Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Aims

The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.

Methods

Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.

Results

The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.

Conclusion

The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

Pharmacogenomic Analysis of CYP3A5*3 and Tacrolimus Trough Concentrations in Vietnamese Renal Transplant Outcomes

Purpose

CYP3A5 polymorphisms have been associated with variations in the pharmacokinetics of tacrolimus (Tac) in kidney transplant patients. Our study aims to quantify how the CYP3A5 genotype influences tacrolimus trough concentrations (C0) in a Vietnamese outpatient population by selecting an appropriate population pharmacokinetic model of Tac for our patients.

Patients and Methods

The external dataset was obtained prospectively from 54 data of adult kidney transplant recipients treated at the 103 Military Hospital. All published Tac population pharmacokinetic models were systematically screened from PubMed and Scopus databases and were selected based on our patient's available characteristics. Mean absolute prediction error (MAPE), mean prediction error, and goodness-of-fit plots were used to identify the appropriate model for finding the formula that identifies the influence of CYP3A5 genotype on the pharmacokinetic data of Vietnamese patients.

Results

The model of Zhu et al had a good predictive ability with MAPE of 19.29%. The influence of CYP3A5 genotype on tacrolimus clearance was expressed by the following formulas: CL/F=27 , 2 × [ ( WT/70 ) 0 , 75 ] × [ ( HCT/0 , 35 ) -0 , 501 ] × [ ( POD/180 ) 0 , 0306 ] × CYP3A5 ( L/h ) . The simulation result showed that Tac C0 was significantly higher in patients not expressing CYP3A5 (p< 0.001).

Conclusion

The incorporation of the CYP3A5 phenotype into Zhu's structural model has significantly enhanced our ability to predict Tacrolimus trough levels in the Vietnamese population. This study's results underscore the valuable role of CYP3A5 phenotype in optimizing the forecast of Tac concentrations, offering a promising avenue to assist health-care practitioners in their clinical decision-making and ultimately advance patient care outcomes.

Pharmacokinetic Model Based on Stochastic Simulation and Estimation for Therapeutic Drug Monitoring of Tacrolimus in Korean Adult Transplant Recipients

BACKGROUND

Tacrolimus shows high variability in inter- and intraindividual pharmacokinetics (PK); therefore, it is important to develop an appropriate model for accurate therapeutic drug monitoring (TDM) procedures. This study aimed to develop a pharmacokinetic model for tacrolimus that can be used for TDM procedures in Korean adult transplant recipients by integrating published models with acquired real-world TDM data and evaluating clinically meaningful covariates.

METHODS

Clinical data of 1829 trough blood samples from 269 subjects were merged with simulated data sets from published models and analyzed using a nonlinear mixed-effect model. The stochastic simulation and estimation (SSE) method was used to obtain the final parameter estimates.

RESULTS

The final estimated values for apparent clearance, the volume of distribution, and absorption rate were 21.2 L/h, 510 L, and 3.1/h, respectively. The number of postoperative days, age, body weight, and type of transplant organs were the major clinical factors affecting tacrolimus PK.

CONCLUSIONS

A tacrolimus PK model that can incorporate published PK models and newly collected data from the Korean population was developed using the SSE method. Despite the limitations in model development owing to the nature of TDM data, the SSE method was useful in retrieving complete information from the TDM data by integrating published PK models while maintaining the variability of the model.

Model-Based Tacrolimus Follow-up Dosing in Adult Renal Transplant Recipients: A Simulation Trial

BACKGROUND

Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.

METHODS

This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C 0,obs ), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C 0 , age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C 0,pred ) were predicted.

RESULTS

Of 190 tacrolimus C 0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C 0,obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C 0,pred ( P = 0.89). The median absolute difference between the tacrolimus C 0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C 0,obs versus 1.6 ng/mL for C 0,pred . In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C 0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL).

CONCLUSIONS

The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.

Effect of drug combination on tacrolimus target dose in renal transplant patients with different CYP3A5 genotypes

Various factors, including genetic polymorphisms, drug-drug interactions, and patient characteristics influence the blood concentrations of tacrolimus in renal transplant patients. In the present study, we established a population pharmacokinetic model to explore the effect of combined use of Wuzhi capsules/echinocandins and the patients' biochemical parameters such as hematocrit on blood concentrations and target doses of tacrolimus in renal transplant patients with different CYP3A5 genotypes. The aim of the study was to propose an individualized tacrolimus administration regimen for early renal transplant recipients.In this retrospective cohort study, we included 240 renal transplant recipients within 21 days of surgery (174 males and 66 females, mean age 39.4 years), who received tacrolimus alone (n = 54), in combination with Wuzhi capsules (99) or caspofungin (57) or micafungin (30). We collected demographic characteristics, clinical indicators, CYP3A5 genotypes, and 1950 steady-state trough concentrations of tacrolimus and included them in population pharmacokinetic model. An additional 110 renal transplant recipients and 625 steady-state trough concentrations of tacrolimus were included for external validation of the model. The population pharmacokinetic model was established and Monte Carlo was used to simulate probabilities for achieving the target concentration for individual tacrolimus administration.A two-compartment model of first-order absorption and elimination was developed to describe the population pharmacokinetics of tacrolimus. CYP3A5 genotypes and co-administration of Wuzhi capsules, as well as time after renal transplantation and hematocrit, were important factors affecting the clearance of tacrolimus. We found no obvious change in trend in the scatter plot of tacrolimus clearance rate vs. hematocrit. The Monte Carlo simulation indicated the following recommended doses of tacrolimus alone: 0.14 mg·kg^-1·d^-1 for genotype CYP3A5*1*1, 0.12 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.10 mg·kg^-1·d^-1 for CYP3A5*3*3. For patients receiving the combination with Wuzhi capsules, the recommended doses of tacrolimus were 0.10 mg·kg^-1·d^-1 for CYP3A5*1*1, 0.08 mg·kg^-1·d^-1 for CYP3A5*1*3, and 0.06 mg·kg^-1·d^-1 for CYP3A5*3*3 genotypes. Caspofungin or micafungin had no effect on the clearance of tacrolimus in renal transplant recipients.The population pharmacokinetics of tacrolimus in renal transplant patients was evaluated and the individual administration regimen of tacrolimus was simulated. For early kidney transplant recipients receiving tacrolimus treatment, not only body weight, but also CYP3A5 genotypes and drugs used in combination should be considered when determining the target dose of tacrolimus.

Population pharmacokinetics of tacrolimus in Chinese adult liver transplant patients

Tacrolimus is widely used in organ transplantation to prevent rejection. However, the narrow therapeutic window and the large inter-and intra-individual variability in the pharmacokinetics (PK) of tacrolimus make it difficult for individualization of dosing. This study aimed at developing a population pharmacokinetic model for estimating the oral clearance of tacrolimus in Chinese liver transplant patients, and identifying factors that contribute to the PK variability of tacrolimus. Data of 151 liver transplant patients who received tacrolimus were analyzed in this study. The population PK model was analyzed and the covariates including population demographic and biochemical characteristics, drug combination, and genetic polymorphism were explored using non-linear mixed-effects modeling approach. A single-compartment population PK model was developed, and the final model was CL/F = (14.6-2.38 × cytochrome P450 (CYP) 3A5-3.72 × WZC+1.04 × (POD/9)+2.48 × COR) × Exp(η_i ), where CYP3A5 was 1 for CYP3A5*3/*3, Wuzhi Capsule (WZC) was 1 when patients took tacrolimus combined with WZC, otherwise it was 0, corticosteroids (COR) was 1 when patients take tacrolimus combined with COR, otherwise, it was 0, POD was the post-operative day. Visual inspection and bootstrap indicated that the final model was stable and robust. In this study, we developed the first tacrolimus population PK model in Chinese adult liver transplant patients. We first determined the influence of WZC on tacrolimus in these people, which could provide useful PK information for the drug combination of tacrolimus and WZC. We also revealed the influence of genetic polymorphism of CYP3A5, POD, and a combination of COR on tacrolimus PK. Therefore, these significant factors should be taken into consideration in optimizing dosage regimens.

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.

Machine learning-based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus (TAC), a first-line immunosuppressant in solid-organ transplant, has a narrow therapeutic window and large inter-individual variability, which affects its use in clinical practice. Successful predictions using machine learning algorithms have been reported in several fields. However, a comparison of 10 machine learning model-based TAC pharmacogenetic and pharmacokinetic dosing algorithms for kidney transplant perioperative patients of Chinese descent has not been reported. The objective of this study was to screen and establish an appropriate machine learning method to predict the individualized dosages of TAC for perioperative kidney transplant patients.

METHODS

The records of 2551 patients were collected from three transplant centres, 80% of which were randomly selected as a 'derivation cohort' to develop the dose prediction algorithm, while the remaining 20% constituted a 'validation cohort' to validate the final algorithm selected. Important features were screened according to our previously established population pharmacokinetic model of tacrolimus. The performances of the algorithms were evaluated and compared using R-squared and the mean percentage in the remaining 20% of patients.

RESULTS AND DISCUSSION

This study identified several factors influencing TAC dosage, including CYP3A5 rs776746, CYP3A4 rs4646437, haematocrit, Wuzhi capsules, TAC daily dose, age, height, weight, post-operative time, nifedipine and the medication history of the patient. According to our results, among the 10 machine learning models, the extra trees regressor (ETR) algorithm showed the best performance in the training set (R-squared: 1, mean percentage within 20%: 100%) and test set (R-squared: 0.85, mean percentage within 20%: 92.77%) of the derivation cohort. The ETR model successfully predicted the ideal TAC dosage in 97.73% of patients, especially in the intermediate dosage range (>5 mg/day to <8 mg/day), whereby the ideal TAC dosage could be successfully predicted in 99% of the patients.

WHAT IS NEW AND CONCLUSION

The results indicated that the ETR algorithm, which was chosen to establish the dose prediction model, performed better than the other nine machine learning models. This study is the first to establish ETR algorithms to predict TAC dosage. This study will further promote the individualized medication of TAC in kidney transplant patients in the future, which has great significance in ensuring the safety and effectiveness of drug use.

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.

Wuzhi Capsule Dosage Affects Tacrolimus Elimination in Adult Kidney Transplant Recipients, as Determined by a Population Pharmacokinetics Analysis

Purpose

In this study, we aimed to establish a tacrolimus population pharmacokinetic model and better understand the drug-drug interaction between Wuzhi capsule and tacrolimus in Chinese renal transplant recipients.

Patients and Methods

We performed a population pharmacokinetic analysis using a non-linear mixed-effects model to determine the suitable Wuzhi capsule dose in combination with tacrolimus. Data on 1378 tacrolimus steady-state concentrations were obtained from 142 patients who received kidney transplant in Changhai Hospital and Huashan Hospital. Demographic characteristics, laboratory tests, genetic polymorphisms, and co-medications were evaluated.

Results

The one-compartment model best described data. Our final model identified creatinine clearance rate, hematocrit, Wuzhi capsule dose, CYP3A5*3 genetic polymorphisms, and tacrolimus daily dose as significant covariates for tacrolimus clearance, with the value of 14.4 L h^-1, and the between-subject variability (BSV) was 25.4%. The Wuzhi capsule showed a dose-dependent effect on tacrolimus pharmacokinetics, demonstrating a stronger inhibitory effect than inductive effect.

Conclusion

Our model can accurately describe population pharmacokinetics of tacrolimus when combined with different doses of Wuzhi capsule. Additionally, this model can be used for individualizing tacrolimus dose following kidney transplantation.

Tacrolimus Exposure in Obese Patients: and A Case-Control Study in Kidney Transplantation

BACKGROUND

Tacrolimus pharmacokinetics in obese (Ob) patients has been poorly studied. In this article, the authors explored the impact of obesity on tacrolimus exposure in kidney transplant recipients (KTRs) and estimated a more suitable initial dosage in this population.

METHODS

A retrospective, observational, monocentric case-control study was performed in obese KTRs (BMI > 30 kg/m2) who received tacrolimus between 2013 and 2017 (initial dose: 0.15 mg/kg/d) (actual weight). Nonobese (Nob) controls (BMI <30 kg/m2) were matched for age and sex. Weekly centralized monitoring of tacrolimus trough levels was performed by liquid chromatography/mass spectrometry until the third month (M3). Target trough levels were set between 8 and 10 ng/mL. All patients received antilymphocyte globulin, corticosteroids, and mycophenolate mofetil.

RESULTS

Of the 541 KTRs, 28 tacrolimus-treated Ob patients were included and compared with 28 NOb-matched controls. With a mean of 22 assays/patient, tacrolimus trough levels were higher in Ob patients (mean 9.9 versus 8.7 ng/mL; P = 0.008); the weight-related dose of Tac was lower at M3 (mean 0.10 versus 0.13 mg/kg/d, P < 0.0001). The tacrolimus concentration to dose (C0/D) was higher in the Ob cohort [mean 116 versus 76 (ng/mL)/(mg/kg/d); P = 0.001]. In Ob patients, a mean decrease of -4.6 mg/d in the 3 months after tacrolimus initiation was required (versus -1.12 in NOb; P = 0.001) to remain within the therapeutic range. Obesity, high mycophenolate mofetil daily dose at M3, and CYP3A5 expression were independently associated with higher tacrolimus exposure. Four dose-adaptation strategies were simulated and compared with the study results.

CONCLUSIONS

An initial dose calculation based on either ideal or lean body weight may allow for faster achievement of tacrolimus trough level targets in Ob KTRs, who are at risk of overexposure when tacrolimus is initiated at 0.15 mg/kg/d. A prospective study is required to validate alternative dose calculation strategies in these patients.

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

WHAT IS KNOWN AND OBJECTIVES

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

METHODS

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

RESULTS AND DISCUSSION

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

WHAT IS NEW AND CONCLUSION

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

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.

Analyses of AUC(0–12) and C0 Compliances within Therapeutic Ranges in Kidney Recipients Receiving Cyclosporine or Tacrolimus

The AUC (area under the concentration time curve) is considered the pharmacokinetic exposure parameter best associated with clinical effects. Unfortunately, no prospective studies of clinical outcomes have been conducted in adult transplant recipients to investigate properly the potential benefits of AUC_(0–12) monitoring compared to the C₀-guided therapy. The aim of the present study was to compare two methods, C₀ (through level) and AUC_(0–12) (area under the concentration time curve), for assessing cyclosporine and tacrolimus concentrations. The study included 340 kidney recipients. The AUC_(0–12) was estimated using a Bayesian estimator and a three-point limited sampling strategy. Therapeutic drug monitoring of tacrolimus performed by using AUC_(0–12) and C₀ showed that tacrolimus in most cases is overdosed when considering C₀, while determination of the AUC_(0–12) showed that tacrolimus is effectively dosed for 27.8–40.0% of patients receiving only tacrolimus and for 25.0–31.9% of patients receiving tacrolimus with MMF (mycophenolate mofetil). In the 1–5 years post-transplantation group, 10% higher CsA (cyclosporine) dose was observed, which was proportionate with a 10% higher AUC_(0–12) exposure value. This indicates good compatibility of the dosage and the AUC(0–12) method. The Bland–Altman plot demonstrated that C₀ and AUC_(0–12) might be interchangeable methods, while the ROC (receiver operating characteristic) curve analysis of the C₀/AUC_(0–12) ratio in the tacrolimus-receiving patient group demonstrated reliable performance to predict IFTA (interstitial fibrosis and tubular atrophy) after kidney transplantation, with an ROC curve of 0.660 (95% confidence interval (CI): 0.576–0.736), p < 0.01. Moreover, AUC_(0–12) and C₀ of tacrolimus depend on concomitant medication and adjustment of the therapeutic range for AUC_(0–12) might influence the results.

Predictors of Adverse Events and Determinants of the Voriconazole Trough Concentration in Kidney Transplantation Recipients

Voriconazole is the mainstay for the treatment of invasive fungal infections in patients who underwent a kidney transplant. Variant CYP2C19 alleles, hepatic function, and concomitant medications are directly involved in the metabolism of voriconazole. However, the drug is also associated with numerous adverse events. The purpose of this study was to identify predictors of adverse events using binary logistic regression and to measure its trough concentration using multiple linear modeling. We conducted a prospective analysis of 93 kidney recipients cotreated with voriconazole and recorded 213 trough concentrations of it. Predictors of the adverse events were voriconazole trough concentration with the odds ratios (OR) of 2.614 (P = 0.016), cytochrome P450 2C19 (CYP2C19), and hemoglobin (OR 0.181, P = 0.005). The predictive power of these three factors was 91.30%. We also found that CYP2C19 phenotypes, hemoglobin, platelet count, and concomitant use of ilaprazole had quantitative relationships with voriconazole trough concentration. The fit coefficient of this regression equation was R²  = 0.336, demonstrating that the model explained 33.60% of interindividual variability in the disposition of voriconazole. In conclusion, predictors of adverse events are CYP2C19 phenotypes, hemoglobin, and voriconazole trough concentration. Determinants of the voriconazole trough concentration were CYP2C19 phenotypes, platelet count, hemoglobin, concomitant use of ilaprazole. If we consider these factors during voriconazole use, we are likely to maximize the treatment effect and minimize adverse events.

Tacrolimus Starting Dose Prediction Based on Genetic Polymorphisms and Clinical Factors in Chinese Renal Transplant Recipients

Aims: Tacrolimus has extensive pharmacokinetic variability among patients and a narrow therapeutic window. The U.S. Clinical Pharmacogenetics Implementation Consortium recommends a starting dose for tacrolimus of 0.15-0.3 mg/kg/day, which is much higher compared with 0.05-0.15 mg/kg/day used in China. The purpose of this study was to investigate the influence of clinical factors and single nucleotide polymorphisms (SNPs) on tacrolimus concentrations in Chinese renal transplant recipients. Methods: This study enrolled 406 tacrolimus-treated patients. After renal transplantation, the first tacrolimus trough concentration and corresponding clinical information were collected from all patients. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to genotype 15 SNPs. The relationship between the genetic and clinical factors and dose-adjusted tacrolimus trough concentration was examined. The tacrolimus starting dose was predicted using a classification and regression tree analysis. Results: Examination of the 15 SNPs and several clinical factors identified the CYP3A5 genotype (p = 5.6 × 10^-11) and hemoglobin (p = 8.4 × 10^-10) as the most significant determinants of tacrolimus C₀/D. Accordingly, a concise tacrolimus recommendation dosage model, a classification scheme, and a regression tree were developed. Conclusion: A new classification and regression tree model was developed for establishing the starting dose of tacrolimus based on the CYP3A5 genotype and hemoglobin values. This result may help clinicians prescribe an appropriate initial tacrolimus dose. ClinicalTrials.gov ID: 2020-KY-147.

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.

Effects of red blood cell concentrate transfusion on blood tacrolimus concentration

Background Elevated blood concentration of tacrolimus is frequently observed following transfusion of red blood cell concentrate in patients after allogeneic hematopoietic stem cell transplantation. Objective The aim of this retrospective study was to clarify the effects of transfusion of red blood cell concentrate on the blood concentration of tacrolimus. Setting Chiba University Hospital in Japan. Method Fifty-two patients (aged 0-65 years) receiving both tacrolimus and transfusion after allogeneic hematopoietic stem cell transplantation were enrolled. The ratio of measurement after transfusion to measurement before transfusion was calculated for hematocrit and blood concentration/dose ratio of tacrolimus (termed the hematocrit ratio and the tacrolimus ratio, respectively). Main outcome measure Change in blood concentration/dose ratio of tacrolimus and variable factors associated with variation in tacrolimus ratio. Results The blood concentration/dose ratio of tacrolimus was increased after transfusion compared with before transfusion (p < 0.001). A statistically significant correlation was seen between the hematocrit ratio and tacrolimus ratio (r = 0.32, p < 0.001). Hematocrit ratio, age or body surface area, and difference in aspartate aminotransferase level before and after transfusion were associated with the variation in tacrolimus ratio. There was no correlation between tacrolimus ratio and change in serum creatinine or potassium level in the short term. Conclusion Change in the blood concentration/dose ratio of tacrolimus was associated with change in the hematocrit ratio after transfusion, and more attention is required for children or patients with small body surface area. Dose adjustment of tacrolimus is required if the blood concentration of tacrolimus is much higher than the target concentration.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Model based development of tacrolimus dosing algorithm considering CYP3A5 genotypes and mycophenolate mofetil drug interaction in stable kidney transplant recipients

This study quantifies the interaction between tacrolimus (TAC) and mycophenolate mofetil (MMF) in kidney transplant recipients. Concentrations of TAC, mycophenolic acid (MPA), and metabolites were analyzed and relevant genotypes were determined from 32 patients. A population model was developed to estimate the effect of interaction. Concentrations of TAC were simulated in clinical scenarios and dose-adjusted trough concentrations per dose (C/D) were compared. Effect of interaction was described as the inverse exponential relationship. Major determinants of trough levels of TAC were CYP3A5 genotype and interaction with MPA. The absolute difference in C/D of TAC according to co-administered MMF was higher in CYP3A5 non-expressers (0.55 ng/mL) than in CYP3A5 expressers (0.35 ng/mL). The effect of MMF in determining the TAC exposure is more pronounced in CYP3A5 non-expressers. Based on population pharmacokinetic model, we suggest the TAC dosing algorithm considering the effects of CYP3A5 and MMF drug interaction in stable kidney transplant recipients.

A Minimal Physiologically-Based Pharmacokinetic Model for Tacrolimus in Living-Donor Liver Transplantation: Perspectives Related to Liver Regeneration and the cytochrome P450 3A5 (CYP3A5) Genotype

In adult patients after living‐donor liver transplantation, postoperative days and the cytochrome P450 3A5 (CYP3A5) genotype are known to affect tacrolimus pharmacokinetics. In this study, we constructed a physiologically‐based pharmacokinetic model adapted to the clinical data and evaluated the contribution of liver regeneration as well as hepatic and intestine CYP3A5 genotypes on tacrolimus pharmacokinetics. As a result, liver function recovered immediately and affected the total body clearance of tacrolimus only during a limited period after living‐donor liver transplantation. The clearance was about 1.35‐fold higher in the recipients who had a liver with the CYP3A5*1 allele than in those with the CYP3A5*3/*3 genotype, whereas bioavailability was ~0.7‐fold higher in the recipients who had intestines with the CYP3A5*1 allele than those with CYP3A5*3/*3. In conclusion, the constructed physiologically‐based pharmacokinetic model clarified that the oral clearance of tacrolimus was affected by the CYP3A5 genotypes in both the liver and intestine to the same extent.

A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients

Aims

The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient.

Methods

Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates.

Results

A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h⁻¹, clearance was 23.0 l h^–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: Dosemg=222nghml–1*22.5lh–1*1.0ifCYP3A5*3/*3or1.62ifCYP3A5*1/*3orCYP3A5*1/*1*1.0ifCYP3A4*1or unknownor0.814ifCYP3A4*22*Age56−0.50*BSA1.930.72/1000Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model.

Conclusions

For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

Prediction of tacrolimus dosage in the early period after heart transplantation: a population pharmacokinetic approach

Aim: The aim of this study was to evaluate tacrolimus population pharmacokinetics and investigate factors that explain tacrolimus variability in adult heart transplant patients. Methods: A total of 707 tacrolimus concentrations from 107 adult heart transplant patients were included in model development. The effects of demographic, clinical factors and CYP3A5 genotype on tacrolimus clearance were evaluated using a nonlinear mixed-effects modeling. 24 patients with 106 tacrolimus concentrations were used for external validation. Results: The pharmacokinetic data were adequately described by a one-compartment model with first-order absorption and elimination. The estimated apparent clearance and volume of distribution of tacrolimus were 13.7 l/h and 791 l, respectively. Tacrolimus apparent clearance was significantly reduced in CYP3A5 nonexpressers (CYP3A5*3/*3), concomitant with azole antifungal drugs and Wuzhi capsule (WZ). A predictive performance was further confirmed in an external validation by Bayesian estimation. Recommended dose regimens were obtained by simulations based on the established model. Conclusion: This is the first population pharmacokinetic study conducted in Chinese heart transplant recipients. These findings are of great importance with regards to tacrolimus dose optimization in heart transplantation patients.

Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Tacrolimus, a calcineurin inhibitor, is a common immunosuppressant prescribed after organ transplantation and has notable inter- and intrapatient pharmacokinetic variability. The sources of variability have been investigated using population pharmacokinetic modeling over the last 2 decades. This article provides an updated synopsis on published nonlinear mixed-effects analyses developed for tacrolimus in transplant recipients. The objectives were to establish a detailed overview of the current data and to investigate covariate relationships determined by the models. Sixty-three published analyses were reviewed, and data regarding the study design, modeling approach, and resulting findings were extracted and summarized. Most of the studies investigated tacrolimus pharmacokinetics in adult and pediatric renal and liver transplants after administration of the immediate-release formulation. Model structures largely depended on the study sampling strategy, with ∼50% of studies developing a 1-compartment model using trough concentrations and a 2-compartment model with delayed absorption from intensive sampling. The CYP3A5 genotype, as a covariate, consistently impacted tacrolimus clearance, and dosing adjustments were required to achieve similar drug exposure among patients. Numerous covariates were identified as sources of interindividual variability on tacrolimus pharmacokinetics with limited consistency across these studies, which may be the result of the study designs. Additional analyses are required to further evaluate the potential impact of these covariates and the clinical implementation of these models to guide tacrolimus dosing recommendations. This article may be useful for guiding the design of future population pharmacokinetic studies and provides recommendations for the selection of an existing optimal model to individualize tacrolimus therapy.

Tacrolimus population pharmacokinetic models according to CYP3A5/CYP3A4/POR genotypes in Chinese Han renal transplant patients

Aim: To develop a population pharmacokinetic (PK) model of tacrolimus in Chinese Han renal transplant population and establish the influence of different covariates (especially different CYP3A5/3A4/POR genotype) on PK properties. Materials & methods: Trough tacrolimus concentrations, clinical characteristics and CYP3A5/CYP3A4/POR genotypes were collected from 141 adult renal transplant recipients after transplantation. The population PK analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 3.4.2. Results: Tacrolimus PK profiles exhibited high interpatient variability. A two compartment model with first-order input and elimination described the tacrolimus PK profiles in the studied population. Among the genotypes, only CYP3A5 genotype was confirmed to have clinical significance. Conclusion: Our final model confirmed that CYP3A5*3 plays a more significant role in tacrolimus PK and could affect the blood concentrations and CL/F (clearance rate/bioavailbility). This model is expected to help to improve individualized tacrolimus dosing.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Tacrolimus Population Pharmacokinetics and Multiple CYP3A5 Genotypes in Black and White Renal Transplant Recipients

Tacrolimus exhibits inter-patient pharmacokinetic variability attributed to CYP3A5 isoenzymes and the efflux transporter, P-glycoprotein. Most black renal transplant recipients require higher tacrolimus doses compared to whites to achieve similar troughs when race-adjusted recommendations are used. An established guideline provides tacrolimus genotype dosing recommendations based on CYP3A5*1(W/T) and loss of protein function variants: CYP3A5*3 (rs776746), CYP3A5*6 (rs10264272), CYP3A5*7 (rs41303343) and may provide more comprehensive race-adjusted dosing recommendations. Our objective was to develop a tacrolimus population pharmacokinetic model evaluating demographic, clinical, and genomic factors in stable black and white renal transplant recipients. A secondary objective investigated race-based tacrolimus regimens and genotype-specific dosing. Sixty-seven recipients receiving oral tacrolimus and mycophenolic acid ≥6 months completed a 12-hour pharmacokinetic study. CYP3A5*3,*6,*7 and ABCB1 1236C>T, 2677G>T/A, 3435C>T polymorphisms were characterized. Patients were classified as extensive, intermediate, and poor metabolizers using a novel CYP3A5*3*6*7 metabolic composite. Modeling and simulation was performed with computer software (NONMEM 7.3, ICON Development Solutions; Ellicott City, Maryland). A 2-compartment model with first-order elimination and absorption with lag time best described the data. The CYP3A5*3*6*7 metabolic composite was significantly associated with tacrolimus clearance (P value < .05), which was faster in extensive (mean: 45.0 L/hr) and intermediate (29.5 L/hr) metabolizers than poor metabolizers (19.8 L/hr). Simulations support CYP3A5*3*6*7 genotype-based tacrolimus dosing to enhance general race-adjusted regimens, with dose increases of 1.5-fold and 2-fold, respectively, in intermediate and extensive metabolizers for comparable exposures to poor metabolizers. This model offers a novel approach to determine tacrolimus dosing adjustments that maintain comparable therapeutic exposure between black and white recipients with different CYP3A5 genotypes.

Increased Exposure of Tacrolimus by Co-administered Mycophenolate Mofetil: Population Pharmacokinetic Analysis in Healthy Volunteers

The objective of the study was to investigate the pharmacokinetic drug-drug interactions between tacrolimus (TAC) and mycophenolate mofetil (MMF) in healthy Korean male volunteers. Seventeen volunteers participated in a three-period, single-dose, and fixed sequence study. They sequentially received MMF, TAC, and the combination. Concentrations of TAC, mycophenolic acid (MPA), and its metabolites MPA 7-O-glucuronide and MPA acyl glucuronide were measured. The variants of CYP3A4, CYP3A5, SLCO1B1, SLCO1B3, ABCC2, UGT1A9, and UGT2B7 were genotyped. Drug interaction was evaluated with a non-compartmental analysis and population pharmacokinetic modelling to quantify the interaction effect. A total of 1,082 concentrations of those analytes were analysed. AUC_0-inf of TAC increased by 22.1% (322.4 ± 174.1 to 393.6 ± 121.7 ng·h/mL; P < 0.05) when co-administered with MMF, whereas the pharmacokinetic parameters of MPA and its metabolites were not changed by TAC. Apparent clearance (CL/F) of TAC was 17.8 L/h [relative standard error (RSE) 11%] or 13.8 L/h (RSE 11%) without or with MMF, respectively. Interaction was explained by the exponential model. The CYP3A5 genotype was the only significant covariate. The population estimate of CL/F of TAC was 1.48-fold (RSE 16%) in CYP3A5 expressers when compared to nonexpressers. CL/F of TAC was decreased when co-administered with MMF in these subjects.

SLC28A3 rs7853758 as a new biomarker of tacrolimus elimination and new-onset hypertension in Chinese liver transplantation patients

AIM

The effect of SLC28A3 on tacrolimus disposition and new-onset hypertension (NOHP) after liver transplantation (LT) remains unclear. Methodology & results: A total of 169 patients in two cohorts from the China Liver Transplant Registry database were included. Rs7853758 in recipients'SLC28A3 could predict tacrolimus pharmacokinetics in two sets. The model of donors' CYP3A5 rs776746 and recipients' CYP3A4 rs2242480 could predict tacrolimus metabolism at week 1 and the model of donors' CYP3A5 rs776746, recipients' CYP3A4 rs2242480, recipients' SLC28A3 rs7853758 and hemoglobin could predict tacrolimus disposition at weeks 2, 3 and 4. Besides, recipients' SLC28A3 rs7853758 was a new risk factor of NOHP after LT.

CONCLUSION

Rs7853758 in recipients' SLC28A3 has a correlation with tacrolimus pharmacokinetics and the risk of NOHP in Chinese LT patients.

Tacrolimus Updated Guidelines through popPK Modeling: How to Benefit More from CYP3A Pre-emptive Genotyping Prior to Kidney Transplantation

Tacrolimus (Tac) is a profoundly effective immunosuppressant that reduces the risk of rejection after solid organ transplantation. However, its use is hampered by its narrow therapeutic window along with its highly variable pharmacological (pharmacokinetic [PK] and pharmacodynamic [PD]) profile. Part of this variability is explained by genetic polymorphisms affecting the metabolic pathway. The integration of CYP3A4 and CY3A5 genotype in tacrolimus population-based PK (PopPK) modeling approaches has been proven to accurately predict the dose requirement to reach the therapeutic window. The objective of the present study was to develop an accurate PopPK model in a cohort of 59 kidney transplant patients to deliver this information to clinicians in a clear and actionable manner. We conducted a non-parametric non-linear effects PopPK modeling analysis in Pmetrics^®. Patients were genotyped for the CYP3A4^∗22 and CYP3A5^∗3 alleles and were classified into 3 different categories [poor-metabolizers (PM), Intermediate-metabolizers (IM) or extensive-metabolizers (EM)]. A one-compartment model with double gamma absorption route described very accurately the tacrolimus PK. In covariate analysis, only CYP3A genotype was retained in the final model (Δ-2LL = -73). Our model estimated that tacrolimus concentrations were 33% IC_95%[20–26%], 41% IC_95%[36–45%] lower in CYP3A IM and EM when compared to PM, respectively. Virtually, we proved that defining different starting doses for PM, IM and EM would be beneficial by ensuring better probability of target concentrations attainment allowing us to define new dosage recommendations according to patient CYP3A genetic profile.

Tacrolimus population pharmacokinetics according to CYP3A5 genotype and clinical factors in Chinese adult kidney transplant recipients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus is characterized by a narrow therapeutic index and a considerable inter- and intraindividual pharmacokinetic variability. The aim of our study was to develop a population pharmacokinetic model of tacrolimus in adult kidney transplant of Chinese patients, identify factors especially CYP3A5*3 genetic polymorphism that explain variability, and determine dosage regimens.

METHODS

Pharmacogenomic data obtained from 83 Chinese kidney transplant patients treated with tacrolimus were determined using polymerase chain reaction-restriction fragment length polymorphism analysis. Trough blood concentration data were collected from all of the patients during the 12 months of post-transplantation days and were analysed using the nonlinear mixed-effects modelling program. After building the final model, 1000 bootstraps were performed to validate the final model.

RESULTS AND DISCUSSION

A one-compartment model with first-order absorption and elimination adequately described the pharmacokinetics of tacrolimus. In this study, we observed that POD, HCT and CYP3A5*3 genotype were determinant factors in CL/F and POD related with V/F of tacrolimus significantly. The final model with the clearance covariates was presented as: Cl/F=THETA(1)*EXP(THETA(4)*(83/POD))*(39.1/HCT)**THETA(5)*EXP(THETA(6)*CYP3A5), and the final model with the volume covariates was presented as: Vd/F=THETA(2)*POD**THETA(3). The Ka was fixed to 4.5 h^-1 .

WHAT IS NEW AND CONCLUSION

The HCT, CYP3A5*3 genetic polymorphism and POD contributed to the interindividual variability of oral tacrolimus in Chinese adult renal transplant patients.

Dynamic effects of CYP3A5 polymorphism on dose requirement and trough concentration of tacrolimus in renal transplant recipients

WHAT IS KNOWN AND OBJECTIVE

Tacrolimus is a widely used immunosuppressive drug with marked pharmacokinetic variability partly due to CYP3A5 polymorphism. Our study aimed to investigate the dynamic effects of CYP3A5 genotypes on dose requirement and trough concentration (C₀ ) of tacrolimus in renal transplant recipients.

METHODS

A total of 194 Chinese renal transplant recipients received oral tacrolimus twice daily. Whole-blood C₀ of tacrolimus were measured on the 3rd day, 7th day, 14th day, 1st month, 3rd month and 6th month post-transplantation. CYP3A5 genotypes were determined and the recipients were categorized as CYP3A5 expressers (CYP3A5*1 allele carriers) and non-expressers (homozygous CYP3A5*3). The correlated serum creatinine, haematocrit and albumin were also detected.

RESULTS

The allele frequencies for CYP3A5*1/*1, *1/*3 and *3/*3 were 7·7%, 44·8% and 47·4%, respectively. There were no significant variability in serum creatinine, haematocrit and albumin values between CYP3A5 expressers and non-expressers. Larger doses were administered to CYP3A5 expressers than to non-expressers after surgery except the initial dose. C₀ were much lower in CYP3A5 expressers than in non-expressers on the 3rd day, 7th day, 14th day and 1st month post-transplantation (P < 0·01); however, no significant differences were found on the 3rd and 6th months post-transplantation. All of the dose-adjusted C₀ in CYP3A5 expressers were significantly lower than non-expressers (P < 0·01). Less of the recipients achieving target C₀ (4-8 ng/mL) were found in CYP3A5 expressers than in non-expressers after initial dose (35·7% vs. 50%). Meanwhile, CYP3A5 non-expressers were detected having higher C₀ (>8 ng/mL) during 3 months post-transplantation. Besides, the proportions in the two groups both increased gradually over time and up to 91·8% and 94% on the 6th month, respectively.

WHAT IS NEW AND CONCLUSION

There are no significant differences in serum creatinine, haematocrit and albumin values between CYP3A5 expressers and non-expressers. CYP3A5 expressers have decreased dose-adjusted tacrolimus C₀ when compared to non-expressers. Dose-adjusted C₀ of tacrolimus increases in a time-dependent manner in both groups.

Influence of everolimus on the pharmacokinetics of tacrolimus in Japanese renal transplant patients

OBJECTIVES

To examine whether a trough concentration of everolimus in the therapeutic range of 3-5 ng/mL affects the pharmacokinetics of tacrolimus in renal transplant patients.

METHODS

A total of 52 Japanese renal transplant patients receiving tacrolimus were enrolled in this study. In 28 of them, everolimus was co-administered on day 14 after surgery. Changes in the dose-adjusted blood trough concentration of tacrolimus from day 14 to 28 after surgery were investigated.

RESULTS

The dose-adjusted blood trough concentration of tacrolimus on day 28 was affected by CYP3A5*3/*3 and hemoglobin level (P < 0.001 and P = 0.007), but not by everolimus (P = 0.171). In addition, there was no change in the dose-adjusted blood trough concentration of tacrolimus in patients before or after everolimus coadministration (P = 0.165). On day 28, there was no correlation between the rate of change in the dose-adjusted blood trough concentration of tacrolimus and the blood trough concentration or area under the plasma concentration-time curve from 0 to 12 h for everolimus after initiation of combination therapy (r = 0.341, P = 0.076 and r = 0.234, P = 0.231).

CONCLUSIONS

A pharmacokinetic interaction between tacrolimus and everolimus was not observed clinically in renal transplant patients. Safe and reliable immunosuppressive therapy in renal transplant patients might be achieved using a combination of tacrolimus and everolimus.

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Pharmacogenetics may Influence Tacrolimus Daily Dose, but not Urinary Tubular Damage Markers in the Long-Term Period after Renal Transplantation

Background

The primary goal of this study was to evaluate the influence of cytochrome P450 (CYP) 3A5 (6986A>G) and ABCB1 (3435C>T) polymorphisms on tacrolimus (TAC) dosage regimen and exposure. Second, we evaluated the influence of TAC dosage regimen and the tested polymorphisms on renal oxidative injury, as well as the urinary activities of tubular ectoenzymes in a long-term period after transplantation. Also, we aimed to determine the association between renal oxidative stress and tubular damage markers in the renal transplant patients.

Methods

The study included 72 patients who were on TAC based immunosuppression. Allele-specific PCR was used for polymorphism determination. We measured the urinary thiobarbituric acid reactive substances (TBARS) and reactive carbonyl derivates (RCD) in order to evaluate oxidative injury, as well as the urinary activities of ectoenzymes (N-acetyl-β-D-glucosaminidase, aminopeptidase N and dipeptidyl peptidase IV) to evaluate tubular damage.

Results

The carriers of CYP 3A5*1 allele required statistically higher daily doses of TAC than CYP *3/*3 carriers, as well as the carriers of C allele of ABCB1 gene compared to those with TT genotype. Also, there were no differences in TBARS, RCD and the activities of ectoenzymes between the patients’ genotypes. Our results showed significant correlations between urinary TBARS and RCD and the ectoenzymes’ activities.

Conclusions

Our findings suggest that CYP 3A5 and ABCB1 3435 polymorphism may affect TAC daily doses, but not the drug’s tubular toxicity. Furthermore, tubular damage may be associated with increased renal oxidative stress.

Population pharmacokinetic analysis of tacrolimus in Mexican paediatric renal transplant patients: role of CYP3A5 genotype and formulation

AIMS

The aims of this study were (i) to develop a population pharmacokinetic (PK) model of tacrolimus in a Mexican renal transplant paediatric population (n = 53) and (ii) to test the influence of different covariates on its PK properties to facilitate dose individualization.

METHODS

Population PK and variability parameters were estimated from whole blood drug concentration profiles obtained at steady-state using the non-linear mixed effect modelling software NONMEM® Version 7.2.

RESULTS

Tacrolimus PK profiles exhibited high inter-patient variability (IPV). A two compartment model with first order input and elimination described the tacrolimus PK profiles in the studied population. The relationship between CYP3A5 genotype and tacrolimus CL/F was included in the final model. CL/F in CYP3A5*1/*1 and *1/*3 carriers was approximately 2- and 1.5-fold higher than in CYP3A5*3/*3 carriers (non-expressers), respectively, and explained almost the entire IPV in CL/F. Other covariates retained in the final model were the tacrolimus dose and formulation type. Limustin® showed markedly lower concentrations than the rest of the formulations.

CONCLUSIONS

Population PK modelling of tacrolimus in paediatric renal transplant recipients identified the tacrolimus formulation type as a significant covariate affecting the blood concentrations and confirmed the previously reported significant effect of CYP3A5 genotype on CL/F. It allowed the design of a proposed dosage based on the final model that is expected to help to improve tacrolimus dosing.

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling

Aims

The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models.

Methods

Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting.

Results

Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h⁻¹ [95% CI 12.6, 18.0 l h⁻¹]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An E_max model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range.

Conclusion

A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.