Importance of hematocrit for a tacrolimus target concentration strategy

Tacrolimus Exposure in Obese Patients: and A Case-Control Study in Kidney Transplantation

BACKGROUND

Tacrolimus pharmacokinetics in obese (Ob) patients has been poorly studied. In this article, the authors explored the impact of obesity on tacrolimus exposure in kidney transplant recipients (KTRs) and estimated a more suitable initial dosage in this population.

METHODS

A retrospective, observational, monocentric case-control study was performed in obese KTRs (BMI > 30 kg/m2) who received tacrolimus between 2013 and 2017 (initial dose: 0.15 mg/kg/d) (actual weight). Nonobese (Nob) controls (BMI <30 kg/m2) were matched for age and sex. Weekly centralized monitoring of tacrolimus trough levels was performed by liquid chromatography/mass spectrometry until the third month (M3). Target trough levels were set between 8 and 10 ng/mL. All patients received antilymphocyte globulin, corticosteroids, and mycophenolate mofetil.

RESULTS

Of the 541 KTRs, 28 tacrolimus-treated Ob patients were included and compared with 28 NOb-matched controls. With a mean of 22 assays/patient, tacrolimus trough levels were higher in Ob patients (mean 9.9 versus 8.7 ng/mL; P = 0.008); the weight-related dose of Tac was lower at M3 (mean 0.10 versus 0.13 mg/kg/d, P < 0.0001). The tacrolimus concentration to dose (C0/D) was higher in the Ob cohort [mean 116 versus 76 (ng/mL)/(mg/kg/d); P = 0.001]. In Ob patients, a mean decrease of -4.6 mg/d in the 3 months after tacrolimus initiation was required (versus -1.12 in NOb; P = 0.001) to remain within the therapeutic range. Obesity, high mycophenolate mofetil daily dose at M3, and CYP3A5 expression were independently associated with higher tacrolimus exposure. Four dose-adaptation strategies were simulated and compared with the study results.

CONCLUSIONS

An initial dose calculation based on either ideal or lean body weight may allow for faster achievement of tacrolimus trough level targets in Ob KTRs, who are at risk of overexposure when tacrolimus is initiated at 0.15 mg/kg/d. A prospective study is required to validate alternative dose calculation strategies in these patients.

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial

Bodyweight‐based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady‐state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C₀). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug‐related toxicity, respectively. To minimize subtherapeutic and supratherapeutic tacrolimus exposure in the immediate post‐transplant phase, a previously developed dosing algorithm to predict an individual’s tacrolimus starting dose was tested prospectively. In this single‐arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweight‐based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C₀, measured for the first time at day 3, was 7.5–12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post‐transplantation, 34 of 59 patients (58%, 90% CI 47–68%) had a tacrolimus C₀ within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy‐proven acute rejection occurred in three patients (5%). In conclusion, algorithm‐based tacrolimus dosing leads to the achievement of the tacrolimus target C₀ in as many as 58% of the patients on day 3 after kidney transplantation.

Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients

Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.

High Variability of Whole-Blood Tacrolimus Pharmacokinetics Early After Thoracic Organ Transplantation

Background and Objective

Oral tacrolimus is initiated perioperatively in heart and lung transplantation patients. There have been few studies on oral tacrolimus pharmacokinetics early post-transplantation, even though tacrolimus-related toxicity may occur early, potentially leading to morbidity and mortality. Therefore, we aimed to study the pharmacokinetics of oral tacrolimus in thoracic organ recipients during the first days after transplantation.

Methods

We conducted a pharmacokinetic study in 30 thoracic organ transplants at intensive care at the University Medical Center Utrecht in the first week post-transplantation. Twelve-hour whole-blood tacrolimus profiles were examined using high-performance liquid chromatography tandem mass spectrometry (HPLC–MS/MS) and analysed via population pharmacokinetic modelling.

Results

The concentration–time profiles showed high variability. Concentrations at 12 h were outside the target range in 69% of the cases. A two-compartment model with mixed first-order and zero-order absorption adequately described tacrolimus concentrations. The typical value of the apparent clearance was 19.6 L/h (95% CI 16.2–22.9), and the apparent distribution volumes of central and peripheral compartments, V1 and V2, were 231 L (95% CI 199–267) and 521 L (95% CI 441–634), respectively. Inter-occasion (dose-to-dose) variability far exceeded the interindividual variability (IIV), with an estimated variability in relative bioavailability of 55% (95% CI 48.5–64.4).

Conclusions

The high variability of tacrolimus pharmacokinetics early after thoracic organ transplantation is largely due to excessive variability in bioavailability, making individualised dosing based on measured concentrations futile. To bypass this bioavailability issue, we suggest administering tacrolimus intravenously and aiming below the upper therapeutic range early post-transplantation.

Clinical Trial Registraion: NTR 3912/EudraCT 2012-001909-24.

Electronic supplementary material

The online version of this article (10.1007/s13318-019-00591-7) contains supplementary material, which is available to authorized users.

Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives

INTRODUCTION

In kidney transplantation, tacrolimus (TAC) is at the cornerstone of current immunosuppressive strategies. Though because of its narrow therapeutic index, it is critical to ensure that TAC levels are maintained within this sharp window through reactive adjustments. This would allow maximizing efficiency while limiting drug-associated toxicity. However, TAC high intra- and inter-patient pharmacokinetic (PK) variability makes it more laborious to accurately predict the appropriate dosage required for a given patient.

AREAS COVERED

This review summarizes the state-of-the-art knowledge regarding drug interactions, demographic and pharmacogenetics factors as predictors of TAC PK. We provide a scoring index for each association to grade its relevance and we present practical recommendations, when possible for clinical practice.

EXPERT OPINION

The management of TAC concentration in transplanted kidney patients is as critical as it is challenging. Recommendations based on rigorous scientific evidences are lacking as knowledge of potential predictors remains limited outside of DDIs. Awareness of these limitations should pave the way for studies looking at demographic and pharmacogenetic factors as well as gut microbiota composition in order to promote tailored treatment plans. Therapeutic approaches considering patients' clinical singularities may help allowing to maintain appropriate concentration of TAC.

Clinical Factors Affecting the Dose Conversion Ratio from Intravenous to Oral Tacrolimus Formulation among Pediatric Hematopoietic Stem Cell Transplantation Recipients

BACKGROUND

Tacrolimus is converted from intravenous to oral formulation for the prophylaxis of graft-versus-host disease when patients can tolerate oral intake and graft-versus-host disease is under control. Oral tacrolimus formulation presents poor bioavailability with intraindividual and interindividual variations; however, some factors affecting its blood concentration among pediatric hematopoietic stem cell transplantation (HCT) recipients are still unclear. This study aimed to identify the clinical factors affecting tacrolimus blood concentrations after switching its formulation.

METHODS

Changes in the blood concentration/dose ratio (C/D) of tacrolimus in pediatric HCT recipients were analyzed after the switching of tacrolimus from intravenous to oral formulation. Clinical records of 57 pediatric patients who underwent allogenic HCT from January 2006 to April 2019 in our institute were retrospectively reviewed. The C/D of tacrolimus before discontinuation of intravenous infusion (C/Div) was compared with the tacrolimus trough level within 10 days after the initiation of oral administration (C/Dpo). Multiple linear regression analysis was performed to identify factors affecting (C/Dpo)/(C/Div).

RESULTS

The constant coefficient of (C/Dpo)/(C/Div) was 0.1692 [95% confidence interval (CI), 0.137-0.2011]. The concomitant use of voriconazole or itraconazole and female sex were significant variables with a beta coefficient of 0.0974 (95% CI, 0.062-0.133) and -0.0373 (95% CI, -0.072 to -0.002), respectively.

CONCLUSIONS

After switching of tacrolimus formulation, pediatric HCT recipients might need oral tacrolimus dose that is 5-6 and 3.5-4.5 times the intravenous dose to maintain tacrolimus blood concentrations and area under the concentration-time curve, respectively. With the concomitant use of voriconazole or itraconazole, an oral tacrolimus dose of 4-5 times the intravenous dose seemed appropriate to maintain blood tacrolimus concentration.

Fasting Status and Circadian Variation Must be Considered When Performing AUC-based Therapeutic Drug Monitoring of Tacrolimus in Renal Transplant Recipients

Therapeutic drug monitoring (TDM) is mandatory for the immunosuppressive drug tacrolimus (Tac). For clinical applicability, TDM is performed using morning trough concentrations. With recent developments making tacrolimus concentration determination possible in capillary microsamples and Bayesian estimator predicted area under the concentration curve (AUC), AUC‐guided TDM may now be clinically applicable. Tac circadian variation has, however, been reported, with lower systemic exposure following the evening dose. The aim of the present study was to investigate tacrolimus pharmacokinetic (PK) after morning and evening administrations of twice‐daily tacrolimus in a real‐life setting without restrictions regarding food and concomitant drug timing. Two 12 hour tacrolimus investigations were performed; after the morning dose and the following evening dose, respectively, in 31 renal transplant recipients early after transplantation both in a fasting‐state and under real‐life nonfasting conditions (14 patients repeated the investigation). We observed circadian variation under fasting‐conditions: 45% higher peak‐concentration and 20% higher AUC following the morning dose. In the real‐life nonfasting setting, the PK‐profiles were flat but comparable after the morning and evening doses, showing slower absorption rate and lower AUC compared with the fasting‐state. Limited sampling strategies using concentrations at 0, 1, and 3 hours predicted AUC after fasting morning administration, and samples obtained at 1, 3, and 6 hours predicted AUC for the other conditions (evening and real‐life nonfasting). In conclusion, circadian variation of tacrolimus is present when performed in patients who are in the fasting‐state, whereas flatter PK‐profiles and no circadian variation was present in a real‐life, nonfasting setting.

Is There a Temporal Relationship Between Trough Whole Blood Tacrolimus Concentration and Acute Rejection in the First 14 Days After Kidney Transplantation?

BACKGROUND

There are inconsistent findings regarding the relationship between trough whole blood tacrolimus concentration (TAC C0) and acute kidney rejection in recipients undergoing TAC therapeutic drug monitoring (TDM). However, studies have not always assessed TAC C0 at the time of rejection or accounted for variability in hematocrit. Therefore, this study aimed to investigate the temporal relationship between TAC C0 and acute rejection, including when accounting for variation in hematocrit.

METHODS

For 38 recipients who developed biopsy-proven acute rejection (BPAR) in the first 14 days after kidney transplantation, daily TAC C0 from TDM and hematocrit was collected from case notes. Differences in log10-transformed TAC C0 between the day of BPAR (log Cr), 1 day before BPAR (log Cr-1), and 2 days before BPAR (log Cr-2) and the combined median concentrations for the days preceding these (log Cprior) were examined by repeated-measures analysis of variance with Dunnett post hoc testing. Generalized linear mixed-effects regression (glmer) examined the ability of TAC C0 to predict acute rejection episodes with and without controlling for hematocrit.

RESULTS

Log Cr-1 [mean difference (95% confidence interval) = -0.13 (-0.21 to -0.048), post hoc P = 0.002] and log Cr [-0.13 (-0.24 to -0.025), post hoc P = 0.013] were significantly lower than log Cprior. TAC C0 was a significant (P = 0.0078) predictor of rejection episodes (area under the receiver operating characteristic curve = 0.79) only in glmer models accounting for variability in hematocrit.

CONCLUSIONS

In recipients who developed BPAR, there was a significant temporal relationship between TAC C0 and BPAR incidence under TAC TDM that may not be detected in cross-sectional studies, especially if variability in hematocrit is not addressed. This supports a TAC C0-rejection relationship, which differs between recipients, and may explain why some recipients do or do not experience rejection within or below the TDM range, respectively. However, studies with larger sample sizes are needed to confirm this finding.

Clinical Pharmacokinetic and Pharmacodynamic Considerations in the Treatment of Ulcerative Colitis

Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) of unknown etiology, probably caused by a combination of genetic and environmental factors. The treatment of patients with active UC depends on the severity, localization and history of IBD medication. According to the classic step-up approach, treatment with 5-aminosalicylic acid compounds is the first step in the treatment of mild to moderately active UC. Corticosteroids, such as prednisolone are used in UC patients with moderate to severe disease activity, but only for remission induction therapy because of side effects associated with long-term use. Thiopurines are the next step in the treatment of active UC but monotherapy during induction therapy in UC patients is not preferred because of their slow onset. Therapeutic drug monitoring (TDM) of the pharmacologically active metabolites of thiopurines, 6-thioguanine nucleotide (6-TGN), has proven to be beneficial. Thiopurine S-methyltransferase (TMPT) plays a role in the metabolic conversion pathway of thiopurines and exhibits genetic polymorphism; however, the clinical benefit and relevance of TPMT genotyping is not well established. In patients with severely active UC refractory to corticosteroids, calcineurin inhibitors such as ciclosporin A (CsA) and tacrolimus are potential therapeutic options. These agents usually have a rather rapid onset of action. Monoclonal antibodies (anti-tumor necrosis factor [TNF] agents, vedolizumab) are the last pharmacotherapeutic option for UC patients before surgery becomes inevitable. Body weight, albumin status and antidrug antibodies contribute to the variability in the pharmacokinetics of anti-TNF agents. Additionally, the use of concomitant immunomodulators (thiopurines/methotrexate) lowers the rate of immunogenicity, and therefore the concomitant use of anti-TNF therapy with an immunomodulator may confer some advantage compared with monotherapy in certain patients. TDM of anti-TNF agents could be beneficial in patients with primary nonresponse and secondary loss of response. The potential benefit of applying TDM during vedolizumab treatment has yet to be determined.

Prospective study of the changes in pharmacokinetics of immunosuppressive medications after laparoscopic sleeve gastrectomy

Laparoscopic sleeve gastrectomy induces weight loss via the creation of a restrictive gastric tube for early satiety and is associated with an accelerated gastric transit time. A prospective, single-dose pharmacokinetic study was performed, prior to and after laparoscopic sleeve gastrectomy, for tacrolimus, extended-release tacrolimus, mycophenolate mofetil, and enteric-coated mycophenolate sodium. The study included 12 morbidly obese patients in chronic renal failure. The median decrease in body mass index was 8.8 kg/m² with an excess body weight loss of 54.9%. The AUC₂₄ of all drugs were increased after laparoscopic sleeve gastrectomy by 46%, 55%, 77%, and 74%, respectively. The maximum concentrations were increased for tacrolimus, extended-release tacrolimus, and mycophenolate mofetil by 43%, 46%, and 65%. The apparent total clearances were decreased for tacrolimus, mycophenolate mofetil, and enteric-coated mycophenolate sodium by 36%, 57%, and 38%. Laparoscopic sleeve gastrectomy can be associated with significant changes in pharmacokinetics of the drugs evaluated. The mechanism is likely decreased apparent drug clearance due to an increased drug exposure (from a more distal site of intestinal absorption with decreased intestinal metabolism), or decreased clearance (liver metabolism). Adapting the monitoring of immunosuppression will be important to avoid overdosing and potential side effects.

Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach

AIMS

The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications.

METHODS

A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies.

RESULTS

A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation.

CONCLUSION

A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

Population Pharmacokinetic Analysis of Immediate-Release Oral Tacrolimus Co-administered with Mycophenolate Mofetil in Corticosteroid-Free Adult Kidney Transplant Recipients

BACKGROUND AND OBJECTIVE

Tacrolimus is the mainstay calcineurin inhibitor frequently administered with mycophenolic acid with or without corticosteroids to prevent graft rejection in adult kidney transplant recipients. The primary objective of this study was to develop and evaluate a population pharmacokinetic model characterizing immediate-release oral tacrolimus co-administered with mycophenolate mofetil (a pro-drug of mycophenolic acid) in adult kidney transplant recipients on corticosteroid-free regimens. The secondary objective was to investigate the effects of clinical covariates on the pharmacokinetics of tacrolimus, emphasizing the interacting effects of mycophenolic acid.

METHODS

Population modeling and evaluation were conducted with Monolix (Suite-2018R1) using the stochastic approximation expectation-maximization algorithm in 49 adult subjects (a total of 320 tacrolimus whole-blood concentrations). Effects of clinical variables on tacrolimus pharmacokinetics were determined by population covariate modeling, regression modeling, and categorical analyses.

RESULTS

A two-compartment, first-order absorption with a lag-time, linear elimination, and constant error model best represented the population pharmacokinetics of tacrolimus. The apparent clearance value for tacrolimus was 17.9 l/h (6.95% relative standard error) in our model, which is lower compared with similar subjects on corticosteroid-based therapy. The glomerular filtration rate had significant effects on the apparent clearance and central compartment volume of distribution. Conversely, mycophenolic acid did not affect the apparent clearance of tacrolimus.

CONCLUSION

We have developed and internally evaluated a novel population pharmacokinetic model for tacrolimus co-administered with mycophenolate mofetil in corticosteroid-free adult kidney transplant patients. These findings are clinically important and provide further reasons for conducting therapeutic drug monitoring in this specific population.

Tacrolimus Levels in the Prophylaxis of Acute Graft-Versus-Host Disease in the Chinese Early After Hematopoietic Stem Cell Transplantation

BACKGROUND

Tacrolimus has been widely accepted as the backbone of acute graft-versus-host disease (aGVHD) prophylaxis in allogeneic hematopoietic stem cell transplantation (alloHSCT). The present work evaluated whether tacrolimus concentrations early after transplant correlate with the incidence of aGVHD in Chinese alloHSCT recipients.

METHODS

One hundred four Chinese alloHSCT recipients were included in this retrospective study. All patients received standard prophylaxis with tacrolimus and short-term methotrexate. Blood samples were taken at steady-state for those on i.v. tacrolimus (Cv) or predose (C0) and 2 hours after the last oral dose (C2).

RESULTS

In the first 8 weeks after alloHSCT, significant variability in Cv, C0, and C2 of Chinese patients was observed. It was found that higher tacrolimus C0 and C2 values tended to be associated with a reduced risk of aGVHD, although this was a nonsignificant trend due to the small sample size involved. Receiver operating characteristic curve analysis indicated that Cv levels of ≥16.52 ng/mL, C0 levels of ≥5.56 ng/mL, and C2 levels of ≥7.83 ng/mL minimized the incidence of treatment failure during weeks 3-4 with intravenous administration and weeks 5-6 with oral administration. There was no statistically significant association of the patient liver and kidney function with the blood concentration of tacrolimus in the desired range of 5-20 ng/mL.

CONCLUSIONS

Tacrolimus therapeutic drug monitoring improved treatment outcomes of Chinese alloHSCT recipients. Cv measurements during weeks 3-4 and C0 or C2 measurements during weeks 5-6 better predicted aGVHD (I-IV) than the concentrations measured at other time points during the first 6 weeks after alloHSCT.

A Minimal Physiologically-Based Pharmacokinetic Model for Tacrolimus in Living-Donor Liver Transplantation: Perspectives Related to Liver Regeneration and the cytochrome P450 3A5 (CYP3A5) Genotype

In adult patients after living‐donor liver transplantation, postoperative days and the cytochrome P450 3A5 (CYP3A5) genotype are known to affect tacrolimus pharmacokinetics. In this study, we constructed a physiologically‐based pharmacokinetic model adapted to the clinical data and evaluated the contribution of liver regeneration as well as hepatic and intestine CYP3A5 genotypes on tacrolimus pharmacokinetics. As a result, liver function recovered immediately and affected the total body clearance of tacrolimus only during a limited period after living‐donor liver transplantation. The clearance was about 1.35‐fold higher in the recipients who had a liver with the CYP3A5*1 allele than in those with the CYP3A5*3/*3 genotype, whereas bioavailability was ~0.7‐fold higher in the recipients who had intestines with the CYP3A5*1 allele than those with CYP3A5*3/*3. In conclusion, the constructed physiologically‐based pharmacokinetic model clarified that the oral clearance of tacrolimus was affected by the CYP3A5 genotypes in both the liver and intestine to the same extent.

Population pharmacokinetics of immediate- and prolonged-release tacrolimus formulations in liver, kidney and heart transplant recipients

Aims

Develop a population pharmacokinetics model of tacrolimus in organ transplant recipients receiving twice‐daily, immediate‐release (IR‐T; Prograf) and/or once‐daily, prolonged‐release (PR‐T; Advagraf or Astagraf XL) tacrolimus.

Methods

Tacrolimus concentration–time profiles were analysed from 8 Phase II studies in adult and paediatric liver, kidney and heart transplant patients receiving IR‐T and/or PR‐T. A tacrolimus population pharmacokinetic model, including identification of significant covariates, was developed using NONMEM.

Results

Overall, 23,176 tacrolimus concentration records were obtained from 408 patients. A 2‐compartment model with first‐order absorption and elimination described the concentration–time profiles. Tacrolimus absorption rate was 50% slower with PR‐T vs IR‐T. Tacrolimus apparent oral clearance was 44.3 L/h in Whites and 59% higher in Asians. Tacrolimus central volume of distribution was 108 L in males and 55% lower in females; trough concentrations were similar between formulations. Tacrolimus relative bioavailability was similar between formulations (geometric mean ratio PR‐T:IR‐T 95%, 90% confidence intervals: 89%, 101%). Asians had 83% and 51% higher relative bioavailability than Whites and Blacks, respectively, for IR‐T and PR‐T. Whites had 49% and 77% higher relative bioavailability than Blacks for PR‐T and IR‐T, respectively. Blacks had 52% lower relative bioavailability than Whites and Asians for IR‐T and PR‐T. Type of organ transplanted and patient population (adult/paediatric) did not have a significant effect on tacrolimus pharmacokinetics.

Conclusions

This population pharmacokinetic model described data from transplant recipients who received IR‐T and/or PR‐T. Tacrolimus trough concentrations and relative bioavailability were similar between formulations, supporting 1 mg:1 mg conversion from Prograf to Advagraf/Astagraf XL in clinical practice.

Characterizing the pharmacokinetics and pharmacodynamics of immunosuppressant medicines and patient outcomes in elderly renal transplant patients

This review examines what is currently known about the pharmacokinetics and pharmacodynamics of commonly prescribed immunosuppressant medicines, tacrolimus, cyclosporine, mycophenolate and prednisolone, in elderly renal transplant recipients, and reported patient outcomes in this cohort. Renal transplantation is increasing rapidly in the elderly, however, currently, long-term patient outcomes are relatively poor compared to younger adults. Some studies have suggested that elderly recipients may have higher dose-adjusted exposure and/or lower clearance of the calcineurin inhibitors tacrolimus and cyclosporine; with one study reporting up to 50% reduction in tacrolimus exposure in the elderly. Elderly transplant recipients do not appear to have higher dosage-adjusted exposure to mycophenolic acid (MPA). The effects of ageing on the pharmacokinetics of prednisolone are unknown. Only one study has examined how aging effects drug target enzymes, reporting no difference in baseline inosine 5'-monophosphate dehydrogenase (IMPDH) activity and MPA-induced IMPDH activity in elderly compared to younger adult renal transplant recipients. In elderly transplant recipients, immunosenescence likely lowers the risk of acute rejection, but increases the risk of drug-related adverse effects. Currently, the three main causes of death in elderly renal transplant recipients are cardiovascular disease, infection and malignancy. One study has showed that renal transplant recipients aged over 65 years are seven times more likely to die with a functioning graft compared with young adults (aged 18-29 years). This suggests that an optimal balance between immunosuppressant medicine efficacy and toxicity is not achieved in elderly recipients, and further studies are needed to foster long-term graft and patient survival. Lower maintenance immunosuppressant targets in elderly recipients may decrease patient susceptibility to drug side effects, however, further studies are required and appropriate targets need to be established.

Intra-patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

BACKGROUND

This study aims to investigate the evolution and factors associated with TAC IPV and its impact on patient outcomes in pediatric LT recipients.

METHODS

This is a retrospective study including 41 children. The TAC IPV was expressed as the coefficient of variation and was calculated for years 1-5 following LT. The number of missed clinic appointments was used as a surrogate marker for therapy adherence.

RESULTS

We identified a decrease in the TAC IPV during the first 3 years after LT (P < 0.01). Serum albumin in the first year (P = 0.03), hematocrit (P = 0.02) and total bilirubin (P = 0.04) in the third year, and therapy adherence (P < 0.01) in the fifth year were associated with TAC IPV. High TAC IPV was associated with biopsy-proven acute allograft rejection (P = 0.04) and the need for biopsy during the first year (P = 0.02). There was a borderline association between TAC IPV and donor-specific antibodies (P = 0.08) and CMV viremia (P = 0.07). High TAC IPV was a predictor of need for liver biopsy and AR with an odds ratio of 1.04 (95% CI 1.0-1.1; P = 0.03) and 1.04 (95% CI 1.0-1.1; P = 0.05), respectively.

CONCLUSIONS

Our results highlight the impact of biological factors on TAC IPV during the early LT follow-up and later also therapy adherence. High TAC IPV may be associated with adverse patient outcomes.

Tacrolimus exposure early after lung transplantation and exploratory associations with acute cellular rejection

AIMS

To (i) describe tacrolimus (TAC) pre-dose concentrations (C₀), (ii) calculate apparent oral TAC clearance (CL/F_HCT) adjusted for measured haematocrit (HCTi) and standardised to a HCT of 45%, across three observation time points and (iii) explore if low TAC C₀ or high mean CL/F_HCT are associated with an increased risk of rejection episodes early after lung transplantation.

METHODS

TAC whole blood concentration-time profiles and transbronchial biopsies were performed prospectively at weeks 3, 6 and 12 after lung transplantation. The TAC pre-dose concentration (C₀) was measured, and CL/F_HCT was determined using non-compartmental analysis. The associations between TAC C₀ and CL/F_HCT and rejection status were explored using repeated measures logistic regression.

RESULTS

Eighteen patients provided 377 TAC whole blood concentrations. Considerable variability around the median (IQR) CL/F_HCT 6.8 (4.2-15.9) L h^-1, and the median C₀ 12.7 (9.9-16.6) μg L^-1 was noted. Despite adjustment for haematocrit, a significant decrease was observed in CL/F_HCT in all patients over time: CL/F_HCT 14 (5.4-23) at week 3, CL/F_HCT 7.7 (4.5-12) at week 6 and CL/F_HCT 3.9 (2.4-11) L h^-1 at week 12 (p < 0.01). Seven (38.9%) patients experienced a single grade 2 rejection, whilst 11 (61.1%) patients experienced no rejection. Higher TAC C₀ were associated with a reduced risk of rejection OR 0.68 (95% CI 0.51-0.91, p = 0.02), and greater mean CL/F_HCT was associated with an increased risk of rejection OR 1.34 (95% CI 1.01-1.81 p = 0.04).

CONCLUSION

Monitoring TAC C₀, HCT and CL/F_HCT in patients after lung transplantation may assist clinicians in detecting patients at risk of acute rejection and may guide future research into TAC and HCT monitoring after lung transplantation.

A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients

Aims

The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient.

Methods

Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates.

Results

A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h⁻¹, clearance was 23.0 l h^–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: Dosemg=222nghml–1*22.5lh–1*1.0ifCYP3A5*3/*3or1.62ifCYP3A5*1/*3orCYP3A5*1/*1*1.0ifCYP3A4*1or unknownor0.814ifCYP3A4*22*Age56−0.50*BSA1.930.72/1000Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model.

Conclusions

For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

The potential impact of hematocrit correction on evaluation of tacrolimus target exposure in pediatric kidney transplant patients

BACKGROUND

Tacrolimus is an important immunosuppressive agent with high intra- and inter-individual pharmacokinetic variability and a narrow therapeutic index. As tacrolimus extensively accumulates in erythrocytes, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations. However, as hematocrit values in pediatric kidney transplant patients are highly variable after kidney transplantation, translating whole blood concentration targets without taking hematocrit into consideration is theoretically incorrect. The aim of this study is to evaluate the potential impact of hematocrit correction on tacrolimus target exposure in pediatric kidney transplant patients.

METHODS

Data were obtained from 36 pediatric kidney transplant patients. Two hundred fifty-five tacrolimus whole blood samples were available, together responsible for 36 area under the concentration-time curves (AUCs) and trough concentrations. First, hematocrit corrected concentrations were derived using a formula describing the relationship between whole blood concentrations, hematocrit, and plasma concentrations. Subsequently, target exposure was evaluated using the converted plasma target concentrations. Ultimately, differences in interpretation of target exposure were identified and evaluated.

RESULTS

In total, 92% of our patients had lower hematocrit (median 0.29) than the reference value of adult kidney transplant patients. A different evaluation of target exposure for either trough level, AUC, or both was defined in 42% of our patients, when applying hematocrit corrected concentrations.

CONCLUSION

A critical role for hematocrit in therapeutic drug monitoring of tacrolimus in pediatric kidney transplant patients is suggested in this study. Therefore, we believe that hematocrit correction could be a step towards improvement of tacrolimus dose individualization.

Clinical aspects of tacrolimus use in paediatric renal transplant recipients

The calcineurin inhibitor tacrolimus, cornerstone of most immunosuppressive regimens, is a drug with a narrow therapeutic window: underexposure can lead to allograft rejection and overexposure can result in an increased incidence of infections, toxicity and malignancies. Tacrolimus is metabolised in the liver and intestine by the cytochrome P450 3A (CYP3A) isoforms CYP3A4 and CYP3A5. This review focusses on the clinical aspects of tacrolimus pharmacodynamics, such as efficacy and toxicity. Factors affecting tacrolimus pharmacokinetics, including pharmacogenetics and the rationale for routine CYP3A5*1/*3 genotyping in prospective paediatric renal transplant recipients, are also reviewed. Therapeutic drug monitoring, including pre-dose concentrations and pharmacokinetic profiles with the available "reference values", are discussed. Factors contributing to high intra-patient variability in tacrolimus exposure and its impact on clinical outcome are also reviewed. Lastly, suggestions for future research and clinical perspectives are discussed.

Retrospective analysis of the correlation between tacrolimus concentrations measured in whole blood and variations of blood cell counts in patients undergoing allogeneic haematopoietic stem cell transplantation

Objective

Tacrolimus is administered to patients undergoing haematopoietic stem cell transplantation (HSCT) as prophylaxis for graft-versus-host disease. As a high blood tacrolimus concentration within a narrow therapeutic range must be maintained after HSCT, therapeutic drug monitoring (TDM) is necessary. We investigated the correlation between blood tacrolimus concentration and blood cell count in HSCT patients to assess how changes in blood cell count affect tacrolimus TDM.

Methods

A retrospective analysis was performed for 24 patients who underwent allogeneic HSCT and received tacrolimus. The correlation between variations in blood tacrolimus concentration and blood cell count was evaluated for three consecutive weeks, starting 1 week after HSCT.

Results

Variations in blood tacrolimus concentration were significantly correlated with variations in red blood cell (RBC) count, haemoglobin level and haematocrit value, but not with variations in white blood cell or platelet counts. Further, the above variations were significantly correlated in patients undergoing cord blood transplantation and peripheral blood stem cell transplantation, but not in those undergoing bone marrow transplantation.

Conclusions

These findings demonstrate that RBC count is associated with variations in blood tacrolimus concentration, with the relevance of this association depending on the source of transfused stem cells. Thus, variations in RBC count might be useful for tacrolimus TDM.

Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Tacrolimus, a calcineurin inhibitor, is a common immunosuppressant prescribed after organ transplantation and has notable inter- and intrapatient pharmacokinetic variability. The sources of variability have been investigated using population pharmacokinetic modeling over the last 2 decades. This article provides an updated synopsis on published nonlinear mixed-effects analyses developed for tacrolimus in transplant recipients. The objectives were to establish a detailed overview of the current data and to investigate covariate relationships determined by the models. Sixty-three published analyses were reviewed, and data regarding the study design, modeling approach, and resulting findings were extracted and summarized. Most of the studies investigated tacrolimus pharmacokinetics in adult and pediatric renal and liver transplants after administration of the immediate-release formulation. Model structures largely depended on the study sampling strategy, with ∼50% of studies developing a 1-compartment model using trough concentrations and a 2-compartment model with delayed absorption from intensive sampling. The CYP3A5 genotype, as a covariate, consistently impacted tacrolimus clearance, and dosing adjustments were required to achieve similar drug exposure among patients. Numerous covariates were identified as sources of interindividual variability on tacrolimus pharmacokinetics with limited consistency across these studies, which may be the result of the study designs. Additional analyses are required to further evaluate the potential impact of these covariates and the clinical implementation of these models to guide tacrolimus dosing recommendations. This article may be useful for guiding the design of future population pharmacokinetic studies and provides recommendations for the selection of an existing optimal model to individualize tacrolimus therapy.

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes

PURPOSE

Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus.

METHODS

A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting.

RESULTS

Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting.

CONCLUSIONS

Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

A New CYP3A5*3 and CYP3A4*22 Cluster Influencing Tacrolimus Target Concentrations: A Population Approach

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients.

METHODS

Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM^® version 7.2.

RESULTS

The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers.

CONCLUSIONS

Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.

Which Kidney Transplant Recipients Can Benefit from the Initial Tacrolimus Dose Reduction?

BACKGROUND

Observational data suggest that the fixed initial recommended tacrolimus (Tc) dosing (0.2 mg/kg/day) results in supratherapeutic drug levels in some patients during the early posttransplant period. The aim of the study was to analyze a wide panel of patient-related factors and their interactions which increase the risk for first Tc blood level > 15 ng/ml.

MATERIALS AND METHODS

We performed a retrospective analysis of 488 consecutive adult kidney transplant recipients who were initially treated with triple immunosuppressive regimen containing tacrolimus twice daily. The analysis included the first assessment of Tc trough blood levels and several demographic, anthropometric, laboratory, and comedication data.

RESULTS

The multiple logistic regression analysis showed that age > 55 years, BMI > 24.6 kg/m2, blood hemoglobin concentration > 9.5 g/dl, and the presence of anti-HCV antibodies independently increased the risk for first Tc level > 15 ng/ml. The relative risk (RR) for first tacrolimus level > 15 ng/ml was 1.88 (95% CI 1.35-2.64, p < 0.001) for patients with one risk factor and 2.81 (2.02-3.89, p < 0.001) for patients with two risk factors.

CONCLUSIONS

Initial tacrolimus dose reduction should be considered in older, overweight, or obese kidney transplant recipients and in subjects with anti-HCV antibodies. Moreover, dose reduction of tacrolimus is especially important in patients with coexisting multiple risk factors.

Recovery of CYP3A Phenotype after Kidney Transplantation

End-stage renal disease impairs drug metabolism via cytochrome P450 CYP3A; however, it is unclear whether CYP3A activity recovers after kidney transplantation. Therefore, the aim of this study was to evaluate the change in CYP3A activity measured as 4β-hydroxycholesterol (4βOHC) concentration after kidney transplantation. In total, data from 58 renal transplant recipients with 550 prospective 4βOHC measurements were included in the study. One sample per patient was collected before transplantation, and 2-12 samples per patient were collected 1-82 days after transplantation. The measured pretransplant 4βOHC concentrations ranged by >7-fold, with a median value of 22.8 ng/ml. Linear mixed-model analysis identified a 0.16-ng/ml increase in 4βOHC concentration per day after transplantation (P < 0.001), indicating a regain in CYP3A activity. Increasing estimated glomerular filtration rate after transplantation was associated with increasing 4βOHC concentration (P < 0.001), supporting that CYP3A activity increases with recovering uremia. In conclusion, this study indicates that CYP3A activity is regained subsequent to kidney transplantation.

The Effect of Weight and CYP3A5 Genotype on the Population Pharmacokinetics of Tacrolimus in Stable Paediatric Renal Transplant Recipients

BACKGROUND

The aim of this study was to develop a population pharmacokinetic model of tacrolimus in paediatric patients at least 1 year after renal transplantation and simulate individualised dosage regimens.

PATIENTS AND METHODS

We included 54 children with median age of 11.1 years (range 3.8-18.4 years) with 120 pharmacokinetic profiles performed over 2 to 4 h. The pharmacokinetic analysis was performed using the non-linear mixed-effects modelling software (NONMEM(®)). The impact of covariates including concomitant medications, age, the cytochrome P450 (CYP) CYP3A5*3 gene and the adenosine triphosphate binding cassette protein B1 (ABCB1) 3435 C→T gene polymorphism on tacrolimus pharmacokinetics was analysed. The final model was externally validated on an independent dataset and dosing regimens were simulated.

RESULTS

A two-compartment model adequately described tacrolimus pharmacokinetics. Apparent oral clearance (CL/F) was associated with weight (allometric scaling) but not age. Children with lower weight and CYP3A5 expressers required higher weight-normalised tacrolimus doses. CL/F was inversely associated with haematocrit (P < 0.05) and γ-glutamyl transpeptidase (γGT) (P < 0.001) and was increased by 45 % in carriers of the CYP3A5*1 allele (P < 0.001). CL/F was not associated with concomitant medications. Dose simulations show that a daily tacrolimus dose of 0.2 mg/kg generates a pre-dose concentration (C 0) ranging from 5 to 10 µg/L depending on the weight and CYP3A5 polymorphism. The median area under the plasma concentration-time curve (AUC) corresponding with a tacrolimus C 0 of 4-8 µg/L was 97 h·µg/L (interquartile range 80-120).

CONCLUSIONS

In patients beyond the first year after transplantation, there is a cumulative effect of CYP3A5*1 polymorphism and weight on the tacrolimus C 0. Children with lower weight and carriers of the CYP3A5*1 allele have higher weight-normalised tacrolimus dose requirements.

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients

WHAT IS KNOWN AND OBJECTIVES

Tacrolimus (TAC) is widely used as part of immunosuppressive regimens. There is great interindividual variation on the disposition of TAC. The aim of this study was to develop a population pharmacokinetic (PPK) model for Chinese liver transplant patients and evaluate genetic polymorphism and other possible factors on the PK parameters. The exposure of TAC is to be estimated through Bayesian modelling.

METHODS

A total of 47 sets of rich-time PK and 1234 conventional therapeutic drug monitoring (TDM) data were collected from 125 Chinese liver transplant patients. The pathophysiological data of these patients were recorded. CYP3A5*3 and ABCB1 genotypes were determined for each patient. The PPK model for TAC was established by nonlinear mixed-effects modelling (nonmem). The impact of pathophysiology and genotype on PPK parameters was evaluated. Bayesian estimators for the area under concentration-time curve (AUC) of TAC were validated.

RESULTS

A two-compartment model with lag time was found to be the most suitable model for the pooled full PK and TDM data for Chinese liver transplant patients. The CL/F, V₂ /F, Q/F, V₃ /F, K_a and lag time were 17.4±0.81 L/h, 165±44.1 L, 54.9±25.8L/h, 594±87.5 L, 0.51±0.095 L/h and 1.57±0.34 h. Post-operative day (POD), creatinine clearance (CLcr) and ABCB1 C3435T genotypes were found to have significant influences on CL/F (P<.01). ABCB1 C3435T genotypes showed a significant correlation with V₂ /F (P<.01). C₀ -C₂ and C₀ -C₂ -C₄ were shown to be suitable for the estimation of AUC in Chinese liver transplant patients.

WHAT IS NEW AND CONCLUSION

A PPK model for TAC was established successfully in Chinese liver transplant patients. POD, CLcr and ABCB1 C3435T genotypes were shown to have significant effects on CL/F. The AUC of TAC in Chinese liver transplant patients could be estimated through Bayesian modelling, based on which individualized immunosuppressive regimens can be designed.

NFAT-regulated cytokine gene expression during tacrolimus therapy early after renal transplantation

AIMS

Despite pharmacokinetic monitoring of calcineurin inhibitors, the long-term outcome after transplantation (Tx) is still hampered by the side effects of these drugs. The aim of the present study was to characterize nuclear factor of activated T cells (NFAT)-regulated gene expression as a potential pharmacodynamic biomarker for further individualization of tacrolimus (Tac) therapy.

METHODS

In 29 renal allograft recipients, samples were drawn once pre-Tx, and before and 1.5 h after Tac dosing at approximately 1 week, 6 weeks and 1 year post-Tx. Tac concentrations were measured by immunoassay, while the expression of genes encoding NFAT-regulated cytokines [interleukin 2 (IL2), interferon gamma (IFNG), colony stimulating factor 2 (CSF2)] and cytochrome P450 3A5 (CYP3A5) genotyping were determined by real-time polymerase chain reaction.

RESULTS

The cytokine response after Tac dosing varied up to 46-fold between patients and changed significantly with time post-engraftment. Tac concentrations 1.5 h postdose (C_1.5 ) >15 μg l^-1 were associated with strong cytokine inhibition and residual gene expression (RGE) ≤10%, while lower Tac C_1.5 resulted in more variable responses (RGE 2.5-68.7%). Patients with ongoing subclinical acute rejection (n = 5) demonstrated limited cytokine inhibition (RGE 39.7-72.6%), while patients with polyoma virus viraemia (n = 3) had relatively strong inhibition of cytokines (RGE 2.5-32.5%). By contrast, there was no association between Tac exposure and rejection or viraemia.

CONCLUSIONS

The findings of our study support the potential of NFAT-regulated gene expression measurements as a pharmacodynamic tool for additional monitoring of Tac therapy, especially in the context of overimmunosuppression and viraemia.

Tacrolimus Updated Guidelines through popPK Modeling: How to Benefit More from CYP3A Pre-emptive Genotyping Prior to Kidney Transplantation

Tacrolimus (Tac) is a profoundly effective immunosuppressant that reduces the risk of rejection after solid organ transplantation. However, its use is hampered by its narrow therapeutic window along with its highly variable pharmacological (pharmacokinetic [PK] and pharmacodynamic [PD]) profile. Part of this variability is explained by genetic polymorphisms affecting the metabolic pathway. The integration of CYP3A4 and CY3A5 genotype in tacrolimus population-based PK (PopPK) modeling approaches has been proven to accurately predict the dose requirement to reach the therapeutic window. The objective of the present study was to develop an accurate PopPK model in a cohort of 59 kidney transplant patients to deliver this information to clinicians in a clear and actionable manner. We conducted a non-parametric non-linear effects PopPK modeling analysis in Pmetrics^®. Patients were genotyped for the CYP3A4^∗22 and CYP3A5^∗3 alleles and were classified into 3 different categories [poor-metabolizers (PM), Intermediate-metabolizers (IM) or extensive-metabolizers (EM)]. A one-compartment model with double gamma absorption route described very accurately the tacrolimus PK. In covariate analysis, only CYP3A genotype was retained in the final model (Δ-2LL = -73). Our model estimated that tacrolimus concentrations were 33% IC_95%[20–26%], 41% IC_95%[36–45%] lower in CYP3A IM and EM when compared to PM, respectively. Virtually, we proved that defining different starting doses for PM, IM and EM would be beneficial by ensuring better probability of target concentrations attainment allowing us to define new dosage recommendations according to patient CYP3A genetic profile.

The CYP3A biomarker 4β-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation

AIMS

Tacrolimus is a cornerstone in modern immunosuppressive therapy after kidney transplantation. Tacrolimus dosing is challenged by considerable pharmacokinetic variability, both between patients and over time after transplantation, partly due to variability in cytochrome P450 3A (CYP3A) activity. The aim of this study was to assess the value of the endogenous CYP3A marker 4β-hydroxycholesterol (4βOHC) for tacrolimus dose individualization early after kidney transplantation.

METHODS

Data were obtained from 79 adult kidney transplant recipients who contributed a total of 625 4βOHC measurements and 1999 tacrolimus whole blood concentrations during the first 2 months after transplantation. The relationships between 4βOHC levels and individual estimates of tacrolimus apparent plasma clearance (CL/F_plasma ) at different time points after transplantation were investigated using scatterplots and population pharmacokinetic modelling.

RESULTS

There was no significant correlation between pre-transplant 4βOHC levels and tacrolimus CL/F_plasma the first week (r = 0.19 [95% CI -0.03-0.40]) or between 4βOHC and tacrolimus CL/F_plasma 1 week (r = 0.20 [-0.11-0.47]), 4 weeks (r = 0.21 [-0.07-0.46]) or 2 months (r = 0.24 [-0.03-0.48]) after transplantation (P ≥ 0.06). In the population analysis, time-varying 4βOHC was not a statistically significant covariate on tacrolimus CL/F_plasma , neither in terms of absolute values (P = 0.11) nor in terms of changes from baseline (P = 0.17). 4βOHC values increased between 1 week and 2 months after transplantation (median change +57% [IQR +22-83%], P < 0.001), indicating increasing CYP3A activity. Contradictorily, tacrolimus CL/F_plasma decreased over the same period (median change -13% [IQR -3 to -26%], P < 0.001).

CONCLUSIONS

4βOHC does not appear to have a clinical potential to improve individualization of tacrolimus doses early after kidney transplantation.

Comparative performance of oral midazolam clearance and plasma 4β-hydroxycholesterol to explain interindividual variability in tacrolimus clearance

AIMS

We compared the CYP3A4 metrics weight-corrected midazolam apparent oral clearance (MDZ Cl/F/W) and plasma 4β-hydroxycholesterol/cholesterol (4β-OHC/C) as they relate to tacrolimus (TAC) Cl/F/W in renal transplant recipients.

METHODS

For a cohort of 147 patients, 8 h area under the curve (AUC) values for TAC and oral MDZ were calculated besides measurement of 4β-OHC/C. A subgroup of 70 patients additionally underwent intravenous erythromycin breath test (EBT) and were administered the intravenous MDZ probe. All patients were genotyped for common polymorphisms in CYP3A4, CYP3A5 and P450 oxidoreductase, among others.

RESULTS

MDZ Cl/F/W, 4β-OHC/C/W, EBT and TAC Cl/F/W were all moderately correlated (r = 0.262-0.505). Neither MDZ Cl/F/W nor 4β-OHC/C/W explained variability in TAC Cl/F/W in CYP3A5 expressors (n = 29). For CYP3A5 non-expressors (n = 118), factors explaining variability in TAC Cl/F/W in a MDZ-based model were MDZ Cl/F/W (R²  = 0.201), haematocrit (R²  = 0.139), TAC formulation (R²  = 0.107) and age (R²  = 0.032; total R²  = 0.479). In the 4β-OHC/C/W-based model, predictors were 4β-OHC/C/W (R²  = 0.196), haematocrit (R²  = 0.059) and age (R²  = 0.057; total R²  = 0.312). When genotype information was ignored, predictors of TAC Cl/F/W in the whole cohort were 4β-OHC/C/W (R²  = 0.167), MDZ Cl/F/W (R²  = 0.045); Tac QD formulation (R²  = 0.036), and haematocrit (R²  = 0.032; total R²  = 0.315). 4β-OHC/C/W, but not MDZ Cl/F/W, was higher in CYP3A5 expressors because it was higher in CYP3A4*1b carriers, which were almost all CYP3A5 expressors.

CONCLUSIONS

A MDZ-based model explained more variability in TAC clearance in CYP3A5 non-expressors. However, 4β-OHC/C/W was superior in a model in which no genotype information was available, likely because 4β-OHC/C/W was influenced by the CYP3A4*1b polymorphism.

A Rationale for Age-Adapted Immunosuppression in Organ Transplantation

Demographic changes are associated with a steady increase of older patients with end-stage organ failure in need for transplantation. As a result, the majority of transplant recipients are currently older than 50 years, and organs from elderly donors are more frequently used. Nevertheless, the benefit of transplantation in older patients is well recognized, whereas the most frequent causes of death among older recipients are potentially linked to side effects of their immunosuppressants.Immunosenescence is a physiological part of aging linked to higher rates of diabetes, bacterial infections, and malignancies representing the major causes of death in older patients. These age-related changes impact older transplant candidates and may have significant implications for an age-adapted immunosuppression. For instance, immunosenescence is linked to lower rates of acute rejections in older recipients, whereas the engraftment of older organs has been associated with higher rejection rates. Moreover, new-onset diabetes mellitus after transplantation is more frequent in the elderly, potentially related to corticosteroids, calcineurin inhibitors, and mechanistic target of rapamycin inhibitors.This review presents current knowledge for an age-adapted immunosuppression based on both, experimental and clinical studies in and beyond transplantation. Recommendations of maintenance and induction therapy may help to improve graft function and to design future clinical trials in the elderly.

Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study

BACKGROUND

Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians.

METHODS

A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately.

RESULTS

Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04).

CONCLUSIONS

Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.