intercompartmental clearance was 2.31 L/h, and the peripheral volume of distribution was 2.46 L. Based on the established model and the target of area under curve (AUC_0-24h)/minimum inhibition concentration (MIC) ≥666, we developed a recommended dose regimen for kidney transplant patients according to their renal function and weight. The daily doses were 4.0±0.31, 4.7±0.36, 5.1±0.40, 5.5±0.43, 5.8±0.45, and 6.1±0.48 mg/kg when the GFRs were 15, 30, 45, 60, 75, and 90 ml/min/1.73 m², respectively.

CONCLUSION

This study provides a reference for individualized daptomycin administration in kidney transplant recipients, and it is a valuable resource for improving the treatment effect and reducing the toxic effects of daptomycin.

Advances in personalized medicine and noninvasive diagnostics in solid organ transplantation

Personalized medicine has been a mainstay and in practice in transplant pharmacotherapy since the advent of the field. Decisions pertaining to the diagnosis, selection, and monitoring of transplant pharmacotherapy are aimed toward the individual, the allograft, and the overall immunologic needs of the patient. Recent advances in pharmacogenomics, noninvasive biomarkers, and artificial intelligence (AI) technologies have the promise of transforming the way we individualize treatment and monitor allograft function. Pharmacogenomic testing can provide clinicians with additional data that can minimize toxicity and maximize therapeutic dosing in high-risk patients, leading to more informed decisions that may decrease the risk of rejection and adverse outcomes related to immunosuppressive therapies. Development of noninvasive strategies to monitor allograft function may offer safer and more convenient methods to detect allograft injury. Cell free DNA and gene expression profiling offer the potential to serve as "liquid biopsies" minimizing the risk to patients and providing clinicians with useful molecular data that may help individualize immunosuppression and rejection treatment. Use of big data in transplant and novel AI platforms, such as the iBox, hold tremendous promise in providing clinicians a "glimpse into the future" thereby allowing for a more individualized approach to immunosuppressive therapy that may minimize future adverse outcomes. Advances in diagnostics, laboratory science, and AI have made the application of personalized medicine even more tailored for solid organ transplant recipients. In this perspective, we summarize the current and emerging tools available, literature supporting use, and the horizon for future personalization of transplantation.

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.

Clinical Pharmacokinetics and Bayesian Estimators for the Individual Dose Adjustment of a Generic Formulation of Tacrolimus in Adult Kidney Transplant Recipients

BACKGROUND

Tacrolimus has a narrow therapeutic range and requires dose adjustment, usually based on the trough blood concentration but preferably on the area under the concentration-time curve over 12 h post-dose (AUC_0-12h). The single-arm, multicentre, clinical study IMPAKT aimed: (i) to develop, in de novo kidney transplant recipients, pharmacokinetic models and maximum a-posteriori Bayesian estimators for a generic, immediate-release, oral formulation of tacrolimus to estimate tacrolimus AUC_0-12h at different post-transplant periods using a limited sampling strategy, and considering the CYP3A5*3 polymorphism as a covariate and (ii) to compare the performance of these Bayesian estimators to those previously developed for the original formulation.

METHODS

Thirty patients were enrolled and 29 provided nine blood samples over 9 h at day 7 and months 1 and 3 post-transplant. Tacrolimus blood profiles measured with liquid chromatography-tandem mass spectrometry were modelled using one-compartment, double gamma absorption, linear elimination models developed in-house. Different limited sampling strategies of three time-points within 4 h post-dose were tested for the maximum a-posteriori Bayesian estimator of tacrolimus AUC_0-12h. The models and estimators were validated internally and their performance compared to that of models previously developed for the original formulation.

RESULTS

The concentration-time curves, AUC_0-12h/dose and trough blood concentration/dose exhibited wide inter-individual variability. The covariate-free pharmacokinetic models developed for the three post-transplant periods closely fitted the individual profiles. Maximum a-posteriori Bayesian estimators based on three different limited sampling strategies and no covariate yielded accurate AUC_0-12h estimates, including for the five cytochrome P450 3A5 expressers and for the four patients without corticosteroids. The 0-1 h-3 h strategy finally chosen had very low bias (- 4.0 to - 2.5%) and imprecision (root mean square error 5.5-9.2%). The maximum a-posteriori Bayesian estimators previously developed for the reference product fitted the generic profiles with similar performance.

CONCLUSIONS

We developed original pharmacokinetic models and accurate maximum a-posteriori Bayesian estimators to estimate patient exposure and adjust the dose of generic tacrolimus, and confirmed that the robust tools previously developed for the original formulation can be applied to this generic.

Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives

INTRODUCTION

In kidney transplantation, tacrolimus (TAC) is at the cornerstone of current immunosuppressive strategies. Though because of its narrow therapeutic index, it is critical to ensure that TAC levels are maintained within this sharp window through reactive adjustments. This would allow maximizing efficiency while limiting drug-associated toxicity. However, TAC high intra- and inter-patient pharmacokinetic (PK) variability makes it more laborious to accurately predict the appropriate dosage required for a given patient.

AREAS COVERED

This review summarizes the state-of-the-art knowledge regarding drug interactions, demographic and pharmacogenetics factors as predictors of TAC PK. We provide a scoring index for each association to grade its relevance and we present practical recommendations, when possible for clinical practice.

EXPERT OPINION

The management of TAC concentration in transplanted kidney patients is as critical as it is challenging. Recommendations based on rigorous scientific evidences are lacking as knowledge of potential predictors remains limited outside of DDIs. Awareness of these limitations should pave the way for studies looking at demographic and pharmacogenetic factors as well as gut microbiota composition in order to promote tailored treatment plans. Therapeutic approaches considering patients' clinical singularities may help allowing to maintain appropriate concentration of TAC.

Population pharmacokinetics and Bayesian estimators for intravenous mycophenolate mofetil in haematopoietic stem cell transplant patients

AIMS

Intravenous mycophenolate mofetil (IV MMF), a prodrug of mycophenolic acid (MPA), is used during nonmyeloablative and reduced-intensity conditioning haematopoetic stem cell transplantation (HCT) to improve engraftment and reduce graft-versus-host disease. The aims of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies to allow for individual dose adjustment of intravenous mycophenolate mofetil administered by infusion in haematopoietic stem cell transplant patients.

METHODS

Sixty-three MPA concentration-time profiles (median [min-max] = 6 [4-7] samples) were collected from 34 HCT recipients transplanted for 14 (1-45) days and administered IV MMF every 8 hours, concomitantly with cyclosporine. The database was split into development (75%) and validation (25%) datasets. Pharmacokinetic models characterized by a single compartment with first-order elimination, combined with two gamma distributions to describe the transformation of MMF into mycophenolic acid, were developed using in parallel nonparametric (Pmetrics) and parametric (ITSIM) approaches. The performances of the models and the derived Bayesian estimators were evaluated in the validation set.

RESULTS

The best limited sampling strategy led to a bias (min, max), root mean square error between observed and modeled interdose areas under the curve in the validation dataset of -11.72% (-31.08%, 5.00%), 14.9% for ITSIM and -2.21% (-23.40%, 30.01%), 12.4% for Pmetrics with three samples collected at 0.33, 2 and 3 hours post dosing.

CONCLUSION

Population pharmacokinetic models and Bayesian estimators for IV MMF in HCT have been developed and are now available online (https://pharmaco.chu-limoges.fr) for individual dose adjustment based on the interdose area under the curve.

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.