Pharmacokinetic role of protein binding of mycophenolic acid and its glucuronide metabolite in renal transplant recipients

Mycophenolic acid (MPA), the active compound of mycophenolate mofetil (MMF), is used to prevent graft rejection in renal transplant recipients. MPA is glucuronidated to the metabolite MPAG, which exhibits enterohepatic recirculation (EHC). MPA binds for 97% and MPAG binds for 82% to plasma proteins. Low plasma albumin concentrations, impaired renal function and coadministration of cyclosporine have been reported to be associated with increased clearance of MPA. The aim of the study was to develop a population pharmacokinetic model describing the relationship between MMF dose and total MPA (tMPA), unbound MPA (fMPA), total MPAG (tMPAG) and unbound MPAG (fMPAG). In this model the correlation between pharmacokinetic parameters and renal function, plasma albumin concentrations and cotreatment with cyclosporine was quantified. tMPA, fMPA, tMPAG and fMPAG concentration–time profiles of renal transplant recipients cotreated with cyclosporine (n = 48) and tacrolimus (n = 45) were analyzed using NONMEM. A 2- and 1-compartment model were used to describe the pharmacokinetics of fMPA and fMPAG. The central compartments of fMPA and fMPAG were connected with an albumin compartment allowing competitive binding (bMPA and bMPAG). tMPA and tMPAG were modeled as the sum of the bound and unbound concentrations. EHC was modeled by transport of fMPAG to a separate gallbladder compartment. This transport was decreased in case of cyclosporine cotreatment (P < 0.001). In the model, clearance of fMPAG decreased when creatinine clearance (CrCL) was reduced (P < 0.001), and albumin concentration was correlated with the maximum number of binding sites available for MPA and MPAG (P < 0.001). In patients with impaired renal function cotreated with cyclosporine the model adequately described that increasing fMPAG concentrations decreased tMPA AUC due to displacement of MPA from its binding sites. The accumulated MPAG could also be reconverted to MPA by the EHC, which caused increased tMPA AUC in patients cotreated with tacrolimus. Changes in CrCL had hardly any effect on fMPA exposure. A decrease in plasma albumin concentration from 0.6 to 0.4 mmol/l resulted in ca. 38% reduction of tMPA AUC, whereas no reduction in fMPA AUC was seen. In conclusion, a pharmacokinetic model has been developed which describes the relationship between dose and both total and free MPA exposure. The model adequately describes the influence of renal function, plasma albumin and cyclosporine co-medication on MPA exposure. Changes in protein binding due to altered renal function or plasma albumin concentrations influence tMPA exposure, whereas fMPA exposure is hardly affected

Pharmacokinetic Modelling of the Plasma Protein Binding of Mycophenolic Acid in Renal Transplant Recipients

Background and Objectives: Renal function and the plasma albumin concentration have been shown to correlate with clearance of total mycophenolic acid (MPA). The hypothesis for the underlying mechanism is that low plasma albumin concentrations and accumulation of the glucuronide metabolite of MPA (MPAG) decrease the binding of MPA to albumin. The subsequent increase in the unbound fraction (f(u)) of MPA (MPA(u)) produces an increase in total MPA (MPA(t)) clearance. This study aimed to develop an empirical population pharmacokinetic model to describe the relationships between renal function and albumin concentration and MPAG, MPA(u) and MPA(t), in order to provide insight into the mechanism by which renal function and plasma albumin affect the disposition of MPA. Methods: 774 MPA(t), 479 MPA(u) and 772 total MPAG (MPAG(t)) plasma concentrations were available from 88 renal transplant recipients on days 11 and 140 after transplantation. Data were analysed using non-linear mixed-effects modelling. Results: Time profiles of MPA(u) and MPAG(t) concentrations were adequately described by two 2-compartment pharmacokinetic models with a link between the central compartments, representing the glucuronidation of MPA(u) to form MPAG. MPA(t) concentrations were modelled using: [MPA(t)]=[MPA(u)] + [MPA(u)].theta(pb), with [MPA(u)].theta(pb) representing the bound MPA concentration, where [MPA(t)], [MPA(u)] and Orb represent MPA(t) concentration, MPA(u) concentration and a factor that correlates to the total number of protein binding places, respectively. According to this equation, f(u) = [MPA(u)]/[MPA(t)] = 1/(1 + theta(pb)).theta(pb), and therefore [MPA(t)], was significantly and independently correlated with creatinine clearance (CLCR), the plasma albumin concentration and the MPAG(t) concentration (all p < 0.001). A reduction in CLCR from 60 to 25 mL/min correlated with an increase in f(u) from 2.7% to 3.5%, accumulation of MPAG(t) concentrations from 50 to 150 mg/L correlated with an increase in f(u) from 2.8% to 3.7%, and a decrease in plasma albumin concentration from 40 to 30 g/L correlated with an increase in f(u) from 2.6% to 3.5%. No significant correlations were detected between MPA(u) clearance and the plasma albumin concentration or CLCR. Conclusion: The model shows that low CLCR, low Plasma albumin concentrations and high MPAG concentrations decrease MPA(t) exposure by affecting MPA binding to albumin

Limited Sampling Strategies for Therapeutic Drug Monitoring of Mycophenolate Mofetil Therapy in Patients With Autoimmune Disease

Mycophenolate mofetil (MMF) is increasingly used for the treatment of autoimmune diseases (AID). In renal transplant recipients, it has been demonstrated that adjustment of the MMF dose according to the area under the plasma concentration versus time curve (AUC) of mycophenolic acid (MPA), the active moiety of MMF, improves clinical outcome. The aim of this study was to develop a maximum a posteriori Bayesian estimator (MAP-BE) to estimate MPA AUC(0-12) in patients with AID using a limited number of samples. The predictive performance of the MAP-BE was compared with a multiple linear regression method. Full MPA concentration versus time curves were available from 38 patients with AID treated with MMF. Nonlinear mixed-effect modeling was used to develop a population pharmacokinetic model. Patients were divided in an index and a validation data set. The pharmacokinetic model derived from the index data set was used to develop several MAP-BEs. The Bayesian estimators were used to predict AUC(0-12) in the validation data set on the basis of a limited number of blood samples. The bias and precision of these predictions were compared with those of limited sampling strategies developed with multiple linear regression. The absorption of MPA was described with 2 first-order processes with a short and a long lag time and a subsequent first-order elimination. The 2-compartment model accounted for the entero-hepatic recirculation of MPA as well. Using 1-4 samples, MPA AUC(0-12) was adequately estimated by the MAP-BE. Bias (-5.5%) was not significantly different from zero, and precision was below 27%. The predictive performance of the multiple linear regression method was comparable. In conclusion, MAP-BEs were developed for the estimation of MPA AUC(0-12) in patients with AID. The predictive performance was good and comparable to those of the multiple linear regression method. Due to its flexibility with respect to sample times, the MAP-BE may be preferred over the multiple linear regression method