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Statistical tools for dose individualization of mycophenolic acid and tacrolimus co‐administered during the first month after renal transplantation
Author(s) -
Musuamba Flora T.,
Mourad Michel,
Haufroid Vincent,
De Meyer Martine,
Capron Arnaud,
Delattre Isabelle K.,
Verbeeck Roger K.,
Wallemacq Pierre
Publication year - 2013
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.12007
Subject(s) - mycophenolic acid , mean squared error , transplantation , mathematics , area under the curve , pharmacokinetics , medicine , chemistry , urology , statistics
Aim To predict simultaneously the area under the concentration−time curve during one dosing interval [ AUC(0,12 h )] for mycophenolic acid ( MPA ) and tacrolimus ( TAC ), when concomitantly used during the first month after transplantation, based on common blood samples. Methods Data were from two different sources, real patient pharmacokinetic ( PK ) profiles from 65 renal transplant recipients and 9000 PK profiles simulated from previously published models on MPA or TAC in the first month after transplantation. Multiple linear regression ( MLR ) and Bayesian estimation using optimal samples were performed to predict MPA and TAC AUC(0,12 h) based on two concentrations. Results The following models were retained: AUC(0,12 h) = 16.5 + 4.9 × C 1.5 + 6.7 × C 3.5 ( r 2 = 0.82, r RMSE = 9%, with simulations and r 2 = 0.66, r RMSE = 24%, with observed data) and AUC(0,12 h) = 24.3 + 5.9 × C 1.5 + 12.2 × C 3.5 ( r 2 = 0.94, r RMSE = 12.3%, with simulations r 2 = 0.74, r RMSE = 15%, with observed data) for MPA and TAC , respectively. In addition, B ayesian estimators were developed including parameter values from final models and values of concentrations at 1.5 and 3.5 h after dose. Good agreement was found between predicted and reference AUC(0,12 h) values: r 2 = 0.90, r RMSE = 13% and r 2 = 0.97, r RMSE = 5% with simulations for MPA and TAC , respectively and r 2 = 0.75, r RMSE = 11% and r 2 = 0.83, r RMSE = 7% with observed data for MPA and TAC , respectively. Conclusion Statistical tools were developed for simultaneous MPA and TAC therapeutic drug monitoring. They can be incorporated in computer programs for patient dose individualization.