Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver 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.

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.

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.

Model-informed Estimation of Acutely Decreased Tacrolimus Clearance and Subsequent Dose Individualization in a Pediatric Renal Transplant Patient with Posterior Reversible Encephalopathy Syndrome

BACKGROUND

Considerable inter-patient and inter-occasion variability has been reported in tacrolimus pharmacokinetics (PK) in the pediatric renal transplant population. The present study investigated tacrolimus PK in a 2-year-old post-renal transplant patient and a known CYP3A5 expresser who developed posterior reversible encephalopathy syndrome (PRES) and had significantly elevated tacrolimus blood concentrations during tacrolimus treatment. A model-informed PK assessment was performed to assist with precision dosing. Tacrolimus clearance was evaluated both before and after the development of PRES on post-transplant day (PTD) 26.

METHODS

A retrospective chart review was conducted to gather dosing data and tacrolimus concentrations, as part of a clinical pharmacology consultation service. Individual PK parameters were estimated by Bayesian estimation using a published pediatric PK model. Oral clearance (CL/F) was estimated for three distinct time periods-before CNS symptoms (PTD 25), during the PRES event (PTD 27-30), and after oral tacrolimus was re-started (PTD 93).

RESULTS

Bayesian estimation showed an estimated CL/F of 15.0 L/h in the days preceding the PRES event, compared to a population mean of 16.3 L/h (95% confidence interval 14.9-17.7 L/h) for CYP3A5 expressers of the same age and weight. Samples collected on PTD 27-30 yielded an estimated CL/F of 3.6 L/h, a reduction of 76%, coinciding with clinical confirmation of PRES and therapy discontinuation. On PTD 93, an additional assessment showed a stable CL/F value of 14.5 L/h one month after re-initiating tacrolimus and was used to recommend a continued maintenance dose.

CONCLUSION

This is the first report to demonstrate acutely decreased tacrolimus clearance in PRES, likely caused by the downregulation of metabolizing enzymes in response to inflammatory cytokines. The results suggest the ability of model-informed Bayesian estimation to characterize an acute decline in oral tacrolimus clearance after the development of PRES, and the role that PK estimation may play in supporting dose selection and individualization.

Textbook outcome among patients undergoing enhanced recovery after liver transplantation stratified by risk. A single-center retrospective observational cohort study

BACKGROUND & AIMS

Liver transplantation (LT) is one of the most complex surgical procedures. Enhanced recovery after surgery (ERAS) aims to reduce the risk of postoperative complications. When patients achieve all desirable outcomes after a procedure, they are considered to have experienced a textbook outcome (TO).

METHODS

Two cohorts of patients undergoing low (n = 101) or medium risk (n = 15) LT were identified. The remaining patients (n = 65) were grouped separately. The ERAS protocol included pre-, intra-, and post-operative steps. TO was defined as the absence of complications, prolonged length of hospital stays, readmission and mortality during the first 90 days.

RESULTS

One third of patients who underwent ERAS after LT experienced a TO. On multivariable analysis, age (OR, 1.05 [95% CI, 1.01-1.09]; P = .02), and having hepatocellular carcinoma (OR, 2.83 [95% CI, 1.37-6.03]; P = .005) were individually associated with a greater probability of achieving a TO. Belonging to the cohorts of medium risk or outside the selection criteria was associated with a lower probability of achieving a TO (OR, 0.46 [96% CI, 0.22-0.93]; P = .03). Patients less likely to experience TO required more hospital resources. Patients who achieved TO were more likely to remain free of chronic kidney disease (achieved TO, 83.8% [82.7-85.6]; failed TO, 67.9% [66.9-70.2]; P < .05). Tacrolimus dose and trough levels were similar.

CONCLUSIONS

A novel finding of our study is that short and medium-term kidney function is better preserved in patients who experience a TO. Better kidney function of patients who achieve TO is not due to lower tacrolimus dosage.

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.

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.

Development of a population pharmacokinetic model and Bayesian estimators for isoniazid in Tunisian tuberculosis patients

This study aimed to develop a population pharmacokinetic model using full pharmacokinetic (PK) profiles of isoniazid (INH) taking into account demographic and genetic covariates and to develop Bayesian estimators for predicting INH area under the curve (AUC) in Tunisian tuberculosis patients. The INH concentrations in the building data set were fitted using a one- to three-compartment model. The impact of the different covariates was assessed on the PK parameters of the best model. The best limited sampling strategy (LSS) for estimating the INH AUC was selected by comparing the predicted values to an independent data set. INH PK was best described using a three-compartment model with lag-time absorption. The different studied covariates did not have any impact on the PK parameters of the building model. The Bayesian estimation using one-point concentrations gave the lowest values of prediction errors for the C₃ LSS model. This model could be sufficient in routine activity for INH monitoring in this population.

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.

Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: The example of tacrolimus

We previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Bayesian estimation (MAP-BE). However, the major limitation in the development of such ML algorithms is the limited availability of large databases of concentration vs. time profiles for such drugs. The objectives of this study were: (i) to develop a Xgboost model to estimate tacrolimus inter-dose AUC based on concentration-time profiles obtained from a literature population pharmacokinetic (POPPK) model using Monte Carlo simulation; and (ii) to compare its performance with that of MAP-BE in external datasets of rich concentration-time profiles. The population parameters of a previously published PK model were used in the mrgsolve R package to simulate 9000 rich interdose tacrolimus profiles (one concentration simulated every 30 min) at steady-state. Data splitting was performed to obtain a training set (75%) and a test set (25%). Xgboost algorithms able to estimate tacrolimus AUC based on 2 or 3 concentrations were developed in the training set and the model with the lowest RMSE in a ten-fold cross-validation experiment was evaluated in the test set, as well as in 4 independent, rich PK datasets from transplant patients. ML algorithms based on 2 or 3 concentrations and a few covariates yielded excellent AUC estimation in the external validation datasets (relative bias < 5% and relative RMSE < 10%), comparable to those obtained with MAP-BE. In conclusion, Xgboost machine learning models trained on concentration-time profiles simulated using literature POPPK models allow accurate tacrolimus AUC estimation based on sparse concentration data. This study paves the way to the development of artificial intelligence at the service of precision therapeutic drug monitoring in different therapeutic areas.

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.

Population pharmacokinetics and pharmacogenomics of tacrolimus in Chinese children receiving a liver transplant: initial dose recommendation

BACKGROUND

In order to improve the precision of treatment with tacrolimus in Chinese patients undergoing pediatric liver transplantation, the optimum initial dose of tacrolimus was determined based on population pharmacokinetics and pharmacogenomics.

METHODS

Demographic data, clinical parameters, drug combinations and pharmacogenomics were integrated to build a population pharmacokinetic model using NONMEM. Additionally, Monte Carlo simulations were used to optimize the recommended initial dose.

RESULTS

Weight, patient cytochrome 450 3A (CYP3A)5 genotype, and co-administration with wuzhi-capsule (WZ) were incorporated into the final model. For children with a CYP3A5*3/*3 genotype not co-administered WZ, 0.10 mg/kg/day split into two doses was recommended for patients weighing 5-17 kg, and 0.05 mg/kg/day split into two doses was recommended for patients weighing 17-60 kg. For children with a CYP3A5*1 allele not co-administered WZ, 0.25 mg/kg/day for patients weighing 5-10 kg, 0.20 mg/kg/day for patients weighing 10-17 kg, 0.15 mg/kg/day for patients weighing 17-36 kg, and 0.10 mg/kg/day for patients weighing 36-60 kg; all split into two doses was recommended. For children with a CYP3A5*3/*3 genotype co-administered WZ, 0.10 mg/kg/day for patients weighing 5-11 kg, and 0.05 mg/kg/day for patients weighing 11-60 kg; both split into two doses was recommended. For children with a CYP3A5*1 allele who were co-administered WZ, 0.20 mg/kg/day for patients weighing 5-10 kg, 0.15 mg/kg/day for patients weighing 10-22 kg, and 0.10 mg/kg/day for patients weighing 22-60 kg all split into two doses was recommended.

CONCLUSIONS

The optimal initial dose of tacrolimus was determined based on population pharmacokinetics and pharmacogenomics in Chinese patients undergoing pediatric liver transplantation.