Kidney podocyte-associated gene polymorphisms affect tacrolimus concentration in pediatric patients with refractory nephrotic syndrome

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.

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.

Pharmacogenetic Aspects of Drug Metabolizing Enzymes and Transporters in Pediatric Medicine: Study Progress, Clinical Practice and Future Perspectives

As the activity of certain drug metabolizing enzymes or transporter proteins can vary with age, the effect of ontogenetic and genetic variation on the activity of these enzymes is critical for the accurate prediction of treatment outcomes and toxicity in children. This makes pharmacogenetic research in pediatrics particularly important and urgently needed, but also challenging. This review summarizes pharmacogenetic studies on the effects of genetic polymorphisms on pharmacokinetic parameters and clinical outcomes in pediatric populations for certain drugs, which are commonly prescribed by clinicians across multiple therapeutic areas in a general hospital, organized from those with the most to the least pediatric evidence among each drug category. We also further discuss the research status of the gene-guided dosing regimens and clinical implementation of pediatric pharmacogenetics. More and more drug-gene interactions are demonstrated to have clinical validity for children, and pharmacogenomics in pediatrics have shown evidence-based benefits to enhance the efficacy and precision of existing drug dosing regimens in several therapeutic areas. However, the most important limitation to the implementation is the lack of high-quality, rigorous pediatric prospective clinical studies, so adequately powered interventional clinical trials that support incorporation of pharmacogenetics into the care of children are still needed.

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC.

Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance.

Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42).

Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach

Purpose

Tacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C₀/D) in Chinese children with refractory NS, and then develop and validate the TAC C₀/D prediction models.

Patients and Methods

The association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C₀/D in the third month since TAC treatment was examined in 171 children with refractory NS. Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. The best prediction models were validated on a cohort of 30 refractory NS patients.

Results

GBDT algorithm performed best in the whole group (R²=0.444, MSE=591.032, MAE=20.782, MedAE=18.980) and CYP3A5 nonexpresser group (R²=0.264, MSE=477.948, MAE=18.119, MedAE=18.771), while ET algorithm performed best in the CYP3A5 expresser group (R²=0.380, MSE=1839.459, MAE=31.257, MedAE=19.399). These prediction models included 3 clinical variables (ALB0, AGE0, and gender) and 10 SNPs (ACTN4 rs3745859, ACTN4 rs56113315, ACTN4 rs62121818, CTLA4 rs4553808, CYP3A5 rs776746, IL2RA rs12722489, INF2 rs1128880, MAP3K11 rs7946115, MYH9 rs2239781, and MYH9 rs4821478).

Conclusion

The association between the clinical and genetic variables and TAC C₀/D was described, and three TAC C₀/D prediction models integrating clinical and genetic variables were developed and validated using machine learning, which may support individualized TAC dosing.

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning

Background and Aims: Tacrolimus(TAC)-induced nephrotoxicity, which has a large individual variation, may lead to treatment failure or even the end-stage renal disease. However, there is still a lack of effective models for the early prediction of TAC-induced nephrotoxicity, especially in nephrotic syndrome(NS). We aimed to develop and validate a predictive model of TAC-induced tubular toxicity in children with NS using machine learning based on comprehensive clinical and genetic variables. Materials and Methods: A retrospective cohort of 218 children with NS admitted between June 2013 and December 2018 was used to establish the models, and 11 children were prospectively enrolled for external validation. We screened 47 clinical features and 244 genetic variables. The changes in urine N- acetyl- β-D- glucosaminidase(NAG) levels before and after administration was used as an indicator of renal tubular toxicity. Results: Five machine learning algorithms, including extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), extremely random trees (ET), random forest (RF), and logistic regression (LR) were used for model generation and validation. Four genetic variables, including TRPC6 rs3824934_GG, HSD11B1 rs846910_AG, MAP2K6 rs17823202_GG, and SCARB2 rs6823680_CC were incorporated into the final model. The XGBoost model has the best performance: sensitivity 75%, specificity 77.8%, accuracy 77.3%, and AUC 78.9%. Conclusion: A pre-administration model with good performance for predicting TAC-induced nephrotoxicity in NS was developed and validated using machine learning based on genetic factors. Physicians can estimate the possibility of nephrotoxicity in NS patients using this simple and accurate model to optimize treatment regimen before administration or to intervene in time after administration to avoid kidney damage.

Effect of donor non-muscle myosin heavy chain (MYH9) gene polymorphisms on clinically relevant kidney allograft dysfunction

BACKGROUND

Despite its established association with chronic kidney disease (CKD) the role of myosin-9 (MYH9) gene variation on transplanted kidney function remains unknown. This study aimed at evaluating the effect of donor MYH9 nephrogenic variants on renal allograft function within the first post transplantation year.

METHODS

In the longitudinal kidney transplant study 207 deceased donors were genotyped for previously known risk MYH9 single nucleotide polymorphisms (SNPs). The predictor was MYH9 high-risk variants status. The primary outcome was mean eGFR found in low vs. high risk MYH9 genotypes between third and twelfth post-transplant month, the secondary outcome was the risk of proteinuria.

RESULTS

Distribution of genotypes remained in Hardy-Weinberg equilibrium. The T allele of rs3752462 (dominant model, TT or TC vs. CC) was associated with higher filtration rate (P = 0.05) in a multivariate analysis after adjusting for delayed graft function and donor sex. Two G alleles of rs136211 (recessive model, GG vs. GA or AA) resulted in doubling the risk of proteinuria (OR = 2.22; 95% CI = 1.18-4.37, P = 0.017) after adjusting for donor and recipient sex.

CONCLUSION

Deceased donor kidneys of European descent harboring MYH9 SNPs rs3752462 T allele show significantly superior estimated filtration rate while those of rs136211 GG genotype excessive risk of proteinuria. These findings, if replicated, may further inform and improve individualization of allocation and treatment policies.