Explaining Variability in Mycophenolic Acid Exposure to Optimize Mycophenolate Mofetil Dosing: A Population Pharmacokinetic Meta-Analysis of Mycophenolic Acid in Renal Transplant Recipients
Reinier M. van Hest*,
Ron A.A. Mathot*,
Mark D. Pescovitz,
Robert Gordon,
Richard D. Mamelok and
Teun van Gelder*,||
* Departments of Hospital Pharmacy and || Internal Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Surgery, Indiana University Medical Center, Indianapolis, Indiana; Roche Laboratories, Inc., Nutley, New Jersey; and Mamelok Consulting, Palo Alto, California
Address correspondence to: Mr. Reinier M. van Hest, Department of Hospital Pharmacy, Clinical Pharmacology Unit, Erasmus MC, Rotterdam, The Netherlands, PO Box 2040, 3000 CA Rotterdam. Phone: +31-10-4633202; Fax: +31-10-4632400; E-mail: r.vanhest{at}erasmusmc.nl
Received for publication October 17, 2005.
Accepted for publication December 6, 2005.
Large between- and within-patient variability has been observedin the pharmacokinetics of mycophenolic acid (MPA). However,conflicting results exist about the influence of patient characteristicsthat explain the variability in MPA exposure. This populationpharmacokinetic meta-analysis of MPA in renal transplant recipientswas performed to explore whether race, renal function, albuminlevel, delayed graft function, diabetes, and co-medication aredeterminants of total MPA exposure. A total of 13,346 MPA concentration-timedata points from 468 renal transplant patients who participatedin six clinical studies were combined and analyzed retrospectively.Sampling occasions ranged from day 1 after transplantation to10 yr after transplantation. Concentration-time data were analyzedwith nonlinear mixed-effect modeling. Exposure to total MPA,as determined by MPA clearance, significantly increased withincreasing renal function, albumin level, and hemoglobin aswell as decreasing cyclosporine predose level (P < 0.001).These variables could explain 18% of the between-patient and38% of the within-patient variability in MPA exposure. Differencesin MPA exposure between patients with or without delayed graftfunction or between patients of different races are likely tobe caused by the effect of renal function on MPA exposure. Diabetesdid not have an effect on MPA exposure. The clinical implicationis that a change in renal function or albumin level providesan indication for therapeutic drug monitoring as MPA exposuremay be altered. Patients in whom cyclosporine and mycophenolatemofetil are combined may need higher mycophenolate mofetil doses,especially during the early phase after transplantation thancurrently recommended for optimal MPA exposure.
Mycophenolate mofetil (MMF) is an immunosuppressive drug thatis used successfully in solid organ transplantation to preventacute allograft rejection (1,2). MMF is a prodrug of mycophenolicacid (MPA), which exhibits rapid and almost complete absorptionfrom the gut. MPA has extensive plasma albumin binding (98%)and is metabolized by uridine glucuronosyl transferase enzymesinto the pharmacologically inactive glucuronide metabolite (MPAG)(3–5). The pharmacokinetics of MPA are characterized furtherby an enterohepatic recirculation, in which MPAG is excretedinto bile and deglucuronidated in the gut back to MPA (3).
Low rates of acute rejection and long-term patient survivalhave been achieved with MMF when used in a standard dose recommendationof 1 g twice daily for adults. A number of pharmacokinetic studieshave shown an increased risk for acute rejection in patientswith lower MPA exposure, suggesting that efficacy may improveby adjusting the dose on the basis of plasma concentrations.On the basis of these studies, a target window has been adoptedfor MPA exposure (area-under-the-curve [AUC] values between30 and 60 mg/L per h) (6–8). Accumulating evidence suggeststhat this target is not reached in every patient with the standardMMF dose, with some studies reporting a 10-fold between-patientvariability of MPA exposure, changes of exposure over time witha fixed MMF dose, and influence of co-medication (4,9–11).Consequently, individualization of the MMF dose may be necessaryto achieve adequate MPA exposure in every patient.
By explaining between-patient variability in MPA pharmacokineticsand identifying the patient characteristics that significantlyinfluence MPA exposure, rational decisions on optimal dosingcan be achieved (12). Earlier pharmacokinetic studies have alreadyattempted to correlate MPA exposure to several explanatory factors.For example, some studies found that impaired renal functionand low albumin levels result in high total MPA clearance andthus low total MPA exposure (5,9,11,13,14), although this couldnot be confirmed in all studies (15–20). Also with regardto the effect of the use of co-medication (3,10,18,21,22), diabetes(23,24), body weight (11,13,17,20), age (5,11,18–20),gender (11,20,23), and race (9,23,25), contrasting results havebeen obtained. Most of these studies were underpowered, basedon MPA predose levels only, and some studies did not controladequately for confounding factors. Consequently, for most variables,it is not clear whether and to what extent they influence MPAexposure and whether individualization of the MMF dose shoulddepend on these variables.
Population pharmacokinetic meta-analysis is known to be verypowerful and can estimate reliably the determinants of pharmacokineticvariability, thereby explaining between-patient differencesin drug exposure (26,27). An important advantage of a populationpharmacokinetic approach is that it allows pharmacokinetic datasets that originate from several studies with different samplingtime points to be combined. In this study, a population pharmacokineticmeta-analysis of MPA in renal transplant recipients was performedto explore whether race; age; gender; weight; renal function;albumin level; delayed graft function (DGF); diabetes; and theuse of antimicrobial agents, gastric pH modulators, cyclosporine,and corticosteroids can explain variability in MPA exposurebetween (subgroups of) patients.
Studies
Total MPA concentration-time data from 468 renal transplantpatients who participated in six different studies were combinedand analyzed retrospectively. All data were provided by RocheLaboratories Inc. Details of these studies were published elsewherepreviously (6,7,23,28–30). Per study, the number of patientsfrom whom samples were drawn for pharmacokinetic analysis, theMMF starting doses, the occasions of pharmacokinetic assessmentafter transplantation, the time of sampling after MMF administration,and the concomitant use of immunosuppressive agents are summarizedin Table 1.
Table 1. Description of studies used for the population PK meta-analysis with regard to PK propertiesa
Data and Definitions
Data on total MPA concentrations, timing of MPA sample drawing,and MMF dosing history from the six studies were pooled in onedata set. Data were also collected on patient characteristics,routine laboratory measurements, co-medication, comorbiditysuch as diabetes, and DGF for every sampling occasion in allpatients. Pretransplantation diabetes was defined as the useof antidiabetic drugs within 60 d before transplantation ora medical history of diabetes. DGF was defined as the need fordialysis in the first 2 wk after transplantation. Three categoriesfor race were defined: White, black, and other. The use of co-administereddrugs was scored as 1 when the drug was used on the day of pharmacokineticassessment; otherwise, co-medication was scored as 0. The useof antiviral agents consisted of acyclovir or ganciclovir. Patientcharacteristics and biochemical parameters are summarized inTable 2.
Table 2. Patient demographics and biochemical parametersa
Pharmacokinetic Analysis
All data were analyzed simultaneously using the nonlinear mixed-effectsmodeling software program (NONMEM Version V, level 1.1; GloboMaxLLC, Ellicott City, MD). NONMEM is a parametric nonlinear multiplemeasurements regression program that was designed for populationpharmacokinetic analyses. This kind of analysis quantifies twotypes of population pharmacokinetic parameters on the basisof linking dosage, time, and observable patient features todrug concentrations (26,27,31,32). The first type is fixed effectparameters, which quantify mean population pharmacokinetic parameters,or typical relationships between patient features, such as genderor race, and individual pharmacokinetic parameters. The secondtype is random-effect parameters, which measure between- andwithin-patient variability of pharmacokinetic parameters (32,33).Using the first-order method in NONMEM, the population pharmacokineticparameters are calculated by simultaneously fitting all datato a pharmacokinetic model (32). This means that NONMEM appropriatelypools data across individuals, which makes the population parameterestimates less dependent on the number of samples per individual,while at the same time it allows easy combination of concentration-timedata collected in different studies with different samplingschemes and at different moments after transplantation (27).
A more detailed description of the technical aspects of themethods that are used for pharmacokinetic modeling are reportedelsewhere (unpublished observations, Van Hest et al., 2005).Briefly, during the first step of the analysis, a compartmentalpopulation pharmacokinetic model was developed describing thepharmacokinetics of MPA and quantifying between- and within-patientvariability in MPA pharmacokinetics. Data were logarithmicallytransformed, and residual variability was modeled additively(27). Individual estimates of the pharmacokinetic parameterswere obtained by Bayesian analysis.
The second step was the investigation of relationships betweenpatient factors and individual estimates of the pharmacokineticparameters. Patient factors that were tested were race; age;gender; weight; albumin level; alanine transferase; bilirubin;alkaline phosphatase; hemoglobin; red blood cell count; DGF;diabetes; cyclosporine dose; cyclosporine predose concentration;MMF dose; corticosteroid dose; the use of antiviral agents,proton pump inhibitors, antacids, H2-antagonists, and sirolimus;and renal function. With regard to renal function, two estimateswere tested: Estimation of creatinine clearance according tothe Cockcroft and Gault formula (C&G) (34) and estimationof the GFR with the abbreviated Modification of Diet in RenalDisease (aMDRD) method (35). First, all different variableswere tested in the model developed during the first step, ina univariate way. Whether a variable had a significant effectwas determined with the likelihood ratio test. P < 0.001was considered to be statistically significant. Second, a multivariateanalysis (backward elimination procedure) was done to obtainthe final model. The final model was refined by estimating between-patientvariability in the relationships between patient factors andpharmacokinetic parameters. This variability parameter takesinto account that a change in the value of a variable may nothave the same effect on a pharmacokinetic parameter in all individuals(36).
MPA AUC values, normalized to 1000 mg of MMF, were calculatedon the basis of the individual estimates for MPA clearance fromthe final model (equation 1): MPA AUC (mg/L per h) = 1000 mg/MPAclearance (L/h).
Statistical Analyses
Statistical analyses were performed with the software packageSPSS 11.5 for Windows (SPSS Inc., Chicago, IL). For comparisonsof continuous parameters between groups and within a group overtime, repeated measures ANOVA was used. P < 0.05 was consideredsignificant.
Data
In total, 13,346 MPA samples that originated from 1894 concentration-timecurves that were obtained from 468 renal transplant recipientswere analyzed. Sampling occasions varied from day 1 to day 3795(>10 yr) after renal transplantation, and MMF doses rangedfrom 250 mg twice daily to 2200 mg twice daily. A total of 884MPA concentration-time curves originated from the first monthafter transplantation, and 280 pharmacokinetic profiles weretaken after the first half year.
Pharmacokinetic Analysis
The model after the first step was a two-compartment model witha lag time that preceded the absorption phase (Figure 1). Theresults of the uni- and multivariate analyses of relationshipsbetween pharmacokinetic parameters and patient factors are summarizedin Table 3. The correlations between pharmacokinetic parametersand C&G were statistically stronger as determined with thelikelihood ratio test than correlations between pharmacokineticparameters and aMDRD. For this reason, C&G was used as themeasure for renal function and aMDRD was rejected.
Figure 1. The population pharmacokinetic model that best fit the data, which was a two-compartment model with time-lagged first-order absorption. MMF, mycophenolate mofetil.
Table 3. Relationships between PK parameters and patient factorsa
After the multivariate analysis, significant relationships werefound between C&G, albumin level, hemoglobin and cyclosporinepredose level, and MPA clearance (all P < 0.001; Table 3).These correlations are reported as relationships with MPA AUC0to 12 (normalized to 1000 mg of MMF, equation 1), as clearanceand dose are the only determinants of AUC0 to 12. MPA AUC0 to12 was higher when renal function was better: AUC0 to 12 was36 mg/L per h with a C&G of 20 ml/min and 45 mg/L per hwhen C&G was 65 ml/min (Figure 2A). A higher albumin levelcorrelated with a higher MPA AUC0 to 12: 42 mg/L per h whenalbumin level was 35 g/L and 48 mg/L per h with an albumin levelof 42 g/L (Figure 2B). Furthermore, AUC0 to 12 was higher witha hemoglobin of 12.5 mg/dl (AUC0 to 12 was 45 mg/L per h) comparedwith a hemoglobin of 10 mg/dl (AUC0 to 12 was 42 mg/L per h;Figure 2C). Finally, a lower cyclosporine predose level correlatedwith a higher AUC0 to 12: 45 mg/L per h with a cyclosporinepredose level of 150 ng/ml and 43 mg/L per h with a predoselevel of 225 ng/ml (Figure 2D). Whereas the separate patientfactors had a small to modest effect on MPA AUC0 to 12, an almostdoubling of AUC0 to 12 from 31 to 56 mg/L per h was found whenthe described effects of renal function, albumin level, hemoglobin,and cyclosporine predose level were combined.
Figure 2. Correlations between mycophenolic acid (MPA) clearance (CL) and renal function (creatinine clearance calculated according to Cockcroft and Gault; A), plasma albumin level (B), hemoglobin (C), and cyclosporine predose level (D). The solid lines represent the correlation estimated by the final population pharmacokinetic model.
Furthermore, absorption half-life was found to be significantlylonger with a lower cyclosporine dose: 0.15 h with a cyclosporinedose of 500 mg and 0.26 h without the use of cyclosporine (P< 0.001), indicating that cyclosporine increased the rateof MPA absorption from the gut. Patients who used antacids hada 37% higher central volume of distribution than patients whodid not use these agents.
The parameter estimates of the final model are summarized inTable 4. The identified relationships between patient factorsand pharmacokinetic parameters after the multivariate analysis(Table 3) explained both between- and within-patient variabilityin the pharmacokinetics of MPA. A total of 18% of the between-patientvariability in clearance and 38% of the within-patient variabilityin clearance was explained. For absorption half-life, 35% between-and 15% within-patient variability could be explained. For centralvolume of distribution, 39% between- and 20% within-patientvariability was explained. Finally, 42% between- and 47% within-patientvariability was explained for intercompartmental clearance.The magnitude of the effect of renal function on MPA clearanceand of albumin level on MPA clearance could be very differentper individual as illustrated by coefficients of variation forthe between-patient variability in these relationships of 66and 112%, respectively (P < 0.001). This indicates that achange of renal function or albumin level may have a significantimpact on MPA exposure in one patient, whereas in another patient,the effect may be considerably less.
Table 4. Parameter estimates for the final model with their SEa
Effects of Cyclosporine Exposure, DGF, Race, and Diabetes
To illustrate further the influence of the use of cyclosporineon MPA exposure, we compared the course of dose-normalized MPAAUC0 to 12 over time after transplantation between patientswho had cyclosporine as concomitant immunosuppressive therapy(n = 144 on day 0 to 4 posttransplantation) and patients whoused an immunosuppressive regimen without cyclosporine (n =102 on day 0 to 4 posttransplantation; Figure 3). Part of thislatter group was concurrently treated with sirolimus (n = 30).Patients who were exposed to cyclosporine exhibited lower mediandose-normalized MPA AUC0 to 12 values than patients who werenot exposed to cyclosporine during the whole study period, withthe exception of the first week. At months 1, 3, and 6 and atyear 1 after transplantation, patients who used cyclosporinehad a median dose-normalized MPA AUC0 to 12 of, respectively,36, 45, 52, and 56 mg/L per h, and patients without cyclosporineexposure had a median MPA AUC0 to 12 of 65, 58, 77, and 72 mg/Lper h. Of note, these values also show that MPA exposure increasedwith time after transplantation.
Figure 3. Course of dose-normalized MPA area-under-the-curve (AUC0 to 12) over time after transplantation for patients who had cyclosporine (CsA) as concomitant immunosuppressive therapy (n = 144 on days 0 to 4 posttransplantation; open box-whisker plots) and for patients who used an immunosuppressive regimen without CsA (n = 102 on days 0 to 4 posttransplantation; closed box-whisker plots). The box indicates the upper and lower quartiles, and the central line represents the median. The whiskers represent the 2.5 and the 97.5% values. The dotted lines represent the adopted therapeutic window for MPA AUC0 to 12 values of 30 to 60 mg/L per h (9). Exposure was significantly different between groups with P < 0.05 at months 1 and 6 and year 1. Exposure was significantly different between groups with P < 0.01 at month 3.
In the univariate analysis, patients with DGF had a significantlylower median MPA AUC0 to 12 compared with those with immediategraft function during the first 4 d after transplantation (23versus 33 mg/L per h, respectively; P < 0.001; Figure 4).However, in the multivariate analysis, DGF was no longer significantlycorrelated with clearance (Table 3), because renal function,as the more broadly defined variable, could explain the lowerMPA exposure in patients with DGF: Median C&G was 10 ml/minin patients with DGF versus 23 ml/min in patients with immediategraft function during the first 4 d after transplantation (P< 0.001). Thereafter, with recovering renal function in patientswith DGF (21 ml/min in week 2), the difference in MPA AUC0 to12 between patients with or without DGF decreased: 27 versus33 mg/L per h during days 5 to 8 and 31 versus 33 mg/L per hduring week 2.
Figure 4. Course of dose-normalized MPA AUC0 to 12 over the first 2 wk after transplantation for patients with immediate graft function (n = 212 on days 0 to 4 posttransplantation; open box-whisker plots) and for patients with delayed graft function (DGF; n = 34 on days 0 to 4 posttransplantation; closed box-whisker plots). The box indicates the upper and lower quartiles, and the central line represents the median. The whiskers represent the 2.5 and the 97.5% values. The dotted lines represent the adopted therapeutic window for MPA AUC0 to 12 values of 30 to 60 mg/L per h (9). Exposure was significantly different between groups with P < 0.05 on days 0 to 4.
Black renal transplant patients exhibited lower median dose-normalizedMPA AUC0 to 12 values during the first month after transplantationcompared with white patients. AUC0 to 12 values on days 0 to4, days 5 to 8, week 2, and month 1 were 30, 25, 25, and 30mg/L per h for black patients and 32, 32, 33, and 38 mg/L perh for white patients. Race, however, was not significantly correlatedwith clearance in the multivariate analysis (Table 3). The differencetherefore may be the result of a lower median renal functionin black patients during the same occasions (10, 16, 25, and52 ml/min) compared with white patients (21, 29, 38, and 55ml/min). The level of renal function over time between bothraces, however, was not statistically significant (P = 0.18).This may be due to the small number of black patients per occasion(n = 17, 8, 6, and 7, respectively), resulting in insufficientpower to find a statistically significant difference.
In this study, patients with diabetes had a small but significantlyincreased Tmax (calculated according to reference [37]) comparedwith patients without diabetes during the first half year aftertransplantation. For example, median Tmax in renal transplantpatients with diabetes at 1 mo posttransplantation was 1.1 hand in patients without diabetes was 0.8 h (P = 0.045).
During standard-dose MMF therapy, the MPA exposure has beenreported to vary 10-fold between patients, resulting in a widerrange of MPA AUC values than the adopted AUC range of the therapeuticwindow (4). This suggests that dose individualization may improveoutcome. Several studies investigated the determinants of thevariability in MPA concentrations, but conflicting results havebeen obtained (3,5,9–11,13–25). To explore whichfactors can explain variability in the pharmacokinetics of MPA,we performed a powerful population pharmacokinetic meta-analysisusing data from 468 renal transplant recipients. Eight variablesthat significantly influenced the pharmacokinetics of MPA wereidentified (Table 3). With these eight variables, 18 to 42%of the between-patient variability and 15 to 47% of the within-patientvariability can be explained in the different pharmacokineticparameters.
Renal function was an important determinant of MPA clearance.MPA clearance decreased with improving renal function. Thiscorrelation could explain 35% of within-patient variability,meaning that recovering renal function can explain an importantpart of the widely known increase of MPA exposure within a patientover time (7). The relationship between renal function and MPAclearance also explained why patients with DGF had a higherMPA clearance and consequently a lower MPA AUC0 to 12 in thefirst days after transplantation compared with patients withimmediate graft function (Figure 4) (9,38). Patients with DGFhad lower MPA exposure as a result of a significantly lowerrenal function during that period compared with patients withoutDGF.
A similar effect may apply to race. Black patients showed atrend toward lower dose-normalized MPA AUC0 to 12 compared withwhite patients in the first month after transplantation. LikeDGF, this difference may be explained by a lower renal functionin black patients, without an additive effect attributable torace. Although speculative, a possible difference in renal functionbetween races and the resulting effect on MPA exposure mighthave contributed to the observation in a previous study thatblack patients benefited from MMF over azathioprine only withdoses of 1.5 g twice daily, instead of the standard dose of1 g twice daily (9,25).
The influence of renal function on MPA clearance was not foundin every study that investigated the pharmacokinetics of MPA(15,16,18,19). This is explained by the fact that renal functionseems to have a clinically relevant effect on MPA clearanceonly when renal function is <25 ml/min (Figure 2A). Changesin renal function above the 25 ml/min threshold have a smallimpact on MPA clearance: An improvement of renal function from65 to 110 ml/min induces a modest decrease of MPA clearancefrom 22 to 19 L/h (Figure 2A). Studies with low proportionsof patients with DGF or impaired graft function postoperativelymay have been underpowered to demonstrate the influence of renalfunction on MPA clearance.
Acidosis, uremia, and accumulation of MPAG, all associated withimpaired renal function, will decrease MPA binding to albumin(9). As MPA is supposed to be a restrictively cleared drug,an increased free fraction leads to an increase of the amountof MPA that is available for glucuronidation and hence to ahigher MPA clearance (9).
The relationship with plasma albumin level and MPA clearanceis also explained through MPA free fraction. When the albuminlevel increases, MPA free fraction may become smaller; consequently,MPA clearance may decrease. The effect that increasing hemoglobincaused a decrease in MPA clearance, which was not found earlier,might also be explained with the same hypothesis. This suggeststhat MPA binds not only to albumin but also to hemoglobin orred blood cells. Unfortunately, free MPA concentrations werenot available in this study to test this hypothesis.
Despite having identified the significant influence of renalfunction and plasma albumin level on MPA clearance, adjustmentsof MMF dose cannot be recommended purely on the basis of thesefactors. The reason is that large between-patient variabilitywas estimated in the effect that renal function and albuminlevel had on MPA clearance (66 and 112%, respectively). Thismeans that the same change in renal function or albumin levelin one patient may result in a clinically relevant change ofMPA clearance, whereas in another patient, hardly any effectwill be present. Consequently, a change in renal function oralbumin level is not in itself an indication for dose adjustmentbut is merely an indication for therapeutic drug monitoringto check whether the MMF dose needs to be adjusted to get orkeep MPA exposure on target. Another reason may be that despitelower total MPA exposure, free MPA concentrations may be unalteredor even elevated in situations of impaired renal function orlow albumin levels (9,39). Because free MPA is regarded as thepharmacologically active moiety (40), MMF dose adjustments wouldnot be indicated then. This issue warrants further researchbefore MMF dose can be based on renal function and albumin level.
The observation that MPA clearance is influenced by cyclosporinepredose level can be explained by cyclosporine-mediated inhibitionof the multidrug resistance protein 2 through which the enterohepaticrecirculation of MPA can be disrupted (10). The result is thatpatients who were treated concurrently with cyclosporine hadlower MPA exposure than patients who did not receive cyclosporineduring the first year after renal transplantation (Figure 3).This observation is in accordance with observations from otherstudies in which patients who were treated concurrently withsirolimus (41) or tacrolimus (21) had higher MPA exposure thancyclosporine-treated patients. Furthermore, Figure 3 shows thathalf of the patients who were treated concurrently with cyclosporinehad MPA exposure below the recommended target window in thefirst week after transplantation. Because optimal MPA exposureearly after transplantation is associated with a lower incidenceof acute rejection (42), outcome in patients in whom MMF iscombined with cyclosporine may be improved with 1500 mg of MMFtwice daily instead of the currently recommended 1000 mg twicedaily in the immediate posttransplantation phase.
The result from a previous study that the tapering of corticosteroidsleads to an increase of MPA concentrations could not be confirmed(22). A positive correlation between the corticosteroid doseand MPA clearance could be identified during the univariateanalysis, but this relationship lost its significance in themultivariate analysis. This indicates that the corticosteroiddose is a confounding factor for the relationships between thepatient factors and MPA clearance in the final model.
A previous study did not show an effect of diabetes on MPA AUC0to 12 (23). Another study found an increased Tmax, but onlyseven patients with diabetes were included (24). This studyconfirms a slightly increased Tmax in renal transplant recipientswith diabetes. The increased Tmax may be related to gastroparesis,which is present in many patients with diabetes (43) but doesnot have a clinically relevant impact on MPA exposure.
Figure 3 shows that dose-normalized MPA AUC0 to 12 increasesover time after renal transplantation as a result of decreasingclearance. Given the identified relationships between MPA clearanceand renal function, hemoglobin, albumin level, and cyclosporinepredose level, this is in part caused by dynamic changes inthese variables. The increase in exposure in the group withoutcyclosporine exposure occurs mainly in the first month aftertransplantation and thus may be caused by improving renal function,increasing albumin level, and climbing hemoglobin (Figure 3).Increasing MPA exposure later after transplantation may be theresult, in part, of a decrease in cyclosporine predose levels(Table 2). This is illustrated in the cyclosporine group inFigure 3, in which median renal function and albumin level werestable between month 1 and year 1 (median renal function increasedfrom 52 to 64 ml/min, and median albumin level increased from37 to 39 g/L), whereas median cyclosporine predose level decreasedfrom 237 to 155 ng/ml during that period. As a result of thegradual increase in MPA exposure, a subset of patients willbe above the target window with standard MMF doses of 1000 mgtwice daily after 6 to 12 mo after transplantation. This ismost likely in patients who are no longer treated with cyclosporineand who have good renal function, albumin level, and hemoglobin(41). The increased MPA exposure may be very welcome in regimensin which cyclosporine is tapered or stopped, and in patientswho tolerate such levels without toxicity, dose reductions maynot be necessary. It is also important that the recommendedtarget window (8) is based on a combination of MMF with a calcineurininhibitor, and other target values may apply for other combinations(44).
With a population pharmacokinetic model, relationships havebeen identified between patient factors and pharmacokineticparameters, thus explaining variability in MPA pharmacokinetics.Exposure to MPA is significantly influenced by renal function,albumin level, and hemoglobin and cyclosporine predose levels.These variables may prove to be useful for more effective therapeuticdrug monitoring and MMF dosing, but this warrants further prospectiveresearch. Differences in MPA exposure between patients withor without DGF or between patients of different races are likelyto be caused by the effect of renal function on MPA exposure.Diabetes and the use of gastric pH modulators other than antacids,corticosteroids, antibiotics, and antiviral agents do not havean effect on MPA exposure.
Acknowledgments
This study was supported by Roche Laboratories, Inc. M.D.P.received research support from Novartis, Roche, Wyeth, and Astellas;R.G. is an employee of Roche; R.D.M. works as an consultantfor Roche; and T.V.G. has received research support from Novartis,Roche, and Wyeth.
We thank Sekhar Bhamidipati for creating the analytic data setand Dennis A. Hesselink for statistical assistance and inspiringdiscussions.
Footnotes
Published online ahead of print. Publication date availableat www.jasn.org.
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Abstract
Introduction
