This executive summary is an overview of the consensus report on Therapeutic Drug Monitoring (TDM) of tacrolimus-personalized therapy that synthesizes the key points and the grading of recommendations regarding tacrolimus exposure in different types of solid organ transplantation (SOT) and other indications, the influence of pharmacogenetic (PG) variables and pharmacodynamic (PD) biomarkers in achieving drug-specific target concentrations, and the role of pharmacokinetics (PK)/PG and PK/PD models in personal drug adjustment.
The new recommendations and knowledge regarding the previous consensus report in 2009 are highlighted. During the last decade, there have been many changes regarding the clinical management and TDM of tacrolimus. The goal of these changes was to minimize the adverse effects, mainly nephrotoxicity, and improve its effectiveness. Several studies have evaluated new target concentrations for tacrolimus significantly lower than those of the previous decade. In addition, these therapeutic ranges for tacrolimus have been refined based on post-transplant time, concomitant immunosuppressive medication, and according to immunological risk.
With the changes in tacrolimus monitoring, there is a requirement for highly standardized, specific, sensitive, and robust methods that allow for monitoring tacrolimus precisely, even at low concentration ranges (2–4 ng/mL). In addition, during this last decade, we have developed a new approach from a pharmacological perspective of monitoring therapeutic drugs that include not only the PK but also some PG and PD variables, since it is this combination that can provide a more personalized treatment. Efforts have been made to evaluate the influence of genetic polymorphisms on the initial dosing of tacrolimus and the subsequent clinical effects such as the incidence of rejections in various populations in SOT. Considering that similar concentrations of tacrolimus may produce different degrees of immunosuppression in treated patients, several groups have focused on evaluating the clinical usefulness of PD and immunological biomarkers predictive of the risk of graft rejection and clinical outcome.
Drug Exposure and Within-Patient Variability of Tacrolimus Concentrations as a Biomarker for TDM
Previous studies in SOT have shown a relationship between tacrolimus exposure and the risk of acute rejection and drug-related adverse events. The area under the concentration–time curve (AUC) is considered the PK exposure parameter best associated with clinical effects. Unfortunately, no prospective studies of clinical outcomes have been conducted in adult and pediatric transplant recipients to investigate properly the potential benefits of AUC0–12 monitoring compared with C0-guided therapy. However, C0 is used in most transplant centers for routine TDM of tacrolimus. The monitoring of tacrolimus AUC has been proposed especially in the early period after transplantation, from time to time to check the overall exposure, and when clinically indicated. Furthermore, the rather poor correlation between C0 and AUC translates into very variable AUC/C0 ratios, which means that patients with identical C0 may have very different AUC0–12h. Therefore, the authors suggest evaluating this ratio at least once in the early period and once in the stable period, for each transplant recipient.
Recent reports indicate that the within-patient variability of tacrolimus concentrations could be a useful tool for optimizing the immunosuppressive therapy in SOT. Most of the time, the within-patient variability is simply evaluated using the coefficient of variation (CV) of trough concentrations (C0). The hypothesis is that significant variability of tacrolimus concentrations may lead alternatively to underexposure and overexposure periods resulting in immune activations with subclinical rejections accumulation favoring organ lesions and drug toxicity associated with adverse events and organ damages. Furthermore, the intrapatient variability may identify patients with low adherence and patients with particular PK profiles. To date, none of the different tacrolimus formulations (immediate release, prolonged, or extended release) has been clearly reported to decrease PK variability of the drug when compared with the other.
In this consensus report, it is explained for the first time that the intrapatient variability must be evaluated; likewise, the monitoring of the C0/AUC ratio is proposed, to identify those patients who are good candidates to analyze AUC-Tac instead of C0.
Recommended Tacrolimus Target Concentration Ranges in Solid Organ Transplantation
Executive Summary on Tacrolimus Exposure Recommendations
In Kidney Transplant Recipients
- In immunological low-risk patients, tacrolimus may be targeted to a C0 of 4–12 ng/mL (and preferably to C0 >7 ng/mL) when prescribed in combination with IL-2 receptor (IL-2R) blocker induction therapy, mycophenolate, and glucocorticoids (A I); or a C0 of 4–7 (month 0–2) and 2–4 (>month 2) ng/mL when combined with everolimus and glucocorticoids and induction therapy (either Thymoglobulin or IL-2R blockers) (B II).
- Tacrolimus C0 targets may be higher in adult patients at higher immunological risks (B II).
- Although only supported by clinical experience, a C0 target range of 10–20 ng/mL may be proposed for pediatric patients (C1 II).
- A minimal AUC0–12h threshold of 150 ng·h/mL may be proposed for the twice-daily formulation in adults (B II). AUC targets corresponding to different C0 ranges were derived from AUC–C0 correlation studies in large adult patient populations, for the twice-daily and once-daily (Advagraf) formulations.
In Liver Transplant Recipients
- When prescribed in combination with mycophenolate or everolimus and corticosteroids, tacrolimus may be targeted to a C0 of 6–10 ng/mL during the first 4 weeks after transplantation and 5–8 ng/mL thereafter (A I).
- Tacrolimus as a monotherapy, or when only associated with induction treatment, may require a higher C0 target (10–15 ng/mL during the first 3 months after transplantation and 5–10 ng/mL afterward) (C1 II).
- A tacrolimus C0 of 10–15 ng/mL may also be aimed for in patients on a corticosteroid-free regimen (even beyond the fourth month after surgery) (C1 II).
For pediatric patients, there is not enough clinical evidence to make recommendations.
In Heart and Lung Transplantation
The C0 ranges recommended almost 20 years ago must be revised (and probably lowered), as they encompassed values between 15 and 20 ng/mL in the first weeks after transplantation, whereas recent studies have shown that the risk of acute kidney injury within the first 2 weeks after transplantation was significantly increased for C0 >15 ng/mL (B II).
In Bone Marrow Transplantation
A C0 of 10–20 ng/mL may be targeted when tacrolimus is prescribed orally in combination with methotrexate, in adults (B II) and in children (B II).
For the use of tacrolimus on other indications, there is not strong enough evidence to make recommendations (C2).
MEASUREMENT OF TACROLIMUS CONCENTRATIONS
This consensus report encompasses the advances made in the field of the analysis of tacrolimus concentrations, with a critical discussion of pros and cons for each method, including new monitoring strategies such as dried blood spots (DBSs) and intracellular tacrolimus concentrations. Stability of tacrolimus has been investigated using both patient blood samples and spiked whole blood samples. The stability of tacrolimus concentrations was proven for samples stored up to 14 days at 22°C or 4°C, as well as at least 1 month at −20°C and 1 year at −70°C.
The use of commercial whole blood-based tacrolimus calibrators is recommended to ensure accurate measurements and to support the harmonization of results between laboratories.
Analytical Methods to Determine Tacrolimus in Whole Blood
Analysis of tacrolimus in whole blood is performed either by immunoassays or by liquid chromatography–tandem mass spectrometry (LC-MS/MS), with both techniques being represented in nearly equal proportions at measurement service providers worldwide.
Most of the LC-MS/MS assays (∼75%) were multianalyte methods that allowed for simultaneous quantification of tacrolimus and other immunosuppressive drugs within one analytical batch, using a whole blood sample.
High analytical selectivity and sensitivity, and the possibility for high-throughput multianalyte assays are important benefits of LC-MS/MS. Thus, this technique has met the evolving clinical requirements for fast, accurate, and precise tacrolimus assays performing well at low concentrations. Nevertheless, skilled laboratory management and trained staff are necessary to establish and operate LC-MS/MS assays for routine TDM.
Tacrolimus is measured in whole blood. Protein precipitation, solid-phase extraction, and liquid–liquid extraction (LLE) have been reported as sample preparation strategies before chromatography in LC-MS/MS assays including tacrolimus. Precipitation of whole blood samples can be performed by either a stepwise or simultaneous addition of zinc sulfate solution and organic solvent.
Chromatographic separation is commonly based on C18 (or C8) stationary phases combined with mobile phases of water and methanol to which an acidified ammonium buffer is added. The mobile phase constituents should be of LC-MS quality.
Most tacrolimus LC-MS/MS assays apply electrospray ionization as the interface to get the sample compounds ionized and into gas phase. The conditions for spraying, evaporation, ionization, and acceleration of tacrolimus ions into the mass spectrometer have to be optimized with respect to the specific instrument type. Several of the ion source parameters are global in a multianalyte assay, and consequently, optimization of these parameters may have to be prioritized for compounds yielding the lowest instrument response at the lower limit of quantification (LLOQ) (usually sirolimus and everolimus when included in a multianalyte assay for immunosuppressive drugs).
First generation assays have been replaced by improved tests. Currently, a choice of immunoassays is available [enzyme-multiplied immunoassay technique (EMIT), electrochemiluminescence immunoassay (ECLIA), others] and the chemiluminescent microparticle immunoassay (CMIA) rapidly became a leader of immunoassay methodology for tacrolimus due to low bias versus chromatography, imprecision even better than LC-MS/MS and close agreement of results for clinical samples in proficiency tests.
Evaluation of the CMIA tacrolimus assay showed no interference with hematocrit, bilirubin, or total protein, but cross-reactivity yielding 94% with 31-O-desmethyl (M-II) and 45% with 15-O-desmethyl (M-III) tacrolimus metabolites. The cross-reactivity with 13-O-desmethyl (M-I) and 12-hydroxy (M-IV) tacrolimus metabolites was negligible.
NEW MONITORING STRATEGIES
Microsample-Based Tacrolimus Concentration Monitoring
The use of DBS on filter cards and other microsampling are innovative, minimal-invasive sample methods, which can replace traditional blood sampling for TDM of tacrolimus. This approach is patient-friendly and can be implemented at home by the patient themselves, collecting very small amounts of blood (typically 10–20 µL). The procedure is both cost and time saving, and it also allows for multiple sampling within a dose interval, simplifying the determination of AUC. Current challenges in microsample implementation include extraction recovery, the hematocrit effect, correlation between venous and capillary blood measurements, the quality of the blood spot, risk of contamination, and sample stability.
New generation microsampling such as volumetric absorptive microsampling (VAMS) can possibly mitigate some of the usual bias encountered with these methods, particularly the hematocrit effect.
Intracellular and Tissue Tacrolimus Concentration Monitoring
Determining tacrolimus drug concentrations where it exerts its immunosuppressive effect might be particularly relevant to achieve personalized therapy. Several groups have contributed to the development of analytical methods to measure tacrolimus concentrations in peripheral blood mononuclear cells (PBMC) of kidney, liver, and heart transplant recipients. The large variability reported in these studies highlighted the critical importance of preanalytical and analytical steps for intracellular concentration assays. Most analytical methods used liquid chromatography with tandem mass spectrometric detection, but there are several analytical issues that have not yet been fully addressed. These methods must be appropriately standardized, harmonized, and validated following international guidelines.
The association between CYP3A5 genotype and tacrolimus dose requirement is consistent and has been observed among kidney, liver, heart, and lung transplant recipients, both adult and pediatric. Although genotyping has proven effective in predicting the starting dose of tacrolimus, this has not influenced outcome of transplanted patients, provided appropriate use of TDM. As the clinical benefit of population PK (popPK) models is being investigated, the value of including CYP3A genotypes and potentially other genetic markers in such models may be re-evaluated.
Executive Summary and Practical Recommendations
- 1. Patients expressing CYP3A5 require approximately 50% higher tacrolimus dose to reach the target therapeutic range compared with nonexpressors (A I).
Although there is evidence from a randomized-controlled clinical trial that basing the tacrolimus starting dose on the CYP3A5 genotype may facilitate tacrolimus dosing, this has not been a universal finding, and there is currently no convincing clinical evidence that a PG-based approach to tacrolimus dosing improves clinical outcomes after SOT.
- 2. Of the many other candidate single nucleotide polymorphisms that have been studied, CYP3A4*22 seems to be the most promising because it explains residual variability in tacrolimus PK (B II, and C2 II, for whites, Asiatic, and African origin populations).
PD BIOMARKERS FOR TACROLIMUS MONITORING
Drug-Specific PD Biomarkers
Calcineurin Phosphatase Activity
The activity of CaN is determined in PBMC that requires cell isolation before the assay can be performed. An inverse relationship between CaN activity in PBMC and calcineurin inhibitor (CNI) concentrations in whole blood has been observed in patients after liver and kidney transplantation. Results from in vitro experiments indicate that, in contrast to cyclosporine, tacrolimus had a relatively high EC50, above the upper limit of the therapeutic range (20 ng/mL). This questions the relevance of CaN activity as a PD marker for tacrolimus' immunosuppressive effects.
Nuclear Factor of Activated T Cells–Regulated Gene Expression
The quantitative analysis of IL-2, IFN-γ, and granulocyte-macrophage colony-stimulating factor (GM-CSF) gene expression in whole blood is established to quantify the inhibition of the transcription of nuclear factor of activated T cells (NFAT)-regulated genes, based on samples collected at tacrolimus C0 and peak concentrations (1.5 hours after dose) after oral administration of tacrolimus.
NFAT-regulated gene expression has been performed in SOT such as kidney (adults and children), liver, heart, and lung transplant recipients, showing that monitoring the residual NFAT-regulated gene expression could identify allograft recipients at higher risk of infections or acute rejection.
The real-time polymerase chain reaction (PCR) technique provides a highly reproducible and sensitive tool and can be set up with satisfactory analytical performance in a routine molecular biological laboratory to be used in larger patient cohorts and in multicenter clinical studies. NFAT-regulated gene expression has the potential to develop into a monitoring tool complementing PK, especially in long-term renal allograft recipients.
Drug Non-Specific PD Biomarkers
Several studies have focused on the utility of intracellular expression of IL-2 and IFNγ as prognostic biomarkers for the risk of acute rejection, as diagnostic biomarkers at the time of rejection, and as markers reflecting the efficacy and the safety of tacrolimus. Flow cytometry and the enzyme-linked immunosorbent spot (ELISPOT) are the 2 mainly used methodologies. An ongoing randomized multicenter European study (Biodrim; Health F2-2012-305147) is currently evaluating the ELISPOT assay during tacrolimus minimization therapy to stratify renal transplant patients into low and high responders. In stable liver transplant recipients, IFN-γ expressing CD4+ and CD8+ T cells has been identified as surrogate markers for the risk of rejection after withdrawal of long-term immunosuppressive treatment.
For the validation of these biomarkers as early predictive biomarkers of the risk of rejection and graft clinical outcome, it is crucial to improve some methodological aspects and harmonize these functional assays.
Donor-Specific Anti–Human Leukocyte Antigen Antibodies
Today, donor-specific anti–human leukocyte antigen (HLA) antibodies (DSAs) and the subsequent development of antibody-mediated rejection (ABMR) are considered to be leading causes for graft loss. The development of standardized highly sensitive solid-phase assays for detecting anti-HLA antibodies has significantly improved the clinical utility. In kidney and heart transplantation, donor-specific anti-HLA antibodies (DSA) is a biomarker of underimmunosuppression, which may be caused by nonadherence but may also occur in both CNI-free and CNI-minimization clinical protocols. Most physicians agree that tacrolimus (with levels >5 ng/mL) is the CNI of choice in case of de novo DSA (dnDSA), although no data from prospective controlled multicenter studies are available.
Graft-Derived Cell-free DNA
Quantification of donor-derived cell-free DNA (dd-cfDNA) in recipient blood or urine has been evaluated as a potential surrogate biomarker of acute injury in the donor organ but lacks the specificity to distinguish between acute rejection and polyomavirus (BK) nephropathy injury. Plasma levels of dd-cfDNA have been correlated with allograft rejection and outcome in renal transplant recipients. In liver transplant recipients, it has been demonstrated that graft-derived cell-free DNA (GcfDNA or d-cfDNA) quantification could be used to assess the minimally effective trough concentrations of tacrolimus.
Although dcfDNA seems to be a promising biomarker for monitoring graft health after transplantation, multicenter, prospective, observational, and interventional studies will be required to better define how it can be used and evaluate its clinical utility before considering it a valid biomarker in SOT.
Executive Summary and Practical Recommendations
- Determination of residual NFAT-regulated gene expression supports the identification of renal transplant recipients at higher risk of acute rejection, opportunistic infections, malignancy, and cardiovascular risk (B II).
- Monitoring residual NFAT-regulated gene expression complements CNI PK as an adjunct to guiding CNI therapy (B III).
- Monitoring intracellular T-cell IFN-γ production (particularly by the enzyme-linked immune-spot, ELISPOT, assay) before and early after transplantation can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II).
PD monitoring of tacrolimus therapy has not entered routine monitoring yet. To advance in the process of validation of PD and immunologic biomarkers, it is crucial to improve and standardize methods. The clinical implementation of these biomarkers as a complement to tacrolimus TDM may have impact on patient and graft care.
Executive Summary and Practical Recommendations
- The utilization of popPK model-based Bayesian estimators has shown improved target achievement compared with standard TDM. Although trough concentration is used in most transplant centers for TDM of tacrolimus, there is some evidence that C0 correlates poorly with AUC0–12.
- The use of popPK model derived Bayesian estimators based on limited sampling strategies, however, provides AUC predictions with bias <5% and an imprecision <20%. This seems to be an applicable way to improve future tacrolimus TDM as compared to continue with standard trough concentration–based TDM, especially when considering home sampling with microsampling devices that currently are under validation (as presented above).
- The authors recommend the integration of CYP3A5*3 and CYP3A4*22 genotype information, when available, in future tacrolimus popPK models, primarily for the opportunity to optimize initial dosing.
- More PK-PD and PB/PK modeling activities are required to enhance the understanding of factors influencing clinical outcomes in transplantation.
Tacrolimus is among the most frequently used immunosuppressive drug in SOT.1,2 Building on 3 decades of experience and a large number of clinical trials, we have arrived at the current principles for the optimal use of this drug.3–6 These include personalization of the dosing by frequent measurements of whole blood concentrations, to apply target concentration ranges, which have gradually been set at lower levels, based on the results from multicenter trials that investigated various combinations of immunosuppressants.7–9 Although these immunosuppressive protocols have reduced the first-year incidence of biopsy-proven acute rejection (BPAR) in renal and liver transplant recipients to, respectively, 15% and 25% or lower, there is still room for improvement. Importantly, there is a range of adverse events that affect the quality of life and life expectancy of transplant patients who need life-long immunosuppression. At the same time, we are faced with large numbers of patients who lost their grafts because of ABMRs, most likely due to underimmunosuppression. Therefore, the search for methods that may reflect personal drug response, to further optimize and personalize tacrolimus dosing to obtain the lowest possible individual exposure, is still warranted.
In September 2017, the Immunosuppressive Drugs Scientific Committee (ISDs SC) of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology decided to prepare an updated consensus report and to discuss the most recent advances in TDM of tacrolimus-personalized therapy. The aim was to improve the standards of practice and to highlight the potential of new methods and principles that may provide individualized therapy and improve patient care.
Ten years ago, a consensus report on the optimization of tacrolimus was presented in this journal.10 This report pointed to the rather poor correlation between tacrolimus trough concentrations and outcome, especially with acute rejection, and recommended AUC measurements for more precise monitoring. Furthermore, because of a high degree of PK and PD between-patient variability, the use of PG and immunologic biomarkers should be considered and properly evaluated in prospective, multicenter clinical trials. To support PK dose individualization, limited-sampling strategies have been introduced, accompanied by popPK models or calculations using Bayesian forecasting.11,12 In the meantime, the use of mass spectrometry in assays to measure tacrolimus has taken over in routine clinical monitoring in many centers. This has led to a modification of practice for efficient processing of large numbers of samples,13 whereas some problems related to analytical specificity have also been eliminated.14 Furthermore, new assays have provided opportunities for measurement in other matrices, such as PBMC and DBS. It should be noted that to improve the prediction efficacy of monitoring, PD and specific immunological biomarkers strongly associated with the mechanism of action of tacrolimus have been assessed in several clinical trials.15
In this new document, the Expert Committee, consisting of 37 international experts in the fields of TDM of tacrolimus and its PG and biomarkers, presents a broad consensus on the current recommendations to achieve optimal personalization of tacrolimus therapy. The consensus is based on in-depth literature research and detailed Expert Committee discussions about pharmacology, PK monitoring (for once-daily and twice-daily formulations), analytical methods, standardization, and new TDM approaches. Calcineurin (CaN) phosphatase activity measurement and a new concept for PD monitoring of CNIs, NFAT-regulated gene expression, and nonspecific PD biomarkers (intracellular cytokines and chemokines production) have also been introduced and documented for tacrolimus, and their potential as a supplement to blood concentration measurements is discussed. With respect to PG, the significance of CYP3A5 variants has been investigated in detail in kidney, liver, heart, and lung transplant recipients, and several popPK models including CYP3A5 genotype have been developed for tacrolimus. The potential contribution of other genetic factors, such as CYP3A4*22 and the efflux transporter adenosine triphosphate-binding cassette subfamily B member 1 (ABCB1 gene), is summarized.
These recommendations and evaluations are outlined for all discussed topics. Of note, recommended tacrolimus target concentrations are defined for each type of organ transplant. Moreover, for PD biomarkers and PG, a systematic weighing of the quality of evidence and strength of recommendations according to the Grading of Recommendations Assessment Development and Evaluation was performed (Table 1).1,2 Furthermore, this consensus document will incorporate the recommendations concerning the clinical utility of combining tacrolimus PK TDM with PG and PD biomarkers to better prevent acute rejection, subclinical rejection, drug-related adverse events, and graft dysfunction.
This consensus report will support all professionals involved in the management of patients treated with tacrolimus in transplantation and other clinical settings and aims to improve both standards of practice in pharmacological tacrolimus TDM (PK/PG/PD) and personalized patient care.
Mechanism of Action
Tacrolimus (known also as FK-506) binds to an immunophilin FK506-binding protein (FKBP) that constitutes the main therapeutic mechanism.16 The tacrolimus–FKBP complex inhibits the activity of CaN, a serine threonine phosphatase, which plays an important role in interleukin 2 (IL-2) promoter induction after T-cell activation.17,18 This process inhibits the translocation of a family of transcription factors of activated T cells. It leads to reduced transcriptional activation of cytokine genes for interleukins (IL-2, IL-3, IL-4, and IL-5), tumor necrosis factor α (TNF-α), interferon-gamma (IFN-γ), and GM-CSF. Finally, proliferation of T lymphocytes is reduced.19,20
Indications (Registered and Off-Label)
The approved indications for tacrolimus vary by country and formulation. With regard to organ transplantation in adult and pediatric patients, the originator's immediate release or intravenous formulations obtained market approval in the United States for the prophylaxis of organ rejection in patients receiving allogeneic liver, kidney, or heart transplants. In Europe, it is approved also for the treatment of allograft rejection resistant to treatment with other immunosuppressive medicinal products. In Japan, where the drug was developed, additional approvals were granted for lung, pancreas, allogeneic small bowel transplants, as well as for the prophylaxis of graft rejection and graft-versus-host disease (GVHD) in bone marrow transplantation. In Latin America, it is also approved for the treatment of severe and mild acute rejection and the prevention of GVHD.
The indications for prolonged or modified release formulations are somewhat more restrictive (except Graceptor in Japan): in the United States, Astagraf is only approved for the prophylaxis of organ rejection in kidney transplant patients in combination with other immunosuppressants. Envarsus has the additional restriction that patients should be converted from tacrolimus immediate-release formulations. In Europe, Advagraf and Envarsus are approved for kidney and liver transplantation but not for heart transplantation, as well as for the treatment of allograft rejection, but only in adults.
Tacrolimus has also been registered for a variety of other nontransplant indications across countries. In Europe, Protopic is registered for the treatment of eczema by topical administration on the skin. In Japan, oral tacrolimus is approved for the treatment of interstitial pneumonia associated with polymyositis and dermatomyositis. In Uruguay, it is indicated for refractory rheumatoid arthritis, refractory atopic dermatitis, and refractory uveitis.
Interestingly, regional indications become off-label use in the other parts of the world, such as hematopoietic stem cell, lung, or small bowel transplantation, to which one can add rare composite tissue transplantations (face, hand, arm, etc.) or refractory autoimmune diseases, not only those approved in Uruguay or Japan, but also the more common psoriasis, lupus nephritis, inflammatory bowel diseases, etc.
PK of Tacrolimus
Oral tacrolimus is rapidly absorbed, with a peak concentration attained within 0.5–1 hour, but it has a flat absorption profile in some liver transplant recipients with absorption that seems to be independent of bile.21,22 The poor dissolution of tacrolimus in gastric juices, administration with food, erratic gastrointestinal motility, extensive presystemic metabolism by CYP3A enzymes in the gut wall and liver, and activity of the efflux-pump P-glycoprotein (P-gp, encoded by, ABCB1 gene) are relevant factors that contribute to the variability in absorption. The mean oral bioavailability is 25%.22
Binding of tacrolimus to red blood cells (RBCs, about 95%) is concentration-dependent.23 In plasma, it is approximately 99% bound to plasma proteins such as α1-acid glycoprotein, albumin, and for a very small part to lipoproteins. Blood: plasma tacrolimus concentration ratios range from 13 to 114 (mean = 15) and are dependent on hematocrit, plasma protein, and tacrolimus concentration.23
Tacrolimus is metabolized mainly by CYP3A4 and CYP3A5 in liver and gut wall, with a minimal contribution of CYP3A7.24 Up to 15 metabolites are formed by monodemethylation and didemethylation, hydroxylation, and to some degree a combination of demethylation and hydroxylation. The major metabolites of tacrolimus are the 13-O-desmethyl tacrolimus (M-I) and the 15-O-desmethyl tacrolimus (M-III). 13-O-desmethyl tacrolimus (M-I) has 10% of the activity of tacrolimus, whereas the 31-O-desmethyl tacrolimus (M-II) has similar activity to the parent drug but the concentrations are low-to-not-detectable in patients.25,26 Tacrolimus is highly lipophilic, has a low clearance, and approximately 95% of its metabolites are eliminated by the biliary route. Less than 1% of unchanged tacrolimus is eliminated by urinary and biliary routes.27 The terminal elimination half-life of tacrolimus has a range between 4 and 41 hours.22
Using PK modeling as described in a separate section later in this article, apparent clearance and central volume of distribution as 24.0–28.5 L/h and 70.6–158.2 L, respectively, have been reported for adult kidney transplant recipients.28CYP3A5 genotype, weight, hematocrit, and postoperative day were identified to affect tacrolimus clearance. The between-individual variability for clearance and central volume of distribution was 54% and 110%.28 In another study, the mean between-occasion variability in tacrolimus clearance was 17% at 6–12 months after renal transplantation.29 Circadian variation with tacrolimus PK was reported, with a lower bioavailability at night30, but a lack of circadian variability, either with early or maintenance tacrolimus therapy, has also been reported.31
Clearance is 2-fold higher after pediatric liver transplantation, with a shorter terminal half-life (11.5 ± 3.8 hours).32 In pediatric liver transplantation, a linear increase of tacrolimus clearance up to 21 days after surgery was reported.33 Thereafter, tacrolimus clearance decreased up to a period of 1 year after transplantation.34 The apparent clearance decreased with time after transplantation because of an increase in hematocrit and albumin.35 In pregnancy, an apparent decrease in α1-acid-glycoprotein, albumin, and RBC resulted in a 39% increase in the total tacrolimus clearance and 100% increase of the unbound fraction of tacrolimus, compared with the postpartum period.36
Temporary elevation of tacrolimus trough concentrations (about 2-fold and more) has been observed in patients with diarrhea.37,38 This may be because intestinal Pgp activity is impaired in patients with persistent diarrhea, leading to increased bioavailability.39
Reduction in the dose of corticosteroids, from the early post-transplant months to 1 year after transplantation, contributes to a decrease in tacrolimus clearance, facilitating a reduction in the tacrolimus dose.35,40 Variability of tacrolimus exposure in African American patients compared with white and Asian patients has been reported and is attributed to ethnic differences in CYP3A5 and P-gp.41 In the section on PG in this review, the past, present, and future of PG testing for tacrolimus will be discussed.
Drug-Drug and Drug–Food Interactions
Compounds which affect CYP3A enzyme activity or P-gp–mediated transport may influence tacrolimus concentrations in blood. Drug-drug interactions of clinical importance are summarized in Table 2, based on reviews.42,43 Drugs that are known inhibitors of CYP3A4/5/7 include calcium antagonists, macrolide antibiotics, and others as listed in Table 2. One group of drugs that have demonstrated profound inhibition of CYP3A enzymes is the triazole antifungals.44,45 In individual cases, large increases in tacrolimus exposure have been reported, as exemplified by voriconazole.46 At initiation of therapy with these drugs, an immediate tacrolimus dose reduction is recommended, as maintaining the tacrolimus dose and waiting for a tacrolimus concentration after a couple of days are likely to cause significant toxicity.
More recent reviews have focused on potent interactions between tacrolimus and the antiretroviral agents used for the treatment of HIV-infected transplant recipients. Among these drugs are classes that inhibit and induce, respectively, the CYP3A enzymes and the P-gp transporter. This knowledge is also important for the selection of the most appropriate anti-HIV regimen to combine with immunosuppressive treatment.47
The direct-acting antiviral agents for Hepatitis C Virus infection can also produce drug interactions with tacrolimus.48–51 Considerable modifications of tacrolimus dose have been recommended for combination with other regimens of the direct acting antivirals against Hepatitis C Virus.
By contrast, as listed in Table 2, drugs that are known inducers of CYP3A include rifampin, antiepileptic drugs, some HIV antivirals, and importantly also the glucocorticoids.40,52 These drugs enhance CYP3A activity, increase the rate of tacrolimus metabolism, and lead to a decrease of tacrolimus concentration that if passing unnoticed may fall below the recommended therapeutic range, which would put a patient at risk for graft rejection. If in the first months after kidney transplantation, the prednisolone dose is gradually tapered, and this affects tacrolimus exposure. In this setting, a rise in serum creatinine as a result of increasing tacrolimus concentrations may be misinterpreted as a rejection after corticosteroid tapering.
The consumption of solid food when taking oral doses of tacrolimus can decrease both the rate and extent of drug absorption from the gastrointestinal tract.53 This food effect is most pronounced after a high-fat meal.54 Therefore, the drug label recommends taking tacrolimus on an empty stomach, or at least 1 hour before or 2–3 hours after a meal. Food–drug interactions with tacrolimus include grapefruit and grapefruit juice.55 St. John's wort is a known inducer of the CYP3A56 and has been shown to decrease tacrolimus exposure in renal transplant recipients.57
Consequently, foods and drugs that are known to alter the activity of drug metabolizing enzymes should be used with caution during tacrolimus therapy, and the use of herbal medications should be avoided. The use of concomitant medications with tacrolimus may put a patient at risk of toxicity or subtherapeutic blood concentrations.43
Galenic Formulations and Generics
Tacrolimus is now also available in immediate or modified release pharmaceutical forms for oral administration (tablet or capsule). The Advagraf prolonged release once-daily formulation was developed to increase treatment adherence, ultimately leading to better prevention of graft rejection. Recently, a new prolonged-release tacrolimus formulation named Envarsus was developed using so-called “solid solution” delivery technology. Envarsus presents higher bioavailability and as a consequence needs to be administered in comparably lower doses based on 0.75-mg, 1-mg, and 4-mg tablets (correction factor of 0.7). A potential benefit of this formulation is that less-fluctuating tacrolimus concentrations have been observed.58
Tacrolimus is a narrow therapeutic index drug. Several generic formulations for Prograf have been registered since patent protection expired. A list of tacrolimus brand names worldwide is currently counting 258 products, all formulations included and mostly due to the large number of immediate release generics.59 The lack of worldwide harmonization in the registration of generic drugs, especially with respect to the criteria for bioequivalence, has resulted in regional differences in the availability of tacrolimus generics. For the once-daily tacrolimus formulations, patent protection was still in place at the time when the present document was written, and generic formulations based on these technologies had not entered the market yet.
Part of the transplant community has been, and still is, concerned that the PK properties of generic tacrolimus formulations may be too different from those of the innovator product, in all or in subgroups of special populations such as patients with cystic fibrosis or elderly patients.60 Various national or international transplant societies have published recommendations regarding the implementation of generic immunosuppressive drugs in the transplant field.61,62 Typically, they recommended caution with the use of generic immunosuppressive drugs until more robust clinical data are available, and adequate regulatory safeguards are instituted. One of the main concerns was that bioequivalence demonstrated in healthy volunteers may not be representative of all transplant recipients. In the literature, there are a large number of published studies on comparisons between one or more generic formulation and the Prograf formulation.63 Endpoints of these studies often include PK parameters, including bioequivalence. Some studies also include clinical endpoints, such as acute rejection or renal function. For the latter endpoints, the sample size is often too small, and the confidence intervals are too wide to conclude whether the substitution to generic formulations is safe or unsafe. For studies that focused on PK endpoints, a major problem is the higher variability in transplanted patients compared with healthy volunteers. In registration studies using healthy volunteers, standardized conditions in a specialized research unit are the norm. However, studies in transplant patients are typically subject to a less-controlled research environment with ensuing variability. One of the clear exceptions is the prospective, replicate dosing, partially blinded, randomized, 3-treatment, 6-period crossover bioequivalence study that was conducted at the University of Cincinnati in patients with kidney (n = 35) or liver transplant (n = 36). In this study, it was concluded that the bioequivalence demonstrated for tacrolimus in healthy volunteers also translates to those receiving a kidney or liver transplant.64 In contrast to this population with a median age of 52 years for kidney and 57 years for liver transplant recipients, another study included 28 Norwegian renal transplant recipients with median age of 69 years (range 60–78 years); in this study, bioequivalence criteria were not met.65 Another example is the immediate-release generic Limustin which in one study including pediatric renal transplant recipients demonstrated very low bioavailability compared with the originator formulation (Prograf).66 Further experiments by these authors showed that the pharmaceutical characteristics of this generic was dissimilar to the original and likely explained the reduced tacrolimus exposure in children.
It is unlikely that large prospective trials comparing generic and innovator tacrolimus formulations in newly transplanted patients will be conducted.67 We consider generic tacrolimus as an attractive therapeutic option, especially in de novo transplant patients. As a standard of practice, the treatment should be guided by TDM, and simultaneously controlled for safety and efficacy. Conversions from innovator drug to generic or from one generic to another generic version need to be performed under careful monitoring of drug exposure and only after adequate instructions to the patient. In view of the risk of mistakes caused by patient confusion, it is important to limit the number of conversions from one formulation to another as much as possible.
Evidence-Based PK Monitoring for Tacrolimus in Specific Clinical Situations
Relationships between tacrolimus exposure and treatment outcomes have been amply reported. The AUC can be regarded as the exposure metrics best associated with tacrolimus clinical effects, but no prospective study has been conducted in adult or pediatric transplantation to investigate the potential benefits on clinical outcomes of between-dose AUC monitoring over therapy guided by trough concentrations, C0 (ie, concentration in samples drawn immediately before dose). C0 is much easier to obtain and used in most transplant centers for routine TDM. The monitoring of tacrolimus AUC has been proposed especially in the early period after transplantation to check the time evolution of the overall exposure and when clinically indicated (eg, to investigate suspected tacrolimus adverse effects or to guide immunosuppression minimization). Furthermore, in different conditions, the AUC/C0 ratio is influenced by the post-transplantation period, the CYP3A5*3 genotype,68 and can be drastically modified in patients with decreased intestinal motility or decreased absorption (eg, diabetes mellitus, ileus, cystic fibrosis, bariatric surgery, gastrectomy, and colectomy), in patients exposed to strong PK drug-drug interactions (eg, on anti-HIV, azole antifungal drugs), or in patients with other sources of tacrolimus metabolism deficiency. The rather poor correlation between C0 and AUC69 translates into very variable AUC/C0 ratios, which means that patients with identical C0 may have very different between-dose AUC. Interestingly, tacrolimus AUC0–24h correlated better with C24h than C0 for both twice-daily and once-daily tacrolimus, and AUC0–12h correlated better with C12h than C0 for twice-daily tacrolimus. The authors concluded that C0 can only be a correct proxy of the overall exposure if blood sampling is perfectly timed.68 However, this ratio was found to be very stable with time in individuals, and the authors suggested evaluating this ratio at least once in the early period (first month) and once in the stable period (after 3 months) for each transplant recipient.70
This section summarizes the updated recommendations for tacrolimus exposure in each type of SOT as well as in other clinical applications.
PK Monitoring in Kidney Transplantation
Tacrolimus Exposure, Efficacy, and Toxicity
Since the previous consensus paper on tacrolimus TDM,10 only a few studies have investigated the relationships between tacrolimus exposure and the risk of acute rejection or toxicity. In the large Genomics of Deterioration of Kidney Allograft Failure (DeKAF) study in adult patients over the first 6 months after transplantation, each 1 ng/mL decrease in tacrolimus C0 was associated with a 7.2% increased risk of acute rejection (P = 0.03).71 A smaller study in low-immunological risk patients without steroids and with a moderate dose of mycophenolic acid concluded that C0 should be maintained >7 ng/mL during the first year after transplantation.72 However, a pooled analysis of 3 other randomized, controlled clinical trials found no relationship between tacrolimus C0 and BPAR.73 Two rather old and small observational studies, respectively, showed a significant association between tacrolimus AUC0–12h and acute rejection, with mean values of 157 ng·h/mL in patient with AR and 215 ng·h/mL in patients without74; and efficacy thresholds to avoid BPAR of 150 ng·h/mL for tacrolimus AUC0–12h and 45 mg·h/L for mycophenolic acid AUC0–12h.75
Evidence is even more limited in pediatric patients. A retrospective study of 58 children showed that a lower frequency of chronic kidney disease and decreased graft function was observed when C0 was maintained at ≥10 ng/mL in the first 3 months after transplantation.76
Tacrolimus exposure has been more consistently associated with the incidence of adverse events,77,78 but overall, the studies which investigated the relationship between tacrolimus exposure and the risk of AR and toxicity were often retrospective and/or included limited numbers of patients and/or involved co-medications different from those used nowadays.
Recommended Tacrolimus Target Concentration Ranges
Based on the current evidence, it is not possible to recommend a single target tacrolimus concentration range.4 Rather, the tacrolimus target exposure has been defined empirically and depends on the perceived risk of acute rejection, the time after transplantation, and the comedication used. In the United States and in Europe, tacrolimus combined with mycophenolate is the backbone of immunosuppressive drug protocols. In most centers, patients also receive induction therapy (either T-cell–depleting antibody therapy or IL-2R blockers) and glucocorticoids.79 The best evidence for such regimens was provided by the SYMPHONY randomized, controlled clinical trial,3 which showed that 75% of the low-immunological risk patients in the tacrolimus arm (which performed best) had a tacrolimus C0 between 4 and 11 ng/mL in the first month after transplantation, between 4 and 10 ng/mL later on, and between 4 and 9 ng/mL between 6 and 12 months.9 Still, as mentioned above, a more recent study advised against C0 <7 ng/mL in a similar low-risk population.72 Also, different targets may be aimed for when tacrolimus is combined with T lymphocyte–depleting antibody therapy or in higher risk patients.
Tacrolimus in combination with everolimus therapy was investigated in the TRANSFORM randomized, controlled clinical trial.80 It showed that in transplant recipients at mild to moderate immunological risk, everolimus plus low-exposure tacrolimus [target C0 of 4–7 ng/mL (months 0–2), 2–5 ng/mL (months 3–6), and 2–4 ng/mL thereafter] is noninferior to mycophenolate plus standard-exposure tacrolimus (target C0 of 8–12, 6–10 and 5–8 ng/mL, respectively) for a binary composite end point assessing immunosuppressive efficacy and preservation of graft function.80
There has been no new study in support of tacrolimus exposure targets in pediatric kidney transplant recipients. As a reminder, the recommendations are to start with an initial tacrolimus dose of 0.15 mg/kg twice a day, to achieve C0 concentrations between 10 and 20 ng/mL during the first 2 months after transplantation and between 5 and 10 ng/mL thereafter.10
Concerning between-dose AUC targets, a minimal threshold of approximately 150 ng·h/mL in adult kidney transplant recipients emerges from the only 2 studies published.74,75 However, based on exposure measurement in very large numbers of patients on twice-daily69 or once-daily70 tacrolimus formulations, between-dose AUC target ranges were derived from the different C0 targets proposed for low-, standard-, and high-risk patients. For twice-daily tacrolimus, the corresponding AUC0–12h/C0 ranges proposed were 75–140 ng·h/mL for 3–7 ng/mL; 100–190 ng·h/mL for 5–10 ng/mL; 140–210 ng·h/mL for 8–12 ng/mL; and 180–270 ng·h/mL for 10–15 ng/mL.69 For once-daily tacrolimus (Advagraf), the corresponding AUC0–24h target ranges proposed were 150–275 ng·h/mL for C0 3–7 ng/mL; 180–350 ng·h/mL for 5–10 ng/mL; 260–400 ng·h/mL for 8–12 ng/mL; and 310–475 ng·h/mL for 10–15 ng/mL.70 Importantly, for a given C0 range, 3 tighter time-adjusted AUC ranges were proposed (for 0–3 months, 3–12 months and >12 months after transplantation) because the correlation between AUC and C0 changes over the first 12 months after transplantation for both formulations, because of the natural decrease in tacrolimus apparent clearance over this period. The AUC ranges proposed above correspond to the combination of the 3 time-adjusted ranges.
PK Monitoring in Liver Transplantation
Tacrolimus Exposure, Efficacy, and Toxicity
AUC is considered to be the PK exposure index best associated with clinical effects. Therefore, the monitoring of tacrolimus AUC has been proposed, when clinically indicated, but routine monitoring of tacrolimus C0 concentrations is usual practice for outpatients.6 In case of combination therapy of tacrolimus with corticosteroids and either mycophenolate or everolimus, a tacrolimus C0 of 6–10 ng/mL should be targeted from day 1 and over the first 4 weeks of treatment, followed by a target of 5–8 ng/mL.7,8,81–86 Waiting 6 weeks before targeting 5–8 ng/mL in combination with mycophenolic acid may be considered as an alternative because the evidence in favor of the target change at 4 weeks is weak. With such immunosuppressive regimen, C0 >10 ng/mL seem to offer little additional advantage in terms of reduced allograft rejection but result in a higher rate of renal dysfunction.85 Also, C0 <5 ng/mL should be avoided, mainly during the first month, because they may lead to worse long-term outcomes.82 Tacrolimus given as monotherapy or only associated with induction treatment may require higher C0 targets (10–15 ng/mL during the first 3 months after transplantation and 5–10 ng/mL afterward), although there is some evidence in favor of minimized targets with such regimens too.87–90 Similarly, higher targets (10–15 ng/mL even beyond the fourth month after surgery) may be aimed for in case of corticosteroid-free treatment. In general, however, C0 value >15 ng/mL should be avoided.91 Liver function usually becomes stable 3 weeks after transplantation. TDM after this period may therefore be reduced in frequency beyond this point in time, if patient condition and C0 levels are stable.
Although there are limited data regarding optimal C0 target range(s) in pediatric liver transplant patients, a retrospective study of 72 pediatric patients aged 0.5–17.6 years indicated that adverse events such as nephrotoxicity were associated with higher tacrolimus C0 during maintenance therapy (median tacrolimus C0 in patients with or without adverse events: 8.2 and 4.8 ng/mL, respectively).92
PK Monitoring in Thoracic Transplantation
Tacrolimus Exposure, Efficacy, and Toxicity
Data on tacrolimus PK and TDM in thoracic transplantation are limited. All studies were conducted in <25 adult patients, except for 1 in 45 heart transplant93 and another in 78 lung transplant recipients.94
Tacrolimus dose after thoracic transplantation is usually adjusted based on C0 levels, mainly targeting the ranges proposed almost 20 years ago for kidney and liver transplant recipients: 15–20 ng/mL for the first 2 months, 10–15 ng/mL from months 3–6, and 8–10 ng/mL after 6 months following heart transplantation5; and 10–25 ng/mL for the first 2 weeks, 10–20 ng/mL for the next 6–10 weeks, and 10–15 ng/mL thereafter in lung transplantation.95 These C0 targets were never formally revised. Moreover, as these targets have not been associated with any type of assay for blood concentration measurement, they should be interpreted and implemented in routine clinical practice with caution, and further research is needed to refine them.96 Two recent studies explored the relationship between tacrolimus C0 and acute kidney injury, in heart97 and in lung98 transplantation, respectively, and showed that the risk of acute kidney injury within the first 2 weeks after transplantation was significantly increased for C0 >15 ng/mL.
As in other SOTs, tacrolimus AUC rather than single concentrations may be used for TDM. A wide range of correlation coefficient values between C0 and AUC0–12 have been reported.99–102 Only one study in heart transplantation has evaluated the relationship between AUC and outcome.101 The AUC0–12 after the first oral dose was significantly lower in patients who experienced acute rejection compared with those who did not (71 versus 168 h·ng/mL, P = 0.012), but no information was provided on when acute rejection occurred. Unfortunately, no prospective studies have been conducted so far in thoracic transplantation to compare different AUC0–12 targets, or to compare AUC monitoring with C0 monitoring.96
PK Monitoring in Bone Marrow Transplantation
Tacrolimus Exposure, Efficacy, and Toxicity
GVHD remains one of the main causes of treatment-related mortality after allohematopoietic stem cell transplantation (HSCT). The concomitant administration of a CNI (tacrolimus or cyclosporine) and short-term methotrexate is one of the standard regimens used to prevent GVHD.
Randomized controlled trials in both adult and pediatric patients have suggested that the combination of methotrexate and tacrolimus may be superior to methotrexate and cyclosporine to reduce acute-GVHD (aGVHD).103–106 Tacrolimus is generally administered by continuous IV infusion beginning on the day before allogeneic transplantation at a dose of 0.03 mg/kg/d.107–109 When patients are able to tolerate oral administration, the factor used for tacrolimus dose conversion from intravenous to oral administration varies between 1:3 and 1:5.109 In most clinical trials and retrospective studies, the C0 target concentration was between 10 and 20 ng/mL.108,110,111 A retrospective cohort study of 120 consecutive adult patients undergoing first allogeneic HSCT found a lower risk of grade 2–4 aGVHD in patients with tacrolimus mean C0 of >12 ng/mL over the first week after grafting, whereas lower tacrolimus concentrations at weeks 2, 3, and 4 were not associated with a higher incidence of aGVHD.112 Tacrolimus C0 target ranges of 10–30 or 10–40 ng/mL have also been reported,113,114 but the incidence of nephrotoxicity was very high113 and significantly increased for C0 >20 ng/mL.114
Few studies have evaluated tacrolimus concentration targets in children undergoing HSCT. A Japanese retrospective study of tacrolimus combined with methotrexate (97 children aged 0.4–18 years) found that mean tacrolimus concentrations ≤7 ng/mL during continuous infusion over the first 4 weeks (ie, not really representative of the steady-state concentration) were associated with an increased risk of aGVHD and poorer survival.115 Another retrospective study, where tacrolimus was combined with mycophenolic acid (60 children aged 0.4–21 years) found that a mean tacrolimus concentration <10 ng/mL during week 3 was associated with increased incidence of aGVHD.116
PK Monitoring in Other Diseases Where Tacrolimus is Prescribed
Tacrolimus is also prescribed (off-label in most cases) for several autoimmune diseases.
A topical tacrolimus ointment is effective against atopic dermatitis (or atopic eczema).117 It did not lead to relevant systemic tacrolimus exposure in adults,118 in children aged 2–17 years,119 or in infants aged younger than 2 years.120 In adults, 94% of tacrolimus blood concentrations were <1 ng/mL, and the highest value at any time point in any patient was 1.38 ng/mL.118
A small proof-of-concept, noncomparative study investigated the safety and efficacy of sequential therapy with short-term oral tacrolimus to achieve rapid disease control followed by maintenance with topical tacrolimus 0.1% ointment in the treatment of severe atopic dermatitis.121 Over the first 3 weeks of the study, all patients received oral tacrolimus as monotherapy. Topical tacrolimus was then added to the oral treatment in weeks 4–6. After week 6, patients were treated with only topical tacrolimus. Clinical improvement was noted in about 2/3 of the patients. The average whole blood tacrolimus levels were 8.1 ng/mL at week 1, 7.7 ng/mL at week 3, and 5.5 ng/mL at week 6.
In patients treated with tacrolimus after renal transplantation, remarkable improvements in incidental psoriasis have been noted.122 Tacrolimus may be more suited than cyclosporine to a patient population with increased cardiovascular risk.123 However, there is no evidence in favor of a tacrolimus concentration target range for this indication. Topical tacrolimus has been suggested as a suitable treatment for more localized psoriasis, such as facial, genital, and intertriginous psoriasis,124 requiring no TDM since systemic drug exposure has been shown to be low.
The standard of care for the induction treatment of proliferative lupus nephritis remains mycophenolate mofetil and cyclophosphamide. CNIs are only recommended as a second-line alternative given their side effects, especially in Asian populations.125 However, a Bayesian network meta-analysis found that a maintenance treatment with tacrolimus in patients with biopsy-proven lupus nephritis class III, IV, or V yielded the best chance of preventing renal relapse as well as the lowest risk of withdrawals because of adverse events and leukopenia, as compared to mycophenolate mofetil, azathioprine, and cyclophosphamide.126 Tacrolimus dose was titrated to achieve a C0 of 4–6 ng/mL in 34 Chinese patients,127 and a C0 of 6–10 ng/mL in the first and second month and 4–8 ng/mL thereafter in a Thai study.128
In pediatric patients, a single-center clinical study found that once-daily tacrolimus at 3 mg/d, resulting in C0 between 1.5 and 7.5 ng/mL, improved the serological parameters, the lupus activity index, and reduced the need for steroids in patients aged 9–25 years. However, no significant relationship between treatment effect and blood concentration was noted over the 2-year study period.129
A meta-analysis of 23 clinical studies, all performed in Asian populations, showed that in most studies, tacrolimus dose was titrated to achieve C0 values of 5–10 ng/mL in the first and second months, and 4–7 ng/mL thereafter.130 Still, the “optimal” tacrolimus blood trough concentration has yet to be determined, and the targets used have largely been chosen to avoid toxic effects.131 Finally, the positive results of tacrolimus in Asian patients cannot be extrapolated to other ethnicities.
Inflammatory Bowel Disease
In a recent review, tacrolimus and infliximab seemed to be equally safe and effective in the short-term treatment of active ulcerative colitis.132 Tacrolimus oral dose was generally adjusted to achieve C0 levels of 5–10 ng/mL thereafter. In a study of 65 patients with moderate to severe active ulcerative colitis, 2 target tacrolimus concentrations (5–10 and 10–15 ng/mL) and a control group on placebo were compared double-blindly.133 Clinical improvement was observed significantly more often in the tacrolimus high-target range group. Bruns et al134 recommended frequent monitoring of whole blood tacrolimus concentrations because efficacy and toxicity are dose-dependent.
Tacrolimus has also been investigated in pediatric patients with inflammatory bowel disease. A prospective multicenter trial including 13 pediatric patients aged 6–20 years with oral tacrolimus started at a dose of 0.1 mg/kg twice-daily and subsequently adjusted to achieve C0 concentrations between 10 and 15 ng/mL found that 69% of patients responded to oral tacrolimus within 14 days.135 A single-center, retrospective study of 18 pediatric patients aged 1–16 years, given tacrolimus at a starting dose of 0.1 mg/kg/dose twice-daily and subsequently adjusted to a target C0 of 10–15 ng/mL for the first 2 weeks and 7–12 ng/mL thereafter, concluded that in patients with steroid-resistant colitis, tacrolimus was capable of inducing short- to medium-term remission but yielded no long-term benefit.136
Executive Summary on Tacrolimus Exposure Recommendations
In Kidney Transplant Recipients
- In low-immunological risk patients, tacrolimus may be targeted to a C0 of 4–12 ng/mL (and preferably to C0 >7 ng/mL) when prescribed in combination with IL-2R blocker induction therapy, mycophenolate, and glucocorticoids (A I); or a C0 of 4–7 (month 0–2) and 2–4 (>month 2) ng/mL when combined with everolimus and glucocorticoids and induction therapy (either Thymoglobulin or IL-2R blockers) (B II).
- Tacrolimus C0 targets may be higher in adult patients at higher immunological risks (B II)
- Although only supported by clinical experience, a C0 target range of 10–20 ng/mL may be proposed for pediatric patients (C1 II)
- A minimal AUC0–12h threshold of 150 ng·h/mL may be proposed for the twice-daily formulation in adults (B II). AUC targets corresponding to different C0 ranges were derived from AUC–C0 correlation studies in large adult patient populations, for the twice-daily and once-daily (Advagraf) formulations.
In Liver Transplant Recipients
For adult patients,
- When prescribed in combination with mycophenolate or everolimus and corticosteroids, tacrolimus may be targeted to a C0 of 6–10 ng/mL during the first 4 weeks after transplantation and 5–8 ng/mL thereafter (A I).
- Tacrolimus as a monotherapy, or when only associated with induction treatment, may require a higher C0 target (10–15 ng/mL during the first 3 months after transplantation and 5–10 ng/mL afterward) (C1 II).
- A tacrolimus C0 of 10–15 ng/mL may also be aimed for in patients on a corticosteroid-free regimen (even beyond the fourth month after surgery) (C1 II).
For pediatric patients, there is not enough clinical evidence to make recommendations.
Heart and lung transplantation—the C0 ranges recommended almost 20 years ago must be revised (and probably lowered) because they encompassed values between 15 and 20 ng/mL in the first weeks after transplantation, whereas recent studies have shown that the risk of acute kidney injury within the first 2 weeks after transplantation was significantly increased for C0 >15 ng/mL (B II).
Bone marrow transplantation—C0 of 10–20 ng/mL may be targeted when tacrolimus is prescribed orally in combination with methotrexate, in adults (B II) and in children (B II).
In the other conditions in which tacrolimus is sometimes used, there is not strong enough evidence to make recommendations (C2).
Within-Patient Variability of Tacrolimus Exposure as a Biomarker for TDM
In addition to the tacrolimus blood concentration itself, its within-patient (between-occasion) variability might also be a valuable tool for optimizing immunosuppressive therapy in SOT.137–139 Within-patient variability can be simply evaluated using the CV of at least 3 consecutive C0 measurements in a given period. To date, none of the different tacrolimus formulations (immediate release, prolonged or extended release) clearly shows lower within-patient PK variability than the others.140,141 Significant variability of tacrolimus concentrations during immunosuppressive treatment may lead alternatively to underexposure and overexposure periods and represent a risk of treatment failure.
The first suggestions to consider the within-patient variability as a potential biomarker of treatment outcome were reported in kidney transplantation.29 The CV of tacrolimus C0 beyond 6 months after transplantation was found to be a more relevant biomarker of tacrolimus toxicity and immunosuppressive efficacy than the sole C0.142,143 Patients with high exposure variability were confirmed to be at higher risk of developing histologic kidney lesions,143 graft loss,142 and of poorer long-term outcomes.29,144–146 Most of the studies conducted in kidney transplantation included patients with stable treatment (at least 6 months after transplantation), in a period when adherence may be regarded as a strong determinant of within-patient variability.
In liver transplantation, whether in the early postoperative period (days 8–30) or at mid-term (after 6 months), the within-patient variability of tacrolimus concentrations was related to patient outcome.147,148 In adult patients, early within-patient variability (between day 8 and day 30) was associated with long-term graft and patient survival and with complications such as nephrotoxicity, cardiotoxicity, and neurological adverse events, meaning that early interventions may be undertaken to reduce variability.148 In pediatric patients, the link between within-patient variability, calculated from month 6 of tacrolimus initiation, and biopsy-proven acute rejection was first found in retrospective studies and later confirmed prospectively, whereas there was no relationship with graft or patient survival.147,149
Very recently, similar results have been reported in heart transplantation. The within-patient variability was calculated retrospectively between month 3 and month 12, and a high CV turned out to be an important risk factor for the onset of rejection.139
Evaluating the within-patient variability of C0 values during tacrolimus treatment should be further considered in the clinical management of solid organ recipients. Early identification of at-risk patients, with higher within-patient variability, may allow for implementing actions aimed at reducing this variability and preventing its clinical consequences.
Executive Summary on Within-Patient Variability of Tacrolimus Exposure
Within-patient variability of tacrolimus concentrations has emerged as a potential tool to predict adverse events during follow-up of kidney, liver, and heart transplant recipients but has not entered routine monitoring yet (B II). Evaluation of within-patient variability can easily be implemented to improve TDM (B II). The use of within-patient variability has not been validated prospectively, and the timing of determination has to be defined.
MEASUREMENT OF TACROLIMUS CONCENTRATIONS
Short- and long-term stability of tacrolimus and its major metabolites (MI, MII, and MIII) have been investigated using both patient blood samples and whole blood samples spiked with these substances. Stability of the measured tacrolimus concentration was proven for samples stored up to 14 days at 22°C or 4°C, as well as at least 1 month at −20°C and 1 year at −70°C.25,150 MI, MII and MIII were found to be stable for at least 3 days at ambient temperature, 1 month at −20°C, and 12 months at −80°C.25,151 Three freeze-thaw cycles were possible without compromising the quantitative results.25,151 These cycles also had no effect on the unbound tacrolimus concentration, but this fraction increased by >30% in samples stored for 6 months at −80°C.152 The authors concluded that samples should be ultrafiltrated when fresh to study the unbound tacrolimus concentration. For long-term storage, the prepared ultrafiltrates can be frozen at −80°C.
Stability of tacrolimus concentrations was also investigated in some alternative sample matrices. For example, in PBMC (CD4+ T-cell and CD19+ B-cell), stability was demonstrated for up to 3 months at both ambient temperature and −30°C153; in oral fluid ambient temperature (no exact time was mentioned) and for up to 1 month at −80°C154; and in human bile samples over 20 hours at ambient temperature and 6 months at −80°C.155 After 3 freeze and thaw cycles, no loss of tacrolimus concentration was observed in oral fluid or bile.
Extended stability of tacrolimus in DBS samples using different grades of Whatman paper (Whatman, Kent, United Kingdom) has been reported. For example, on Whatman 31 ET CHR paper, tacrolimus concentrations were consistent over 28 days when stored at either 22°C or 37°C,156 over 30 days on Whatman 903 Protein Saver Cards at temperatures ranging between −20°C and 25°C, and at least 5 days at 60°C.157
Analytical Methods to Determine Tacrolimus in Whole Blood
More than 60% of the requests for measurement of immunosuppressive drug concentrations in clinical laboratories concerns this drug. Because TDM was recommended with the introduction of tacrolimus in the early 1990s, many analytical methods determining tacrolimus concentrations have been developed and established for clinical services.14 These methods offer different advantages but may also suffer a variety of disadvantages.158 Analytical laboratories face a number of alternatives when choosing the method that is most appropriate for their local circumstances. Analysis of tacrolimus in whole blood is performed either by immunoassays or by LC-MS/MS, with both techniques being represented in nearly equal proportions at measurement service providers worldwide.159
In a 2013 international survey, 53% of TDM laboratories reported that LC-MS/MS was used as their routine tacrolimus method.159 Approximately 60% of the LC-MS/MS procedures were laboratory developed tests (LDTs), whereas the remainder was based on kits launched on the market by the diagnostic industry. Most of the LC-MS/MS assays (∼75%) were multianalyte methods that allowed for the simultaneous quantification of tacrolimus and other immunosuppressive drugs within one analytical batch, using a whole blood sample.159
High analytical selectivity and sensitivity, and the possibility for high throughput multianalyte assays are important benefits of LC-MS/MS. Thus, this technique has met the evolving clinical requirements for fast, accurate, and precise tacrolimus assays performing well at low concentrations. The ease of use and robustness of LC-MS/MS instruments and also the combination with automated or semiautomated sample preparation have gradually been improved and further facilitated the widespread implementation in clinical laboratories. Nevertheless, skilled laboratory management and specially trained staff are necessary to establish and operate LC-MS/MS assays for routine TDM. Furthermore, for LC-MS/MS, a rather large upfront financial investment is necessary.
The design, validation, and continuous quality assurance of immunosuppressive drug LC-MS/MS assays are critical for routine performance, and these aspects have recently been reviewed by the IATDMCT Immunosuppressive Drugs Scientific Committee.14 The process of LC-MS/MS–based quantification of tacrolimus in clinical samples can be divided into sample preparation, chromatographic separation, mass-spectrometric selection as well as detection, data processing, and validation, finally leading to reportable results.
Tacrolimus is extensively distributed into RBCs, and so, it is measured in whole blood. Ethylenediaminetetraacetic acid (EDTA) is the preferred anticoagulant.14 Protein precipitation, solid-phase extraction, and liquid–liquid extraction have been reported as sample preparation strategies before chromatography in LC-MS/MS assays including tacrolimus.160 The sample preparation and chromatography should be designed to balance each other: clean extracts may allow for simple and fast chromatography, whereas crude extracts should be compensated for by more thorough chromatographic clean-up and separation to avoid impairment of the MS/MS response.
Precipitation of whole blood samples can be performed by either a stepwise or simultaneous addition of zinc sulfate solution and organic solvent. Annesley and Clayton developed a protocol in which 5 volumes of deionized water was added to whole blood before step-wise addition of zinc sulfate 0.1 mol/L and methanol. Apparently, the water hemolysis step improved the extraction efficiency and lowered the imprecision of the tacrolimus measurements in clinical samples.161 This extraction protocol was further validated by Seger et al as part of a standardized multianalyte LC-MS/MS assay: 50-µL EDTA whole blood was added to 250-µL water and 750-µL methanol:zinc sulfate 0.1 mol/L (2:1), then vortex mixed at room temperature and centrifuged at 4°C.162 The water hemolysis step and subsequent precipitation with methanol: zinc sulfate may be regarded as state-of-the-art for this type of sample preparation. However, visual inspection of the precipitate is recommended.
The hydrophobic nature of tacrolimus makes it suitable for reversed-phase chromatography. Conditions for chromatographic separation are commonly based on C18 (or C8) stationary phases combined with mobile phases of water and methanol to which an acidified ammonium buffer (eg, formic acid plus ammonium acetate) is added.160 The mobile-phase constituents should be of LC-MS quality. Tacrolimus is retained on a C8/C18 analytical column with methanol content up to approximately 50% in the mobile phase, thereby allowing for the separation of less hydrophobic compounds. Further on, tacrolimus can be eluted with adequate peak symmetry by increasing the methanol content to ≥90%.13,162 Some chromatographic compromises may be necessary when cyclosporine is included in the assay because the latter demands highly optimized conditions to obtain acceptable peak shapes. Column temperatures, in the range 35°C–75°C, may be used to facilitate peak narrowing, also allowing a higher flow rate due to reduced back-pressure.
Online extraction with two-dimensional chromatography is a widely used strategy for clean-up before directing the extracts into the mass spectrometer. The prepared matrix is then injected on an extraction column with large particles and a low-organic mobile phase is pumped at a high rate (turbulent flow). Subsequently, the system switches to a high-organic mobile phase that back-flushes tacrolimus from the extraction column and through an analytical column.160 Such online clean-up techniques may be applied to avoid potential ion suppression effects on the signal intensity and also to minimize contamination of the mass spectrometer.
The chromatography of tacrolimus should be designed to minimize coelution with compounds causing ion suppression/ion enhancement. Tracking of the time intervals with potential signal suppression/enhancement (postcolumn infusion) and monitoring the retention times for glycerophosphocholines will guide such achievements during assay development.163,164
Most tacrolimus LC-MS/MS assays apply electrospray ionization as the interface to get the mobile-phase solvent and sample compounds into gas phase.165 The conditions for spraying, evaporation, ionization, and acceleration of tacrolimus ions into the mass spectrometer have to be optimized with respect to the specific instrument. Several of the ion-source parameters are global in a multianalyte assay, and consequently, optimization of these parameters may have to be prioritized for compounds yielding the lowest instrument response at the LLOQ (usually sirolimus and everolimus when included in a multianalyte immunosuppressive drugs assay).
Sample matrix components will potentially influence the evaporation and ionization efficiency of tacrolimus (ion suppression or enhancement). Proper sample preparation and chromatographic separation is mandatory to minimize such matrix effects on the analyte response. Highly sensitive mass spectrometers enable less impact of matrix effects since they allow less biological matrix per sample to be loaded into the system. It is generally recommended to use a stable isotope-labeled internal standard (SIL-IS) to correct variations throughout the assay procedure, including compensation of matrix effects. There are commercially available SIL-IS for tacrolimus (eg, 13C, D2-TAC).
Selective reaction monitoring is applied for tacrolimus quantification, usually with the positively charged ammonium adduct as precursor ion (m/z 821.5) and product ion set at m/z 768.5.160 The corresponding mass transitions should be used for the IS.
Assuring Adequate Quality of Clinical Tacrolimus LC-MS/MS Assays
The use of commercial whole blood–based tacrolimus calibrators is recommended to ensure accurate measurements and to support the harmonization of results between laboratories. Indeed, 78% of TDM laboratories have reported the usage of commercial tacrolimus calibrators.159 Preferably, the quality control samples should be sourced independently from the calibrators. Tacrolimus measurements should be based on a multilevel calibration curve, and a proper calibration model with adequate weighting should be applied. Natural tacrolimus isotopes may contribute to the IS response when an SIL-IS is used, and a nonlinear calibration curve should be considered in such cases. Also, there may be tacrolimus impurities in the IS solution generating an analyte response in tacrolimus-free samples. Impurity interferences like this should be balanced against requirements for the LLOQ.
Reproducibility of the peak integration can be ensured with automated data processing using custom-adjusted algorithms in the instrument software. Manual integration should generally be avoided, but, if necessary, it should be justified and documented in each case. Since the MS/MS signal intensity may fluctuate over time on an instrument, it is useful to include daily testing of the absolute assay response to verify that requirements for the LLOQ are fulfilled.
Hospital TDM laboratories may receive tacrolimus samples from both in-house and outpatient clinics, and the requirements for the turnaround time will often be different depending on the clinical setting. LC-MS/MS assays should be designed to meet a turnaround time of approximately 3 hours for in-house tacrolimus samples. Alternating injections into 2 LC channels coupled to a single MS/MS allow for overlapping chromatography and can be used as a strategy to obtain a reduced batch run time.13 Laboratories performing therapeutic monitoring of tacrolimus should perform adequate assay validation or verification before implementation of the assay as a routine service. They should adhere to predefined acceptance criteria for analytical performance of their routine runs and participate in external proficiency testing (PT).14
Tacrolimus TDM has benefited extensively from immunochemical methods since the drug was introduced into clinical practice. First-generation assays including the PRO-Trac II ELISA (DiaSorin), the Microparticle Enzyme Immunoassay produced by Abbott, and the Cloned Enzyme Donor Immunoassay originally produced by Microgenics, subsequently Thermo Fisher, were replaced by improved tests. Currently, a choice of immunoassay is available.
The EMIT is offered by Siemens (formerly Dade-Behring) from the early 2000s. The assay may be performed on several analyzers (eg, Vital Viva—different models, Roche Cobas Mira—different models and Integra, Beckman Synchron LX20, Bayer Advia 1650, Abbott Architect c8000, Hitachi 902 etc). The EMIT reagents suffer from cross-reactivity with tacrolimus metabolites, resulting in significant overestimation of tacrolimus concentrations in patient samples as compared to LC-MS/MS, reaching up to 30%–36%.25,166 It has also been noted that another factor contributing to the bias could be nonspecific cross-reactivity.25 Poor LLOQ ranging between 2.8 and 4.6 ng/mL has been reported,167–169 suggesting that EMIT is not reliable for monitoring tacrolimus concentrations below about 3.0–5.0 ng/mL. LeGatt et al reported total imprecision of 13.7% and 6.0% for tacrolimus concentrations of 3.4 and 19.1 ng/mL, respectively.167 Commonly observed poor repeatability of determinations between analytical runs and a wide dispersion of the results by EMIT seen in PT14,169 were partially related to reagent instability after opening and method calibration at a particular laboratory. Currently, use of EMIT for tacrolimus monitoring is steadily diminishing.
Antibody-Conjugated Magnetic Immunoassay
Another assay from Siemens is the Antibody-Conjugated Magnetic Immunoassay (ACMIA) developed for the Dimension analyzer family. This application, which has been available since 2007 for general biochemistry analyzers, had the advantage of no manual pretreatment step.
The LLOQ for ACMIA was reported as 2.5–5.36 ng/mL.169–172
A mean positive bias of 1.78 ± 1.51 ng/mL versus LC-MS/MS was found in patient samples by Tempestilli et al171 and practically no bias (+1.7%) by Cangemi et al.173 Total imprecision of ≤12.9%174,175 was reported. Tempestilli et al observed the influence of albumin on tacrolimus concentrations and suggested the risk of inappropriately low tacrolimus dosage in low-albumin patients.171 However, ACMIA results were not affected by hematocrit values.176,177
Interestingly, the method's main asset became its primary disadvantage. No pretreatment could lead to lower accuracy and to an overestimation of the results in as many as 1% of patient samples.171,178 In some patients, the assay was affected by endogenous blood constituents (incl. heterophilic or anti β-galactosidase antibodies) giving falsely elevated results.173,178
Recently, the manufacturer has made modifications,179 which are expected to resolve the problem. The “new” ACMIA for tacrolimus is characterized by an improved LLOQ of 0.86 ng/mL, fitting well with recent TDM requirements,14 negligible bias of 1.7% to LC-MS/MS, and total imprecision of ≤5.5%.179 If the modified ACMIA proves resistant to erroneous results, it could be an attractive tool for tacrolimus TDM.180
The CMIA was developed by Abbott for a family of Architect analyzers. After receiving Food and Drug Administration (FDA) approval, the CMIA can be used for routine tacrolimus TDM in US clinical laboratories.181 The first analytical step is a manual pretreatment of a whole blood sample to precipitate proteins and extract tacrolimus into a supernatant, followed by immunoassay. Evaluation of the CMIA tacrolimus assay showed no interference with hematocrit, bilirubin, or total protein170,172,182 but cross-reactivity yielding 94% with 31-O-desmethyl tacrolimus (M-II), and 45% with 15-O-desmethyl tacrolimus (M-III) tacrolimus metabolites. The cross-reactivity with 13-O-desmethyl tacrolimus (M-I) and 12-hydroxy tacrolimus (M-IV) metabolites was negligible.172
Functional sensitivity (CV = 20% of the fitted curve) of 0.5 ng/mL,170,182,183 even lower than the manufacturer's claim (0.8 ng/mL) was reported. However, CV <10% was noted only beyond 1-ng/mL tacrolimus concentration.170 In Wallemacq's multicenter study, the total CMIA imprecision was ≤8.2% using QC materials,172 and it was ≤8.8% using samples of Chinese renal transplant patients as reported by Li et al.184 Saint-Marcoux et al noted 4.4 ± 0.2% bias versus LC-MS/MS in pooled clinical samples, whereas in 2 analytical sites involved in Wallemacq's comparison, the bias was reported as +0.51 and +1.63 ng/mL.172,185 The assay rapidly became a leader of immunoassay methodology for tacrolimus due to a comparatively low bias versus chromatography, imprecision even better than LC-MS/MS, and close agreement of results for clinical samples in PT data.
The ECLIA (Elecsys) is a semiautomated diagnostic assay developed by Roche for use in a family of Cobas e analyzers to monitor tacrolimus in whole blood samples. A comprehensive study has been conducted in 5 centers in Europe to evaluate the performance of ECLIA for tacrolimus determination. Three different models of Cobas analyzers (e 411, e 601, and e 602) were used.186 The manufacturer declares that the assay shows no interference from bilirubin, hematocrit, or total protein, and that cross-reactivity is 70% for metabolite M-II but no detectable cross-reactivity with tacrolimus metabolites: M-I, M-III, and M-IV. A study by Shipkova showed method linearity between 0.5 and 40 ng/mL, functional sensitivity (CV ≤20%) was 0.3 ng/mL, and CV ≤10% was at 0.8 ng/mL186; Within-run imprecision was ≤8.9% and laboratory-to-laboratory imprecision ≤12.1%. This performance was confirmed in more recently published studies.181,187 Fung et al reported total imprecision of 3.9%–9.4%.188 Although the ECLIA tacrolimus assay has a shorter analysis time (18 versus 30 minutes) than the CMIA, it uses a higher sample volume (300 versus 200 µL), a factor to be considered when choosing the assay best suited for a particular laboratory.186,188,189
Immunoassays Under Evaluation
A new test for tacrolimus monitoring, the Quantitative Microsphere System (QMS) Tacrolimus Immunoassay, has been introduced recently by Thermo Scientific (Fremont, CA). Method description as well as its clinical evaluation is available in a publication by Leung et al.190 They found that the assay is free from interference from bilirubin, hemolysis, and lipemia. The method was reported as linear up to 30 ng/mL with a LLOQ at 0.7 ng/mL (CV = 14.4%). Imprecision was stated as 3.9%–8.1% and 4.7%–10.0% (within-run and between-run, respectively). A comparison made between QMS and LC-MS/MS measures in 145 patient samples showed a bias of +1.6 ng/mL.190
A similar type of assay, latex agglutination turbidimetric immunoassay (LTIA, Nanopia TDM Tacrolimus assay kit) from Sekisui Medical has been tested in Japan. LTIA was compared with 3 other IAs (CMIA, ECLIA, and ACMIA) by Akamine et al.191 In this evaluation, LTIA had the best profile for cross-reactivity with 3 major tacrolimus metabolites (M-I, M-II, and M-III), and yet, LTIA presented the highest bias (+1.88 ng/mL) which was additionally influenced by CYP3A5 genotype and hematocrit value.191 Further investigations are necessary to judge whether the LTIA tacrolimus assay is suitable for tacrolimus TDM.
The evolution observed over 20 years for tacrolimus immunoassays shows improved specificity, precision, limit of quantification, and time of analysis and automation. Thus, the new generation of immunoassays is increasingly an alternative to LC-MS/MS methods in tacrolimus monitoring.
Consistency of Tacrolimus Results Generated by Different Analytical Methods
Method inconsistency may have an impact on patient care for several reasons including but not limited to its effect on clinical decisions and drug dosing. It also has an impact on long-term outcomes for patients and on the correctness of retrospective analysis of clinical data or the interpretation of pooled data from clinical trials, since these analyses may be used for regulatory purposes or to establish clinical decision points. The range of immunoassays available for tacrolimus each shows a different spectrum of cross-reactivity to tacrolimus metabolites and different susceptibilities to interactions with heterophilic antibodies and endogenous factors such as hematocrit or albumin.14 Therefore, dealing with the issue of consistency of results generated with different methods is not easy. A recent study of the performance of current tacrolimus immunoassays among Japanese hospitals demonstrated large between-laboratory variability even when using samples spiked with the drug.169 The very low level of method harmonization with LDTs (eg, most LC-MS/MS methods) presents further obstacles. According to data from the Analytical Services International (ASI) PT program collected between 2014 and 2017 (Figs. 1A and 2A), the ratio between the maximum and minimum averages of the concentrations determined by different methods when analyzing the same samples was about 1.3-fold. This was true both for spiked and pooled patient samples. However, considering the within-method CV (%) found for each of the different peer groups (Figs. 1B and 2B), a much greater difference between minimum and maximum reported results can be anticipated. Recently Agrawal et al192 evaluated, under “real-world” clinical conditions, the effect of a change in analytical methodology from CMIA (run on an Abbott Architect system) to a kit-based LC-MS/MS method (Waters TQD Acquity mass-spectrometer with Mass Trak) on patient classification according to the tacrolimus concentration. Although a good analytical performance has been demonstrated for both of these methods in the past,193,194 the results of this study demonstrated that 40% of patient samples were discordantly classified by the 2 methods when the applied therapeutic ranges were 2 ng/mL wide. The discordance rate improved to 3% when the width of the target range was increased to 4 ng/mL. A change in patient classification due to lack of comparability between methods might lead to the patient receiving either an insufficient dose and rejecting the organ, or receiving a high, potentially toxic dose. Although introducing laboratory- (or method-)specific target ranges may be a helpful approach to attenuate the impact of between-method differences on patient classification, this approach may pose a hidden danger, particularly when laboratories need to change the methodology at short notice (eg, due to problems with reagent supply) or when transplant physicians have to simultaneously interpret results provided by different laboratories.
The narrow therapeutic index of tacrolimus sets stringent requirements for the performance of analytical methods. For instance, estimation by different approaches that try to take into account the within-individual biological variability of predose tacrolimus concentrations shows that an adequate application of a 4- to 5-ng/mL-wide target concentration range would require analytical imprecision and bias of ≤6% each.14,195 As discussed in recently published IATDMCT recommendations for proper analytical performance14 and demonstrated in Figures 1 and 2, such goals are hardly achievable in current routine TDM services. Targeting of even more narrow therapeutic ranges for clinical purposes seems, with reference to current analytical practices, unrealistic.159 The recommended achievement of between-run imprecision of at least ≤10% with a total error (TE) of ≤15% (among other performance characteristics) should be considered obligatory for tacrolimus analytical methods to rate them as acceptable.14
Furthermore, to properly deal with patient, nonadherence issues that are particularly critical in transplantation not only method precision but also the ability to measure very low concentrations are important. The recommended LLOQ for tacrolimus is ≤1 ng/mL and can be achieved currently by LC-MS/MS methods, the ECLIA, and CMIA techniques,14 as well as the newest generation of ACMIA. The measurement range of the QMS method as reported by the manufacturer can theoretically also cover such low concentrations, but the imprecision observed in PT programs is broader than those of the latter methods (Fig. 1B). Hence, between-method consistency in analytical sensitivity is lacking too.
Last but not least, the fact that tacrolimus is prescribed as a long-term therapy clearly highlights the importance of consistent analytical performance of methods and laboratories over long periods. However, as can been seen in Figure 1, long-term inconsistency with analytical methods over time is still an issue, and the within-method variability of the bias to the same spiked concentrations frequently reached 15% and even more when looking at the interquartile ranges.
In summary, when looking back to the early years of tacrolimus TDM, a continuous improvement of method performance can be seen. However, the analytical performance of currently available methods still varies broadly which needs critical consideration when implementing or changing TDM services for tacrolimus.
Method Calibration and Proficiency Testing
In the case of tacrolimus analytics, there are multiple PT programs available (both national and international) with the College of American Pathologists scheme and the programs formerly run by ASI (now provided by LGC Standards Proficiency Testing) being most popular. Results from these programs have been used in the past to reveal analytical problems and their causes.196 When choosing a PT program, it is important to consider whether specimens based on human whole blood spiked with tacrolimus metabolites, real patient material from transplant recipients, and blank samples without tacrolimus, and metabolites are part of the distributions by the program. Such samples are important to check for matrix effects on the accuracy of the measurement.14
In general, users can check on accuracy using third party prepared calibrators or controls or compare their results with those given by PT data. Fortunately, since 2013, a higher order whole blood reference material for tacrolimus has been available commercially (ERM-DA110a) and listed in the database of the Joint Committee for Traceability in Laboratory Medicine (JCTLM). Because commercially available calibrators and controls are in general traceable to this material, their use in routine services is preferable to reduce the risk of calibration bias. Participation in PT is particularly supportive if own results can be compared not only with the peers but also with results traceable to a higher order reference method; however, unfortunately, this possibility is still not available for tacrolimus. In this situation, comparison to results generated with the LC-MS/MS technique (the LC-MS/MS participant group) can provide some helpful information when evaluating immunoassays. However, the drawback here is that the LC-MS/MS group is not consistent,194 and the level of validation as well as the overall quality performance management at the participating analytical sites are unknown. Therefore, for the evaluation and verification of immunoassays accuracy, confirmation of their results using a fully validated LC-MS/MS as a reference and real (not pooled) patient samples is advised before application to routine services. Acceptance criteria for such between-method comparisons have been recommended by the IATDMCT Immunosuppressive Drugs Scientific Committee14:
- A linear regression slope within ±10% of the theoretical value of 1.0.
- A linear regression intercept not significantly different from zero.
- A standard error for the estimate, Syx ≤10% of the average of the therapeutic concentrations.
Figures 1 and 2 summarize data regarding the performance of currently used tacrolimus methods as collected between 2014 and 2017 (after the introduction of ERM-DA110a) from the ASI PT program. They show that, although, the bias of method means when compared with spiked target concentrations (4, 8, and 12 ng/mL, Fig. 1A) lies with few exceptions (EMIT at 4 ng/mL) within a range of ±10%, it varies broadly between distributions. When comparing the performance of immunoassays with pooled patient samples (Fig. 2A) versus the LC-MS/MS group, all of them showed some overestimation, in agreement with published data generated with nonpooled patient samples.170,186 In contrast to the EMIT and QMS assays, the median overestimation with the CMIA, ECLIA, and ACMIA was <10%.
Looking at the long-term method-specific percentage CVs (both for spiked and pooled patient samples), they clearly favor LC-MS/MS, CMIA, and ECLIA methods compared with ACMIA, QMS, and EMIT (Figs. 1B and 2B). Moreover, the percentage CVs of the first group of methods seems not to be concentration-dependent, in contrast to the methods of the second group, for which much broader within-group variability at the lowest concentration (4 ng/mL) compared with 8 and 12 ng/mL was identified. During the observation period, the CMIA and ECLIA methods demonstrated better long-term precision than the very heterogeneous LC-MS/MS group.
All these results underline the strengths of PT and demonstrate that there is without doubt a high potential for improvement of tacrolimus analytics to further standardize method and laboratory performance.
Standardization of Tacrolimus TDM
The applicability and reliability of analytical figures generated by laboratories is strongly linked with data quality, particularly their accuracy. This general remark holds true for any kind of measurement service and is not limited to clinically relevant entities such as tacrolimus. Laboratory medicine adopted relatively early the general metrological concept of traceability197,198 and established a close relationship with national metrological institutes. By founding the JCTLM located at the International Bureau of Weights and Measures,199 chemical and biological entities in laboratory medicine have been raised to the same level of international consistency and used classical SI units for measuring time, weight, and length.
Measurement procedure accuracy is achieved through ensuring specificity of the applied methods. These methods are the responsibility of individual laboratory units offering defined measurement services. Total error or measurement uncertainty calculations200–202 can be used to investigate the error components associated with the processes of measuring, namely bias and precision that do combine up to the accuracy of a measurement system.203 Error budgets may be used to estimate the expected TE that combines systematic error components (associated with bias) and stochastic (random) error components (associated with precision). They can be either calculated by “top-down” or “bottom-up” approaches. Although bottom-up approaches are used in the assessment of reference measurement systems,202 top-down approaches allow for the monitoring of systems in routine use, for example, either individual users or global measurement services. For tacrolimus, a recent top-down investigation showed that this approach, either using single laboratory validation data or PT data, gave identical results.204 Different top-down approaches to estimate measurement uncertainty of whole blood tacrolimus mass concentration values meet the assay quality criteria set up by a recent IATDMCT guidance paper.14
If individual laboratories use the same test principle, for example, an automated immunoassay, traceability of locally applied calibrators to an “industrial master calibration” is usually assured and guaranteed by the assay vendor. The vendor [eg, operating under FDA clearance or within the framework of an obtained European CE Marking for In Vitro Diagnostic (IVD) devices (IVD-CE) certification] has responsibility for the trueness, that is, lack of bias of the local calibration to this master calibration. The expected assay precision is also stated by the assay producer; local deviations to the given numbers must be carefully monitored because they increase the TE of an assay. If such deviations exceed (definable) thresholds, the local laboratory should ensure the assay vendor takes corrective actions (eg, running an additional service, changing a pipetting unit, etc.) to prevent the occurrence of irregular analytical errors.205 If a laboratory has decided to develop an “in house assay” (LDT), the responsibility for maintaining the trueness and precision of a measurement service is entirely the responsibility of the individual laboratory.206 Failure can be minimized if the trueness of the assay is kept under control by using commercial calibrator materials. In the past, it has been impressively shown that this approach minimizes the risk of between-laboratory imprecision for both 25-OH vitamin D207 and tacrolimus,193 which improves the comparability of patient results obtained by individual laboratories.
In the current global situation, with several FDA-approved/IVD-CE–certified tacrolimus measurement systems on the market and at least 2 additional calibrator material vendors serving the LTD community, one has to assume that the trueness between this tacrolimus TDM measurement platform realizations is limited. Proficiency data (Figs. 1A and 2A) support this assumption,14 emphasizing the need for traceability of individual tacrolimus measurement services beyond the industrial or commercial calibrator systems.
LGC Standards Proficiency Testing, a National Measurement Institutes of the United Kingdom, took up this challenge some years ago and presented 2 certified reference materials to the public: ERM-DA110a, a whole blood matrix containing tacrolimus containing whole blood matrix in 2014 (secondary higher order reference material) and, in 2017, ERM-AC022a, which is pure tacrolimus (neat substance, primary higher order reference material). ERM-AC022a was characterized by quantitative Nuclear Magnetic Resonance, but it is not clear which reference method was applied for value assignment because the primary reference method has not been disclosed, as has been performed in other fields, for example, for steroid hormone measurements.208,209 However, ERM-AC022a is listed by the JCTLM, implying that the responsible JCTLM working group has reviewed the reports associated with ERM-AC022a and concluded that they are in accordance with the JCTLM regulations. Nevertheless, the availability of peer-reviewed reference methods for tacrolimus is long overdue. Unfortunately, in the whole field of ISD-TDM, only one such method has been presented, and it was for cyclosporine.210 Within the Scientific Division (SD) of the International Federation of Clinical Chemistry (IFCC), a work group (WG-ID) was founded in 2018 to focus on activities in this field, including the generation of reference materials and the placement of appropriate reference procedures for immunosuppressive drugs including tacrolimus.211
New TDM Approaches
It should be noted that all these new TDM approaches were made possible by important analytical improvements developed during the last decade (ie, sensitive and precise liquid chromatography with tandem mass spectrometry).
Microsample-Based Tacrolimus Concentration Monitoring (DBS and Others)
DBSs on filter cards and VAMS are innovative, minimal invasive sample methods, which can replace traditional blood sampling for the TDM of immunosuppressive drugs but can also be used for a number of other applications. This approach is patient-friendly and can be implemented at home by the patient themselves, collecting very small amounts of blood (typically 10–20 µL). The procedure is both cost and time saving, and it also allows for multiple sampling within a dose interval, enabling the determination of a patient's AUC. The AUC reflects patient's systemic drug exposure. Microsampled AUC determinations have advantages in special populations such as neonates, pediatric, and older patients, in whom venipunctures are difficult, and the blood volume that can safely be collected is limited. Furthermore, in a busy out-patient clinic, it is logistically difficult to draw multiple blood samples throughout a 12- to 24-hour dosing interval. Thus, for tacrolimus TDM, a microsample approach is particularly appealing.
Preanalytical and Analytical Requirements and Pitfalls
Microsample methods require extensive validation with some criteria beyond the usual recommendations for analytical method validation.212,213 This approach also requires sufficient analytical sensitivity because of the low volume of blood collected. The use of microsamples with dried blood is a destructive method meaning that the complete sample is used for the analysis, and no confirmation of drug measurement can be conducted using the same sample, only using parallel samples. Current challenges in microsample implementation include but are not limited to extraction recovery, matrix effect on blood volume (hematocrit effect), correlation between venous and capillary blood measurements, the quality of the blood spot, risk of contamination, and sample stability. These issues require additional validation steps and have to be properly addressed during method validation.214 New generation microsampling such as VAMS can possibly mitigate some of the usual bias encountered with these methods, particularly hematocrit effect.215 Several DBS methods for tacrolimus TDM have been published. These methods are often multiplexed with other immunosuppressive drugs and may include creatinine determination, which is of particular relevance in kidney transplantation.216–218
Future Developments and Clinical Perspective
The use of microsampling for tacrolimus TDM needs to be cross-validated with established TDM strategies based on venous EDTA whole blood, to determine whether this approach is suitable for clinical purposes. One of the main advantages over established sampling procedures for tacrolimus TDM is the potential to improve the patient's quality of life. One study has been conducted recently in transplanted children. In parents and children completing the satisfaction survey, all but one reported preference for DBS sampling over venous blood sampling.219 Clinical performance of microsampling in routine practice should also be evaluated in various transplant settings. Analytical performance together with patient training to ensure appropriate sample quality and motivation will be key factors for the implementation of microsampling in clinical practice.
Intracellular and Tissue Tacrolimus Concentration Monitoring
TDM of immunosuppressive drugs results in a decrease in acute cellular rejection (ACR) rates and a decrease in treatment-related toxicity, particularly the nephrotoxic effects of the CNI.91 However, despite intensive use of TDM, the global outcomes of transplantation seem to be only marginally improved.10 This has led pharmacologists to develop newer ways to optimize drug treatment, including measuring the concentration of immunosuppressive drugs directly at their site of action. Currently, standard TDM of tacrolimus is based on the measurement of drug concentrations in whole blood. Determining tacrolimus drug concentrations where it exerts its immunosuppressive effect, in the T cell or, for practical reasons, in PBMC, might be particularly relevant. Moreover, measuring tacrolimus directly in graft tissue may be of interest as it is reasonable to expect that local concentrations better reflect drug effect. Interest in such approaches has been strengthened by observations of the weak relationship between intracellular and whole blood tacrolimus concentrations in patients after various types of transplantation, suggesting potential added value.220–223 A study confirming the link between intracellular tacrolimus concentrations and patient outcomes after liver transplantation suggested that this new TDM approach was a valuable option,224 but definitive clinical verification and validation remain to be generated. The work conducted on this topic has been reviewed recently.225,226
Preanalytical and Analytical Requirements and Pitfalls
The first investigations evaluating intracellular concentrations of tacrolimus were conducted in the 1990s. Several groups have contributed to the development of analytical methods to measure tacrolimus concentrations in PBMC of kidney, liver, and heart transplant recipients.153,220,222,223,227,228 The large variability reported in these studies highlighted the critical importance of preanalytical and analytical steps for intracellular concentration assays. These methods must be appropriately validated following international guidelines and be sufficiently sensitive to quantify low intracellular concentrations, especially in the setting of immunosuppressant minimization protocols. Most analytical methods have involved liquid chromatography with tandem mass spectrometry detection. Normalization of the concentrations measured is also needed and can be achieved based on the number of cells, on the mean corpuscular volume of cells for PBMC, or based on the sample weight for tissues. There are several potential issues that have not yet been fully addressed, such as tacrolimus passive or active transport across the cell membrane before cell separation, equilibrium modifications during cell separation or washing steps, and the risk of sample contamination with RBCs. Proper evaluation of the impact of each of these potential issues has to be conducted as these may cause measurement bias. There is a crucial need for practice harmonization for the TDM of intracellular tacrolimus concentrations. Moreover, to date, no quality control programs exist.
Relationship With Whole Blood Concentrations
Early studies to evaluate the relationship between whole blood and cellular concentrations of tacrolimus have reported a lack of correlation.222–224 However, as the approach is further refined, a trend to better correlations has emerged. Notably, a recent study conducted in a large population of stable kidney transplant recipients (n = 213) showed a linear relationship between whole blood and intracellular tacrolimus concentrations, although agreement between the 2 concentrations remained modest (r = 0.67).229 Among factors influencing tacrolimus compartmentalization in cells, the role of P-gp (or ABCB1) has already been identified as a determinant of tacrolimus efflux and ABCB1 genotype relates to intracellular tacrolimus exposure.222 Other factors may include the circulating free-fraction (influenced by protein binding and hematocrit) and the intracellular expression of CYP3A5/4.222 More research must be performed to further explore the role of these factors in the high variability of immunosuppressive efficacy of tacrolimus. A more detailed knowledge of such factors may also allow for the identification of subpopulations of patients who may benefit from alternative TDM strategies, such as intracellular measurement of tacrolimus.
Clinical Evidences and Future Perspectives
To date, the most convincing proof of concept study was reported by Wallemacq's group in Belgium.224 In a study conducted in 90 liver transplant recipients treated with tacrolimus monotherapy, they reported a relationship between tacrolimus concentrations in PBMC as well as in liver biopsy samples and histological rejection grade determined at day 7 after transplantation, whereas whole blood concentrations were not associated with the severity of rejection.224 The results of this study suggested that intracellular tacrolimus concentrations early after liver transplantation seem to be related to rejection risk and severity. The lack of a relationship between whole blood tacrolimus concentrations and clinical outcome during this early period may be a good argument for using intracellular tacrolimus concentrations as a biomarker of immunosuppressive drug effect. Another observational study highlighted the case of a liver transplant recipient experiencing an ACR while having the lowest intracellular tacrolimus exposure among study participants.230 However, the definitive evidence for the relevance of intracellular tacrolimus concentrations as a longitudinal biomarker usable for clinical practice with standard immunosuppressive regimens is still lacking. The time-consuming preanalytical sample workup makes this approach complex and still requires extensive validation. Another perspective could be obtained by multivariate mathematical modeling predicting intracellular concentrations, based on increasing experience in genetic polymorphism, together with a better understanding and identification of covariates influencing intracellular tacrolimus concentrations. Besides intracellular measurement, determining the tacrolimus free-fraction also seems to be an appealing approach. This unbound (ie, the pharmacologically active moiety) fraction of the drug might be of interest in reflecting immunosuppressive drug effect. This newer way of TDM should also be investigated but is still at an early stage of development.152,231 Finally, it should be mentioned that the analysis of tacrolimus in oral fluid samples has been investigated. One study concluded that the correlation with whole blood concentrations was poor and due to several problematic methodological problems could not be recommended.232 A more recent report indicated that some of these problems could be overcome; however, blood contamination may still pose a problem,233 and this kind of sampling seems to not have gained any further interest so far.
After oral administration, tacrolimus is metabolized by gastrointestinal and hepatic cytochrome P450 (CYP) 3A isoenzymes, predominantly CYP3A4 and CYP3A5 (other members of the CYP3A family are CYP3A7 and CYP3A43). The main enzyme involved in tacrolimus biotransformation is CYP3A5, with CYP3A4 having a lower efficiency for catalysis.234
The expression of CYP3A5 is largely determined by genetic polymorphisms, with only 15%–25% of whites expressing CYP3A5 at a detectable level. The main genetic factor responsible for this is an intron 3 single-nucleotide polymorphism (SNP; CYP3A5*3, rs776746, g.6986A>G), which causes aberrant splicing, resulting in the absence of protein and, thus, CYP3A5 activity.235,236 Individuals with at least one CYP3A5*1 allele (defined as the “wild-type” allele) are classified as CYP3A5 expressors (CYP3A5*1/*1 or CYP3A5*1/*3). The minor allele frequency (MAF) of CYP3A5*3 varies widely across different ethnicities, resulting in significant differences in CYP3A5 expression (Table 3). Another rare SNP located in exon 7 (CYP3A5*6, rs10264272, g.14690 G>A) has also been shown to result in loss of functional CYP3A5 activity.235
Until recently, and in contrast to CYP3A5, no common SNP in the CYP3A4 gene could be related to CYP3A4 activity and to explain the significant between-individual variability in CYP3A4 activity. However, in 2011, Wang et al demonstrated that an intron 6 SNP (CYP3A4*22, rs35599367, C>T) significantly influenced CYP3A4 hepatic expression, suggesting that this SNP may be a biomarker for the prediction of the response to drugs metabolized by CYP3A4.237
In addition to CYP3A4 and CYP3A5, the efflux transporter ABCB1 also plays a role in tacrolimus PK.238,239 Over the last decade, more than 50 SNPs have been identified in ABCB1.240,241 The 3 most common SNPs in the protein-encoding region are rs1128503 (1236C>T, Gly412Gly), rs2032582 (2677G>T/A, Ala893Ser/Thr), and rs1045642 (3435C>T, Ile1145Ile).242,243 These 3 SNPs (with a MAF of around 50% in whites) are in strong linkage disequilibrium and have been investigated extensively. Other less-frequent SNPs have been described and, potentially, can explain part of the between-individual variability observed in the expression and/or function of ABCB1. Of particular interest, the ABCB1 1199G>A coding SNP located in exon 11 (rs2229109) is relatively frequent, with a reported allelic frequency of about 6% in the white population. This SNP is associated with a serine to asparagine substitution at position 400 in a cytoplasmic loop of ABCB1, which is involved in substrate recognition and with intracellular accumulation of tacrolimus in HEK293 and K562 recombinant T-cell lines.244
An SNP in the gene-encoding P450 oxidoreductase (POR*28; rs1057868, C>T, Ala503Val) has been associated with an increased in vivo CYP3A activity using midazolam as a drug probe.245 Because tacrolimus is metabolized by CYP3A isoenzymes, this SNP might affect tacrolimus PK.
Finally, 2 different SNPs in peroxisome proliferator-activated receptor alpha (PPARα) (rs4253728 G>A and rs4823613 A>G) have been shown to influence CYP3A4 activity both in vitro and in vivo.246
PK-PG Relationships in Kidney Transplantation
In relation to PK parameters and TDM, it has been clearly demonstrated that the CYP3A5*3 variant is the main genetic factor influencing tacrolimus dose requirement (defined as the dose-adjusted, whole blood predose concentration or C0) in stable renal transplant recipients, both in the early and late phase after transplantation (ie, several weeks to months after transplantation).247–249 Since the original publication, this genetic association has been confirmed in a large number of studies250 and meta-analysis.251,252 Based on these observations, dose adjustments have been proposed for the first tacrolimus dose after kidney transplantation, according to the CYP3A5 genotype of the recipient, with a doubling of the tacrolimus starting dose in patients who are CYP3A5 expressors (carrying at least one wild-type CYP3A5*1 allele).253 Indeed, genotype-based adjustment of the initial tacrolimus dose has already proven useful, with a greater proportion of patients reaching the therapeutic concentration range shortly after transplantation.254 Such a pre-emptive strategy, without additional dose adaptation (only based on CYP3A5 genotype) during the first 4 days after transplantation, has been proven to be safe and easy to implement.255 The level of evidence of this PG–PK association justified a specific guideline from the Clinical PG Implementation Consortium (CPIC).256 However, the benefit in terms of reaching the targeted tacrolimus exposure earlier with the use of CYP3A5 genotype–based dosing has not been a universal finding,257 and no trials have so far demonstrated improved clinical outcomes.
Besides CYP3A5*3, CYP3A4 activity has also been shown to be an important factor influencing tacrolimus dose requirement and clearance in renal transplant patients.258 To a lesser extent than CYP3A5*3, CYP3A4*22 has been shown to influence tacrolimus dose-adjusted pre-dose concentrations and dose requirements in stable renal transplant patients.259,260 A similar approach to genotype-based dose adjustment has been proposed with the advice that both CYP3A5*3 and CYP3A4*22 should be taken into consideration.261 Updated guidelines for the tacrolimus starting dose based on the recipient's CYP3A5 and CYP3A4 combined genotype (Table 4) have been validated through popPK modeling.262,263 However, the clinical utility of this approach still remains to be proven in prospective studies before it can be included in a future revised version of the CPIC guidelines.
Although numerous studies have shown no influence of ABCB1 genotype on tacrolimus PK parameters,247,249,264,265 some studies and/or meta-analyses have reported weak but significant associations,266,267 mainly during the first month after transplantation.268 In those, latter publications, ABCB1 3435 CC patients were reported to have a lower tacrolimus concentration-to-dose ratio and a higher dose requirement for tacrolimus compared with patients having the CT genotype and TT genotype, although no genotype-based dose adjustments have been proposed according to ABCB1 genotype of the recipient. An explanation for the lack of the anticipated correlation between ABCB1 variants and tacrolimus PK could be that the expression and the function of ABCB1 is highly variable and is influenced by several factors, including nongenetic factors.269
It is interesting to note that ABCB1 genotype has been shown to influence intracellular tacrolimus concentrations, particularly in PBMCs. Indeed, 3435T and 1199A carriers had a significantly higher intracellular tacrolimus concentration compared with homozygous wild-type patients, suggesting a reduced ABCB1 activity toward tacrolimus in PBMCs of patients with these ABCB1 variants.270 Theoretically, such effects of ABCB1 genotype on PBMC concentrations could influence PD without any significant impact on whole blood PK parameters.271
In addition, in relation to PK parameters and TDM, it has been demonstrated that patients carrying at least one POR*28 allele have a higher tacrolimus dose requirement than those not carrying this allele (POR*1/*1), but this association was only found in CYP3A5 expressors.272–275 Only one study reported no association between POR genotype and tacrolimus PK parameters.276 Going one step further, another study, which confirmed that the POR*28 allele was associated with increased in vivo CYP3A5 activity toward tacrolimus in CYP3A5 expressors, also showed that POR*28 homozygosity (POR*28/*28) was associated with a significantly higher CYP3A4 activity toward tacrolimus in CYP3A5 nonexpressors.277 Up until now, no specific genotype-based dose adjustments have been proposed according to recipient's POR genotype, but, based on the consistency of the available data, future recommendations could include CYP3A5*3, CYP3A4*22, and POR*28 genetic information.
Finally, tacrolimus dose-adjusted predose concentrations were not different depending on peroxisome proliferator–activated receptor alpha PPARA genotype in a cohort of 241 white kidney transplant patients.276 In another study (n = 177), a limited effect (15% higher tacrolimus concentration) was observed in the PPARA variant allele carriers.272
PG–PD Relationships in Kidney Transplantation
FK-binding protein-12 (FKBP-12), the principal binding protein for tacrolimus, is polymorphically expressed. The same holds true for CaN and the NFAT, which is the main downstream target of CaN (reviewed in Pouche et al278). In an in vitro study, it was demonstrated that genetic polymorphisms in the genes encoding proteins of the CaN pathway were associated with the inhibitory effects of tacrolimus.279 However, in a study including 160 kidney transplant recipients, no statistically significant associations between these genetic variants and tacrolimus PD was demonstrated.280 Taken together, the limited available data do not suggest a clinically relevant effect of genetic polymorphisms in FKBP-12, CaN, and NFAT and tacrolimus PD.
PG in Liver Transplantation
CYP3A isoenzymes are expressed in both the liver and the intestine. Both organs contribute to the presystemic metabolism of tacrolimus, but, unlike other forms of SOT, the hepatic enzyme content is determined by the donor genome in liver transplantation, whereas the intestinal content is determined by the genome of the recipient.
The association between tacrolimus PK and the CYP3A5 genotype of both the intestine (recipient) and the liver (donor) has been assessed in living-donor liver transplantation (LDLT) and in deceased DLT. Some studies have demonstrated that donor and recipient CYP3A5*3 genotypes are of major influence for tacrolimus blood concentration to dose ratio (C/D), which serves as an index of clearance and tacrolimus dose requirement,281–283 whereas other authors have suggested that donor CYP3A5*3 seems to contribute more than the recipient.284,285 Of note, the lowest (C/D) ratio values were seen when both donor and recipient were CYP3A5 expressors.283,286,287 A meta-analysis combining data from living and deceased DLT (694 donor and recipient genotypes) confirmed that both the genotype of the donor and recipient are important in determining the C/D ratio. The CYP3A5 genotype of the recipient, the determinant of intestinal genotype, has the greatest influence on tacrolimus dose requirement in the immediate post-transplant period, whereas the donor genotype, affecting hepatic expression, becomes more important after the first few weeks.288
Another study, which included more than 400 living-DLT cases from Japan suggested that the CYP3A5 genotype of the recipient was more important than that of the grafted liver as an indicator of systemic tacrolimus exposure for at least 5 weeks after transplantation.289 The increasing role of the donor CYP3A5 genotype with time is compatible with the progressive liver graft recovery and growth. Of note, in this study, there was a higher frequency of ACR among patients receiving a liver with at least one CYP3A5*1 allele compared with those receiving a liver with the CYP3A5*3/*3 genotype, suggesting an association between local (intrahepatic) concentration of unmetabolized tacrolimus and rejection. This effect of donor and recipient CYP3A5 genotype is apparently independent of the tacrolimus formulation. This association was reported in 2 studies in stable liver transplant recipients treated with the once-daily tacrolimus formulation.281,290
Opinion is divided on the association of the CYP3A5 genotype with the nephrotoxicity of tacrolimus. In both liver and heart transplantation, it was reported that CYP3A5 expression in the kidneys could play a role in the individual susceptibility to the nephrotoxic effects of tacrolimus.291–293 Another report suggested that expression of CYP3A5 (in both the graft liver and the intestine of the recipient) may result in a higher incidence of infectious complications in living-DLT recipients. The exact mechanism for these complications related to excessive immunosuppression is unclear, but it was postulated that these may relate to differences in tacrolimus metabolism.294,295
Only a few studies have been conducted on CYP3A4*22 in liver transplantation. In stable liver transplant recipients, Moes et al found no association between this SNP and the PK of the once-daily formulation of tacrolimus (considering both the donor and the recipient genotypes).290 The CYP3A4*22 SNP was also investigated among Asian patients but was not identified in this population.296 Results from adult liver transplant recipients receiving a twice-daily formulation of tacrolimus have shown that donor CYP3A4*22 genotype influences tacrolimus PK; a substantial reduction in tacrolimus dose was required in the first weeks after transplantation to achieve target concentrations.297,298 In addition, donor and recipient CYP3A4*22 genotypes seem to have opposite effects, with absence of effect of the combined recipient and donor statuses on tacrolimus C/D ratio value.287 In pediatric liver transplantation, a popPK study suggested that the apparent clearance of tacrolimus decreased by 29% when a graft carried a CYP3A4*22 allele (with no effect of the recipient genotype). This effect was quantitatively similar to that observed in the case a donor liver expressing CYP3A5 (ie, an increase of 30% for apparent clearance).299 However, comparison between pediatric and adult liver transplant patients is difficult since young transplant recipients have a higher liver graft to body weight ratio, leading to a higher tacrolimus apparent clearance and dose requirement.
Results from these studies revealed controversial data, and so, further studies are needed to better evaluate the apparently limited influence of CYP3A4*22 that might be masked by the more dominant effect of CYP3A5*3 on tacrolimus PK.
Regarding CYP2C19 status and its influence on tacrolimus exposure, liver transplant patients with a CYP2C19 defect genotype have a higher risk of clinically significant drug-drug interactions between tacrolimus and drugs whose major metabolic pathway involves CYP2C19, such as omeprazole and lanzoprazole300 and voriconazole.45
As for other types of transplantation, the association between various ABCB1 SNPs and tacrolimus PK and PD in liver transplantation is debated. The mean tacrolimus C0 correlated significantly with ABCB1 messenger RNA (mRNA) expression in PBMC, and the ABCB1 messenger RNA (mRNA) expression level in the small intestine was associated with acute rejection risk and 1-year graft survival.301
PG in Heart Transplantation
As in other solid organ transplant populations, the effect of the defective CYP3A5*3 allele on tacrolimus dose requirement is also well established in heart transplant recipients. In fact, 7 different candidate–gene association studies related this SNP to differential tacrolimus exposure regardless of the period after transplantation or the design of the study (ie, longitudinal or cross-sectional).302–308 It has also been consistently reported that the tacrolimus dose required by CYP3A5 expressors to achieve the target therapeutic range is approximately 2-fold higher when compared with CYP3A5 nonexpressors. However, it is estimated that only about 25% of the variability in tacrolimus dose requirement is explained by a patient's CYP3A5 genotype, which is slightly lower than the value observed among the renal transplant population. This observation suggests that other PG markers may explain part of this residual variability.
So far, only 2 studies have assessed the effect of the CYP3A4*22 allele on tacrolimus dose requirement in adult heart transplant recipients. The first of these studies was characterized by a cross-sectional design with a relatively small number of patients (n = 76).306 In contrast to observations made in kidney and liver transplantation, no significant association was found between the CYP3A4*22 SNP and tacrolimus dose requirement, even if the combined CYP3A4 and CYP3A5 genotype was studied. In the second study, no association between CYP3A4 genotype and tacrolimus dose requirements was observed. However, only one CYP3A4*22 carrier was detected among the 52 tacrolimus-treated heart transplant recipients.308 By contrast, in a cohort of 60 pediatric heart transplant recipients, it was observed that CYP3A4*22 carriers needed a 30% lower tacrolimus dose to reach similar exposure compared with CYP3A4*1/*1 patients.305 This study was characterized by a longitudinal design in which 13 time points were analyzed during the first 2 weeks after transplantation. This more rigorous design might explain why the difference was significant despite the low number of patients included in the analysis. The differences were even more significant when patients were categorized into the different CYP3A genotype clusters. It was demonstrated that CYP3A poor metabolizers required 17% less tacrolimus compared with intermediate metabolizers and 48% less than extensive metabolizers. These findings suggest that the combined CYP3A4 and CYP3A5 genotype can provide useful information to guide tacrolimus immunosuppressive therapy after heart transplantation.
As for other SOT populations, most studies have not reported a clear association between ABCB1 genotype and tacrolimus PK or dose requirement among heart transplant recipients.302,304,307,308 A minor association between the ABCB1 1236C>T SNP and tacrolimus dose requirement was observed in a mixed cohort of 60 heart transplant recipients309 in which 36 SNPs were investigated in relation to the efficacy, safety, and PK of immunosuppressive drugs, including tacrolimus (n = 23). In this small study, it was shown that patients homozygous for the 1236C allele had a lower tacrolimus C0/D than carriers of the 1236T variant allele, although this difference was not statistically significant.
Only one study in heart transplantation has evaluated the association between POR*28 and tacrolimus PK.308 In this study, associations between 7 SNPs and dose-adjusted tacrolimus C0 at 1, 3, 6, and 12 months after heart transplantation were evaluated in 52 patients. Carriers of the POR*28 variant had a higher dose-adjusted tacrolimus C0 at all time points, but the differences were only significant at months 3 (P = 0.025) and 6 (P = 0.047) after transplantation. Interestingly, this POR*28 effect was observed without consideration of the CYP3A5*3 allelic status, whereas in other forms of solid organ transplant, the defect caused by the POR*28 allele was only apparent among CYP3A5 expressors.
PG in Lung Transplantation
As expected, among lung transplant recipients, CYP3A5*1 carriers require higher doses of tacrolimus to reach the target therapeutic range throughout the first post-transplant year, suggesting that doubling the dose in CYP3A5 expressors might also be an option in lung transplantation.310 This was confirmed in a popPK study in which the tacrolimus clearance was found to be 40% higher compared with nonexpressors.94 With regard to the influence of ABCB1 genotype, available data are limited, but 2 studies suggested that ABCB1 haplotypes associated with high ABCB1 pump function are characterized by a lower exposure to tacrolimus in adult lung transplant recipients.310,311 This association is controversial because this observation was not confirmed in studies investigating a mixed population of CYP3A5 expressors and nonexpressors.310 This suggests that the effect of ABCB1 genotype on tacrolimus exposure is probably limited and likely clinically not relevant. No study on the association between CYP3A4*22 and tacrolimus PK/dose requirement after lung transplantation has been reported.
Executive Summary and Practical Recommendations
The association between CYP3A5 genotype and tacrolimus dose requirements is consistent and has been observed among kidney, liver, heart, and lung transplant recipients, both adult and pediatric. Patients expressing CYP3A5 require an at least 50% higher tacrolimus dose to reach the target therapeutic range compared with nonexpressors (A I). Although there is evidence from a randomized-controlled clinical trial that basing the tacrolimus starting dose on an individual's CYP3A5 genotype may facilitate tacrolimus dosing, this has not been a universal finding, and there is currently no convincing clinical evidence that a PG-based approach to tacrolimus dosing improves clinical outcomes after SOT. Of the many other candidate SNPs that have been studied, CYP3A4*22 seems to be the most promising because it explains residual variability in tacrolimus PK (B II, and C2 II, for whites, Asiatic, and African origin populations, respectively). We believe that future studies should investigate the clinical benefit of popPK models including CYP3A5 and CYP3A4 genotype (and possibly other genetic markers), to guide tacrolimus dosing.
PD BIOMARKERS FOR TACROLIMUS MONITORING
PD Biomarkers for the Action of Tacrolimus
PD biomarkers can be either drug-specific or non–drug-specific.312 In the case of tacrolimus, drug-specific biomarkers are related to the signal transduction pathways and enzyme activities inhibited by the drug, whereas nonspecific biomarkers reflect the inhibition of T-cell activation and proliferation in general, including cytokine production. PD biomarkers can be determined either directly in whole blood, in whole blood stimulated with mitogens, antibodies, in donor leucocytes, or third-party cells and in isolated lymphocytes, either quiescent or stimulated. PD biomarkers used to monitor tacrolimus PD effects and activities are listed in Table 5 and illustrated in Figure 3.
Drug-Specific PD Biomarkers
Calcineurin Phosphatase Activity
Attempts to measure CaN activity to monitor the effect of tacrolimus have been pursued since very early after the release of the drug. One of the earliest approaches was to follow the dephosphorylation of a radioactively labeled peptide substrate by measuring the 32P released.313 To avoid radio isotopes, a phosphorylated peptide substrate R II has been synthesized, which can be measured in its dephosphorylated state by high-performance liquid chromatography.314 However, it turned out that this approach is less sensitive compared with the radiometric method and recently an LC-MS/MS–based method was published by Carr et al.315 All methods to measure CaN activity using a synthetic substrate suffer from nonspecificity because other intracellular phosphatases besides CaN also dephosphorylate the peptide. Therefore, this nonspecific activity must either be measured separately or subtracted from the assay readout, or specific inhibitors, and CaN activators must be added to the incubation mixtures to achieve a CaN-specific result.316 CaN activity is determined in PBMC, which requires cell isolation before the assay can be performed. Unfortunately, results from different assays are reported in method-specific units, which make the comparison of data difficult. Several groups have measured CaN activity in patients after liver and kidney transplantation and, in general, observed an inverse relationship between CNI concentrations in whole blood and CaN activity in PBMC, but with a high between-individual variability.317–319 A common observation with in vitro experiments is that, in contrast to the CNI cyclosporine, tacrolimus does not attain 100% inhibition of the enzyme even at very high concentrations.315,318,319 In one study, the EC50 of tacrolimus was 26.4 ng/mL above the accepted upper limit of the therapeutic range (20 ng/mL).318 In line with this observation, tacrolimus C0 have been shown to be without effect on the CaN activity.315 This questions the measurement of CaN activity as a PD marker of tacrolimus effect, particularly in blood samples that are drawn as trough samples immediately before the next dose. However, several groups observed an association between CaN activity and clinical events such as nephrotoxicity in liver transplant patients318 or rejection in kidney graft recipients.320 Surprisingly, CaN measured by high-performance liquid chromatography did not correlate at all with intracellular tacrolimus concentrations in PBMCs.230
There are contradicting results about the appropriate time point for PBMC isolation after tacrolimus administration. Some authors observed better effects 2 hours after dosing, which reflects tacrolimus peak concentrations, whereas others noted a greater inhibition of enzyme activity after 4 hours.314 An intrinsic drawback of all PD assays to measure enzyme activities in isolated PBMC is the fact that the drug is lost during the isolation and washing steps, and that the cells are incubated in a nonphysiological environment. Furthermore, CaN assays are not standardized and are, except for the LC-MS/MS method, barely validated in terms of their analytical performance.315
Nuclear Translocation of NFAT by Flow Cytometry
A downstream event of CaN is the translocation of the dephosphorylated NFAT to the nucleus. NFAT is a family of transcription factors, and NFAT has been reported to be particularly sensitive to inhibition by tacrolimus.321 Intranuclear translocation of NFAT1 can be followed by flow cytometry image stream (AMNIS).322,323 For this purpose, whole blood is stimulated ex vivo with phorbol myristate acetate/ionomycin for 30 minutes, and the nuclear localization of NFAT1 is visualized in stimulated and nonstimulated CD4+ and CD8+ T cells using an anti-NFAT1 antibody and 4′,6-diamidino-2-phenylindole (DAPI) staining.323 Maguire et al323 observed in a small study with 3 renal transplant patients an inverse correlation between tacrolimus concentrations and nuclear translocation of NFAT1. The assay is time-consuming, requires special equipment and fresh blood samples. Furthermore, it has yet to be thoroughly validated.
Using multiparametric flow cytometry, Noceti et al investigated the effect of tacrolimus along the CaN pathway in lymphocytes in healthy human participants (n = 35)324 and in patients on the waiting list for liver transplantation (n = 19).279 As described by I/Imax models, increasing doses of tacrolimus proportionally inhibit NFAT1 translocation as well as IL-2 and CD25 expression. Moreover, IL-2 and CD25 response to the inhibition of NFAT1 expression in PBMC nuclei follow allosteric sigmoidal models, suggesting tight signal translation along the CaN pathway.324
Between-individual variability was higher under nonstimulated than understimulated conditions, as well as in the presence of tacrolimus.325 IL-2+CD8+ cells at tacrolimus Imax showed the highest tacrolimus between-individual variability, suggesting its usefulness as a biomarker of individual effects integrating many different sources of regulation and variability. Multivariate statistical analysis showed the influence of PG polymorphisms on tacrolimus PD.279,324
The aforementioned study also demonstrated the feasibility of using nonstimulated PBMCs to explore the CaN pathway under more physiologic conditions while integrating more variability than in the equivalent stimulated status.279 The largest variability was observed at tacrolimus IC50, which is in line with the large between-individual differences observed in clinical drug effects among patients. NFAT1 translocation might reflect the degree of individual immunological capacity, as Sommerer et al and Zhan et al, reported a correlation between lower NFAT-regulated gene expression and increased frequency of infection episodes in transplant patients.326,327
NFAT1 translocation to PBMC nuclei promises to be a suitable candidate biomarker to monitor tacrolimus PD after transplantation. However, further evidence in terms of potential associations of the extent of NFAT1 translocation with clinical outcomes is still needed.
NFAT-Regulated Gene Expression
As CNI trough or peak concentrations are only marginally associated with clinical outcome, if at all, several approaches to measure the biologic effects of CNI-based immunosuppression have been developed, including the assessment of CaN phosphatase activity, cytokine release, and gene expression.313,328–334
Quantitative analysis of gene expression has been established to calculate the functional effects of CaN inhibition, specifically the inhibition of the transcription of NFAT-regulated genes in peripheral blood.313,326,335,336 This assay is based on the quantitative analysis of IL-2, IFN-γ, and GM-CSF gene expression in whole blood samples collected at cyclosporine/tacrolimus C0, and peak concentrations (2 hours for cyclosporine and 1.5 hours for tacrolimus) after oral administration.
The real-time PCR technique provides a rapid, highly reproducible, and sensitive tool for the quantitative analysis of gene expression.335 This method can be semiautomated and standardized and performed in specialized laboratories. Whole blood samples are stable for 24 hours at 20°C. Although overall gene expression is reduced upon storage, the relative degree of NFAT inhibition remains stable during this period. NFAT-regulated gene expression has shown low analytical variability (less than 10%) in repeated measurements. Although between-patient variability is high, within-individual variability is low in patients on stable CNI doses and stable immunosuppressive comedication.334,335
This assay can be set up with satisfactory analytical performance in a routine molecular biological laboratory.326,337–339 Linearity, imprecision, and limit of quantification, as well as sample stability, were investigated. A between-laboratory comparison showed comparable results.337 The reproducibility of the NFAT-regulated gene expression assay across laboratories could facilitate the implementation of this assay for the PD routine monitoring of CNI in different centers. Therefore, this monitoring technique could be used in larger patient cohorts and in multicenter clinical studies.
NFAT-regulated gene expression has been performed in SOT such as kidney (adults and children), liver, heart, and lung transplant recipients.316,331,337 Beneficial effects in the early post-transplant period were confirmed, as well as in the long-term follow-up after transplantation.316,338,340 However, most of the evaluations included maintenance allograft recipients.316,326,341–348 These results summarized data on acute rejection, opportunistic infections, malignancy (eg, nonmelanoma skin cancer), and cardiovascular risk or outcome. In observational, cross-sectional and prospective clinical trials, including one prospective case–control study, monitoring of residual NFAT-regulated gene expression has been proven as a tool to reduce cyclosporin A therapy in stable renal allograft recipients.316,331 In one prospective randomized controlled clinical study, the novel approach of monitoring residual NFAT-regulated gene expression led to a significantly reduced cardiovascular risk and improved allograft function in stable renal transplant recipients compared with the standard monitoring by cyclosporine trough concentrations.349
In a considerable proportion of patients treated with tacrolimus, the inhibition of NFAT-regulated gene expression is lower compared with cyclosporine treatment, possibly partially due to a low relative increase of tacrolimus concentrations from C0 to Cmax.346 The lack of CaN inhibition in patients treated with tacrolimus has been described previously by direct quantification of the enzyme activity,315,318,319 suggesting additional, as yet unidentified targets of the drug. Nevertheless, several studies on tacrolimus treatment showed that monitoring residual NFAT-regulated gene expression could identify allograft recipients at higher risk of infections or acute rejection.338,343,346,347
NFAT-regulated gene expression is a promising biomarker in CNI therapy in regard to infectious complications, malignancies, acute rejection, and cardiovascular risk. Within the last years, an assay which detects NFAT-regulated gene expression was introduced in clinical studies in several transplant centers in Europe and United States with encouraging results. 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 patients treated with cyclosporine or tacrolimus, NFAT-regulated gene expression has the potential to develop into a monitoring tool complementing PK, especially in long-term renal allograft recipients.
The inhibitory effect of tacrolimus on the phosphorylation of intracellular signaling molecules can be measured by phospho-specific flow cytometry in T cells and their subsets. There are scarce examples in which this approach has been used. One recent study by Kannegieter et al350 followed the phosphorylation of p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase (ERK), and protein kinase B (AKT) in CD3+, CD4+, and CD8+ T cells. In nonstimulated whole blood samples, p38 MAPK and AKT were inhibited after kidney transplantation in CD4+ and ERK in CD8+ cells in patients treated with a tacrolimus-based immunosuppressive therapy. Stimulation of whole blood samples with phorbol myristate acetate/ionomycin revealed lower phosphorylation of p38 MAPK and AKT in CD4+ and CD8+ cells compared with samples obtained before initiation of immunosuppressive therapy. Patients experiencing acute rejection episodes, but on therapy with belatacept, showed higher phosphorylation of ERK in both CD4+ and CD8+ cells compared to patients without rejection. Authors from the same group expanded monitoring of p38 MAPK, ERK, and AKT phosphorylation to CD14+ monocytes in 20 kidney transplant recipients receiving a tacrolimus-based immunosuppressive regimen. However, no association with clinical outcome or tacrolimus dose adjustments was reported.351 A small conversion study of a twice-daily to a once-daily formulation of tacrolimus with 12 stable kidney graft recipients was accompanied by the monitoring of p38 MAPK phosphorylation. After conversion to the once-daily formulation, a higher phosphorylation of 11.4% in CD4+ T cells and 15.6% in CD8+ T cells was observed despite comparable tacrolimus C0. The authors considered phosphoflow as a sensitive approach to assess the PD effects of tacrolimus.352 An advantage of phosphoflow cytometry is that no cell isolation is required. However, samples should be freshly analyzed, and experience with the technique and appropriate instruments are required, which currently precludes a wider application.
IL-2 mRNA Expression and Production in Leukocytes
The terminal downstream effector of the CaN pathway is the cytokine IL-2, which can be followed as a PD read-out of tacrolimus action by studying its mRNA expression. Alternatively, the intracellular formation can be followed in T cells by flow cytometry or the release from leukocytes into cell culture media.353 In a study with 8 renal transplant patients treated with tacrolimus, mycophenolic acid, and steroids, PBMC were isolated and stimulated with an anti-CD3 mouse monoclonal antibody.353 The authors observed an inverse association between tacrolimus whole blood concentrations and IL-2 mRNA expression, the number of IL-2 producing CD4+ T cells, and the extracellular IL-2 concentration.353 Vafadari et al271 showed an effect of the ABCB1 genotype on intracellular IL-2 expression as a PD read-out of the tacrolimus effect on T cells from kidney transplant recipients. The ABCB1 3435CT SNP has been shown to affect transporter activity, whereby the CC genotype has been associated with a higher activity compared with the TT genotype. In turn, this leads to lower tacrolimus concentrations inside the cells, which are associated with less IL-2 expression.
Drug Non-Specific PD Biomarkers
ATP Release From CD4+ T Cells
T-cell activation can be assessed by a commercial assay (ImmuKnow; Viracor-IBT Laboratories, Lee's Summit, MO) to follow ATP release from stimulated CD4+ T cells. The assay has been used to assess the PD effect of switching from a twice-daily formulation of tacrolimus (Prograf) to a once-daily formulation (Advagraf) in simultaneous pancreas–kidney graft recipients.354 Unlike the effects observed in kidney transplant patients on p38 MAPK phosphorylation,350 no significant effect was seen on the ATP production, suggesting an equal PD effect of both formulations.354 Similar results have been reported before in living-donor liver transplant recipients.355 In a recent study involving liver transplant patients, the tacrolimus dose was individualized in 102 of 202 patients on the basis of the ImmuKnow assay results. Tacrolimus doses were either reduced or increased by 25% when ATP concentrations were <130 ng/mL (strong-immune response) or >450 ng/mL (low-immune response), respectively. Patient survival was higher, and the incidence of infections lower in the interventional arm compared with the 200 controls who were dosed according to trough blood level results only. Patients without adverse events had, in general, a lower tacrolimus dose and trough concentrations.356
T-Cell Proliferation and Surface Activation Markers
The impact of tacrolimus on inhibition of T-cell proliferation was measured ex vivo by flow cytometric quantification of the proliferation cell nuclear antigen in peripheral blood of heart transplant recipients.357 Furthermore, in patients with chronic kidney disease, ex vivo peripheral blood analysis by flow cytometry showed inhibitory effects of tacrolimus on T-cell proliferation after labeling with carboxyl fluorescein diacetate succinimidyl ester (CFSE).358
Regarding early T-cell activation markers, investigations involving renal transplant patients have demonstrated that CD40L and CD69 are suppressed during tacrolimus immunosuppression,328 which was even more evident after conversion from cyclosporine to tacrolimus.359 In general, tacrolimus treatment suppresses T-cell markers (CD25, CD95, and CD154), costimulatory molecules (CD28 and ICOS), and adhesion molecules (CD54) in patients after SOT (see Table 1, Supplemental Digital Content 1, http://links.lww.com/TDM/A321).328,357,359–362
Tacrolimus immunosuppression also affects T-cell differentiation into distinct effector cell types such as CD4+, CD8+, and Th17 cells. Although the percentage of CD4+ and CD8+ T cells has been reported to be reduced359,363,364 without changing the CD4+/CD8+ T-cell ratio,365 the CD4 suppressor activity was enhanced.359 The percentage of natural killer (NK) cells was not affected by tacrolimus treatment; however, the NK cell proliferation, NK cytotoxicity, and cytokine secretion in response to IL-2 were inhibited after tacrolimus treatment.366 In addition, tacrolimus impaired IL-2R and signal transducer and activator of transcription 3 (STAT3) signaling and induced a downregulation of NK receptors, which in turn induced proliferative and functional defects of NK cells366 resulting in an impaired innate immunity.363
Investigation of the effects of T-cell activation has its limitations. For example, the immunosuppressive effects of tacrolimus are rapidly lost once dosing is briefly interrupted,364 and it is unknown to what extent tacrolimus-impaired T-cell function may be altered by infection or rejection of a transplanted organ.357 Furthermore, the effects of tacrolimus, which has a 10- to 100-fold greater immunosuppressive potency than cyclosporine,328 could be influenced by genetic variations. For example, a strong association between tacrolimus PD and polymorphisms in the gene-encoding cyclophilin A, CaN catalytic subunit, and CD25 has been reported.324 Moreover, the pharmaceutical formulation of tacrolimus may result in different concentration–time curves, which may impact T-cell activation. A study based on liver transplant patients suggested that the originator formulation of tacrolimus may affect regulatory T cell (Tregs) differently than generic tacrolimus formulations.366 Furthermore, switching between once- and twice-daily tacrolimus formulations may influence drug efficacy.352,367 The fact that T-cell proliferation and activation measured by CD25 and CD71 expression trended higher after conversion from mycophenolate mofetil to enteric coated mycophenolate sodium,368 suggests that investigating the effects of tacrolimus on immune function also needs to consider immunosuppressive comedication.
Based on its mechanism of action, the clinical utility of analyzing the expression of different cytokines in lymphocytes, with a notable role on T-cell response (CD4+ and CD8+ T cells), has been evaluated to assess the individual effects of tacrolimus on the immune response after SOT.15,369–371 Most studies published so far focused on the intracellular expression of IL-2 and IFN-γ, 2 cytokines that play a key role in the activation of the alloresponse.15,370,372
From an analytical point of view, 2 methodologies are mainly used: flow cytometry and the enzyme-linked immunosorbent spot (ELISPOT).
Several studies have focused on the utility of intracellular expression of IL-2 and IFNγ as prognostic biomarkers for the risk of acute rejection, as diagnostic biomarkers at the time of rejection, and as markers reflecting the efficacy and the safety of tacrolimus. Until now, only a few multicentre prospective trials have been reported. Millán et al373 evaluated the ability of these biomarkers to predict the risk of acute rejection in 142 transplant recipients (63 liver/79 kidney) recruited from 4 Spanish centers. Changes in the percentage expression of IL-2 in CD8+CD69+, IFN-γ in CD4+CD69+, and IFN-γ in CD8+CD69+ were evaluated using flow cytometry before transplantation and during 1 year after transplantation. The results demonstrated that, in those patients who suffered acute rejection, a significant increase of the intracellular expression of these cytokines was observed.
An ongoing randomized multicenter European study (Biodrim; Health F2-2012-305147) is currently evaluating the utility of IFN-γ and IL-2 ELISPOT assay during tacrolimus minimization therapy to stratify renal transplant patients into low and high responders. The CTOT-01, a prospective, multicenter, observational study designed to determine the diagnostic and prognostic utility of a panel of noninvasive biomarkers for transplant outcomes in renal recipients included IFN-γ ELISPOT analysis before and after transplantation. The same group recently evaluated the adverse outcomes of tacrolimus withdrawal in a cohort of nonsensitized primary renal transplant recipients. Analysis of donor-reactive IFN-γ ELISPOT correlated with the development of DSA and/or acute humoral rejection in the tacrolimus withdrawal group.374
Results from Boleslawski et al375 showed that percentage of CD3+CD8+IL-2+ expression (measured before and after transplantation) could be a surrogate marker to identify patients treated with tacrolimus or cyclosporine at high risk for acute rejection. In line with these results, Akoglu et al376 found that IL-2 production in CD8+ T cells correlated with Banff score (Spearman's rho = 0.81; P = 0.027) in adult liver transplant recipients treated with CNI. Ahmed et al377 demonstrated that the frequency of CD8+ and CD8− cells that synthesized IL-2 and IFN-γ correlated with the biologic effect of tacrolimus in kidney and liver transplant patients. In stable liver transplant recipients undergoing weaning from immunosuppressive therapy, Millan et al378,379 identified the percentage of IFN-γ expressing CD4+ and CD8+ T cells as surrogate markers for the risk of rejection in stable liver transplant recipients after withdrawal of long-term immunosuppressive treatment, using flow cytometry378 and also in de novo adult liver transplant recipients receiving tacrolimus, mycophenolic acid, and prednisone.379 Furthermore, the latter study379 demonstrated that the analysis of the degree of inhibition of IFN-γ and IL-2 expression in CD8+ T cells could be useful in identifying those patients with a high susceptibility to tacrolimus, avoiding unnecessarily high levels of immunosuppression in this group. Patients with an inhibition of these biomarkers lower than 40% during the first week after transplantation developed acute rejection. Moreover, the capacity to inhibit IFN-γ during the first week after transplantation was related to the severity of acute rejection, in agreement with the results reported by Akoglu et al.376 All together, these results suggest that measuring expression of IL-2 and IFN-γ in lymphocytes may reflect the individual response to tacrolimus and may be useful for dose adjustment. Recently, Noceti et al324 investigated the CaN/NFAT pathway in a new model of nonstimulated PBMC and its response to increasing tacrolimus concentrations. This group reported that intracellular IL-2 expression in CD4+ T cells correlated with tacrolimus concentrations, and in agreement with other groups, a large between-individual variability was found, especially for IL-2–producing CD8+ cells.
Another intracellular cytokine that has been explored to evaluate tacrolimus effect on T-cell activation is IL-17. This proinflammatory cytokine is produced by the third subset of effector T cells named Th17.380 To date, only a few studies have assessed the effect of tacrolimus on Th17 responses. First, Abadja et al381 evaluated by ELISPOT how tacrolimus and mycophenolic acid interfere with IL-17 production. The main finding of this study was that both drugs are capable of inhibiting Th17-related response. Mycophenolic acid seems to lead to an even stronger IL-17 suppression than tacrolimus. The combination of mycophenolic acid with low doses of tacrolimus tends to produce lower circulating IL-17 levels than after treatment with a conventional dose tacrolimus given alone. Thus, tacrolimus minimization strategies might help to better control Th17 immunity when mycophenolic acid is part of the immunosuppressive therapy. Second, in a prospective sequential study including renal allograft recipients receiving tacrolimus-based therapy, Chung et al382 showed that Th1 effector T-cell subsets were decreased after transplantation due to the effect of the immunosuppressive therapy, but not Th17-producing and IL-17–producing effector memory T cells. One possible explanation for these discrepancies between both studies was the difference in PBMC sampling: in one study, PBMC were sampled from patients exposed to tacrolimus for a long time and who showed a stable clinical course, while the second PBMC sampling was performed within 3 months after transplantation. More recently, Merino et al383 demonstrated in an in vitro study that tacrolimus reduced the production of IL-2 in memory T cells, whereas it completely inhibited naive T cells, but the production of IL-17 did not change significantly. Therefore, tacrolimus seems to be more effective in controlling alloreactive memory T cells responsible for potential rejection episodes.
In summary, the measurement of intracellular cytokines, such as IFN-γ and IL-2, may reflect the status of T-cell response and the immunomodulatory effect of tacrolimus in each transplant patient, whereas the role of IL-17 requires further investigation. To advance in the process of validation of these biomarkers as early predictive biomarkers of the risk of rejection and graft clinical outcome, it is crucial to improve some methodological aspects. Thus, commercial and common cellular reagent standards and panel of reactive T cells (eg, pool of donor antigens that reflect the potential of organ donors) should be available to further standardize and harmonize these functional assays.
Donor-Specific Anti-HLA Antibodies
With recent advances in the diagnostic armamentarium, the understanding of the importance of anti-HLA antibodies has increased.384–386 The development of standardized highly sensitive solid-phase assays for detecting anti-HLA antibodies has significantly improved the clinical utility of this biomarker to predict ABMR and allograft injury in kidney transplant recipients. However, current methods have their limitations and are only semiquantitative.387 Today, DSA and the subsequent development of ABMR are considered to be leading causes for graft loss. The negative impact of dnDSA on long-term outcome after kidney transplantation has been demonstrated in many studies,384–390 and it is estimated that 20%–30% of kidney allograft recipients will develop dnDSA after transplantation. The detection of anti-HLA DSA is widely implemented in clinical practice as an important biomarker for the assessment of pre-transplant and post-transplant risks of ABMR and allograft loss.384 However, predictive capabilities of this biomarker alone are limited in the individual.387,388,390 Outcome in patients with dnDSA is variable, with some patients rapidly developing acute ABMR, while others having an indolent clinical course for years.384–390 It is believed that among other factors, differences between DSA with regard to the complement-fixing capability, IgG subclass, concentration, affinity, and avidity are responsible for the observed differences in outcome.384–387,391 In addition, the presence of complement-fixing DSA may guide treatment392 and scoring systems with additional clinical data improving its utility.393 Although many questions on the predictive capabilities of DSA are under investigation,394 there is consensus that dnDSA constitute an important noninvasive biomarker after renal transplantation, and regular testing is recommended.384,386
Despite the progress in diagnostics, there is an unmet medical need with respect to therapeutic approaches,394 and data on immunosuppression and in particular data on effects of tacrolimus exposure on DSA are scarce. For 2 decades, tacrolimus is an integral part in immunosuppressive protocols for immunologically high-risk patients and in desensitization protocols.395 Despite lack of firm evidence, higher tacrolimus exposure is usually used in high-risk patients despite potential overimmunosuppression and toxicity concerns. Several studies demonstrate that poor adherence is an important risk factor for the development of dnDSA.384–386,396,397 Poor adherence may lead to “drug holidays,” to low and variable tacrolimus trough levels, which might result in high within-patient tacrolimus variability. Several retrospective studies demonstrate a negative impact on long-term outcome in patients with high within-patient tacrolimus variability137,398,399 (see Pharmacokinetics Section). Similar to nonadherence, physician-guided CNIs withdrawal may result in the development of dnDSA.374,400 In this regard, it is interesting to note that CNIs have no direct effect on B-cell proliferation and function, and thus, effective suppression of the T-cell interaction with B cells seems important for the prevention of dnDSA.401,402 So far, only very few studies have investigated tacrolimus levels with regard to the development of DSA.403,404 Still, retrospective studies have to be interpreted with caution, as bias by indication may limit the conclusion: for example, patients with low CNI levels may have been put on purpose on low exposure (eg, due to CNIs toxicity, elderly marginal kidney) or low exposure is a consequence of nonadherence. Only one prospective study404 of a steroid-free cohort treated with 1.2- to 1.4-g/d mycophenolic acid reported that patients with a 50% lower tacrolimus dose starting at month 4 after transplantation developed more dnDSA (tacrolimus levels at month 6: mean 4.1 versus 6.7 ng/mL). Obviously, more data are needed to address the important question.
DSA after heart transplantation are integral to the development of ABMR,405 which is, accompanied with graft failure, a major cause of mortality up to 40%406 or even higher in terms of a late onset after heart transplantation.407 Furthermore, DSA also evidently promote cardiac allograft vasculopathy.408 ABMR rates of approximately 25%–40% occur in desensitized patients after heart transplantation. Moreover, approximately 20%–30% of nonsensitized heart transplant recipients develop dnDSA with associated ABMR in most cases. Although an ISHLT consensus report on the management of antibodies preoperatively and postoperatively in heart transplantation,409 well-designed studies in heart transplantation are scarce, and published results are mainly about retrospective single-center experience as published recently.410 Thus, strategies of monitoring and treatment of DSA vary widely among heart transplant centers.
In general, DSAs develop as a result from inadequate immunosuppression in the long-term after heart transplantation or because of activation of established memory responses to alloantigens in sensitized heart transplant recipients. Consequently, the first step after detection of DSA is to initiate or to intensify tacrolimus exposure as the CNI of choice411 because tacrolimus significantly decreases rejection compared with cyclosporine.412
In summary, in kidney and heart transplantation, DSAs are a biomarker of underimmunosuppression, which may happen if patients are nonadherent but may also occur in both CNI-free and CNI-minimization clinical protocols. Most physicians agree that tacrolimus (with trough blood levels >5 ng/mL) is the CNI of choice in case of dnDSA, although no data from prospective controlled multicentre studies are available.
Graft-Derived Cell-Free DNA
Quantification of dd-cfDNA in recipient blood or urine has been evaluated as a potential diagnostic biomarker for graft injury413–415 and acute rejection.413,416 Results from Sigdel et al417 demonstrate that serial monitoring of urinary dd-cfDNA can be a sensitive surrogate biomarker of acute injury in the donor organ but lacks the specificity to distinguish between acute rejection and BK virus nephropathy injury. More recently, plasma levels of dd-cfDNA, from 102 kidney transplant recipients, were correlated with allograft rejection and outcome. The results suggest that dd-cfDNA may be used to assess allograft rejection (T-cell-mediated as well as antibody-mediated) and injury.418
In a study including 10 adult liver graft recipients, Oellerich et al investigated whether graft-derived cell-free DNA (GcfDNA or d-cfDNA) quantification could be used to assess the minimally effective trough concentrations of tacrolimus, they observed an increase in GcfDNA of more than 10% at a tacrolimus cutoff of 6.8 ng/mL.419 The authors concluded that measuring GcfDNA could be useful to establish effective tacrolimus concentrations in liver transplant patients and to individualize immunosuppressive drug regimens. Later, a prospective, observational multicentric study including 107 adult liver transplant recipients420 showed that monitoring GcfDNA in plasma by droplet digital PCR based on a limited number of predefined SNPs allowed for earlier and more sensitive discrimination of acute rejection as compared with conventional liver function tests.
Although dcfDNA seems to be a promising biomarker for monitoring graft health after transplantation,421 multicenter, prospective, observational, and interventional studies will be required to better define how it can be used and evaluate its clinical utility before considering it a valid biomarker in SOT.
Executive Summary and Practical Recommendations
- Determination of residual NFAT-regulated gene expression supports the identification of renal transplant recipients at higher risk of acute rejection, opportunistic infections, malignancy, and cardiovascular risk (B II).
- Monitoring residual NFAT-regulated gene expression complements CNI PK as an adjunct to guiding CNI therapy (B III).
- Monitoring intracellular T-cell IFN-γ production (particularly by the enzyme-linked immune-spot, ELISPOT, assay) before and early after transplantation can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II).
PD monitoring of tacrolimus therapy has not entered routine monitoring yet. To advance the validation of PD and immunologic biomarkers, it is crucial to improve and standardize some methodological aspects. The clinical implementation of these biomarkers as a complement of tacrolimus TDM may have a tremendous impact in patient and graft care.
PK/PG MODELING AND PK/PD MODELING FOR TACROLIMUS
Population PK Modeling of Tacrolimus
The PK of tacrolimus have been described extensively in the literature using 1- and 2-compartmental disposition models with first-order elimination in 61% and 39% of PopPK studies, respectively.12 About a fifth of models incorporated a lag time to describe delayed drug absorption, while a few studies attempted to describe a more complex absorption process using a gamma distribution to describe the absorption rate and then to convolute it with a single exponential or multiexponential impulse response.422 The integral of this function represents an asymmetrical, S-shape absorption phase, the asymmetry of which increases when the absorption rate decreases, followed by drug distribution in 1 or 2 compartments and elimination from the central compartment. An Erlang model with a range of transit compartments has also been proposed, which represent a special case of the gamma distribution, in which the exponent is an integer that represents the number of transit compartments that the drug has to cross to reach the central compartment.423 Most models have been based on oral PK curves of whole blood tacrolimus concentrations, and most commonly, tacrolimus whole blood apparent clearance (CL/F) was characterized.
Although the absolute bioavailability of tacrolimus has been reported to be on average 25%–30%,424 in 4 studies in which both intravenous and oral PK data for twice-daily tacrolimus were available, typical bioavailability (F) was estimated and reported to range from 7.3% to 19.7%.12
Variability in tacrolimus whole blood apparent clearance among transplant recipients in these models was most commonly related to CYP3A5 genotype (rs776746), patient hematocrit, patient weight, corticosteroid dose, postoperative day, and a significant reduction in hepatic function (aspartate aminotransferase). Although comedication is also an important determinant of tacrolimus disposition, most data sets did not contain information about the use of strong CYP3A inhibitors or inducers.12
Trough concentrations are used in most transplant centers for the TDM of tacrolimus. Although much easier to obtain and convenient for the patient, C0 monitoring seems far from the ideal biomarker. Indeed, it has been shown that rejection and toxicity could occur even if the C0 was within the proposed therapeutic window.10,91 As explained previously in the Pharmacokinetic Section, there is some evidence that C0 correlates poorly with AUC0–12.425,426
An alternative to single time point measurements or full concentration–time profiling to calculate AUC is the use of a population PK model in a Maximum A Posteriori (MAP) Bayesian forecasting technique to estimate AUC based on a limited number of measurements, generally taken in the first few hours of the dosing interval. MAP Bayesian forecasting with use of this population PK models can be a beneficial tool for accurate TDM.11
Bias in the prediction of tacrolimus AUC using PopPK models has ranged from 15% to 10% (assessed by mean prediction errors), imprecision ranging from 0.81% to 40% (measured as root mean squared error or mean absolute prediction error), and R2 values ranged from 0.27 to 0.99. About two-thirds of MAP Bayesian forecasting models that used 2 or more tacrolimus concentrations showed bias of 10% or less, but only 39% showed imprecision ≤10%.12
Currently, 5 tacrolimus formulations have been described in the transplant population (intravenous, twice-daily oral suspension, twice-daily oral capsule, once-daily oral capsule, and once-daily melting dose) all with slightly different models and optimal limited sampling models. The combination of sampling times at 0, 1, and 3 hours after dose consistently showed bias and imprecision values of less than 15% with one exception; the new prolonged tacrolimus melting dose formulation requires sampling times of 0, 8, and 12 hours after dose intake.12,427 This difference suggests that an alternative blood sampling procedure is required, such as dried blood spot monitoring, to make AUC monitoring feasible for this formulation.
It is now well accepted that CYP3A5*3 (rs776746) genetic profiling is informative to guide initial tacrolimus dosing,256,428 and several popPK models including CYP3A5 genotype have been developed for tacrolimus, as summarized above. As outlined in the different previous sections, PG may influence a large part of the between-individual difference in the clearance of tacrolimus or its intracellular distribution. This influence might be quantified through popPK modeling strategies. There are several advantages of popPK modeling over other PG analyses: (1) the ability to quantify the effect of covariables of interest and, thereby, make quantitative assumptions about the magnitude of SNP effects on all PK; (2) it facilitates routine dose adjustments using MAP-BE; and (3) it enables simulation studies, allowing for dose recommendations.11 This approach has given rise to numerous interesting studies, but except for CYP3A polymorphisms, no clear conclusions can be drawn at this stage, especially due to the ethnic diversity in allelic frequencies and the lack of large studies and/or unbiased data.
Multiple popPK models describing the PK effect of CYP3A5*3 have been developed so far in different types of transplantation populations, and on average, they showed that introducing the CYP3A5*3 genetic status of the patient in the model explains approximately 30% of the variability in the tacrolimus CL when using a parametric model approach.28,33,94,290,299,429–445 Statistical considerations demonstrated that it also decreased the Akaike information criteria and improved the population and individual predictions in nonparametric models.263,445
In addition, confirming results observed in simple candidate gene association studies, it seems that popPK models including CYP3A4*22 (rs35599367) are more accurate than those not taking this SNP into consideration. However, this SNP seems relevant in whites not expressing CYP3A5 only because it has been demonstrated that CYP3A4*22 alone does not significantly improve the performance of tacrolimus popPK models.290,439 By contrast, 2 recent studies have shown that including CYP3A4*22 genotype information beyond CYP3A5*3 for clustering the patients into poor metabolizer (PM), intermediate metabolizer (IM), and extensive metabolizer (EM) improved the predictability performance of such models.263,439 Indeed, almost 40% of the residual variability was explained by CYP3A clustering in a popPK study using a parametric approach,439 whereas this clustering strategy was associated with the highest reduction in −2 log-likelihood in a popPK study using a nonparametric approach.263
By performing dosage simulations with their popPK model, Andreu et al439 defined that the highest percentage of patients with a C0 within the target therapeutic range (5–10 ng/mL) occurred after 4, 3, and 2 mg every 12 hours for EMs, IMs, and PMs, respectively (for hematocrit fixed at 34%). Consistently, Woillard et al developed a double gamma absorption model using a nonparametric approach (Pmetrics) including CYP3A4*22/CYP3A5*3 to refine the initial dose requirement of tacrolimus. Monte Carlo simulations were performed leading to the recommended starting doses of 0.07 mg/kg twice-daily for poor metabolizers, 0.13 mg/kg twice-daily for intermediate metabolizers, and 0.2 mg/kg twice-daily for extensive metabolizers. These recommendations have been approved recently and discussed and refined in a discriminant analysis of principal component. These proposed revisions are summarized in Figure 4 and are in line with the CPIC or PG National French Network recommendations.428 The principal difference between the developed strategy based on popPK studies and CPIC recommendations consists of decreasing the dose for CYP3A4*22 carriers-CYP3A5 defectives, that is, the so-called PM clusters, and to allow up to 0.4 mg/kg/d prescribed to the EM cluster. For these EM, it is specifically proposed to start therapy at 0.35 mg/kg/d and to further fine tune these doses using TDM.
Other SNPs of interest such as POR*28 (rs1057868) or CYP3A4*1 G (rs2242480) have also been investigated, and some researchers have even tried to model their impact on tacrolimus PK variability using popPK models. However, in comparison with CYP3A5*3 or CYP3A4*22, their effect on tacrolimus systemic exposure seems to be clinically nonsignificant, at least in the white population. By contrast, in the African population in which CYP3A4*22 has not yet been described and CYP3A5*1 carriers are the majority, other SNPs might still be important for explaining the residual PK variability. In this particular case, POR*28 status might be of interest to individualize the tacrolimus dose among CYP3A5 expressors as the POR*28 allele has been noted to influence tacrolimus PK but only in CYP3A5 expressors. To the best of our knowledge, none of the popPK studies reported to date has explored that possibility in the African population. In addition, in Asian patients, in whom the number of CYP3A5 expressors is between those of whites and Africans, it has been demonstrated that the combined genotype of CYP3A5-POR was the only covariant significantly related to the apparent clearance of tacrolimus. The situation is less clear for CYP3A4*1 G, found exclusively in the Asian population. Although this SNP has been associated with increased CYP3A4 activity and linked with tacrolimus PK in simple association studies in renal446 and hepatic transplantation,447 this effect has not yet been observed in popPK studies,448 limiting the relevance of these associations. Many other SNPs (eg, ABCB1, NR1I2, IL10, and PPARa) have also been investigated in popPK models, but mostly their inclusion failed to demonstrate any significant improvement of the model predictability and/or applicability. Concerning ABCB1 SNPs, the popPK studies investigating the influence of ABCB1 SNPs, such as the coding but synonymous 3435C>T or even the nonsynonymous 1199G>A, suggest a limited impact of these SNPs on tacrolimus blood exposure or bioavailability. Thus, it is generally accepted that ABCB1 genotype is not likely to have a clinical value when considering systemic PK. However, it seems that ABCB1 variants (associated with a decrease in its transport) may explain differences in tacrolimus tissue distribution and might influence the effective fraction of the drug that is available to exert its immunosuppressive activity in lymphocytes244,270 or its toxic effect in the kidneys.449–451 Unfortunately, none of the reported studies has used a popPK modeling approach to analyze those intracellular PK data. Hopefully, with the improvement of analytical techniques, this domain will be more extensively explored in the near future.
The number of studies addressing tacrolimus PK–PD relationships using modeling approaches is still very few compared to those related with pure PK modeling. Similar to other therapeutic areas,452,453 modeling and simulation methods for immunosuppressive drugs in transplantation are crucial for the quantitation and prediction for new clinical scenarios. This has already been outlined in the last biomarker consensus document,15 but very few advances have occurred since then. Improved analytical techniques have enhanced our ability to measure various biomarkers that could be related to CNI use. The increasing knowledge of the underlying physiological mechanisms involved in CNI PD could allow for the transition from an empirical to a quantitative framework. Development of PK-PD models could help to confirm the underlying physiological mechanisms and to facilitate the expansion and improvement of immunosuppressive treatments.
The Table 2 (see Supplemental Digital Content 1, http://links.lww.com/TDM/A321) gives an overview of some of the most relevant studies that have attempted to use mathematical or statistical modeling approaches in transplantation. Some of the so-called PK-PD studies found in the literature are based on correlations between PD effects and drug exposure454–456 [and additional references of interest that are shown in Table 2 (see Supplemental Digital Content 1, http://links.lww.com/TDM/A321)],319,338,346,353,357,373,378,379,457–459 while others have considered the use of PD models, the most widely applied being the direct inhibitory Emax models.317,318,320 None of these cases applied the indirect response models proposed by Jusko et al460 or disease progression models461 to account for changes in disease unrelated to drug action. Most of the models were developed for descriptive purposes, without the evaluation of their predictive capability being reported. Lately, some new PK-PD models based on relationships between longitudinal measurements of drug exposure and clinical outcomes have seemed, but still efforts should be made to move forward from the current scenario to modeling discrete data.462–464
In conclusion, more PK-PD and PB/PK modeling activities are still required to enhance the understanding of factors influencing clinical outcomes in transplantation. Despite possible inconveniences for routine clinical practice, optimal sampling study designs are essential to allow for robust conclusions.
Executive Summary and Practical Recommendations
- The utilization of popPK model–based Bayesian estimators has shown improved target achievement compared with standard TDM.465 Although trough concentration is used in most transplant centers for TDM of tacrolimus, there is some evidence that C0 correlates poorly with AUC0–12. The use of popPK model–derived Bayesian estimators based on limited sampling strategies, however, provides AUC predictions with bias <5% and an imprecision <20%. This seems to be an applicable way to improve future tacrolimus TDM as compared to continue with standard trough concentration based TDM, especially when considering home sampling using microsampling devices that are currently under validation (as presented above).
- We strongly recommend the integration of CYP3A5*3 and CYP3A4*22 genotype information, when available, in future tacrolimus popPK models, primarily for the opportunity to optimize initial dosing.
- More PK-PD and PB/PK modeling activities are required to enhance the understanding of factors influencing clinical outcomes in transplantation.
In this consensus report, we have assessed the evolution of best practice for the TDM of tacrolimus to allow for personalized treatment. We have taken into consideration the influence of standardized and harmonized analytical methods, as well as PG, PD and immunologic biomarkers, and their ability to act as early predictors of clinical events, such as rejection or drug-related adverse events.
The first consensus report on optimization of tacrolimus therapy10 confirmed that the TDM of tacrolimus has a significant impact on patient management. However, it was concluded that there was the need to concentrate efforts on developing and establishing new standardized and harmonized analytical strategies to fine tune the target concentrations to be achieved in adult and pediatric populations, considering risk factors, comedication drug interactions, and validated biomarkers.
One decade later, the evolution of graft and patient clinical outcomes in transplantation remains insufficiently studied in the context of personalized immunosuppressive treatment early and long-term after transplantation. Tacrolimus, in its different formulations, remains and is considered the cornerstone of immunosuppressive therapy. Tacrolimus, mycophenolic acid, and steroids are the most commonly used treatment combinations in SOT. However, our understanding of immunosuppression to achieve personalized therapies is still evolving.
For some time now, tacrolimus-related nephrotoxicity has been considered to be a major risk factor, and dose minimization was readily applied in an attempt to prevent kidney damage. Currently, the histologic graft lesions are also known to be attributed to other factors, one of the main causes being an actively maintained alloimmunity. Several studies have demonstrated that it is essential to ensure consistent tacrolimus exposure over time, and instead of aiming for very low tacrolimus trough concentrations, clinicians have to put more emphasis on the prevention of underimmunosuppression and a lower limit of 4 ng/mL or greater is suggested on most indications. In the present consensus report, whenever possible, the recommendations for achieving specific target concentrations of tacrolimus are based on the results obtained from multicenter prospective clinical trials for different types of SOT in specific cohorts of low or high immunological risk patients, also taking the combination with other immunosuppressive drugs into consideration. With the recent developments of novel sampling techniques and improved dosing methods (eg, popPK model-based Bayesian estimators), future focus on finding optimal AUC target should be emphasized. Few studies have evaluated the optimal tacrolimus target concentrations in children undergoing different types of clinical transplantation or in adult and pediatric populations when tacrolimus has been administered for other clinical indications. So, there is still room to better determine and adjust the optimal tacrolimus target concentrations for each patient group.
When looking back on the last 10 years of tacrolimus TDM, there has been a continuous and notable improvement of analytical assay performance. Several FDA-approved and IVD-CE–certified tacrolimus measurement systems have been introduced, but only 2 standardized calibrator materials are available. Tacrolimus assay inconsistencies may have a negative impact on clinical decisions, drug adjustment, and patient outcomes, demonstrating the need for traceability, the generation of standardized reference materials, and the placement of appropriate reference procedures for tacrolimus monitoring.
When interpreting whole blood drug exposure (C0 target concentrations, AUC ranges) and clinical trial outcomes, clinicians should keep in mind the analytical aspects (ie, the assay used and the between-method bias) and PG characteristics associated with tacrolimus disposition and effects. Furthermore, it is well-known that immunosuppressive drugs modulate the immune system of each patient differently. As tacrolimus is a narrow therapeutic index drug, small variations in systemic exposure can lead to substantial differences in the PD response influencing graft and patient clinical outcomes. The combined knowledge of PK, with PG together with PD biomarkers, could provide further prognostic and diagnostic information regarding the risk of rejection and condition of the allograft at earlier time points and allow antirejection therapy to be adjusted at an early stage before severe graft injury ensues. Accordingly, the recommendations of the consensus report regarding PG, PD, and immunological biomarkers are summarized as follows:
- The association between CYP3A5 genotype and tacrolimus dose requirement is robust and has been observed among both adult and pediatric kidney, liver, heart, and lung transplant recipients (grading of recommendation A I), but currently, there is no convincing clinical evidence that a PG-based approach to tacrolimus dose selection improves graft and patient clinical outcomes after SOT. CYP3A4 *22 genotype is associated with residual variability in tacrolimus PK, but further studies should investigate the clinical benefit of some PG clusters in tacrolimus disposition and effect.
- PD monitoring of tacrolimus therapy has not entered routine monitoring yet, but NFAT-regulated gene expression is a candidate biomarker for personal response to tacrolimus and the identification of renal and liver transplant recipients at high risk of rejection and infection (B II).
- Monitoring intracellular T-cell IFN-γ production, particularly by the ELISPOT assay before and early after transplantation, can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II).
The authors believe that future studies should continue to investigate the clinical benefit of PopPK models including PG phenotypes, as well as PK/PD modeling and the potential application of such models into clinical routine, to facilitate personalized tacrolimus dosing.
In conclusion, the Expert Committee emphasized that it is reasonable to expect that routine monitoring of tacrolimus PK, combined with PG and predictive PD and immunologic biomarkers will modify and control risk factors, improves long-term outcomes post-transplant as well as graft and patient survival. There is a need to harness the information we have generated, re-evaluate how we monitor tacrolimus exposure/effect, and graft outcomes by incorporating early predictive biomarkers into multidisciplinary designed prospective robust clinical trials to support evidence for patient stratification and immunosuppression guidance, and achieve timely regulatory approval.
The authors acknowledge Professor David Holt for his contributions to improvement of the use of English language in this article.
1. 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.
2. UpToDate. Grading guide [web site]. 2019. Available at: https://www.uptodate.com/home/grading-guide
. Accessed March 19, 2019.
3. Ekberg H, Tedesco-Silva H, Demirbas A, et al. Reduced exposure to calcineurin inhibitors in renal transplantation. N Engl J Med. 2007;357:2562–2575.
4. Kidney Disease: Improving Global Outcomes (KDIGO)Transplant Work Group. KDIGO clinical practice guideline for the care of kidney transplant recipients. Am J Transplant. 2009;9(suppl 3):S1–S155.
5. Taylor DO, Barr ML, Meiser BM, et al. Suggested guidelines for the use of tacrolimus in cardiac transplant recipients. J Heart Lung Transplant. 2001;20:734–738.
6. Yano I, Masuda S, Egawa H, et al. Significance of trough monitoring for tacrolimus blood concentration and calcineurin activity in adult patients undergoing primary living-donor liver transplantation. Eur J Clin Pharmacol. 2012;68:259–266.
7. De Simone P, Nevens F, De Carlis L, et al. Everolimus with reduced tacrolimus improves renal function in de novo liver transplant recipients: a randomized controlled trial. Am J Transplant. 2012;12:3008–3020.
8. Boudjema K, Camus C, Saliba F, et al. Reduced-dose tacrolimus with mycophenolate mofetil vs. standard-dose tacrolimus in liver transplantation: a randomized study. Am J Transplant. 2011;11:965–976.
9. Ekberg H, Mamelok RD, Pearson TC, et al. The challenge of achieving target drug concentrations in clinical trials: experience from the Symphony study. Transplantation. 2009;87:1360–1366.
10. Wallemacq P, Armstrong VW, Brunet M, et al. Opportunities to optimize tacrolimus therapy in solid organ transplantation: report of the European consensus
conference. Ther Drug Monit. 2009;31:139–152.
11. Woillard JB, Saint-Marcoux F, Debord J, et al. Pharmacokinetic models to assist the prescriber in choosing the best tacrolimus dose. Pharmacol Res. 2018;130:316–321.
12. Brooks E, Tett SE, Isbel NM, et al. Population pharmacokinetic modelling and bayesian estimation of tacrolimus exposure: is this clinically useful for dosage prediction yet? Clin Pharmacokinet. 2016;55:1295–1335.
13. Pohanka A, Rosenborg S, Lindh JD, et al. Experiences from using LC-MS/MS for analysis of immunosuppressive drugs in a TDM service. Clin Biochem. 2016;49:1024–1031.
14. Seger C, Shipkova M, Christians U, et al. Assuring the proper analytical performance of measurement procedures for immunosuppressive drug concentrations in clinical practice: recommendations of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology Immunosuppressive Drug Scientific Committee. Ther Drug Monit. 2016;38:170–189.
15. Brunet M, Shipkova M, van Gelder T, et al. Barcelona consensus
on biomarker-based immunosuppressive drugs management in solid organ transplantation. Ther Drug Monit. 2016;38(suppl 1):S1–S20.
16. Schreiber SL, Crabtree GR. The mechanism of action of cyclosporin A and FK506. Immunol Today. 1992;13:136–142.
17. Spencer CM, Goa KL, Gillis JC. Tacrolimus. An update of its pharmacology and clinical efficacy in the management of organ transplantation. Drugs. 1997;54:925–975.
18. Thomson AW, Bonham CA, Zeevi A. Mode of action of tacrolimus (FK506): molecular and cellular mechanisms. Ther Drug Monit. 1995;17:584–591.
19. Klintmalm GB. FK 506: an update. Clin Transplant. 1994;8:207–210.
20. Jusko WJ, Thomson AW, Fung J, et al. Consensus
document: therapeutic monitoring of tacrolimus (FK-506). Ther Drug Monit. 1995;17:606–614.
21. Venkataramanan R, Jain A, Warty VW, et al. Pharmacokinetics
of FK 506 following oral administration: a comparison of FK 506 and cyclosporine. Transplant Proc. 1991;23:931–933.
22. Venkataramanan R, Swaminathan A, Prasad T, et al. Clinical pharmacokinetics
of tacrolimus. Clin Pharmacokinet. 1995;29:404–430.
23. Jusko WJ, Piekoszewski W, Klintmalm GB, et al. Pharmacokinetics
of tacrolimus in liver transplant patients. Clin Pharmacol Ther. 1995;57:281–290.
24. Hesselink DA, Bouamar R, Elens L, et al. The role of pharmacogenetics
in the disposition of and response to tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2014;53:123–139.
25. Dubbelboer IR, Pohanka A, Said R, et al. Quantification of tacrolimus and three demethylated metabolites in human whole blood using LC-ESI-MS/MS. Ther Drug Monit. 2012;34:134–142.
26. Iwasaki K. Metabolism of tacrolimus (FK506) and recent topics in clinical pharmacokinetics
. Drug Metab Pharmacokinet. 2007;22:328–335.
27. Moller A, Iwasaki K, Kawamura A, et al. The disposition of 14C-labeled tacrolimus after intravenous and oral administration in healthy human subjects. Drug Metab Dispos. 1999;27:633–636.
28. Bergmann TK, Hennig S, Barraclough KA, et al. Population pharmacokinetics
of tacrolimus in adult kidney transplant patients: impact of CYP3A5 genotype on starting dose. Ther Drug Monit. 2014;36:62–70.
29. Borra LC, Roodnat JI, Kal JA, et al. High within-patient variability in the clearance of tacrolimus is a risk factor for poor long-term outcome after kidney transplantation. Nephrol Dial Transplant. 2010;25:2757–2763.
30. Park SI, Felipe CR, Pinheiro-Machado PG, et al. Circadian and time-dependent variability in tacrolimus pharmacokinetics
. Fundam Clin Pharmacol. 2007;21:191–197.
31. Satoh S, Kagaya H, Saito M, et al. Lack of tacrolimus circadian pharmacokinetics
and CYP3A5 pharmacogenetics
in the early and maintenance stages in Japanese renal transplant recipients. Br J Clin Pharmacol. 2008;66:207–214.
32. Wallemacq PE, Furlan V, Moller A, et al. Pharmacokinetics
of tacrolimus (FK506) in paediatric liver transplant recipients. Eur J Drug Metab Pharmacokinet. 1998;23:367–370.
33. Fukudo M, Yano I, Masuda S, et al. Population pharmacokinetic and pharmacogenomic analysis of tacrolimus in pediatric living-donor liver transplant recipients. Clin Pharmacol Ther. 2006;80:331–345.
34. Christiaans M, van Duijnhoven E, Beysens T, et al. Effect of breakfast on the oral bioavailability of tacrolimus and changes in pharmacokinetics
at different times posttransplant in renal transplant recipients. Transplant Proc. 1998;30:1271–1273.
35. Undre NA, Schafer A. Factors affecting the pharmacokinetics
of tacrolimus in the first year after renal transplantation. European Tacrolimus Multicentre Renal Study Group. Transplant Proc. 1998;30:1261–1263.
36. Zheng S, Easterling TR, Umans JG, et al. Pharmacokinetics
of tacrolimus during pregnancy. Ther Drug Monit. 2012;34:660–670.
37. Nakamura A, Amada N, Haga I, et al. Effects of elevated tacrolimus trough levels in association with infectious enteritis on graft function in renal transplant recipients. Transplant Proc. 2014;46:592–594.
38. Maes BD, Lemahieu W, Kuypers D, et al. Differential effect of diarrhea on FK506 versus cyclosporine A trough levels and resultant prevention of allograft rejection in renal transplant recipients. Am J Transplant. 2002;2:989–992.
39. Lemahieu W, Maes B, Verbeke K, et al. Cytochrome P450 3A4 and P-glycoprotein activity and assimilation of tacrolimus in transplant patients with persistent diarrhea. Am J Transplant. 2005;5:1383–1391.
40. Hesselink DA, Ngyuen H, Wabbijn M, et al. Tacrolimus dose requirement in renal transplant recipients is significantly higher when used in combination with corticosteroids. Br J Clin Pharmacol. 2003;56:327–330.
41. Tang JT, Andrews LM, van Gelder T, et al. Pharmacogenetic aspects of the use of tacrolimus in renal transplantation: recent developments and ethnic considerations. Expert Opin Drug Metab Toxicol. 2016;12:555–565.
42. Christians U, Jacobsen W, Benet LZ, et al. Mechanisms of clinically relevant drug interactions associated with tacrolimus. Clin Pharmacokinet. 2002;41:813–851.
43. van Gelder T. Drug interactions with tacrolimus. Drug Saf. 2002;25:707–712.
44. Groll AH, Townsend R, Desai A, et al. Drug-drug interactions between triazole antifungal agents used to treat invasive aspergillosis and immunosuppressants metabolized by cytochrome P450 3A4. Transpl Infect Dis. 2017;19:e12751.
45. Vanhove T, Bouwsma H, Hilbrands L, et al. Determinants of the magnitude of interaction between tacrolimus and voriconazole/posaconazole in solid organ recipients. Am J Transplant. 2017;17:2372–2380.
46. Kuypers DR, Claes K, Evenepoel P, et al. Clinically relevant drug interaction between voriconazole and tacrolimus in a primary renal allograft recipient. Transplantation. 2006;81:1750–1752.
47. van Maarseveen EM, Rogers CC, Trofe-Clark J, et al. Drug-drug interactions between antiretroviral and immunosuppressive agents in HIV-infected patients after solid organ transplantation: a review. AIDS Patient Care STDS. 2012;26:568–581.
48. Badri P, Dutta S, Coakley E, et al. Pharmacokinetics
and dose recommendations for cyclosporine and tacrolimus when coadministered with ABT-450, ombitasvir, and dasabuvir. Am J Transplant. 2015;15:1313–1322.
49. Badri PS, Parikh A, Coakley EP, et al. Pharmacokinetics
of tacrolimus and cyclosporine in liver transplant recipients receiving 3 direct-acting antivirals as treatment for hepatitis C infection. Ther Drug Monit. 2016;38:640–645.
50. Feng HP, Caro L, Fandozzi CM, et al. Pharmacokinetic interactions between elbasvir/grazoprevir and immunosuppressant drugs in healthy volunteers. J Clin Pharmacol. 2018;58:666–673.
51. Kwo PY, Badshah MB. New hepatitis C virus therapies: drug classes and metabolism, drug interactions relevant in the transplant settings, drug options in decompensated cirrhosis, and drug options in end-stage renal disease. Curr Opin Organ Transplant. 2015;20:235–241.
52. Lam S, Partovi N, Ting LS, et al. Corticosteroid interactions with cyclosporine, tacrolimus, mycophenolate, and sirolimus: fact or fiction? Ann Pharmacother. 2008;42:1037–1047.
53. Stifft F, Undre N, van Hooff JP, et al. Effect of breakfast on the exposure of the once-daily tacrolimus formulation in stable kidney transplant recipients. Ther Drug Monit. 2016;38:456–462.
54. Bekersky I, Dressler D, Mekki QA. Effect of low- and high-fat meals on tacrolimus absorption following 5 mg single oral doses to healthy human subjects. J Clin Pharmacol. 2001;41:176–182.
55. Liu C, Shang YF, Zhang XF, et al. Co-administration of grapefruit juice increases bioavailability of tacrolimus in liver transplant patients: a prospective study. Eur J Clin Pharmacol. 2009;65:881–885.
56. Liu MZ, Zhang YL, Zeng MZ, et al. Pharmacogenomics and herb-drug interactions: merge of future and tradition. Evid Based Complement Alternat Med. 2015;2015:321091.
57. Mai I, Stormer E, Bauer S, et al. Impact of St John's wort treatment on the pharmacokinetics
of tacrolimus and mycophenolic acid in renal transplant patients. Nephrol Dial Transplant. 2003;18:819–822.
58. Budde K, Bunnapradist S, Grinyo JM, et al. Novel once-daily extended-release tacrolimus (LCPT) versus twice-daily tacrolimus in de novo kidney transplants: one-year results of phase III, double-blind, randomized trial. Am J Transplant. 2014;14:2796–2806.
59. Micromedex. IBM Micromedex [web site]. 2019. Available at: https://www.micromedexsolutions.com/micromedex2/librarian/
. Accessed January 23, 2019.
60. Christians U, Klawitter J, Clavijo CF. Bioequivalence testing of immunosuppressants: concepts and misconceptions. Kidney Int. 2010;115:s1.
61. Harrison JJ, Schiff JR, Coursol CJ, et al. Generic immunosuppression in solid organ transplantation: a Canadian perspective. Transplantation. 2012;93:657–665.
62. van Gelder T. European society for organ transplantation advisory committee recommendations on generic substitution of immunosuppressive drugs. Transpl Int. 2011;24:1135–1141.
63. Molnar AO, Fergusson D, Tsampalieros AK, et al. Generic immunosuppression in solid organ transplantation: systematic review and meta-analysis. BMJ. 2015;350:h3163.
64. Alloway RR, Vinks AA, Fukuda T, et al. Bioequivalence between innovator and generic tacrolimus in liver and kidney transplant recipients: a randomized, crossover clinical trial. PLoS Med. 2017;14:e1002428.
65. Robertsen I, Asberg A, Ingero AO, et al. Use of generic tacrolimus in elderly renal transplant recipients: precaution is needed. Transplantation. 2015;99:528–532.
66. Jacobo-Cabral CO, Garcia-Roca P, Reyes H, et al. Limustin(R), a non-innovator tacrolimus formulation, yields reduced drug exposure in pediatric renal transplant recipients. Pediatr Transplant. 2014;18:706–713.
67. van Gelder T. What is the future of generics in transplantation? Transplantation. 2015;99:2269–2273.
68. Marquet P, Albano L, Woillard JB, et al. Comparative clinical trial of the variability factors of the exposure indices used for the drug monitoring of two tacrolimus formulations in kidney transplant recipients. Pharmacol Res. 2018;129:84–94.
69. Saint-Marcoux F, Woillard JB, Jurado C, et al. Lessons from routine dose adjustment of tacrolimus in renal transplant patients based on global exposure. Ther Drug Monit. 2013;35:322–327.
70. Marquet P, Bedu A, Monchaud C, et al. Pharmacokinetic therapeutic drug monitoring of Advagraf in more than 500 adult renal transplant patients, using an expert system online. Ther Drug Monit. 2018;40:285–291.
71. Israni AK, Riad SM, Leduc R, et al. Tacrolimus trough levels after month 3 as a predictor of acute rejection following kidney transplantation: a lesson learned from DeKAF Genomics. Transpl Int. 2013;26:982–989.
72. Gatault P, Kamar N, Buchler M, et al. Reduction of extended-release tacrolimus dose in low-immunological-risk kidney transplant recipients increases risk of rejection and appearance of donor-specific antibodies: a randomized study. Am J Transplant. 2017;17:1370–1379.
73. Bouamar R, Shuker N, Hesselink DA, et al. Tacrolimus predose concentrations do not predict the risk of acute rejection after renal transplantation: a pooled analysis from three randomized-controlled clinical trials(dagger). Am J Transplant. 2013;13:1253–1261.
74. Undre NA, van Hooff J, Christiaans M, et al. Low systemic exposure to tacrolimus correlates with acute rejection. Transplant Proc. 1999;31:296–298.
75. Kuypers DR, Claes K, Evenepoel P, et al. Clinical efficacy and toxicity profile of tacrolimus and mycophenolic acid in relation to combined long-term pharmacokinetics
in de novo renal allograft recipients. Clin Pharmacol Ther. 2004;75:434–447.
76. Larkins N, Matsell DG. Tacrolimus therapeutic drug monitoring and pediatric renal transplant graft outcomes. Pediatr Transplant. 2014;18:803–809.
77. Böttiger Y, Brattström C, Tyden G, et al. Tacrolimus whole blood concentrations correlate closely to side-effects in renal transplant recipients. Br J Clin Pharmacol. 1999;48:445–448.
78. Kershner RP, Fitzsimmons WE. Relationship of FK506 whole blood concentrations and efficacy and toxicity after liver and kidney transplantation. Transplantation. 1996;62:920–926.
79. Matas AJ, Smith JM, Skeans MA, et al. OPTN/SRTR 2013 annual data report: kidney. Am J Transplant. 2015;15(suppl 2):1–34.
80. Pascual J, Berger SP, Witzke O, et al. Everolimus with reduced calcineurin inhibitor exposure in renal transplantation. J Am Soc Nephrol. 2018;29:1979–1991.
81. Chapman WC, Brown RS Jr, Chavin KD, et al. Effect of early everolimus-facilitated reduction of tacrolimus on efficacy and renal function in de novo liver transplant recipients: 24-month results for the North American subpopulation. Transplantation. 2017;101:341–349.
82. Jia JJ, Lin BY, He JJ, et al. “Minimizing tacrolimus” strategy and long-term survival after liver transplantation. World J Gastroenterol. 2014;20:11363–11369.
83. Neuberger JM, Mamelok RD, Neuhaus P, et al. Delayed introduction of reduced-dose tacrolimus, and renal function in liver transplantation: the “ReSpECT” study. Am J Transplant. 2009;9:327–336.
84. Otero A, Varo E, de Urbina JO, et al. A prospective randomized open study in liver transplant recipients: daclizumab, mycophenolate mofetil, and tacrolimus versus tacrolimus and steroids. Liver Transpl. 2009;15:1542–1552.
85. Rodriguez-Peralvarez 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.
86. Rodriguez-Peralvarez 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.
87. Becker T, Foltys D, Bilbao I, et al. Patient outcomes in two steroid-free regimens using tacrolimus monotherapy after daclizumab induction and tacrolimus with mycophenolate mofetil in liver transplantation. Transplantation. 2008;86:1689–1694.
88. Hu AB, Wu LW, Tai Q, et al. Safety and efficacy of four steroid-minimization protocols in liver transplant recipients: 3-year follow-up in a single center. J Dig Dis. 2013;14:38–44.
89. Lerut JP, Pinheiro RS, Lai Q, et al. Is minimal, [almost] steroid-free immunosuppression a safe approach in adult liver transplantation? Long-term outcome of a prospective, double blind, placebo-controlled, randomized, investigator-driven study. Ann Surg. 2014;260:886–891; discussion 891–882.
90. Margarit C, Bilbao I, Castells L, et al. A prospective randomized trial comparing tacrolimus and steroids with tacrolimus monotherapy in liver transplantation: the impact on recurrence of hepatitis C. Transpl Int. 2005;18:1336–1345.
91. Venkataramanan R, Shaw LM, Sarkozi L, et al. Clinical utility of monitoring tacrolimus blood concentrations in liver transplant patients. J Clin Pharmacol. 2001;41:542–551.
92. Riva N, Schaiquevich P, Caceres Guido P, et al. Pharmacoepidemiology of tacrolimus in pediatric liver transplantation. Pediatr Transplant. 2017;21:e12982.
93. Alloway R, Vanhaecke J, Yonan N, et al. Pharmacokinetics
in stable heart transplant recipients after conversion from twice-daily to once-daily tacrolimus formulations. J Heart Lung Transplant. 2011;30:1003–1010.
94. Monchaud C, de Winter BC, Knoop C, et al. Population pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation. Clin Pharmacokinet. 2012;51:175–186.
95. Garrity ER Jr, Hertz MI, Trulock EP, et al. Suggested guidelines for the use of tacrolimus in lung-transplant recipients. J Heart Lung Transplant. 1999;18:175–176.
96. Monchaud C, Marquet P. Pharmacokinetic optimization of immunosuppressive therapy in thoracic transplantation: part I. Clin Pharmacokinet. 2009;48:419–462.
97. Sikma MA, Hunault CC, Kirkels JH, et al. Association of whole blood tacrolimus concentrations with kidney injury in heart transplantation patients. Eur J Drug Metab Pharmacokinet. 2018;43:311–320.
98. Sikma MA, Hunault CC, van de Graaf EA, et al. High tacrolimus blood concentrations early after lung transplantation and the risk of kidney injury. Eur J Clin Pharmacol. 2017;73:573–580.
99. Aumente Rubio MD, Arizon del Prado JM, Lopez Malo de Molina MD, et al. Clinical pharmacokinetics
of tacrolimus in heart transplantation: new strategies of monitoring. Transplant Proc. 2003;35:1988–1991.
100. Sgrosso JL, Araujo GL, Vazquez MC. Tacrolimus pharmacokinetics
in heart transplant. Transplant Proc. 2002;34:142–143.
101. Undre NA, Stevenson PJ. Pharmacokinetics
of tacrolimus in heart transplantation. Transplant Proc. 2002;34:1836–1838.
102. Wang CH, Ko WJ, Chou NK, et al. Therapeutic drug monitoring of tacrolimus in cardiac transplant recipients: a comparison with cyclosporine neoral. Transplant Proc. 2004;36:2386–2387.
103. Hiraoka A, Ohashi Y, Okamoto S, et al. Phase III study comparing tacrolimus (FK506) with cyclosporine for graft-versus-host disease prophylaxis after allogeneic bone marrow transplantation. Bone Marrow Transplant. 2001;28:181–185.
104. Nash RA, Antin JH, Karanes C, et al. Phase 3 study comparing methotrexate and tacrolimus with methotrexate and cyclosporine for prophylaxis of acute graft-versus-host disease after marrow transplantation from unrelated donors. Blood. 2000;96:2062–2068.
105. Oshima K, Sato M, Terasako K, et al. Target blood concentrations of CYA and tacrolimus in randomized controlled trials for the prevention of acute GVHD after hematopoietic SCT. Bone Marrow Transplant. 2010;45:781–782.
106. Ram R, Gafter-Gvili A, Yeshurun M, et al. Prophylaxis regimens for GVHD: systematic review and meta-analysis. Bone Marrow Transplant. 2009;43:643–653.
107. Jacobson P, Ng J, Ratanatharathorn V, et al. Factors affecting the pharmacokinetics
of tacrolimus (FK506) in hematopoietic cell transplant (HCT) patients. Bone Marrow Transplant. 2001;28:753–758.
108. Kanda Y, Kobayashi T, Mori T, et al. A randomized controlled trial of cyclosporine and tacrolimus with strict control of blood concentrations after unrelated bone marrow transplantation. Bone Marrow Transplant. 2016;51:103–109.
109. Suetsugu K, Ikesue H, Miyamoto T, et al. Analysis of the variable factors influencing tacrolimus blood concentration during the switch from continuous intravenous infusion to oral administration after allogeneic hematopoietic stem cell transplantation. Int J Hematol. 2017;105:361–368.
110. Yano S, Mori S, Saito T, et al. Pharmacokinetics
for once-daily modified release formulation of tacrolimus hydrate in unrelated hematopoietic stem cell transplantation. Ann Hematol. 2015;94:491–496.
111. Mori T, Kato J, Shimizu T, et al. Effect of early posttransplantation tacrolimus concentration on the development of acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation from unrelated donors. Biol Blood Marrow Transplant. 2012;18:229–234.
112. Ganetsky A, Shah A, Miano TA, et al. Higher tacrolimus concentrations early after transplant reduce the risk of acute GvHD in reduced-intensity allogeneic stem cell transplantation. Bone Marrow Transplant. 2016;51:568–572.
113. Wingard JR, Nash RA, Przepiorka D, et al. Relationship of tacrolimus (FK506) whole blood concentrations and efficacy and safety after HLA-identical sibling bone marrow transplantation. Biol Blood Marrow Transplant. 1998;4:157–163.
114. Ratanatharathorn V, Nash RA, Przepiorka D, et al. Phase III study comparing methotrexate and tacrolimus (prograf, FK506) with methotrexate and cyclosporine for graft-versus-host disease prophylaxis after HLA-identical sibling bone marrow transplantation. Blood. 1998;92:2303–2314.
115. Watanabe N, Matsumoto K, Muramatsu H, et al. Relationship between tacrolimus blood concentrations and clinical outcome during the first 4 weeks after SCT in children. Bone Marrow Transplant. 2010;45:1161–1166.
116. Offer K, Kolb M, Jin Z, et al. Efficacy of tacrolimus/mycophenolate mofetil as acute graft-versus-host disease prophylaxis and the impact of subtherapeutic tacrolimus levels in children after matched sibling donor allogeneic hematopoietic cell transplantation. Biol Blood Marrow Transplant. 2015;21:496–502.
117. Cury Martins J, Martins C, Aoki V, et al. Topical tacrolimus for atopic dermatitis. Cochrane Database Syst Rev. 2015:Cd009864.
118. Undre NA, Moloney FJ, Ahmadi S, et al. Skin and systemic pharmacokinetics
of tacrolimus following topical application of tacrolimus ointment in adults with moderate to severe atopic dermatitis. Br J Dermatol. 2009;160:665–669.
119. McCollum AD, Paik A, Eichenfield LF. The safety and efficacy of tacrolimus ointment in pediatric patients with atopic dermatitis. Pediatr Dermatol. 2010;27:425–436.
120. Mandelin JM, Rubins A, Remitz A, et al. Long-term efficacy and tolerability of tacrolimus 0.03% ointment in infants:* a two-year open-label study. Int J Dermatol. 2012;51:104–110.
121. Keaney TC, Bhutani T, Sivanesan P, et al. Open-label, pilot study examining sequential therapy with oral tacrolimus and topical tacrolimus for severe atopic dermatitis. J Am Acad Dermatol. 2012;67:636–641.
122. Mele C, Salerno MP, Romagnoli J, et al. Complete clinical remission of psoriasis 6 months after renal transplantation. Transplant Proc. 2013;45:2788–2789.
123. Malecic N, Young H. Tacrolimus for the management of psoriasis: clinical utility and place in therapy. Psoriasis (Auckl). 2016;6:153–163.
124. Wang C, Lin A. Efficacy of topical calcineurin inhibitors in psoriasis. J Cutan Med Surg. 2014;18:8–14.
125. Hannah J, Casian A, D'Cruz D. Tacrolimus use in lupus nephritis: a systematic review and meta-analysis. Autoimmun Rev. 2016;15:93–101.
126. Lee YH, Song GG. Comparative efficacy and safety of tacrolimus, mycophenolate mofetil, azathioprine, and cyclophosphamide as maintenance therapy for lupus nephritis: a Bayesian network meta-analysis of randomized controlled trials. Z Rheumatol. 2017;76:904–912.
127. Chen W, Liu Q, Chen W, et al. Outcomes of maintenance therapy with tacrolimus versus azathioprine for active lupus nephritis: a multicenter randomized clinical trial. Lupus. 2012;21:944–952.
128. Kamanamool N, Ingsathit A, Rattanasiri S, et al. Comparison of disease activity between tacrolimus and mycophenolate mofetil in lupus nephritis: a randomized controlled trial. Lupus. 2018;27:647–656.
129. Tanaka H, Oki E, Tsuruga K, et al. Management of young patients with lupus nephritis using tacrolimus administered as a single daily dose. Clin Nephrol. 2009;72:430–436.
130. Kraaij T, Bredewold OW, Trompet S, et al. TAC-TIC use of tacrolimus-based regimens in lupus nephritis. Lupus Sci Med. 2016;3:e000169.
131. Mok CC. Therapeutic monitoring of the immuno-modulating drugs in systemic lupus erythematosus. Expert Rev Clin Immunol. 2017;13:35–41.
132. Takeuchi K, Shimoyama T, Yamamoto T. Comparison of safety and efficacy of tacrolimus versus infliximab for active ulcerative colitis. Dig Dis. 2018;36:106–112.
133. Ogata H, Matsui T, Nakamura M, et al. A randomised dose finding study of oral tacrolimus (FK506) therapy in refractory ulcerative colitis. Gut. 2006;55:1255–1262.
134. Bruns T, Stallmach A. Drug monitoring in inflammatory bowel disease: helpful or dispensable? Dig Dis. 2009;27:394–403.
135. Bousvaros A, Kirschner BS, Werlin SL, et al. Oral tacrolimus treatment of severe colitis in children. J Pediatr. 2000;137:794–799.
136. Ziring DA, Wu SS, Mow WS, et al. Oral tacrolimus for steroid-dependent and steroid-resistant ulcerative colitis in children. J Pediatr Gastroenterol Nutr. 2007;45:306–311.
137. Whalen HR, Glen JA, Harkins V, et al. High intrapatient tacrolimus variability is associated with worse outcomes in renal transplantation using a low-dose tacrolimus immunosuppressive regime. Transplantation. 2017;101:430–436.
138. Del Bello A, Congy-Jolivet N, Danjoux M, et al. High tacrolimus intra-patient variability is associated with graft rejection, and de novo donor-specific antibodies occurrence after liver transplantation. World J Gastroenterol. 2018;24:1795–1802.
139. Gueta I, Markovits N, Yarden-Bilavsky H, et al. High tacrolimus trough level variability is associated with rejections after heart transplantation. Am J Transplant. 2018;18:2571–2578.
140. Shuker N, Cadogan M, van Gelder T, et al. Conversion from twice-daily to once-daily tacrolimus does not reduce intrapatient variability in tacrolimus exposure. Ther Drug Monit. 2015;37:262–269.
141. Lemaitre F, Tron C, Rayar M. Fluctuation does not mean variability: a pharmacokinetic point of view. Am J Transplant. 2017;17:1691–1692.
142. Vanhove T, Vermeulen T, Annaert P, et al. High intrapatient variability of tacrolimus concentrations predicts accelerated progression of chronic histologic lesions in renal recipients. Am J Transplant. 2016;16:2954–2963.
143. Rodrigo E, Segundo DS, Fernandez-Fresnedo G, et al. Within-patient variability in tacrolimus blood levels predicts kidney graft loss and donor-specific antibody development. Transplantation. 2016;100:2479–2485.
144. O'Regan JA, Canney M, Connaughton DM, et al. Tacrolimus trough-level variability predicts long-term allograft survival following kidney transplantation. J Nephrol. 2016;29:269–276.
145. Shuker N, Shuker L, van Rosmalen J, et al. A High intra-patient variability in tacrolimus exposure is associated with poor long-term outcome of kidney transplantion. Transpl Int. 2016;29:1158–1167.
146. Sapir-Pichhadze R, Wang Y, Famure O, et al. Time-dependent variability in tacrolimus trough blood levels is a risk factor for late kidney transplant failure. Kidney Int. 2014;85:1404–1411.
147. Shemesh E, Bucuvalas JC, Anand R, et al. The medication level variability index (MLVI) predicts poor liver transplant outcomes: a prospective multi-site study. Am J Transplant. 2017;17:2668–2678.
148. Rayar M, Tron C, Jezequel C, et al. High intrapatient variability of tacrolimus exposure in the early period after liver transplantation is associated with poorer outcomes. Transplantation. 2018;102:e108–e114.
149. Christina S, Annunziato RA, Schiano TD, et al. Medication level variability index predicts rejection, possibly due to nonadherence, in adult liver transplant recipients. Liver Transpl. 2014;20:1168–1177.
150. Freeman DJ, Stawecki M, Howson B. Stability of FK 506 in whole blood samples. Ther Drug Monit. 1995;17:266–267.
151. Chen YL, Hirabayashi H, Akhtar S, et al. Simultaneous determination of three isomeric metabolites of tacrolimus (FK506) in human whole blood and plasma using high performance liquid chromatography-tandem mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2006;830:330–341.
152. Stienstra NA, Sikma MA, van Dapperen A, et al. Development of a simple and rapid method to measure the free fraction of tacrolimus in plasma using ultrafiltration and LC-MS/MS. Ther Drug Monit. 2016;38:722–727.
153. Romano P, da Luz Fernandes M, de Almeida Rezende Ebner P, et al. UPLC-MS/MS assay validation for tacrolimus quantitative determination in peripheral blood T CD4+ and B CD19+ lymphocytes. J Pharm Biomed Anal. 2018;152:306–314.
154. Ghareeb M, Akhlaghi F. Development and validation of a sensitive and selective LC-MS/MS method for determination of tacrolimus in oral fluids. J Chromatogr B Analyt Technol Biomed Life Sci. 2016;1038:136–141.
155. Tron C, Rayar M, Petitcollin A, et al. A high performance liquid chromatography tandem mass spectrometry for the quantification of tacrolimus in human bile in liver transplant recipients. J Chromatogr A. 2016;1475:55–63.
156. Koster RA, Alffenaar JW, Greijdanus B, et al. Fast LC-MS/MS analysis of tacrolimus, sirolimus, everolimus and cyclosporin A in dried blood spots and the influence of the hematocrit and immunosuppressant concentration on recovery. Talanta. 2013;115:47–54.
157. Sadilkova K, Busby B, Dickerson JA, et al. Clinical validation and implementation of a multiplexed immunosuppressant assay in dried blood spots by LC-MS/MS. Clin Chim Acta. 2013;421:152–156.
158. Brandhorst G, Oellerich M, Maine G, et al. Liquid chromatography-tandem mass spectrometry or automated immunoassays: what are the future trends in therapeutic drug monitoring? Clin Chem. 2012;58:821–825.
159. Christians U, Vinks AA, Langman LJ, et al. Impact of laboratory practices on interlaboratory variability in therapeutic drug monitoring of immunosuppressive drugs. Ther Drug Monit. 2015;37:718–724.
160. Korecka M, Shaw LM. Review of the newest HPLC methods with mass spectrometry detection for determination of immunosuppressive drugs in clinical practice. Ann Transplant. 2009;14:61–72.
161. Annesley TM, Clayton L. Simple extraction protocol for analysis of immunosuppressant drugs in whole blood. Clin Chem. 2004;50:1845–1848.
162. Seger C, Tentschert K, Stoggl W, et al. A rapid HPLC-MS/MS method for the simultaneous quantification of cyclosporine A, tacrolimus, sirolimus and everolimus in human blood samples. Nat Protoc. 2009;4:526–534.
163. Little JL, Wempe MF, Buchanan CM. Liquid chromatography-mass spectrometry/mass spectrometry method development for drug metabolism studies: examining lipid matrix ionization effects in plasma. J Chromatogr B Analyt Technol Biomed Life Sci. 2006;833:219–230.
164. Vethe NT, Gjerdalen LC, Bergan S. Determination of cyclosporine, tacrolimus, sirolimus and everolimus by liquid chromatography coupled to electrospray ionization and tandem mass spectrometry: assessment of matrix effects and assay performance. Scand J Clin Lab Invest. 2010;70:583–591.
165. Zhang Y, Zhang R. Recent advances in analytical methods for the therapeutic drug monitoring of immunosuppressive drugs. Drug Test Anal. 2018;10:81–94.
166. Ansermot N, Fathi M, Veuthey JL, et al. Quantification of cyclosporine and tacrolimus in whole blood. Comparison of liquid chromatography-electrospray mass spectrometry with the enzyme multiplied immunoassay technique. Clin Biochem. 2008;41:910–913.
167. LeGatt DF, Shalapay CE, Cheng SB. The EMIT 2000 tacrolimus assay: an application protocol for the Beckman Synchron LX20 PRO analyzer. Clin Biochem. 2004;37:1022–1030.
168. Boer K, Deufel T, Schmidt D, et al. Application of the EMIT 2000 Tacrolimus assay on the Abbott Architect c8000 high volume clinical chemistry analyzer. Clin Biochem. 2006;39:1041–1043.
169. Miura M, Masuda S, Egawa H, et al. Inter-laboratory variability of current immunoassay methods for tacrolimus among Japanese hospitals. Biol Pharm Bull. 2016;39:1331–1337.
170. Bazin C, Guinedor A, Barau C, et al. Evaluation of the Architect tacrolimus assay in kidney, liver, and heart transplant recipients. J Pharm Biomed Anal. 2010;53:997–1002.
171. Tempestilli M, Di Stasio E, Basile MR, et al. Low plasma concentrations of albumin influence the affinity column-mediated immunoassay method for the measurement of tacrolimus in blood during the early period after liver transplantation. Ther Drug Monit. 2013;35:96–100.
172. Wallemacq P, Goffinet JS, O'Morchoe S, et al. Multi-site analytical evaluation of the Abbott ARCHITECT tacrolimus assay. Ther Drug Monit. 2009;31:198–204.
173. Cangemi G, Barco S, Bonifazio P, et al. Comparison of antibody-conjugated magnetic immunoassay and liquid chromatography-tandem mass spectrometry for the measurement of cyclosporine and tacrolimus in whole blood. Int J Immunopathol Pharmacol. 2013;26:419–426.
174. Ventura E, Bonardet A, Pageaux GP, et al. Calcineurin inhibitor determination in whole blood with the RXL dimension analyzer: a useful tool for immunosuppressive drug monitoring. Transplant Proc. 2009;41:707–709.
175. Griffey MA, Hock KG, Kilgore DC, et al. Performance of a no-pretreatment tacrolimus assay on the Dade Behring Dimension RxL clinical chemistry analyzer. Clin Chim Acta. 2007;384:48–51.
176. Doki K, Homma M, Hori T, et al. Difference in blood tacrolimus concentration between ACMIA and MEIA in samples with low haematocrit values. J Pharm Pharmacol. 2010;62:1185–1188.
177. Ju MK, Chang HK, Kim HJ, et al. Is the affinity column-mediated immunoassay method suitable as an alternative to the microparticle enzyme immunoassay method as a blood tacrolimus assay? Transplant Proc. 2008;40:3673–3678.
178. Moscato D, Nonnato A, Adamo R, et al. Therapeutic monitoring of tacrolimus: aberrant results by an immunoassay with automated pretreatment. Clin Chim Acta. 2010;411:77–80.
179. Bargnoux AS, Sutra T, Badiou S, et al. Evaluation of the New Siemens Tacrolimus Assay on the dimension EXL integrated chemistry system analyzer: comparison with an ultra-performance liquid chromatography-tandem mass spectrometry method. Ther Drug Monit. 2016;38:808–812.
180. Kaneko T, Fujioka T, Suzuki Y, et al. Comparison of whole-blood tacrolimus concentrations measured by different immunoassay systems. J Clin Lab Anal. 2018;32:e22587.
181. Dasgupta A, Khalil SA, Johnson-Davis KL. Analytical performance evaluation of a New Cobas Tacrolimus Assay on Cobas e411 analyzer: comparison of values obtained by the CMIA Tacrolimus Assay and a liquid chromatography combined with tandem mass spectrometric method. Ann Clin Lab Sci. 2016;46:204–208.
182. De BK, Jimenez E, De S, et al. Analytical performance characteristics of the Abbott Architect i2000 Tacrolimus assay; comparisons with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Abbott IMx methods. Clin Chim Acta. 2009;410:25–30.
183. Marubashi S, Nagano H, Kobayashi S, et al. Evaluation of a new immunoassay for therapeutic drug monitoring of tacrolimus in adult liver transplant recipients. J Clin Pharmacol. 2010;50:705–709.
184. Li ZY, Yan CL, Yan R, et al. Analytical performance of the Abbott Architect i2000 tacrolimus assay in Chinese patients after renal transplantation. Transplant Proc. 2010;42:4534–4537.
185. Saint-Marcoux F, Debord J, Parant F, et al. Development and evaluation of a simulation procedure to take into account various assays for the Bayesian dose adjustment of tacrolimus. Ther Drug Monit. 2011;33:171–177.
186. Shipkova M, Vogeser M, Ramos PA, et al. Multi-center analytical evaluation of a novel automated tacrolimus immunoassay. Clin Biochem. 2014;47:1069–1077.
187. Qin X, Rui J, Xia Y, et al. Multi-center performance evaluations of tacrolimus and cyclosporine electrochemiluminescence immunoassays in the asia-pacific region. Ann Lab Med. 2018;38:85–94.
188. Fung AWS, Knauer MJ, Blasutig IM, et al. Evaluation of electrochemiluminescence immunoassays for immunosuppressive drugs on the Roche cobas e411 analyzer. F1000Research. 2017;6:1832.
189. Sasano M, Kimura S, Maeda I, et al. Analytical performance evaluation of the Elecsys(R) Cyclosporine and Elecsys(R) Tacrolimus assays on the cobas e411 analyzer. Pract Lab Med. 2017;8:10–17.
190. Leung EK, Yi X, Gloria C, et al. Clinical evaluation of the QMS(R) Tacrolimus immunoassay. Clin Chim Acta. 2014;431:270–275.
191. Akamine Y, Kagaya H, Ohkubo T, et al. A comparison of the effects of CYP3A5 polymorphism on tacrolimus blood concentrations measured by 4 immunoassay methods in renal transplant patients. J Clin Pharm Ther. 2018;43:181–188.
192. Agrawal YP, Cid M, Westgard S, et al. Transplant patient classification and tacrolimus assays: more evidence of the need for assay standardization. Ther Drug Monit. 2014;36:706–709.
193. Annesley TM, McKeown DA, Holt DW, et al. Standardization of LC-MS for therapeutic drug monitoring of tacrolimus. Clin Chem. 2013;59:1630–1637.
194. Levine DM, Maine GT, Armbruster DA, et al. The need for standardization of tacrolimus assays. Clin Chem. 2011;57:1739–1747.
195. Haeckel R, Wosniok W, Gurr E, et al. Permissible limits for uncertainty of measurement in laboratory medicine. Clin Chem Lab Med. 2015;53:1161–1171.
196. Steele BW, Wang E, Soldin SJ, et al. A longitudinal replicate study of immunosuppressive drugs: a College of American Pathologists study. Arch Pathol Lab Med. 2003;127:283–288.
197. Vesper HW, Myers GL, Miller WG. Current practices and challenges in the standardization and harmonization of clinical laboratory tests. Am J Clin Nutr. 2016;104(suppl 3):907s–912s.
198. Siekmann L. Metrological traceability—a concept for standardization in laboratory medicine. Clin Chem Lab Med. 2013;51:953–957.
199. Jones GR, Jackson C. The Joint Committee for Traceability in Laboratory Medicine (JCTLM)—its history and operation. Clin Chim Acta. 2016;453:86–94.
200. Westgard JO. Error methods are more practical, but uncertainty methods may still be preferred. Clin Chem. 2018;64:636–638.
201. Westgard JO, Westgard SA. Measuring analytical quality: total analytical error versus measurement uncertainty. Clin Lab Med. 2017;37:1–13.
202. Theodorsson E. Uncertainty in measurement and total error: tools for coping with diagnostic uncertainty. Clin Lab Med. 2017;37:15–34.
203. Menditto A, Patriarca M, Magnusson B. Understanding the meaning of accuracy, trueness and precision. Accreditation Qual Assur. 2007;12:45–47.
204. Rigo-Bonnin R, Blanco-Font A, Canalias F. Different top-down approaches to estimate measurement uncertainty of whole blood tacrolimus mass concentration values. Clin Biochem. 2018;57:56–61.
205. Vogeser M, Seger C. Irregular analytical errors in diagnostic testing—a novel concept. Clin Chem Lab Med. 2018;56:386–396.
206. Vogeser M, Seger C. Quality management in clinical application of mass spectrometry measurement systems. Clin Biochem. 2016;49:947–954.
207. Yates AM, Bowron A, Calton L, et al. Interlaboratory variation in 25-hydroxyvitamin D2 and 25-hydroxyvitamin D3 is significantly improved if common calibration material is used. Clin Chem. 2008;54:2082–2084.
208. Botelho JC, Shacklady C, Cooper HC, et al. Isotope-dilution liquid chromatography-tandem mass spectrometry candidate reference method for total testosterone in human serum. Clin Chem. 2013;59:372–380.
209. Botelho JC, Ribera A, Cooper HC, et al. Evaluation of an isotope dilution HPLC tandem mass spectrometry candidate reference measurement procedure for total 17-beta estradiol in human serum. Anal Chem. 2016;88:11123–11129.
210. Grote-Koska D, Czajkowski S, Klauke R, et al. A candidate reference measurement procedure for cyclosporine A in whole blood. Accreditation Qual Assur. 2014;19:147–157.
211. IFCC. The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC); Scientific Division; SD Working Groups; Immunosuppressive Drugs (WG-ID) [web site] 2018. Available at: http://www.ifcc.org/ifcc-scientific-division/sd-working-groups/wg-id/
. Accessed April 30, 2019.
212. Edelbroek PM, van der Heijden J, Stolk LM. Dried blood spot methods in therapeutic drug monitoring: methods, assays, and pitfalls. Ther Drug Monit. 2009;31:327–336.
213. Mei JV, Zobel SD, Hall EM, et al. Performance properties of filter paper devices for whole blood collection. Bioanalysis. 2010;2:1397–1403.
214. Enderle Y, Foerster K, Burhenne J. Clinical feasibility of dried blood spots: analytics, validation, and applications. J Pharm Biomed Anal. 2016;130:231–243.
215. Kita K, Mano Y. Application of volumetric absorptive microsampling device for quantification of tacrolimus in human blood as a model drug of high blood cell partition. J Pharm Biomed Anal. 2017;143:168–175.
216. Martial LC, Hoogtanders KEJ, Schreuder MF, et al. Dried blood spot sampling for tacrolimus and mycophenolic acid in children: analytical and clinical validation. Ther Drug Monit. 2017;39:412–421.
217. Koster RA, Greijdanus B, Alffenaar JW, et al. Dried blood spot analysis of creatinine with LC-MS/MS in addition to immunosuppressants analysis. Anal Bioanal Chem. 2015;407:1585–1594.
218. Koster RA, Veenhof H, Botma R, et al. Dried blood spot validation of five immunosuppressants, without hematocrit correction, on two LC-MS/MS systems. Bioanalysis. 2017;9:553–563.
219. Al-Uzri A, Freeman KA, Wade J, et al. Longitudinal study on the use of dried blood spots for home monitoring in children after kidney transplantation. Pediatr Transplant. 2017;21:e12983.
220. Pensi D, De Nicolo A, Pinon M, et al. An UPLC-MS/MS method coupled with automated on-line SPE for quantification of tacrolimus in peripheral blood mononuclear cells. J Pharm Biomed Anal. 2015;107:512–517.
221. Krogstad V, Vethe NT, Robertsen I, et al. Determination of tacrolimus concentration and protein expression of P-glycoprotein in single human renal core biopsies. Ther Drug Monit. 2018;40:292–300.
222. Ghareeb M, Akhlaghi F. Alternative matrices for therapeutic drug monitoring of immunosuppressive agents using LC-MS/MS. Bioanalysis. 2015;7:1037–1058.
223. Lemaitre F, Antignac M, Fernandez C. Monitoring of tacrolimus concentrations in peripheral blood mononuclear cells: application to cardiac transplant recipients. Clin Biochem. 2013;46:1538–1541.
224. 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.
225. Lemaitre F, Antignac M, Verdier MC, et al. Opportunity to monitor immunosuppressive drugs in peripheral blood mononuclear cells: where are we and where are we going? Pharmacol Res. 2013;74:109–112.
226. Capron A, Haufroid V, Wallemacq P. Intra-cellular immunosuppressive drugs monitoring: a step forward towards better therapeutic efficacy after organ transplantation? Pharmacol Res. 2016;111:610–618.
227. Bahmany S, de Wit LEA, Hesselink DA, et al. Highly sensitive and rapid determination of tacrolimus in peripheral blood mononuclear cells by liquid chromatography-tandem mass spectrometry. Biomed Chromatogr. 2018:e4416.
228. Klaasen RA, Bergan S, Bremer S, et al. A longitudinal study of tacrolimus in lymphocytes during the first year after kidney transplantation. Ther Drug Monit. 2018;40:558–566.
229. Han SS, Yang SH, Kim MC, et al. Monitoring the intracellular tacrolimus concentration in kidney transplant recipients with stable graft function. PLoS One. 2016;11:e0153491.
230. Lemaitre F, Blanchet B, Latournerie M, et al. Pharmacokinetics
of tacrolimus in liver transplant recipients: inside the white blood cells. Clin Biochem. 2015;48:406–411.
231. Bittersohl H, Schniedewind B, Christians U, et al. A simple and highly sensitive on-line column extraction liquid chromatography-tandem mass spectrometry method for the determination of protein-unbound tacrolimus in human plasma samples. J Chromatogr A. 2018;1547:45–52.
232. Belostotsky V, Adaway J, Keevil BG, et al. Measurement of saliva tacrolimus levels in pediatric renal transplant recipients. Pediatr Nephrol. 2011;26:133–138.
233. Ghareeb M, Gohh RY, Akhlaghi F. Tacrolimus concentration in saliva of kidney transplant recipients: factors influencing the relationship with whole blood concentrations. Clin Pharmacokinet. 2018;57:1199–1210.
234. Kamdem LK, Streit F, Zanger UM, et al. Contribution of CYP3A5 to the in vitro hepatic clearance of tacrolimus. Clin Chem. 2005;51:1374–1381.
235. Kuehl P, Zhang J, Lin Y, et al. Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Genet. 2001;27:383–391.
236. Hustert E, Haberl M, Burk O, et al. The genetic determinants of the CYP3A5 polymorphism. Pharmacogenetics
237. Wang D, Guo Y, Wrighton SA, et al. Intronic polymorphism in CYP3A4 affects hepatic expression and response to statin drugs. Pharmacogenomics J. 2011;11:274–286.
238. Hebert MF. Contributions of hepatic and intestinal metabolism and P-glycoprotein to cyclosporine and tacrolimus oral drug delivery. Adv Drug Deliv Rev. 1997;27:201–214.
239. Saeki T, Ueda K, Tanigawara Y, et al. Human P-glycoprotein transports cyclosporin A and FK506. J Biol Chem. 1993;268:6077–6080.
240. Schwab M, Eichelbaum M, Fromm MF. Genetic polymorphisms of the human MDR1 drug transporter. Annu Rev Pharmacol Toxicol. 2003;43:285–307.
241. Cascorbi I, Gerloff T, Johne A, et al. Frequency of single nucleotide polymorphisms in the P-glycoprotein drug transporter MDR1 gene in white subjects. Clin Pharmacol Ther. 2001;69:169–174.
242. Hoffmeyer S, Burk O, von Richter O, et al. Functional polymorphisms of the human multidrug-resistance gene: multiple sequence variations and correlation of one allele with P-glycoprotein expression and activity in vivo. Proc Natl Acad Sci U S A. 2000;97:3473–3478.
243. Kim RB, Leake BF, Choo EF, et al. Identification of functionally variant MDR1 alleles among European Americans and African Americans. Clin Pharmacol Ther. 2001;70:189–199.
244. Dessilly G, Elens L, Panin N, et al. ABCB1 1199G>A genetic polymorphism (Rs2229109) influences the intracellular accumulation of tacrolimus in HEK293 and K562 recombinant cell lines. PLoS One. 2014;9:e91555.
245. Oneda B, Crettol S, Jaquenoud Sirot E, et al. The P450 oxidoreductase genotype is associated with CYP3A activity in vivo as measured by the midazolam phenotyping test. Pharmacogenet Genomics. 2009;19:877–883.
246. Klein K, Thomas M, Winter S, et al. PPARA: a novel genetic determinant of CYP3A4 in vitro and in vivo. Clin Pharmacol Ther. 2012;91:1044–1052.
247. 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
248. Thervet E, Anglicheau D, King B, et al. Impact of cytochrome p450 3A5 genetic polymorphism on tacrolimus doses and concentration-to-dose ratio in renal transplant recipients. Transplantation. 2003;76:1233–1235.
249. Hesselink DA, van Schaik RH, van der Heiden IP, et al. Genetic polymorphisms of the CYP3A4, CYP3A5, and MDR-1 genes and pharmacokinetics
of the calcineurin inhibitors cyclosporine and tacrolimus. Clin Pharmacol Ther. 2003;74:245–254.
250. Picard N, Bergan S, Marquet P, et al. Pharmacogenetic biomarkers
predictive of the pharmacokinetics
of immunosuppressive drugs. Ther Drug Monit. 2015;38(suppl 1):S57–S61.
251. Zong YP, Wang ZJ, Zhou WL, et al. Effects of CYP3A5 polymorphisms on tacrolimus pharmacokinetics
in pediatric kidney transplantation: a systematic review and meta-analysis of observational studies. World J Pediatr. 2017;13:421–426.
252. Rojas L, Neumann I, Herrero MJ, et al. Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: a systematic review and meta-analysis of observational studies. Pharmacogenomics J. 2015;15:38–48.
253. 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.
254. Thervet E, Loriot MA, Barbier S, et al. Optimization of initial tacrolimus dose using pharmacogenetic testing. Clin Pharmacol Ther. 2010;87:721–726.
255. De Meyer M, Haufroid V, Kanaan N, et al. Pharmacogenetic-based strategy using de novo tacrolimus once daily after kidney transplantation: prospective pilot study. Pharmacogenomics. 2016;17:1019–1027.
256. Birdwell KA, Decker B, Barbarino JM, et al. Clinical pharmacogenetics
implementation Consortium (CPIC) guidelines for CYP3A5 genotype and tacrolimus dosing. Clin Pharmacol Ther. 2015;98:19–24.
257. Shuker N, Bouamar R, van Schaik RH, et al. A randomized controlled trial comparing the efficacy of Cyp3a5 genotype-based with body-weight-based tacrolimus dosing after living donor kidney transplantation. Am J Transplant. 2016;16:2085–2096.
258. de Jonge H, de Loor H, Verbeke K, et al. In vivo CYP3A4 activity, CYP3A5 genotype, and hematocrit predict tacrolimus dose requirements and clearance in renal transplant patients. Clin Pharmacol Ther. 2012;92:366–375.
259. Elens L, van Schaik RH, Panin N, et al. Effect of a new functional CYP3A4 polymorphism on calcineurin inhibitors' dose requirements and trough blood levels in stable renal transplant patients. Pharmacogenomics. 2011;12:1383–1396.
260. Elens L, Bouamar R, Hesselink DA, et al. A new functional CYP3A4 intron 6 polymorphism significantly affects tacrolimus pharmacokinetics
in kidney transplant recipients. Clin Chem. 2011;57:1574–1583.
261. Elens L, Capron A, van Schaik RH, et al. Impact of CYP3A4*22 allele on tacrolimus pharmacokinetics
in early period after renal transplantation: toward updated genotype-based dosage guidelines. Ther Drug Monit. 2013;35:608–616.
262. Elens L, Haufroid V. Genotype-based tacrolimus dosing guidelines: with or without CYP3A4*22? Pharmacogenomics. 2017;18:1473–1480.
263. Woillard JB, Mourad M, Neely M, et al. Tacrolimus updated guidelines through popPK modeling: how to benefit more from CYP3A pre-emptive genotyping prior to kidney transplantation. Front Pharmacol. 2017;8:358.
264. Kurzawski M, Dabrowska J, Dziewanowski K, et al. CYP3A5 and CYP3A4, but not ABCB1 polymorphisms affect tacrolimus dose-adjusted trough concentrations in kidney transplant recipients. Pharmacogenomics. 2014;15:179–188.
265. Yoon SH, Cho JH, Kwon O, et al. CYP3A and ABCB1 genetic polymorphisms on the pharmacokinetics
of tacrolimus and its metabolites (M-I and M-III). Transplantation. 2013;95:828–834.
266. Li Y, Hu X, Cai B, et al. Meta-analysis of the effect of MDR1 C3435 polymorphism on tacrolimus pharmacokinetics
in renal transplant recipients. Transpl Immunol. 2012;27:12–18.
267. Singh R, Srivastava A, Kapoor R, et al. Do drug transporter (ABCB1) SNPs influence cyclosporine and tacrolimus dose requirements and renal allograft outcome in the posttransplantation period? J Clin Pharmacol. 2011;51:603–615.
268. Terrazzino S, Quaglia M, Stratta P, et al. The effect of CYP3A5 6986A>G and ABCB1 3435C>T on tacrolimus dose-adjusted trough levels and acute rejection rates in renal transplant patients: a systematic review and meta-analysis. Pharmacogenet Genomics. 2012;22:642–645.
269. Metalidis C, Lerut E, Naesens M, et al. Expression of CYP3A5 and P-glycoprotein in renal allografts with histological signs of calcineurin inhibitor nephrotoxicity. Transplantation. 2011;91:1098–1102.
270. 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.
271. Vafadari R, Bouamar R, Hesselink DA, et al. Genetic polymorphisms in ABCB1 influence the pharmacodynamics
of tacrolimus. Ther Drug Monit. 2013;35:459–465.
272. 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.
273. Gijsen VM, van Schaik RH, Soldin OP, et al. P450 oxidoreductase *28 (POR*28) and tacrolimus disposition in pediatric kidney transplant recipients—a pilot study. Ther Drug Monit. 2014;36:152–158.
274. de Jonge H, Metalidis C, Naesens M, et al. The P450 oxidoreductase *28 SNP is associated with low initial tacrolimus exposure and increased dose requirements in CYP3A5-expressing renal recipients. Pharmacogenomics. 2011;12:1281–1291.
275. Pulk RA, Schladt DS, Oetting WS, et al. Multigene predictors of tacrolimus exposure in kidney transplant recipients. Pharmacogenomics. 2015;16:841–854.
276. Kurzawski M, Malinowski D, Dziewanowski K, et al. Impact of PPARA and POR polymorphisms on tacrolimus pharmacokinetics
and new-onset diabetes in kidney transplant recipients. Pharmacogenet Genomics. 2014;24:397–400.
277. Elens L, Hesselink DA, Bouamar R, et al. Impact of POR*28 on the pharmacokinetics
of tacrolimus and cyclosporine A in renal transplant patients. Ther Drug Monit. 2014;36:71–79.
278. Pouche L, Stojanova J, Marquet P, et al. New challenges and promises in solid organ transplantation pharmacogenetics
: the genetic variability of proteins involved in the pharmacodynamics
of immunosuppressive drugs. Pharmacogenomics. 2016;17:277–296.
279. Noceti O, Pouche L, Esperon P, et al. Activity of the calcineurin pathway in patients on the liver transplantation waiting list: factors of variability and response to tacrolimus inhibition. Clin Chem. 2017;63:1734–1744.
280. Pouche L, Koitka M, Stojanova J, et al. A candidate gene approach of the calcineurin pathway to identify variants associated with clinical outcomes in renal transplantation. Pharmacogenomics. 2016;17:375–391.
281. Buendia JA, Otamendi E, Kravetz MC, et al. Combinational effect of CYP3A5 and MDR-1 polymorphisms on tacrolimus pharmacokinetics
in liver transplant patients. Exp Clin Transplant. 2015;13:441–448.
282. Gomez-Bravo MA, Salcedo M, Fondevila C, et al. Impact of donor and recipient CYP3A5 and ABCB1 genetic polymorphisms on tacrolimus dosage requirements and rejection in Caucasian Spanish liver transplant patients. J Clin Pharmacol. 2013;53:1146–1154.
283. Uesugi M, Masuda S, Katsura T, et al. Effect of intestinal CYP3A5 on postoperative tacrolimus trough levels in living-donor liver transplant recipients. Pharmacogenet Genomics. 2006;16:119–127.
284. Provenzani A, Notarbartolo M, Labbozzetta M, et al. Influence of CYP3A5 and ABCB1 gene polymorphisms and other factors on tacrolimus dosing in Caucasian liver and kidney transplant patients. Int J Mol Med. 2011;28:1093–1102.
285. Yu S, Wu L, Jin J, et al. Influence of CYP3A5 gene polymorphisms of donor rather than recipient to tacrolimus individual dose requirement in liver transplantation. Transplantation. 2006;81:46–51.
286. Goto M, Masuda S, Kiuchi T, et al. CYP3A5*1-carrying graft liver reduces the concentration/oral dose ratio of tacrolimus in recipients of living-donor liver transplantation. Pharmacogenetics
287. Debette-Gratien M, Woillard JB, Picard N, et al. Influence of donor and recipient CYP3A4, CYP3A5, and ABCB1 genotypes on clinical outcomes and nephrotoxicity in liver transplant recipients. Transplantation. 2016;100:2129–2137.
288. Buendia JA, Bramuglia G, Staatz CE. Effects of combinational CYP3A5 6986A>G polymorphism in graft liver and native intestine on the pharmacokinetics
of tacrolimus in liver transplant patients: a meta-analysis. Ther Drug Monit. 2014;36:442–447.
289. Uesugi M, Kikuchi M, Shinke H, et al. Impact of cytochrome P450 3A5 polymorphism in graft livers on the frequency of acute cellular rejection in living-donor liver transplantation. Pharmacogenet Genomics. 2014;24:356–366.
290. Moes DJ, van der Bent SA, Swen JJ, et al. Population pharmacokinetics
of once daily tacrolimus formulation in stable liver transplant recipients. Eur J Clin Pharmacol. 2016;72:163–174.
291. de Denus S, Zakrzewski M, Barhdadi A, et al. Association between renal function and CYP3A5 genotype in heart transplant recipients treated with calcineurin inhibitors. J Heart Lung Transplant. 2011;30:326–331.
292. Kuypers DR, de Jonge H, Naesens M, et al. CYP3A5 and CYP3A4 but not MDR1 single-nucleotide polymorphisms determine long-term tacrolimus disposition and drug-related nephrotoxicity in renal recipients. Clin Pharmacol Ther. 2007;82:711–725.
293. Fukudo M, Yano I, Yoshimura A, et al. Impact of MDR1 and CYP3A5 on the oral clearance of tacrolimus and tacrolimus-related renal dysfunction in adult living-donor liver transplant patients. Pharmacogenet Genomics. 2008;18:413–423.
294. Hosohata K, Uesugi M, Hashi S, et al. Association between CYP3A5 genotypes in graft liver and increase in tacrolimus biotransformation from steroid treatment in living-donor liver transplant patients. Drug Metab Pharmacokinet. 2014;29:83–89.
295. Muraki Y, Usui M, Isaji S, et al. Impact of CYP3A5 genotype of recipients as well as donors on the tacrolimus pharmacokinetics
and infectious complications after living-donor liver transplantation for Japanese adult recipients. Ann Transplant. 2011;16:55–62.
296. Shi Y, Li Y, Tang J, et al. Influence of CYP3A4, CYP3A5 and MDR-1 polymorphisms on tacrolimus pharmacokinetics
and early renal dysfunction in liver transplant recipients. Gene. 2013;512:226–231.
297. Gomez-Bravo MA, Apellaniz-Ruiz M, Salcedo M, et al. Influence of donor liver CYP3A4*20 loss-of-function genotype on tacrolimus pharmacokinetics
in transplanted patients. Pharmacogenet Genomics. 2018;28:41–48.
298. Monostory K, Toth K, Kiss A, et al. Personalizing initial calcineurin inhibitor dosing by adjusting to donor CYP3A-status in liver transplant patients. Br J Clin Pharmacol. 2015;80:1429–1437.
299. Guy-Viterbo V, Baudet H, Elens L, et al. Influence of donor-recipient CYP3A4/5 genotypes, age and fluconazole on tacrolimus pharmacokinetics
in pediatric liver transplantation: a population approach. Pharmacogenomics. 2014;15:1207–1221.
300. Hosohata K, Masuda S, Katsura T, et al. Impact of intestinal CYP2C19 genotypes on the interaction between tacrolimus and omeprazole, but not lansoprazole, in adult living-donor liver transplant patients. Drug Metab Dispos. 2009;37:821–826.
301. Masuda S, Goto M, Fukatsu S, et al. Intestinal MDR1/ABCB1 level at surgery as a risk factor of acute cellular rejection in living-donor liver transplant patients. Clin Pharmacol Ther. 2006;79:90–102.
302. Zheng H, Webber S, Zeevi A, et al. Tacrolimus dosing in pediatric heart transplant patients is related to CYP3A5 and MDR1 gene polymorphisms. Am J Transplant. 2003;3:477–483.
303. 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.
304. Diaz-Molina B, Tavira B, Lambert JL, et al. Effect of CYP3A5, CYP3A4, and ABCB1 genotypes as determinants of tacrolimus dose and clinical outcomes after heart transplantation. Transplant Proc. 2012;44:2635–2638.
305. Gijsen VM, van Schaik RH, Elens L, et al. CYP3A4*22 and CYP3A combined genotypes both correlate with tacrolimus disposition in pediatric heart transplant recipients. Pharmacogenomics. 2013;14:1027–1036.
306. Deininger KM, Vu A, Page RL II, et al. CYP3A pharmacogenetics
and tacrolimus disposition in adult heart transplant recipients. Clin Transplant. 2016;30:1074–1081.
307. Gijsen V, Mital S, van Schaik RH, et al. Age and CYP3A5 genotype affect tacrolimus dosing requirements after transplant in pediatric heart recipients. J Heart Lung Transplant. 2011;30:1352–1359.
308. Lesche D, Sigurdardottir V, Setoud R, et al. CYP3A5*3 and POR*28 genetic variants influence the required dose of tacrolimus in heart transplant recipients. Ther Drug Monit. 2014;36:710–715.
309. Sanchez-Lazaro I, Herrero MJ, Jordan-De Luna C, et al. Association of SNPs with the efficacy and safety of immunosuppressant therapy after heart transplantation. Pharmacogenomics. 2015;16:971–979.
310. Zheng H, Zeevi A, Schuetz E, et al. Tacrolimus dosing in adult lung transplant patients is related to cytochrome P4503A5 gene polymorphism. J Clin Pharmacol. 2004;44:135–140.
311. Ruiz J, Herrero MJ, Boso V, et al. Impact of single nucleotide polymorphisms (SNPs) on immunosuppressive therapy in lung transplantation. Int J Mol Sci. 2015;16:20168–20182.
312. Shipkova M, Wieland E. Editorial: immune monitoring in solid organ transplantation. Clin Biochem. 2016;49:317–319.
313. 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.
314. Blanchet B, Hulin A, Duvoux C, et al. Determination of serine/threonine protein phosphatase type 2B PP2B in lymphocytes by HPLC. Anal Biochem. 2003;312:1–6.
315. 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.
316. Sommerer C, Giese T. Nuclear factor of activated T cells-regulated gene expression as predictive biomarker of personal response to calcineurin inhibitors. Ther Drug Monit. 2016;38(suppl 1):S50–S56.
317. Blanchet B, Duvoux C, Costentin CE, et al. Pharmacokinetic-pharmacodynamic assessment of tacrolimus in liver-transplant recipients during the early post-transplantation period. Ther Drug Monit. 2008;30:412–418.
318. Fukudo M, Yano I, Masuda S, et al. Pharmacodynamic analysis of tacrolimus and cyclosporine in living-donor liver transplant patients. Clin Pharmacol Ther. 2005;78:168–181.
319. Koefoed-Nielsen PB, Karamperis N, Hojskov C, et al. The calcineurin activity profiles of cyclosporin and tacrolimus are different in stable renal transplant patients. Transpl Int. 2006;19:821–827.
320. 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.
321. Hogan PG, Chen L, Nardone J, et al. Transcriptional regulation by calcium, calcineurin, and NFAT. Genes Dev. 2003;17:2205–2232.
322. George T, Harmon I, Peterson EJ, et al. Measurement of nuclear translocation in primary cells using correlation analysis of images obtained on the ImageStream imaging flow cytometer (87.13). J Immunol. 2007;178:S130.
323. Maguire O, Tornatore KM, O'Loughlin KL, et al. Nuclear translocation of nuclear factor of activated T cells (NFAT) as a quantitative pharmacodynamic parameter for tacrolimus. Cytometry A. 2013;83:1096–1104.
324. Noceti OM, Woillard JB, Boumediene A, et al. Tacrolimus pharmacodynamics
along the calcineurin pathway in human lymphocytes. Clin Chem. 2014;60:1336–1345.
325. Macian F, Lopez-Rodriguez C, Rao A. Partners in transcription: NFAT and AP-1. Oncogene. 2001;20:2476–2489.
326. 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.
327. Zahn A, Schott N, Hinz U, et al. Immunomonitoring of nuclear factor of activated T cells-regulated gene expression: the first clinical trial in liver allograft recipients. Liver Transpl. 2011;17:466–473.
328. 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.
329. Giese T, Sommerer C, Zeier M, et al. Approaches towards individualized immune intervention. Dig Dis. 2010;28:45–50.
330. 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.
331. Sommerer C, Meuer S, Zeier M, et al. Calcineurin inhibitors and NFAT-regulated gene expression. Clin Chim Acta. 2012;413:1379–1386.
332. 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.
333. Halloran PF, Helms LM, Kung L, et al. The temporal profile of calcineurin inhibition by cyclosporine in vivo. Transplantation. 1999;68:1356–1361.
334. 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.
335. 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.
336. 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.
337. Abdel-Kahaar E, Giese T, Sommerer C, et al. Analytical validation and cross-validation of a NFAT-regulated gene expression assay for pharmacodynamic monitoring of therapy with calcineurin inhibitors. Ther Drug Monit. 2016;38:711–716.
338. Bremer S, Vethe NT, Skauby M, et al. NFAT-regulated cytokine gene expression during tacrolimus therapy early after renal transplantation. Br J Clin Pharmacol. 2017;83:2494–2502.
339. Greenland JR, Chong T, Wang AS, et al. Suppressed calcineurin-dependent gene expression identifies lung allograft recipients at increased risk of infection. Am J Transplant. 2018;18:2043–2049.
340. Sommerer C, Zeier M, Meuer S, et al. Monitoring of calcineurin inhibitors by NFAT-regulated gene expression in de novo renal allograft recipients on cyclosporine A. Clin Nephrol. 2015;84:165–172.
341. Keller F, Sommerer C, Giese T, et al. Correlation between pharmacokinetics
of tacrolimus and pharmacodynamics
on NFAT-regulated gene expression in stable kidney transplant recipients. Clin Nephrol. 2017;87:93–99.
342. 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.
343. 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.
344. 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.
345. 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.
346. 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.
347. 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.
348. 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.
349. Sommerer C, Brocke J, Bruckner T, et al. Improved pulse wave velocity and renal function in individualized calcineurin inhibitor treatment by immunomonitoring: the randomized controlled calcineurin inhibitor-sparing trial. Transplantation. 2018;102:510–520.
350. Kannegieter NM, Hesselink DA, Dieterich M, et al. Differential T cell signaling pathway activation by tacrolimus and belatacept after kidney transplantation: post hoc analysis of a randomised-controlled trial. Sci Rep. 2017;7:15135.
351. Kannegieter NM, Hesselink DA, Dieterich M, et al. Pharmacodynamic monitoring of tacrolimus-based immunosuppression in CD14+ monocytes after kidney transplantation. Ther Drug Monit. 2017;39:463–471.
352. Kannegieter NM, Shuker N, Vafadari R, et al. Conversion to once-daily tacrolimus results in increased p38MAPK phosphorylation in T lymphocytes of kidney transplant recipients. Ther Drug Monit. 2016;38:280–284.
353. Albring A, Wendt L, Harz N, et al. Relationship between pharmacokinetics
of calcineurin inhibitors in renal transplant patients. Clin Transplant. 2015;29:294–300.
354. Cattral M, Luke S, Knauer MJ, et al. Randomized open-label crossover assessment of Prograf vs Advagraf on immunosuppressant pharmacokinetics
in simultaneous pancreas-kidney patients. Clin Transplant. 2018;32:e13180.
355. Tanaka T, Takatsuki M, Soyama A, et al. Evaluation of immune function under conversion from Prograf to Advagraf in living donor liver transplantation. Ann Transplant. 2013;18:293–298.
356. Ravaioli M, Neri F, Lazzarotto T, et al. Immunosuppression modifications based on an immune response assay: results of a randomized, controlled trial. Transplantation. 2015;99:1625–1632.
357. Barten MJ, Tarnok A, Garbade J, et al. Pharmacodynamics
of T-cell function for monitoring immunosuppression. Cell Prolif. 2007;40:50–63.
358. Kurata Y, Kato M, Kuzuya T, et al. Pretransplant pharmacodynamic analysis of immunosuppressive agents using CFSE-based T-cell proliferation assay. Clin Pharmacol Ther. 2009;86:285–289.
359. Weimer R, Melk A, Daniel V, et al. Switch from cyclosporine A to tacrolimus in renal transplant recipients: impact on Th1, Th2, and monokine responses. Hum Immunol. 2000;61:884–897.
360. 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.
361. Barten MJ, Rahmel A, Garbade J, et al. Pharmacodynamic monitoring of the conversion of cyclosporine to tacrolimus in heart and lung transplant recipients. Transplant Proc. 2005;37:4532–4534.
362. Bai YJ, Wang LL, Cai B, et al. Regulatory function of tacrolimus and CsA on CD4/CD8 T lymphocyte subgroups and costimulators on them in allo-liver recipients [in Chinese]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2008;24:989–992.
363. Shi G, Zeng Q, Zhao Z, et al. Variation of CD4(+)CD25(+)Foxp3(+) regulatory T cells and Th17 cells in the peripheral blood of human liver allograft patients with long-term survival. Transplant Proc. 2017;49:1834–1840.
364. Laskin BL, Jiao J, Baluarte HJ, et al. The effects of tacrolimus on T-cell proliferation are short-lived: a pilot analysis of immune function testing. Transplant Direct. 2017;3:e199.
365. Gong N, Chen Z, Wang J, et al. Immunoregulatory effects of sirolimus vs. tacrolimus treatment in kidney allograft recipients. Cell Immunol. 2015;297:87–93.
366. Kim TJ, Kim N, Kang HJ, et al. FK506 causes cellular and functional defects in human natural killer cells. J Leukoc Biol. 2010;88:1089–1097.
367. Nakamura Y, Hama K, Katayama H, et al. Safety and efficacy of conversion from twice-daily tacrolimus (prograf) to once-daily prolonged-release tacrolimus (graceptor) in stable kidney transplant recipients. Transplant Proc. 2012;44:124–127.
368. Bohler T, Canivet C, Galvani S, et al. Pharmacodynamic monitoring of the conversion from mycophenolate mofetil to enteric-coated mycophenolate sodium in stable kidney-allograft recipients. IntImmunopharmacol. 2008;8:769–773.
369. Boix F, Mrowiec A, Muro M. Cytokine expression profile as predictive surrogate biomarkers
for clinical events in the field of solid organ transplantation. Curr Protein Pept Sci. 2017;18:240–249.
370. Millan O, Brunet M. Cytokine-based immune monitoring. Clin Biochem. 2016;49:338–346.
371. Brunet M, Millan Lopez O, Lopez-Hoyos M. T-cell cytokines as predictive markers of the risk of allograft rejection. Ther Drug Monit. 2016;38(suppl 1):S21–S28.
372. Hricik DE, Rodriguez 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.
373. Millan 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.
374. 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.
375. 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.
376. 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.
377. 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.
378. Millan O, Benitez C, Guillen D, et al. Biomarkers
of immunoregulatory status in stable liver transplant recipients undergoing weaning of immunosuppressive therapy. Clin Immunol. 2010;137:337–346.
379. Millan O, Rafael-Valdivia L, Torrademe 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.
380. Korn T, Bettelli E, Oukka M, et al. IL-17 and Th17 cells. Annu Rev Immunol. 2009;27:485–517.
381. Abadja F, Atemkeng S, Alamartine E, et al. Impact of mycophenolic acid and tacrolimus on Th17-related immune response. Transplantation. 2011;92:396–403.
382. Chung BH, Kim KW, Kim BM, et al. Dysregulation of Th17 cells during the early post-transplant period in patients under calcineurin inhibitor based immunosuppression. PLoS One. 2012;7:e42011.
383. Merino D, San Segundo D, Medina JM, et al. Different in vitro proliferation and cytokine-production inhibition of memory T-cell subsets after calcineurin and mammalian target of rapamycin inhibitors treatment. Immunology. 2016;148:206–215.
384. Tait BD, Susal C, Gebel HM, 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.
385. Lefaucheur C, Viglietti D, Mangiola M, et al. From humoral theory to performant risk stratification in kidney transplantation. J Immunol Res. 2017;2017:5201098.
386. Loupy A, Lefaucheur C. Antibody-mediated rejection of solid-organ allografts. N Engl J Med. 2018;379:1150–1160.
387. Karahan GE, Claas FHJ, Heidt S. Technical challenges and clinical relevance of single antigen bead C1q/C3d testing and IgG subclass analysis of human leukocyte antigen antibodies. Transpl Int. 2018;31:1189–1197.
388. Bamoulid J, Roodenburg A, Staeck O, et al. Clinical outcome of patients with de novo C1q-binding donor-specific HLA antibodies after renal transplantation. Transplantation. 2017;101:2165–2174.
389. Lachmann N, Terasaki PI, Budde K, et al. Anti-human leukocyte antigen and donor-specific antibodies detected by luminex posttransplant serve as biomarkers
for chronic rejection of renal allografts. Transplantation. 2009;87:1505–1513.
390. Wiebe C, Gareau AJ, Pochinco D, et al. Evaluation of C1q status and titer of de novo donor-specific antibodies as predictors of allograft survival. Am J Transplant. 2017;17:703–711.
391. Bouquegneau A, Loheac C, Aubert O, et al. Complement-activating donor-specific anti-HLA antibodies and solid organ transplant survival: a systematic review and meta-analysis. PLoS Med. 2018;15:e1002572.
392. Viglietti D, Bouatou Y, Kheav VD, et al. Complement-binding anti-HLA antibodies are independent predictors of response to treatment in kidney recipients with antibody-mediated rejection. Kidney Int. 2018;94:773–787.
393. Viglietti D, Loupy A, Aubert O, et al. Dynamic prognostic score to predict kidney allograft survival in patients with antibody-mediated rejection. J Am Soc Nephrol. 2018;29:606–619.
394. Budde K, Durr M. Any progress in the treatment of antibody-mediated rejection? J Am Soc Nephrol. 2018;29:350–352.
395. Malvezzi P, Jouve T, Noble J, et al. Desensitization in the setting of HLA-incompatible kidney transplant. Exp Clin Transplant. 2018;16:367–375.
396. Sellares J, de Freitas DG, Mengel M, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant. 2012;12:388–399.
397. Wiebe C, Gibson IW, Blydt-Hansen TD, et al. Rates and determinants of progression to graft failure in kidney allograft recipients with de novo donor-specific antibody. Am J Transplant. 2015;15:2921–2930.
398. Sablik KA, Clahsen-van Groningen MC, Hesselink DA, et al. Tacrolimus intra-patient variability is not associated with chronic active antibody mediated rejection. PLoS One. 2018;13:e0196552.
399. Shuker N, van Gelder T, Hesselink DA. Intra-patient variability in tacrolimus exposure: causes, consequences for clinical management. Transplant Rev (Orlando). 2015;29:78–84.
400. Dugast E, Soulillou JP, Foucher Y, et al. Failure of calcineurin inhibitor (tacrolimus) weaning randomized trial in long-term stable kidney transplant recipients. Am J Transplant. 2016;16:3255–3261.
401. Heidt S, Roelen DL, Eijsink C, et al. Calcineurin inhibitors affect B cell antibody responses indirectly by interfering with T cell help. Clin Exp Immunol. 2010;159:199–207.
402. Matz M, Lehnert M, Lorkowski C, et al. Combined standard and novel immunosuppressive substances affect B-lymphocyte function. Int Immunopharmacol. 2013;15:718–725.
403. O'Leary JG, Samaniego M, Barrio MC, et al. The influence of immunosuppressive agents on the risk of de novo donor-specific HLA antibody production in solid organ transplant recipients. Transplantation. 2016;100:39–53.
404. Wiebe C, Rush DN, Nevins TE, et al. Class II eplet mismatch modulates tacrolimus trough levels required to prevent donor-specific antibody development. J Am Soc Nephrol. 2017;28:3353–3362.
405. Nath DS, Angaswamy N, Basha HI, et al. Donor-specific antibodies to human leukocyte antigens are associated with and precede antibodies to major histocompatibility complex class I-related chain A in antibody-mediated rejection and cardiac allograft vasculopathy after human cardiac transplantation. Hum Immunol. 2010;71:1191–1196.
406. Lund LH, Edwards LB, Dipchand AI, et al. The registry of the international society for heart and lung transplantation: thirty-third adult heart transplantation report-2016; focus theme: primary diagnostic indications for transplant. J Heart Lung Transplant. 2016;35:1158–1169.
407. Hodges AM, Lyster H, McDermott A, et al. Late antibody-mediated rejection after heart transplantation following the development of de novo donor-specific human leukocyte antigen antibody. Transplantation. 2012;93:650–656.
408. Frank R, Molina MR, Goldberg LR, et al. Circulating donor-specific anti-human leukocyte antigen antibodies and complement C4d deposition are associated with the development of cardiac allograft vasculopathy. Am J Clin Pathol. 2014;142:809–815.
409. Kobashigawa J, Colvin M, Potena L, et al. The management of antibodies in heart transplantation: an ISHLT consensus
document. J Heart Lung Transplant. 2018;37:537–547.
410. Coutance G, d'Orio V, Belin L, et al. Favorable outcome of an exclusively posttransplant prophylactic strategy after heart transplantation in recipients with high immunological risk. Transplantation. 2018 [epub ahead of print]. DOI: 10.1097/TP.0000000000002503.
411. Barten MJ, Schulz U, Beiras-Fernandez A, et al. The clinical impact of donor-specific antibodies in heart transplantation. Transpl Rev (Orlando). 2018;32:207–217.
412. Kobashigawa JA, Miller LW, Russell SD, et al. Tacrolimus with mycophenolate mofetil (MMF) or sirolimus vs. cyclosporine with MMF in cardiac transplant patients: 1-year report. Am J Transplant. 2006;6:1377–1386.
413. Garcia Moreira V, Prieto Garcia B, Baltar Martin JM, et al. Cell-free DNA as a noninvasive acute rejection marker in renal transplantation. Clin Chem. 2009;55:1958–1966.
414. 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.
415. Gielis EM, Ledeganck KJ, De Winter BY, et al. Cell-free DNA: an upcoming biomarker in transplantation. Am J Transplant. 2015;15:2541–2551.
416. Verhoeven J, Boer K, Van Schaik RHN, et al. Liquid biopsies to monitor solid organ transplant function: a review of new biomarkers
. Ther Drug Monit. 2018;40:515–525.
417. Sigdel TK, Vitalone MJ, Tran TQ, et al. A rapid noninvasive assay for the detection of renal transplant injury. Transplantation. 2013;96:97–101.
418. Bloom RD, Bromberg JS, Poggio ED, et al. Cell-free DNA and active rejection in kidney allografts. J Am Soc Nephrol. 2017;28:2221–2232.
419. Oellerich M, Schutz 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.
420. Schutz E, Fischer A, Beck J, et al. Graft-derived cell-free DNA, a noninvasive early rejection and graft damage marker in liver transplantation: a prospective, observational, multicenter cohort study. PLoS Med. 2017;14:e1002286.
421. Knight SR, Thorne A, Faro MLL. Donor-specific cell-free DNA as a biomarker in solid organ transplantation. A systematic review. Transplantation. 2019;103:273–283.
422. Debord J, Risco E, Harel M, et al. Application of a gamma model of absorption to oral cyclosporin. Clin Pharmacokinet. 2001;40:375–382.
423. Savic RM, Jonker DM, Kerbusch T, et al. Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies. J Pharmacokinet Pharmacodyn. 2007;34:711–726.
424. Staatz CE, Tett SE. Clinical pharmacokinetics
of tacrolimus in solid organ transplantation. ClinPharmacokinet. 2004;43:623–653.
425. Wong KM, Shek CC, Chau KF, et al. Abbreviated tacrolimus area-under-the-curve monitoring for renal transplant recipients. Am J Kidney Dis. 2000;35:660–666.
426. Knoop C, Thiry P, Saint-Marcoux F, et al. Tacrolimus pharmacokinetics
and dose monitoring after lung transplantation for cystic fibrosis and other conditions. Am J Transplant. 2005;5:1477–1482.
427. Woillard JB, Debord J, Monchaud C, et al. Population pharmacokinetics
and bayesian estimators for refined dose adjustment of a new tacrolimus formulation in kidney and liver transplant patients. Clin Pharmacokinet. 2017;56:1491–1498.
428. Woillard JB, Chouchana L, Picard N, et al. Pharmacogenetics
of immunosuppressants: state of the art and clinical implementation - recommendations from the French National Network of Pharmacogenetics
(RNPGx). Therapie. 2017;72:285–299.
429. Chen B, Shi HQ, Liu XX, et al. Population pharmacokinetics
and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients. J Clin Pharm Ther. 2017;42:679–688.
430. Zhu L, Yang J, Zhang Y, et al. Effects of CYP3A5 genotypes, ABCB1 C3435T and G2677T/A polymorphism on pharmacokinetics
of Tacrolimus in Chinese adult liver transplant patients. Xenobiotica. 2015;45:840–846.
431. Jacobo-Cabral CO, Garcia-Roca P, Romero-Tejeda EM, et al. Population pharmacokinetic analysis of tacrolimus in Mexican paediatric renal transplant patients: role of CYP3A5 genotype and formulation. Br J Clin Pharmacol. 2015;80:630–641.
432. Zhao W, Fakhoury M, Baudouin V, et al. Population pharmacokinetics
of once daily prolonged-release formulation of tacrolimus in pediatric and adolescent kidney transplant recipients. Eur J Clin Pharmacol. 2013;69:189–195.
433. Storset E, Holford N, Midtvedt K, et al. Importance of hematocrit for a tacrolimus target concentration strategy. Eur J Clin Pharmacol. 2014;70:65–77.
434. Musuamba FT, Mourad M, Haufroid V, et al. A simultaneous d-optimal designed study for population pharmacokinetic analyses of mycophenolic Acid and tacrolimus early after renal transplantation. J Clin Pharmacol. 2012;52:1833–1843.
435. Zuo XC, Ng CM, Barrett JS, et al. Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics
in Chinese adult renal transplant recipients: a population pharmacokinetic analysis. Pharmacogenet Genomics. 2013;23:251–261.
436. Han N, Yun HY, Hong JY, et al. Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients. Eur J Clin Pharmacol. 2013;69:53–63.
437. Woillard JB, de Winter BC, Kamar N, et al. Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations—twice daily Prograf and once daily Advagraf. Br J Clin Pharmacol. 2011;71:391–402.
438. Benkali K, Rostaing L, Premaud A, et al. Population pharmacokinetics
and Bayesian estimation of tacrolimus exposure in renal transplant recipients on a new once-daily formulation. Clin Pharmacokinet. 2010;49:683–692.
439. Andreu F, Colom H, Elens L, et al. A new CYP3A5*3 and CYP3A4*22 cluster influencing tacrolimus target concentrations
: a population approach. Clin Pharmacokinet. 2017;56:963–975.
440. Jalil MH, Hawwa AF, McKiernan PJ, et al. Population pharmacokinetic and pharmacogenetic analysis of tacrolimus in paediatric liver transplant patients. Br J Clin Pharmacol. 2014;77:130–140.
441. Zhao W, Elie V, Roussey G, et al. Population pharmacokinetics
of tacrolimus in de novo pediatric kidney transplant recipients. Clin Pharmacol Ther. 2009;86:609–618.
442. Andrews LM, Hesselink DA, van Gelder T, et al. A population pharmacokinetic model to predict the individual starting dose of tacrolimus following pediatric renal transplantation. Clin Pharmacokinet. 2018;57:475–489.
443. Press RR, Ploeger BA, den Hartigh J, et al. Explaining variability in tacrolimus pharmacokinetics
to optimize early exposure in adult kidney transplant recipients. Ther Drug Monit. 2009;31:187–197.
444. Ogasawara K, Chitnis SD, Gohh RY, et al. Multidrug resistance-associated protein 2 (MRP2/ABCC2) haplotypes significantly affect the pharmacokinetics
of tacrolimus in kidney transplant recipients. Clin Pharmacokinet. 2013;52:751–762.
445. Asberg A, Midtvedt K, van Guilder M, et al. Inclusion of CYP3A5 genotyping in a nonparametric population model improves dosing of tacrolimus early after transplantation. Transpl Int. 2013;26:1198–1207.
446. Zhou LY, Zuo XC, Chen K, et al. Significant impacts of CYP3A4*1G and CYP3A5*3 genetic polymorphisms on the pharmacokinetics
of diltiazem and its main metabolites in Chinese adult kidney transplant patients. J Clin Pharm Ther. 2016;41:341–347.
447. Liu Y, Zhang T, Zhang X, et al. A new donors' CYP3A5 and recipients' CYP3A4 cluster predicting tacrolimus disposition, and new-onset hypertension in Chinese liver transplant patients. Oncotarget. 2017;8:70250–70261.
448. Zhang JJ, Liu SB, Xue L, et al. The genetic polymorphisms of POR*28 and CYP3A5*3 significantly influence the pharmacokinetics
of tacrolimus in Chinese renal transplant recipients. Int J Clin Pharmacol Ther. 2015;53:728–736.
449. Knops N, van den Heuvel LP, Masereeuw R, et al. The functional implications of common genetic variation in CYP3A5 and ABCB1 in human proximal tubule cells. Mol Pharm. 2015;12:758–768.
450. 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.
451. Woillard JB, Gatault P, Picard N, et al. A donor and recipient candidate gene association study of allograft loss in renal transplant recipients receiving a tacrolimus-based regimen. Am J Transplant. 2018;18:2905–2913.
452. Lon HK, Liu D, Jusko WJ. Pharmacokinetic/pharmacodynamic modeling in inflammation. Crit Rev Biomed Eng. 2012;40:295–312.
453. Lovern M, Sargentini-Maier ML, Otoul C, et al. Population pharmacokinetic and pharmacodynamic analysis in allergic diseases. Drug Metab Rev. 2009;41:475–485.
454. Koefoed-Nielsen PB, Karamperis N, Jorgensen KA. Comparison of the temporal profile of calcineurin inhibition by cyclosporine and tacrolimus in renal transplant patients. Transplant Proc. 2005;37:1736–1738.
455. Koefoed-Nielsen PB, Jorgensen KA. Alterations in calcineurin phosphatase activity in tacrolimus-treated renal transplant patients. Transplant Proc. 2002;34:1743–1744.
456. Millan O, Brunet M, Campistol JM, et al. Pharmacodynamic approach to immunosuppressive therapies using calcineurin inhibitors and mycophenolate mofetil. Clin Chem. 2003;49:1891–1899.
457. Millan O, Budde K, Sommerer C, et al. Urinary miR-155-5p and CXCL10 as prognostic and predictive biomarkers
of rejection, graft outcome
and treatment response in kidney transplantation. Br J Clin Pharmacol. 2017;83:2636–2650.
458. Billing H, Breil T, Schmidt J, et al. Pharmacodynamic monitoring by residual NFAT-regulated gene expression in stable pediatric liver transplant recipients. Pediatr Transplant. 2012;16:187–194.
459. Steinebrunner N, Sandig C, Sommerer C, et al. Pharmacodynamic monitoring of nuclear factor of activated T cell-regulated gene expression in liver allograft recipients on immunosuppressive therapy with calcineurin inhibitors in the course of time and correlation with acute rejection episodes—a prospective study. Ann Transplant. 2014;19:32–40.
460. Jusko WJ, Ko HC. Physiologic indirect response models characterize diverse types of pharmacodynamic effects. Clin Pharmacol Ther. 1994;56:406–419.
461. Holford N. Clinical pharmacology = disease progression + drug action. Br J Clin Pharmacol. 2015;79:18–27.
462. Holford N. A time to event tutorial for pharmacometricians. CPT Pharmacometrics Syst Pharmacol. 2013;2:e43.
463. Plan EL. Modeling and simulation of count data. CPT Pharmacometrics Syst Pharmacol. 2014;3:e129.
464. Paule I, Girard P, Freyer G, et al. Pharmacodynamic models for discrete data. Clin Pharmacokinet. 2012;51:767–786.
465. Storset E, Asberg A, Skauby M, et al. Improved tacrolimus target concentration achievement using computerized dosing in renal transplant recipients—a prospective, randomized study. Transplantation. 2015;99:2158–2166.