Last updated on March 2018

OPTImization of the Dose of tacroliMUS by Bayesian Prediction


Brief description of study

The pharmacokinetics of tacrolimus (TAC) are characterized by high inter- and intra-individual variability with narrow therapeutic range. Currently, the limiting point of Tac drug monitoring is the inability to individualize doses during the first few days after transplantation. Our group developed a population pharmacokinetic model (PPK) identifying CYP3A4 * 22 and CYP3A5 * 3 polymorphisms and hematocrit as explanatory variables of the observed variability in pre-dose (Co) concentrations. According to this model, the proportion of patients that do not reach the therapeutic target is 40

Detailed Study Description

The results of the study of genetic polymorphisms performed in renal transplant patients at our hospital demonstrated the influence of CYP3A5 * 3 and CYP3A4 * 22 single nucleotide polymorphism (SNPs) on exposure to Tac. From these results, the first population pharmacokinetic model was developed, which included CYP3A5 * 3 and CYP3A4 * 22 polymorphisms as well as hematocrit as explanatory variables of interindividual variability. Based on our population model and using the simulation tool, the percentage of patients reaching the therapeutic target based on Co values within the range of 6-10 ng / mL was determined after being dosed according to the strategy of Conventional dosage empirically according to Co achieved. The simulation of 50 Co values according to the conventional dosage allowed to determine the proportion of patients reaching the therapeutic target in each case and their confidence interval. 40% of the patients did not reach the therapeutic objective. Based on the clusters of the two polymorphisms, the percentages of patients on or below exposed varied according to whether they were slow or fast metabolizers respectively. Due to this high variability in Tac PK, the individualization of the Tac posology was calculated by calculating the initial dose according to the population model previously developed and adjusting the subsequent doses, as a function of the Tac Co through Bayesian approximations with the inclusion of genotyping and Hematocrit, can contribute greatly to achieve optimal exposure to the drug from the start of treatment in the immediate post-transplant and reduce the variability observed in the Co-achieved; This may be particularly important for patients with a slow and rapid metabolizer profile. All of this may contribute to minimizing adverse effects, ensuring greater efficacy in the target population, reducing the risk of acute rejection, and reducing associated costs.

In the present study we intend to incorporate pharmacogenomics for its application in de novo patients, which will allow us to perform a more individualized therapy for each patient based on the values of target Co and the CYP3A5 * 3 and CYP3 A4 * 22 polymorphisms of the patient since The initiation of immunosuppressive therapy and thus improve efficiency and decrease adverse effects.

Clinical Study Identifier: NCT03465410

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