Study of the Influence of a Combination of Pharmacogenetic Variables on Tacrolimus Exposure: A Population Pharmacokinetic Approach
[eng] The calcineurin inhibitor Tacrolimus (Tac) is used to prevent acute rejection after renal transplant. Unfortunately, the clinical use of Tac is complicated by its considerable toxicity, narrow therapeutic window, and high interindividual pharmacokinetic variability. Therapeutic drug monitoring...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/118369 |
| Acceso en línea: | https://hdl.handle.net/2445/118369 http://hdl.handle.net/10803/457629 |
| Access Level: | acceso abierto |
| Palabra clave: | Nefrologia Farmacocinètica Farmacogenètica Immunosupressió Nephrology Pharmacokinetics Pharmacogenetics Immunosuppression |
| Sumario: | [eng] The calcineurin inhibitor Tacrolimus (Tac) is used to prevent acute rejection after renal transplant. Unfortunately, the clinical use of Tac is complicated by its considerable toxicity, narrow therapeutic window, and high interindividual pharmacokinetic variability. Therapeutic drug monitoring is commonly applied to individualize Tac therapy in renal transplant recipients using trough concentrations. When concentrations are out of the target range, the physicians roughly estimate what should be the appropriate change of dose. Despite trough concentrations are the most used exposure parameters, the Area Under the Curve (AUC) correlates better with the clinical outcomes. In the clinical setting, an AUC tiered-dosing is not feasible, thus an alternate approach is that based on limited-sampling strategy by means of Bayesian prediction. In this sense, the use of a population pharmacokinetic (PPK) model can assist for the first dose calculation at the start of treatment but also for dose adaptation based on predefined target by means of Maximum A Posteriori Bayesian forecasting technique, supporting therapeutic drug monitoring. Recent discovery of new polymorphism has led to further investigations on that file aiming to reduce the unexplained interindividual variability in Tacrolimus exposure. The main objective of the present work was to design a population-based Bayesian prediction tool for initial dose calculation and dose adaptation during the post-transplant period through: 1. Characterizing the Tacrolimus population PK using an intensive sampling and confirming the best limiting sampling strategy to be applied during dose adaptation. 2. To deeply Investigate in tacrolimus pharmacogenetic predictors of interindividual variability 3. Implementing new genetic information as well as other clinical factors to generate a refined population pharmacokinetic model reducing unexplained variability. A Tacrolimus population PK model was designed to characterize accurately the population absorption phase as well as quantify the inter and intra-individual variability. The first population PK model led to obtain an optimal sampling strategy using only trough concentrations for dose tailoring through Bayesian prediction. The CYP3A4*22 and CYP3A5*3 alleles are all independently associated with Tac exposure during the first year after transplantation. Proofs that a combined CYP3A4 and 5 genotype cluster is of relevant importance when deciding on the initial Tac dose. Poor metabolizers patients related to the cluster of CYP3A4*1/*22 and CYP3A5*3/*3, had lower dose requirements to achieve the target concentrations. Extensive metabolizers patients related to the cluster of CYP3A4*1/*1 and CYP3A5*1/*3, had higher dose requirements to achieve the target concentrations. A new refined PPK model was then developed using the combination of the cluster of CYP3A5*3 and CYP3A4*22 polymorphisms, age and hematocrit to describe Tacrolimus pharmacokinetics. The CYP3A extensive metabolizers patients may require about 2-fold higher doses compared to poor metabolizers. Moreover, intermediate metabolizers may require about 1.5-fold higher doses compared to poor metabolizers. |
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