Tacrolimus therapeutic drug monitoring in Tunisian renal transplant recipients: Effect of post-transplantation period

A prospective validation of a population pharmacokinetic model of tacrolimus in Tunisian kidney transplant patients

BACKGROUND

A prospective validation of pharmacokinetic population (Pk pop) of Tacrolimus (Tac) for dose adjustment in kidney transplant patients was assessed in only one study. The present study was aimed at prospectively evaluating the performance of our previously developed Tac- Pk pop model in predicting trough concentration (C0) in Tunisian kidney transplant patients.

PATIENTS AND METHOD

It was a prospective study including patients who had undergone kidney transplantation at Monastir-Nephrology Department. The population study was divided into adherence and control groups.

RESULTS

A total of 198 C0 (30 patients) were analyzed. The proportion of C0 within TR was 63.9% and 38.0% in the adhesion and control group, respectively. The percentage of C0 within TR was significantly higher in the adherence group during both early and late post-transplant period (p = 0.03 and 0.04, respectively). This percentage was found to be significantly higher during the third C0 monitoring and thereafter in the adherence group compared with the control group (65.8% vs 41%, respectively).

CONCLUSION

Tac dose proposal based on this model could be helpful to improve clinical outcomes in our population by reducing the risk of acute rejection and this immunosuppressant's toxic side effects.

Effect of CYP3 A4, CYP3 A5 and ABCB1 gene polymorphisms on the clinical efficacy of tacrolimus in the treatment of nephrotic syndrome

Background

The efficacy of tacrolimus (TAC) is variable in the treatment of nephrotic syndrome (NS), which might be related to genetic variation among patients. Therefore, we aim to investigate the effects of CYP3 A4, CYP3 A5 and ABCB1 gene polymorphisms on the clinical efficacy of TAC in the treatment of NS patients.

Methods 100 NS patients were treated with TAC and prednisone and followed up for 3 months. Genotype differences (CYP3 A4*1G, CYP3 A5*3, ABCB1 1236C > T and ABCB1 2677G > T/A) were detected by Sanger sequencing. The clinical efficacy was evaluated by the 24 h urinary protein quantitation, albumin, renal function and the degree of edema. Multivariable logistic regression was used to analyze the effect of gene polymorphisms on the clinical efficacy of TAC.

Results

There were 35 patients (35%) with complete remission, 43 patients (43%) with partial remission, 22 patients (22%) without remission, and no patients with recurrence. For CYP3A4, there were 56, 42, and 2 patients with *1/*1, *1/*1G and *1G/*1G genotype, respectively. For CYP3A5, there were 8, 36 and 56 cases with*1/*1, *1/*3 and *3/*3 genotype, respectively. For ABCB1 C1236T, there were 10, 44, and 46 cases with 1236CC, 1236CT and 1236TT genotype, respectively. For ABCB1 G2677 T/A, there were 13, 57, and 30 patients with 2677GG genotype, 2677GT/GA genotype and 2677TT/AA/TA genotype, respectively. The mutant allele frequencies of CYP3A4*1G, CYP3A5*3, ABCB1 C1236T and ABCB1 G2677 T/A were 23%, 74%, 68% and 58.5%, respectively. Results reveal that the gene polymorphisms of CYP3A4 and CYP3A5 and CCB do not affect the clinical efficacy of TAC. For ABCB1 C1236T,TT genotype can increase the effectiveness 12.085 times compared with CC and CT genotype (P = 0.018, OR = 12.085, 95%CI 1.535–95.148). For ABCB1 G2677 T/A, the clinical efficacy of patients with mutant genotype is 8.683 times than that of wild-type and heterozygous patients (P = 0.042, OR = 8.683, 95%CI 1.080–69.819). Overweight patients can improve the clinical efficacy by 15.838 times (P = 0.020, OR = 15.838, 95%CI1.550–161.788).

Conclusions

ABCB1 C1236T, ABCB1 G2677 T/A genotype and BMI are probably the factors influencing the clinical efficacy of TAC in treating patients with NS.

Electronic supplementary material

The online version of this article (10.1186/s40360-018-0202-9) contains supplementary material, which is available to authorized users.

Influence of combined CYP3A4 and CYP3A5 single-nucleotide polymorphisms on tacrolimus exposure in kidney transplant recipients: a study according to the post-transplant phase

AIM

The present study investigated in Tunisian renal transplant patients, genetic polymorphisms of CYP3A4 -392A>G and CYP3A5 6986A>G and their influence on tacrolimus (Tac) pharmacokinetics during early and late post-transplant (PT) phases and established customized ranges of Tac doses matching the C0 target levels according to CYP3A4 and CYP3A5 genotype combination and the PT phase.

PATIENTS & METHODS

We included adult Tunisian patients having received Tac for de novo kidney grafts and undergone a therapeutic drug monitoring of Tac by morning C0 monitoring during early (1 to 90 days) and late (over 90 days) PT phases. The genomic DNA was extracted from peripheral blood mononuclear cells using a salting-out procedure. CYP3A4 promoter (rs2740574; -392A>G) and CYP3A5 (rs776746; 6986A>G) SNP genotyping was analyzed using PCR-RFLP.

RESULTS

Fifty-two patients were enrolled in the study. During the early PT phase, the CYP3A5 polymorphism but not that of CYP3A4, correlates significantly with Tac dose-normalized C0 (C0/D ratio). During the late PT phase, the effect of CYP3A4 polymorphism becomes significant and that of CYP3A5 becomes nonsignificant on Tac C0/D Tac. The mean daily doses (mg/kg) matching therapeutic C0, regardless of the CYP3A genotypes, were 0.16 ± 0.05 and 0.10 ± 0.05 during early and late PT phase, respectively. Carriers of the CYP3A4*1B allele require higher doses to maintain the C0 in the therapeutic range during the two PT phases. However, patients carrying the CYP3A5*1 require significant higher Tac doses, only during the early phase.

CONCLUSION

Our data support a critical role of the CYP3A5 6986A>G and CYP3A4 -392A>G polymorphisms on the variation of Tac exposure during the early and the late PT phase, respectively. The establishment of customized Tac doses, according to CYP3A4/CYP3A5 genotype combination and the PT time, may allow preventing graft rejection and improving the safety profile of this drug. Further studies are needed to investigate this issue.

Development of Limited Sampling Strategies for the Estimation of Tacrolimus Area Under the Curve in Adult Kidney Transplant Recipients According to the Posttransplantation Time

BACKGROUND

Limited sampling strategies (LSS), using few sampling times after dosing, have been used to reliably predict tacrolimus area under the 12-hour concentration-time curve (AUC). Because the pharmacokinetics of tacrolimus is subject to significant changes over the exposure time to this drug, it can be hypothesized that the reliability of the LSS would also change. This study aimed to develop a reliable and practical LSS allowing the estimation of tacrolimus AUC in Tunisian kidney transplant recipients taking into account the posttransplantation time.

METHODS

Thirty Tunisian patients were enrolled into 3 groups (10 in each group) according to the posttransplantation period: period 1: between 1 day and 3 months, period 2: between 3 and 12 months and period 3 over 12 months, as defined by the European consensus conference on the therapeutic drug monitoring of tacrolimus. Samples were collected just before and 0.5, 1, 2, 4, 6, 8, and 12 hours after tacrolimus administration. The full pharmacokinetic profiles obtained from these timed concentration data were used to choose the best sampling times. Error indices (mean absolute prediction error and the root mean squared prediction error) were used to evaluate the predictive performance.

RESULTS

Among the 1-point estimations, the C4-predicted AUC showed the highest correlation with the measured one during period 1 and period 2 (r = 0.94 and 0.91, respectively) but not period 3 (r = 0.76). The C0-predicted and the measured AUC become less and less correlated from period 1 to period 3 (r = 0.81, 0.75, and 0.66), respectively. Only the model including the C0/C2 provided a high correlation between predicted and measured tacrolimus AUC regardless of the posttransplant period (r = 0.95, 0.96, 0.98 and root mean squared prediction error = 4.1, 5.8, 4.2 during periods 1, 2, and 3, respectively).

CONCLUSIONS

Our data clearly indicate that the predictive performance of LSS is prone to change according to the posttransplantation time. A 2-time point LSS was found to be sufficient to predict tacrolimus AUC. The LSS using C0 and C2 is reliable, accurate, and practical to estimate the AUC of tacrolimus regardless of the posttransplantation time.