Associations of HSD11B1 polymorphisms with tacrolimus concentrations in Chinese renal transplant recipients with prednisone combined therapy

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.

Subtherapeutic bupropion and hydroxybupropion serum concentrations in a patient with CYP2C19*1/*17 genotype suggesting a rapid metabolizer status

Bupropion is hydroxylated to its primary active metabolite hydroxybupropion by cytochrome P450 enzyme CYP2B6. In vitro data suggest the existence of alternative hydroxylation pathways mediated by the highly polymorphic enzyme CYP2C19. However, the impact of its genetic variants on bupropion metabolism in vivo is still under investigation. We report the case of a 28-year-old male Caucasian outpatient suffering from major depressive disorder who did not respond to a treatment with bupropion. Therapeutic drug monitoring revealed very low serum concentrations of both bupropion and hydroxybupropion. Genotyping identified a heterozygous status for the gain-of-function allele with the genotype CYP2C19*1/*17 predicting enhanced enzymatic activity. The present case shows a reduced bupropion efficacy, which may be explained by a reduced active moiety of bupropion and its active metabolite hydroxybupropion, due to alternative hydroxylation pathways mediated by CYP2C19 in an individual with CYP2C19 rapid metabolizer status. The case report thus illustrates the clinical relevance of therapeutic drug monitoring in combination with pharmacogenetics diagnostics for a personalized treatment approach.

Tacrolimus Levels in the Prophylaxis of Acute Graft-Versus-Host Disease in the Chinese Early After Hematopoietic Stem Cell Transplantation

BACKGROUND

Tacrolimus has been widely accepted as the backbone of acute graft-versus-host disease (aGVHD) prophylaxis in allogeneic hematopoietic stem cell transplantation (alloHSCT). The present work evaluated whether tacrolimus concentrations early after transplant correlate with the incidence of aGVHD in Chinese alloHSCT recipients.

METHODS

One hundred four Chinese alloHSCT recipients were included in this retrospective study. All patients received standard prophylaxis with tacrolimus and short-term methotrexate. Blood samples were taken at steady-state for those on i.v. tacrolimus (Cv) or predose (C0) and 2 hours after the last oral dose (C2).

RESULTS

In the first 8 weeks after alloHSCT, significant variability in Cv, C0, and C2 of Chinese patients was observed. It was found that higher tacrolimus C0 and C2 values tended to be associated with a reduced risk of aGVHD, although this was a nonsignificant trend due to the small sample size involved. Receiver operating characteristic curve analysis indicated that Cv levels of ≥16.52 ng/mL, C0 levels of ≥5.56 ng/mL, and C2 levels of ≥7.83 ng/mL minimized the incidence of treatment failure during weeks 3-4 with intravenous administration and weeks 5-6 with oral administration. There was no statistically significant association of the patient liver and kidney function with the blood concentration of tacrolimus in the desired range of 5-20 ng/mL.

CONCLUSIONS

Tacrolimus therapeutic drug monitoring improved treatment outcomes of Chinese alloHSCT recipients. Cv measurements during weeks 3-4 and C0 or C2 measurements during weeks 5-6 better predicted aGVHD (I-IV) than the concentrations measured at other time points during the first 6 weeks after alloHSCT.