Population pharmacokinetic analysis of tacrolimus in Chinese myasthenia gravis 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.

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.

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 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.

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.

Multiple genetic factors affecting the pharmacokinetic and pharmacodynamic processes of tacrolimus in Chinese myasthenia gravis patients

PURPOSE

Tacrolimus is a novel effective immunosuppressant for myasthenia gravis (MG) patients. However, the narrow therapeutic window, and high inter- and intrapatient variation in bioavailability largely limited its clinical application. This article intended to find the SNPs influencing clinical outcome and discover the possible mechanisms.

METHODS

Based on the tagSNPs genotyped by Improved Multiple Ligase Detection Reaction, Plink 1.07 was used to find the SNPs having close interaction to tacrolimus serum concentration, QMG score changes or even reasonable drug dose. Then we searched several databases to predict the possible miRNA binding rs15524 sequence. Based on the prediction, dual-luciferase reporter assay and miRNA transfection were used to discover the mechanism of how SNP rs15524 controls tacrolimus serum concentration through influencing CYP3A5 expression.

RESULTS

In this article, we found multiple SNPs on CYP3A4, CYP3A5, FKBP1A, NFATC2 genes were predicted closely related to tacrolimus serum concentration, therapeutic effect which reflected by QMG score changes or even reasonable drug dose. After in silico miRNA selection, possible relationship between hsa-miR-500a and rs15524 was found. With the help of dual-luciferase reporter assay, wild-type rs15524 (T allele) was found having a stronger binding affinity for hsa-miR-500a. Higher expression of CYP3A5 may also led by lower hsa-miR-500a level.

CONCLUSIONS

SNP rs15524 may control CYP3A5 expression by affecting the binding affinity between CYP3A5 3'UTR and hsa-miR-500a. Wild type (T allele) 3'UTR of CYP3A5 has stronger binding affinity to hsa-miR-500a and cause lower CYP3A5 expression and higher tacrolimus serum concentration.