Population pharmacokinetics and pharmacogenetics of tacrolimus in de novo pediatric kidney transplant recipients

Effect of early post-hematopoietic stem cell transplant tacrolimus concentration on transplant outcomes in pediatric recipients: One facility's ten-year experience of immunosuppression with tacrolimus

Acute graft-versus-host disease (GVHD) is a common life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT), ranking as the second leading cause of death among recipients, surpassed only by disease relapse. Tacrolimus is commonly used for GVHD prophylaxis, but achieving therapeutic blood levels is challenging, particularly in pediatrics, due to the narrow therapeutic window and the high interindividual variability. The retrospective study conducted at IRCCS "Burlo Garofolo" in Italy aimed to assess the impact of early post-HSCT tacrolimus levels on transplant-related outcomes in pediatric recipients. The population pharmacokinetic model (POP/PK) was set up to describe tacrolimus pharmacokinetics. Elevated tacrolimus (>12-15 ng/ml) levels within the initial weeks post-HSCT are associated with reduced post-transplant infections (p < 0.0001) and decreased incidence of early transplant-related events (p < 0.01), including a lower incidence of acute GVHD (p < 0.05 on day 0). High tacrolimus exposure can lead to an increased risk of chronic GVHD (p < 0.0001) and reduced overall survival (p < 0.01). Personalized dosing and therapeutic monitoring of tacrolimus are crucial to ensure optimal outcomes. POP/PK could help achieve this goal, giving us a model by which we can balance immunosuppression while looking at the patient's general well-being and providing the necessary treatment.

Population pharmacokinetic models of tacrolimus in paediatric solid organ transplant recipients: A systematic review

AIMS

This study aimed to provide up-to-date information on paediatric population pharmacokinetic models of tacrolimus and to identify factors influencing tacrolimus pharmacokinetic variability.

METHODS

Systematic searches in the Web of Science, PubMed, Scopus, Science Direct, Cochrane, EMBASE databases and reference lists of articles were conducted from inception to March 2023. All population pharmacokinetic studies of tacrolimus using nonlinear mixed-effect modelling in paediatric solid organ transplant patients were included.

RESULTS

Of the 21 studies reviewed, 62% developed from liver transplant recipients and 33% from kidney transplant recipients. Most studies used a 1-compartment model to describe tacrolimus pharmacokinetics. Body weight was a significant predictor for tacrolimus volume of distribution (Vd/F). The estimated Vd/F for 1-compartment models ranged from 20 to 1890 L, whereas the peripheral volume of distribution (Vp/F) for 2-compartment models was between 290 and 1520 L. Body weight, days post-transplant, CYP3A5 genotype or haematocrit were frequently reported as significant predictors of tacrolimus clearance. The estimated apparent clearance values range between 0.12 and 2.18 L/h/kg, with inter-individual variability from 13.5 to 110.0%. Only 29% of the studies assessed the generalizability of the models with external validation.

CONCLUSION

This review highlights the potential factors, modelling approaches and validation methods that impact tacrolimus pharmacokinetics in a paediatric population. The clinician could predict tacrolimus clearance based on body weight, CYP3A5 genotype, days post-transplant or haematocrit. Further research is required to determine the relationship between pharmacogenetics and tacrolimus pharmacodynamics in paediatric patients and confirm the applicability of nonlinear kinetics in this population.

External assessment and refinement of a population pharmacokinetic model to guide tacrolimus dosing in pediatric heart transplant

STUDY OBJECTIVE

The immunosuppressant tacrolimus is a first-line agent to prevent graft rejection following pediatric heart transplant; however, it suffers from extensive inter-patient variability and a narrow therapeutic window. Personalized tacrolimus dosing may improve transplant outcomes by more efficiently achieving and maintaining therapeutic tacrolimus concentrations. We sought to externally validate a previously published population pharmacokinetic (PK) model that was constructed with data from a single site.

DATA SOURCE

Data were collected from Seattle, Texas, and Boston Children's Hospitals, and assessed using standard population PK modeling techniques in NONMEMv7.2.

MAIN RESULTS

While the model was not successfully validated for use with external data, further covariate searching identified weight (p < 0.0001 on both volume and elimination rate) as a model-significant covariate. This refined model acceptably predicted future tacrolimus concentrations when guided by as few as three concentrations (median prediction error = 7%; median absolute prediction error = 27%).

CONCLUSION

These findings support the potential clinical utility of a population PK model to provide personalized tacrolimus dosing guidance.

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.

Factors Affecting Day-to-Day Variations in Tacrolimus Concentration among Children and Young Adults Undergoing Allogeneic Hematopoietic Stem Cell Transplantation

Tacrolimus is widely used as prophylaxis for graft-versus-host disease (GVHD) in allogeneic stem cell transplantation (allo-HSCT). It has a narrow therapeutic index range; high tacrolimus concentrations are associated with toxicity, whereas low concentrations are associated with an increased risk of GVHD. Although dose adjustments based on therapeutic drug monitoring are performed, unexpected large variations in tacrolimus concentration are sometimes encountered. The available evidence suggests that the factors affecting tacrolimus concentration are not fully understood. This study was aimed primarily at investigating the factors affecting day-to-day variations in tacrolimus concentration in children and young adults who received continuous tacrolimus infusion after allo-HSCT. The secondary objective was to identify the factors causing large variations (>20%) in tacrolimus concentrations. This retrospective cohort study comprised 123 consecutive pediatric and young adult patients (age <25 years) who received continuous i.v. tacrolimus infusion after allo-HSCT at Shinshu University Hospital, Matsumoto, Japan, between January 2009 and December 2021. To compare day-to-day variations in tacrolimus concentration without consideration of the tacrolimus dose, 2 consecutive days when the tacrolimus dose was not changed were selected from between the first post-allo-HSCT day of a tacrolimus concentration >7 ng/mL and day 28 post-allo-HSCT. Subsequently, information for the subsequent 24 hours was collected along with the tacrolimus concentrations and hematocrit values. Tacrolimus concentration was determined using whole blood samples. Tacrolimus concentrations were significantly higher in patients who received red blood cell concentrate (RCC) transfusions (P < .0001) and methotrexate (P = .0162), patients with persistent fever (P = .0056), and patients with a decline in fever (P = .0003). In contrast, tacrolimus concentrations were significantly lower in patients who received platelet concentrate (PC) transfusions (P < .0001), who redeveloped fever (P = .0261), and who had a replaced tacrolimus administration route set (P = .0008). Variations in tacrolimus concentration were significantly correlated with variations in hematocrit (r = .556; P < .0001). Body weight (P < .0001), RCC transfusion (P < .0001), methotrexate use (P = .0333), persistent fever (P = .0150), and decline in fever (P = .0073) were associated with a sharp increase in tacrolimus concentration. In contrast, body weight (P < .0001), PC transfusion (P = .0025), and replacement of the tacrolimus administration route set (P = .0025) were associated with a sharp decrease in tacrolimus concentration. RCC and PC transfusions, fever, methotrexate administration, and replacement of the tacrolimus administration route set were independent factors affecting day-to-day variations in tacrolimus concentration. In addition to these factors, low body weight was a risk factor for both sharp increases and decreases in tacrolimus concentration. These findings suggest the need for better control of tacrolimus concentration using whole blood samples.

Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation

BACKGROUND

This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation.

METHODS

Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C₀ . PCR was used to determine the donors' and recipients' CYP3A5 genotypes. Multivariate stepwise regression analysis and stepwise elimination covariates were used for covariates selection. Thirteen machine learning algorithms were applied for the development of prediction models. APE, the ratio of the APE ≤3 ng ml^-1 and ideal rate (the proportion of the predicted value with a relative error of 30% or less) were used to evaluate the predictive performance of the model.

RESULTS

A total of 163 infant patients were included in this study. In the case of the optimal combination of covariates, the Ridge model had the lowest APE, 2.01 (0.85, 3.35 ng ml^-1 ). The highest ratio of the APE ≤3 ng ml^-1 was the LAR model (71.77%). And the Ridge model showed the highest ideal rate (55.05%). For the Ridge model, GRWR was the most important predictor.

CONCLUSIONS

Compared with other ML models, the Ridge model had good predictive performance and potential clinical application.

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.

Personalized Dose of Adjuvant Imatinib in Patients with Gastrointestinal Stromal Tumors: Results from a Population Pharmacokinetic Analysis

Purpose

Imatinib is the first-line treatment for patients with gastrointestinal stromal tumors (GIST) after surgery. However, its pharmacokinetic profile varies remarkably between individuals and has not been well characterized in postoperative Chinese patients with GIST. Therefore, this study aimed to develop a population pharmacokinetic (PPK) model and recommend appropriate doses for different patients to achieve the target trough concentration in such a population.

Patients and Methods

A total of 110 surgically treated GIST patients were enrolled, of which 85 were applied to conduct a PPK analysis with a nonlinear mixed-effect model and 25 for external validation of the model. Demographic and biomedical covariates, as well as six single nucleotide polymorphisms were tested to explore the sources of variation in pharmacokinetic parameters of imatinib. Monte Carlo simulations were performed to establish the initial dosing regimens.

Results

A one-compartment model was established in postoperative GIST patients. The red blood cell count (RBC) and ABCG2 rs2231142 were observed to have a significant effect on the clearance of imatinib. The typical values estimated by the final model were 9.72 L/h for clearance (CL/F) and 229 L for volume of distribution (V/F). Different from the fixed dose regimen of 400 mg each day, patients carrying rs2231142 heterozygous type and with a lower level of RBC (2.9 × 10¹²/L), 300 mg imatinib daily is enough to achieve the target trough concentration. When RBC rises to 4.9 × 10¹²/L, 500 mg daily is recommended. For patients with rs2231142 GG genotype, 500 mg a day is required at RBCs of 3.9 × 10¹²/L and 4.9 × 10¹²/L.

Conclusion

RBC and rs2231142 contribute to the pharmacokinetic variation of imatinib and personalized dose recommendations based on patient characteristics may be necessary.

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.

Population pharmacokinetics and dosage optimization of tacrolimus coadministration with Wuzhi capsule in adult liver transplant patients

1. This study aimed to establish a population pharmacokinetic model of tacrolimus coadministration with Wuzhi capsule and optimize the dosage regimen in adult liver transplant patients.2. Totally 1327 tacrolimus trough concentrations from 116 adult liver transplant patients were obtained for model development. A one-compartment model with first-order absorption and elimination was used to analyse the data, and the final model was internally verified using a goodness-of-fit diagnostic plot, bootstrap methods, and visual prediction test. A total of 29 patients with 250 tacrolimus trough concentrations was used for external validation via prediction-based diagnostics. Additionally, the simulation was used to optimize the recommended dose of tacrolimus and Wuzhi capsules.3. The estimated apparent clearance and volume of the distribution of tacrolimus were 15.4 L/h and 1210 L, respectively. The tacrolimus daily dose, Wuzhi capsule daily dose, postoperative time, alanine transaminase, haemoglobin, total bilirubin, direct bilirubin, estimated glomerular filtration rate, and urea, concomitant with voriconazole and fluconazole, were identified as significant covariates affecting the pharmacokinetic parameters. Internal and external validation showed that the final model was stable and reliable for predicting performance.4. The final model could provide guidance for dosage optimization of tacrolimus coadministered with Wuzhi capsules in adult liver transplantation patients.

A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease

Drug-drug interactions (DDIs) and adverse drug reactions (ADR) are experienced by many patients, especially by elderly population due to their multiple comorbidities and polypharmacy. Databases such as PubMed contain hundreds of abstracts with DDI and ADR information. PubMed is being updated every day with thousands of abstracts. Therefore, manually retrieving the data and extracting the relevant information is tedious task. Hence, automated text mining approaches are required to retrieve DDI and ADR information from PubMed. Recently we developed a hybrid approach for predicting DDI and ADR information from PubMed. There are many other existing approaches for retrieving DDI and ADR information from PubMed. However, none of the approaches are meant for retrieving DDI and ADR specific to patient population, gender, pharmacokinetics, and pharmacodynamics. Here, we present a text mining protocol which is based on our recent work for retrieving DDI and ADR information specific to patient population, gender, pharmacokinetics, and pharmacodynamics from PubMed.

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.

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

Background and Objective

Bodyweight-based dosing of tacrolimus is considered standard care. Currently, at first steady state, a third of pediatric kidney transplant recipients has a tacrolimus pre-dose concentration within the target range. We investigated whether adaptation of the starting dose according to a validated dosing algorithm could increase this proportion.

Methods

This was a multi-center, single-arm, prospective trial with a planned interim analysis after 16 patients, in which the tacrolimus starting dose was based on bodyweight, cytochrome P450 3A5 genotype, and donor status (living vs. deceased donor).

Results

At the interim analysis, 31% of children had a tacrolimus pre-dose concentration within the target range. As the original dosing algorithm was poorly predictive of tacrolimus exposure, the clinical trial was terminated prematurely. Next, the original model was improved by including the data of the children included in this trial, thereby doubling the number of children in the model building cohort. Data were best described with a two-compartment model with inter-individual variability, allometric scaling, and inter-occasion variability on clearance. Cytochrome P450 3A5 genotype, hematocrit, and creatinine influenced the tacrolimus clearance. A new starting dose model was developed in which the cytochrome P450 3A5 genotype was incorporated. Both models were successfully internally and externally validated.

Conclusions

The weight-normalized starting dose of tacrolimus should be higher in patients with a lower bodyweight and in those who are cytochrome P450 3A5 expressers.

Electronic supplementary material

The online version of this article (10.1007/s40262-019-00831-8) contains supplementary material, which is available to authorized users.

Case Report: Low Hematocrit Leading to Tacrolimus Toxicity

Tacrolimus is a calcineurin inhibitor characterized by a narrow therapeutic index and high intra- and inter-individual pharmacokinetic variability. Therapeutic drug monitoring in whole-blood is the standard monitoring procedure. However, tacrolimus extensively binds to erythrocytes, and tacrolimus whole-blood distribution and whole-blood trough concentrations are strongly affected by hematocrit. High whole-blood tacrolimus concentrations at low hematocrit may result in high unbound plasma concentrations and increased toxicity. We present the case of a 16-year-old girl with kidney and liver transplant in whom low concentrations of tacrolimus in the context of low hematocrit led to significant increase in the dosage of tacrolimus and participate, along with a genetic polymorphism of ABCB1, in nephrotoxicity.

Treatment optimization of maintenance immunosuppressive agents in pediatric renal transplant recipients

Introduction: Graft survival in pediatric kidney transplant patients has increased significantly within the last three decades, correlating with the discovery and utilization of new immunosuppressants as well as improvements in patient care. Despite these developments in graft survival for patients, there is still improvement needed, particularly in long-term care in pediatric patients receiving grafts from deceased donor patients. Maintenance immunosuppressive therapies have narrow therapeutic indices and are associated with high inter-individual and intra-individual variability.Areas covered: In this review, we examine the impact of pharmacokinetic variability on renal transplantation and its association with age, genetic polymorphisms, drug-drug interactions, drug-disease interactions, renal insufficiency, route of administration, and branded versus generic drug formulation. Pharmacodynamics are outlined in terms of the mechanism of action for each immunosuppressant, potential adverse effects, and the utility of pharmacodynamic biomarkers.Expert opinion: Acquiring abetter quantitative understanding of immunosuppressant pharmacokinetics and pharmacodynamic components should help clinicians implement treatment regimens to maintain the balance between therapeutic efficacy and drug-related toxicity.

Analysis of the side effect of QTc interval prolongation in the bedaquiline regimen in drug resistant tuberculosis patients

OBJECTIVES

Indonesia is one of the top 20 countries with the highest prevalence of drug resistant tuberculosis (DR-TB) worldwide with a percentage of new cases of 2.4% and retreatment of 13%. Bedaquiline (BDQ) is one of the drugs that used in the individual long regimen treating DR-TB. BDQ is also combined with levofloxacin (LFX) and/or clofazimine (CFZ) that can cause QTc interval prolongation. The aim was to study the differences in the use of BDQ regimens to the lengthening of the QTc interval and to study risk factors (diabetes, hypokalemia, sex, BMI, and age) in BDQ regimen.

METHODS

This study was an observational retrospective study with a total sampling method, which was conducted at Dr. Soetomo General Hospital Surabaya. Samples from this study were patients diagnosed with DR-TB at Dr. Soetomo General Hospital Surabaya in the period of January 2015-December 2019 who used BDQ regimen and met the inclusion criteria. The ECG data were analyzed from the mean of each group (BDQ regimen and risk factors), also analyzed using statistical analysis.

RESULTS

Data obtained from total sample in this study were 73 patients. The most widely used different regimens in this study were the combination of BDQ + LFX by 36 patients (49.3%), BDQ + LFX + CFZ by 16 patients (21.9%), BDQ by 11 patients (15.1%) and BDQ + CFZ 10 patients (13.7%). Out of 73 patients, 52 patients (71.2%) experienced lengthening of the QT interval and grade 1 of QTc interval prolongation occurred in most patients and also the onset was mostly one month after using BDQ regimen. The side effects of QTc interval prolongation from groups of combination and risk factors were no difference in each month (p>0.05).

CONCLUSIONS

This study can be concluded that there were no differences in the QTc prolongation between the groups of BDQ regimen (BDQ, BDQ + LFX, BDQ + CFZ and BDQ + LFX + CFZ) and the groups of risk factors.

CYP3A5*3 polymorphism and age affect tacrolimus blood trough concentration in myasthenia gravis patients

The study aims to identify clinical factors affecting tacrolimus blood trough concentration (C0) in myasthenia gravis (MG) patients and to optimize the initial dose of tacrolimus in MG treatment. A total of 103 MG patients participated in this study, and their clinical factors, medication regimens, C0 values and CYP3A5*3 polymorphisms were collected in detail. We used a linear mixed model to analyze the effect of multiple factors on the dosage-weighted C0 (C0:D) and performed subgroup analyses to investigate the consistency of correlations between influencing factors and the C0:D ratios. Among all factors, CYP3A5*3 polymorphism and age showed a strong positive correlation with C0:D ratios. The C0:D ratios (ng/ml·mg^-1) were higher for CYP3A5*3/*3 than for CYP3A5*1 (mean difference: 1.038, 95% confidence interval [CI]: 0.820-1.256, P-value <0.001), and for age in the range of 45-64 and ≥ 65 years than for age < 45 years (mean difference [95% CI] and P-value: 0.531[0.257-0.805] and P-value <0.001, 0.703 [0.377-1.029] and P-value <0.001, respectively). The C0:D ratios were not related to corticosteroid dosage, body weight, sex, hematocrit or the concomitant use of calcium channel blockers. The consistencies of the correlations between C0:D ratios and CYP3A5*3 polymorphism or age were confirmed by subgroup analyses. Thus, CYP3A5*3 polymorphism and age should be considered in optimizing the initial dose of tacrolimus for MG treatment.

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.

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.

Evaluation of the performance of a prior tacrolimus population pharmacokinetic kidney transplant model among adult allogeneic hematopoietic stem cell transplant patients

Abstract

Tacrolimus is a calcineurin inhibitor used to prevent acute graft versus host disease in adult patients receiving allogeneic hematopoietic stem cell transplantation (HCT). Previous population pharmacokinetic (PK) models have been developed in solid organ transplant, yet none exists for patients receiving HCT. The primary objectives of this study were to (1) use a previously published population PK model in adult patients who underwent kidney transplant and apply it to allogeneic HCT; (2) evaluate model‐predicted tacrolimus steady‐state trough concentrations and simulations in patients receiving HCT; and (3) evaluate covariates that affect tacrolimus PK in allogeneic HCT. A total of 252 adult patients receiving allogeneic HCT were included in the study. They received oral tacrolimus twice daily (0.03 mg/kg) starting 3 days prior to transplant. Data for these analyses included baseline clinical and demographic data, genotype data for single nucleotide polymorphisms in CYP3A4/5 and ABCB1, and the first tacrolimus steady‐state trough concentration. A dosing simulation strategy based on observed trough concentrations (rather than model‐based predictions) resulted in 12% more patients successfully achieving tacrolimus trough concentrations within the institutional target range (5–10 ng/ml). Stepwise covariate analyses identified HLA match and conditioning regimen (myeloablative vs. reduced intensity) as significant covariates. Ultimately, a previously published tacrolimus population PK model in kidney transplant provided a platform to help establish a model‐based dose adjustment strategy in patients receiving allogenic HCT, and identified HCT‐specific covariates to be considered for future prospective studies.

Analysis of time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus is used to treat patients with lupus nephritis; however, its time course and dose effect on proteinuria in lupus nephritis patients remain unknown. The purpose of this study was to determine the time course and dose effect of tacrolimus on proteinuria in lupus nephritis patients via model-based meta-analysis (MBMA).

METHODS

PubMed, Web of Science, Cochrane Library and ClinicalTrials.gov databases were systematically searched for information on the efficacy of tacrolimus against proteinuria in lupus nephritis patients. Useful data were extracted to build a model for the population studied using a non-linear mixed-effect model (NONMEM). This model was applied to simulate time course of tacrolimus on proteinuria using Monte Carlo simulations.

RESULTS

Ten clinical studies that recruited 222 patients with lupus nephritis were included. Based on various diagnostic plots, we found that the established model described the observed data reasonably well. In addition, the typical E_max and ET₅₀ of tacrolimus for 24-hour proteinuria in lupus nephritis patients were -5.88 g and 0.37 months, respectively. The baseline value of 24-hour proteinuria affected E_max . No significant dose-response relationship was observed in the range of tacrolimus concentration used in the present study (3-10 ng/mL), indicating that the effect of tacrolimus on proteinuria depends on effective concentration range and not the dose. However, the time course relationship was obvious; the efficacy of tacrolimus increased over time, reaching a plateau (80% E_max ) at approximately 1.48 months from the beginning of treatment.

WHAT IS NEW AND CONCLUSION

When the concentration range of tacrolimus is maintained at 3-10 ng/mL, at least 1.48 months of treatment is required to achieve a better outcome with regard to proteinuria in lupus nephritis patients.

Oral generic tacrolimus initiation and substitution in the Medicaid population: a new user cohort study

OBJECTIVE

Although existing studies have compared clinical efficacy and pharmacokinetics of generic with brand tacrolimus, little is known about generic tacrolimus utilization patterns or factors predicting generic substitution. This study examines associations between patient-level factors and generic tacrolimus initiation or substitution among new users of oral generic or brand-name tacrolimus.

METHODS

This new user retrospective cohort study identified 374 beneficiaries who initiated generic or brand tacrolimus treatment, using 100% Medicaid administrative claims data from 11 states in 2011-2012. Outcomes were generic tacrolimus initiation and substitution within 12 months of treatment initiation. Multivariable logistic regression and Cox proportional hazards models were used to identify factors associated with generic tacrolimus initiation and substitution.

RESULTS

Two-thirds of oral tacrolimus new users initiated generic tacrolimus. Patients who were older age and black were significantly more likely to initiate generic tacrolimus (adjusted odds ratio [AOR] = 1.02, 95% confidence interval [CI] = 1.001-1.03; AOR = 2.54, 95% CI = 1.40-4.62; respectively). Patients with more concomitant prescriptions, more comorbidities, or higher initial daily dosage had significantly lower likelihoods of initiating generic tacrolimus (AOR = 0.93, 95% CI = 0.87-0.99; AOR = 0.87, 95% CI = 0.77-0.99; AOR = 0.96, 95% CI = 0.93-0.993). Among brand tacrolimus new users, 23.6% substituted with generics within 12 months, and an addition of prior hospitalization or unit of initial daily dosage increment was associated with 35% (subdistribution hazard ratio [SHR] = 1.35, 95% CI = 1.02-1.76) or 2% (SHR = 1.02, 95% CI = 1.00-1.04) increase in likelihood of generic substitution, respectively.

CONCLUSIONS

Understanding associations between patient-level factors with generic tacrolimus initiation and substitution could help practitioners and policymakers monitor treatment effect and facilitate generic tacrolimus utilization.

Population pharmacokinetics model and initial dose optimization of tacrolimus in children and adolescents with lupus nephritis based on real-world data

The present study aimed to establish a population pharmacokinetics model of tacrolimus and further optimize the initial dosing regimen of tacrolimus in pediatric and adolescent patients with lupus nephritis (LN). Pediatric and adolescent patients with LN were recruited between August 2014 and September 2019 at the Children's Hospital of Fudan University (Shanghai, China). Relevant information was used to set up a population pharmacokinetics model with a Nonlinear Mixed Effect Model and the initial dosage regimen was simulated with the Monte Carlo method. Body weight and co-administration of wuzhi capsule were indicated to influence tacrolimus clearance in pediatric and adolescent patients with LN, and at the same body weight, the rate of tacrolimus clearance in patients without vs. with co-administration of wuzhi capsule was 1:0.71. In addition, in patients who were not administered wuzhi capsule, an initial dosage regimen of 0.15 mg/kg/day was recommended for a body weight of 10-23 kg and 0.10 mg/kg/day for 23-60 kg; in patients who were administered wuzhi capsule, an initial dosage regimen of 0.10 mg/kg/day was recommended for a body weight of 10-23 kg and 0.05 mg/kg/day for 23-60 kg. To the best of our knowledge, the present study was the first to establish a population pharmacokinetics model of tacrolimus in order to determine the optimal initial dosage regimen of tacrolimus in pediatric and adolescent patients with LN.

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.

Tacrolimus treatment in childhood refractory nephrotic syndrome: A retrospective study on efficacy, therapeutic drug monitoring, and contributing factors to variable blood tacrolimus levels

Tacrolimus, an immunosuppressive drug, was recommended by the 2012 KDIGO guidelines to treat nephrotic syndrome (NS) in children and adults. However, it has high interpatient pharmacokinetic variability and exposure levels should be monitored, although there are no specified target concentrations. This retrospective study aimed to review efficacy and safety after concomitant treatment with tacrolimus and prednisone, and to identify factors that contribute to the variable blood-trough-concentration-to-dose (C₀/Dose) ratio in children with refractory NS (RNS). A 6-month therapy induced complete or partial remission in 95% of patients. One-year follow-up indicated a high remission rate and low nephrotoxicity. Under maintenance dosages, approximately 95% of the C₀ values were 2-7 ng/mL. Body weight (BW), age, CYP3A5 polymorphisms were the factors affecting the C₀/Dose ratio. The C₀/Dose ratio in patients with a BW of <20 kg was 1.5-fold than that in patients with BW of ≥40 kg. Moreover, the C₀/Dose ratio in patients aged 1-≤6 and 6-≤12 years was significantly lower than that in patients aged 12-≤18 years, by 25% and 48%, respectively. There were no significant association between CYP3A5 genotyping and C₀/Dose ratio in younger children (1-≤6 years), rather than older children (6-≤18 years). In conclusion, routine CYP3A5 genotyping should be considered in children aged over 6 years and exposure levels (C₀) of 2-7 ng/mL may be feasible when tacrolimus is combined with low-dose prednisone to treat childhood RNS.

Association of Genetic Variants in CYP3A4, CYP3A5, CYP2C8, and CYP2C19 with Tacrolimus Pharmacokinetics in Renal Transplant Recipients

BACKGROUND

Our study aimed to investigate the pharmacogenetics of cytochrome P3A4 (CYP3A4), CYP3A5, CYP2C8, and CYP2C19 and their influence on TAC Pharmacokinetics (PKs) in short-term renal transplant recipients.

METHODS

A total of 105 renal transplant recipients were enrolled. Target Sequencing (TS) based on next-generation sequencing technology was used to detect all exons, exon/intron boundaries, and flanking regions of CYP3A4, CYP3A5, CYP2C8, and CYP2C19. After adjustment of Minor Allele Frequencies (MAF) and Hardy-Weinberg Equilibrium (HWE) analysis, tagger Single-nucleotide Polymorphisms (SNPs) and haplotypes were identified. Influence of tagger SNPs on TAC concentrations was analyzed.

RESULTS

A total of 94 SNPs were identified in TS analysis. Nine tagger SNPs were selected, and two SNPs (rs15524 and rs4646453) were noted to be significantly associated with TAC PKs in short-term post-transplant follow-up. Measurement time points of TAC, body mass index (BMI), usage of sirolimus, and incidence of Delayed Graft Function (DGF) were observed to be significantly associated with TAC PKs. Three haplotypes were identified, and rs15524-rs4646453 was found to remarkably contribute to TAC PKs. Recipients carrying H2/H2 (GG-AA) haplotype also showed significantly high weight- and dose-adjusted TAC concentrations in posttransplant periods of 7, 14, and 30 days and 3 and 6 months.

CONCLUSIONS

Two tagger SNPs, namely, rs15524 and rs4646453, are significantly related to the variability of TAC disposition, and TAC measurement time points, BMI, usage of sirolimus, and incidence of DGF contribute to this influence. Recipients carrying H2/H2 (GG-AA) haplotype in rs15524-rs4646453 may require a low dosage of TAC during 1-year follow-up posttransplant.

Off-label use of tacrolimus in children with glomerular disease: Effectiveness, safety and pharmacokinetics

Glomerular diseases are leading causes of end-stage renal disease in children. Tacrolimus is frequently used off-label in the treatment of glomerular diseases. The effectiveness, safety and pharmacokinetic data of tacrolimus in the treatment of glomerular diseases in children are reviewed in this paper to provide evidence to support its rational use in clinical practice. The remission rates in previously published studies were different. In 19 clinical trials on children with nephrotic syndrome, the overall remission rate was 52.6-97.6%. In four clinical trials on children with lupus nephritis, the overall remission rate was 81.8-89.5%. In a pilot study with paediatric Henoch-Schönlein purpura nephritis patients, the overall remission rate was 100.0%. Infection, nephrotoxicity, gastrointestinal symptoms and hypertension are the most common adverse events. Body weight, age, CYP3A5 genotype, cystatin-C and daily dose of tacrolimus may have significant effects on the pharmacokinetics of tacrolimus in children with glomerular disease. More prospective controlled trials with long follow-up are needed to demonstrate definitely the effectiveness, safety and pharmacokinetics of tacrolimus in children with glomerular diseases.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Population pharmacokinetics of cyclosporine in Chinese children receiving hematopoietic stem cell transplantation

Cyclosporine (CsA) is characterized by a narrow therapeutic window and high interindividual pharmacokinetic variability, particularly in juvenile patients. The aims of this study were to build a population pharmacokinetic model of CsA in Chinese children with hematopathy who received allogeneic hematopoietic stem cell transplantation (allo-HSCT) and to identify covariates affecting CsA pharmacokinetics. A total of 86 Chinese children aged 8.4 ± 3.8 years (range 1.1–16.8 years) who received allo-HSCT were enrolled. Whole blood samples were collected before allo-HSCT. Genotyping was performed using an Agena MassARRAY system. A total of 1010 trough plasma concentration values of CsA and clinical data were collected. The population pharmacokinetic model of CsA was constructed using nonlinear mixed-effects modeling (NONMEM) software. The stability and performance of the final model were validated using bootstrapping and normalized prediction distribution errors. We showed that a one-compartment model with first-order elimination adequately described the pharmacokinetics of CsA. The typical values for clearance (CL) and volume of distribution (V) were 42.3 L/h and 3100 L, respectively. Body weight, postoperative days, CYP3A4*1 G genotype, estimated glomerular filtration rate and coadministration of triazole antifungal drugs were identified as significant covariates for CL. Weight and postoperative days were significant covariates for the V of CsA. Our model can be adopted to optimize the CsA dosing regimen for Chinese children with hematopathy receiving allo-HSCT.

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

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

Efficacy and safety of tacrolimus in de novo pediatric transplant recipients randomized to receive immediate- or prolonged-release tacrolimus

BACKGROUND AND AIMS

This multicenter trial compared immediate-release tacrolimus (IR-T) vs prolonged-release tacrolimus (PR-T) in de novo kidney, liver, and heart transplant recipients aged <16 years. Each formulation had similar pharmacokinetic (PK) profiles. Follow-up efficacy and safety results are reported herein.

MATERIALS AND METHODS

Patients, randomized 1:1, received once-daily, PR-T or twice-daily, IR-T within 4 days of surgery. After a 4-week PK assessment, patients continued randomized treatment for 48 additional weeks. At Year 1, efficacy assessments included the number of clinical acute rejections, biopsy-confirmed acute rejection (BCAR) episodes (including severity), patient and graft survival, and efficacy failure (composite of death, graft loss, BCAR, or unknown outcome). Adverse events were assessed throughout.

RESULTS

The study included 44 children. At Year 1, mean ± standard deviation tacrolimus trough levels were 6.6 ± 2.2 and 5.4 ± 1.6 ng/mL, and there were 2 and 7 acute rejection episodes in the PR-T and IR-T groups, respectively. No cases of graft loss or death were reported during the study. The overall efficacy failure rate was 18.2% (PR-T n = 1; IR-T n = 7).

CONCLUSIONS

In pediatric de novo solid organ recipients, the low incidence of BCAR and low efficacy failure rate suggest that PR-T-based immunosuppression is effective and well tolerated to 1-year post-transplantation.

CYP3A5 genotype affects time to therapeutic tacrolimus level in pediatric kidney transplant recipients

BACKGROUND

Optimal management of immunosuppression in kidney transplantation requires a delicate balance of efficacy and toxicity. Tacrolimus (TAC) dose requirements are significantly impacted by genetic variation in CYP3A5 polymorphisms, however the impact that genotype has on clinical outcomes in the pediatric kidney transplant population remains unclear.

METHODS

We evaluated a retrospective cohort of 98 pediatric kidney transplant recipients. The primary exposure was CYP3A5 genotype, which classified each recipient into the expresser (at least one CYP3A5*1 allele) or non-expresser group (only CYP3A5*3 alleles). The primary outcome was time to achieve a steady therapeutic TAC concentration. Secondary outcomes include incidence of early allograft rejection and calcineurin inhibitor (CNI) nephrotoxicity during the first year post-transplant.

RESULTS

The study cohort included 55 (56%) expressers and 43 (44%) non-expressers of the CYP3A5*1 allele. Expressers had a significantly longer time to achieve a steady therapeutic TAC concentration than non-expressers (log rank, P = 0.03). Expressers had a trend for higher incidence of early allograft rejection (29.1% vs 16.3%, log rank, P = 0.16). Early biopsy-proven CNI nephrotoxicity was seen in 60% of recipients, with no differences in the rate between expressers and non-expressers.

CONCLUSIONS

Pediatric kidney transplant recipients with the CYP3A5*1 allele (expressers) take a longer time to achieve therapeutic TAC levels than those with the CYP3A5*3 allele (non-expressers). However, we observed no significant differences in acute rejection or CNI nephrotoxicity based on CYP3A5 genotype. Thus CYP3A5 genotype was not observed to have an immediate impact on early transplant outcomes.

A Population Pharmacokinetic Model to Predict the Individual Starting Dose of Tacrolimus Following Pediatric Renal Transplantation

Background

Multiple clinical, demographic, and genetic factors affect the pharmacokinetics of tacrolimus in children, yet in daily practice, a uniform body-weight based starting dose is used. It can take weeks to reach the target tacrolimus pre-dose concentration.

Objectives

The objectives of this study were to determine the pharmacokinetics of tacrolimus immediately after kidney transplantation and to find relevant parameters for dose individualization using a population pharmacokinetic analysis.

Methods

A total of 722 blood samples were collected from 46 children treated with tacrolimus over the first 6 weeks after renal transplantation. Non-linear mixed-effects modeling (NONMEM^®) was used to develop a population pharmacokinetic model and perform a covariate analysis. Simulations were performed to determine the optimal starting dose and to develop dosing guidelines.

Results

The data were accurately described by a two-compartment model with allometric scaling for bodyweight. Mean tacrolimus apparent clearance was 50.5 L/h, with an inter-patient variability of 25%. Higher bodyweight, lower estimated glomerular filtration rate, and higher hematocrit levels resulted in lower total tacrolimus clearance. Cytochrome P450 3A5 expressers and recipients who received a kidney from a deceased donor had a significantly higher tacrolimus clearance. The model was successfully externally validated. In total, these covariates explained 41% of the variability in clearance. From the significant covariates, the cytochrome P450 3A5 genotype, bodyweight, and donor type were useful to adjust the starting dose to reach the target pre-dose concentration. Dosing guidelines range from 0.27 to 1.33 mg/kg/day.

Conclusion

During the first 6 weeks after transplantation, the tacrolimus weight-normalized starting dose should be higher in pediatric kidney transplant recipients with a lower bodyweight, those who express the cytochrome P450 3A5 genotype, and those who receive a kidney from a deceased donor.

Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese pediatric hematopoietic stem cell transplantation patients

1. Several tacrolimus population pharmacokinetic (PPK) models in hematopoietic stem cell transplantation (HSCT) patients have been set up to recommend an optimal dosage schedule. However, the PPK model of Chinese pediatric HSCT patients has not been reported. The study is to investigate whether published PPK models of HSCT patients can be used to simulate Chinese pediatric HSCT patients and establish the tacrolimus PPK model of Chinese pediatric HSCT patients.2. Published PPK models were collected from the literature and assessed using Chinese pediatric HSCT patients via the individual prediction error method. The establishment of tacrolimus PPK model in Chinese pediatric HSCT patients was characterized with nonlinear mixed-effects modeling (NONMEM).3. Three published HSCT PPK models were identified, two of which could be applied to our external dataset. However, these models were dissatisfactory in terms of individual prediction error and, hence, inadequate for extrapolation. Finally, a new tacrolimus PPK model in Chinese pediatric HSCT patients was established. Based on the simulation results of our model, new initial dosage suggestions were recommended. In conclusion, the tacrolimus PPK model in Chinese pediatric HSCT patients was presented and the model could be used to predict individualized dosing regimens in children with HSCT.

Population pharmacokinetics of tacrolimus in pediatric refractory nephrotic syndrome and a summary of other pediatric disease models

Different tacrolimus (TAC) population pharmacokinetic (PPK) models have been established in various pediatric disease populations. However, a TAC PPK model for pediatric refractory nephrotic syndrome (PRNS) has not been well characterized. The current study aimed to establish a TAC PPK model in Chinese PRNS and provide a summary of previous literature concerning TAC PPK models in different pediatric diseases. A total of 147 TAC conventional therapeutic drug monitoring (TDM) data from multiple blood samples obtained from 65 Chinese patients with PRNS were characterized using nonlinear mixed-effects modeling. The impacts of demographic features, biological characteristics and drug combination were evaluated. Model validation was assessed using the bootstrap method. A one-compartment model with first-order absorption and elimination was determined to be the most suitable model for TDM data in PRNS. The absorption rate constant (Ka) was set at 4.48 h⁻¹. The typical values of apparent oral clearance (CL/F) and apparent volume of distribution (V/F) in the final model were 5.46 l/h and 57.1 l, respectively. The inter-individual variability of CL/F and V/F were 22.2 and 0.2%, respectively. The PPK equation for TAC was: CL/F = 5.46 × exponential function (EXP)(0.0323 × age) × EXP(−0.359 × cystatin-C) × EXP(0.148 × daily dose of TAC). No significant effects of covariates on V/F were observed. In conclusion, the current study developed and validated the first TAC PPK model for patients with PRNS. The study also provided a summary of previous literature concerning other TAC PPK models in different pediatric diseases.

A Theoretical Physiologically-Based Pharmacokinetic Approach to Ascertain Covariates Explaining the Large Interpatient Variability in Tacrolimus Disposition

Physiologically‐based pharmacokinetic (PBPK) modeling allows assessment of the covariates contributing to the large pharmacokinetic (PK) variability of tacrolimus; these include multiple physiological and biochemical differences among patients. A PBPK model of tacrolimus was developed, including a virtual population with physiological parameter distributions reflecting renal transplant patients. The ratios of predicted to observed dose‐normalized maximum plasma concentration (C_max), 0–12‐hour area under the concentration–time curve (AUC_0–12 hour), and trough plasma concentration (C_trough) ranged from 0.92‐fold to 1.15‐fold, indicating good predictive performance. The model quantitatively indicated the impact of cytochrome P450 (CYP)3A4 abundance, hematocrit, and serum albumin levels, in addition to CYP3A5 genotype status, on tacrolimus PK and associated variability. Age‐dependent change in tacrolimus trough concentration in pediatric patients was mainly attributed to the CYP3A ontogeny profile. This study demonstrates the utility of PBPK modeling as a tool for mechanistic and quantitative assessment of the impact of patient physiological differences on observed large PK variability.

The potential impact of hematocrit correction on evaluation of tacrolimus target exposure in pediatric kidney transplant patients

BACKGROUND

Tacrolimus is an important immunosuppressive agent with high intra- and inter-individual pharmacokinetic variability and a narrow therapeutic index. As tacrolimus extensively accumulates in erythrocytes, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations. However, as hematocrit values in pediatric kidney transplant patients are highly variable after kidney transplantation, translating whole blood concentration targets without taking hematocrit into consideration is theoretically incorrect. The aim of this study is to evaluate the potential impact of hematocrit correction on tacrolimus target exposure in pediatric kidney transplant patients.

METHODS

Data were obtained from 36 pediatric kidney transplant patients. Two hundred fifty-five tacrolimus whole blood samples were available, together responsible for 36 area under the concentration-time curves (AUCs) and trough concentrations. First, hematocrit corrected concentrations were derived using a formula describing the relationship between whole blood concentrations, hematocrit, and plasma concentrations. Subsequently, target exposure was evaluated using the converted plasma target concentrations. Ultimately, differences in interpretation of target exposure were identified and evaluated.

RESULTS

In total, 92% of our patients had lower hematocrit (median 0.29) than the reference value of adult kidney transplant patients. A different evaluation of target exposure for either trough level, AUC, or both was defined in 42% of our patients, when applying hematocrit corrected concentrations.

CONCLUSION

A critical role for hematocrit in therapeutic drug monitoring of tacrolimus in pediatric kidney transplant patients is suggested in this study. Therefore, we believe that hematocrit correction could be a step towards improvement of tacrolimus dose individualization.

Clinical aspects of tacrolimus use in paediatric renal transplant recipients

The calcineurin inhibitor tacrolimus, cornerstone of most immunosuppressive regimens, is a drug with a narrow therapeutic window: underexposure can lead to allograft rejection and overexposure can result in an increased incidence of infections, toxicity and malignancies. Tacrolimus is metabolised in the liver and intestine by the cytochrome P450 3A (CYP3A) isoforms CYP3A4 and CYP3A5. This review focusses on the clinical aspects of tacrolimus pharmacodynamics, such as efficacy and toxicity. Factors affecting tacrolimus pharmacokinetics, including pharmacogenetics and the rationale for routine CYP3A5*1/*3 genotyping in prospective paediatric renal transplant recipients, are also reviewed. Therapeutic drug monitoring, including pre-dose concentrations and pharmacokinetic profiles with the available "reference values", are discussed. Factors contributing to high intra-patient variability in tacrolimus exposure and its impact on clinical outcome are also reviewed. Lastly, suggestions for future research and clinical perspectives are discussed.