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.

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