The Expert Committee communicated frequently by e-mail, and met in person on 2 occasions, the second time to arrive at a consensus. All members of the Barcelona Consensus Document Committee complied with the policy on conflicts of interest, which requires disclosure of any financial or other interest that might be construed as constituting an actual, potential, or apparent conflict. Potential conflicts of interest are listed in the disclosures of the article. The committee will determine the need for revisions to the consensus document at 3 yearly intervals.
This consensus document will incorporate, for the first time, the opinion of several groups of experts in the field. It has been designed to discuss the utility of measuring selected currently available biomarkers shown to be associated with the risk of rejection, immunosuppression requirements, drug-related efficacy and toxicity, and graft function. Biomarkers should help to tailor immunosuppressive therapy to the needs of the individual patient. The aim is to identify biomarkers with documented clinical utility that have been evaluated using standardized and validated methodologies in independent populations. The Expert Committee has decided that donor-specific antibodies (DSA)/anti-HLA antibodies will not be covered here, because these biomarkers are discussed in depth in the literature. Likewise, other interesting biomarkers such as gene microarrays3,4 and miRNAs5 will not be considered in this document; the authors' general position is that further studies are required to assess the combination of these biomarkers with TDM of immunosuppressive drugs.
BIOMARKERS ASSOCIATED WITH THE ASSESSMENT OF THE RISK OF REJECTION
T-Cell IFN-γ and IL-2 Cytokines as Predictive Markers of the Risk of Allograft Rejection
Multiple cytokines can mediate effector and regulatory effects on the immune response,6,7 and their production and secretion can be modified by immunosuppressive drugs after ex vivo stimulation. The impact of these drugs on the synthesis of the cytokines interleukin (IL)-2 and interferon (IFN)-γ has shown wide interindividual variability, which suggests that monitoring cytokines may be useful both for predicting the risk of rejection, by identifying biomarkers of alloreactivity, and for reflecting personal susceptibility to immunosuppressive drugs.8,9
IFN-γ as a Predictive Biomarker of Individual Alloreactivity and Risk of Rejection
- Monitoring intracellular or total IFN-γ before and early after transplantation can help to identify kidney and liver transplant recipients at high risk of acute rejection (B, II).
- Monitoring IFN-γ production with donor-specific stimulation can help identify patients who are candidates for immunosuppression minimization (B, II).
- Ongoing multicenter clinical trials using validated methods are now evaluating the clinical utility of IFN-γ production, both pretransplantation and posttransplantation as an early predictive biomarker of the risk of rejection and graft clinical outcome.
IFN-γ has a pleiotropic effect10; in some physiological circumstances, it elicits inflammatory T helper (Th) 1–driven immune responses, whereas in others, it enables regulatory T cells (Treg) to control immune responses.11
Assessment of IFN-γ by the enzyme-linked immunospot (ELISPOT) assay has been used to evaluate the pretransplantation and early posttransplantation frequency of donor-specific IFN-γ-producing T cells and their impact on posttransplantation clinical outcome.12 High frequencies of donor-reactive memory effector T cells are associated with increased IFN-γ production, a high risk of acute rejection, and poorer first-year renal graft function.13–15 Graft function was defined by the authors considering the simplified modification of diet in renal disease (MDRD) formula to calculate the glomerular filtration rate (GFR) and creatinine levels. Furthermore, delayed graft function was defined as the need for dialysis during the first week after transplantation, and acute rejection episode was defined as an increased creatinine level that was not attributable to other reasons, with a subsequent return to baseline after antirejection treatment. Moreover, the finding that circulating donor-specific alloreactive T cells were detectable long after transplantation suggests that T-cell-mediated chronic graft damage may persist in the long term and that those biomarkers of alloimmunity could be useful to identify patients with progressive immune-mediated graft injury.16
Because the type of cell subpopulation can determine whether the immune response will be effector or regulatory, there is a growing interest in determining which cell subpopulations synthesize specific cytokines. Several studies have used flow cytometry to analyze intralymphocytary IFN-γ changes, in alloreactive T cells, as a biomarker of risk of rejection.17 In stable liver transplant recipients undergoing weaning from immunosuppressive therapy, %CD3+CD4+IFN-γ+ and %CD3+CD8+IFN-γ+ were identified as surrogate markers for the risk of rejection.18 This promising finding was corroborated later in de novo adult liver transplant recipients.19,20 Patients with acute rejection had an early significant increase in IFN-γ production by CD4+ and CD8+ cells during the first month after transplantation before the acute rejection was diagnosed (biopsy-proven acute rejection [BPAR]).
In line with these findings, the results from a multicenter prospective study indicate that pretransplantation and posttransplantation analysis of intracellular %CD3+CD4+CD69+IFN-γ+ and %CD3+CD8+CD69+IFN-γ+ T cells, measured by interlaboratory standardized methods, can help to identify liver and kidney transplant recipients at high risk of acute rejection.19 All patients who rejected organs showed pretransplantation levels of %CD3+CD4+CD69+IFN-γ+ and %CD3+CD8+CD69+IFN-γ+ above the cutoff value established for the risk of acute rejection.19
IL-2 as Predictive Biomarker of Individual Alloreactivity and Risk of Rejection
- Monitoring intracellular IL-2 before and early after transplantation can help to identify kidney and liver transplant recipients at high risk of acute rejection (B, II).
- IL-2 inhibition may reflect interindividual response to CNIs (B, II).
- Ongoing multicenter clinical trials using validated methods are now evaluating the clinical utility of IL-2 production, both before and after transplantation, as an early predictive biomarker of the risk of rejection and personal susceptibility to CNIs.
IL-2 drives T-cell growth, induces T regulatory (Treg) differentiation, and mediates activation-induced cell death.21,22 Several studies have shown that IL-2 is necessary for the survival of activated cells and the successful generation of effector responses23 and regulatory responses.24
The %CD3+CD8+IL-2+ expression could be a surrogate marker to identify patients at high risk of rejection.18,25 Pretransplantation IL-2 production in CD8+ T cells was closely related to the onset of acute rejection and was also correlated with the Banff score in adult liver transplant recipients.26 In stable liver recipients, an increase in the %CD8+IL-2+ during withdrawal was identified as a prelude to rejection.18 More recently, in a cohort of de novo liver transplant recipients, %CD3+CD8+IL-2+ was significantly higher in rejectors than in nonrejectors, both before and at 1 week after transplantation.20 Along the same lines, in a multicenter prospective study,19 both liver and kidney transplant patients with acute rejection showed significantly higher pretransplantation IL-2 production in CD3+CD8+CD69+ T cells.
Preliminary studies have shown that intracellular IL-2 may reflect the individual response to CNI.20,27 The incidence of BPAR was significantly related to inhibition of %CD3+CD8+IL-2+ and %CD3+CD8+IFN-γ+ during the first week after transplantation, and was unrelated to Tac exposure. BPAR occurred in patients with less than 40% inhibition of %CD3+CD8+IL-2+ and %CD3+CD8+IFN-γ+ during the first week after transplantation, compared with pretransplantation values.20
Limitations of the Methods and Clinical Use of IFN-γ and IL-2 Cytokine Assessment
Monitoring IFN-γ production with donor-specific stimulation can identify patients with an increased immune response to a defined donor antigen. Interestingly, cross-validation data of the IFN-γ ELISPOT assay performed in several European laboratories have shown that this method is effective for the assessment of circulating alloreactive memory effector T cells in renal transplant recipients.28 However, there are 2 clear disadvantages to the ELISPOT assay: first, donor-specific cells are not usually available in routine clinical practice; and second, it is impossible to simultaneously analyze different lymphocyte subsets and/or effector/regulatory cytokines, which are donor nonspecific immune parameters that can also correlate with graft outcome.
Multiparameter flow cytometry has the advantage of allowing simultaneous analysis of multiple cell phenotypic markers and intracellular cytokine production. Results from multicenter studies19,29 indicate that protocols for this type of peripheral blood cell phenotyping can be successfully transferred to multiple laboratories with experienced personnel and provide highly comparable results. One drawback of this method is its lack of specificity, given that the intracellular production of some cytokines may also be modulated by other inflammatory conditions (eg, infections). The validated IFN-γ ELISPOT assay28 with donor-specific stimulation and intracellular cytokine measurement by flow cytometry19 have shown similar median interlaboratory and intralaboratory coefficients of variation. For both methods, the results of the analyses are interpreted on the basis of cutoff values determined in previous multicenter studies in kidney and liver transplant recipients.16,25
Further investigation in this field is warranted. The impact of confounding clinical factors in the application of these biomarkers for predicting the risk of rejection must be more appropriately evaluated. The optimal time point(s) and frequency for monitoring these cytokines as predictive biomarkers of risk assessment have yet to be established. Combined with other biomarkers and drug exposure, the %CD3+CD4+IFN-γ+, %CD3+CD8+IFN-γ+, and %CD3+CD8+IL-2+ may complement pharmacokinetic TDM in transplant recipients receiving CNIs.
T-Cell Surface Antigens
T lymphocytes play a central role in the cellular-mediated process of acute graft rejection after solid organ transplantation (SOT).30 They are characterized by expression of the CD3 (CD = cluster of differentiation) receptor or T-cell receptor on their surface. T-cell activation is a hallmark of the early rejection process in SOT.31 Upregulated surface antigens as markers of activated T cells (eg, CD25, CD26, CD28, CD38, CD44, CD69, CD71, CD95, CD134, CD152, CD154, CXCR3, CCR5, and HLA-DR) can be assessed either in nonstimulated whole blood or after ex vivo stimulation of whole blood, as well as in isolated peripheral blood mononuclear cells (PBMC) in cell function assays.31 Some surface antigens are also cleaved off the cell surface and can be determined in the serum or plasma.
- Donor-specific CD154 expression in T-cytotoxic memory cells with a United States Food and Drug Administration (FDA)-approved ex vivo cell function assay may be used to predict the risk of transplant rejection after liver and small bowel transplantation in patients <21 years (B, II).
- Soluble CD30 (sCD30) in the serum/plasma before and shortly after renal transplantation is associated with long-term kidney graft outcome, but its usefulness as a biomarker to predict acute rejection in SOT is not yet entirely clear (B, II).
- Assessing surface antigens on T cells stimulated in vitro and ex vivo with mitogens in cell function assays reflects the inhibitory effect of immunosuppressants on lymphocyte activation (C2, II).
- CD26 and CD28 surface antigens on T cells assessed directly in nonstimulated whole blood are associated with acute rejection and/or malignancy after kidney and liver transplantation (C2, III).
The effect of immunosuppressants on T-cell surface antigens has been shown in vitro by supplementing incubation media of cell function assays with various concentrations of immunosuppressive drugs, or by stimulating cells isolated from immunosuppressed patients ex vivo. As a proof of principle, dose–response curves with immunosuppressants have shown their inhibitory effect on surface antigen expression in activated T cells in vitro,32 and stimulated PBMC isolated from immunosuppressed patients showed less surface marker upregulation ex vivo when compared with cells from healthy controls.33,34 There are only a few reports on the direct assessment of T-cell subsets (CD4 or CD8) expressing the costimulatory molecules CD26 or CD28. An association with acute rejection and long-term outcome (malignancy) has been reported for CD28 in liver transplantation,35,36 and with acute rejection in renal transplantation for CD26.37 One group has extensively explored the surface marker CD30, in its soluble form in serum (sCD30), and found an association with acute rejection and long-term kidney function in renal transplantation.38–40 This was true for plasma concentrations determined both before and after transplantation.39,41 High serum sCD30 concentrations before transplantation combined with panel reactive antibodies were associated with remarkably poor graft outcome.40 However, a more recent meta-analysis questioned the value of pretransplantation sCD30 in predicting acute rejection.42 The significance of sCD30 for organs other than the kidney is less clear. The soluble IL-2 receptor is another T-cell activation marker that can be measured like sCD30 by immunoassay of the serum or plasma and has been shown to be associated with acute rejection in renal transplantation.43
T-Cell Surface Antigens: Methods and Association With Clinical Outcome
Stimulation in cell function assays can be achieved by donor alloantigens, third party antigens (cells or peptides), antibodies to T cells or T-cell surface proteins, or by mitogens. Antigen expression is usually followed by flow cytometry using fluorescent antibodies. Immunoassays are available to measure soluble surface antigens in the serum/plasma.
Most data on the association between T-cell surface antigens and clinical outcome have been reported in the early phase after kidney transplantation, concluding that surface marker expression was more useful to rule out, rather than to predict acute rejection (high negative predictive values). This is conceivable because T-cell activation is not restricted to immune activation due to tissue incompatibility but can also be triggered by other events such as infections. However, the costimulatory molecule CD154 can be used to predict acute rejection in young patients with liver and small bowel transplantation.44,45 The Pleximmune cell function assay, using donor-specific stimulation, has obtained FDA clearance for this indication. The assay is performed in a central laboratory in the United States, which improves its reproducibility, but limits its worldwide dissemination and turn-around time.
Limitations of the Clinical Use of T-Cell Surface Antigen Assessment
A drawback of most cell function assays is the need for cell isolation and incubation times from 7 hours to 120 hours.46 Another difficulty is the lack of assay standardization and limited cell stability, which are obstacles for multicenter trials. In contrast, soluble proteins in the circulation can be assessed by commercial assays, thereby improving comparability between laboratories. However, this approach also has limitations; because surface antigens can also be released from cells other than T cells, such as activated endothelial and B cells, compromising their specificity as biomarkers for T-cell activation.47,48 It is a matter of debate whether nonspecific stimulation (eg, by mitogens) or donor-specific stimulation by donor cells or donor antigens is more meaningful in cell function tests. In the first case, the general effect of immunosuppression on T-cell activation can be compared between individuals; in the second case, a donor-specific effect is observed, which may be more useful to personalize immunosuppression.
T-Cell Surface Antigens: Clinical Implementation
None of the surface antigens can be currently recommended to tailor immunosuppression in clinical transplantation, or to complement TDM. No data have been reported to justify the use of surface antigens to predict the individual response to a specific drug. CD154 is intended to predict acute rejection in patients aged <21 years with liver and small bowel transplantation, whereas sCD30 may be used to estimate kidney graft outcome. Although information from the Pleximmune assay and sCD30 is potentially considered by transplant physicians in their choice of immunosuppression, there are no controlled prospective clinical trials that have proven that adjusting immunosuppression based on surface antigen expression or the concentration of soluble surface antigens in the serum will improve the outcome of graft recipients.
T-Cell Regulatory Populations
Tregs are basically defined by their capacity to suppress effector immune responses, and in the context of transplantation, to control alloreactive responses. This is why they have been considered as potential biomarkers for SOT, to monitor immunosuppression, and to predict clinical events.
Among Tregs, the most extensively studied ones are the CD4+ Tregs. In humans, they are characterized by high expression of CD25 (the α-chain of the IL-2 receptor), in contrast to the effector CD4+ T cells, which express lower, transient levels of CD25, and expression of the transcription factor Foxp3.49 It has recently been demonstrated that the expression of Foxp3 is not as specific, and the promoter must be demethylated to be specific to Tregs.50 The phenotype that best characterizes Tregs at present is defined as CD4+CD25highFoxP3+CD27+CD127low/−.51,52 Other Treg subsets have also been identified, such as Tr1 or Th3, although mostly in experimental models, not yet extensively studied in humans as CD25+ Tregs. In addition, several Treg subsets have been described, and the CD45RO expression on CD4+CD25high Treg cells has been shown to identify activated Tregs with highly suppressive capacity.53
- Low numbers of circulating activated Tregs before transplantation may help to identify renal transplant recipients at high risk of acute rejection (B, II).
- Increased levels of circulating Tregs may help to identify renal transplant recipients at high risk of developing squamous cell cancer (B, II).
Tregs and Clinical Outcome
The first evidence of the possible role of Tregs in organ transplantation was found in biopsies from renal transplant patients undergoing acute rejection.54 However, it was later found that this was not specific to rejection. Because Tregs have the potential to control immune responses, many subsequent studies were conducted, in an attempt to demonstrate their role as biomarkers in SOT. Many were performed on whole blood instead of on biopsies, to have a minimally invasive biomarker. A serious limitation was that most of these were not multicenter studies, were limited to special clinical situations, and had very few time points in follow-up. Moreover, most did not measure Tregs in both biopsies and peripheral blood. A few studies monitored the numbers of Tregs in peripheral blood during the first 2 years after transplantation, reporting a decrease of these cells in patients with acute rejection.55–57 Furthermore, increased levels of circulating Tregs during the first year after renal transplantation were associated with better graft survival at 4 years after transplantation.58 None of the studies were able to demonstrate any predictive value for rejection from circulating Treg levels. It has also been proposed that the decreased levels of these cells in patients with acute rejection could be more related to a high load of immunosuppression.56 However, measuring the whole Treg population in peripheral blood might not reflect the cell subset involved in alloreactivity control. Thus, the presence of increased pretransplantation levels of activated Tregs with the phenotype CD4+CD25highCD62L+CD45RO+ was associated with increased risk of acute rejection within the first year after transplantation.57,59
There is even less evidence of Tregs being associated with chronic rejection, and results in many cases are discordant.60–62 In fact, international consortia, such as the European RISET or the North American ITN, did not consider monitoring circulating Tregs to be useful in renal transplantation,61,62 but they did find an exacerbated humoral immune profile. In this regard, another regulatory cell subset, B regulatory cells (Bregs), has been proposed very recently. Data on Bregs in human transplantation are still limited. Moreover, it is unclear whether they represent a cell subset, and to date, no cell lineage transcription factor has been identified. Most of the evidence is limited to mouse models.63
The utility of Tregs as biomarkers of rejection or graft outcome in other SOT is even less well studied, and far from being demonstrated.
Other possible clinical applications of Tregs should be explained in the context of patients receiving chronic immunosuppression. The aim is to define drugs able to suppress the effector T-cell responses while maintaining or inducing the activity of Tregs. In line with the previous argument, mammalian target of rapamycin (mTOR) inhibitors (mTORi) may favor the action of Tregs.64 In addition, long-term treatment with CNIs in stable human renal transplantation produces a decrease in the number of circulating Treg cells, whereas mTORi maintains the number of circulating Tregs.65 These findings suggest that mTORi treatment could help to recover the blood levels of Tregs in patients previously treated with CNIs. Furthermore, the presence of high numbers of circulating Tregs before conversion from CNI therapy to mTORi treatment could predict renal recipients who develop squamous cell cancer.66 This could be one of the most promising clinical applications of monitoring circulating Tregs. However, there are very few studies on the effect of induction therapies on Tregs in renal transplantation. Most are not comparable because of the use of different immunosuppression maintenance regimens. Finally, Tregs have been proposed as a tool to achieve donor tolerance in transplantation, although their implementation in clinical transplantation is limited by the relative success of immunosuppression to avoid acute rejection.67
Tregs: Methods and Clinical Implementation
There is clearly a need for clinical trials that investigate the determination of Tregs as biomarkers in peripheral blood. The main difficulties entailed in such trials are the number of phenotypic markers required to define Tregs, and the lack of standardized methods to measure them in peripheral blood. To our knowledge, there is only 1 multicenter study in which standard operating procedures were followed to quantify the numbers of circulating Tregs, even using the same reagent lots to minimize interlaboratory variability.57 The lack of large, randomized, prospective multicenter cohorts has meant that monitoring Tregs as clinical biomarkers in organ transplantation is far from being implemented in routine clinical practice.
BIOMARKERS THAT REFLECT THE INDIVIDUAL RESPONSE TO IMMUNOSUPPRESSANTS
The combination of synergistic drugs is the main strategy to prevent early acute rejection and to provide long-term effective rejection prophylaxis after organ transplantation. The necessary TDM of immunosuppressive drugs in clinical practice is currently based on measuring drug concentration levels in blood (PK). However, such PK monitoring of immunosuppressants may not predict the individual pharmacological effects on immune cells.68 Thus, the direct determination of drug targets (eg, enzyme activity or T-cell subsets) as a PD surrogate of the immunosuppressive drug effects may help to better assess the individual response to the immunosuppressant. This review does not discuss the effect of different biologicals on lymphocyte populations but focuses on the clinical relevance and published methods for monitoring PD targets of commonly used classical chemical immunosuppressive drugs.
Target Enzyme Activity as Specific Biomarkers in Transplantation
It is notable that current combination maintenance immunosuppression is mainly based on the inhibition of different enzymes in immune cells, eg, inhibition of calcineurin activity by cyclosporine (CsA) or Tac, inhibition of inosine-monophosphate-dehydrogenase (IMPDH) by MPA, and inhibition of the mTOR complex by everolimus (EVR) or sirolimus (SRL). It is obvious that direct determination of target enzyme activity would provide a straightforward PD approach to directly determine the effect of the immunosuppressant in the individual. Despite 2 decades of research, clinical applicability of this approach is often limited by the complexity of the test systems. Development of a rapid, reliable, and robust assay system, which can be used in clinical practice, is a prerequisite for any PK-PD investigation in larger patient populations.32 In addition to methodological issues, the validation of and transfer of such PD biomarkers to clinical practice is a long, step-by-step process, largely depending on international collaboration networks.69
IMPDH Measurement Methods and Clinical Outcome
- Determination of IMPDH activity before transplantation might be useful to identify renal transplant recipients at higher risk of acute rejection or MPA-associated side effects (B, II).
- Monitoring IMPDH activity may complement the determination of MPA PK to better guide MPA therapy (B, II).
- Ongoing multicenter clinical trials are using cross-validated methods to evaluate the clinical utility of IMPDH activity to predict the risk of rejection or MPA-associated side effects.
Development of a rapid, reliable, and robust IMPDH assay system, which can be used in clinical practice, was an important step for thorough PK-PD investigations in larger numbers of MPA-treated patients.70,71 New insights into the mechanism of action of MPA were obtained by this direct PD assay.72,73 It was used in several clinical studies, including pediatric cohorts, by different research groups, and is based on the chromatographic determination of newly generated xanthosine 5′monophosphate (XMP) in mononuclear cell lysates. The assay requires only reasonable amounts of blood and can reliably be used in multicenter trials. Pretransplant IMPDH activity may be linked to the genetic background and may provide some valuable indications for the further clinical course (eg, risk of rejection or MPA-associated side effects), which could result in better tailored MPA dosing strategies. Although pretransplant IMPDH activity is not affected by MPA, all subsequent IMPDH determinations are directly influenced by the ongoing MPA treatment. Given the complexities of MPA PK, the best time point (eg, predose) and/or IMPDH sampling strategy (eg, maximum inhibition, area under the effect curve) has yet to be determined as a PD surrogate marker of MPA-associated immunosuppressive effects. In addition, more clinical data from larger cohorts are needed to determine the clinical utility of IMPDH monitoring.
mTOR Activity: Methods and Clinical Outcome
Monitoring P-p70S6 kinase (phospho-70-kDa ribosomal protein S6 kinase)/pS6RP (phospho ribosomal S6 protein) may complement the determination of mTOR inhibitor trough concentrations to better guide mTOR inhibitor therapy (C1, III).
With respect to monitoring mTORi, early results using the Western blot or enzyme-linked immunosorbent assay providing data on measurement of mTOR pathway compounds (p70S6 kinase or pS6RP) seem to be promising for enhanced TDM of SRL and EVR after organ transplantation.74–76 Compared with the Western blot and enzyme-linked immunosorbent assay, the technique of phospho-flow cytometry offers the ability to detect phosphorylated proteins, and to differentiate between activation-induced changes of signaling molecules inside the cell relative to unstimulated populations of identical cells in the same sample.77
Additionally, only microliters of whole blood are needed for multiparametric flow cytometric analysis to measure drug potencies and efficacies in vivo78 and are therefore the ideal tool for PD cell monitoring.33 At present, only the phospho-flow pS6RP assay has been validated in vitro for the analysis of SRL effects on phosphorylated S6 ribosomal protein (pS6RP) in vitro.79 Phospho-flow analysis revealed that SRL suppressed pS6RP in human T cells in a dose-dependent manner. In the experience of some groups, storage of whole blood for 24 hours at room temperature or 4°C before analysis seems to display adequate robustness for its clinical use, although data on stability are not consistent across laboratories. Further evaluation of this pS6RP whole-blood assay in 87 EVR-treated heart transplant recipients showed that CsA blood concentration, the duration of EVR treatment, the comedication with thiazide diuretics, and different metabolic parameters could have an influence on the expression of pS6RP in T cells. Additionally, 4 different patterns of EVR responses on pS6RP expression were observed.80
Another phospho-flow assay measured p70S6K phosphorylation in PBMCs in renal transplant recipients.81 Phosphorylation was significantly reduced in isolated PBMC from patients treated with CNIs and mTORi compared with patients on CNIs and mycophenolate. However, the effect did not correlate with the whole-blood trough concentrations of the mTORi. Additionally, it was observed that in the CD4+CD25low/− subset of T cells, the p70S6K phosphorylation was significantly reduced for patients on EVR, whereas in the circulating CD4+CD25high Treg cells, the phosphorylation was not affected by the mTORi. This assay has not been validated, so far.
IMPDH and mTOR Activity: Clinical Implementation
To demonstrate clinical relevance, both specific biomarkers of target enzyme activity, IMPDH and mTOR, must be validated in clinical settings and multicenter studies. Based on current findings, any future multicenter prospective study should be carefully designed to (1) formulate the study population, (2) identify inclusion and exclusion criteria, (3) establish a time frame for optimal enzyme activity measurement, and (4) assess baseline values for IMPDH and p70S6 kinase/pS6RP to investigate the outcome of MPA- and mTORi-treated patients after SOT.
Nuclear Factor of Activated T-Cell-Regulated Gene Expression
Several approaches have been undertaken to measure the biologic effects of CNI-based immunosuppression (CsA; Tac) including calcineurin phosphatase activity, cytokine release, and gene expression.82–90
Measuring calcineurin phosphatase activity has been proposed as a PD approach to optimize CNI dosing at the molecular target.91,92 Only small cohorts have been monitored to date, and a consistent correlation between CNI concentrations and calcineurin activity in transplant patients has not been found. A new assay based on liquid chromatography–tandem mass spectrometry (LC–MS/MS) in multiple reaction monitoring (MRM) mode has been described recently, but data in large clinical cohorts are lacking.93
Quantitative analysis of gene expression has been established to calculate the functional effects of calcineurin inhibition, specifically inhibition of the transcription of nuclear factor of activated T-cell (NFAT)-regulated genes in peripheral blood.94,95 This assay is based on the quantitative analysis of IL-2, IFN-γ, and granulocyte macrophage colony-stimulating factor (GM-CSF) gene expression in whole-blood samples collected at CsA/Tac troughs (C0), and peak levels (2 hours for CsA and 1.5 hours for Tac) after an oral dose.
- Determination of residual NFAT-regulated gene expression helps to identify renal transplant recipients at higher risk of opportunistic infections, malignancy, acute rejection, and cardiovascular risk (B, II).
- Monitoring residual NFAT-regulated gene expression complements CNI PK to better guide CNI therapy (B, II).
- Ongoing multicenter clinical trials are using cross-validated methods to predict the risk of opportunistic infection, malignancy, and acute rejection.
NFAT Gene Expression: Method
The real-time polymerase chain reaction (RT-PCR) technique provides a rapid, highly reproducible, and sensitive tool for the quantitative analysis of gene expression.96 The test can be semiautomated, standardized, and performed in a specialized laboratory. Whole-blood samples are stable for 24 hours at 20°C. Although the overall gene expression is reduced on storage, the relative degree of NFAT inhibition remains stable in this period. Therefore, this monitoring technique can be used in larger patient cohorts and in multicenter clinical studies.
NFAT-regulated gene expression has shown low analytical variability in repeated measurements. Although interpatient variability is high, intraindividual variability is low in patients on stable CNI doses.97 Establishment of this PD monitoring assay in other specialized laboratories, and external validation of the method, is currently ongoing.
NFAT-Regulated Gene Expression and Clinical Outcome
Beneficial effects have been confirmed in long-term follow-up after transplantation, because most evaluations included maintenance allograft recipients.97–101 These results summarize mostly data on opportunistic infections, malignancy (eg, nonmelanoma skin cancer), acute rejection, and cardiovascular risk. Monitoring of residual NFAT-regulated gene expression has been proven in observational cross-sectional and prospective clinical trials, including 1 prospective case–control study, as a beneficial and safe tool to reduce CsA therapy in stable renal allograft recipients.88 An ongoing randomized controlled clinical study is evaluating the improvement in cardiovascular risk in stable renal allograft recipients on a CsA regimen by monitoring standard CsA trough levels, compared with the novel approach by monitoring residual NFAT-regulated gene expression.102
In Tac-treated patients, inhibition of NFAT-regulated gene expression is lower compared with CsA treatment, possibly because of a low relative increase of Tac levels from C0 to Cmax.101 However, several studies on Tac treatment show that monitoring residual NFAT-regulated gene expression may help to identify allograft recipients at higher risk of infections or acute rejection.101,103,104
NFAT-regulated gene expression is a promising biomarker in CNI therapy as regards infectious complications, malignancies, acute rejection, and cardiovascular risk. A residual NFAT-regulated gene expression below <10% on CsA treatment and <30% on Tac treatment might be a risk factor for infectious complications and malignoma, whereas a residual NFAT-regulated gene expression above 40% in CsA-treated patients and 60%–80% in Tac-treated patients is a risk for rejection. Prospective interventional studies and randomized controlled studies are ongoing to confirm these encouraging results.
NFAT-Regulated Gene Expression and Clinical Implementation
The assessment of residual expression of NFAT-regulated genes is a minimally invasive, rapid, robust, and reliable assay system, which has proven its validity and practicality in clinical and research settings. In CsA-treated patients, NFAT-regulated gene expression has the potential to develop into a monitoring tool complementing PK, especially in long-term renal allograft recipients. However, the benefit of monitoring in de novo allograft recipients and in patients on Tac therapy has yet to be evaluated in additional long-term studies, to confirm the preliminary data in Tac-treated patients.
PHARMACOGENETIC MARKERS PREDICTIVE OF PK AND PD
PG is based on the identification of constitutive genetic markers located in the genes influencing drug response. The majority of genes explored in the context of SOT are those coding for metabolizing enzymes or membrane drug transporters. Pharmacogenetic biomarkers useful to refine dose selection or, more interestingly, to select a priori the initial dose have been identified in rare cases but are not homogeneously used across transplantation centers. In addition, pharmacogenetic markers related to the fate of immunosuppressants in particular tissues (eg, lymphocytes, kidney graft) or to drug PD may be identified and implemented in the clinical decision process.
CNIs, Cyclosporine and Tac
- CYP3A5 genotype-based dose adjustment of immediate-release Tac clearly improves initial dosing in renal transplantation (A, I). This is not the case for cyclosporine.
- No benefit on clinical outcomes has been demonstrated so far.
- Other candidate biomarkers requiring prospective validation include CYP3A4*22, especially for CsA (C2, III), and donor ABCB1 variants (C1, III), for CsA.
The CYP3A5*3 allele (associated with decreased enzyme expression) is the main genetic biomarker of immediate-release Tac dosing requirements. In renal transplantation, genotype-based adjustment of initial dosing improves drug exposure105,106 and, although not proven prospectively, might also improve clinical outcomes. The recently described CYP3A4*22 allele, associated with decreased enzyme activity, might help to refine dose proposals, but its clinical utility has still to be proven in prospective studies.
Some studies and meta-analyses have also suggested a slight, and less significant, influence of CYP3A5*3 single-nucleotide polymorphism (SNP) on CsA PK.107–109 CYP3A4*22 resulted in lower CsA clearance (−15%)110 and higher CsA C2/dose (+53%).111 However, no genotype-based dose adjustment has been proposed so far for CsA in organ transplantation, as there is no evidence that this would improve clinical outcomes.108,112,113
The influence of ABCB1 polymorphisms on the whole-blood PK of CNI is more controversial, with at best, weak associations between the c.3435C>T (rs1045642; Ile1145Ile) genotype and concentration-to-dose ratios and dose requirements. However, different ABCB1 variants have been shown to influence intracellular CNI concentrations,114,115 particularly in PBMC, an effect that, in turn, may theoretically influence PD parameters, because low intracellular CNI concentrations have been associated with a higher risk of acute rejection in renal and liver transplantation.116,117 The recipient ABCB1 genotypes have apparently no effect on Tac nephrotoxicity,118 whereas the situation is less clear for CsA, with a few reports of positive associations with decreased GFR and/or higher risk of delayed graft function in 3435T carriers.119,120
Donor ABCB1 genotypes (at position 3435 or studied as haplotypes) can be considered as very promising biomarkers in renal transplantation, as they have been associated with nephrotoxicity and graft loss after CsA administration,121,122 as well as with interstitial fibrosis (IF)/tubular atrophy severity over the first 3 years after transplantation, and with the degradation of renal graft function in Tac-treated patients.123,124
In summary, data supports the use of pretransplant CYP3A5*3 genotyping to adjust the initial Tac dose, which may be further individualized using CYP3A4*22. Initial CsA dosing may be improved by pretransplant CYP3A4*22 determination. However, these genotypes may not add much to the precision of dose recommendations based on whole-blood concentrations in the maintenance phase. Donor ABCB1 variant haplotype or genotype may be promising as a predictive biomarker of CNI-related nephrotoxicity.
- UGT1A9 genotype may serve as a biomarker to predict initial dosing of MPA in patients cotreated with Tac (C2, III).
- IMPDH1 and IMDPH2 genotype may explain, at least in part, some of the variability in the response to and toxicity of MPA when added as covariates to PK/PD population models (C2, III).
Of the many variants in the various UGT genes, 3 SNPs in UGT1A9 seem to be the most promising as biomarkers. UGT1A9 c.-2152C>T and c.-275T>A, which are in linkage disequilibrium, have been associated with reduced exposure to MPA, and patients carrying these SNPs may have an increased risk of acute renal graft rejection when treated with concomitant Tac therapy125,126; UGT1A9 c.-98T>C ([or UGT1A9*3]) has been associated with higher MPA exposure, but data demonstrating a reduced rejection risk or increased toxicity are lacking.125–128
In some studies, selected IMPDH1 gene variants have been correlated with rejection episodes,129–131 leukopenia, and other adverse events, whereas other major studies have not reproduced these findings.132,133 Although variants of IMPDH2 were expected to influence the effect and outcome on the basis of their upregulation in activated lymphocytes, the influence of genetic variants has not been conclusive for this isoform.132,134 The conflicting results may in some cases relate to relevant, but low-frequency, gene variants,131 whereas for others, the relevance for IMPDH activity of some variants has not been identified.129–131
The potential of UGT1A9, IMPDH1, and IMPDH2 genotyping as biomarkers for MPA dose individualization and to predict outcome has not yet been clarified and is a definite role for these as biomarkers will require further evidence.
mTOR Inhibitors (mTORi), Sirolimus and EVR
- There are no validated pharmacogenetic biomarkers for mTORi.
- CYP3A5 genotyping might be useful for the initial dose adjustment of SRL provided that CNI are not coadministered (the PG of mTORi being presumably influenced by drug interactions with CNI) (C1).
Although still controversial, the CYP3A5*3 allele may influence SRL PK, but without any proven impact on the risk of acute rejection, graft clinical outcomes, or adverse effects. This effect would only concern renal transplant patients not receiving concomitant CNI treatment, perhaps because they compete with SRL for CYP3A5.135–137 In contrast, there is no evidence so far to recommend the prospective genotyping of CYP3A5 for EVR dose adjustment.110,138–142 The defective CYP3A4*22 allele might have a moderate influence on EVR and SRL hepatic metabolism, but probably is not strong enough to justify dose adjustments.110,143
No clinically significant ABCB1 pharmacogenetic effect has been reported on SRL or EVR PK, or on SRL effects in vivo in SOT.135,137,139,140,142,144 Only few data are available regarding the impact of these polymorphisms on intracellular mTORi concentration. A recent study suggests that ABCB1-mediated efflux of EVR would have a minor role in its distribution in PBMC; ABCB1 SNPs showed no effect on this distribution.145
In summary, there is no clinical evidence as yet to support the usefulness of mTORi pharmacogenetic biomarkers.
BIOMARKERS ASSOCIATED WITH GRAFT DYSFUNCTION OR INJURY
Chemokines as Biomarkers of Graft Clinical Outcome
Chemoattractant cytokines or chemokines (CXCs) are small-molecular-weight proteins (8–14 kDa) that are secreted by several types of cells.146 The chemokine protein family consists of at least 45 ligands and 20 receptors.146,147 They direct leukocyte navigation and are associated with inflammation and immune response after transplantation,148,149 among other conditions. An increasing number of studies have suggested that the IFN-γ-inducible CXC-receptor 3 (CXCR-3) ligands CXCL-9 and CXCL-10 are rapidly increased after reperfusion and are abundant in rejecting allografts. They are assessed by either protein or mRNA levels in urine, serum, and the transplant organ, and are associated with Banff scores of T-cell and antibody-mediated rejection after kidney transplantation.150 Graft parenchymal cells can secrete CXCL-9 and CXCL-10, thus recruiting CXCR3+ T cells into the transplanted organ, which enhances the alloimmune response.151
- CXCL-9 and CXCL-10 proteins in urine as markers for kidney graft inflammation and alloimmune response have been validated in multicenter clinical trials, providing sufficient evidence to support the next steps toward clinical implementation (A, II).
- Urinary CCL-2 has been found to be a promising marker for inflammation and IF in renal allografts. Further validation in multicenter trials is justified (B, II).
Chemokines and Clinical Outcome
Earlier studies in renal transplant patients indicated that urinary CXCL-9 and CXCL-10 concentrations could differentiate patients with acute graft rejection and BK virus infections from stable patients152 and could identify patients with subclinical tubulitis.153 CXCL-9 and CXCL-10 had better diagnostic sensitivity and specificity than serum creatinine concentrations.152 Evidence that CXCL-10 may also be a predictor for short- and long-term kidney graft function has been reported.154
In a multicenter study, the serially collected protein and mRNA levels in urine from 280 adult and pediatric de novo kidney transplant patients were analyzed. CXCL-9 mRNA and protein indicated the presence or absence of active inflammation in the graft, and were associated with BPAR within the first 6 months after transplantation.155 Moreover, low urinary CXCL-9 protein levels 6 months after transplantation indicated a low risk of acute rejection and decreased GFR 6–24 months after transplantation, which suggests that CXCL-9 may be used for risk stratification of renal transplant patients.155
The benefit of urinary CXCL-9 and CXCL-10 levels in the diagnosis and prognosis of antibody-mediated rejection was studied in a highly sensitized cohort of 244 renal allograft recipients, 67 of whom had preformed donor–specific antibodies.156 Urinary CXCL-9 and CXCL-10 levels, with or without normalization to urine creatinine concentrations, were correlated with tubule interstitial and microvascular inflammation. CXCL-10 normalized to urine creatinine concentrations were also associated with T-cell-mediated and antibody-mediated rejection, even in the absence of tubule interstitial inflammation. Moreover, the results suggested that the combination of urinary CXCL-10 levels normalized to urine creatinine with donor-specific antibody monitoring, significantly improved the noninvasive diagnosis of antibody-mediated rejection, and may allow for the stratification of patients at high risk for graft loss.156
In addition, it was found that urinary CXCL-10 levels normalized to urine creatinine levels are related to microvascular inflammation, and are a potential sensitive and specific biomarker for subclinical and clinical T-cell-mediated rejection in children.157
In a prospective study, nonsensitized stable living donor renal transplant patients were randomized to remain on or to be withdrawn from Tac.158 CXCL-9 was measured in serially collected urine samples, and it was found that high urinary CXCL-9 levels predated clinical detection of acute rejection by a median of 15 days.158
Other chemokines also seem to be of potential interest as markers after transplantation. In renal transplant patients, 6-month urinary CCL-2 concentrations normalized to urine creatinine were found to be associated with IF and tubular atrophy in 24-month biopsies159 and were a predictor of death-censored graft loss.160 In a follow-up study, urinary CCL-2 levels normalized to urine creatinine concentrations in samples collected 6 months after transplantation were independently correlated with IF and inflammation scores in biopsies after 6 months and 24 months.161 Moreover, 6-month urinary CCL-2 normalized to urine creatinine was also able to differentiate between the absence or presence of inflammation in renal tissue.161
The potential value of chemokines as biomarkers after liver and lung transplantation has also been explored. There is evidence that chemokines are involved in organ damage such as ischemia/reperfusion injury, rejection, inflammation, viral infection, biliary injury, fibrosis, and cirrhosis after liver transplantation.147 In a study in 94 liver transplant patients, serum CXCL-9 concentrations were significantly higher before transplantation and on day 1 after liver transplantation in patients with acute cellular rejection within the first 6 months.162 This is consistent with the results of an earlier study in liver transplant patients showing that, among other markers, high serum CCL-2, CXCL-9, and CXCL-10 concentrations were associated with early allograft dysfunction.163 Plasma CXCL-10 levels at 6 months after liver transplantation in recipients with recurrent hepatitis C (n = 130) were lower in patients with slow, compared with rapid, fibrosis progression.164 In this study, 6-month plasma CXCL-10 concentrations correlated with fibrosis stages and necroinflammatory scores in liver biopsies, as well as serum transaminases 12 months after liver transplantation.
Chronic lung allograft dysfunction, which limits long-term survival after lung transplantation, is heterogeneous, and different clinical phenotypes have been identified. In a biomarker discovery study in lung transplant patients, CXCL-8, CXCL-10, CCL-2, CCL-3, CCL-4, and CCL-7 in bronchial lavage fluid could differentiate between neutrophilic bronchiolitis obliterans syndrome (n = 17 patients) and restrictive allograft syndrome (n = 20), as well as discriminate between those from patients with stable (n = 20) and nonneutrophilic bronchiolitis obliterans syndrome (n = 20).165
Chemokines and Clinical Implementation
Clinical validation studies have provided sufficient information and agreement, specifically in terms of CXCL-9 and CXCL-10 protein in urine as markers for kidney graft inflammation and alloimmune response, to justify further steps toward implementation of these markers in clinical practice.150 Based on the published evidence, as briefly summarized above, it is reasonable to expect that these chemokine markers will help to guide and individualize immunosuppressive regimens, predict acute and chronic T-cell and antibody-mediated rejection, and may be a useful tool for risk stratification of patients. It has also been shown that measurement using standard immunoassay platforms is adequate,155 which should facilitate clinical implementation and acceptance.
Graft-Derived Cell-Free DNA as a Marker of Transplant Injury
Graft-derived circulating cell-free DNA (GcfDNA) is a promising new approach in the detection of graft injury.166–168 Plasma donor DNA is a cell death marker, released from necrotic or apoptotic cells in the transplanted organ. GcfDNA accounts for a small fraction of total cfDNA in the recipient's blood. Because organ transplants are also genome transplants, GcfDNA could be specifically determined in plasma and used as a marker of allograft injury, like a “liquid biopsy.”169 During acute rejection, high amounts of GcfDNA are shed into the blood stream.170 Monitoring GcfDNA could potentially detect rejection episodes at early stages when other diagnostic methods are still ineffective.
- Graft-derived circulating cell-free DNA (GcfDNA) as a “liquid biopsy” may be useful for early detection of graft injury due to subclinical or full-blown rejection, specific infections, or ischemia (A, II).
- Serial GcfDNA determinations can help to guide changes in immunosuppression, and to monitor minimization in combination with TDM, to achieve personalized immunosuppression.
- Ongoing multicenter clinical trials are currently evaluating the clinical utility of this biomarker as a potential universal marker of graft injury.
Graft-Derived Cell-Free DNA Measurement: Methods and Association With Clinical Outcome
Current methods do not provide rapid and cost-effective direct assessment of graft integrity after SOT,171–175 and there is a lack of reliable conventional, noninvasive markers for cardiac rejection. A newly developed droplet digital PCR (ddPCR) method166 has advantages over expensive high-throughput sequencing methods176 in the rapid quantification of GcfDNA percentages and absolute amounts. This procedure does not require donor DNA and can therefore be applied to any organ donor–recipient pair. GcfDNA rises sharply after engraftment, because of ischemia reperfusion damage. It then decays to the baseline level within about 1 week. This can be used as a threshold for the diagnosis of acute rejection. Episodes of acute rejection are accompanied by a significant increase of GcfDNA (>5-fold) compared with values in patients without complications.170 Elevated GcfDNA values were already observed 6–10 days before early acute graft rejection after liver transplantation177 and 2–3 months before late acute rejection in heart transplantation.176 The direct measurement of graft integrity using GcfDNA can be used to establish the minimally effective concentrations of immunosuppressive drugs in the individual patient.178 The test could therefore be helpful for guiding the minimization of immunosuppression. The ddPCR method permits early, sensitive, specific, and cost-effective direct assessment of graft integrity.
Graft-Derived Cell-Free DNA Measurement: Clinical Implementation
Although prospective, multicenter clinical trials in liver (n ≈ 120), heart (n = 80), and kidney (n = 300) transplant patients have not been completed,170 interim results suggest that GcfDNA can be combined with TDM to guide changes in immunosuppression and to monitor immunosuppression minimization to provide more effective, less toxic treatment. Gielis et al167 have recently reviewed currently published studies on this promising biomarker in transplantation. GcfDNA monitoring will provide actionable health care information, with the aim of achieving the right therapy for the right patient. Effective, truly personalized immunosuppression has the potential to shift emphasis from reaction to prevention and to reduce the cost of health care.
ANALYTICAL ASPECTS OF BIOMARKER MEASUREMENT
Because there is a broad consensus that not a single biomarker but rather a panel of complementary components is needed to cover most clinically relevant issues, such analytical strategies may have to deal with a wide variety of molecules with very different properties and behaviors.179 To meet this challenge, a large body of techniques combined with a plethora of assay protocols is available. Some of these strategies, particularly those allowing for multiplexing, require complex software-based data evaluation and reporting. Although some analytical procedures are of great value for research purposes, they may be too complex for implementation in a clinical setting. From a clinical point of view, potential biomarkers should be noninvasive or minimally invasive, available within a reasonable time frame to allow timely adjustment of the immunosuppressive therapy, not too laborious, accurate, precise, and cost effective. Importantly, they should be robust and suitable for standardization to ensure reproducibility of results across laboratories.179,180 Assay performance should guarantee that the observed tendency in a biomarker is related to clinical evolution and not an analytical artifact.
New biomarkers have to compete with current biochemical markers (eg, creatinine, troponins, bilirubin), which often have limited diagnostic performance if used for monitoring immunosuppressive therapy, but for which well-established analytical methods with highly optimized performance are available around the clock. This is a challenging goal for new, more comprehensive, yet more complex biomarkers. Assays to measure such biomarkers are often “in house” developments, and publications of clinical studies commonly do not report details of the analytical protocol or their analytical performance, which limits their implementation in other laboratories. Commercial kits are available for only a small number of biomarkers; they are rarely approved for clinical use, often do not have established cutoff values to guide clinical decisions, and are also seldom cross-validated among laboratories. Consolidation of a panel of biomarkers available so far is limited to single technical platforms (eg, Luminex, MesoScale Discovery), and measuring different biomarkers often requires multiple instruments and expensive consumables and reagents. This is further accompanied by the need for in-depth expertise of the operators, and training has to be continuously provided. Many procedures are laborious, time-consuming and difficult to automate (eg, functional cell-based assays of cell isolation, culture, and stimulation are needed).
Appropriate method validation and standardization of the analytical process, 2 issues of critical importance to allow clinical implementation of biomarkers, are still insufficiently addressed. Both are often aggravated by many factors: the fact that biomarkers are mostly endogenous molecules; many of them, such as proteins, represent complex biopolymers; their biological origin and heterogeneity complicates development of appropriate reference standards; and their stability is a complex issue including chemical and physical properties and biological integrity. Potential predictive biomarkers clearly need to be analytically validated, using different patient cohorts before being integrated into routine clinical practice (Table 3). Although an analytical validation plan should be adopted to cover the specifics of the diverse techniques, the availability of general uniform guidance (currently often absent) is a prerequisite for method harmonization and standardization. Although the proof of “fitness for purpose” is appropriate for validation of biomarker assays used in exploratory drug development studies, a higher level of analytical validation must be achieved before diagnostic application in a clinical setting. Guidelines for method validation published by national and international authorities, eg, FDA, European Medicines Agency,181,182 the College of American Pathologists (CAP, www.cap.org), the Clinical and Laboratory Standards Institute (CLSI, www.clsi.org), and the International Organization for Standardization (ISO, www.iso.org), together with some proposals for the validation of specific methodologies,183–187 offer an advanced basis for a consensus on method validation.
It is important that method validation and efforts for method harmonization or standardization should cover all steps of the analytical process. This starts with the choice of appropriate sample matrices, collection and handling, including storage, sample preparation for analysis, and bioanalytical procedures, and ends with postanalytical issues such as the appropriateness of proposed cutoff levels and translation of results into valid clinical recommendations. New biostatistics models should be developed to establish the most appropriate correlation among biomarkers, drug effect and clinical outcome, which allows personalized treatment.
Important considerations for clinical implementation of promising new biomarkers are:
- To select analytical techniques and protocols appropriate for use beyond a research setting and capable of providing the analytical performance needed to ensure that data generated with the assay are reliable for the intended diagnostic application.
- To define the most appropriate sample matrices and clear protocols for sample collection, handling, storage, and shipment.
- To have consensus on method validation plans and acceptance criteria.
- To evaluate the feasibility and develop strategies for standardization of the analytical process.
- To establish training programs.
- To endeavor to make reference materials, stable calibrators, and quality control materials available and to develop external quality assurance tools.
A major initiative to foster the establishment of standardized protocols for monitoring of transplant recipients, suitable for sharing within the global transplant community and offering the capability for providing appropriate training (The Global Virtual Laboratory for Transplantation), was recently launched.188
NEW MODELS TO DESCRIBE AND PREDICT THE PK/PD RELATIONSHIP
In medicine, physicians face increasing amounts of complex information. In the past, decisions for patient care were based on medical history, physical examination, some basic laboratory tests, and an x-ray; but now, information from advanced biomedical techniques needs to be integrated into patient management. Typically, such data are too complex to be handled by individual MDs, and clinical decision support is required for implementation in patient care, both to reduce variability in decision making and to reach personalized medicine.
Biomarkers can provide guidance in clinical decision-making, by adding information on disease severity, treatment effects, or adverse events. By integrating biomarkers in mathematical models, the relationship between drug exposure (PK variables) and drug response (PD variables) can be characterized. With these models, both desired and undesired clinical outcomes can be studied and hopefully predicted. The models describe the time course of disease and the effects of interventions. Furthermore, the relationship between drug treatment, changes in the biomarker, and various clinical outcomes can be studied. A better understanding of PK and PD is therefore required to optimize drug therapy in transplant patients, corresponding to integrating pharmacometrics—the science of quantitative pharmacology—in clinical practice to develop evidence-based personalized pharmacotherapy.
Although limited to the clinical practice of large centers, modeling is increasingly performed for drug therapy. For optimal dosing strategies, it is important to be aware of the concentration–effect relationship and of the factors that influence the variability in drug exposure in individual patients. Population PK modeling is used to select the best dose for complex patients. Data from a patient population are first fitted into a model, which is tested to see whether the model adequately describes the data. New data from individual patients can then be entered, and using Bayesian estimation, the next dose for a particular patient is defined. These techniques have been applied in drug development for a long time, but have now reached the clinic as well, and are most used for critical dose drugs, in particular in patients treated with antibiotics. Especially in patients in intensive care, many factors will influence drug exposure, and for serious infections in these vulnerable patients, it is essential that target concentrations are reached as quickly as possible. Population PK can account for an increased clearance or for a changed volume of distribution in critically ill patients. After the assessment of patient-specific drug exposure data, adaptive feedback control algorithms can predict the best dose adjustment to reach the target concentrations.
Biomarker development, and subsequent implementation of biomarkers into transplant patient management, would benefit from following a similar approach. For the research side, the ultimate methodology is systems biology. In systems biology, there is an integration of complex interactions within biological systems to describe and understand physiological and pathophysiological processes. The term “systems pharmacology” is also used to describe the effects of drugs on these processes. Such models however are very complex and are not suitable for clinical application.
Typically E-max models are used to describe the relationship between drug concentrations and biomarkers that reflect the PD effect of this drug. The inhibition of IMPDH by MPA has been promoted as a method to monitor the effects of MPA treatment.189 If there is a better correlation between the PD parameter and outcome, than between the drug concentration and outcome, the PD marker should be studied in more detail. Several investigators have used multivariate logistic regression analyses to determine the influence of multiple variables on clinical outcome after transplantation.190,191 A complicating factor in these analyses is the fact that risk of rejection depends on various covariates including the time after transplantation and that the target concentrations for most of the drugs used to prevent rejection change over time. To deal with this problem, new PK-PD models have recently been proposed.192 These so-called time-to-event models are of special interest for the transplant field, as they consider the whole longitudinal history of the explanatory time-dependent variable.193
If a single biomarker had sufficient positive or negative predictive power to be used as a stand-alone variable on which to base drug treatment, then supportive models would not be required. However, in our view, it is unlikely that in the transplant setting, in which many factors influence the outcome, such a highly predictive biomarker will be found. The more likely scenario is that the information provided by the biomarker will need to be integrated with parameters such as time after transplantation, concentrations, or dosages of one or more immunosuppressive drugs, and previously observed rejections and infectious complications. Assistance from clinical pharmacologists or pharmacists will be necessary to develop the models and to generate treatment recommendations for individual patients. For the multidisciplinary field that SOT already is, this should not be a major hurdle. Improvement and increased use of PK-PD modeling are most likely to occur in the coming decade.
- Monitoring a panel of valid biomarkers in combination with TDM by applying appropriate PK-PD models may be a better approach to designing personal immunosuppressive therapy to improve outcomes and long-term graft survival.
- Preliminary proposal for a panel of biomarkers (discussed above), currently under clinical evaluation in ongoing multicentre clinical trials:
- Expression of IFN-γ and IL-2 for the assessment of the risk of rejection and graft outcome.
- Urine CXCL10 synthesis for short- and long-term kidney graft function.
- Residual NFAT-regulated gene expression for personal response to CsA and Tac as well as risk of rejection and infections.
- GcfDNA for early detection of graft injury.
- CYP3A5*1 genotype for Tac dose requirement.
When deciding to implement a new biomarker, laboratories should be aware that:
- A higher level of analytical method validation must be achieved before diagnostic application, compared with exploratory drug development studies or general research projects. Suitable validation plans should follow well-established guidelines (eg, CLSI, ISO) for the use of biomarkers in a clinical setting and should be adapted to reflect technique-specific characteristics. Data derived from validation experiments should be compared against the predefined performance goals that reflect clinical needs, rather than simply the capability of a technique, to guarantee the intended support of therapeutic decisions.
- Appropriate cutoff values that should prompt intervention need to be defined and validated in independent populations, and at the interlaboratory level in multicenter, randomized controlled clinical trials.
- A system for assay life cycle management should be established to ensure consistency of results over time, namely a comprehensive internal quality assurance program that includes quality controls, system suitability testing, and continuous revalidations of critical analytical parameters. The quality assurance program should address not only analytical but also preanalytical issues such as sample collection, storage, and transport. In addition, measures for a permanent education and training of both the personnel involved and the customers should be implemented. Laboratories should have established protocols for all of these procedures, covering all these aspects.
- To ensure that results for biomarker analysis are comparable between laboratories, long-term external quality assurance programs should be established. Before this is achieved, cross-validation between laboratories is recommended on a regular basis. In addition, efforts to harmonize and standardize analytical services should be obligatory.
- To develop and establish new PK-PD models, particularly time-to-event models. In the transplant setting, in which the outcome is influenced by many factors, information provided by some biomarkers will have to be combined with parameters such as time after transplantation, immunosuppressive drugs concentrations or doses, previously observed rejections, and infectious complications. Assistance from clinical pharmacologists or pharmacists will be necessary to develop the models and to issue treatment recommendations for individual patients. In addition, efforts to perform external/cross validation of standardized PK-PD model between laboratories should be mandatory.
The Expert Committee of this consensus document, as members of the BWG of the Immunosuppressive Drugs Scientific Committee of the IATDMCT, has a commitment to optimize the analysis of the biomarkers discussed. It intends to:
- Develop and disseminate standard operating procedures for monitoring immune responses and immunosuppression adjustment in transplant recipients to the transplantation community.
- Develop and make available through the Educational Web site of IATDMCT measures for permanent education and training that facilitate the implementation and maintenance of these biomarker assays with the aim to ensure that procedures are being performed properly.
- Actively participate in the multidisciplinary design and conduct of multicenter, randomized controlled clinical trials for biomarker evaluation in SOT.
- Revise the consensus document and update the proposed panel of biomarkers on a regular basis. The need for updating will be determined at 3-year intervals.
The authors acknowledge Astellas Pharma, Novartis Pharma, and Teva Europe for their financial support.
2. Guyatt GH, Oxman AD, Vist GE, et al.. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336:924–926.
3. Hollander Z, Chen V, Sidhu K, et al.. Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression. J Heart Lung Transplant. 2013;32:259–265.
4. Halloran PF, Pereira AB, Chang J, et al.. Microarray diagnosis of antibody-mediated rejection in kidney transplant biopsies: an international prospective study (INTERCOM). Am J Transplant. 2013;13:2865–2874.
5. Suthanthiran M, Schwartz JE, Ding R, et al.. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med. 2013;369:20–31.
6. Mitchell P, Afzali B, Lombardi G, et al.. The T helper 17-regulatory T cell axis in transplant rejection and tolerance. Curr Opin Organ Transplant. 2009;14:326–331.
7. Wood KJ, Zaitsu M, Goto R. Cell mediated rejection. In: Zachary AA, Leffell MS, eds. Transplantation Immunology: Methods and Protocols. New York, NY: Humana Press, Springer; 2013:71–83.
8. Heeger PS, Greenspan NS, Kuhlenschmidt S, et al.. Pretransplant frequency of donor-specific, IFN-gamma-producing lymphocytes is a manifestation of immunologic memory and correlates with the risk of posttransplant rejection episodes. J Immunol. 1999;163:2267–2275.
9. Millán O, Brunet M, Campistol JM, et al.. Pharmacodynamic approach to immunosuppressive therapies using calcineurin inhibitors and mycophenolate mofetil. Clin Chem. 2003;49:1891–1899.
10. Evaristo C, Alegre ML. IFN-gamma: the Dr. Jekyll and Mr. Hyde of immunology? Am J Transplant. 2013;13:3057–3058.
11. Wood KJ, Sawitzki B. Interferon gamma: a crucial role in the function of induced regulatory T cells in vivo. Trends Immunol. 2006;27:183–187.
12. Hricik DE, Rodríguez V, Riley J, et al.. Enzyme linked immunosorbent spot (ELISPOT) assay for interferon-gamma independently predicts renal function in kidney transplant recipients. Am J Transplant. 2003;3:878–884.
13. Kim SH, Oh EJ, Kim MJ, et al.. Pretransplant donor-specific interferon-gamma ELISPOT assay predicts acute rejection episodes in renal transplant recipients. Transplant Proc. 2007;39:3057–3060.
14. Nickel P, Bold G, Presber F, et al.. High levels of CMV-IE-1-specific memory T cells are associated with less alloimmunity and improved renal allograft function. Transpl Immunol. 2009;20:238–242.
15. Nickel P, Presber F, Bold G, et al.. Enzyme-linked immunosorbent spot assay for donor-reactive interferon-gamma-producing cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation. 2004;78:1640–1646.
16. Bestard O, Nickel P, Cruzado JM, et al.. Circulating alloreactive T cells correlate with graft function in longstanding renal transplant recipients. J Am Soc Nephrol. 2008;19:1419–1429.
17. Okanami Y, Tsujimura K, Mizuno S, et al.. Intracellular interferon-gamma staining analysis of donor-specific T-cell responses in liver transplant recipients. Transplant Proc. 2012;44:548–554.
18. Millán O, Benítez C, Guillén D, et al.. Biomarkers of immunoregulatory status in stable liver transplant recipients undergoing weaning of immunosuppressive therapy. Clin Immunol. 2010;137:337–346.
19. Millán O, Rafael-Valdivia L, San Segundo D, et al.. Should IFN-gamma, IL-17 and IL-2 be considered predictive biomarkers of acute rejection in liver and kidney transplant? Results of a multicentric study. Clin Immunol. 2014;154:141–154.
20. Millán O, Rafael-Valdivia L, Torrademé E, et al.. Intracellular IFN-gamma and IL-2 expression monitoring as surrogate markers of the risk of acute rejection and personal drug response in de novo liver transplant recipients. Cytokine. 2013;61:556–564.
21. Ho IC, Kim JI, Szabo SJ, et al.. Tissue-specific regulation of cytokine gene expression. Cold Spring Harb Symp Quant Biol. 1999;64:573–584.
22. Liao W, Lin JX, Leonard WJ. IL-2 family cytokines: new insights into the complex roles of IL-2 as a broad regulator of T helper cell differentiation. Curr Opin Immunol. 2011;23:598–604.
23. Long M, Adler AJ. Cutting edge: paracrine, but not autocrine, IL-2 signaling is sustained during early antiviral CD4 T cell response. J Immunol. 2006;177:4257–4261.
24. de la Rosa M, Rutz S, Dorninger H, et al.. Interleukin-2 is essential for CD4+CD25+ regulatory T cell function. Eur J Immunol. 2004;34:2480–2488.
25. Boleslawski E, Conti F, Sanquer S, et al.. Defective inhibition of peripheral CD8+ T cell IL-2 production by anti-calcineurin drugs during acute liver allograft rejection. Transplantation. 2004;77:1815–1820.
26. Akoglu B, Kriener S, Martens S, et al.. Interleukin-2 in CD8+ T cells correlates with Banff score during organ rejection in liver transplant recipients. Clin Exp Med. 2009;9:259–262.
27. Ahmed M, Venkataraman R, Logar AJ, et al.. Quantitation of immunosuppression by tacrolimus using flow cytometric analysis of interleukin-2 and interferon-gamma inhibition in CD8(-) and CD8(+) peripheral blood T cells. Ther Drug Monit. 2001;23:354–362.
28. Bestard O, Crespo E, Stein M, et al.. Cross-validation of IFN-gamma Elispot assay for measuring alloreactive memory/effector T cell responses in renal transplant recipients. Am J Transplant. 2013;13:1880–1890.
29. Streitz M, Miloud T, Kapinsky M, et al.. Standardization of whole blood immune phenotype monitoring for clinical trials: panels and methods from the ONE study. Transplant Res. 2013;2:17.
30. Ingulli E. Mechanism of cellular rejection in transplantation. Pediatr Nephrol. 2010;25:61–74.
31. Shipkova M, Wieland E. Surface markers of lymphocyte activation and markers of cell proliferation. Clin Chim Acta. 2012;413:1338–1349.
32. Böhler T, Nolting J, Kamar N, et al.. Validation of immunological biomarkers for the pharmacodynamic monitoring of immunosuppressive drugs in humans. Ther Drug Monit. 2007;29:77–86.
33. Barten MJ, Tarnok A, Garbade J, et al.. Pharmacodynamics of T-cell function for monitoring immunosuppression. Cell Prolif. 2007;40:50–63.
34. Stalder M, Bîrsan T, Holm B, et al.. Quantification of immunosuppression by flow cytometry in stable renal transplant recipients. Ther Drug Monit. 2003;25:22–27.
35. Boleslawski E, BenOthman S, Grabar S, et al.. CD25, CD28 and CD38 expression in peripheral blood lymphocytes as a tool to predict acute rejection after liver transplantation. Clin Transplant. 2008;22:494–501.
36. Boleslawski E, Othman SB, Aoudjehane L, et al.. CD28 expression by peripheral blood lymphocytes as a potential predictor of the development of de novo malignancies in long-term survivors after liver transplantation. Liver Transpl. 2011;17:299–305.
37. Wieland E, Shipkova M, Martius Y, et al.. Association between pharmacodynamic biomarkers and clinical events in the early phase after kidney transplantation: a single-center pilot study. Ther Drug Monit. 2011;33:341–349.
38. Pelzl S, Opelz G, Wiesel M, et al.. Soluble CD30 as a predictor of kidney graft outcome. Transplantation. 2002;73:3–6.
39. Süsal C, Opelz G. Posttransplant sCD30 as a biomarker to predict kidney graft outcome. Clin Chim Acta. 2012;413:1350–1353.
40. Süsal C, Pelzl S, Döhler B, et al.. Identification of highly responsive kidney transplant recipients using pretransplant soluble CD30. J Am Soc Nephrol. 2002;13:1650–1656.
41. Sengul S, Keven K, Gormez U, et al.. Identification of patients at risk of acute rejection by pretransplantation and posttransplantation monitoring of soluble CD30 levels in kidney transplantation. Transplantation. 2006;81:1216–1219.
42. Chen Y, Tai Q, Hong S, et al.. Pretransplantation soluble CD30 level as a predictor of acute rejection in kidney transplantation: a meta-analysis. Transplantation. 2012;94:911–918.
43. Mehta R, Shah G, Adler W, et al.. Soluble interleukin 2 receptor (sIL-2R) levels in renal transplant recipients. Clin Transplant. 2004;18(suppl 12):67–71.
44. Ashokkumar C, Talukdar A, Sun Q, et al.. Allospecific CD154+ T cells associate with rejection risk after pediatric liver transplantation. Am J Transplant. 2009;9:179–191.
45. Sindhi R, Ashokkumar C, Higgs BW, et al.. Allospecific CD154 + T-cytotoxic memory cells as potential surrogate for rejection risk in pediatric intestine transplantation. Pediatr Transplant. 2012;16:83–91.
46. Canavan JB, Afzali B, Scotta C, et al.. A rapid diagnostic test for human regulatory T-cell function to enable regulatory T-cell therapy. Blood. 2012;119:e57–66.
47. Favaloro EJ. Differential expression of surface antigens on activated endothelium. Immunol Cell Biol. 1993;71:571–581.
48. Kennedy MK, Willis CR, Armitage RJ. Deciphering CD30 ligand biology and its role in humoral immunity. Immunology. 2006;118:143–152.
49. Sakaguchi S. Naturally arising Foxp3-expressing CD25+CD4+ regulatory T cells in immunological tolerance to self and non-self. Nat Immunol. 2005;6:345–352.
50. Janson PC, Winerdal ME, Marits P, et al.. FOXP3 promoter demethylation reveals the committed Treg population in humans. PLoS One. 2008;3:e1612.
51. Coenen JJ, Koenen HJ, van Rijssen E, et al.. Rapamycin, not cyclosporine, permits thymic generation and peripheral preservation of CD4+ CD25+ FoxP3+ T cells. Bone Marrow Transplant. 2007;39:537–545.
52. Liu W, Putnam AL, Xu-Yu Z, et al.. CD127 expression inversely correlates with FoxP3 and suppressive function of human CD4+ T reg cells. J Exp Med. 2006;203:1701–1711.
53. Miyara M, Yoshioka Y, Kitoh A, et al.. Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity. 2009;30:899–911.
54. Muthukumar T, Dadhania D, Ding R, et al.. Messenger RNA for FOXP3 in the urine of renal-allograft recipients. N Engl J Med. 2005;353:2342–2351.
55. Kim SH, Oh EJ, Ghee JY, et al.. Clinical significance of monitoring circulating CD4+CD25+ regulatory T cells in kidney transplantation during the early posttransplant period. J Korean Med Sci. 2009;24(suppl):S135–S142.
56. San Segundo D, Fernández-Fresnedo G, Ruiz JC, et al.. Two-year follow-up of a prospective study of circulating regulatory T cells in renal transplant patients. Clin Transplant. 2010;24:386–393.
57. San Segundo D, Millán O, Muñoz-Cacho P, et al.. High proportion of pretransplantation activated regulatory T cells (CD4+CD25highCD62L+CD45RO+) predicts acute rejection in kidney transplantation: results of a multicenter study. Transplantation. 2014;98:1213–1218.
58. San Segundo D, Fernández-Fresnedo G, Rodrigo E, et al.. High regulatory T-cell levels at 1 year posttransplantation predict long-term graft survival among kidney transplant recipients. Transplant Proc. 2012;44:2538–2541.
59. Schaier M, Seissler N, Schmitt E, et al.. DR(high+)CD45RA(-)-Tregs potentially affect the suppressive activity of the total Treg pool in renal transplant patients. PLoS One. 2012;7:e34208.
60. Ashton-Chess J, Dugast E, Colvin RB, et al.. Regulatory, effector, and cytotoxic T cell profiles in long-term kidney transplant patients. J Am Soc Nephrol. 2009;20:1113–1122.
61. Newell KA, Asare A, Kirk AD, et al.. Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest. 2010;120:1836–1847.
62. Sagoo P, Perucha E, Sawitzki B, et al.. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J Clin Invest. 2010;120:1848–1861.
63. Rosser EC, Mauri C. Regulatory B cells: origin, phenotype, and function. Immunity. 2015;42:607–612.
64. Battaglia M, Stabilini A, Roncarolo MG. Rapamycin selectively expands CD4+CD25+FoxP3+ regulatory T cells. Blood. 2005;105:4743–4748.
65. San Segundo D, Ruiz JC, Fernández-Fresnedo G, et al.. Calcineurin inhibitors affect circulating regulatory T cells in stable renal transplant recipients. Transplant Proc. 2006;38:2391–2393.
66. Carroll RP, Hester J, Wood KJ, et al.. Conversion to sirolimus in kidney transplant recipients with squamous cell cancer and changes in immune phenotype. Nephrol Dial Transplant. 2013;28:462–465.
67. Cobbold SP, Waldmann H. Regulatory cells and transplantation tolerance. Cold Spring Harb Perspect Med. 2013;3:1–17.
68. Oellerich M, Barten MJ, Armstrong VW. Biomarkers: the link between therapeutic drug monitoring and pharmacodynamics. Ther Drug Monit. 2006;28:35–38.
69. Spaan S, Fransman W, Warren N, et al.. Variability of biomarkers in volunteer studies: the biological component. Toxicol Lett. 2010;198:144–151.
70. Glander P, Sombogaard F, Budde K, et al.. Improved assay for the nonradioactive determination of inosine 5'-monophosphate dehydrogenase activity in peripheral blood mononuclear cells. Ther Drug Monit. 2009;31:351–359.
71. Wieland E, Olbricht CJ, Süsal C, et al.. Biomarkers as a tool for management of immunosuppression in transplant patients. Ther Drug Monit. 2010;32:560–572.
72. Glander P, Hambach P, Liefeldt L, et al.. Inosine 5'-monophosphate dehydrogenase activity as a biomarker in the field of transplantation. Clin Chim Acta. 2012;413:1391–1397.
73. Weigel G, Griesmacher A, Zuckermann AO, et al.. Effect of mycophenolate mofetil therapy on inosine monophosphate dehydrogenase induction in red blood cells of heart transplant recipients. Clin Pharmacol Ther. 2001;69:137–144.
74. Di Paolo S, Leogrande D, Teutonico A, et al.. In reply. Am J Kidney Dis. 2008;51:531–532.
75. Hartmann B, He X, Keller F, et al.. Development of a sensitive phospho-p70 S6 kinase ELISA to quantify mTOR proliferation signal inhibition. Ther Drug Monit. 2013;35:233–239.
76. Hartmann B, Schmid G, Graeb C, et al.. Biochemical monitoring of mTOR inhibitor-based immunosuppression following kidney transplantation: a novel approach for tailored immunosuppressive therapy. Kidney Int. 2005;68:2593–2598.
77. Baan C, Bouvy A, Vafadari R, et al.. Phospho-specific flow cytometry for pharmacodynamic monitoring of immunosuppressive therapy in transplantation. Transplant Res. 2012;1:20.
78. Dieterlen MT, Eberhardt K, Tarnok A, et al.. Flow cytometry-based pharmacodynamic monitoring after organ transplantation. Methods Cell Biol. 2011;103:267–284.
79. Dieterlen MT, Bittner HB, Klein S, et al.. Assay validation of phosphorylated S6 ribosomal protein for a pharmacodynamic monitoring of mTOR-inhibitors in peripheral human blood. Cytometry B Clin Cytom. 2012;82:151–157.
80. Dieterlen MT, Bittner HB, Tarnok A, et al.. Flow cytometric evaluation of T cell activation markers after cardiopulmonary bypass. Surg Res Pract. 2014;2014:801643.
81. Hoerning A, Wilde B, Wang J, et al.. Pharmacodynamic monitoring of mammalian target of rapamycin inhibition by phosphoflow cytometric determination of p70S6 kinase activity. Transplantation. 2015;99:210–219.
82. Fukudo M, Yano I, Katsura T, et al.. A transient increase of calcineurin phosphatase activity in living-donor kidney transplant recipients with acute rejection. Drug Metab Pharmacokinet. 2010;25:411–417.
83. Giese T, Sommerer C, Zeier M, et al.. Approaches towards individualized immune intervention. Dig Dis. 2010;28:45–50.
84. Halloran PF, Helms LM, Kung L, et al.. The temporal profile of calcineurin inhibition by cyclosporine in vivo. Transplantation. 1999;68:1356–1361.
85. Hartel C, Fricke L, Schumacher N, et al.. Delayed cytokine mRNA expression kinetics after T-lymphocyte costimulation: a quantitative measure of the efficacy of cyclosporin A-based immunosuppression. Clin Chem. 2002;48:2225–2231.
86. Hartel C, Schumacher N, Fricke L, et al.. Sensitivity of whole-blood T lymphocytes in individual patients to tacrolimus (FK 506): impact of interleukin-2 mRNA expression as surrogate measure of immunosuppressive effect. Clin Chem. 2004;50:141–151.
87. Sommerer C, Giese T, Meuer S, et al.. Pharmacodynamic monitoring of calcineurin inhibitor therapy: is there a clinical benefit? Nephrol Dial Transplant. 2009;24:21–27.
88. Sommerer C, Meuer S, Zeier M, et al.. Calcineurin inhibitors and NFAT-regulated gene expression. Clin Chim Acta. 2012;413:1379–1386.
89. Stein CM, Murray JJ, Wood AJ. Inhibition of stimulated interleukin-2 production in whole blood: a practical measure of cyclosporine effect. Clin Chem. 1999;45:1477–1484.
90. Fruman DA, Klee CB, Bierer BE, et al.. Calcineurin phosphatase activity in T lymphocytes is inhibited by FK 506 and cyclosporin A. Proc Natl Acad Sci U S A. 1992;89:3686–3690.
91. Caruso R, Perico N, Cattaneo D, et al.. Whole-blood calcineurin activity is not predicted by cyclosporine blood concentration in renal transplant recipients. Clin Chem. 2001;47:1679–1687.
92. Sanquer S, Amrein C, Grenet D, et al.. Expression of calcineurin activity after lung transplantation: a 2-year follow-up. PLoS One. 2013;8:e59634.
93. Carr L, Gagez AL, Essig M, et al.. Calcineurin activity assay measurement by liquid chromatography-tandem mass spectrometry in the multiple reaction monitoring mode. Clin Chem. 2014;60:353–360.
94. Giese T, Zeier M, Meuer S. Analysis of NFAT-regulated gene expression in vivo: a novel perspective for optimal individualized doses of calcineurin inhibitors. Nephrol Dial Transplant. 2004;19(suppl 4):iv55–iv60.
95. Giese T, Zeier M, Schemmer P, et al.. Monitoring of NFAT-regulated gene expression in the peripheral blood of allograft recipients: a novel perspective toward individually optimized drug doses of cyclosporine A. Transplantation. 2004;77:339–344.
96. Derveaux S, Vandesompele J, Hellemans J. How to do successful gene expression analysis using real-time PCR. Methods. 2010;50:227–230.
97. Sommerer C, Konstandin M, Dengler T, et al.. Pharmacodynamic monitoring of cyclosporine a in renal allograft recipients shows a quantitative relationship between immunosuppression and the occurrence of recurrent infections and malignancies. Transplantation. 2006;82:1280–1285.
98. Konstandin MH, Sommerer C, Doesch A, et al.. Pharmacodynamic cyclosporine A-monitoring: relation of gene expression in lymphocytes to cyclosporine blood levels in cardiac allograft recipients. Transpl Int. 2007;20:1036–1043.
99. Sommerer C, Hartschuh W, Enk A, et al.. Pharmacodynamic immune monitoring of NFAT-regulated genes predicts skin cancer in elderly long-term renal transplant recipients. Clin Transplant. 2008;22:549–554.
100. Sommerer C, Schnitzler P, Meuer S, et al.. Pharmacodynamic monitoring of cyclosporin A reveals risk of opportunistic infections and malignancies in renal transplant recipients 65 years and older. Ther Drug Monit. 2011;33:694–698.
101. Sommerer C, Zeier M, Meuer S, et al.. Individualized monitoring of nuclear factor of activated T cells-regulated gene expression in FK506-treated kidney transplant recipients. Transplantation. 2010;89:1417–1423.
102. Sommerer C, Schaier M, Morath C, et al.. The Calcineurin Inhibitor-Sparing (CIS) Trial—individualised calcineurin-inhibitor treatment by immunomonitoring in renal allograft recipients: protocol for a randomised controlled trial. Trials. 2014;15:489.
103. Sommerer C, Zeier M, Czock D, et al.. Pharmacodynamic disparities in tacrolimus-treated patients developing cytomegalus virus viremia. Ther Drug Monit. 2011;33:373–379.
104. Steinebrunner N, Sandig C, Sommerer C, et al.. Reduced residual gene expression of nuclear factor of activated T cells-regulated genes correlates with the risk of cytomegalovirus infection after liver transplantation. Transpl Infect Dis. 2014;16:379–386.
105. Haufroid V, Wallemacq P, VanKerckhove V, et al.. CYP3A5 and ABCB1 polymorphisms and tacrolimus pharmacokinetics in renal transplant candidates: guidelines from an experimental study. Am J Transplant. 2006;6:2706–2713.
106. Thervet E, Loriot MA, Barbier S, et al.. Optimization of initial tacrolimus dose using pharmacogenetic testing. Clin Pharmacol Ther. 2010;87:721–726.
107. Haufroid V, Mourad M, Van Kerckhove V, et al.. The effect of CYP3A5 and MDR1 (ABCB1) polymorphisms on cyclosporine and tacrolimus dose requirements and trough blood levels in stable renal transplant patients. Pharmacogenetics. 2004;14:147–154.
108. Tang HL, Ma LL, Xie HG, et al.. Effects of the CYP3A5*3 variant on cyclosporine exposure and acute rejection rate in renal transplant patients: a meta-analysis. Pharmacogenet Genomics. 2010;20:525–531.
109. Zhu HJ, Yuan SH, Fang Y, et al.. The effect of CYP3A5 polymorphism on dose-adjusted cyclosporine concentration in renal transplant recipients: a meta-analysis. Pharmacogenomics J. 2011;11:237–246.
110. Moes DJ, Swen JJ, den Hartigh J, et al.. Effect of CYP3A4*22, CYP3A5*3, and CYP3A combined genotypes on cyclosporine, everolimus, and tacrolimus pharmacokinetics in renal transplantation. CPT Pharmacometrics Syst Pharmacol. 2014;3:e100.
111. Lunde I, Bremer S, Midtvedt K, et al.. The influence of CYP3A, PPARA, and POR genetic variants on the pharmacokinetics of tacrolimus and cyclosporine in renal transplant recipients. Eur J Clin Pharmacol. 2014;70:685–693.
112. Kreutz R, Zurcher H, Kain S, et al.. The effect of variable CYP3A5 expression on cyclosporine dosing, blood pressure and long-term graft survival in renal transplant patients. Pharmacogenetics. 2004;14:665–671.
113. Bouamar R, Hesselink DA, van Schaik RH, et al.. Polymorphisms in CYP3A5, CYP3A4, and ABCB1 are not associated with cyclosporine pharmacokinetics nor with cyclosporine clinical end points after renal transplantation. Ther Drug Monit. 2011;33:178–184.
114. Crettol S, Venetz JP, Fontana M, et al.. Influence of ABCB1 genetic polymorphisms on cyclosporine intracellular concentration in transplant recipients. Pharmacogenet Genomics. 2008;18:307–315.
115. Capron A, Mourad M, De Meyer M, et al.. CYP3A5 and ABCB1 polymorphisms influence tacrolimus concentrations in peripheral blood mononuclear cells after renal transplantation. Pharmacogenomics. 2010;11:703–714.
116. Falck P, Asberg A, Guldseth H, et al.. Declining intracellular T-lymphocyte concentration of cyclosporine a precedes acute rejection in kidney transplant recipients. Transplantation. 2008;85:179–184.
117. Capron A, Lerut J, Latinne D, et al.. Correlation of tacrolimus levels in peripheral blood mononuclear cells with histological staging of rejection after liver transplantation: preliminary results of a prospective study. Transpl Int. 2012;25:41–47.
118. Gijsen VM, Madadi P, Dube MP, et al.. Tacrolimus-induced nephrotoxicity and genetic variability: a review. Ann Transplant. 2012;17:111–121.
119. Cattaneo D, Ruggenenti P, Baldelli S, et al.. ABCB1 genotypes predict cyclosporine-related adverse events and kidney allograft outcome. J Am Soc Nephrol. 2009;20:1404–1415.
120. García M, Macías RM, Cubero JJ, et al.. ABCB1 polymorphisms are associated with cyclosporine-induced nephrotoxicity and gingival hyperplasia in renal transplant recipients. Eur J Clin Pharmacol. 2013;69:385–393.
121. Hauser IA, Schaeffeler E, Gauer S, et al.. ABCB1 genotype of the donor but not of the recipient is a major risk factor for cyclosporine-related nephrotoxicity after renal transplantation. J Am Soc Nephrol. 2005;16:1501–1511.
122. Woillard JB, Rerolle JP, Picard N, et al.. Donor P-gp polymorphisms strongly influence renal function and graft loss in a cohort of renal transplant recipients on cyclosporine therapy in a long-term follow-up. Clin Pharmacol Ther. 2010;88:95–100.
123. Naesens M, Lerut E, de Jonge H, et al.. Donor age and renal P-glycoprotein expression associate with chronic histological damage in renal allografts. J Am Soc Nephrol. 2009;20:2468–2480.
124. Tavira B, Gómez J, Díaz-Corte C, et al.. The donor ABCB1 (MDR-1) C3435T polymorphism is a determinant of the graft glomerular filtration rate among tacrolimus treated kidney transplanted patients. J Hum Genet. 2015;60:273–276.
125. Kuypers DR, Naesens M, Vermeire S, et al.. The impact of uridine diphosphate-glucuronosyltransferase 1A9 (UGT1A9) gene promoter region single-nucleotide polymorphisms T-275A and C-2152T on early mycophenolic acid dose-interval exposure in de novo renal allograft recipients. Clin Pharmacol Ther. 2005;78:351–361.
126. van Schaik RH, van Agteren M, de Fijter JW, et al.. UGT1A9 -275T>A/-2152C>T polymorphisms correlate with low MPA exposure and acute rejection in MMF/tacrolimus-treated kidney transplant patients. Clin Pharmacol Ther. 2009;86:319–327.
127. Johnson LA, Oetting WS, Basu S, et al.. Pharmacogenetic effect of the UGT polymorphisms on mycophenolate is modified by calcineurin inhibitors. Eur J Clin Pharmacol. 2008;64:1047–1056.
128. Lévesque E, Delage R, Benoit-Biancamano MO, et al.. The impact of UGT1A8, UGT1A9, and UGT2B7 genetic polymorphisms on the pharmacokinetic profile of mycophenolic acid after a single oral dose in healthy volunteers. Clin Pharmacol Ther. 2007;81:392–400.
129. Gensburger O, van Schaik RH, Picard N, et al.. Polymorphisms in type I and II inosine monophosphate dehydrogenase genes and association with clinical outcome in patients on mycophenolate mofetil. PharmacogenetGenomics. 2010;20:537–543.
130. Kagaya H, Miura M, Saito M, et al.. Correlation of IMPDH1 gene polymorphisms with subclinical acute rejection and mycophenolic acid exposure parameters on day 28 after renal transplantation. Basic Clin Pharmacol Toxicol. 2010;107:631–636.
131. Wang J, Yang JW, Zeevi A, et al.. IMPDH1 gene polymorphisms and association with acute rejection in renal transplant patients. Clin Pharmacol Ther. 2008;83:711–717.
132. Shah S, Harwood SM, Döhler B, et al.. Inosine monophosphate dehydrogenase polymorphisms and renal allograft outcome. Transplantation. 2012;94:486–491.
133. Woillard JB, Picard N, Thierry A, et al.. Associations between polymorphisms in target, metabolism, or transport proteins of mycophenolate sodium and therapeutic or adverse effects in kidney transplant patients. Pharmacogenet Genomics. 2014;24:256–262.
134. Grinyó J, Vanrenterghem Y, Nashan B, et al.. Association of four DNA polymorphisms with acute rejection after kidney transplantation. Transpl Int. 2008;21:879–891.
135. Anglicheau D, Le Corre D, Lechaton S, et al.. Consequences of genetic polymorphisms for sirolimus requirements after renal transplant in patients on primary sirolimus therapy. Am J Transplant. 2005;5:595–603.
136. Le Meur Y, Djebli N, Szelag JC, et al.. CYP3A5*3 influences sirolimus oral clearance in de novo and stable renal transplant recipients. Clin Pharmacol Ther. 2006;80:51–60.
137. Miao LY, Huang CR, Hou JQ, et al.. Association study of ABCB1 and CYP3A5 gene polymorphisms with sirolimus trough concentration and dose requirements in Chinese renal transplant recipients. Biopharm Drug Dispos. 2008;29:1–5.
138. Kniepeiss D, Renner W, Trummer O, et al.. The role of CYP3A5 genotypes in dose requirements of tacrolimus and everolimus after heart transplantation. Clin Transplant. 2011;25:146–150.
139. Lemaitre F, Bezian E, Goldwirt L, et al.. Population pharmacokinetics of everolimus in cardiac recipients: comedications, ABCB1, and CYP3A5 polymorphisms. Ther Drug Monit. 2012;34:686–694.
140. Moes DJ, Press RR, den Hartigh J, et al.. Population pharmacokinetics and pharmacogenetics of everolimus in renal transplant patients. Clin Pharmacokinet. 2012;51:467–480.
141. Picard N, Rouguieg-Malki K, Kamar N, et al.. CYP3A5 genotype does not influence everolimus in vitro metabolism and clinical pharmacokinetics in renal transplant recipients. Transplantation. 2011;91:652–656.
142. Schoeppler KE, Aquilante CL, Kiser TH, et al.. The impact of genetic polymorphisms, diltiazem, and demographic variables on everolimus trough concentrations in lung transplant recipients. Clin Transplant. 2014;28:590–597.
143. Woillard JB, Kamar N, Coste S, et al.. Effect of CYP3A4*22, POR*28, and PPARA rs4253728 on sirolimus in vitro metabolism and trough concentrations in kidney transplant recipients. Clin Chem. 2013;59:1761–1769.
144. Mourad M, Mourad G, Wallemacq P, et al.. Sirolimus and tacrolimus trough concentrations and dose requirements after kidney transplantation in relation to CYP3A5 and MDR1 polymorphisms and steroids. Transplantation. 2005;80:977–984.
145. Robertsen I, Vethe NT, Midtvedt K, et al.. Closer to the site of action; everolimus concentrations in peripheral blood mononuclear cells correlate well with whole blood concentrations. Ther Drug Monit. 2015;37:675–680.
146. Barker CE, Ali S, O'Boyle G, et al.. Transplantation and inflammation: implications for the modification of chemokine function. Immunology. 2014;143:138–145.
147. Liu B, Li J, Yan LN. Chemokines in chronic liver allograft dysfunction pathogenesis and potential therapeutic targets. Clin Dev Immunol. 2013;2013:325318.
148. el-Sawy T, Fahmy NM, Fairchild RL. Chemokines: directing leukocyte infiltration into allografts. Curr Opin Immunol. 2002;14:562–568.
149. Lo DJ, Weaver TA, Kleiner DE, et al.. Chemokines and their receptors in human renal allotransplantation. Transplantation. 2011;91:70–77.
150. Fairchild RL, Suthanthiran M. Urine CXCL10/IP-10 fingers ongoing antibody-mediated kidney graft rejection. J Am Soc Nephrol. 2015;26:2607–2609.
151. Lo DJ, Kaplan B, Kirk AD. Biomarkers for kidney transplant rejection. Nat Rev Nephrol. 2014;10:215–225.
152. Jackson JA, Kim EJ, Begley B, et al.. Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am J Transplant. 2011;11:2228–2234.
153. Schaub S, Nickerson P, Rush D, et al.. Urinary CXCL9 and CXCL10 levels correlate with the extent of subclinical tubulitis. Am J Transplant. 2009;9:1347–1353.
154. Matz M, Beyer J, Wunsch D, et al.. Early post-transplant urinary IP-10 expression after kidney transplantation is predictive of short- and long-term graft function. Kidney Int. 2006;69:1683–1690.
155. Hricik DE, Nickerson P, Formica RN, et al.. Multicenter validation of urinary CXCL9 as a risk-stratifying biomarker for kidney transplant injury. Am J Transplant. 2013;13:2634–2644.
156. Rabant M, Amrouche L, Lebreton X, et al.. Urinary c-X-c motif chemokine 10 independently improves the noninvasive diagnosis of antibody-mediated kidney allograft rejection. J Am Soc Nephrol. 2015;26:2840–2851.
157. Blydt-Hansen TD, Gibson IW, Gao A, et al.. Elevated urinary CXCL10-to-creatinine ratio is associated with subclinical and clinical rejection in pediatric renal transplantation. Transplantation. 2015;99:797–804.
158. Hricik DE, Formica RN, Nickerson P, et al.. Adverse outcomes of tacrolimus withdrawal in immune-quiescent kidney transplant recipients. J Am Soc Nephrol. 2015;26:3114–3122.
159. Ho J, Rush DN, Gibson IW, et al.. Early urinary CCL2 is associated with the later development of interstitial fibrosis and tubular atrophy in renal allografts. Transplantation. 2010;90:394–400.
160. Ho J, Wiebe C, Rush DN, et al.. Increased urinary CCL2: Cr ratio at 6 months is associated with late renal allograft loss. Transplantation. 2013;95:595–602.
161. Ho J, Wiebe C, Gibson IW, et al.. Elevated urinary CCL2: Cr at 6 months is associated with renal allograft interstitial fibrosis and inflammation at 24 months. Transplantation. 2014;98:39–46.
162. Raschzok N, Reutzel-Selke A, Schmuck RB, et al.. CD44 and CXCL9 serum protein levels predict the risk of clinically significant allograft rejection after liver transplantation. Liver Transplant. 2015;21:1195–1207.
163. Friedman BH, Wolf JH, Wang L, et al.. Serum cytokine profiles associated with early allograft dysfunction in patients undergoing liver transplantation. Liver Transplant. 2012;18:166–176.
164. Joshi D, Carey I, Foxton M, et al.. CXCL10 levels identify individuals with rapid fibrosis at 12 months post-transplant for hepatitis C virus and predict treatment response. Clin Transplant. 2014;28:569–578.
165. Verleden SE, Ruttens D, Vos R, et al.. Differential cytokine, chemokine and growth factor expression in phenotypes of chronic lung allograft dysfunction. Transplantation. 2015;99:86–93.
166. Beck J, Bierau S, Balzer S, et al.. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin Chem. 2013;59:1732–1741.
167. Gielis EM, Ledeganck KJ, De Winter BY, et al.. Cell-free DNA: an upcoming biomarker in transplantation. Am J Transplant. 2015;15:2541–2551.
168. Lo YM, Tein MS, Pang CC, et al.. Presence of donor-specific DNA in plasma of kidney and liver-transplant recipients. Lancet. 1998;351:1329–1330.
169. De Vlaminck I, Valantine HA, Snyder TM, et al.. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci Transl Med. 2014;6:241ra277.
170. Beck J, Oellerich M, Schulz U, et al.. Donor-derived cell-free DNA is a novel universal biomarker for allograft rejection in solid organ transplantation. Transplant Proc. 2015;47:2400–2403.
171. American Society of Nephrology renal research report. J Am Soc Nephrol. 2005;16:1886–1903.
172. Meeusen JW, Lieske JC. Looking for a better creatinine. Clin Chem. 2014;60:1036–1039.
173. Miller CA, Fildes JE, Ray SG, et al.. Non-invasive approaches for the diagnosis of acute cardiac allograft rejection. Heart. 2013;99:445–453.
174. Rodríguez-Perálvarez M, Germani G, Darius T, et al.. Tacrolimus trough levels, rejection and renal impairment in liver transplantation: a systematic review and meta-analysis. Am J Transplant. 2012;12:2797–2814.
175. Rodríguez-Perálvarez M, Germani G, Papastergiou V, et al.. Early tacrolimus exposure after liver transplantation: relationship with moderate/severe acute rejection and long-term outcome. J Hepatol. 2013;58:262–270.
176. Snyder TM, Khush KK, Valantine HA, et al.. Universal noninvasive detection of solid organ transplant rejection. Proc Natl Acad Sci U S A. 2011;108:6229–6234.
177. Oellerich M, Kanzow P, Beck J, et al.. Graft-derived cell-free DNA (GcfDNA) as a sensitive measure of individual graft integrity after liver transplantation [Abstract #A7]. Am J Transplant. 2014;14:874.
178. Oellerich M, Schütz E, Kanzow P, et al.. Use of graft-derived cell-free DNA as an organ integrity biomarker to reexamine effective tacrolimus trough concentrations after liver transplantation. Ther Drug Monit. 2014;36:136–140.
179. Wieland E, Shipkova M, Oellerich M. Biomarkers in transplantation medicine: guide to the next level in immunosuppressive therapy. Clin Chim Acta. 2012;413:1309.
180. Cravedi P, Heeger PS. Immunologic monitoring in transplantation revisited. Curr Opin Organ Transplant. 2012;17:26–32.
181. U. S. Food and Drug Admnistration (FDA). Guidance for Industry: Bioanalytical Method Validation (DRAFT GUIDANCE). Rockville, MD: Food and Drug Administration; 2013.
182. European Medicines Agency (EMEA). Guideline on Bioanalytical Method Validation (EMEA/CHMP/EWP/192217/2009 Rev. 1 Corr. 2**). London, United Kingom: Committee for Medicinal Products for Human Use (CHMP), European Medicines Agency (EMEA); 2011.
183. Ashoor I, Najafian N, Korin Y, et al.. Standardization and cross validation of alloreactive IFNgamma ELISPOT assays within the clinical trials in organ transplantation consortium. Am J Transplant. 2013;13:1871–1879.
184. Davis BH, Wood B, Oldaker T, et al.. Validation of cell-based fluorescence assays: practice guidelines from the ICSH and ICCS—part I—rationale and aims. Cytometry B Clin Cytom. 2013;84:282–285.
185. Keslar KS, Lin M, Zmijewska AA, et al.. Multicenter evaluation of a standardized protocol for noninvasive gene expression profiling. Am J Transplant. 2013;13:1891–1897.
186. Mattocks CJ, Morris MA, Matthijs G, et al.. A standardized framework for the validation and verification of clinical molecular genetic tests. Eur J Hum Genet. 2010;18:1276–1288.
187. Valentin MA, Ma S, Zhao A, et al.. Validation of immunoassay for protein biomarkers: bioanalytical study plan implementation to support pre-clinical and clinical studies. J Pharm Biomed Anal. 2011;55:869–877.
188. Geissler EK, Tullius SG, Chong AS. Establishment of a global virtual laboratory for transplantation: a symposium report. Transplantation. 2015;99:381–384.
189. Smits TA, Cox S, Fukuda T, et al.. Effects of unbound mycophenolic acid on inosine monophosphate dehydrogenase inhibition in pediatric kidney transplant patients. Ther Drug Monit. 2014;36:716–723.
190. Glander P, Hambach P, Braun KP, et al.. Pre-transplant inosine monophosphate dehydrogenase activity is associated with clinical outcome after renal transplantation. Am J Transplant. 2004;4:2045–2051.
191. Sommerer C, Müller-Krebs S, Schaier M, et al.. Pharmacokinetic and pharmacodynamic analysis of enteric-coated mycophenolate sodium: limited sampling strategies and clinical outcome in renal transplant patients. Br J Clin Pharmacol. 2010;69:346–357.
192. Rizopoulos D. JM: an R package for the joint modeling of longitudinal and time-to event data J Stat Softw. 2010;35:1–33.
193. Daher Abdi Z, Premaud A, Essig M, et al.. Exposure to mycophenolic acid better predicts immunosuppressive efficacy than exposure to calcineurin inhibitors in renal transplant patients. Clin Pharmacol Ther. 2014;96:508–515.
Keywords:Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.
biomarkers of immunosuppression; immunologic biomarkers; consensus; assessment of acute rejection; graft outcome; graft injury; pharmacogenetics; pharmacokinetics; pharmacodynamics; personalized immunosuppression; solid organ transplantation