Our results show that Advagraf bioequivalence cannot be ensured in this population. Significant changes in tacrolimus levels and dose were observed on long-term follow-up.

The recommended dose of Advagraf for conversion from Prograf is considered to be 1:1 on a milligram basis. However, the long-term equivalence of Prograf and Advagraf has been questioned. The relative bioavailability of Advagraf and Prograf was evaluated in a single-center, open-label study of Prograf-to-Advagraf conversion in 20 patients, ranging in age from 12 to 18 years, who had a stable liver transplant and were receiving Prograf. After the supervised administration of Prograf for 7 days, the patients were converted to Advagraf. On days 7 and 14, serial blood samples were obtained for tacrolimus determinations. The pharmacokinetic parameters were calculated with a noncompartmental approach, and the relative bioavailability of both formulations was calculated according to standard statistical methods. Polymorphisms in cytochrome P450 3A5 (rs776746), adenosine triphosphate-binding cassette B1 (rs1045642), POR*28 (rs1057868), and POR (rs2868177) were determined with standard methods. The clinical and analytical data from a 1-year follow-up period were collected for all patients 30, 90, 180, and 360 days after conversion. The mean ratios for Cmax and AUC0-24 were 96.9 (90% confidence interval = 85.37-110.19) and 100.1 (90% confidence interval = 90.8-112.1), respectively. No relationship was found between the patients' genotypes and the pharmacokinetic tacrolimus values. During the follow-up, biochemical parameters (aspartate aminotransferase, alanine aminotransferase, bilirubin, cystatin C, and creatinine) did not change significantly; 3 patients presented with relevant clinical events, but no event was considered to be related to tacrolimus. A decrease in tacrolimus blood levels and an increase in dose/level ratios were observed 3 and 6 months after conversion, but they returned to basal levels by month 12. In conclusion, conversion from Prograf to Advagraf with a 1:1 dose equivalence is appropriate as an initial guideline. Our 1-year follow-up showed a transient decrease in tacrolimus levels, so closer monitoring of tacrolimus levels may be required after conversion.

SummaryTo develop limited sampling strategies (LSSs) to predict total tacrolimus exposure (AUC 0-24 ) after the administration of Advagraf â and Prograf â (Astellas Pharma S.A, Madrid, Spain) to pediatric patients with stable liver or kidney transplants. Forty-one pharmacokinetic profiles were obtained after Prograf â and Advagraf â administration. LSSs predicting AUC 0-24 were developed by linear regression using three extraction time points. Selection of the most accurate LSS was made based on the r 2 , mean error, and mean absolute error. All selected LSSs had higher correlation with AUC 0-24 than the correlation found between C 0 and AUC 0-24 . Best LSS for Prograf â in liver transplants was C 0_1.5_4 (r 2 = 0.939) and for kidney transplants C 0_1_3 (r 2 = 0.925). For Advagraf â , the best LSS in liver transplants was C 0_1_2.5 (r 2 = 0.938) and for kidney transplants was C 0_0.5_4 (r 2 = 0.931). Excluding transplant type variable, the best LSS for Prograf â is C 0-1-3 (r 2 = 0.920) and the best LSS for Advagraf â was C 0_0.5_4 (r 2 = 0.926). Considering transplant type irrespective of the formulation used, the best LSS for liver transplants was C 0_2_3 (r 2 = 0.913) and for kidney transplants was C 0_0.5_4 (r 2 = 0.898). Best LSS, considering all data together, was C 0_1_4 (r 2 = 0.898). We developed several LSSs to predict AUC 0-24 for tacrolimus in children and adolescents with kidney or liver transplants after Prograf â and/or Advagraf â treatment.

Tacrolimus (TAC) is highly effective for the prevention of acute organ rejection. However, its clinical use may be challenging due to its large interindividual pharmacokinetic variability, which can be partially explained by genetic variations in TAC-metabolizing enzymes and transporters. The aim of this study was to evaluate the influence of genetic and clinical factors on TAC pharmacokinetic variability in 21 stable pediatric renal transplant patients. This study was nested in a previous Prograf to Advagraf conversion clinical trial. CYP3A5, ABCB1 and two POR genotypes were assessed by real-time PCR. The impact on TAC pharmacokinetics of individual genetic variants on CYP3A5 nonexpressors was evaluated by genetic score. Explicative models for TAC AUC C and C after Advagraf were developed by linear regression. The built genetic scores explain 13.7 and 26.5% of the total AUC and C total variability, respectively. Patients genetic information should be considered to monitorizate and predict TAC exposure.

No abstract

No abstract