Impact of longitudinal exposure to mycophenolic acid on acute rejection in renal-transplant recipients using a joint modeling approach

Assessing cumulative exposure to maintenance immunosuppressive drugs: Metrics, outcomes, and implications for transplant patients

Immunosuppressive drugs are used in the management of transplant patients to prevent organ rejection. However, immunosuppression can be associated with adverse effects such as infections and cancers. This study aimed to characterize the measures of cumulative immunosuppressive drug exposure (CIDE) used in the literature and their associated outcomes in transplant patients. A literature search was conducted in Ovid MEDLINE, Ovid EMBASE, Cochrane CENTRAL, and Cochrane Database of Systematic Reviews using search terms related to maintenance immunosuppressants and CIDE. Study risk of bias was assessed using the Quality in Prognostic Studies tool. Thirty-one articles were included in this qualitative synthesis. Sixteen articles (52 %) calculated the total dose of immunosuppression over the treatment period, while eight (26 %) used area-under-the-curve of trough level concentrations to quantify CIDE. Five (16 %) articles investigated time-weighted metrics of calcineurin inhibitors and four (13 %) used other metrics that could not be categorized into the previous groups. Most studies investigated CIDE with calcineurin inhibitors and used additive dosing methods. This approach was also popular with corticosteroids and multi-drug exposures. The variety of metrics used in the literature reveals a lack of standardization in the evaluation of CIDE and long-term outcomes. Future studies should validate these metrics for clinical application, especially pertaining to infectious outcomes.

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome

In this study, we jointly modeled longitudinal CD4 count data and survival outcome (time-to-first occurrence of composite outcome of death, cardiac tamponade or constriction) in other to investigate the effects of Mycobacterium indicus pranii immunotherapy and the CD4 count measurements on the hazard of the composite outcome among patients with HIV and tuberculous (TB) pericarditis. In this joint modeling framework, the models for longitudinal and the survival data are linked by an association structure. The association structure represents the hazard of the event for 1-unit increase in the longitudinal measurement. Models fitting and parameter estimation were carried out using R version 4.2.3. The association structure that represents the strength of the association between the hazard for an event at time point j and the area under the longitudinal trajectory up to the same time j provides the best fit. We found that 1-unit increase in CD4 count results in 2 % significant reduction in the hazard of the composite outcome. Among HIV and TB pericarditis individuals, the hazard of the composite outcome does not differ between of M.indicus pranii versus placebo. Application of joint models to investigate the effect of M.indicus pranii on the hazard of the composite outcome is limited. Hence, this study provides information on the effect of M.indicus pranii on the hazard of the composite outcome among HIV and TB pericarditis patients.

Noninvasive testing for mycophenolate exposure in children with renal transplant using urinary metabolomics

BACKGROUND

Despite the common use of mycophenolate in pediatric renal transplantation, lack of effective therapeuic drug monitoring increases uncertainty over optimal drug exposure and risk for adverse reactions. This study aims to develop a novel urine test to estimate MPA exposure based using metabolomics.

METHODS

Urine samples obtained on the same day of MPA pharmacokinetic testing from two prospective cohorts of pediatric kidney transplant recipients were assayed for 133 unique metabolites by mass spectrometry. Partial least squares (PLS) discriminate analysis was used to develop a top 10 urinary metabolite classifier that estimates MPA exposure. An independent cohort was used to test pharmacodynamic validity for allograft inflammation (urinary CXCL10 levels) and eGFR ratio (12mo/1mo eGFR) at 1 year.

RESULTS

Fifty-two urine samples from separate children (36.5% female, 12.0 ± 5.3 years at transplant) were evaluated at 1.6 ± 2.5 years post-transplant. Using all detected metabolites (n = 90), the classifier exhibited strong association with MPA AUC by principal component regression (r = 0.56, p < .001) and PLS (r = 0.75, p < .001). A practical classifier (top 10 metabolites; r = 0.64, p < .001) retained similar accuracy after cross-validation (LOOCV; r = 0.52, p < .001). When applied to an independent cohort (n = 97 patients, 1053 samples), estimated mean MPA exposure over Year 1 was inversely associated with mean urinary CXCL10:Cr (r = -0.28, 95% CI -0.45, -0.08) and exhibited a trend for association with eGFR ratio (r = 0.35, p = .07), over the same time period.

CONCLUSIONS

This urinary metabolite classifier can estimate MPA exposure and correlates with allograft inflammation. Future studies with larger samples are required to validate and evaluate its clinical application.

Individualized prediction for the occurrence of acute kidney injury during the first postoperative week following cardiac surgery

STUDY OBJECTIVE

To develop individualized dynamic predictions for the occurrence of acute kidney injury (AKI) during the first postoperative week after cardiac surgery.

DESIGN

Observational retrospective cohort study.

SETTING

Single university teaching hospital in Madrid, Spain.

PATIENTS

3960 cases of major cardiac surgery performed from January 2002 to December 2013.

MEASUREMENTS

Baseline demographic and clinical characteristics, intraoperative risk factors, and repeated postoperative estimated glomerular filtration rates (eGFR). The primary outcome was AKI during the first postoperative week (stage 1 or higher of the Acute Kidney Injury Network). The dataset was split in two random samples (exploratory and validation). By combining time-to-event outcomes (AKI), and longitudinal data (repeated postoperative eGFR), we developed two different joint models for patients with normal and high baseline levels of serum creatinine (sCr).

MAIN RESULTS

AKI occurred in 1105 patients (31%, 95% confidence interval [CI] 29.5-32.5) in the exploratory sample and 128 (32.2%, 95% CI 27.6-36.8) in the validation sample. For high baseline sCr patients, the risk of an AKI event was associated with the eGFR trajectory (hazard ratio [HR] 0.91, 95% CI 0.90-0.92), as well as with age, and cardiopulmonary bypass time. The normal baseline sCr model incorporated the same covariates and intraoperative transfusion. In this second model, the risk of an AKI event was associated with both the eGFR trajectory (HR 0.91, 95% CI 0.91-0.92, for the current value of eGFR), and with its slope at that point (HR 0.96, 95% CI 0.94-0.99). So AKI risk decreased when the eGFR values increased, in accordance with the speed of this rise. Internal validation showed good discrimination and calibration of both joint models. The AUCs were always higher than 0.7.

CONCLUSIONS

The joint models obtained combining both patient risk factors and postoperative eGFR values, are useful to predict individualized risk of cardiac surgery-associated AKI. Predictions can be updated as new information is gathered.

Cognition-Mortality Associations Are More Pronounced When Estimated Jointly in Longitudinal and Time-to-Event Models

With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults (N = 5,954; ages 50–87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology.

Inosine 5'-Monophosphate Dehydrogenase Activity for the Longitudinal Monitoring of Mycophenolic Acid Treatment in Kidney Allograft Recipients

BACKGROUND

Mycophenolic acid (MPA) is a standard immunosuppressant in organ transplantation. A simple monitoring biomarker for MPA treatment has not been established so far. Here, we describe inosine 5'-monophosphate dehydrogenase (IMPDH) monitoring in erythrocytes and its application to kidney allograft recipients.

METHODS

IMPDH activity measurements were performed using a high-performance liquid chromatography assay. Based on 4203 IMPDH measurements from 1021 patients, we retrospectively explored the dynamics early after treatment start. In addition, we analyzed the influence of clinically relevant variables on IMPDH activity in a multivariate model using data from 711 stable patients. Associations between IMPDH activity and clinical events were evaluated in hospitalized patients.

RESULTS

We found that IMPDH activity reflects MPA exposure after 8 weeks of constant dosing. In addition to dosage, body mass index, renal function, and coimmunosuppression affected IMPDH activity. Significantly lower IMPDH activities were found in patients with biopsy-proven acute rejection as compared to patients without rejection (median [interquartile range]: 696 [358-1484] versus 1265 [867-1618] pmol xanthosine-5'-monophosphate/h/mg hemoglobin, P < 0.001). The highest IMPDH activities were observed in hospitalized patients with clinically evident MPA toxicity as compared to patients with hospitalization not related to MPA treatment (1548 [1021-2270] versus 1072 [707-1439] pmol xanthosine-5'-monophosphate/h/mg hemoglobin; P < 0.001). Receiver operating characteristic curve analyses underlined the usefulness of IMPDH to predict rejection episodes (area, 0.662; confidence interval, 0.584-0.740; P < 0.001) and MPA-associated adverse events (area, 0.632; confidence interval, 0.581-0.683; P < 0.001), respectively.

CONCLUSIONS

IMPDH measurement in erythrocytes is a novel and useful strategy for the longitudinal monitoring of MPA treatment.

Evaluation of Longitudinal Exposure to Tacrolimus as a Risk Factor of Chronic Kidney Disease Occurrence Within the First-year Post-Liver Transplantation

BACKGROUND

Renal failure is predictive of mortality in the early postliver-transplantation period and calcineurin inhibitors toxicity is a main challenge. Our aim is to assess the impact of longitudinal tacrolimus exposure (TLE) and other variables on chronic kidney disease (CKD)-free 1-year-survival.

METHODS

Retrospective data of consecutive patients transplanted between 2011 and 2016 and treated with tacrolimus were collected. TLE and all relevant pre- and post-liver transplantation (LT) predictive factors of CKD were tested and included in a time-to-event model. CKD was defined by repeated estimated glomerular filtration rate (eGFR) values below 60 mL/min/1.73m2 at least for the last 3 months before M12 post-LT.

RESULTS

Data from 180 patients were analyzed. CKD-free survival was 74.5% and was not associated with TLE. Pre-LT acute kidney injury (AKI) and eGFR at 1-month post-LT (eGFRM1) <60 mL/min/1.73m2 were significant predictors of CKD. By distinguishing 2 situations within AKI (ie, with or without hepatorenal syndrome [HRS]), only HRS-AKI remained associated to CKD. HRS-AKI and eGFRM1 <60 mL/min/1.73m2 increased the risk of CKD (hazard ratio, 2.5; 95% confidence interval, 1.2-4.9; hazard ratio, 4.8; 95% confidence interval, 2.6-8.8, respectively).

CONCLUSIONS

In our study, TLE, unlike HRS-AKI and eGFRM1, was not predictive of CKD-free survival at 1-year post-LT. Our results once again question the reversibility of HRS-AKI.

Therapeutic drug monitoring of immunosuppressive drugs in hepatology and gastroenterology

Immunosuppressive drugs have been key to the success of liver transplantation and are essential components of the treatment of inflammatory bowel disease (IBD) and autoimmune hepatitis (AIH). For many but not all immunosuppressants, therapeutic drug monitoring (TDM) is recommended to guide therapy. In this article, the rationale and evidence for TDM of tacrolimus, mycophenolic acid, the mammalian target of rapamycin inhibitors, and azathioprine in liver transplantation, IBD, and AIH is reviewed. New developments, including algorithm-based/computer-assisted immunosuppressant dosing, measurement of immunosuppressants in alternative matrices for whole blood, and pharmacodynamic monitoring of these agents is discussed. It is expected that these novel techniques will be incorporate into the standard TDM in the next few years.

Personalized Therapy for Mycophenolate: Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

ABSTRACT

When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.

Variability of Prognostic Results Based on Biological Parameters in Sickle Cell Disease Cohort Studies in Children: What Should Clinicians Know?

BACKGROUND

Many pediatric studies describe the association between biological parameters (BP) and severity of sickle cell disease (SCD) using different methods to collect or to analyze BP. This article assesses the methods used for collection and subsequent statistical analysis of BP, and how these impact prognostic results in SCD children cohort studies.

METHODS

Firstly, we identified the collection and statistical methods used in published SCD cohort studies. Secondly, these methods were applied to our cohort of 375 SCD children, to evaluate the association of BP with cerebral vasculopathy (CV).

RESULTS

In 16 cohort studies, BP were collected either once or several times during follow-up. The identified methods in the statistical analysis were: (1) one baseline value per patient (2) last known value; (3) mean of all values; (4) modelling of all values in a two-stage approach. Applying these four different statistical methods to our cohort, the results and interpretation of the association between BP and CV were different depending on the method used.

CONCLUSION

The BP prognostic value depends on the chosen statistical analysis method. Appropriate statistical analyses of prognostic factors in cohort studies should be considered and should enable valuable and reproducible conclusions.

Dynamic prediction of relapse in patients with acute leukemias after allogeneic transplantation: Joint model for minimal residual disease

INTRODUCTION

Relapse remains the leading cause of treatment failure after allogeneic hematopoietic stem cell transplantation (alloHSCT) in leukemia. Numerous investigations have demonstrated that minimal residual disease (MRD) before or after alloHSCT is prognostic of relapse risk. These MRD data were collected at specific checkpoints and could not dynamically predict the relapse risk after alloHSCT, which needs serial monitoring.

METHODS

In the present study, we retrospectively analyzed MRD measured with multi-parameter flow cytometry in 207 acute myeloid leukemia (AML) patients (acute promyelocytic leukemia excluded), and 124 acute B lymphoblastic leukemia (ALL) patients. A three-step method based on joint model was used to build a relapse risk prediction model.

RESULTS

The 3-year overall survival and relapse-free survival rates of the entire cohort were 67.1% ± 2.8% and 61.6% ± 2.8%, respectively. The model included disease status before alloHSCT, acute and chronic graft-versus-host disease, and serial MRD data. The time-dependent receiver operating characteristics was used to evaluate the ability of the model. It fitted well with actual incidence of relapse. The serial MRD data collected after alloHSCT had better discrimination capabilities for recurrence prediction with the area under the curve from 0.67 to 0.91 (AML: 0.66-0.89; ALL: 0.70-0.96).

CONCLUSION

The joint model was able to dynamically predict relapse-free probability after alloHSCT, which would be a useful tool to provide important information to guide decision-making in the clinic and facilitate the individualized therapy.

Optimizing Mycophenolic Acid Exposure in Kidney Transplant Recipients: Time for Target Concentration Intervention

The immunosuppressive agent mycophenolate is used extensively in kidney transplantation, yet dosing strategy applied varies markedly from fixed dosing ("one-dose-fits-all"), to mycophenolic acid (MPA) trough concentration monitoring, to dose optimization to an MPA exposure target (as area under the concentration-time curve [MPA AUC0-12]). This relates in part to inconsistent results in prospective trials of concentration-controlled dosing (CCD). In this review, the totality of evidence supporting mycophenolate CCD is examined: pharmacological characteristics, observational data linking exposure to efficacy and toxicities, and randomized controlled trials of CCD, with attention to dose optimization method and exposure achieved. Fixed dosing of mycophenolate consistently leads to underexposure associated with rejection, as well as overexposure associated with toxicities. When CCD is driven by pharmacokinetic calculation to a target concentration (target concentration intervention), MPA exposure is successfully controlled and clinical benefits are seen. There remains a need for consensus on practical aspects of mycophenolate target concentration intervention in contemporary tacrolimus-containing regimens and future research to define maintenance phase exposure targets. However, given ongoing consequences of both overimmunosuppression and underimmunosuppression in kidney transplantation, impacting short- and long-term outcomes, these should be a priority. The imprecise "one-dose-fits-all" approach should be replaced by the clinically proven MPA target concentration strategy.

A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation

Identification of patients at risk of kidney graft loss relies on early individual prediction of graft failure. Data from 616 kidney transplant recipients with a follow-up of at least one year were retrospectively studied. A joint latent class model investigating the impact of serum creatinine (Scr) time-trajectories and onset of de novo donor-specific anti-HLA antibody (dnDSA) on graft survival was developed. The capacity of the model to calculate individual predicted probabilities of graft failure over time was evaluated in 80 independent patients. The model classified the patients in three latent classes with significantly different Scr time profiles and different graft survivals. Donor age contributed to explaining latent class membership. In addition to the SCr classes, the other variables retained in the survival model were proteinuria measured one-year after transplantation (HR=2.4, p=0.01), pretransplant non-donor-specific antibodies (HR=3.3, p<0.001), and dnDSA in patient who experienced acute rejection (HR=15.9, p=0.02). In the validation dataset, individual predictions of graft failure risk provided good predictive performances (sensitivity, specificity, and overall accuracy of graft failure prediction at ten years were 77.7%, 95.8%, and 85%, resp.) for the 60 patients who had not developed dnDSA. For patients with dnDSA individual risk of graft failure was not predicted with a so good performance.

Flexible Bayesian additive joint models with an application to type 1 diabetes research

The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease-specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. Using Bayesian P-splines, we are in particular able to capture highly nonlinear subject-specific marker trajectories as well as a time-varying association between the marker and event process allowing new insights into disease progression. The model is estimated within a Bayesian framework and implemented in the R-package bamlss.

Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis

BACKGROUND

Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical literature. During this review we aim to assess the current standard of reporting of joint models applied in the literature, and to determine whether current reporting standards would allow or hinder future aggregate data meta-analyses of model results.

METHODS

We undertook a literature review of non-methodological studies that involved joint modelling of longitudinal and time-to-event medical data. Study characteristics were extracted and an assessment of whether separate meta-analyses for longitudinal, time-to-event and association parameters were possible was made.

RESULTS

The 65 studies identified used a wide range of joint modelling methods in a selection of software. Identified studies concerned a variety of disease areas. The majority of studies reported adequate information to conduct a meta-analysis (67.7% for longitudinal parameter aggregate data meta-analysis, 69.2% for time-to-event parameter aggregate data meta-analysis, 76.9% for association parameter aggregate data meta-analysis). In some cases model structure was difficult to ascertain from the published reports.

CONCLUSIONS

Whilst extraction of sufficient information to permit meta-analyses was possible in a majority of cases, the standard of reporting of joint models should be maintained and improved. Recommendations for future practice include clear statement of model structure, of values of estimated parameters, of software used and of statistical methods applied.

Joint modelling of repeated measurement and time-to-event data: an introductory tutorial

BACKGOUND

The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (eGFR) measurements and time to initiation of renal replacement therapy (RRT). Joint models typically combine linear mixed effects models for repeated measurements and Cox models for censored survival outcomes. Our aim in this paper is to present an introductory tutorial on joint modelling methods, with a case study in nephrology.

METHODS

We describe the development of the joint modelling framework and compare the results with those obtained by the more widely used approaches of conducting separate analyses of the repeated measurements and survival times based on a linear mixed effects model and a Cox model, respectively. Our case study concerns a data set from the Chronic Renal Insufficiency Standards Implementation Study (CRISIS). We also provide details of our open-source software implementation to allow others to replicate and/or modify our analysis.

RESULTS

The results for the conventional linear mixed effects model and the longitudinal component of the joint models were found to be similar. However, there were considerable differences between the results for the Cox model with time-varying covariate and the time-to-event component of the joint model. For example, the relationship between kidney function as measured by eGFR and the hazard for initiation of RRT was significantly underestimated by the Cox model that treats eGFR as a time-varying covariate, because the Cox model does not take measurement error in eGFR into account.

CONCLUSIONS

Joint models should be preferred for simultaneous analyses of repeated measurement and survival data, especially when the former is measured with error and the association between the underlying error-free measurement process and the hazard for survival is of scientific interest.

Pharmacology and toxicology of mycophenolate in organ transplant recipients: an update

This review aims to provide an update of the literature on the pharmacology and toxicology of mycophenolate in solid organ transplant recipients. Mycophenolate is now the antimetabolite of choice in immunosuppressant regimens in transplant recipients. The active drug moiety mycophenolic acid (MPA) is available as an ester pro-drug and an enteric-coated sodium salt. MPA is a competitive, selective and reversible inhibitor of inosine-5'-monophosphate dehydrogenase (IMPDH), an important rate-limiting enzyme in purine synthesis. MPA suppresses T and B lymphocyte proliferation; it also decreases expression of glycoproteins and adhesion molecules responsible for recruiting monocytes and lymphocytes to sites of inflammation and graft rejection; and may destroy activated lymphocytes by induction of a necrotic signal. Improved long-term allograft survival has been demonstrated for MPA and may be due to inhibition of monocyte chemoattractant protein 1 or fibroblast proliferation. Recent research also suggested a differential effect of mycophenolate on the regulatory T cell/helper T cell balance which could potentially encourage immune tolerance. Lower exposure to calcineurin inhibitors (renal sparing) appears to be possible with concomitant use of MPA in renal transplant recipients without undue risk of rejection. MPA displays large between- and within-subject pharmacokinetic variability. At least three studies have now reported that MPA exhibits nonlinear pharmacokinetics, with bioavailability decreasing significantly with increasing doses, perhaps due to saturable absorption processes or saturable enterohepatic recirculation. The role of therapeutic drug monitoring (TDM) is still controversial and the ability of routine MPA TDM to improve long-term graft survival and patient outcomes is largely unknown. MPA monitoring may be more important in high-immunological recipients, those on calcineurin-inhibitor-sparing regimens and in whom unexpected rejection or infections have occurred. The majority of pharmacodynamic data on MPA has been obtained in patients receiving MMF therapy in the first year after kidney transplantation. Low MPA area under the concentration time from 0 to 12 h post-dose (AUC0-12) is associated with increased incidence of biopsy-proven acute rejection although AUC0-12 optimal cut-off values vary across study populations. IMPDH monitoring to identify individuals at increased risk of rejection shows some promise but is still in the experimental stage. A relationship between MPA exposure and adverse events was identified in some but not all studies. Genetic variants within genes involved in MPA metabolism (UGT1A9, UGT1A8, UGT2B7), cellular transportation (SLCOB1, SLCO1B3, ABCC2) and targets (IMPDH) have been reported to effect MPA pharmacokinetics and/or response in some studies; however, larger studies across different ethnic groups that take into account genetic linkage and drug interactions that can alter a patient's phenotype are needed before any clinical recommendations based on patient genotype can be formulated. There is little data on the pharmacology and toxicology of MPA in older and paediatric transplant recipients.