Statistical tools for dose individualization of mycophenolic acid and tacrolimus co‐administered during the first month after renal transplantation. (8th April 2013)

• Record Type: Journal Article
• Authors: Musuamba, Flora T.; Mourad, Michel; Haufroid, Vincent; De Meyer, Martine; Capron, Arnaud; Delattre, Isabelle K.; Verbeeck, Roger K.; Wallemacq, Pierre
• Is Part Of: British journal of clinical pharmacology. Volume 75:Number 5(2013:May)
• Page Start: 1277
• Page End: 1288
• Publication Date: 2013-04-08
• Subjects: Bayesian estimators -- dose individualization -- multiple linear regression -- mycophenolate -- optimality -- tacrolimus; Pharmacology -- Periodicals; Drugs -- Periodicals; 615.1
• DOI: 10.1111/bcp.12007
• Languages: English
• ISSNs: 0306-5251

Abstract

Abstract : 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, rRMSE = 9%, with simulations and r 2 = 0.66, rRMSE = 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, rRMSE = 12.3%, with simulations r 2 = 0.74, rRMSE = 15%, with observed data) for MPA and TAC, respectively. In addition, Bayesian 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, rRMSE = 13% and r 2 = 0.97, rRMSE = 5% with simulations for MPA and TAC, respectively and r 2 = 0.75, rRMSE = 11% and r 2 = 0.83, rRMSE = 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.