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Journal of Laboratory Medicine

Official Journal of the German Society of Clinical Chemistry and Laboratory Medicine

Editor-in-Chief: Schuff-Werner, Peter

Ed. by Ahmad-Nejad, Parviz / Bidlingmaier, Martin / Bietenbeck, Andreas / Conrad, Karsten / Findeisen, Peter / Fraunberger, Peter / Ghebremedhin, Beniam / Holdenrieder, Stefan / Kiehntopf, Michael / Klein, Hanns-Georg / Kohse, Klaus P. / Kratzsch, Jürgen / Luppa, Peter B. / Meyer, Alexander von / Nebe, Carl Thomas / Orth, Matthias / Röhrig-Herzog, Gabriele / Sack, Ulrich / Steimer, Werner / Weber, Thomas / Wieland, Eberhard / Winter, Christof / Zettl, Uwe K.

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Volume 38, Issue 6


Biomarkers in blood for individualization of the pharmacotherapy with immunosuppressive drugs after transplantation of solid organs

Eberhard Wieland
  • Corresponding author
  • Central Institute of Clinical Chemistry and Laboratory Medicine, Klinikum Stuttgart, Kriegsbergstr. 62, 70174 Stuttgart, Germany
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/ Maria Shipkova
  • Central Institute of Clinical Chemistry and Laboratory Medicine, Klinikum Stuttgart, Stuttgart, Germany
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Published Online: 2015-04-18 | DOI: https://doi.org/10.1515/labmed-2015-0037


The success of transplantation medicine is closely associated with the introduction of potent immunosuppressive drugs. Therapy guidance of the calcineurin inhibitors cyclosporine and tacrolimus as well as the mammalian target of rapamycin (mTOR) inhibitors sirolimus and everolimus is achieved by therapeutic drug monitoring (TDM). For other immunosuppressive drugs such as mycophenolic acid, TDM is not established. It has been suggested that a better individualization of pharmacotherapy could be helpful in avoiding over- and under-immunosuppression, which have been associated with poor long-term outcome of grafts and patients. Therefore, a search for biomarkers that could complement TDM, thereby allowing a better individualization of therapy, is ongoing worldwide. Pharmacodynamic biomarkers in transplantation medicine can be either non-drug-specific, aiming at assessing the global effect on the immune system, or drug-specific, aiming at recording the pharmacologic effect on a drug target. Non-specific pharmacodynamic biomarkers can reflect the degree of immune activation by measuring T- or B-cell activation, the development of operational tolerance by monitoring, e.g., regulatory T-cells, or the graft damage by measuring cell-free DNA released by the graft in response to injury. Drug-specific pharmacodynamic biomarkers have been published for mycophenolic acid, calcineurin, and mTOR inhibitors. The field of biomarker research to complement TDM for a better individualization of immunosuppression is still in its infancy, and controlled prospective clinical trials are needed to unravel or dismiss the potential of particular biomarkers or biomarker combinations in various patient cohorts with different grafts.

Reviewed publication


Keywords: cell-free DNA; donor-specific antibodies; pharmacodynamic; regulatory T cells; T-cell activation; T-cell function assays


The success of transplantation medicine is closely associated with the development and introduction of potent immunosuppressive drugs. The individualization of this therapy is carried out for the calcineurin inhibitors cyclosporine and tacrolimus as well as the mTOR (mammalian target of rapamycin) inhibitors sirolimus and everolimus via therapeutic drug monitoring (TDM). For drugs with a narrow therapeutic index, TDM enables dose adjustment, which is superior to treatment control by way of a fixed dose [1]. Especially in immunosuppressive therapy, however, it has been shown that patients may respond very differently to the treatment, although concentrations of the drugs are in the therapeutic range. One must therefore ask whether the dose adjustment via TDM is sufficient to individualize the therapy correctly. Pharmacokinetic monitoring can always only be a surrogate for the pharmacodynamic effect of therapy, and the therapeutic ranges are statistically determined target values on the basis of a large number of patients. Pharmacodynamic biomarkers, which specifically detect the effect of the drug on its pharmacological target molecule or, non-specifically, the general effect on the patient’s immune system, therefore, are an attractive addition to TDM to achieve immunosuppression that is better adapted individually [2]. An assessment of the immunological status prior to the transplantation would be helpful in order to be able to adapt the therapy optimally immediately after the transplantation. Patients with a strongly pre-activated immune system would benefit from a more robust therapy, while patients with a moderate immune status, or even a tendency to develop tolerance, would be better served with a weaker therapy. In combination therapies with various medications, one also observes synergistic and antagonistic effects. This can result in chronic over- or under-immunosuppression. In fact, the long-term outcome of the transplantation, despite the great success in the early stage to reduce the incidence of acute rejection, is not encouraging. Chronic humoral rejection, the long-term side effects of immunosuppressive drugs that manifest themselves in kidney damage, cardiovascular diseases, infections and malignant neoplasms cause the graft to be lost or the premature death of organ recipients. Suitable biomarkers could help avoid these complications through better individualization of therapy. Biomarkers are defined as a characteristic that is objectively measured and evaluated and used as an indicator for normal or pathogenic biological processes or pharmacological responses to therapeutic interventions [3].

Non-specific markers of immune response

Biomarkers of immune activation

ATP concentration in CD4 T-cells

The intracellular ATP (iATP) concentration increases as a result of the activation of the T-cells. A commercial test kit is available for measurement in CD4 T-cells which is approved by the FDA for diagnostic purposes (ImmuKnow®, Viracor IBT Laboratoriesc., Lee’s Summit, MO, USA). The test requires an incubation of whole blood overnight in the presence of the mitogen phytohemagglutinin (PHA) and an isolation of CD4 T-cells with magnetic particles. The ATP is then measured by luminometry [4]. Whole blood anticoagulated with heparin can be stored up to 30 h at room temperature [5]. The result of the ATP measurement is not dependent on the number of CD4 cells, and there is no dependency on the concentrations of immunosuppressive drugs [6]. A meta-analysis has shown that the iATP concentrations in stable kidney-transplant patients with rejection were higher than in those without rejection, and lower in patients with infections than in patients without infections [7]. According to a multicenter study, iATP concentrations >375 ng/mL before the transplantation were linked to a higher risk of acute rejection [8], whereas a recent study with 583 transplant recipients did not find any correlation between rejection or infection and a one-time determination of the ATP concentration [9]. Although a statistically determined target range is recommended, it can be assumed that strong, inter-individual variations may occur in the immune response, which is why it is also recommended that individual patients be monitored individually over time, but this is very time-consuming and expensive. Similar results to kidney transplantation have also been obtained in the field of heart transplantation [10, 11].

Patients after a lung transplantation with infections also had lower iATP levels than those without infection; with reduction of immunosuppressive therapy and the healing of the infections, the levels increased [12]. Low iATP concentrations were found especially in infections with CMV, other viral infections and pneumonia caused by bacteria, whereas this was less the case for fungal infections [13]. For liver transplants as well, low iATP levels were associated with a high incidence of infections [14]. To date, such clear results have not been published in connection with any organ regarding the association between elevated iATP levels and rejection as a sign of under-immunosuppression, which is why the assay seems to be better suited to indicate over-immunosuppression and the risk of developing infections. Studies that have prospectively examined whether an adjustment of immunosuppression based on iATP levels is reasonable, however, have not been published to date.

Interferon-γ release from alloreactive T-cells (ELISPOT)

Activated T-cells synthesize cytokines, and CD4 cells can be divided into subgroups based on the cytokine pattern. These are the type 1 (Th1) and type 2 helper cells (Th2). Th1 cells produce IL-2, IFN-γ and TNF-α, while IL-4, IL-5, IL-6 and IL-10 are cytokines typical of Th2 cells [15]. An elegant way to track the production of cytokine is the ELISPOT assay (enzyme-linked immunosorbent spot assay). It has been used particularly for the detection of IFN-γ-producing lymphocytes. For this purpose, mononuclear leukocytes are incubated for 18–24 h with donor-specific or non-specific antigens on a membrane, and the cytokine-producing cells are spot-stained with a monoclonal antibody against the cytokine and then counted [16]. This way, Augustine and colleagues have been able to show for kidney transplant recipients that there are memory cells, which can be activated by the alloantigen, even before the transplantation. These memory cells correlate with a higher incidence of rejection after transplantation [17]. This is especially important, because it is believed today that these cells in particular may have a negative impact on the long-term outcome of the transplantation [18]. Hricik et al. have demonstrated that there is, after the transplantation, an association between the activation of T-cells by both donor-specific and non-specific antigens and renal function [19]. To be able to also predict the immunological risk to the organ recipient prior to a post-mortem donation, a panel of donor-foreign cells with major HLA antigens has been developed [20, 21]. However, the results were more convincing in the case of the living donation with donor-specific antigens [22]. A weaker IFN-γ formation was associated with better renal function, allowing a milder immunosuppression [23]. Similar to the ImmuKnow® test, the ELISPOT assay can be standardized and controlled very well [24, 25], which significantly improves the comparability of results between different laboratories. This allows for multicenter studies and increases the chances of routine use if the relevant clinical data are available. In that respect, Bestard et al. have recently demonstrated that INF-γ ELISPOT approaches with donor-specific cells can be usefully employed for the control of immunosuppression [26]. A disadvantage of the method is the long duration of the assay, as it always requires an incubation phase of several hours. For the donor-specific IFN-γ ELISPOT, storage of mononuclear leukocytes of the organ donor is also a problem if follow-up tests are to be conducted following the transplantation.

T-cell surface markers

T-cell activation leads not only to ATP synthesis and cytokine production, but also to the expression of specific surface molecules [27]. These molecules are, for example, receptors, co-stimulatory molecules, adhesion molecules and HLA molecules. Examples of receptors are CD25 (IL-2 receptor) and CD71 (transferrin receptor). Co-stimulatory molecules include CD26, CD28, CD30 and CD154. LFA-1 (lymphocyte function-associated antigen-1) and ICAM-1 (intercellular adhesion molecule 1), also known as CD54, are adhesion molecules that are expressed in larger numbers on activated T-cells. CXCR3 and CCR5 are chemokine receptors that are also found on activated T-cells in larger numbers. The MHC class II molecules include all isotypes [28]. For immune monitoring in transplantation medicine, mainly receptor proteins and the expression of co-stimulatory molecules have been studied. Surface markers can be determined by means of flow cytometry using fluorescence-labeled monoclonal antibodies. This often involves cell function tests, in which either anticoagulated and diluted whole blood or isolated mononuclear leukocytes are stimulated with mitogens or antibodies. Alternatively, a mixed lymphocyte culture with donor-specific or non-specific cells is used. The effect of immunosuppressive drugs can be examined in these incubations through ex vivo additions. In such an approach, as has been demonstrated by Böhler et al., the expression of CD25 and CD71 on CD3 cells (T-cells) can be inhibited dose-dependently by cyclosporine, tacrolimus, mycophenolic acid, sirolimus and methylprednisolone [29]. The imprecision of the assay with cells from a healthy donor in series was <10%, and between different donor cells <21%. The expression of CD25 and CD71 in cells of kidney transplant patients under immunosuppression was also lower than in cells from healthy controls and patients on dialysis [30, 31]. An inverse correlation between the mycophenolic acid concentration in plasma and the expression of CD25 and CD71 was observed in kidney and liver transplant patients [32, 33]. In another study, the change from cyclosporine to tacrolimus produced a decrease in the expression of CD28, CD54 and CD154 [34]. This observation was interpreted as a more potent immunosuppression by means of tacrolimus.

There are only very few data on the connection between surface markers and clinical events. In heart transplant patients, the CD25 and CD71 expressions on CD3 and CD4 cells could support a distinction between organ dysfunction and rejection, while fewer cells with CD25 and CD71 were interpreted as a sign of over-immunosuppression with subsequent organ dysfunction [35, 36]. Our own studies involving kidney transplant recipients suggest that a low expression of CD26 on T-cells is associated with a low risk of acute rejection, while a reduction of the CD71 expression could be a sign of over-immunosuppression because this observation was made especially in patients with infections [37]. CD154 expression has been shown to be associated with acute rejection in pediatric small bowel transplantation in a donor antigen specific cell function assay [38] and is commercially offered in the US as Pleximmune™ test by a central laboratory (Plexision, Pittsburgh, PA, USA). In contrast to the complicated cell function assays using ex vivo stimulation, the measurement of CD26 on CD3 cells directly in whole blood seems to allow for a statement about the degree of activation of the T-cells in vivo. Boleslawski et al. report, in connection with liver transplantation, that CD28 on CD8 cells can be associated with both rejections in the early phase after transplantation (many CD28/CD8 cells) and tumor diseases over the long term (few CD28/CD8 cells) [39, 40].

Soluble CD30 in serum (sCD30)

CD30 is a co-stimulatory molecule that is released into the blood by activated T-cells [41]. It can therefore be measured as a marker of T-cell activation in serum or plasma, for which commercial ELISA methods are available. [42]. In the meantime, there is also a Luminex method with beads that carry antibodies against CD30 [43]. The source of sCD30 seems to be particularly memory T-cells [44]. In recent years, a number of studies have been carried out on the benefit of this biomarker in transplantation medicine [45–58].

In kidney transplantation, it has been found that a concentration >100 U/mL before surgery is associated with an increased incidence of rejection responses in the first year and an organ loss in the first 5 years [49]. If the sCD30 levels remain high even after the transplantation (day 3–5), it poses an additional risk for acute rejection [45]. A significantly worse graft survival has been observed when there are concentrations >40 U/mL on day 30 after the transplantation [50]. It was not always possible to reproduce the promising results with sCD30 [51–53], which could be partly due to the high intra-individual variation of the marker [54]. The sCD30 concentrations fluctuated by about 20% in renal failure patients on the waiting list, which suggests that sequential determination, for example, on a quarterly basis, would be useful. For other organs and tissues (lung, liver, heart, islet cells), the results are discrepant [55–58]. Unfortunately, there have not been any studies to examine the effect of different immunosuppressive regimens on sCD30.

Lymphocyte proliferation

The proliferation of lymphocytes can be tracked using various techniques. These include, for example, the use of labeled DNA building blocks such as 3H-thymidine or bromodeoxyuridine (BrdU), which are incorporated into the newly synthesized DNA and which can be measured in a scintillation counter or by means of ELISA [59, 60]. The distribution of the fluorescent dye carboxyfluorescein diacetate succinimidyl ester (CFSE) in connection with cell division to the daughter cells can be monitored by flow cytometry [61]. The proliferating cell nuclear antigen (PCNA), as an intracellular protein, can be measured using flow cytometry, or its mRNA expression by quantitative PCR (qPCR) [62, 63].

Increased cell proliferation in stable liver transplant patients has been associated with increased proliferation of CD8 cells when rejection responses were observed [64]. In a small study of 55 kidney recipients, a low PCNA with mRNA expression was associated with the risk of infections [63]. According to the CFSE dilution assay, increased proliferation was linked to an increased rejection rate in 28 children after a small bowel transplantation [38]. The incorporation of BrdU in lymphocytes was lower in kidney transplant patients with leukopenia [37].

Donor-specific antibodies

Antibody-mediated rejection (humoral rejection) plays an important role, alongside cellular rejection, especially when it comes to chronic damage of the graft. The antibodies produced by B-cells can be directed very specifically against HLA class I and II characteristics of the organ donor or other antigens, such as major histocompatibility complex class I-related chain A (MICA) or the angiotensin II type 1 receptor. These are called donor-specific antibodies (DSA). DSA may already be present before the transplantation or occur after the surgery. Emerging post-transplant DSA, so-called de novo DSA, are associated with a worse prognosis [65]. The measurement is usually performed today using solid phase assays. In the Luminex technology, microparticles are used to which HLA antigens or other non-HLA antigens are fixed [66]. Antibodies from the serum of the transplant recipient are detected by fluorescence signals. Complement activating antibodies in kidney recipients, in particular, are associated with a significantly increased risk of rejection [67]. The occurrence of de novo DSA can be interpreted as a sign of under-immunosuppression. A study on dose reduction of immunosuppressive drugs yielded an inverse relationship between the drug levels in the blood and the occurrence of de novo DSA [68]. Immunosuppressive drugs may, perhaps, differ in terms of the risk of developing DSA, because during treatment with the mTOR inhibitor everolimus, there were more DSA and humoral rejections than those observed with cyclosporine [69]. Also, in heart and liver transplantations, the incidence of de novo DSA is associated with a poorer graft survival [70, 71]. An antibody-mediated rejection was the most common cause of organ failure in kidney transplant patients in a study performed by Sellarés et al. [72]. The unreliable taking of immunosuppressive drugs was, in half of the cases, associated with the occurrence of DSA, and this lack of compliance could possibly have been uncovered via TDM, but this would not have affected the other half. This emphasizes the notion that the determination of DSA as biomarkers of immune activation may be useful in addition to TDM to better identify vulnerable patients and adjust their immunosuppression. A recently published consensus guideline describes a proposed procedure for routine monitoring, which is summarized here in a simplified manner. The recommendation calls for DSA to be measured in patients at high immunological risk over the first 3 months after transplantation; in patients with a medium risk, only in the first month, and in patients with a low risk, once between the third and 12th months [66].

Biomarkers indicating damage to the graft

Under- or over-immunosuppression and toxic drug effects can damage transplanted organs. To measure the damage organ-specifically, the usual parameters, such as transaminase, bilirubin, LDH, creatinine, cystatin C or troponins, are analyzed in the blood. For the kidney, urine-based biomarkers can also be used. These include, for example, neutrophil gelatinase-associated lipocalin (NGAL) or the kidney injury molecule-1 (KIM-1). The widespread use of these biomarkers is due to the fact that there are well-validated CE-certified tests. Newer biomarkers that are based on the detection of mRNA or protein patterns have been published. Ling et al. have described a panel of 40 peptides in the urine of kidney transplant patients, which was typical of acute rejection. Six genetic biomarkers in the blood (COL1A2, COL3A1, UMOD, MMP-7, SERPING1, TIMP1) were highly significant for acute rejection [73]. Using mass spectrometry, it was possible to identify in the blood genomic and proteome-based biomarkers of nephropathy, which were validated by means of biopsy findings [74]. In 2009, Moreira et al. reported on the possibility of diagnosing an acute rejection in kidney transplant recipients on the basis of free DNA circulating in the blood of the recipient that is released from the transplanted organ [75]. The release of DNA from the donor organ also increases in heart transplant patients during rejection events [76]. New technical developments such as the digital droplet PCR allow for a rapid and relatively inexpensive way of detecting and quantifying this circulating DNA from the graft [77]. A relatively new publication shows that the measurement of cell-free DNA from the donor organ could also help control the immunosuppression [78].

Biomarkers for the development of tolerance

In order to identify patients who may be able to cope with weak immunosuppression, biomarkers are sought that indicate the development of tolerance. Regulatory T-cells (Tregs), which can prevent rejection of the new organ, have been known for years. The detection of such cells could serve as an indicator of tolerance. These cells can be identified by their surface markers (e.g., CD4/CD25/Foxp3) by means of flow cytometry. A better alternative is the PCR-based determination of the demethylated locus on the Foxp3 gene, also called TSDR (Treg-specific demethylated region) [79]. In fact, the determination of the amount of such Tregs was used to examine the tolerance-inducing potential of various immunosuppressive protocols. While the mTOR inhibitors sirolimus and everolimus were able to be linked to the development of tolerance, calcineurin inhibitors led to a smaller number of Tregs [80]. In a recent study, Bouvy et al. demonstrated the different effect of induction therapy with anti-thymocyte globulin (ATG) and the IL-2 receptor antibody basiliximab on the repopulation of Tregs, which was better under ATG [81].

A new direction in the study of the development of tolerance puts the focus on the role played by B-cells in this process [82]. Through typical patterns of gene expression in B-cells, it was possible to detect tolerant patients [83, 84]. Biomarkers of tolerance, for example, are currently being studied by the RISET (Reprogramming the Immune System for Establishment of Tolerance) consortium, which is supported by the European Commission (www.risetfp6.org).

Specific pharmacodynamic biomarkers

Enzymes as a pharmacological target of immunosuppressive drugs

The calcineurin inhibitors cyclosporine and tacrolimus inhibit the enzyme calcineurin phosphatase in lymphocytes. Halloran et al. [85] have been able to show that the inhibition of the enzyme activity in lymphocytes is associated with the cyclosporine concentrations in whole blood. The greatest inhibition 1 and 2 h after dosing corresponded with the highest concentration in the blood. Although these experiments demonstrate the feasibility of such an approach, the method is technically very demanding and, in principle, not suitable for routine use [86]. It has therefore not yet come into widespread use in clinical studies.

The enzyme protein 70S6 kinase (p70S6 kinase) is phosphorylated by the kinase mTOR. The decrease in phosphorylation of the enzyme by the inhibition of mTOR is an indicator of the effect of the mTOR inhibitors sirolimus and everolimus [87]. A small study on kidney transplant patients has shown that there is no correlation between the phosphorylation of p70S6 kinase and the concentrations of sirolimus in the blood [88]. A little-decreased phosphorylation was observed in the Western blot of five patients who experienced an acute rejection, while patients with a greatly weakened phospho-p70S6 kinase band did not experience rejection. The assay is time-consuming and requires a specialized laboratory, which is why routine use is doubtful. However, an ELISA has been developed for the detection of phospho-p70S6 kinase [89], but it has not been validated in clinical trials yet. An alternative method is the so-called phospho-flow technique, in which intracellular phosphorylated proteins are detected by means of a flow cytometer. Methods for the detection of the phosphorylated p70S6 kinase and of the phosphorylated ribosomal S6 protein (rS6P) have been published [90, 91]. Hoerning et al. have been able to demonstrate that phosphorylation of p70S6 kinase is reduced significantly in kidney transplant patients undergoing mTOR therapy compared to controls without treatment or under calcineurin inhibitor therapy. There was no correlation between the pharmacodynamic biomarkers and the whole blood levels of sirolimus or everolimus [91].

The enzyme inosine monophosphate dehydrogenase (IMPDH) is inhibited in mononuclear leukocytes by the immunosuppressant mycophenolic acid. In 79 hemodialysis patients, the activity of the enzyme exhibited a high degree of inter-individual variability before the kidney transplantation [92]. Patients with activity above a certain threshold, for whom the mycophenolic acid dose was reduced after the transplantation, showed the highest rates of rejection. The determination of enzyme activity prior to the transplantation could therefore be used to individualize the dose after the transplantation. After the transplantation, the IMPDH activity was inversely associated with the mycophenolic acid concentration in plasma, which is why it was hypothesized that the combination of concentration measurements of the immunosuppressant in plasma and enzyme activity in leukocytes could be useful for controlling the treatment with mycophenolic acid [93]. To date, however, there is no sufficient evidence from clinical studies to apply this approach to routine patient care. In contrast to a point measurement of IMPDH activity, enzyme activity was measured in a study during the first 4 h after administration of the drug, and the area under the concentration time curve (AUEC) was calculated [94]. It was shown that in the first 2 weeks after the transplantation, the IMPDH inhibition was significantly lower in patients with rejection than in patients without rejection.

The relationship between genetic polymorphisms in type I and type II IMPDH and clinical events was examined in two large studies involving kidney transplant recipients [95]. It was observed that a polymorphism in the IMPDH I gene was associated with a lower risk of acute rejection and leukopenia in the first year after the transplantation.

Interleukin-2 in CD8 T-cells

Interleukin-2 (IL-2) is essential for the activation of T-cells. As such, T-cells produce IL-2 themselves and stimulate themselves or paracrinally via the IL-2 receptor. The calcineurin inhibitors cyclosporine and tacrolimus inhibit the IL-2 effect and thus prevent the activation and proliferation of effector T-cells (CD8). The percentage of CD8 cells producing IL-2 was increased in patients with rejection after a liver transplant already before the transplantation [96], indicating activation of the immune system. These results were confirmed in two studies [64, 97]. The measurement of IL-2-producing T-cells is relatively easy and reproducible by means of flow cytometry, which is why this biomarker seems suitable for use in clinical trials with calcineurin inhibitors, which, however, have not been conducted yet.

Expression of NFAT-regulated genes

The inhibition of the enzyme calcineurin phosphatase through cyclosporine and tacrolimus results in the prevention of dephosphorylation of the so-called nuclear factor of activated T lymphocytes (NFAT). Dephosphorylated NFAT translocates into the nucleus and switches on certain genes, such as IL-2, INF-γ and GM-CSF, which are required for T-cell activation. The group around Zeier has studied intensively the expression of these genes in recent years as pharmacodynamic biomarkers of the calcineurin inhibitor effect [98]. Using qPCR, the expression of the three genes is measured in stimulated leukocytes by means of the LightCycler instrument (Roche Diagnostics, Mannheim, Germany) before and 2 h or 1.5 h after taking cyclosporine or tacrolimus. There is a commercially available kit for this. The gene expression was inversely associated with the cyclosporine or tacrolimus concentration in the blood. A very low expression of the three genes (<15%) was observed in patients with recurrent infections and tumors while receiving cyclosporine and interpreted as a sign of over-immunosuppression [99]. There was no association between clinical events and the concentrations of the calcineurin inhibitor in the blood. Dose reduction was accompanied by an increase in the gene expression, and as long as a cut-off of 30% was not exceeded, there were no rejection responses [100]. These results were reproducible in other studies involving stable pediatric patients and patients following liver or heart transplants [101–104]. Reports in the literature on treatment with tacrolimus have been less convincing [105, 106].


The control of immunosuppressive therapy via TDM has made the treatment safer and above all has helped avoid toxic side effects. However, it has also been found that some patients respond very differently to therapy and that the achievements in the early phase after the transplantation are clouded by modest long-term results. Individualization of therapy seems sensible. As an addition to TDM, the determination of biomarkers in the blood or other body fluids could represent an approach that better reflects the individual responses of the immune system to the treatment [18]. Although pharmacodynamic biomarkers have been developed that can capture, directly and specifically, the pharmacological effect on a target molecule or, more generally and non-specifically, the effect on the immune system, these have not really been validated in larger prospective clinical trials and have rarely entered clinical routine. A major drawback of most biomarkers is the complicated and time-consuming analysis. Many in-house procedures are also not well validated analytically and certainly not standardized, making it difficult to compare results from different laboratories and studies. The greatest potential for clinical application is found in assays that are commercially available as kits or which are performed in central laboratories, such as the ImmuKnow® test, the Pelximmune™ test, the NFAT-regulated gene expression, the IFN-γ ELISPOT or the determination of sCD30 with commercially available ELISA tests and the measurement of DSA on the basis of the Luminex technology. The determination of the DNA of the donor organ in the blood of the recipient by way of the digital droplet-PCR analysis is also promising in analytical terms. However, it seems unlikely that a biomarker has the necessary diagnostic sensitivity and specificity for all test situations. Rather, it is to be expected that a combination of analytically robust and clinically validated biomarkers could be a complementary diagnostic tool for the diagnosis of complications, therapy management and prognosis for recipients of solid organs. At this point, it is impossible to say which combinations are useful, but having a group of markers for immune activation (T-cells and B-cells), development of tolerance and organ damage seems to be the right approach.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


  • 1.

    Koch-Weser J. Drug therapy. Serum drug concentrations as therapeutic guides. N Engl J Med 1972;287:227–31.CrossrefGoogle Scholar

  • 2.

    Wieland E, Olbricht CJ, Süsal C, Gurragchaa P, Böhler T, Israeli M, et al. Biomarkers as a tool for management of immunosuppression in transplant patients. Ther Drug Monit 2010;32:560–72.CrossrefGoogle Scholar

  • 3.

    Biomarkers Definition Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Therapeutics 2001;69:89–95.Google Scholar

  • 4.

    Kowalski R, Post D, Schneider MC, Britz J, Thomas J, Deierhoi M, et al. Immune cell function testing: an adjunct to therapeutic drug monitoring in transplant patient management. Clin Transplant 2003;17:77–88.CrossrefGoogle Scholar

  • 5.

    Kowalski RJ, Zeevi A, Mannon RB, Britz JA, Carruth LM. Immunodiagnostics: evaluation of functional T-cell immunocompetence in whole blood independent of circulating cell numbers. J Immunotoxicol 2007;4:225–32.CrossrefGoogle Scholar

  • 6.

    Akhlaghi F, Gohh RY. The level of ATP production in mitogen-stimulated CD4+ lymphocytes is independent of the time of ingestion of immunosuppressive agents. Ther Drug Monit 2010;32:116–7.CrossrefGoogle Scholar

  • 7.

    Kowalski RJ, Post DR, Mannon RB, Sebastian A, Wright HI, Sigle G, et al. Assessing relative risks of infection and rejection: a meta-analysis using an immune function assay. Transplantation 2006;82:663–8.CrossrefGoogle Scholar

  • 8.

    Reinsmoen NL, Cornett KM, Kloehn R, Burnette AD, McHugh L, Flewellen BK, et al. Pretransplant donor-specific and non-specific immune parameters associated with early acute rejection. Transplantation 2008;85:462–70.CrossrefGoogle Scholar

  • 9.

    Huskey J, Gralla J, Wiseman AC. Single time point immune function assay (ImmuKnowTM) testing does not aid in the prediction of future opportunistic infections or acute rejection. Clin J Am Soc Nephrol 2011;6:423–9.CrossrefGoogle Scholar

  • 10.

    Israeli M, Ben-Gal T, Yaari V, Valdman A, Matz I, Medalion B, et al. Individualized immune monitoring of cardiac transplant recipients by noninvasive longitudinal cellular immunity tests. Transplantation 2010;89:968–76.CrossrefGoogle Scholar

  • 11.

    Kobashigawa JA, Kiyosaki KK, Patel JK, Kittleson MM, Kubak BM, Davis SN, et al. Benefit of immune monitoring in heart transplant patients using ATP production in activated lymphocytes. J Heart Lung Transplant 2010;29:504–8.CrossrefGoogle Scholar

  • 12.

    Bhorade SM, Janata K, Vigneswaran WT, Alex CG, Garrity ER. Cylex ImmuKnow assay levels are lower in lung transplant recipients with infection. J Heart Lung Transplant 2008;27: 990–4.CrossrefGoogle Scholar

  • 13.

    Husain S, Raza K, Pilewski JM, Zaldonis D, Crespo M, Toyoda Y, et al. Experience with immune monitoring in lung transplant recipients: correlation of low immune function with infection. Transplantation 2009:87:1852–7.CrossrefGoogle Scholar

  • 14.

    Xue F, Zhang J, Han L, Li Q, Xu N, Zhou T, et al. Immune cell functional assay in monitoring of adult liver transplantation recipients with infection. Transplantation 2010;89:620–6.CrossrefGoogle Scholar

  • 15.

    Nankivell B, Alexander SI. Rejection of the kidney allograft. N Engl J Med 2010;363:1451–62.Google Scholar

  • 16.

    Augustine JJ, Hricik DE. T-cell immune monitoring by the ELISPOT assay for interferon gamma. Clin Chim Acta 2012;413:1359–63.CrossrefGoogle Scholar

  • 17.

    Augustine JJ, Siu DS, Clemente MJ, Schulak JA, Heeger PS, Hricik DE. Pre-transplant IFN-gamma ELISPOTs are associated with post-transplant renal function in African American renal transplant recipients. Am J Transplant 2005;5:1971–5.CrossrefGoogle Scholar

  • 18.

    Cravedi P, Heeger PS. Immunologic monitoring in transplantation revisited. Curr Opin Organ Transplant 2012;17:26–32.CrossrefGoogle Scholar

  • 19.

    Hricik DE, Rodriguez V, Riley J, Bryan K, Tary-Lehmann M, Greenspan N, et al. Enzyme linked immunosorbent spot (ELISPOT) assay for interferon-gamma independently predicts renal function in kidney transplant recipients. Am J Transplant 2003;3:878–84.CrossrefGoogle Scholar

  • 20.

    Andree H, Nickel P, Nasiadko C, Hammer MH, Schönemann C, Pruss A, et al. Identification of dialysis patients with panel-reactive memory T cells before kidney transplantation using an allogeneic cell bank. J Am Soc Nephrol 2006;17:573–80.CrossrefGoogle Scholar

  • 21.

    Poggio ED, Clemente M, Hricik DE, Heeger PS. Panel of reactive T cells as a measurement of primed cellular alloimmunity in kidney transplant candidates. J Am Soc Nephrol 2006;17:564–72.CrossrefGoogle Scholar

  • 22.

    Kim SH, Oh EJ, Kim MJ, Park YJ, Han K, Yang HJ, et al. Pretransplant donor-specific interferon-gamma ELISPOT assay predicts acute rejection episodes in renal transplant recipients. Transplant Proc 2007;39:3057–60.CrossrefGoogle Scholar

  • 23.

    Bestard O, Nickel P, Cruzado JM, Schoenemann C, Boenisch O, Sefrin A, et al. Circulating alloreactive T cells correlate with graft function in longstanding renal transplant recipients. J Am Soc Nephrol 2008;19:1419–29.CrossrefGoogle Scholar

  • 24.

    Ashoor I, Najafian N, Korin Y, Reed EF, Mohanakumar T, Ikle D, et al. Standardization and cross validation of alloreactive IFNγ ELISPOT assays within the clinical trials in organ transplantation consortium. Am J Transplant 2013;1:1871–9.CrossrefGoogle Scholar

  • 25.

    Sanchez AM, Rountree W, Berrong M, Garcia A, Schuetz A, Cox J, et al. The External Quality Assurance Oversight Laboratory (EQAPOL) proficiency program for IFN-gamma enzyme-linked immunospot (IFN-γ ELISpot) assay. J Immunol Methods 2014;409:31–43.CrossrefGoogle Scholar

  • 26.

    Bestard O, Cruzado JM, Lucia M, Crespo E, Casis L, Sawitzki B, et al. Prospective assessment of antidonor cellular alloreactivity is a tool for guidance of immunosuppression in kidney transplantation. Kidney Int 2013;84:1226–36.CrossrefGoogle Scholar

  • 27.

    Shipkova M, Wieland E. Surface markers of lymphocyte activation and markers of cell proliferation. Clin Chim Acta 2012;413:1338–49.CrossrefGoogle Scholar

  • 28.

    Holling TM, Schooten E, van Den Elsen PJ. Function and regulation of MHC class II molecules in T-lymphocytes: of mice and men. Hum Immunol 2004;65:282–90.CrossrefGoogle Scholar

  • 29.

    Böhler T, Nolting J, Kamar N, Gurragchaa P, Reisener K, Glander P, et al. Validation of immunological biomarkers for the pharmacodynamic monitoring of immunosuppressive drugs in humans. Ther Drug Monit 2007:29:77–86.CrossrefGoogle Scholar

  • 30.

    Stalder M, Bîrsan T, Holm B, Haririfar M, Scandling J, Morris RE. Quantification of immunosuppression by flow cytometry in stable renal transplant recipients. Ther Drug Monit 2003;25:22–27.CrossrefGoogle Scholar

  • 31.

    Böhler T, Canivet C, Nguyen PN, Galvani S, Thomsen M, Durand D, et al. Cytokines correlate with age in healthy volunteers, dialysis patients and kidney-transplant patients. Cytokine 2009,45:169–73.CrossrefGoogle Scholar

  • 32.

    Prémaud A, Rousseau A, Johnson G, Canivet C, Gandia P, Muscari F, et al. Inibition of T-cell activation and proliferation by mycophenolic acid in patients awaiting liver transplantation: PK/PD relationships. Pharmacol Res 2011;63:432–8.CrossrefGoogle Scholar

  • 33.

    Kamar N, Glander P, Nolting J, Böhler T, Hambach P, Liefeldt L, et al. Pharmacodynamic evaluation of the first dose of mycophenolate mofetil before kidney transplantation Clin J Am Soc Nephrol 2009;4:936–42.CrossrefGoogle Scholar

  • 34.

    Weimer R, Zipperle S, Daniel V, Carl S, Staehler G, Opelz G. Pretransplant CD4 helper function and interleukin 10 response predict risk of acute kidney graft rejection. Transplantation 1996;62:1606–14.CrossrefGoogle Scholar

  • 35.

    Deng MC, Erren M, Roeder N, Dreimann V, Günther F, Kerber S, et al. T-cell and monocyte subsets, inflammatory molecules, rejection, and hemodynamics early after cardiac transplantation. Transplantation 1998;65:1255–61.CrossrefGoogle Scholar

  • 36.

    Chang DM, Ding YA, Kuo SY, Chang ML, Wei J. Cytokines and cell surface markers in prediction of cardiac allograft rejection. Immunol Invest 1996;25:13–21.CrossrefGoogle Scholar

  • 37.

    Wieland E, Shipkova M, Martius Y, Hasche G, Klett C, Bolley R, 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;3:341–9.CrossrefGoogle Scholar

  • 38.

    Ashokkumar C, Bentlejewski C, Sun Q, Higgs BW, Snyder S, Mazariegos GV, et al. Allospecific CD154+ B cells associate with intestine allograft rejection in children. Transplantation 2010;90:1226–31.Google Scholar

  • 39.

    Boleslawski E, BenOthman S, Grabar S, Correia L, Podevin P, Chouzenoux 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.Google Scholar

  • 40.

    Boleslawski E, Othman SB, Aoudjehane L, Chouzenoux S, Scatton O, Soubrane O, 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.Google Scholar

  • 41.

    Süsal C, Opelz G. Posttransplant sCD30 as a biomarker to predict kidney graft outcome. Clin Chim Acta 2012;413: 1350–3.Google Scholar

  • 42.

    Josimovic-Alasevic O, Dürkop H, Schwarting R, Backé E, Stein H, Diamantstein T. Ki-1 (CD30) antigen is released by Ki-1-positive tumor cells in vitro and in vivo. I. Partial characterization of soluble Ki-1 antigen and detection of the antigen in cell culture supernatants and in serum by an enzyme-linked immunosorbent assay. Eur J Immunol 1989;19:157–62.Google Scholar

  • 43.

    Pavlov I, Martins TB, Delgado JC. Development and validation of a fluorescent microsphere immunoassay for soluble CD30 testing. Clin Vaccine Immunol 2009;16:1327–31.Google Scholar

  • 44.

    Velásquez SY, García LF, Opelz G, Alvarez CM, Süsal C. Release of soluble CD30 after allogeneic stimulation is mediated by memory T cells and regulated by IFN-γ and IL-2. Transplantation 2013;96:154–61.Google Scholar

  • 45.

    Pelzl S, Opelz G, Daniel V, Wiesel M, Süsal C. Evaluation of posttransplantation soluble CD30 for diagnosis of acute renal allograft rejection. Transplantation 2003;75:421–3.Google Scholar

  • 46.

    Weimer R, Süsal C, Yildiz S, Staak A, Pelzl S, Renner F, et al. Post-transplant sCD30 and neopterin as predictors of chronic allograft nephropathy: impact of different immunosuppressive regimens. Am J Transplant 2006;6:1865–74.Google Scholar

  • 47.

    López-Hoyos M, San Segundo D, Benito MJ, Fernández-Fresnedo G, Ruiz JC, Rodrigo E, et al. Association between serum soluble CD30 and serum creatinine before and after renal transplantation. Transplant Proc 2008;40:2903–5.Google Scholar

  • 48.

    Spiridon C, Nikaein A, Lerman M, Hunt J, Dickerman R, Mack M. CD30, a marker to detect the high-risk kidney transplant recipients. Clin Transplant 2008;22:765–9.Google Scholar

  • 49.

    Süsal C, Pelzl S, Döhler B, Opelz G. Identification of highly responsive kidney transplant recipients using pretransplant soluble CD30. J Am Soc Nephrol 2002;13:1650–6.CrossrefGoogle Scholar

  • 50.

    Süsal C, Döhler B, Sadeghi M, Salmela KT, Weimer R, Zeier M, et al. Posttransplant sCD30 as a Predictor of Kidney Graft Outcome. Transplantation 2011;91:1364–9.Google Scholar

  • 51.

    Halim MA, Al-Otaibi T, Al-Muzairai I, Mansour M, Tawab KA, Awadain WH, et al. Serial soluble CD30 measurements as a predictor of kidney graft outcome. Transplant Proc 2010;42:801–3.Google Scholar

  • 52.

    Kovač J, Arnol M, Vidan Jeras B, Bren AF, Kandus A. Pretransplant soluble CD30 serum concentration does not affect kidney graft outcomes 3 years after transplantation. Transplant Proc 2010;42:4043–6.Google Scholar

  • 53.

    Chen Y, Tai Q, Hong S, Kong Y, Shang Y, Liang W, et al. Pretransplantation soluble CD30 level as a predictor of acute rejection in kidney transplantation: a meta-analysis. Transplantation 2012;94:911–8.Google Scholar

  • 54.

    Altermann W, Schlaf G, Rothhoff A, Seliger B. High variation of individual soluble serum CD30 levels of pre-transplantation patients: sCD30 a feasible marker for prediction of kidney allograft rejection? Nephrol Dial Transplant 2007;22:2795–9.Google Scholar

  • 55.

    Shah AS, Leffell MS, Lucas D, Zachary AA. Elevated pretransplantation soluble CD30 is associated with decreased early allograft function after human lung transplantation. Hum Immunol 2009;70:101–3.Google Scholar

  • 56.

    Ypsilantis E, Key T, Bradley JA, Morgan CH, Tsui S, Parameshwar J, et al. Soluble CD30 levels in recipients undergoing heart transplantation do not predict post-transplant outcome. J Heart Lung Transplant 2009;28:1206–10.Google Scholar

  • 57.

    Hire K, Hering B, Bansal-Pakala P. Relative reductions in soluble CD30 levels post-transplant predict acute graft function in islet allograft recipients receiving three different immunosuppression protocols. Transpl Immunol 2010;23:209–14.Google Scholar

  • 58.

    Kim KH, Oh EJ, Jung ES, Park YJ, Choi JY, Kim DG, et al. Evaluation of pre- and posttransplantation serum interferon-gamma and soluble CD30 for predicting liver allograft rejection. Transplant Proc 2006;38:1429–31.Google Scholar

  • 59.

    Wu J, Palladino MA, Figari IS, Morris RE. Comparative immunoregulatory effects of rapamycin, FK 506 and cyclosporine on mitogen-induced cytokine production and lymphoproliferation Transplant Proc 1991;23:238–40.Google Scholar

  • 60.

    Shipkova M, Wieland E, Schütz E, Wiese C, Niedmann PD, Oellerich M, et al. The acyl glucuronide metabolite of mycophenolic acid inhibits the proliferation of human mononuclear leukocytes. Transplant Proc 2001;33:1080–1.CrossrefGoogle Scholar

  • 61.

    Lyons AB, Parish CR. Determination of lymphocyte division by flow cytometry. J. Immunol Methods 1994;71:131–7.CrossrefGoogle Scholar

  • 62.

    Böhler T, Waiser J, Budde K, Lichter S, Jauho A, Fritsche L, et al. The in vivo effect of rapamycin derivative SDZ RAD on lymphocyte proliferation. Transplant Proc 1998;30:2195–7.CrossrefGoogle Scholar

  • 63.

    Niwa M, Miwa Y, Kuzuya T, Iwasaki K, Haneda M, Ueki T, et al. Stimulation index for PCNA mRNA in peripheral blood as immune function monitoring after renal transplantation. Transplantation 2009;87:1411–4.CrossrefGoogle Scholar

  • 64.

    Millán O, Benitez C, Guillén D, Lopez A, Rimola A, Sánchez-Fueyo A, et al. Biomarkers of immunoregulatory status in stable liver transplant recipients undergoing weaning of immunosuppressive therapy. Clin Immunol 2010;137:337–46.CrossrefGoogle Scholar

  • 65.

    Terasaki PI, Ozawa M, Castro R. Four-year follow-up of a prospective trial of HLA and MICA antibodies on kidney graft survival. Four-year follow-up of a prospective trial of HLA and MICA antibodies on kidney graft survival. Am J Transplant 2007;7:408–15.CrossrefGoogle Scholar

  • 66.

    Tait BD, Süsal C, Gebel HM, Nickerson PW, Zachary AA, Claas FH, et al. Consensus guidelines on the testing and clinical management issues associated with HLA and non-HLA antibodies in transplantation. Transplantation 2013;95:19–47.CrossrefGoogle Scholar

  • 67.

    Loupy A, Lefaucheur C, Vernerey D, Prugger C, Duong van Huyen JP, Mooney N, et al. Complement-binding anti-HLA antibodies and kidney-allograft survival. N Engl J Med 2013;369:1215–26.CrossrefGoogle Scholar

  • 68.

    Hoshino J, Kaneku H, Everly MJ, Greenland S, Terasaki PI. Using donor-specific antibodies to monitor the need for immunosuppression. Transplantation 2012;93:1173–8.CrossrefGoogle Scholar

  • 69.

    Liefeldt L, Brakemeier S, Glander P, Waiser J, Lachmann N, Schönemann C, et al. Donor-specific HLA antibodies in a cohort comparing everolimus with cyclosporine after kidney transplantation. Am J Transplant 2012;12:1192–8.CrossrefGoogle Scholar

  • 70.

    Reinsmoen NL, Lai CH, Mirocha J, Cao K, Ong G, Naim M, et al. Increased negative impact of donor HLA-specific together with non-HLA-specific antibodies on graft outcome. Transplantation 2014;97:595–601.CrossrefGoogle Scholar

  • 71.

    O’Leary JG, Demetris AJ, Friedman LS, Gebel HM, Halloran PF, Kirk AD, et al. The role of donor-specific HLA alloantibodies in liver transplantation. Am J Transplant 2014;14:779–87.CrossrefGoogle Scholar

  • 72.

    Sellarés J, de Freitas DG, Mengel M, Reeve J, Einecke G, Sis B, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant 2012;12:388–99.CrossrefGoogle Scholar

  • 73.

    Ling XB, Sigdel TK, Lau K, Ying L, Lau I, Schilling J, et al. Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection. J Am Soc Nephrol 2010;21:646–53.CrossrefGoogle Scholar

  • 74.

    Kurian SM, Heilman R, Mondala TS, Nakorchevsky A, Hewel JA, Campbell D, et al. Biomarkers for early and late stage chronic allograft nephropathy by proteogenomic profiling of peripheral blood. PLoS One 2009;4:e6212.CrossrefGoogle Scholar

  • 75.

    Garcáa Moreira V, Prieto García B, Baltar Martín JM, Ortega Suárez F, Alvarez FV. Cell-free DNA as a noninvasive acute rejection marker in renal transplantation. Clin Chem 2009;55:1958–66.CrossrefGoogle Scholar

  • 76.

    Snyder TM, Khush KK, Valantine HA, Quake SR. Universal noninvasive detection of solid organ transplant rejection. Proc Natl Acad Sci USA 2011;108:6229–34.CrossrefGoogle Scholar

  • 77.

    Beck J, Bierau S, Balzer S, Andag R, Kanzow P, Schmitz J, 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;59:1732–41.CrossrefGoogle Scholar

  • 78.

    Oellerich M, Schütz E, Kanzow P, Schmitz J, Beck J, Kollmar O, 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;36:136–40.CrossrefGoogle Scholar

  • 79.

    Wieczorek G, Asemissen A, Model F, Turbachova I, Floess S, Liebenberg V, et al. Quantitative DNA methylation analysis of FOXP3 as a new method for counting regulatory T cells in peripheral blood and solid tissue. Cancer Res 2009;69:599–608.Google Scholar

  • 80.

    San Segundo D, Fernández-Fresnedo G, Gago M, Beares I, Ruiz-Criado J, González M, et al. Number of peripheral blood regulatory T cells and lymphocyte activation at 3 months after conversion to mTOR inhibitor therapy. Transplant Proc 2010;42:2871–3.Google Scholar

  • 81.

    Bouvy AP, Klepper M, Kho MM, Boer K, Betjes MG, Weimar W, et al. The impact of induction therapy on the homeostasis and function of regulatory T cells in kidney transplant patients. Nephrol Dial Transplant 2014;29:1587–97.CrossrefGoogle Scholar

  • 82.

    Dugast E, Chesneau M, Soulillou JP, Brouard S. Biomarkers and possible mechanisms of operational tolerance in kidney transplant patients. Immunol Rev 2014;258:208–17.CrossrefGoogle Scholar

  • 83.

    Newell KA, Asare A, Kirk AD, Gisler TD, Bourcier K, Suthanthiran M, et al. Immune Tolerance Network ST507 Study Group. Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest 2010;120:1836–47.Google Scholar

  • 84.

    Sagoo P, Perucha E, Sawitzki B, Tomiuk S, Stephens DA, Miqueu P, et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J Clin Invest 2010;120:1848–61.CrossrefGoogle Scholar

  • 85.

    Halloran PF, Helms LM, Kung L, Noujaim J. The temporal profile of calcineurin inhibition by cyclosporine in vivo. Transplantation 1999;68:1356–61.CrossrefGoogle Scholar

  • 86.

    Marquet P. Is pharmacokinetic or pharmacodynamic monitoring of calcineurin inhibition therapy necessary? Clin Chem 2010;56:736–9.CrossrefGoogle Scholar

  • 87.

    O’Reilly T, McSheehy PM. Biomarker development for the clinical activity of the mTOR inhibitor everolimus (RAD001): processes, limitations, and further proposals. Transl Oncol 2010;3:65–79.CrossrefGoogle Scholar

  • 88.

    Hartmann B, Schmid G, Graeb C, Bruns CJ, Fischereder M, Jauch KW, et al. Biochemical monitoring of mTOR inhibitor-based immunosuppression following kidney transplantation: a novel approach for tailored immunosuppressive therapy. Kidney Int 2005;68:2593–8.CrossrefGoogle Scholar

  • 89.

    Hartmann B, He X, Keller F, Fischereder M, Guba M, Schmid H. Development of a sensitive phospho-p70 S6 kinase ELISA to quantify mTOR proliferation signal inhibition. Ther Drug Monit 2013;35:233–9.Google Scholar

  • 90.

    Dieterlen MT, Bittner HB, Klein S, von Salisch S, Mittag A, Tárnok A, 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;82:151–7.Google Scholar

  • 91.

    Hoerning A, Wilde B, Wang J, Tebbe B, Jing L, Wang X, et al. Pharmacodynamic monitoring of mammalian target of rapamycin inhibition by phosphoflow cytometric determination of p70S6 kinase activity. Transplantation 2015;99:210–9.CrossrefGoogle Scholar

  • 92.

    Glander P, Hambach P, Braun KP, Fritsche L, Giessing M, Mai I, et al. Pre-transplant inosine monophosphate dehydrogenase activity is associated with clinical outcome after renal transplantation. Am J Transplant 2004;4:2045–51.CrossrefGoogle Scholar

  • 93.

    Fukuda T, Goebel J, Thøgersen H, Maseck D, Cox S, Logan B, et al. Inosine monophosphate dehydrogenase (IMPDH) activity as a pharmacodynamic biomarker of mycophenolic acid effects in pediatric kidney transplant recipients. J Clin Pharmacol 2011;51:309–20.CrossrefGoogle Scholar

  • 94.

    Raggi MC, Siebert SB, Steimer W, Schuster T, Stangl MJ, Abendroth DK. Customized mycophenolate dosing based on measuring inosine-monophosphate dehydrogenase activity significantly improves patients’ outcomes after renal transplantation. Transplantation 2010;90:1536–41.CrossrefGoogle Scholar

  • 95.

    Gensburger O, Van Schaik RH, Picard N, Le Meur Y, Rousseau A, Woillard JB, et al. Polymorphisms in type I and II inosine monophosphate dehydrogenase genes and association with clinical outcome in patients on mycophenolate mofetil. Pharmacogenet Genomics 2010;20:537–43.CrossrefGoogle Scholar

  • 96.

    Boleslawski E, Conti F, Sanquer S, Podevin P, Chouzenoux S, Batteux F, et al. Defective inhibition of peripheral CD8+ T cell IL-2 production by anti-calcineurin drugs during acute liver allograft rejection. Transplantation 2004;77:1815–20.Google Scholar

  • 97.

    Akoglu B, Kriener S, Martens S, Herrmann E, Hofmann WP, Milovic V, et al. Interleukin-2 in CD8+ T cells correlates with Banff score during organ rejection in liver transplant recipients. Clin Exp Med 2009;9:259–62.CrossrefGoogle Scholar

  • 98.

    Giese T, Zeier M, Schemmer P, Uhl W, Schoels M, Dengler T, 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;77:339–44.CrossrefGoogle Scholar

  • 99.

    Sommerer C, Konstandin M, Dengler T, Schmidt J, Meuer S, Zeier M, 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 2006,82:1280–5.CrossrefGoogle Scholar

  • 100.

    Sommerer C, Giese T, Schmidt J, Meuer S, Zeier M. Ciclosporin a tapering monitored by NFAT-regulated gene expression: a new concept of individual immunosuppression. Transplantation 2008;85:15–21.CrossrefGoogle Scholar

  • 101.

    Zahn A, Schott N, Hinz U, Stremmel W, Schmidt J, Ganten T, et al. Immunomonitoring of nuclear factor of activated T cells-regulated gene expression: the first clinical trial in liver allograft recipients. Liver Transpl 2011;17:466–73.CrossrefGoogle Scholar

  • 102.

    Billing H, Giese T, Sommerer C, Zeier M, Feneberg R, Meuer S, et al. Pharmacodynamic monitoring of cyclosporine A by NFAT-regulated gene expression and the relationship with infectious complications in pediatric renal transplant recipients. Pediatr Transplant 2010;14:844–51.CrossrefGoogle Scholar

  • 103.

    Konstandin MH, Sommerer C, Doesch A, Zeier M, Meuer SC, Katus HA, et al. Pharmacodynamic cyclosporine A-monitoring: relation of gene expression in lymphocytes to cyclosporine blood levels in cardiac allograft recipients. Transpl Int 2007;20:1036–43.CrossrefGoogle Scholar

  • 104.

    Sommerer C, Zeier M, Schnitzler P, Meuer S, Giese T. Pharmacodynamic monitoring of ciclosporin a reveals risk of opportunistic infections and malignancies in renal transplant recipients 65 years and older. Ther Drug Monit 2011;33:694–8.CrossrefGoogle Scholar

  • 105.

    Sommerer C, Zeier M, Czock D, Schnitzler P, Meuer S, Giese T. Pharmacodynamic disparities in tacrolimus-treated patients developing cytomegalus virus viremia. Ther Drug Monit 2011;33:373–9.CrossrefGoogle Scholar

  • 106.

    Sommerer C, Zeier M, Meuer S, Giese T. Individualized monitoring of nuclear factor of activated T cells-regulated gene expression in FK506-treated kidney transplant recipients. Transplantation 2010;89:1417–23.CrossrefGoogle Scholar

Article note:

Original German online version at: http://www.degruyter.com/view/j/labm.2014.38.issue-6/labmed-2014-0043/labmed-2014-0043.xml?format=INT. The German article was translated by Compuscript Ltd. and authorized by the authors.

About the article

Correspondence: Prof. Dr. med. Eberhard Wieland, Central Institute of Clinical Chemistry and Laboratory Medicine, Klinikum Stuttgart, Kriegsbergstr. 62, 70174 Stuttgart, Germany, E-Mail:

Received: 2014-11-13

Accepted: 2014-11-13

Published Online: 2015-04-18

Citation Information: LaboratoriumsMedizin, Volume 38, Issue 6, ISSN (Online) 1439-0477, ISSN (Print) 0342-3026, DOI: https://doi.org/10.1515/labmed-2015-0037.

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