A Population Pharmacokinetic Model Does Not Predict the Optimal Starting Dose of Tacrolimus in Pediatric Renal Transplant Recipients in a Prospective Study: Lessons Learned and Model Improvement

CXCR2 Activated JAK3/STAT3 Signaling Pathway Exacerbating Hepatotoxicity Associated with Tacrolimus

Purpose

Tacrolimus could induce hepatotoxicity during clinical use, and the mechanism was still unclear, which posed new challenge for the prevention and treatment of tacrolimus-induced hepatotoxicity. The aim of this study was to investigate the mechanism of tacrolimus-induced hepatotoxicity and provide reference for drug development target.

Methods

In this study, biochemical analysis, pathological staining, immunofluorescent staining, immunohistochemical staining, transcriptomic analysis, Western blotting was used to investigate the mechanism of tacrolimus-induced hepatotoxicity in gene knockout mice and Wistar rats.

Results

In gene knockout mice, compared to wild-type mice, CXCR2-deficiency alleviated tacrolimus-induced hepatotoxicity (P < 0.05 or P < 0.01). In Wistar rats, compared to control group, CXCL2-CXCR2, JAK3/STAT3 signaling pathway (phosphorylation of JAK3 and STAT3) were up-regulated, the expression of CIS was lowered and the expression of PIM1 was raised, inducing liver pathological change (P < 0.05 or P < 0.01); Inversely, blocking CXCR2 could reverse the expression of p-JAK3/p-STAT3 and tacrolimus-induced hepatotoxicity (P < 0.05 or P < 0.01).

Conclusion

CXCR2 activated JAK3/STAT3 signaling pathway (phosphorylation of JAK3 and STAT3) exacerbating hepatotoxicity associated with tacrolimus, meanwhile the expression of CIS was down-regulated, the expression of PIM1 was up-regulated. Blocking CXCR2 could reverse the expression of p-JAK3/p-STAT3, CIS, PIM1, and tacrolimus-induced hepatotoxicity.

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.

A systematic review and meta-analysis recite the efficacy of Tacrolimus treatment in renal transplant patients in association with genetic variants of CYP3A5 gene

Tacrolimus is an immunosuppressant with a narrow therapeutic index and pharmacokinetic variability. This variability may be attributed to genetic variants in gene CYP3A5 associated with Tacrolimus metabolism. Studies focusing on genetic variants in the CYP3A5 gene associated with Tacrolimus metabolism have been published, a meta-analysis of these published articles may provide a direction that can change the future research and clinical management of renal transplant patients. In this systematic review and meta-analysis, we have reviewed and analyzed the studies and clinical trials conducted to determine the association between genetic variants of CYP3A5 and Tacrolimus metabolism from the PubMed database and clinical trials (www.clinicaltrials.gov). This meta-analysis also assessed the correlation of CYP3A5 genotype (rs776746) with concentration/dose (Co/D) of Tacrolimus in renal transplant patients. The 59 published articles on genetic association of the CYP3A5 on Tacrolimus doses were reviewed for this systematic review. Meta-analysis showed that the Tacrolimus Co/D ratio is significantly lower in the CYP3A5 expressor group as compared with non-expressor in Asian, European as well as in mixed populations at any post-transplant period (P<0.0001). Our study further confirmed that the CYP3A5 variant (rs776746) is clinically relevant for the dose determination of Tacrolimus. Variations in Tacrolimus Co/D have been found to be significantly linked to the patient's CYP3A5 genetic variant (rs776746). The addition of other genetic variants involved in the pharmacokinetic of Tacrolimus may determine efficient regimen for drug dose. Our meta-analysis confirmed that the CYP3A5 genetic variant (rs776746) analysis is relevant in personalizing the Tacrolimus dose determination in renal transplant patients.

Individualized dosing algorithms for tacrolimus in kidney transplant recipients: current status and unmet needs

INTRODUCTION

Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus' pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations.

AREAS COVERED

Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023.

EXPERT OPINION

Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.

Predictive Capacity of Population Pharmacokinetic Models for the Tacrolimus Dose Requirements of Pediatric Solid Organ Transplant Recipients

BACKGROUND

Transplant recipients require individualized tacrolimus doses to maximize graft survival. Multiple pediatric tacrolimus population pharmacokinetic (PopPK) models incorporating CYP3A5 genotype and other covariates have been developed. Identifying the optimal popPK model is necessary for clinical implementation in pediatric solid organ transplant. The primary objective was to compare the dose prediction capabilities of the developed models in pediatric kidney and heart transplant recipients.

METHODS

Pediatric kidney or heart transplant recipients treated with tacrolimus and available CYP3A5 genotype data were identified. The initial weight-based tacrolimus dose and first therapeutic tacrolimus dose were collected retrospectively. Three published popPK models were used to predict the tacrolimus dose required to achieve a tacrolimus trough concentration of 10 ng/mL. Model dose predictions were compared with the initial and first therapeutic doses using Friedman test. The first therapeutic dose was plotted against the model-predicted dose.

RESULTS

The median initial dose approximately 2-fold lower than the first therapeutic dose for CYP3A5 expressers. The Chen et al model provided the closest estimates to the first therapeutic dose for kidney transplant recipients; however, all 3 models tended to underpredict the observed therapeutic dose. For heart transplant recipients, Andrews et al model predicted doses that were higher than the initial dose but similar to the actual therapeutic dose.

CONCLUSIONS

Weight-based tacrolimus dosing appears to underestimate the tacrolimus dose requirements. The development of a separate popPK model is necessary for heart transplant recipients. A genotype-guided strategy based on the Chen et al model provided the best estimates for doses in kidney transplant recipients and should be prospectively evaluated.

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.

The Use of Machine Learning Algorithms and the Mass Spectrometry Lipidomic Profile of Serum for the Evaluation of Tacrolimus Exposure and Toxicity in Kidney Transplant Recipients

Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance between maximizing efficacy and minimizing dose-related toxic effects. Currently, there is no specific tacrolimus toxicity biomarker except a graft biopsy. Our study aimed to identify specific serum metabolites correlated with tacrolinemia levels using serum high-precision liquid chromatography–mass spectrometry and standard laboratory evaluation. Three machine learning algorithms were used (Naïve Bayes, logistic regression, and Random Forest) in 19 patients with high tacrolinemia (8 ng/mL) and 23 patients with low tacrolinemia (5 ng/mL). Using a selected panel of five lipid metabolites (phosphatidylserine, phosphatidylglycerol, phosphatidylethanolamine, arachidyl palmitoleate, and ceramide), Mg2+, and uric acid, all three machine learning algorithms yielded excellent classification accuracies between the two groups. The highest classification accuracy was obtained by Naïve Bayes, with an area under the curve of 0.799 and a classification accuracy of 0.756. Our results show that using our identified five lipid metabolites combined with Mg2+ and uric acid serum levels may provide a novel tool for diagnosing tacrolimus toxicity in kidney transplant recipients. Further validation with targeted MS and biopsy-proven TAC toxicity is needed.

Population Pharmacokinetic Model Development of Tacrolimus in Pediatric and Young Adult Patients Undergoing Hematopoietic Cell Transplantation

Background: With a notably narrow therapeutic window and wide intra- and interindividual pharmacokinetic (PK) variability, initial weight-based dosing along with routine therapeutic drug monitoring of tacrolimus are employed to optimize its clinical utilization. Both supratherapeutic and subtherapeutic tacrolimus concentrations can result in poor outcomes, thus tacrolimus PK variability is particularly important to consider in the pediatric population given the differences in absorption, distribution, metabolism, and excretion among children of various sizes and at different stages of development. The primary goals of the current study were to develop a population PK (PopPK) model for tacrolimus IV continuous infusion in the pediatric and young adult hematopoietic cell transplant (HCT) population and implement the PopPK model in a clinically available Bayesian forecasting tool.

Methods: A retrospective chart review was conducted of 111 pediatric and young adult patients who received IV tacrolimus by continuous infusion early in the post-transplant period during HCT from February 2016 to July 2020 at our institution. PopPK model building was performed in NONMEM. The PopPK model building process included identifying structural and random effects models that best fit the data and then identifying which patient-specific covariates (if any) further improved model fit.

Results: A total of 1,648 tacrolimus plasma steady-state trough concentrations were included in the PopPK modeling process. A 2-compartment structural model best fit the data. Allometrically-scaled weight was a covariate that improved estimation of both clearance and volume of distribution. Overall, model predictions only showed moderate bias, with minor under-prediction at lower concentrations and minor over-prediction at higher predicted concentrations. The model was implemented in a Bayesian dosing tool and made available at the point-of-care.

Discussion: Novel therapeutic drug monitoring strategies for tacrolimus within the pediatric and young adult HCT population are necessary to reduce toxicity and improve efficacy in clinical practice. The model developed presents clinical utility in optimizing the use of tacrolimus by enabling model-guided, individualized dosing of IV, continuous tacrolimus via a Bayesian forecasting platform.

Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients

BACKGROUND AND OBJECTIVE

Tacrolimus is a narrow therapeutic index drug with high pharmacokinetic variability, and several tacrolimus population pharmacokinetic (PopPK) models were developed to guide individualized drug dosing. These models, however, may not perform well in other clinical settings. Therefore, we aimed to assess the predictive ability of published tacrolimus PopPK models using a dataset of Thai kidney transplant patients.

METHODS

The external dataset was retrospectively collected from medical records of Bhumibol Adulyadej Hospital, Thailand. Published tacrolimus PopPK models were systematically searched from PubMed, Science Direct, CINAHL Complete, and Scopus databases. Models conducted using a nonlinear mixed-effects approach with covariate resemblance to our external dataset were selected. The external dataset consisted of Thai kidney transplant patients receiving oral immediate- or extended-release tacrolimus formulations twice or once daily, respectively. Accuracy and precision of predicted concentrations were evaluated using mean absolute prediction error (MAPE), root mean square error (RMSE), and goodness of fit plots.

RESULTS

Only three models produced acceptable population predictions with the MAPE of < 50%. By using the Bayesian posthoc estimate of individual pharmacokinetic parameters, all models well performed with the MAPE and RMSE of < 30% and 40%, respectively, except two models; one could not successfully converge and the other substantially underpredicted tacrolimus concentrations.

CONCLUSION

We evaluated ten tacrolimus PopPK models, and eight models resulted in satisfactorily individual predicted tacrolimus concentrations in Thai kidney transplant patients and may be used to aid tacrolimus dose adjustment along with a clinical judgment.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Towards next-generation personalization of tacrolimus treatment: a review on advanced diagnostic and therapeutic approaches

The benefit of personalized medicine is that it allows the customization of drug therapy - maximizing efficacy while avoiding side effects. Genetic polymorphisms are one of the major contributors to interindividual variability. Currently, the only gold standard for applying personalized medicine is dose titration. Because of technological advancements, converting genotypic data into an optimum dose has become easier than in earlier years. However, for many medications, determining a personalized dose may be difficult, leading to a trial-and-error method. On the other hand, the technologically oriented pharmaceutical industry has a plethora of smart drug delivery methods that are underutilized in customized medicine. This article elaborates the genetic polymorphisms of tacrolimus as case study, and extensively covers the diagnostic and therapeutic technologies which aid in the delivery of personalized tacrolimus treatment for better clinical outcomes, thereby providing a new strategy for implementing personalized medicine.

Evaluating the Impact of CYP3A5 Genotype on Post-Transplant Healthcare Resource Utilization in Pediatric Renal and Heart Transplant Recipients Receiving Tacrolimus

Purpose

CYP3A5 genotype is a significant contributor to inter-individual tacrolimus exposure and may impact the time required to achieve therapeutic concentrations and number of tacrolimus dose adjustments in transplant patients. Increased modifications to tacrolimus therapy may indicate a higher burden on healthcare resources. The purpose of this study was to evaluate whether CYP3A5 genotype was predictive of healthcare resource utilization in pediatric renal and heart transplant recipients.

Patients and Methods

Patients <18 years of age with a renal or heart transplant between 6/1/2014–12/31/2018 and tacrolimus-based immunosuppression were included. Secondary use samples were obtained for CYP3A5 genotyping. Clinical data was retrospectively collected from the electronic medical record. Healthcare resource utilization measures included the number of dose changes, number of tacrolimus concentrations, length of stay, number of clinical encounters, and total charges within the first year post-transplant. Rejection and donor-specific antibody (DSA) formation within the first year were also collected. The impact of CYP3A5 genotype was evaluated via univariate analysis for the first year and multivariable analysis at 30, 90, 180, 270, and 365 days post-transplant.

Results

Eighty-five subjects were included, 48 renal transplant recipients and 37 heart transplant recipients. CYP3A5 genotype was not associated with any outcomes in renal transplant, however, a CYP3A5 expresser phenotype was a predictor of more dose changes, more tacrolimus concentrations, longer length of stay, and higher total charges in heart transplant recipients. CYP3A5 genotype was not associated with rejection or DSA formation. Age and induction therapy were associated with higher total charges.

Conclusion

CYP3A5 genotype may predict healthcare resource utilization in the first year post-transplant, although this may be mitigated by differences in tacrolimus management. Future studies should evaluate the impact of genotype-guided dosing strategies for tacrolimus on healthcare utilization resources.

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Bodyweight‐based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady‐state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C₀). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug‐related toxicity, respectively. To minimize subtherapeutic and supratherapeutic tacrolimus exposure in the immediate post‐transplant phase, a previously developed dosing algorithm to predict an individual’s tacrolimus starting dose was tested prospectively. In this single‐arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweight‐based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C₀, measured for the first time at day 3, was 7.5–12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post‐transplantation, 34 of 59 patients (58%, 90% CI 47–68%) had a tacrolimus C₀ within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy‐proven acute rejection occurred in three patients (5%). In conclusion, algorithm‐based tacrolimus dosing leads to the achievement of the tacrolimus target C₀ in as many as 58% of the patients on day 3 after kidney transplantation.

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.