A pharmacokinetic/statistical modelling approach to improve dosage individualization after renal transplantation
(2010)
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- (en) Improvement is still needed to improve the patient outcome after kidney transplantation and the biggest challenge is now extending the half-life of the allograft and the prognosis of patient survival long term after transplantation. The survival of the allograft after transplantation depends on several factors including the immunosuppressive (IS) therapy. Not only the most efficient IS drug or combinations of drugs must be chosen, but also effective and non-toxic levels must be reached in the patients as soon as possible after starting the therapy. This has become a big challenge since most of the IS drugs are characterized but a high pharmacokinetic (PK) and pharmacodynamic (PD) variability making the use of a standard dosage regimen inappropriate. The aim of the present thesis is to manage the unexplained variability in the PK of IS drugs (particularly mycophenolic acid (MPA) and tacrolimus (TAC) with the ultimate objective to contribute to a safer and more efficient use of these drugs. To achieve these objectives, two approaches have been used: 1. The population PK (POP PK) analysis approach that models the time course of drug concentrations in a group of patients after administration of the drug of interest and aims to explain and therefore reduce at least partly the unexplained PK variability by taking into account certain patient characteristics (covariates). This would allow group dosing or improve therapeutic drug monitoring (TDM). 2. Limited sampling strategies (LSS) that allow patient dosage individualization based not only on his/her individual characteristics but also on the characteristics of the population to which he/she belong. First, we present the results of the validation of a UPLC analytical method for simultaneous quantification of MPA and its metabolites in human plasma. This method is one of the fastest used to date for determination of MPA and its metabolites as part of clinical trial and TDM. A POP PK modelling study on TAC to identify covariates that could explain its inter- and intra-individual variability is presented. Time of drug administration, analytical method, CYP3A5 and ABCB1 genotypes were found to be significant covariates. We have also developed and validated MLR-based and Bayesian estimators based LSS for MPA and TAC TDM early and long tem after renal transplantation. Patients were co-medicated with cyclosporine or sirolimus and corticosteroids. Lastly, we show that Bayesian estimation approach is better than MLR and trough level based approaches for TDM of MPA and TAC when only trough levels are available.
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Musuamba Tshinanu, F. (2010). A pharmacokinetic/statistical modelling approach to improve dosage individualization after renal transplantation. https://hdl.handle.net/2078.5/131110