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• W2143073126 abstract "Mycophenolic acid (MPA)-based therapies are widely used in combination with calcineurin inhibitors as maintenance immunosuppression for kidney transplant recipients (1). The two MPA therapies used in clinical transplantation are mycophenolate mofetil (MMF [brand name CellCept, Roche Pharmaceuticals, Nutley, NJ]) and mycophenolate sodium (MPS [brand name Myfortic, Norvartis Pharmaceuticals, Nutley, NJ]). MMF has been used for more than a decade and is a prodrug of MPA. The standard dosage of MMF in combination with cyclosporine (CsA) is 1 g given twice daily, although the dosage may be somewhat lower when co-administered with tacrolimus. MPS is an enteric coated form of MPA that was more recently introduced into the clinical arena. A dosage of 720 mg of MPS provides bioequivalence to a dosage of 1000 mg of MMF in kidney transplant patients (2). Immunosuppression afforded by MPA is achieved via reversible and uncompetitive inhibition of inosine monophosphate dehydrogenase (IMPDH), resulting in inhibition of guanine nucleotide biosynthesis (3,4). This consequently leads to suppression of both new DNA synthesis and other pathways that depend on a continuous supply of guanine nucleotide pool, such as T cell surface antigens and other glycosylated membrane proteins (4). Although there has been increased interest to incorporate MPA therapeutic drug monitoring into routine clinical practice (5–9), this has not yet become widespread in the United States for several possible reasons, including (1) lack of availability of US Food and Drug Administration–approved automated simple assays, (2) attainment of low rejection rates using empiric dosing of MMF in many maintenance immunosuppression regimens, (3) the complex pharmacokinetics (PK) of MPA, and (4) absence of overt organ toxicity. Here we discuss and review the pertinent information and study data regarding (1) our current understanding of MPA PK and major factors that can influence MPA clearance, (2) the performance characteristics of MPA analysis methods and prospects for more widespread availability of simple automated methods, (3) results and limitations of clinical outcome studies in renal transplant patients, (4) the status of ongoing prospective trials to evaluate various concentration-control approaches for dosing MMF in renal transplant patients under a variety of contemporary maintenance immunosuppression protocols, and (5) suggested rationale and guidelines for monitoring MPA. MPA PK MMF, the morpholinoethylester of MPA is rapidly hydrolyzed, mostly in the upper gastrointestinal tract, to produce MPA and hydroxyethyl morpholine, an inactive metabolite that is rapidly metabolized and excreted in urine (10). Within blood, MPA is largely distributed in plasma, where it is avidly bound to human serum albumin (10). The 12-h dose-interval MPA plasma concentration versus time profile, AUC0–12 h, is characterized by rapid absorption that reaches maximal concentration within 1 h followed by rapid distribution to tissues and falling plasma concentration, reaching a plateau within 3 to 4 h (Figures 1 and 2). A small fraction of MPA circulates in a free, unbound form, but most is bound to serum albumin. MPA is metabolized mostly via the uridine diphosphate glucuronyltransferase (UGT) system. The primary inactive metabolite is the phenolic glucuronide mycophenolic acid glucuronide (MPAG), which is transported from liver cells into bile most likely via the ATP-binding cassette transporter MDR1-related protein 2 (Mrp2) (11). Biliary MPAG then enters the gastrointestinal tract, where, under the catalytic action of glucuronidase that is shed from the intestinal flora, it is hydrolyzed back to MPA, which is then recycled into the bloodstream, the so-called enterohepatic circulation (EHC) pathway (Figure 1). The EHC pathway results in secondary peaks that typically occur between 4 and 8 h after ingestion of a dose of MMF. Besides MPAG, MPA may also be metabolized to the minor acylglucuronide, AcMPAG. Of note, the enteric coating of MPS results in maximal MPA concentrations that are achieved later in comparison with MMF. Several factors can significantly alter the elimination of MPA and can therefore influence MPA plasma concentrations. Such factors include one or more of the following situations: Significant renal or liver disease, drug–drug interactions, genetics, diarrhea, and patient noncompliance. Renal and Liver Dysfunction Kidney or liver disease can influence clearance by increasing the free fraction of MPA. Thus in kidney recipients who receive concomitant CsA and have poor renal function in the first days after transplant surgery, there can be a two- to three-fold increase in the free fraction (12). It is important to appreciate that in this situation, free MPA concentrations are usually stable, not increased, and comparable to the average values obtained in patients with good early renal function, at least in patients who are on CsA co-therapy and on a 1-g twice daily dose of MMF (12). The increased MPA free fraction is believed to be caused by increased concentrations of MPAG that competitively displace MPA from albumin binding sites, alterations of albumin produced by uremia, that reduces the binding avidity for MPA and by low albumin concentration that reduces binding capacity for MPA (10). As predicted by the equation clearance = free fraction × intrinsic clearance, for restrictively cleared drugs, the increased MPA free fraction results in enhanced MPA clearance, leading to reduced total MPA AUC values but stable and not increased free MPA concentrations. As renal function improves to baseline in these patients, the average free fraction approaches values obtained during stable graft function, a pattern that is paralleled by clearance and total MPA AUC values normalizing as well but free MPA concentrations remaining stable. This situation is comparable to that obtained for restrictively cleared drugs, such as phenytoin, in which this drug's free fraction can increase by two-fold or more in patients with uremia but free phenytoin concentrations remain stable as long as the dosage of the drug remains the same. Patients with good early kidney transplant function have near-normal MPA free fraction values and MPA clearance from the early posttransplantation period and consequently MPA AUC levels that typically resemble those obtained when graft stabilization has occurred (12). Similar perturbations can be observed in the early posttransplantation period in liver transplant patients as a result of one or more disturbances of MPA binding caused by renal dysfunction and elevated MPAG concentrations, hyperbilirubinemia that competitively displaces MPA from albumin binding sites, and hypoalbuminemia. A more extreme picture emerges in transplant patients with chronic kidney disease, in whom greater increases in free fraction and free MPA concentrations can occur as a result of even greater increased MPA free fraction (13,14) and abrogation of the restrictive clearance mechanism. Possible direct influences of renal and hepatic disease on UGT activity or on the EHC pathway have not been established, although there are renal failure animal model data showing inhibition of UGT activity and consequent reduced rate of MPA glucuronidation by the isolated perfused liver prepared from the diseased animal (15). Thus, in the special circumstance of chronic renal failure, the possible reduction of intrinsic clearance combined with the observed elevation of MPA free fraction could explain the observed higher free MPA but normal total MPA concentrations (13,14). In patients with chronic renal failure, measurement of both total and free MPA is warranted for accurate assessment of the possibility of overexposure to very high concentrations of free MPA that can predispose the patient to increased risk for low white blood cell counts and infection (13,14). Drug–Drug Interactions Drugs may interfere with MPA clearance by different mechanisms. Agents such as corticosteroids and rifampin (16), when introduced into the regimen, stimulate the biosynthesis of UGT, thereby increasing MPA clearance with consequent decreased MPA concentration. Discontinuation of such inducing drugs reduces MPA clearance and consequently increases MPA concentrations. Such effects result from changes in the intrinsic UGT enzymatic clearance of MPA, and the degree of effect may vary depending on genetic factors. Another mechanism by which drug–drug interactions may affect MPA exposure is by suppression of the EHC pathway by certain antibiotics secondary to loss of glucuronidase activity normally shed by gastrointestinal tract bacteria (17,18). The prime example of a drug that suppresses the EHC pathway is CsA, which, when co-administered with MPA therapy, results in a significant reduction of dosage-adjusted MPA concentrations. This can be seen clearly in Figure 2, which shows the much lower concentrations from 3 through 12 h for renal transplant patients who receive CsA and MMF. For renal transplant patients who receive tacrolimus and MMF, the presence of a greater contribution of the EHC to the dose interval MPA AUC also gives rise to greater variability in the postabsorption MPA concentrations (see the sample times from 3 h on in Figure 2, showing the consistently higher and more variable MPA concentrations in the tacrolimus patients). This explains the observation in many studies of the approximately 50% lower MPA trough concentrations, adjusted for MMF dosage, in patients on concomitant CsA compared with patients on concomitant tacrolimus. The proposed mechanism for this interaction supported by animal model data is the inhibition of transport of MPAG by CsA at the Mrp2 transporter site in the hepatocyte canalicular membrane (19–21). This is also the likely explanation for the differential MPA drug dosage requirement that is typically observed in patients who are on tacrolimus versus CsA. Genetics The interindividual PK variability observed with MPA use is likely explained in part by genetic variation. MPA is metabolized both in the liver and in the intestine by at least three enzymes of the UGT system: UGT1A8, UGT1A9, and UGT1A10. In addition, there is evidence that a fourth UGT, UGT2B7, is responsible for production of the acylglucuronide metabolite that might contribute to some of the gastrointestinal toxicity that is associated with MPA (22). Genetic variability within these important biotransformation pathways has been described, and this is predicted to have effects on the PK of MPA. This prediction is supported by a study in renal transplant patients that showed a 50% decrease in MPA trough concentrations in patients with promoter variants within the UGT1A9 gene (23). Another study conducted in healthy volunteers further supports the impact of the UGT1A9 −275/−2152 promoter polymorphisms on MPA exposure and suggests a further role for UGT1A8 and UGT2B7 polymorphisms (24). In addition to variation in metabolism, transport of MPA and its metabolites is potentially affected by genetic variation. The main MPA glucuronide metabolite, MPAG, is believed to be transported into bile via Mrp2, the same transporter responsible for bilirubin export from hepatocytes into bile. Mrp2 is the transporter site at which the inhibition of MPAG export into bile by CsA but not tacrolimus is believed to occur (20). Polymorphisms in this and other related transporters that might affect absorption, distribution, and EHC of MPA have been described (20). Studies in renal transplant patients have shown modest effects of Mrp2 polymorphisms on MPA AUC values thus far (25,26). Beyond PK variation, genetic factors may also contribute to pharmacodynamic variability of MPA. Polymorphisms within the IMPDH I and II genes that decrease the expression of the active enzyme (27), increase their expression, or alter these enzymes, such that they become less or more sensitive to inhibition by MPA, may exist and affect both the efficacy and the toxicity of this drug. Given the difficulty in predicting MPA PK behavior and pharmacodynamic outcomes, genotyping in combination with serum concentration monitoring may permit additional personalized dosing of MMF. The information available to date is insufficient to draw any conclusions on the value of genetic information for this drug. Nevertheless, future studies aimed at understanding the impact of genetic variation on MPA PK and pharmacodynamics are clearly warranted, currently under way, and eagerly awaited (Table 3). Other Factors Diarrhea or any other disease process that affects the gastrointestinal tract has the potential to result in impaired absorption of MPA and consequent lowered MPA plasma concentrations. Patient noncompliance is another factor that can cause an acute change in MPA exposure in a patient in routine clinical practice. Of note, two investigations published to date compared MPA PK in clinically stable cohorts of black and white patients. Neither study could identify a difference in any PK parameter between the two groups (12,28). This excludes ethnicity alone as a significant contributor to MPA clearance variability. Gender and diabetes were also excluded as significant contributors to MPA PK variability in two investigations (28,29). Population PK Models Several investigators have developed population PK models for MPA in renal transplant patients (Table 1) (29–34). In each of these investigations, a PK model that best described the set of concentration versus time profiles for renal transplant patients was first established. Following this and using the developed PK model and NONMEM (GloboMax, Ellicott City, MD), the widely used nonlinear mixed effects modeling software, the MPA concentration data were fit to the model and the most likely contributory factors explaining inter- and intrasubject variability in MPA concentrations were assessed. The model that seems representative of this work to date includes rapid increase in MPA concentration during the absorption phase that is often preceded by a lag time, followed by a distribution and an elimination phase. Thus, for studies in which MPA alone is measured and CsA is the calcineurin inhibitor, a two-compartment model with time-lagged absorption provides an adequate PK model. In these models, one compartment is the central compartment (primarily blood and interstitial fluid), and the other is the peripheral compartment (tissues such as liver, kidney, heart and lung) with which MPA in the central compartment rapidly equilibrates. The gastrointestinal tract is the site from which orally administered drug enters the central (first) compartment (Figure 1). In one investigation, approximately half of the patients received CsA and the other half tacrolimus as concomitant calcineurin inhibitor (33). MPAG and MPA concentration profiles were included in the study, and this permitted addition of another “compartment” that represented MPAG, produced in liver (an important peripheral compartment tissue), transport into the intestinal tract and conversion to MPA there, followed by recycling into the central compartment (Figure 1). This model fit the MPA concentration data for the tacrolimus patients well but did not add improved fitting of the data for the patients who received concomitant CsA. This additional compartment representing MPAG conversion back to MPA is consistent with EHC, presumed to be intact in tacrolimus patients but suppressed in CsA patients. With the use of population PK modeling and including the early posttransplantation period (days 3 to 140), major factors identified thus far that correlate significantly with MPA clearance include creatinine clearance, plasma albumin concentration, and CsA daily dosage (32). In a meta-analysis of longitudinal data in which MPA PK values were available from day 1 after transplantation out to 10 yr, it was estimated that kidney function, plasma albumin concentration, hemoglobin concentration, and CsA predose concentration could explain 18% of the between-patient and 38% of the within-patient variability in MPA exposure (29). In a follow-up to this longitudinal study, the authors further showed that for the first 6 mo, a decreased MPA clearance from 32 to 19 L/h correlated with improved kidney function, for this population of patients, from 19 to 71 ml/min, albumin concentration from 35 to 40 g/L, hemoglobin concentration from 9.7 to 12 g/dl, and CsA trough concentration from 225 to 100 ng/ml (35). In a cross-sectional study in patients with stable renal function (>6 mo after transplantation), only the patient's body weight was positively correlated with oral clearance (31), and inclusion of this in the population model resulted in a reduction of the interpatient variability of oral clearance from 34.8 to 28.2%, a very modest improvement. Other factors, such as kidney function, age, gender, height, and liver function tests, were not correlated with MPA oral clearance (31). The finding of no effect of renal or liver function on MPA oral clearance in this study is an expected result because renal function and liver function were stable in this cross-sectional patient cohort. Possible genetic factors that may significantly influence MPA clearance, described previously, were not included in any of these population PK models. Future studies that incorporate genetic factors will be required to assess more fully their influence on MPA clearance in various transplant populations. None of the studies reported to date has included free MPA concentration data, and this is possibly one of the missing PK variables that will need to be included in future studies that model the increase in MPA concentration with time after transplantation. It is not likely, however, that this would be a significant variable during stable graft function when the free fraction of MPA is stable. Limited Sampling Strategies for Estimation of MPA AUC The dose interval MPA AUC0–12 h is generally regarded as the most reliable PK parameter index of risk for acute rejection (6,8,9,36–39) but is impractical to measure in routine clinical practice. Single time-point samples such as the trough concentration or others do not correlate well with the MPA AUC, especially in the early posttransplantation period (10). Several investigators have shown that a good estimate of MPA AUC0–12 h can be obtained by limited sampling during 2 to 3 h, and algorithms with acceptable predictive performance have been reported for adult and pediatric renal transplant patients and for regimens that include MMF plus CsA, tacrolimus, or sirolimus (10,40–47). Three sample abbreviated algorithms developed specifically for adults and children and for regimens that include either CsA or tacrolimus (9) are in use in prospective studies of MPA therapeutic drug monitoring (9). A recently reported study provided validation of the predictive performance of a three-point algorithm for pediatric patients showing prognostic sensitivity and specificity values comparable to the values obtained using the full AUC0–12 h (48). Although the limited sampling estimation of the dose interval MPA AUC over 2 to 3 h has the important advantage of providing a practical approach, it is important to recognize that each developed limited sampling strategy applies to a specific patient population and immunosuppressive drug regimen (10). Analytical Methods for MPA Analysis The methods used to measure total and free MPA concentration include HPLC with ultraviolet (UV) detection, as well as mass spectrometric detection. An enzyme-multiplied immunoassay technique that can be automated has been described and is used in a number of centers for total MPA concentration. The performance characteristics of these methods have been compared and described in detail in numerous publications elsewhere (10). Validated HPLC with UV detection or with mass spectrometric detection is used primarily as a research tool in clinical trials and, occasionally, in routine clinical practice in some centers. Comparable clinical sensitivity and specificity have been reported for the enzyme-multiplied immunoassay technique and validated HPLC-UV method (49). A new automated enzyme receptor assay has been developed with IMPDH as the enzyme/receptor (50,51). The data for this new method in comparison with validated HPLC-UV and liquid chromatography–mass spectrometry methods have demonstrated excellent agreement for total and free MPA concentration values using samples from renal and heart transplant patients (50,51). This new assay has just recently been approved by the US Food and Drug Administration for use in patient monitoring. Because the lack of availability of a simple MPA test that can be automated is one of the limitations standing in the way of implementing monitoring routinely in many centers, this development may provide an impetus for more widespread use of MPA monitoring. IMPDH Activity From a mechanistic perspective, the measurement of the effect of a drug or its pharmacodynamic effect may more tightly correlate with clinical outcome than a PK parameter (52). This hypothesis is the basis for the development and evaluation of assays that measure IMPDH activity in circulating lymphocytes. In this type of assay, the patient's IMPDH expression phenotype, determined by multiple factors including genetics, is evaluated directly for use in therapeutic decision making (52). Nevertheless, monitoring of MPA PK parameters has assumed a more prominent place in therapeutic trials as well as in clinical practice partly as a result of the practical difficulties in performing the pharmacodynamic assays (53). A well-studied IMPDH assay in peripheral blood mononuclear cells has been developed (54,55). Among the interesting questions raised by studies using this assay are the following: (1) Do the significant individual differences in baseline IMPDH activity account for the outcome differences observed in MMF-treated patients? (2) Does low IMPDH activity predict a higher risk for adverse effects? (3) Does high IMPDH activity predict a higher risk for acute rejection (52,56)? Additional studies on larger patient cohorts will be required to evaluate these possibilities further. Relationship between MPA Exposure and Clinical Outcomes The addition of MMF to immunosuppression regimens has been associated with decreased rates of acute rejection and decreased long-term graft loss. Moreover, the incorporation of MPA-based adjunctive therapies into most contemporary maintenance regimens in the past decade has helped to spawn an era of minimization of the other immunotherapies to mitigate their respective toxicities (e.g., corticosteroid withdrawal, calcineurin inhibitor–sparing therapy). In the setting of immunosuppression minimization, the potential utility of MPA monitoring is particularly appealing. As is true for other maintenance immunosuppressants, MPA PK are widely variable, with reported dose-interval AUC values, adjusted for MMF dosage, ranging up to 10-fold in solid organ transplant patients (53,57,58). Two multicenter, prospective, concentration-control studies (37,58,59), involving renal transplant patients who received concomitant CsA, showed that when concentration control of the MPA dose interval AUC0–12 h is practiced in a highly organized and regimented manner and AUC values of at least 30 to 40 mg/h per L are achieved in the early posttransplantation period, the risk for acute rejection can be minimized. Two major multicenter investigations, one the Fixed Dose versus Concentration Control study (FDCC) (60) and the other the Opticept trial (61), are ongoing to determine the clinical utility of abbreviated estimation of MPA AUC and of trough concentrations compared with empiric dosing. The design of both of these investigations more nearly mimics the routine clinic setting in that specific guidelines for dosage adjustment were provided but left to each investigator to implement. The reports on the results of these two trials are anxiously awaited. The addition of MMF to immunosuppression regimens has been associated with decreased rates of acute rejection and decreased graft loss. At the time of introduction of MMF into clinical practice, empiric “one dose fits all” became the usual practice at most centers (7) and the view has been expressed, in a review of contemporary immunosuppressive therapy, that “MMF is effective in combination with many other agents, simple to use without monitoring, and free from organ toxicity and cardiovascular risk” (62). Nevertheless, the discovery and characterization of the highly variable MPA PK and of the relationships between MPA concentration and risk for acute rejection and adverse effects and the desire for more targeted therapy, especially in high-risk patients, has led to further investigation of these relationships. The current practice of empirically dosing MMF to toxicity of the gastrointestinal tract or bone marrow, with dosage reductions when toxicity is observed, has been characterized by one investigator as analogous to prescribing warfarin anticoagulation without knowledge of international normalized ratio but instead having to wait for bleeding or thrombosis before making dosage adjustments (7). The routine availability of validated MPA measurement in the most suitable timed specimen can provide an objective measurement of MPA concentration. The real challenge is correct interpretation of the MPA concentration data, especially single values of trough levels. In various experimental settings, significant correlations have been observed between MPA concentrations and (1) the inhibition of IMPDH activity in lymphocytes obtained from transplant patients during the 12-h dose interval, (2) inhibition of new DNA synthesis in proliferating lymphocytes, (3) rejection histology scores in a rat heart allograft model, and (4) risk for acute rejection in renal transplant patients (10). The study of the relationship between MPA steady-state AUC as well as trough concentration values and the risk for early acute rejection was first reported for a cohort of Japanese renal transplant patients (36). This was followed by a multicenter, randomized, double-blind, concentration-controlled trial in which MMF dosing was based on a Bayesian dosing algorithm and targeting the dose interval MPA AUC to one of three values: 16.1, 32.2, or 60.6 mg/h per L. This study tested the hypothesis that MPA AUC predicted the risk for acute rejection in the early posttransplantation period for the widely used CsA + MMF + corticosteroid regimen in use at that time in cadaver renal transplant patients (37). Two important results from this trial were that (1) reaching or exceeding a predetermined target MPA AUC (32.2 mg/h per L) achieved a significantly lower rate of acute rejection as compared with the rate at the lower targeted MPA AUC (16.1 mg/h per L; P < 0.001) and (2) the risk for acute rejection decreased as the MPA AUC increased (37). In a preliminary report of a prospective concentration-control versus fixed-dose study that was conducted in France (the APOMYGRE study) and used a target AUC of 40 mg/h per L calculated with a Bayesian estimator based on three timed samples (59), in the concentration-control cohort, this finding was confirmed. A full report of the latter study will be required for a more complete analysis of this study. In the randomized, concentration-controlled trial, MPA trough values also correlated with rejection risk but less significantly (P < 0.01), presumably as a result of the greater inherent variability of trough concentration values (37). In three retrospective review studies of systematically collected MPA PK data in adult (63,64) or pediatric (38) renal transplant patients who received MMF + CsA + corticosteroids, the early estimated MPA AUC predicted the risk for rejection (Table 2). In two studies that involved adult renal transplant patients who were receiving MMF + CsA + corticosteroids, MPA AUC did not correlate with the rate of acute rejection, but this may have been due to the small number of rejection events recorded in these studies: Five of 46 patients and seven of 42 (65,66). In addition to the possibility that the low number of rejection events precluded meaningful correlation analyses with MPA concentrations, a possible reason for this finding is that one AUC value, on posttransplantation day 5, was used for prediction of early acute rejection (66). This time in relationship to the time of rejection may not have been optimal, and this issue of when to sample and which type of sample (trough or abbreviated sampling AUC) needs further study. Fewer studies have been conducted on the relationship between MPA concentration and risk for early acute rejection for the combination of MMF + tacrolimus + corticosteroids. Two retrospective studies evaluated the relationship between rejection risk and either MPA AUC (67) or trough concentration (68). No significant differences in the day 7 MPA AUC or trough concentration were observed between patients with biopsy-proven acute rejection and those who did not experience rejection when concentrations of each immunosuppressant were considered separately (67). When these patients were divided into four groups according to AUC0–12 h values (cutoff values of 45 mg/h per L for MPA and 150 μg/h per L for tacrolimus), the incidence of acute rejection trended higher (P = 0.07) in the group with the low MPA and low tacrolimus AUC values (26.3%) compared with the group with the high MPA and high tacrolimus AUC values (7.7%). The risk for rejection was intermediate for the patients who had low values for only one of the two drugs (67). A significant association was found for the median value for MPA trough concentration and risk for rejection within the first 30 d after transplantation (68). The FDCC and Opticept prospective therapeutic drug monitoring trials will hopefully provide more comprehensive data on which to base the selection of sampling schedule and best sample timing for renal transplant patients who receive concomitant tacrolimus therapy. The data supporting definition of the upper end of the target therapeutic range are limited for several reasons, including the highly variable timing of plasma sampling in relation to time of adverse effects and the highly variable set of patient factors from study to study causing variable risk for adverse effects (37,64). For renal transplant patients who are on CsA co-therapy, there is no further reduction in acute rejection at AUC values >60 mg/h per L (37,58); therefore, avoidance of higher exposure would seem prudent on the basis of this information. There is also the possibility that other PK measurements, such as free MPA concentration and the concentration of the acylglucuronide, may be better predictors of adverse effects (38,52,65,66). Ongoing trials to evaluate various concentration-control approaches for dosing MMF in renal transplant patients under a variety of immunosuppression regimens and including measurement of free MPA and the acylglucuronide will hopefully provide more definitive data relating all pertinent MPA PK parameters to risk for rejection and adverse effects. Guideline for Therapeutic Drug Monitoring Recent reviews have suggested provisional target therapeutic ranges for MPA AUC and trough concentrations when using MMF in combination with either CsA or tacrolimus. When combined with CsA, the recommended target ranges are 1 to 3.5 mg/L and 30 to 60 mg/h per L for trough concentrations and AUC, respectively. For the combination with tacrolimus, the target ranges of 1.9 to 4.0 mg/L and 30 to 60 mg/h per L for trough and AUC measurements, respectively, have been suggested (9). Two ongoing concentration-control versus fixed-dosage trials in renal transplant patients will hopefully provide the basis for rigorous assessment of these target ranges in the setting of contemporary practice. A proposed schedule for objective assessment of MPA exposure is presented in Table 4. We hope that the ongoing trials will provide more definitive data on which to base the selection of sample type, test schedule, and the cost–benefit analysis of MPA therapeutic monitoring. Disclosures L.M.S. has received grant support and consulting fees from Roche Pharmaceuticals and grant support from Novartis. R.D.B. has received grant support and consulting fees from Roche Pharmaceuticals and Novartis.Figure 1: Sites for absorption, distribution, metabolism, enterohepatic recycling (EHC), and elimination of mycophenolic acid (MPA). The gastrointestinal tract (G.I.), presumably the stomach and upper small intestine, is the site where mycophenolate mofetil (MMF) is spontaneously hydrolyzed to produce MPA, the active drug, which is absorbed into the circulation (together with interstitial fluid, the circulation constitutes the central compartment). From the central compartment, MPA distributes throughout the body into organs and tissues, including liver, kidney, heart, lung, and others in the peripheral compartment. The glucuronide metabolites including the primary inactive mycophenolic acid glucuronide (MPAG) and the acyl glucuronide, are produced by uridine diphosphate glucuronyltransferases (UGT) in liver, GI, tract and possibly kidney. The glucuronide metabolites produced by the liver are transported, likely via the MDR1-related protein 2 (Mrp2) transporter, into bile. After excretion into bile, MPAG is hydrolyzed back to MPA via glucuronidases shed by GI tract bacteria. MPA thus formed in the GI tract reenters the circulation, thereby completing the EHC pathway. MPAG also appears in the circulation after its production from MPA in various tissues. Elimination of MPAG from the circulation occurs via renal excretion. The inhibitory effect of cyclosporine (CsA) on MPAG transport into bile is symbolized by the ⊗ symbol.Figure 2: Average MPA plasma concentration values versus time during a 12-h dose interval. The error bars are the SD values for each average MPA concentration and the percentage values are the percent coefficient of variation (%CV) calculated from the ratio × 100 of the SD and the average MPA concentrations at each time point. ▪ and ▴, Average MPA concentration values for 40 profiles or 26 profiles from, respectively, renal transplant patients who receive as concomitant immunosuppression CsA or tacrolimus (TAC). The error bars are in the downward or upward direction only for the CsA or the TAC patients, respectively. For both the CsA and TAC patient groups, the %CV values during the first 2 h are fairly comparable but are consistently greater in the TAC versus the CsA cohorts thereafter.Table 1: Population PK studies in renal transplant patientsaTable 2: Correlation between MMF PK and clinical outcomes in renal transplantationaTable 3: Primary factors that can alter MPA exposureaTable 4: Proposed schedule for MPA monitoring in renal transplant patients" @default.
• W2143073126 created "2016-06-24" @default.
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• W2143073126 date "2007-09-01" @default.
• W2143073126 modified "2023-10-16" @default.
• W2143073126 title "Therapeutic Drug Monitoring of Mycophenolic Acid" @default.
• W2143073126 cites W1526833570 @default.
• W2143073126 cites W1598969666 @default.
• W2143073126 cites W1955028047 @default.
• W2143073126 cites W1971278669 @default.
• W2143073126 cites W1973334335 @default.
• W2143073126 cites W1977126062 @default.
• W2143073126 cites W1979599521 @default.
• W2143073126 cites W1980168956 @default.
• W2143073126 cites W1981535286 @default.
• W2143073126 cites W1982951044 @default.
• W2143073126 cites W1985331805 @default.
• W2143073126 cites W1988002956 @default.
• W2143073126 cites W1991303398 @default.
• W2143073126 cites W1992309965 @default.
• W2143073126 cites W1993063721 @default.
• W2143073126 cites W1994447807 @default.
• W2143073126 cites W2001677024 @default.
• W2143073126 cites W2002199646 @default.
• W2143073126 cites W2004936561 @default.
• W2143073126 cites W2005229698 @default.
• W2143073126 cites W2005312001 @default.
• W2143073126 cites W2006198853 @default.
• W2143073126 cites W2006870443 @default.
• W2143073126 cites W2016100084 @default.
• W2143073126 cites W2017010426 @default.
• W2143073126 cites W2022411944 @default.
• W2143073126 cites W2024619081 @default.
• W2143073126 cites W2025501899 @default.
• W2143073126 cites W2029380738 @default.
• W2143073126 cites W2029830810 @default.
• W2143073126 cites W2032807691 @default.
• W2143073126 cites W2037681070 @default.
• W2143073126 cites W2039029671 @default.
• W2143073126 cites W2048189244 @default.
• W2143073126 cites W2056054156 @default.
• W2143073126 cites W2059681951 @default.
• W2143073126 cites W2066128566 @default.
• W2143073126 cites W2067810520 @default.
• W2143073126 cites W2069790510 @default.
• W2143073126 cites W2072486418 @default.
• W2143073126 cites W2073504343 @default.
• W2143073126 cites W2073533596 @default.
• W2143073126 cites W2081355624 @default.
• W2143073126 cites W2081858766 @default.
• W2143073126 cites W2101399909 @default.
• W2143073126 cites W2105444402 @default.
• W2143073126 cites W2116626206 @default.
• W2143073126 cites W2116823219 @default.
• W2143073126 cites W2123995479 @default.
• W2143073126 cites W2149093312 @default.
• W2143073126 cites W2150895558 @default.
• W2143073126 cites W2164907989 @default.
• W2143073126 cites W2167078188 @default.
• W2143073126 cites W2167685828 @default.
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