OPTImization of the Dose of tacroliMUS by Bayesian Prediction

Study Overview
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Brief Summary
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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 Description
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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.

Official Title
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OPTIMIZATION of the Dose of tacroliMUS by Bayesian Prediction in Renal Transplant Patients Including Pharmacogenetic Variables

Conditions
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KIDNEY TRANSPLANTATION

Intervention / Treatment
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* Drug: Standard dosage of Tacrolimus
* Drug: Bayesian Prediction Tacrolimus dosage


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Kidney transplant recipients, from cadaveric and living donor. Patients that are going to receive immediate release Tacrolimus (Prograf/Adoport) as part of immunosuppressive treatment. Concomitant immunosuppression with Mycophenolate mofetil/Mycophenolic acid and steroids. Induction with Basiliximab is permitted. Subjects able to provide written informed consent. Female subjects of child-bearing potential must have a negative serum pregnancy test and must be practicing an effective, reliable, and medically approved contraceptive regimen during the study. Exclusion Criteria: Subjects treated with drugs that can potentially interfere with Tacrolimus, especially CYP3A4 inhibitors (telaprevir, boceprevir, ritonavir, ketoconazole, voriconazole, itraconazole, telithromycin or clarithromycin) or CYP3A4 inductors (rifampicin or rifabutine). Subjects that receive induction treatment with Thymoglobulin or rituximab. Subjects participating in another investigational study within 30 days before inclusion. Subjects with hepatopathy. Subject or donor with a history of any malignancy (within the past 5 years), except non-metastatic basal or squamous cell carcinoma of the skin . Female subjects who are pregnant or breast feeding. Subjects receiving an ABO incompatible kidney. Subjects have Donor Specific Antibodies. Recipients of an allograft with ischemic cold time > or = 24 hours. Subjects with a history of active hepatitis C virus (HCV-RNA positive) and/or hepatitis B virus (DNA-HBV or HBsAg positive) infection. Subjects with a history of human immunodeficiency virus (HIV-Ab positive) infection. Subjects with psychiatric or physical illness that could interfere with the ability of the subject to participate in the study.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: A randomized, prospective, multicenter study
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: GROUP I Standard adjustment<br>Standard dosage of Tacrolimus | Drug: Standard dosage of Tacrolimus<br>* Immediate release Tacrolimus (Prograf/Adoport)<br>* Other names: Tacrolimus;|
| Experimental: GROUP II Bayesian prediction adjustment<br>Bayesian prediction Tacrolimus dosage | Drug: Bayesian Prediction Tacrolimus dosage<br>* Immediate release Tacrolimus (Prograf/Adoport)<br>* Other names: Tacrolimus;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Tacrolimus plasmatic concentrations measurement | Concentration measured at day 6, 10, 15, 30, 60 and 90 post transplant and after each Tacrolimus dose adjustment | Day 6, 10, 15, 30, 60 and 90 post transplant | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time to achieve Tacrolimus therapeutic target levels | Time to achieve Tacrolimus therapeutic target levels within the 90 days post transplant | 90 days | 
| Number of Tacrolimus dose changes to achieve target levels | Number of Tacrolimus dose changes to achieve target levels | 90 days |