Evaluation of Pharmacogenetics of Drug-Metabolizing Enzymes and Drug Efflux Transporter in Renal Transplants Receiving Immunosuppressants

Retrospective pharmacogenetic study in a cohort of pediatric tuberous sclerosis complex patients using everolimus

Aim: Tuberous sclerosis complex (TSC) is a rare disease that produces multisystemic disorders. Everolimus (EVR) is the only immunosuppressive drug approved to control the symptoms and progression of the disease. The aim was to evaluate the genotype-phenotype association to improve the pediatric TSC pharmacotherapeutic outcome. Patients & methods: Ten pediatric TSC patients were recruited. Concomitant treatment and main metabolic enzymes and transporter coding gene variants of EVR were analyzed. Results: Significant associations were found between CYP3A4*22 allele and concomitant treatment with valproic acid (CYP3A4-inhibitor) with a poor metabolizer phenotype and the presence of pneumonia. Conclusion: This is the first pharmacogenetic study of EVR in pediatric TSC patients. The authors propose to consider concomitant treatment and pharmacogenetics due to their multifactorial status.

Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants

BACKGROUND

Single nucleotide polymorphisms influence the effects of tacrolimus and cyclosporine in renal transplants.

AIM

We set out to use machine learning algorithms (MLAs) to identify variables that predict the therapeutic effects and adverse events following tacrolimus and cyclosporine administration in renal transplant patients.

METHOD

We sampled 120 adult renal transplant patients (on cyclosporine or tacrolimus). Generalized linear model (GLM), support vector machine (SVM), artificial neural network (ANN), Chi-square automatic interaction detection, classification and regression tree, and K-nearest neighbors were the chosen MLAs. The mean absolute error (MAE), relative mean square error (RMSE), and regression coefficient (β) with a 95% confidence interval (CI) were used as the model parameters.

RESULTS

For a stable dose of tacrolimus, the MAEs (RMSEs) of GLM, SVM, and ANN were 1.3 (1.5), 1.3 (1.8), and 1.7 (2.3) mg/day, respectively. GLM revealed that the POR*28 genotype and age significantly predicted the stable dose of tacrolimus as follows: POR*28 (β -1.8; 95% CI -3, -0.5; p = 0.006), and age (β -0.04; 95% CI -0.1, -0.006; p = 0.02). For a stable dose of cyclosporine, MAEs (RMSEs) of 93.2 (103.4), 79.1 (115.2), and 73.7 (91.7) mg/day were observed with GLM, SVM, and ANN, respectively. GLM revealed the following predictors of a stable dose of cyclosporine: CYP3A5*3 (β -80.8; 95% CI -130.3, -31.2; p = 0.001), and age (β -3.4; 95% CI -5.9, -0.9; p = 0.007).

CONCLUSION

We observed that various MLAs could identify significant predictors that were useful to optimize tacrolimus and cyclosporine dosing regimens; yet, the findings must be externally validated.

A Low Tacrolimus Concentration-to-Dose Ratio Increases Calcineurin Inhibitor Nephrotoxicity and Cytomegalovirus Infection Risks in Kidney Transplant Recipients: A Single-Center Study in Japan

BACKGROUND

Tacrolimus (TAC) has several problems due to its narrow therapeutic window and variations pharmacokinetics and pharmacodynamics. Recently, several studies reported that TAC metabolism, defined by TAC blood trough concentration to dose (C/D) ratio, was associated with TAC toxicity. Reports on once-daily extended-release TAC (TAC-ER) are limited. The present study aimed to investigate the effect of the TAC metabolic rate on TAC-ER and compare TAC area under the curve (AUC) between fast and slow metabolizers.

METHODS

A total of 58 recipients were included in this study. The optimal cut-off value and time of the C/D ratio on TAC-ER for fast and slow metabolizers was determined using receiver operating characteristic curve analysis for biopsy-proven calcineurin inhibitor (CNI) nephrotoxicity.

RESULTS

The optimal time to evaluate the C/D ratio was 1 month after kidney transplantation (KT) and the cut-off value was 0.9. The multivariate analysis for CNI nephrotoxicity risk showed that only TAC metabolism was associated with CNI nephrotoxicity (hazard ratio 10.60, P = .005, 95% CI 2.03-55.22). Cytomegalovirus infection occurred more frequently in fast metabolizers when the cut-off value of the C/D ratio was set to 0.9 at 3 months after KT (P = .04). The TAC C₄, AUC_2-8, was higher in fast metabolizers than in slow metabolizers (P < .01, P = .03, respectively).

CONCLUSION

The study revealed that TAC fast metabolizers on TAC-ER may be classified as a high-risk group for CNI nephrotoxicity and cytomegalovirus infection. The result of TAC AUC supported the hypothesis that fast metabolizers tended to be overexposed to immunosuppressive agents early after oral administration.

Effects of NR1I2 and ABCB1 Genetic Polymorphisms on Everolimus Pharmacokinetics in Japanese Renal Transplant Patients

The purpose of this study was to evaluate the effects of NR1I2 (7635G>A and 8055C>T) and ABCB1 (1236C>T, 2677G>T/A, and 3435C>T) genetic polymorphisms on everolimus pharmacokinetics in 98 Japanese renal transplant patients. On day 15 after everolimus administration, blood samples were collected just prior to and 1, 2, 3, 4, 6, 9, and 12 h after administration. The dose-adjusted area under the blood concentration−time curve (AUC0-12) of everolimus was significantly lower in patients with the NR1I2 8055C/C genotype than in those with other genotypes (p = 0.022) and was significantly higher in male patients than female patients (p = 0.045). Significant correlations between the dose-adjusted AUC0-12 of everolimus and age (p = 0.001), aspartate transaminase (p = 0.001), and alanine transaminase (p = 0.005) were found. In multivariate analysis, aging (p = 0.008) and higher alanine transaminase levels (p = 0.032) were independently predictive of a higher dose-adjusted everolimus AUC0-12. Aging and hepatic dysfunction in patients may need to be considered when evaluating dose reductions in everolimus. In renal transplant patients, management using everolimus blood concentrations after administration may be more important than analysis of NR1I2 8055C>T polymorphism before administration.