Franc Andreu (1), Helena Colom (1), Ana Caldés (2), Joan Torras (2), Federico Oppenheimer (3), Jaime Sanchez-Plumed (4), Miguel A. Gentil (5), Dirk R. Kuypers (6), Mercè Brunet (7), Josep M. Cruzado (2), Josep M. Grinyó (2) and Núria Lloberas (2)
(1) Department of Pharmacy and pharmaceutical technology, Faculty of Pharmacy, University of Barcelona. (2) Nephrology service, Hospital de Bellvitge, Barcelona. (3) Nephrology Service, Hospital Clinic i Provincial, Barcelona, Spain. (4) Nephrology Service, Hospital La Fe, Valencia, Spain. (5) Nephrology Service, Hospital Virgen del RocÃo, Sevilla, Spain. (6) Department of Nephrology and Transplantation University Hospital, Leuven, Belgium. (7) Centre de Diagnostic Biomedic, Barcelona, Spain.
Objectives: (i) To establish an integrated population pharmacokinetic (PPK) model for MPA, MPAG and AcMPAG in renal transplant recipients on an immunosuppressive regimen with MMF and cyclosporine (CsA) or macrolides (tacrolimus or sirolimus); (ii) to quantify the effect of MRP2 polymorphism and CsA treatment on MPA and its metabolites disposition.
Methods: 56 patients received MMF 1g twice daily in combination with CsA or macrolides. 2038 (MPA), 2054 (MPAG) and 1043 (AcMPAG) concentration-time values were simultaneously analyzed with NONMEM 7.2[1] using PsN v3.5.3 and R code v3.0.1. The FOCE-I method was used for estimation and internal validation was performed with VPC[2], PPC[3] and NPDE[4].
Results: Two two-compartment models for MPA and MPAG and a one-compartment model for AcMPAG, with time-lagged first-order transformation/absorption process, provided the best fit of the data. MPA was converted to MPAG and AcMPAG according to two parallel first-order elimination processes. The metabolic clearance of MPA was estimated by CLMPA·fm + CLMPA·(1-fm), where fm was the ratio of the fraction of MPA metabolized to MPAG. Enterohepatic circulation (EHC) was modeled with a first-order transfer rate constant (KT) from the MPAG central compartment to the absorption site. Both metabolites showed a linear elimination. Between-patient variability was associated with CLMPA, CLMPAG, CLAcMPA, VcMPA, and KT. Between-occasion variability could not be included in the model due to high computational intensity. Residual error of the three compounds was adequately described by an additive model for logtransformed data.
MPAG and AcMPAG plasma clearances significantly decreased with renal function. No significant influence of multidrug-resistant-associated protein-2 C24T single-nucleotide polymorphism was found. The model adequately predicted the increase in MPAG/AcMPAG exposures in CsA and macrolide patients with decreased renal function. As a consequence, higher MPA exposures in macrolide patients were observed compared to CsA patients. Increased MPA exposures with renal function changes from 25 to 10ml/min in macrolide patients was found by the enhanced MPAG enterohepatic circulation. The lowest-percentage of EHC occurred with the highest CtroughCsA and renal function values.
Conclusions: A PPK model has been developed to describe MPA and its major metabolites disposition, supporting CLCR and co-medication-tiered dosing regimen for MMF to standardize exposure during the post-transplant treatment.
References:
[1] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2011. Icon Development Solutions, Ellicott City, Maryland, USA
[2] Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J
[3] Yano Y, Beal SL, Sheiner LB. Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check . J. Pharmacokinet. Pharmacodyn.
[4] Mentre F, Escolano S. Prediction discrepancies for the evaluation of nonlinear mixed-effects models . J. Pharmacokinet. Pharmacodyn.
Reference: PAGE 23 (2014) Abstr 3099 [www.page-meeting.org/?abstract=3099]
Poster: Drug/Disease modeling - Other topics