Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

A joint population pharmacokinetic model to assess the high variability of whole-blood and intracellular tacrolimus in early adult renal transplant recipients

Tacrolimus (TAC) has high pharmacokinetic (PK) variability during the early transplantation period. The relationships between whole-blood and intracellular TAC concentrations and clinical outcomes remain controversial. This study identifies the factors affecting the PK variability of TAC and characterizes the relationships between whole-blood and intracellular TAC concentrations. Data regarding whole-blood TAC concentrations of 1,787 samples from 215 renal transplant recipients (<90 days postoperative) across two centers and intracellular TAC concentrations (648 samples) digitized from previous studies were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were screened, and the distribution of whole-blood to intracellular TAC concentration ratios (RWB:IC) was estimated. The final model was evaluated using bootstrap, goodness of fit, and prediction-corrected visual predictive checks. The optimal dosing regimens and target ranges for each type of immune cell subsets were determined using Monte Carlo simulations. A two-compartment model adequately described the data, and the estimated mean TAC CL/F was 23.6 L·h-1 (relative standard error: 11.5 %). The hematocrit level, CYP3A5*3 carrier status, co-administration with Wuzhi capsules, and tapering prednisolone dose may contribute to the high variability of TAC PK variability during the early post-transplant period. The estimated RWB:IC of all TAC concentrations in peripheral blood mononuclear cells (PBMCs) was 4940, and inter-center variability of PBMCs was observed. The simulated TAC target range in PBMCs was 20.2-85.9 pg·million cells-1. Inter-center variability in intracellular concentrations should be taken into account in further analyses. TAC dosage adjustments can be guided based on PK/PD variability and simulated intracellular concentrations.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

BACKGROUND

The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients.

METHODS

Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.

RESULTS

In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).

CONCLUSIONS

Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

Rapid identification of potential nonsteroidal anti-inflammatory drug overdose-induced liver toxicity and prediction of follow-up exposure: Integrating bioanalytical and population pharmacokinetic assay

Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most frequently used drugs that can cause liver toxicity. The aim of this study was to integrate bioanalytical and population pharmacokinetic (PopPK) assay to rapidly screen and quantify the concentrations of NSAIDs in plasma and monitor clinical safety. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the simultaneous quantification of acetaminophen (APAP), flurbiprofen (FLB), aspirin (ASP), and ibuprofen (IBP), four commonly used NSAIDs. The PopPK model of the signature toxicant was analyzed based on the published literature. The LC-MS/MS method was successfully validated and applied to determine NSAID concentrations in patient plasma samples. APAP, ASP, and IBP data were best fitted using a one-compartment model, and FLB data were best fitted using a two-compartment model. Bootstrapping and visual predictive checks suggested that a robust and reliable pharmacokinetic model was developed. A fast, simple, and sensitive LC-MS/MS method was developed and validated for determining APAP, FLB, ASP, and IBP in human plasma. Combined with the PopPK model, this method was applied to rapidly analyze the concentrations of NSAIDs in clinical samples from patients presenting to the emergency department with acute liver dysfunction and monitored NSAIDs clinical safety.

CYP3A5 Genotype-Dependent Drug-Drug Interaction Between Tacrolimus and Voriconazole in Chinese Kidney Transplant Patients

BACKGROUND

The effect of drug-drug interaction (DDI) between tacrolimus and voriconazole on the pharmacokinetics of tacrolimus in different CYP3A5 genotypes has not been reported in previous studies.

OBJECTIVE

The objective of this study was to investigate whether CYP3A5 genotype could influence tacrolimus-voriconazole DDI in Chinese kidney transplant patients.

METHODS

All kidney transplant patients were divided into combination and non-combination groups based on whether tacrolimus was combined with or without voriconazole. Each group was subdivided into CYP3A5 expresser (CYP3A5*1/*1 or CYP3A5*1/*3) and CYP3A5 nonexpresser (CYP3A5*3/*3). A retrospective analysis compared tacrolimus dose (D)-corrected trough concentrations (C0) (C0/D) between combination and non-combination groups, respectively. Tacrolimus C0/D was also compared between CYP3A5 expresser and nonexpresser in both groups.

RESULTS

The C0/D values of tacrolimus were significantly different between CYP3A5 expresser and nonexpresser in combination group (378.20 [219.38, 633.48] ng/mL/[mg/kg/d] vs 720.00 [595.35, 1681.50] ng/mL/[mg/kg/d], P = 0.0010). Either in CYP3A5 expresser or nonexpresser, we found a statistically significant difference in tacrolimus C0/D between combination and non-combination group (P < 0.0001). The increase in CYP3A5 nonexpresser was 1.38 times higher than that in CYP3A5 expresser (320.93% vs 232.19%).

CONCLUSION AND RELEVANCE

The median C0/D values were 90.38% higher in kidney transplant recipients with CYP3A5*3/*3 genotype than in those with CYP3A5*1/*1 or CYP3A5*1/*3 genotype when treated with both tacrolimus and voriconazole. A CYP3A5 genotype-dependent DDI was found between tacrolimus and voriconazole. Therefore, personalized therapy accounting for CYP3A5 genotype detection and therapeutic drug monitoring is necessary for kidney transplant patients when treating with tacrolimus and voriconazole.

Meta-analysis of the effects of CYP3A5*3 gene polymorphisms on tacrolimus blood concentration and effectiveness in Chinese patients with membranous nephropathy

Objective

The study aimed to systematically evaluate the relationship between CYP3A5*3 gene polymorphisms and the blood concentration and effectiveness of tacrolimus (TAC) in patients with membranous nephropathy (MN).

Methods

PubMed, Cochrane Library, Embase, Web of Science, China Biomedical, China National Knowledge Infrastructure, Wanfang, Vipshop, ReadShow, Clinical Trials Registry, and other databases were searched. Studies on the relationship between CYP3A5*3 gene polymorphism and TAC blood concentration in MN patients were collected, and meta-analysis was performed using Stata 16 software.

Results

A total of eight publications were included in the study, including 498 MN patients. CYP3A5*3 gene polymorphisms are associated with tacrolimus blood levels in patients with MN. The results of the relationship between CYP3A5*3 genotype polymorphisms and tacrolimus blood trough concentrations of the AA + AG genotype were lower than those of the GG genotype at ≤1 month [WMD = -2.08, 95% CI (-2.57, -1.59), p < 0.001] and 1-6 months [WMD = -0.63, 95% CI (-0.98, -0.27), p < 0.001]; however, they were not statistically significant at ≥6 months (p = 0.211). Furthermore, the subgroup analysis revealed that the dose-adjusted concentration of tacrolimus (C0/D) of the AA + AG genotype was lower than that of the GG genotype at ≤1 month [SMD = -1.93, 95% CI (-2.79, -1.08), p < 0.001], 1-6 months [SMD = -2.25, 95% CI (-2.71, -1.79), p < 0.001], and ≥6 months [SMD = -2.36, 95% CI (-2.86, -1.86), p < 0.001]. In addition, there was no statistically significant difference in effectiveness between the two groups at 3, 6, and 12 months of TAC administration (p > 0.05).

Conclusion

Serum TAC concentrations in MN patients were correlated with CYP3A5*3 genotype polymorphisms. Detection of the CYP3A5*3 genotype before the administration of TAC may provide some clinical value for optimizing the treatment of MN patients.

Systematic Review Registration

https://inplasy.com/, identifier [INPLASY202430083].

Drug-drug interaction between tacrolimus and caspofungin in Chinese kidney transplant patients with different CYP3A5 genotypes

Background

The effect of drug-drug interaction between tacrolimus and caspofungin on the pharmacokinetics of tacrolimus in different CYP3A5 genotypes has not been reported in previous studies.

Objectives

To investigate the effect of caspofungin on the blood concentration and dose of tacrolimus under different CYP3A5 genotypes.

Design

We conducted a retrospective cohort study in The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital from January 2015 to December 2022. All kidney transplant patients were divided into the combination or non-combination group based on whether tacrolimus was combined with caspofungin or not. Patients were subdivided into CYP3A5 expressers (CYP3A5*1/*1 or CYP3A5*1/*3) and CYP3A5 non-expressers (CYP3A5*3/*3).

Methods

Data from the combination and the non-combination groups were matched with propensity scores to reduce confounding by SPSS 22.0. A total of 200 kidney transplant patients receiving tacrolimus combined with caspofungin or not were enrolled in this study. Statistical analysis was conducted on the dose-corrected trough concentrations (C0/D) and dose requirements (D) of tacrolimus using independent sample two-sided t-test and nonparametric tests to investigate the impact on patients with different.

Results

In this study, the C0/D values of tacrolimus were not significantly different between the combination and non-combination groups (p = 0.054). For CYP3A5 expressers, there was no significant difference in tacrolimus C0/D or D values between the combination and non-combination groups (p = 0.359; p = 0.851). In CYP3A5 nonexpressers, the C0/D values of tacrolimus were significantly lower in the combination than in the non-combination groups (p = 0.039), and the required daily dose of tacrolimus was increased by 11.11% in the combination group.

Conclusion

Co-administration of caspofungin reduced tacrolimus blood levels and elevated the required daily dose of tacrolimus. In CYP3A5 non-expressers, co-administration of caspofungin had a significant effect on tacrolimus C0/D values. An approximate 10% increase in the weight-adjusted daily dose of tacrolimus in CYP3A5 non-expressers is recommended to ensure the safety of tacrolimus administration.

Tacrolimus Drug Exposure Level and Smoking Are Modifiable Risk Factors for Early De Novo Malignancy After Liver Transplantation for Alcohol-Related Liver Disease

De novo malignancy (DNM) is the primary cause of mortality after liver transplantation (LT) for alcohol-related liver disease (ALD). However, data on risk factors for DNM development after LT are limited, specifically in patients with ALD. Therefore, we retrospectively analyzed all patients transplanted for ALD at our center before October 2016. Patients with a post-LT follow-up of <12 months, DNM within 12 months after LT, patients not on tacrolimus in the 1st year post-LT, and unknown smoking habits were excluded. Tacrolimus drug exposure level (TDEL) was calculated by area under the curve of trough levels in the 1st year post-LT. 174 patients received tacrolimus of which 19 (10.9%) patients developed a DNM between 12 and 60 months post-LT. Multivariate cox regression analysis identified TDEL [HR: 1.710 (1.211-2.414); p = 0.002], age [1.158 (1.076-1.246); p < 0.001], number of pack years pre-LT [HR: 1.021 (1.004-1.038); p = 0.014] and active smoking at LT [HR: 3.056 (1.072-8.715); p = 0.037] as independent risk factors for DNM. Tacrolimus dose minimization in the 1st year after LT and smoking cessation before LT might lower DNM risk in patients transplanted for ALD.

Effects of Clarithromycin and Ketoconazole on FK506 Metabolism in Different CYP3A4 Genotype Recombinant Metabolic Enzyme Systems

OBJECTIVE

This study aimed to investigate the effects of clarithromycin and ketoconazole on the pharmacokinetic properties of tacrolimus in different CYP3A4 genotype recombinant metabolic enzyme systems, so as to understand the drug interactions and their mechanisms further.

METHOD

The experiment was divided into three groups: a blank control group, CYP3A4*1 group and CYP3A4*18 recombinant enzyme group. Each group was added with tacrolimus (FK506) of a series of concentrations. Then 1 umol/L clarithromycin or ketoconazole was added to the recombinant enzyme group and incubated in the NADPH system for 30 minutes to examine the effects of clarithromycin and ketoconazole on the metabolizing enzymes' activity of different genotypes. The remaining concentration of FK506 in the reaction system was determined using UPLC-MS/MS, and the enzyme kinetic parameters were calculated using the software.

RESULTS

The metabolism of CYP3A4*18 to FK506 was greater than that of CyP3А4*1B. Compared with the CYP3A4*1 group, the metabolic rate and clearance of FK506 in the CYP3A4*18 group significantly increased, with Km decreasing. Clarithromycin and ketoconazole inhibit the metabolism of FK506 by affecting the enzyme activity of CYP3A4*1B and CYP3A4*18B. After adding clarithromycin or ketoconazole, the metabolic rate of FK506 significantly decreased in CYP3A4*1 and CYP3A4*18, with Km increasing, Vmax and Clint decreasing.

CONCLUSION

Compared with CYP3A4*1, CYP3A4*18 has a greater metabolism of FK506, clarithromycin and ketoconazole can inhibit both the enzymatic activities of CYP3A4*1 and CYP3A4*18, consequently affecting the metabolism of FK506 and the inhibitory on CYP3A4*1 is stronger.

Insights into the population pharmacokinetics and pharmacodynamics of quetiapine: a systematic review

INTRODUCTION

Quetiapine exhibits notable pharmacokinetic and pharmacodynamic (PK/PD) variability, the origins of which are poorly understood. This systematic review summarizes published population PK/PD studies and identifies significant covariates accounting for this variability to inform precision dosing.

METHODS

We systematically searched the PubMed, Web of Science, and Embase databases and compared study characteristics, model parameters, and covariate effects. Visual predictive distributions were used to compare different models. Forest plots and Monte Carlo simulations were used to assess the influence of covariates.

RESULTS

Six population PK and three population PK/PD studies were included. The median apparent clearance in adults was 87.7 L/h. Strong and moderate cytochrome P450 3A4 inducers increased the apparent clearance approximately fourfold, while strong cytochrome P450 3A4 inhibitors reduced it by 93%. The half-maximum effect concentrations were 82.8 ng/mL for the Brief Psychiatric Rating Scale and 583 ng/mL for dopamine D2 receptor occupancy. Both treatment duration and quetiapine exposure were associated with weight gain.

CONCLUSIONS

Concurrent administration of potent or moderate CYP3A4 inducers and inhibitors need to be avoided in quetiapine-treated patients. When co-medication is required, it is recommended to adjust the dosage based on therapeutic drug monitoring. Additional research is warranted to delineate the dose-exposure-response relationships of quetiapine and active metabolite norquetiapine in pediatrics, geriatrics, hepatically-impaired patients, and women using contraceptives or are pregnant or menopausal.

PROSPERO REGISTRATION

CRD42023446654.

Induction of CYP3A activity by dexamethasone may not be strong, even at high doses: insights from a case of tacrolimus co-administration

BACKGROUND

Dexamethasone (DEX) induces CYP3A activity in a concentration-dependent manner. However, no study has examined changes in the blood concentration of CYP3A substrate drugs when DEX is administered at high doses. Herein, we present a case in which tacrolimus (TAC), a typical CYP3A substrate drug, was co-administered with a chemotherapy regimen that included high-dose DEX.

CASE PRESENTATION

A 71-year-old woman underwent liver transplantation for hepatocellular carcinoma 18 years prior to her inclusion in this case study. She was receiving TAC orally at 2 mg/day and had a stable trough blood concentration of approximately 4 ng/mL and a trough blood concentration/dose (C/D) ratio of approximately 2. The patient was diagnosed with post-transplant lymphoproliferative disease (histological type: Burkitt's lymphoma) after admission. Thereafter, the patient received cyclophosphamide-prednisolone (CP), followed by two courses of R-HyperCVAD (rituximab, cyclophosphamide, doxorubicin, vincristine, and DEX) and R-MA (rituximab, methotrexate, and cytarabine) replacement therapy. DEX (33 mg/day) was administered intravenously on days 1-4 and days 11-14 of R-HyperCVAD treatment, and aprepitant (APR) was administered on days 1-5 in both courses. The TAC C/D ratio decreased to approximately 1 on day 11 during both courses, and then increased. Furthermore, a decreasing trend in the TAC C/D ratio was observed after R-MA therapy. The decrease in the TAC C/D ratio was attributed to APR administration rather than to DEX.

CONCLUSION

The induction of CYP3A activity by a high dose of DEX may not be strong. The pharmacokinetic information on DEX and in vitro enzyme activity induction studies also suggested that CYP3A activity induction is not prominent under high-dose DEX treatment.

A Physiologically Based Pharmacokinetic Approach to Recommend an Individual Dose of Tacrolimus in Adult Heart Transplant Recipients

Tacrolimus is the principal immunosuppressive drug which is administered after heart transplantation. Managing tacrolimus therapy is challenging due to a narrow therapeutic index and wide pharmacokinetic (PK) variability. We aimed to establish a physiologically based pharmacokinetic (PBPK) model of tacrolimus in adult heart transplant recipients to optimize dose regimens in clinical practice. A 15-compartment full-PBPK model (Simbiology® Simulator, version 5.8.2) was developed using clinical observations from 115 heart transplant recipients. This study detected 20 genotypes associated with tacrolimus metabolism. CYP3A5*3 (rs776746), CYP3A4*18B (rs2242480), and IL-10 G-1082A (rs1800896) were identified as significant genetic covariates in tacrolimus pharmacokinetics. The PBPK model was evaluated using goodness-of-fit (GOF) and external evaluation. The predicted peak blood concentration (Cmax) and area under the drug concentration-time curve (AUC) were all within a two-fold value of the observations (fold error of 0.68-1.22 for Cmax and 0.72-1.16 for AUC). The patients with the CYP3A5*3/*3 genotype had a 1.60-fold increase in predicted AUC compared to the patients with the CYP3A5*1 allele, and the ratio of the AUC with voriconazole to alone was 5.80 when using the PBPK model. Based on the simulation results, the tacrolimus dosing regimen after heart transplantation was optimized. This is the first PBPK model used to predict the PK of tacrolimus in adult heart transplant recipients, and it can serve as a starting point for research on immunosuppressive drug therapy in heart transplant patients.

Dosage optimization of tacrolimus based on the glucocorticoid dose and pharmacogenetics in adult patients with systemic lupus erythematosus

BACKGROUND

The purpose of the study was to develop a genotype-incorporated population pharmacokinetic (PPK) model of tacrolimus (TAC) in adults with systemic lupus erythematosus (SLE) to investigate the factors influencing TAC pharmacokinetics and to develop an individualized dosing regimen based on the model. In addition, a non-genotype-incorporated model was also established to assess its predictive performance compared to the genotype-incorporated model.

METHODS

A total of 365 trough concentrations from 133 adult SLE patients treated with TAC were collected to develop a genotype-incorporated PPK model and a non-genotype-incorporated PPK model of TAC using a nonlinear mixed-effects model (NONMEM). External validation of the two models was performed using data from an additional 29 patients. Goodness-of-fit diagnostic plots, bootstrap method, and normalized predictive distribution error test were used to validate the predictive performance and stability of the final models. The goodness-of-fit of the two final models was compared using the Akaike information criterion (AIC). The dosing regimen was optimized using Monte Carlo simulations based on the developed optimal model.

RESULTS

The typical value of the apparent clearance (CL/F) of TAC estimated in the final genotype-incorporated model was 14.3 L h-1 with inter-individual variability of 27.6%. CYP3A5 polymorphism and coadministered medication were significant factors affecting TAC-CL/F. CYP3A5 rs776746 GG genotype carriers had only 77.3% of the TAC-CL/F of AA or AG genotype carriers. Omeprazole reduced TAC-CL/F by 3.7 L h-1 when combined with TAC, while TAC-CL/F increased nonlinearly as glucocorticoid dose increased. Similar findings were demonstrated in the non-genotype-incorporated PPK model. Comparing these two models, the genotype-incorporated PPK model was superior to the non-genotype-incorporated PPK model (AIC = 643.19 vs. 657.425). Monte Carlo simulation based on the genotype-incorporated PPK model indicated that CYP3A5 rs776746 AA or AG genotype carriers required a 1/2-1 fold higher dose of TAC than GG genotype carriers to achieve the target concentration. And as the daily dose of prednisone increases, the dose of TAC required to reach the target concentration increases appropriately.

CONCLUSIONS

We developed the first pharmacogenetic-based PPK model of TAC in adult patients with SLE and proposed a dosing regimen based on glucocorticoid dose and CYP3A5 genotype according to the model, which could facilitate individualized dosing for TAC.

A prospective controlled, randomized clinical trial of kidney transplant recipients developed personalized tacrolimus dosing using model-based Bayesian Prediction

For three decades, tacrolimus (Tac) dose adjustment in clinical practice has been calculated empirically according to the manufacturer's labeling based on a patient's body weight. Here, we developed and validated a Population pharmacokinetic (PPK) model including pharmacogenetics (cluster CYP3A4/CYP3A5), age, and hematocrit. Our study aimed to assess the clinical applicability of this PPK model in the achievement of Tac Co (therapeutic trough Tac concentration) compared to the manufacturer's labelling dosage. A prospective two-arm, randomized, clinical trial was conducted to determine Tac starting and subsequent dose adjustments in 90 kidney transplant recipients. Patients were randomized to a control group with Tac adjustment according to the manufacturer's labeling or the PPK group adjusted to reach target Co (6-10 ng/ml) after the first steady state (primary endpoint) using a Bayesian prediction model (NONMEM). A significantly higher percentage of patients from the PPK group (54.8%) compared with the control group (20.8%) achieved the therapeutic target fulfilling 30% of the established superiority margin defined. Patients receiving PPK showed significantly less intra-patient variability compared to the control group, reached the Tac Co target sooner (5 days vs 10 days), and required significantly fewer Tac dose modifications compared to the control group within 90 days following kidney transplant. No statistically significant differences occurred in clinical outcomes. Thus, PPK-based Tac dosing offers significant superiority for starting Tac prescription over classical labeling-based dosing according to the body weight, which may optimize Tac-based therapy in the first days following transplantation.

A Drug Safety Concept (I) to Avoid Polypharmacy Risks in Transplantation by Individual Pharmacotherapy Management in Therapeutic Drug Monitoring of Immunosuppressants

For several, also vital medications, such as immunosuppressants in solid organ and hematopoietic stem cell transplantation, therapeutic drug monitoring (TDM) remains the only strategy for fine-tuning the dosage to the individual patient. Especially in severe clinical complications, the intraindividual condition of the patient changes abruptly, and in addition, drug-drug interactions (DDIs) can significantly impact exposure, due to concomitant medication alterations. Therefore, a single TDM value can hardly be the sole basis for optimal timely dose adjustment. Moreover, every intraindividually varying situation that affects the drug exposure needs synoptic consideration for the earliest adjustment. To place the TDM value in the context of the patient's most detailed current condition and concomitant medications, the Individual Pharmacotherapy Management (IPM) was implemented in the posttransplant TDM of calcineurin inhibitors assessed by the in-house laboratory. The first strategic pillar are the defined patient scores from the electronic patient record. In this synopsis, the Summaries of Product Characteristics (SmPCs) of each drug from the updated medication list are reconciled for contraindication, dosing, adverse drug reactions (ADRs), and DDIs, accounting for defined medication scores as a second pillar. In parallel, IPM documents the resulting review of each TDM value chronologically in a separate electronic Excel file throughout each patient's transplant course. This longitudinal overview provides a further source of information at a glance. Thus, the applied two-arm concept of TDM and IPM ensures an individually tailored immunosuppression in the severely susceptible early phase of transplantation through digital interdisciplinary networking, with instructive and educative recommendations to the attending physicians in real-time. This concept of contextualizing a TDM value to the precise patient's condition and comedication was established at Halle University Hospital to ensure patient, graft, and drug safety.

Investigating Tacrolimus Disposition in Paediatric Patients with a Physiologically Based Pharmacokinetic Model Incorporating CYP3A4 Ontogeny, Mechanistic Absorption and Red Blood Cell Binding

Tacrolimus is a crucial immunosuppressant for organ transplant patients, requiring therapeutic drug monitoring due to its variable exposure after oral intake. Physiologically based pharmacokinetic (PBPK) modelling has provided insights into tacrolimus disposition in adults but has limited application in paediatrics. This study investigated age dependency in tacrolimus exposure at the levels of absorption, metabolism, and distribution. Based on the literature data, a PBPK model was developed to predict tacrolimus exposure in adults after intravenous and oral administration. This model was then extrapolated to the paediatric population, using a unique reference dataset of kidney transplant patients. Selecting adequate ontogeny profiles for hepatic and intestinal CYP3A4 appeared critical to using the model in children. The best model performance was achieved by using the Upreti ontogeny in both the liver and intestines. To mechanistically evaluate the impact of absorption on tacrolimus exposure, biorelevant in vitro solubility and dissolution data were obtained. A relatively fast and complete release of tacrolimus from its amorphous formulation was observed when mimicking adult or paediatric dissolution conditions (dose, fluid volume). In both the adult and paediatric PBPK models, the in vitro dissolution profiles could be adequately substituted by diffusion-layer-based dissolution modelling. At the level of distribution, sensitivity analysis suggested that differences in blood plasma partitioning of tacrolimus may contribute to the variability in exposure in paediatric patients.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients

OBJECTIVE

Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset.

METHOD

PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance.

RESULT

A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting.

CONCLUSION

The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

A Joint Pharmacokinetic Model for the Simultaneous Description of Plasma and Whole Blood Tacrolimus Concentrations in Kidney and Lung Transplant Recipients

BACKGROUND AND OBJECTIVE

Historically, dosing of tacrolimus is guided by therapeutic drug monitoring (TDM) of the whole blood concentration, which is strongly influenced by haematocrit. The therapeutic and adverse effects are however expected to be driven by the unbound exposure, which could be better represented by measuring plasma concentrations.

OBJECTIVE

We aimed to establish plasma concentration ranges reflecting whole blood concentrations within currently used target ranges.

METHODS

Plasma and whole blood tacrolimus concentrations were determined in samples of transplant recipients included in the TransplantLines Biobank and Cohort Study. Targeted whole blood trough concentrations are 4-6 ng/mL and 7-10 ng/mL for kidney and lung transplant recipients, respectively. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. Simulations were performed to infer plasma concentration ranges corresponding to whole blood target ranges.

RESULTS

Plasma (n = 1973) and whole blood (n = 1961) tacrolimus concentrations were determined in 1060 transplant recipients. A one-compartment model with fixed first-order absorption and estimated first-order elimination characterised observed plasma concentrations. Plasma was linked to whole blood using a saturable binding equation (maximum binding 35.7 ng/mL, 95% confidence interval (CI) 31.0-40.4 ng/mL; dissociation constant 0.24 ng/mL, 95% CI 0.19-0.29 ng/mL). Model simulations indicate that patients within the whole blood target range are expected to have plasma concentrations (95% prediction interval) of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

CONCLUSION

Whole blood tacrolimus target ranges, currently used to guide TDM, were translated to plasma concentration ranges of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

Population PK/PD model of tacrolimus for exploring the relationship between accumulated exposure and quantitative scores in myasthenia gravis patients

Tacrolimus is an important immunosuppressant used in the treatment of myasthenia gravis (MG). However, the population pharmacokinetic (PK) characteristics together with the exposure-response of tacrolimus in the treatment of MG remain largely unknown. In this study, we aimed to develop a population PK/pharmacodynamic (PK/PD) model of tacrolimus in patients with MG, in order to explore the relationships among tacrolimus dose, exposure, and its therapeutic efficacy. The genotype of CYP3A5, Osserman's classification, and status of thymus, as well as demographic characteristics and other biomarkers from laboratory testing were tested as covariate, and simulations were performed based on the final model. The population PK model was described using a one-compartment model with first-order elimination and fixed absorption parameters. CYP3A5 genotype significantly influenced the apparent clearance, and total protein (TP) influenced the apparent volume of distribution as covariates. The quantitative MG scores were characterized by the cumulated area under curve of tacrolimus in a maximum effect function. Osserman's classification was a significant covariate on the initial score of patients with MG. The simulations demonstrated that tacrolimus showed an unsatisfying effect possibly due to insufficient exposure in some patients with MG. A starting dose of 2 mg/d and even higher dose for patients with CYP3A5 *1/*1 and *1/*3 and lower TP level were required for the rapid action of tacrolimus. The population PK/PD model quantitatively described the relationships among tacrolimus dose, exposure, and therapeutic efficacy in patients with MG, which could provide reference for the optimization of tacrolimus dosing regimen at the individual patient level.

Development and validation of a population pharmacokinetic model to guide perioperative tacrolimus dosing after lung transplantation

Background

Tacrolimus therapy is standard of care for immunosuppression after lung transplantation. However, tacrolimus exposure variability during the early postoperative period may contribute to poor outcomes in this population. Few studies have examined tacrolimus pharmacokinetics (PK) during this high-risk time period.

Methods

We conducted a retrospective pharmacokinetic study in lung transplant recipients at the University of Pennsylvania who were enrolled in the Lung Transplant Outcomes Group (LTOG) cohort. We derived a model in 270 patients using NONMEM (version 7.5.1) and examined validity in a separate cohort of 114 patients. Covariates were examined with univariate analysis and multivariable analysis was developed using forward and backward stepwise selection. Performance of the final model in the validation cohort was examined with calculation of mean prediction error (PE).

Results

We developed a one-compartment base model with a fixed rate absorption constant. Significant covariates in multivariable analysis were postoperative day, hematocrit, transplant type, CYP3A5 genotype, total body weight, and time-varying postoperative day, hematocrit, and CYP inhibitor drugs. The strongest predictor of tacrolimus clearance was postoperative day, with median predicted clearance increasing more than threefold over the 14 day study period. In the validation cohort, the final model showed a mean PE of 36.4% (95%CI 30.8%-41.9%) and a median PE of 7.2% (IQR -29.3%-70.53%).

Conclusion

Postoperative day was the strongest predictor of tacrolimus exposure in the early post-lung transplant period. Future multicenter studies employing intensive sampling to examine a broad set of variables related to critical illness physiology are needed to understand determinants of clearance, volume of distribution and absorption in this population.

Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review

BACKGROUND AND OBJECTIVES

Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research.

METHODS

The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach.

RESULTS

Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h^-1 (median of 14.9 L·h^-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1.

CONCLUSION

The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.

Population Pharmacokinetic Analysis for Model-Based Therapeutic Drug Monitoring of Tacrolimus in Chinese Han Heart Transplant Patients

BACKGROUND AND OBJECTIVE

Tacrolimus has become the first-line immunosuppressant for preventing rejection after heart transplantation. The present study aimed to investigate genetic variants and clinical factors affecting the variability of tacrolimus in Chinese Han heart transplant patients using a population pharmacokinetic approach.

METHODS

The retrospective study included 53 hospitalized patients with 547 tacrolimus concentrations for analysis. Nonlinear mixed-effects modeling was used to develop the population pharmacokinetics model for tacrolimus in patients with heart transplants, followed by Monte Carlo simulations to design initial dosing regimens.

RESULTS

In our study, the mutation rate of CYP3A4*18B (C>T) was 27.36%. An oral one-compartment model with first-order absorption and elimination was used to describe the pharmacokinetics of tacrolimus in heart transplant patients. In the final model, the estimated apparent clearance (CL/F) and volume of distribution (V/F) were 532.5 L/h [12.20% interindividual variability, IIV] and 16.87 L (23.16% IIV), respectively. Albumin, postoperative time, and rs2242480 (CYP3A4*18B) gene polymorphisms were the significant covariates affecting CL/F, and creatinine clearance had significant effects on the V/F.

CONCLUSION

The population pharmacokinetic model of tacrolimus in heart transplant patients can better estimate the population and individual pharmacokinetic parameters of patients and can provide a reference for the design of individualized dosing regimens.

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 Pharmacokinetic Evaluation with External Validation of Tacrolimus in Chinese Primary Nephrotic Syndrome Patients

PURPOSE

The generalizability of numerous tacrolimus population pharmacokinetic (popPK) models constructed to promote optimal tacrolimus dosing in patients with primary nephrotic syndrome (PNS) is unclear. This study aimed to evaluate the predictive performance of published tacrolimus popPK models for PNS patients with an external data set.

METHODS

We prospectively collected 223 concentrations from 50 Chinese adult patients with PNS who were undergoing tacrolimus treatment. Data on published tacrolimus popPK models for adults and children with PNS were extracted from the literature. Model predictability was evaluated with prediction-based and simulation-based diagnostics and Bayesian forecasting.

RESULTS

In prediction-based evaluation, none of the 11 identified published popPK models of tacrolimus had met a predefined criteria of a mean prediction error ≤  ± 20%, and the prediction error within ± 30% of the identified models didn't exceed 50%. Simulation-based diagnostics also indicated unsatisfactory predictability. Bayesian forecasting demonstrated amelioration in the model predictability with the inclusion of 2-3 prior observations. Moreover, the predictive performance of nonlinear models was not better than that of one-compartment models.

CONCLUSIONS

The prediction of tacrolimus concentrations for patients with PNS remains challenging; published models are not applicable for extrapolation to other hospitals. Bayesian forecasting significantly improved model predictability and thereby helped to individualize tacrolimus dosing.

Body composition is associated with tacrolimus pharmacokinetics in kidney transplant recipients

Purpose

A population pharmacokinetic (popPK) model may be used to improve tacrolimus dosing and minimize under- and overexposure in kidney transplant recipients. It is unknown how body composition parameters relate to tacrolimus pharmacokinetics and which parameter correlates best with tacrolimus exposure. The aims of this study were to investigate which body composition parameter has the best association with the pharmacokinetics of tacrolimus and to describe this relationship in a popPK model.

Methods

Body composition was assessed using bio-impedance spectroscopy (BIS). Pharmacokinetic analysis was performed using nonlinear mixed effects modeling (NONMEM). Lean tissue mass, adipose tissue mass, over-hydration, and phase angle were measured with BIS and then evaluated as covariates. The final popPK model was evaluated using goodness-of-fit plots, visual predictive checks, and a bootstrap analysis.

Results

In 46 kidney transplant recipients, 284 tacrolimus concentrations were measured. The base model without body composition parameters included age, plasma albumin, plasma creatinine, CYP3A4 and CYP3A5 genotypes, and hematocrit as covariates. After full forward inclusion and backward elimination, only the effect of the phase angle on clearance (dOFV =  − 13.406; p < 0.01) was included in the final model. Phase angle was positively correlated with tacrolimus clearance. The inter-individual variability decreased from 41.7% in the base model to 34.2% in the final model. The model was successfully validated.

Conclusion

The phase angle is the bio-impedance spectroscopic parameter that correlates best with tacrolimus pharmacokinetics. Incorporation of the phase angle in a popPK model can improve the prediction of an individual’s tacrolimus dose requirement after transplantation.

Supplementary information

The online version contains supplementary material available at 10.1007/s00228-022-03323-0.

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.

Effects Of Voriconazole Exposure on The Pharmacokinetics of Tacrolimus in Lung Transplantation Patients: Based on Therapeutic Drug Monitoring Data

Tacrolimus and voriconazole are usually used simultaneously in lung transplantations. Voriconazole can increase tacrolimus concentrations by inhibiting the CYP enzyme, which poses a great challenge for dose adjustment. The aim of this study is to clarify the correlation between voriconazole exposure and tacrolimus trough concentrations (C₀ ), and to establish a population pharmacokinetic model including voriconazole trough concentrations (VOZ) as a covariate for dose optimization. All data were retrospectively collected from lung transplantation patients who were subjected to the therapeutic drug monitoring of tacrolimus and voriconazole. The correlation between C₀ and VOZ or voriconazole daily doses was analyzed by Spearman's correlation. A total of 52 patients accounting for 351 pairs of tacrolimus and voriconazole trough concentrations were included. C₀ and C₀ /DD had a significant correlation with VOZ (P<0.01) rather than voriconazole daily doses. A linear one-compartment model with first-order absorption and elimination was used as basic model in population pharmacokinetic analysis. The body weight (WT), daily dose of tacrolimus (DD), VOZ, and hematocrit (HCT) were included as covariates in the final model. With the increase in voriconazole concentrations, the apparent total clearance (CL/F) of tacrolimus decreased significantly. The simulation results showed that the highest proportion of C₀ within the target range can only reach lower than 50% when optimal initial drug regimen was given. Therefore, both tacrolimus and voriconazole concentrations need to be continuously monitored during treatments in lung transplantation patients, and the tacrolimus dose can be optimized according to VOZ based on the established pharmacokinetic model. This article is protected by copyright. All rights reserved.

Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software

BACKGROUND

Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations.

METHODS

A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.

RESULTS

The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.

CONCLUSIONS

Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

BACKGROUND AND AIM

Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments.

METHODS

Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use.

RESULTS

Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations.

CONCLUSION

All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Effects of Postoperative Day and NR1I2 on Tacrolimus Clearance in Chinese Liver Transplant Recipients-A Population Model Approach

We aimed to explore the new biomarkers influencing tacrolimus in vivo behavior in Chinese liver transplant recipients. A total of 418 drug concentration samples of 41 liver transplant patients were collected for modeling. A population pharmacokinetic model was developed using the nonlinear mixed-effects modeling approach. The potential covariates, such as postoperative day (POD), age, body weight, hepatic and renal function, and recipient genetic polymorphisms (ABCB1, CYP3A4, CYP3A5, NR1I2) were evaluated using forward-inclusion and backward-elimination methods. A 1-compartment model was used describing the in vivo behavior of tacrolimus in liver transplant patients. The estimates of CL/F and V/F were 8.88 L/h and 495.82 L, respectively. Two covariates, POD and NR1I2 rs2276707 genotypes, were incorporated into the final population pharmacokinetic model, and they could significantly impact the CL/F: CL/F (L/h) = 8.88 × (POD/16)^0.18 × e^{0.91 × NR1I2} × e^ηCL . The model evaluation and validation indicated a stable and precise performance of the final model. The functional annotation using ENCODE data indicated that rs2276707 was located on the higher peak of the H3K4Me1 and H3K4Me3 histone marker. To our knowledge, this is the first report indicating NR1I2 rs2276707 genotypes is another biomarker impacting tacrolimus clearance in liver transplant recipients. The NR1I2 gene polymorphism may affect the in vivo behavior of tacrolimus by regulating gene expression.

Identifying the association between tacrolimus exposure and toxicity in heart and lung transplant recipients: A systematic review

AIMS

Tacrolimus is the cornerstone of immunosuppression management in heart and lung transplant recipients, improving overall survival. However, tacrolimus-associated toxicities, including nephrotoxicity, neurotoxicity, new-onset diabetes mellitus after transplant (NODAT), and gastrointestinal toxicity, are known contributors to increased post-transplant morbidity outcomes and reduced graft and recipient survival rates. The aim of this systematic review was to identify correlations between pharmacokinetic measures of tacrolimus exposure in heart and lung recipients and tacrolimus toxicities.

METHODS

MEDLINE, Embase, the Cochrane Library, CENTRAL and WHO Clinical Trial Registries were searched for published studies evaluating tacrolimus toxicities and their correlation to pharmacokinetic monitoring parameters in thoracic transplant recipients. Studies were reviewed by two authors, with data extracted for evaluation. Risk of bias was assessed using the PEDro scale for randomised control trials and the Newcastle Ottawa Scale for non-randomised cohort studies.

RESULTS

Eighteen studies were eligible; a randomised control trial, 11 observational cohort studies, and 6 case series or studies. Of these, 9 studies were in heart transplant recipients alone and 5 in lung transplant recipients alone, 2 studies were in heart and lung transplant recipients and 2 were heart, lung, liver or renal transplant recipients. Studies used variable criteria to define toxicities. Tacrolimus trough concentration (C₀) was the marker of tacrolimus exposure most commonly used. Ten studies reported on nephrotoxicity. Elevated tacrolimus C₀ was associated with acute kidney injury occurrence and severity in three observational studies. Increasing C₀ was a predictor of renal impairment in 6 studies. One study found that for each 5 ng/mL per year of tacrolimus exposure, defined by consecutive AUC, eGFR declined by 1.3 mL/min/1.73m² (p < 0.001). Comparatively, 2 studies failed to find a significant association between nephrotoxicity and tacrolimus exposure. Seven studies reported on neurotoxicity, including neuro-encephalopathies, polyneuropathies and symptomatic change in neurological status. Neurotoxicity occurred both with tacrolimus C₀ within therapeutic range and with supratherapeutic C₀. No significant association was found between NODAT and tacrolimus C₀ in two studies. One study reported on gastrointestinal toxicity, with supratherapeutic C₀ and elevated peak concentration in one lung transplant recipient three days prior to symptom development.

CONCLUSION

No clearly defined relationship between tacrolimus exposure and toxicities is described in the literature. Studies with clear toxicity criteria and pharmacokinetic markers of tacrolimus exposure are required to provide valuable information that may optimise tacrolimus therapy, helping to reduce toxicities in heart and lung transplant recipients.

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.

Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients

Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.