Association of CYP3A4-392A/G, CYP3A5-6986A/G, and ABCB1-3435C/T Polymorphisms with Tacrolimus Dose, Serum Concentration, and Biochemical Parameters in Mexican Patients with Kidney Transplant

Tacrolimus (TAC) is an immunosuppressant drug that prevents organ rejection after transplantation. This drug is transported from cells via P-glycoprotein (ABCB1) and is a metabolic substrate for cytochrome P450 (CYP) 3A enzymes, particularly CYP3A4 and CYP3A5. Several single-nucleotide polymorphisms (SNPs) have been identified in the genes encoding CYP3A4, CYP3A5, and ABCB1, including CYP3A4-392A/G (rs2740574), CYP3A5 6986A/G (rs776746), and ABCB1 3435C/T (rs1045642). This study aims to evaluate the association among CYP3A4-392A/G, CYP3A5-6986A/G, and ABCB1-3435C/T polymorphisms and TAC, serum concentration, and biochemical parameters that may affect TAC pharmacokinetics in Mexican kidney transplant (KT) patients.

METHODS

Forty-six kidney transplant recipients (KTR) receiving immunosuppressive treatment with TAC in different combinations were included. CYP3A4, CYP3A5, and ABCB1 gene polymorphisms were genotyped using qPCR TaqMan. Serum TAC concentration (as measured) and intervening variables were assessed. Logistic regression analyses were performed at baseline and after one month to assess the extent of the association between the polymorphisms, intervening variables, and TAC concentration.

RESULTS

The GG genotype of CYP3A5-6986 A/G polymorphism is associated with TAC pharmacokinetic variability OR 4.35 (95%CI: 1.13-21.9; p = 0.0458) at one month of evolution; in multivariate logistic regression, CYP3A5-6986GG genotype OR 9.32 (95%CI: 1.54-93.08; p = 0.028) and the use of medications or drugs that increase serum TAC concentration OR 9.52 (95%CI: 1.79-88.23; p = 0.018) were strongly associated with TAC pharmacokinetic variability.

CONCLUSION

The findings of this study of the Mexican population showed that CYP3A5-6986 A/G GG genotype is associated with a four-fold increase in the likelihood of encountering a TAC concentration of more than 15 ng/dL. The co-occurrence of the CYP3A5-6986GG genotype and the use of drugs that increase TAC concentration correlates with a nine-fold increased risk of experiencing a TAC at a level above 15 ng/mL. Therefore, these patients have an increased susceptibility to TAC-associated toxicity.

Pharmacokinetics of Tacrolimus in Pregnant Solid-Organ Transplant Recipients: A Retrospective Study

Data on the pharmacokinetics of tacrolimus during pregnancy are limited. Therefore, the aim of this retrospective study was to characterize the whole-blood pharmacokinetics of tacrolimus throughout pregnancy. In this single-center retrospective cohort study, whole-blood tacrolimus trough concentrations corrected for the dose (concentration-to-dose [C/D] ratios) were compared before, monthly during, and after pregnancy in kidney, liver, and lung transplant recipients who became pregnant and gave birth between 2000 and 2022. Descriptive statistics and linear mixed models were used to characterize changes in tacrolimus C/D ratios before, during, and after pregnancy. The total study population included 46 pregnancies (31 pregnant women). Nineteen, 21, and 6 pregnancies were following kidney, liver, and lung transplantation, respectively. Immediate-release or extended-release formulations were used in 54.5% and 45.5% of the women, respectively. Tacrolimus C/D ratios significantly (P < .001) decreased (-48%) compared to the prepregnancy state at 7 months of pregnancy. These ratios recovered within 3 months postpartum (P = .002). C/D ratios tended to be lower during treatment with an extended-release formulation than with an immediate-release formulation (P = .071). Transplantation type did not significantly affect C/D ratios during pregnancy (P = .873). In conclusion, we found that tacrolimus whole-blood pharmacokinetics change throughout pregnancy, with the lowest C/D ratios (48% decrease) in the 7th month of pregnancy. In general, the decrease in C/D ratios seems to stabilize from month 4 onward compared to prepregnancy.

Effect of Tacrolimus Formulation (Prolonged-Release vs Immediate-Release) on Its Susceptibility to Drug-Drug Interactions with St. John's Wort

Tacrolimus is metabolized by cytochrome P450 3A (CYP3A) and is susceptible to interactions with the CYP3A and P-glycoprotein inducer St. John's Wort (SJW). CYP3A isozymes are predominantly expressed in the small intestine and liver. Prolonged-release tacrolimus (PR-Tac) is largely absorbed in distal intestinal segments and is less susceptible to CYP3A inhibition. The effect of induction by SJW is unknown. In this randomized, crossover trial, 18 healthy volunteers received single oral tacrolimus doses (immediate-release [IR]-Tac or PR-Tac, 5 mg each) alone and during induction by SJW. Concentrations were quantified using ultra-high performance liquid chromatography coupled with tandem mass spectrometry and non-compartmental pharmacokinetics were evaluated. SJW decreased IR-Tac exposure (area under the concentration-time curve) to 73% (95% confidence interval 60%-88%) and maximum concentration (Cmax ) to 61% (52%-73%), and PR-Tac exposure to 67% (55%-81%) and Cmax to 69% (58%-82%), with no statistical difference between the 2 formulations. The extent of interaction appeared to be less pronounced in volunteers with higher baseline CYP3A4 activity and in CYP3A5 expressors. In contrast to CYP3A inhibition, CYP3A induction by SJW showed a similar extent of interaction with both tacrolimus formulations. A higher metabolic baseline capacity appeared to attenuate the extent of induction by SJW.

A longitudinal study of long-term renal outcome after pediatric liver transplantation in relation to CNI exposure

BACKGROUND

Chronic kidney disease (CKD) is reported in 20%-30% of children after liver transplantation (LT). One of the proposed underlying causes is the long-term exposure to tacrolimus, a calcineurin inhibitor (CNI), which is the main immunosuppressive drug used after LT. Variation in tacrolimus absolute exposure and relative dose requirements are believed to be important risk factors for developing CNI-associated nephrotoxicity.

AIM

To describe the long-term renal outcome of pediatric LT recipients and determine the effects of tacrolimus exposure on renal outcome parameters.

METHODS

Retrospective single center study of renal function (GFR, proteinuria) and pharmacokinetic parameters (C0 , AUC0-12h ) obtained during annual follow-up in children after liver transplantation, between 1998 and 2019. Relevant pharmacogenetic variants for tacrolimus disposition (CYP3A5 and ABCB1) were determined in recipients and donors. The evolution of individual renal function and tacrolimus exposure was evaluated using linear mixed models for repeated measurements.

RESULTS

Twenty-six children were included (mean follow-up: 10.4 years (range 2-18.9)). Mean estimated GFR was 109.3 (SE: 7.4), vs. measured: 91.3 mL/min/1.73 m2 (SE: 6.3), which remained stable during follow-up. CKD stage ≥2 was observed in 32.8% of the visits based on eGFR versus 50.0% on mGFR. CKD stage ≥3 was uncommon (4.1% and 6.2% resp.). Mean tacrolimus C0 was 5.3 ng/mL (SE: 2.5) with a AUC0-12h of 72.7 ng*h/mL (SE: 30.3), which demonstrated a small decrease during follow-up. There was a negative correlation between C0 and mGFR (rS  = -0.3; p < .001). We found no correlation between GFR and tacrolimus dose requirements ((ng/mL)/(mg/kg)) or pharmacogenetic background.

CONCLUSION

Renal function during long-term follow-up after pediatric LT remained stable for the majority of our cohort. However, mild CKD was relatively common, warranting follow-up into adulthood. Although absolute tacrolimus exposure has a small depressing effect on concurrent GFR, there is no progressive deterioration of GFR due to long-term exposure, dose requirements or genetic background under the current target levels. These findings should be confirmed in a larger sample set, ideally including data from multiple centers.

Population pharmacokinetic models of tacrolimus in paediatric solid organ transplant recipients: A systematic review

AIMS

This study aimed to provide up-to-date information on paediatric population pharmacokinetic models of tacrolimus and to identify factors influencing tacrolimus pharmacokinetic variability.

METHODS

Systematic searches in the Web of Science, PubMed, Scopus, Science Direct, Cochrane, EMBASE databases and reference lists of articles were conducted from inception to March 2023. All population pharmacokinetic studies of tacrolimus using nonlinear mixed-effect modelling in paediatric solid organ transplant patients were included.

RESULTS

Of the 21 studies reviewed, 62% developed from liver transplant recipients and 33% from kidney transplant recipients. Most studies used a 1-compartment model to describe tacrolimus pharmacokinetics. Body weight was a significant predictor for tacrolimus volume of distribution (Vd/F). The estimated Vd/F for 1-compartment models ranged from 20 to 1890 L, whereas the peripheral volume of distribution (Vp/F) for 2-compartment models was between 290 and 1520 L. Body weight, days post-transplant, CYP3A5 genotype or haematocrit were frequently reported as significant predictors of tacrolimus clearance. The estimated apparent clearance values range between 0.12 and 2.18 L/h/kg, with inter-individual variability from 13.5 to 110.0%. Only 29% of the studies assessed the generalizability of the models with external validation.

CONCLUSION

This review highlights the potential factors, modelling approaches and validation methods that impact tacrolimus pharmacokinetics in a paediatric population. The clinician could predict tacrolimus clearance based on body weight, CYP3A5 genotype, days post-transplant or haematocrit. Further research is required to determine the relationship between pharmacogenetics and tacrolimus pharmacodynamics in paediatric patients and confirm the applicability of nonlinear kinetics in this population.

Modelling changes in the pharmacokinetics of tacrolimus during pregnancy after kidney transplantation: A retrospective cohort study

AIMS

Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model.

METHODS

Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis.

RESULTS

A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7 L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P ≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance.

CONCLUSIONS

Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is required.

Tacrolimus Pharmacokinetics is Associated with Gut Microbiota Diversity in Kidney Transplant Patients: Results from a Pilot Cross-Sectional Study

Clinical use of tacrolimus (TAC), an essential immunosuppressant following transplantation, is complexified by its high pharmacokinetic (PK) variability. The gut microbiota gains growing interest but limited investigations have evaluated its contribution to TAC PKs. Here, we explore the associations between the gut microbiota composition and TAC PKs. In this pilot cross-sectional study (Clinicaltrial.gov NCT04360031), we recruited 93 CYP3A5 non-expressers stabilized kidney transplant recipients. Gut microbiota composition was characterized by 16S rRNA gene sequencing, TAC PK parameters were computed, and additional demographic and medical covariates were collected. Associations between PK parameters or diabetic status and the gut microbiota composition, as reflected by α- and β-diversity metrics, were evaluated. Patients with higher TAC area under the curve AUC/(dose/kg) had higher bacterial richness, and TAC PK parameters were associated with specific bacterial taxa (e.g., Bilophila) and amplicon sequence variant (ASV; e.g., ASV 1508 and ASV 1982 (Veillonella/unclassified Sporomusaceae); ASV 664 (unclassified Oscillospiraceae)). Building a multiple linear regression model showed that ASV 1508 (co-abundant with ASV 1982) and ASV 664 explained, respectively, 16.0% and 4.6% of the interindividual variability in TAC AUC/(dose/kg) in CYP3A5 non-expresser patients, when adjusting for hematocrit and age. Anaerostipes relative abundance was decreased in patients with diabetes. Altogether, this pilot study revealed unprecedented links between the gut microbiota composition and diversity and TAC PKs in stable kidney transplant recipients. It supports the relevance of studying the gut microbiota as an important contributor to TAC PK variability. Elucidating the causal relationship will offer new perspectives to predict TAC inter- and intra-PK variability.

A model-based pharmacokinetic assessment of drug-drug interaction between tacrolimus and nirmatrelvir/ritonavir in a kidney transplant patient with COVID-19

We experienced a patient with a remarkable and prolonged increase in tacrolimus blood concentrations when nirmatrelvir/ritonavir was concomitantly used. The inhibitory intensity and duration of nirmatrelvir/ritonavir on tacrolimus pharmacokinetics were examined using a model-based analysis. A renal transplant patient taking oral tacrolimus continuously was treated with nirmatrelvir/ritonavir for 5 days. The baseline tacrolimus trough blood concentration was 4.2 ng/mL. Tacrolimus was discontinued on Day 6 after the concomitant administration of nirmatrelvir/ritonavir, and the trough concentration increased to 96.4 ng/mL on Day 7. The model-based analysis showed that tacrolimus clearance decreased to 35% and bioavailability increased by 18.7-fold after the coadministration of nirmatrelvir/ritonavir, compared with before the coadministration. Therefore, nirmatrelvir/ritonavir drastically decreased both the apparent clearance and apparent volume of distribution. Simulated tacrolimus concentrations could be best fitted to the observed concentrations when the inhibitory effects of nirmatrelvir/ritonavir were modeled to disappear over about 10 days by first-order elimination. In conclusion, nirmatrelvir/ritonavir greatly increases tacrolimus concentrations by not only reducing clearance, but also increasing bioavailability. Interactions between nirmatrelvir/ritonavir and low-bioavailability drugs which are substrates for CYP3A and P-glycoprotein, such as tacrolimus, are harmful, and concomitant use of these medicines should be avoided.

Effectiveness and safety of once-daily tacrolimus formulations in de novo liver transplant recipients: The PRETHI study

Data comparing long-term effectiveness and safety of once-daily tacrolimus formulations in de novo liver transplantation are scarce. We compared the effectiveness, pharmacokinetic profile, and safety of LCPT (Envarsus) and PR-Tac (Advagraf) for up to 12 months post-transplant. Adult de novo liver transplant recipients who started IR-Tac (Prograf) and were converted to LCPT or PR-Tac 3-5 days post-transplant were included. Data from 163 patients were analyzed, 87 treated with LCPT and 76 with PR-Tac. The incidence of treatment failure was 30.5% in the LCPT group versus 23.0% in the PR-Tac group (p = .291). Biopsy-proven acute rejection (BPAR) was reported in 26.8% of patients in the LCPT group and 17.6% in the PR-Tac group (p = .166). Graft loss was experienced in one patient (1.2%) in the LCPT group and three patients (4.1%) in the PR-Tac group (p = .346). Death was registered in three patients (3.7%) in the LCPT group and three patients (4.1%) in the PR-Tac group (p > .999). Patients in the LCPT group showed 45.7% higher relative bioavailability (Cmin /total daily dose [TDD]; p < .01) with similar Cmin and 33.3% lower TDD versus PR-Tac (p < .01). The evolution of renal function, safety profile, and the incidence of post-transplant renal failure, dyslipidemia, obesity, hypertension, and diabetes mellitus were similar in patients treated with LCPT and PR-Tac. In de novo liver transplant patients, LCPT and PR-Tac showed comparable effectiveness with higher relative bioavailability, similar Cmin and lower TDD in the LCPT group. Renal function, safety, and post-transplant complications were comparable in LCPT and PR-Tac groups.

Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease

BACKGROUND

Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance ( CLint ) of tacrolimus as a case example.

METHODS

Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate ( eGFR ) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method.

RESULTS

At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m 2 . A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients.

CONCLUSIONS

Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.

Tacrolimus population pharmacokinetics in adult heart transplant patients

INTRODUCTION

Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0).

MATERIAL AND METHODS

Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0.

RESULTS

The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor).

CONCLUSION

The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

Area under the curve of tacrolimus using microsampling devices: towards precision medicine in solid organ transplantation?

PURPOSE

Therapeutic drug monitoring of tacrolimus using trough concentration (Cmin) is mandatory to ensure drug efficacy and safety in solid organ transplantation. However, Cmin is just a proxy for the area under the curve of drug concentrations (AUC) which is the best pharmacokinetic parameter for exposure evaluation. Some studies suggest that patients may present discrepancies between these two parameters. AUC is now easily available through mini-invasive microsampling approach. The aim of this study is to evaluate the relationship between AUC and Cmin in patients benefiting from a complete pharmacokinetic profile using a microsampling approach.

METHODS

Fifty-one transplant recipients benefited from a complete pharmacokinetic profile using a microsampling approach, and their 24-h AUC were calculated using the trapezoidal method. The correlation with Cmin was then explored. In parallel, we estimated AUC using the sole Cmin and regression equations according to the post-transplantation days and the galenic form.

RESULTS

Weak correlations were found between 24-h AUC observed and the corresponding Cmin (R2 = 0.60) and between AUC observed and expected using the sole Cmin (R2 = 0.62). Therapeutic drug monitoring of tacrolimus using Cmin leads to over- or under-estimate drug exposure in 40.3% of patients.

CONCLUSION

Tacrolimus Cmin appears to be an imperfect reflection of drug exposure. Evaluating AUC using a microsampling approach offers a mini-invasive strategy to monitor tacrolimus treatment in transplant recipients.

A Joint Pharmacokinetic Model for the Simultaneous Description of Plasma and Whole Blood Tacrolimus Concentrations in Kidney and Lung Transplant Recipients

BACKGROUND AND OBJECTIVE

Historically, dosing of tacrolimus is guided by therapeutic drug monitoring (TDM) of the whole blood concentration, which is strongly influenced by haematocrit. The therapeutic and adverse effects are however expected to be driven by the unbound exposure, which could be better represented by measuring plasma concentrations.

OBJECTIVE

We aimed to establish plasma concentration ranges reflecting whole blood concentrations within currently used target ranges.

METHODS

Plasma and whole blood tacrolimus concentrations were determined in samples of transplant recipients included in the TransplantLines Biobank and Cohort Study. Targeted whole blood trough concentrations are 4-6 ng/mL and 7-10 ng/mL for kidney and lung transplant recipients, respectively. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. Simulations were performed to infer plasma concentration ranges corresponding to whole blood target ranges.

RESULTS

Plasma (n = 1973) and whole blood (n = 1961) tacrolimus concentrations were determined in 1060 transplant recipients. A one-compartment model with fixed first-order absorption and estimated first-order elimination characterised observed plasma concentrations. Plasma was linked to whole blood using a saturable binding equation (maximum binding 35.7 ng/mL, 95% confidence interval (CI) 31.0-40.4 ng/mL; dissociation constant 0.24 ng/mL, 95% CI 0.19-0.29 ng/mL). Model simulations indicate that patients within the whole blood target range are expected to have plasma concentrations (95% prediction interval) of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

CONCLUSION

Whole blood tacrolimus target ranges, currently used to guide TDM, were translated to plasma concentration ranges of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

Influence of genetic variants on the pharmacokinetics and pharmacodynamics of sirolimus: a systematic review

Sirolimus is an antiproliferative and immunosuppressive compound inhibiting the mTOR pathway, which is often activated in congenital low-flow vascular malformations. Studies have demonstrated the efficacy of sirolimus for this disease. Studies in kidney transplant patients suggest that genetic variants can influence these pharmacokinetic parameters. Therefore, a systematic literature search was performed to gain insight into pharmacogenetic studies with sirolimus. Most studies investigated CYP3A4 and CYP3A5, with inconsistent results. No pharmacogenetic studies focusing on sirolimus have been performed for low-flow vascular malformations. We analyzed two common variants of CYP3A4 and CYP3A5 (CYP3A4*22 and CYP3A5*3, respectively) in patients (n = 59) with congenital low-flow vascular malformations treated with sirolimus. No association with treatment outcome was identified in this small cohort of patients.

Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

AIM

Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction.

METHODS

Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.

RESULTS

Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P < .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing.

CONCLUSION

A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.

Pharmacokinetic Model Based on Stochastic Simulation and Estimation for Therapeutic Drug Monitoring of Tacrolimus in Korean Adult Transplant Recipients

BACKGROUND

Tacrolimus shows high variability in inter- and intraindividual pharmacokinetics (PK); therefore, it is important to develop an appropriate model for accurate therapeutic drug monitoring (TDM) procedures. This study aimed to develop a pharmacokinetic model for tacrolimus that can be used for TDM procedures in Korean adult transplant recipients by integrating published models with acquired real-world TDM data and evaluating clinically meaningful covariates.

METHODS

Clinical data of 1829 trough blood samples from 269 subjects were merged with simulated data sets from published models and analyzed using a nonlinear mixed-effect model. The stochastic simulation and estimation (SSE) method was used to obtain the final parameter estimates.

RESULTS

The final estimated values for apparent clearance, the volume of distribution, and absorption rate were 21.2 L/h, 510 L, and 3.1/h, respectively. The number of postoperative days, age, body weight, and type of transplant organs were the major clinical factors affecting tacrolimus PK.

CONCLUSIONS

A tacrolimus PK model that can incorporate published PK models and newly collected data from the Korean population was developed using the SSE method. Despite the limitations in model development owing to the nature of TDM data, the SSE method was useful in retrieving complete information from the TDM data by integrating published PK models while maintaining the variability of the model.

Monitoring of blood immunosuppressant concentrations and lymphocyte activation for predicting viral infections following kidney transplantation: A pilot study

The current standard pharmacokinetic monitoring of immunosuppressive therapy does not consider inter- and intra-individual differences in the biological response to multidrug immunosuppressive therapy. The authors evaluated the blood levels of the immunosuppressive drugs IL-2 and IFN-γ in circulating lymphocytes as surrogate indicators of the development of viral infections after living kidney transplantation. This single-center prospective study included 20 kidney transplant recipients who underwent living-donor transplantation at the Mie University Hospital. All the study participants received tacrolimus, mycophenolic acid, methylprednisolone, and basiliximab. The area under the concentration curves (AUCs) of blood tacrolimus and serum mycophenolic acid were measured 1 day prior to transplantation and on post-transplantation days (PTD) for up to 5 months. IL-2 and IFN-γ levels in circulating lymphocytes were measured simultaneously. One recipient experienced an acute graft rejection. Although the AUC of tacrolimus at PTD 7 was significantly higher in the virus-infected group than that in the non-infected group, the AUC of mycophenolic acid did not differ significantly between the 2 groups. The expression levels of IFN-γ+ NK, IFN-γ+ CD4+ T, and CD8+ T cells in the infected group also tended to be higher than those in the noninfected group. During the study period, there was a clear difference in the expression of IFN-γ+ CD8+ T cells, which increased significantly during or after infection. Circulating IFN-γ+ CD8+ T cell counts may serve as promising biomarkers for predicting opportunistic viral infections early after kidney transplantation.

Tacrolimus Population Pharmacokinetic Model in Adult Chinese Patients with Nephrotic Syndrome and Dosing Regimen Identification Using Monte Carlo Simulations

BACKGROUND

The study aimed to establish a population pharmacokinetic (PPK) model of tacrolimus for Chinese patients with nephrotic syndrome using the patient's genotype and Wuzhi capsule dosage as the main test factors.

METHODS

Ninety-six adult patients with nephrotic syndrome, who were receiving tacrolimus treatment, were enrolled. A nonlinear mixed-effects model was used to determine the influencing factors of interindividual tacrolimus metabolism variation and establish a PPK model. To optimize the tacrolimus dosage, 10,000 Monte Carlo simulations were performed.

RESULTS

The 1-chamber model of first-order absorption and elimination was the most suitable model for the data in this study. The typical population tacrolimus clearance (CL/F) value was 16.9 L/h. The percent relative standard error (RSE%) of CL/F was 12%. Increased Wuzhi capsule and albumin doses both decreased the tacrolimus CL/F. In CYP3A5 homozygous mutation carriers, the CL/F was 39% lower than that of carriers of the wild-type and heterozygous mutation. The tacrolimus CL/F in patients who were coadministered glucocorticoids was 1.23-fold higher than that of the control. According to the patient genotype and combined use of glucocorticoids, 26 combinations of Wuzhi capsule and tacrolimus doses were matched. The Monte Carlo simulation identified the most suitable combination scheme.

CONCLUSIONS

An improved tacrolimus PPK model for patients with nephrotic syndrome was established, and the most suitable combination of Wuzhi capsule and tacrolimus doses was identified, thus, facilitating the selection of a more economical and safe administration regimen.

Composite CYP3A phenotypes influence tacrolimus dose-adjusted concentration in lung transplant recipients

OBJECTIVES

Interpatient variability in tacrolimus pharmacokinetics is attributed to metabolism by cytochrome P-450 3A4/5 isoenzymes (encoded by CYP3A4 and CYP3A5). Guidelines for adjusting tacrolimus based on CYP3A5 test results are published; however, CYP3A4 variants also contribute to the variability in tacrolimus pharmacokinetics. The effects of composite phenotypes incorporating CYP3A5 and CYP3A4 increased (*1G, *1B) and decreased (*22) function variants have not been evaluated. The objective of this study is to investigate the impact of both increased and decreased function CYP3A variants on weight and dose-adjusted tacrolimus concentration (C0/D).

METHODS

We performed a single-center retrospective cohort study of lung transplant recipients to evaluate the median tacrolimus C0/D by composite CYP3A phenotype groups during the index transplant hospitalization. CYP3A4 and CYP3A5 alleles were used to classify patients into four CYP3A groups from least to most CYP3A activity. Exploratory analyses of ABCB1 and additional candidate genes were also assessed.

RESULTS

Of the 92 included individuals, most (58) were CYP3A Group 2. The median tacrolimus C0/D differed significantly between CYP3A groups (P = 0.0001). CYP3A Group 2 median tacrolimus C0/D was 190.5 (interquartile range: 147.6-267.5) (ng/ml)/(mg/kg/d) and significantly higher than Group 4 [107.9 (90.4-116.1), P = 0.0001)]. Group 2 median tacrolimus C0/D did not significantly differ from Group 1 and Group 3 [373.5 (149.2-490.3) and 81.4 (62.6-184.1), respectively]. No significant differences in tacrolimus C0/D were found for the ABCB1 diplotypes.

CONCLUSION

These data indicate that a composite CYP3A phenotype incorporating both increase and decrease variant information from CYP3A4 in addition to CYP3A5 may significantly influence tacrolimus C0/D during the early postoperative period.

Examination of the Impact of CYP3A4/5 on Drug-Drug Interaction between Schizandrol A/Schizandrol B and Tacrolimus (FK-506): A Physiologically Based Pharmacokinetic Modeling Approach

Schizandrol A (SZA) and schizandrol B (SZB) are two active ingredients of Wuzhi capsule (WZC), a Chinese proprietary medicine commonly prescribed to alleviate tacrolimus (FK-506)-induced hepatoxicity in China. Due to their inhibitory effects on cytochrome P450 (CYP) 3A enzymes, SZA/SZB may display drug–drug interaction (DDI) with tacrolimus. To identify the extent of this DDI, the enzymes’ inhibitory profiles, including a 50% inhibitory concentration (IC₅₀) shift, reversible inhibition (RI) and time-dependent inhibition (TDI) were examined with pooled human-liver microsomes (HLMs) and CYP3A5-genotyped HLMs. Subsequently, the acquired parameters were integrated into a physiologically based pharmacokinetic (PBPK) model to quantify the interactions between the SZA/SZB and the tacrolimus. The metabolic studies indicated that the SZB displayed both RI and TDI on CYP3A4 and CYP3A5, while the SZA only exhibited TDI on CYP3A4 to a limited extent. Moreover, our PBPK model predicted that multiple doses of SZB would increase tacrolimus exposure by 26% and 57% in CYP3A5 expressers and non-expressers, respectively. Clearly, PBPK modeling has emerged as a powerful approach to examine herb-involved DDI, and special attention should be paid to the combined use of WZC and tacrolimus in clinical practice.

Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software

BACKGROUND

Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations.

METHODS

A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.

RESULTS

The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.

CONCLUSIONS

Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients

BACKGROUND AND OBJECTIVE

Tacrolimus is a narrow therapeutic index drug with high pharmacokinetic variability, and several tacrolimus population pharmacokinetic (PopPK) models were developed to guide individualized drug dosing. These models, however, may not perform well in other clinical settings. Therefore, we aimed to assess the predictive ability of published tacrolimus PopPK models using a dataset of Thai kidney transplant patients.

METHODS

The external dataset was retrospectively collected from medical records of Bhumibol Adulyadej Hospital, Thailand. Published tacrolimus PopPK models were systematically searched from PubMed, Science Direct, CINAHL Complete, and Scopus databases. Models conducted using a nonlinear mixed-effects approach with covariate resemblance to our external dataset were selected. The external dataset consisted of Thai kidney transplant patients receiving oral immediate- or extended-release tacrolimus formulations twice or once daily, respectively. Accuracy and precision of predicted concentrations were evaluated using mean absolute prediction error (MAPE), root mean square error (RMSE), and goodness of fit plots.

RESULTS

Only three models produced acceptable population predictions with the MAPE of < 50%. By using the Bayesian posthoc estimate of individual pharmacokinetic parameters, all models well performed with the MAPE and RMSE of < 30% and 40%, respectively, except two models; one could not successfully converge and the other substantially underpredicted tacrolimus concentrations.

CONCLUSION

We evaluated ten tacrolimus PopPK models, and eight models resulted in satisfactorily individual predicted tacrolimus concentrations in Thai kidney transplant patients and may be used to aid tacrolimus dose adjustment along with a clinical judgment.

Unraveling the Genomic Architecture of the CYP3A Locus and ADME Genes for Personalized Tacrolimus Dosing

BACKGROUND

Tacrolimus (TAC) is an immunosuppressant widely prescribed following an allogenic organ transplant. Due to wide interindividual pharmacokinetic (PK) variability, optimizing TAC dosing based on genetic factors is required to minimize nephrotoxicity and acute rejections.

METHODS

We enrolled 1133 participants receiving TAC from 4 cohorts, consisting of 3 with kidney transplant recipients and 1 with healthy males from clinical trials. The effects of clinical factors were estimated to appropriately control confounding variables. A genome-wide association study, haplotype analysis, and a gene-based association test were conducted using the Korea Biobank Array or targeted sequencing for 114 pharmacogenes.

RESULTS

Genome-wide association study verified that CYP3A5*3 is the only common variant associated with TAC PK variability in Koreans. We detected several CYP3A5 and CYP3A4 rare variants that could potentially affect TAC metabolism. The haplotype structure of CYP3A5 stratified by CYP3A5*3 was a significant factor for CYP3A5 rare variant interpretation. CYP3A4 rare variant carriers among CYP3A5 intermediate metabolizers displayed higher TAC trough levels. Gene-based association tests in the 61 absorption, distribution, metabolism, and excretion genes revealed that CYP1A1 are associated with additional TAC PK variability: CYP1A1 rare variant carriers among CYP3A5 poor metabolizers showed lower TAC trough levels than the noncarrier controls.

CONCLUSIONS

Our study demonstrates that rare variant profiling of CYP3A5 and CYP3A4, combined with the haplotype structures of CYP3A locus, provide additive value for personalized TAC dosing. We also identified a novel association between CYP1A1 rare variants and TAC PK variability in the CYP3A5 nonexpressers that needs to be further investigated.

Intravenous Infusion of Fentanyl Has No Effect on Blood Concentration of Tacrolimus in Patients Receiving Hematopoietic Stem-Cell Transplantation

BACKGROUND

Both tacrolimus (TAC) and fentanyl are frequently used in patients receiving allogeneic hematopoietic stem-cell transplantation. A recently published report demonstrated that fentanyl can reduce the total body clearance of TAC; however, most patients in this study were administered concomitantly with azole antifungal agents, which are known to be strong inhibitors of CYP3A. Hence, the exact effect of fentanyl on TAC pharmacokinetics was unclear. In the current study, the authors retrospectively investigated the pharmacokinetic interaction between TAC and fentanyl in patients who were not concomitantly administered drugs that affect TAC metabolism.

METHODS

Patients with continuous infusion of TAC and fentanyl after hematopoietic stem-cell transplantation at the Tokyo Medical and Dental University between January 2014 and December 2018 were enrolled. The total body clearance of TAC was compared before and after the initiation or discontinuation of fentanyl.

RESULTS

Thirty patients (24 men and 6 women; median age, 11 years) were screened for their eligibility. Twenty-eight patients were enrolled for evaluating the effects of the fentanyl initiation on TAC pharmacokinetics; 2 patients were excluded because of the absence of data related to the TAC blood concentrations or the concomitant use of azole antifungals. Twenty patients were enrolled for investigating the effects of fentanyl discontinuation on TAC pharmacokinetics, whereas 10 patients were excluded because of the absence of data related to the blood concentration of TAC or the additional administration of azole antifungals. Furthermore, the total body clearance of TAC was not significantly affected by the initiation or discontinuation of fentanyl, although there were large interindividual variations. In addition, the results remained the same even when the analysis was performed independently for adults and children.

CONCLUSIONS

Intravenous infusion of fentanyl does not affect the pharmacokinetics of TAC.

High Intrapatient Variability in Tacrolimus Exposure Calculated Over a Long Period Is Associated With De Novo Donor-Specific Antibody Development and/or Late Rejection in Thai Kidney Transplant Patients Receiving Concomitant CYP3A4/5 Inhibitors

BACKGROUND

High intrapatient variability in tacrolimus trough levels (Tac IPV) is associated with poor allograft outcomes. Tac IPV was previously calculated using trough levels 6-12 months after kidney transplantation (KT). Data on the accuracy of Tac IPV calculation over a longer period, the association between high Tac IPV and donor-specific antibody (DSA) development after KT in Asian patients, and the role of IPV in patients receiving concomitant cytochrome P450 (CYP)3A4/5 inhibitors (CYPinh) are limited.

METHODS

A retrospective review of patients who underwent KT at our center in 2005-2015, and who received Tac with mycophenolate during the first 2 years after KT was performed. IPV was calculated using Tac levels adjusted by dosage. DSA was monitored annually after KT using a Luminex microbead assay.

RESULTS

In total, 236 patients were enrolled. CYPinh were prescribed to 189 patients (80.1%): 145 (61.4%), 31 (13.1%), and 13 (5.5%) received diltiazem, fluconazole, and ketoconazole, respectively. Mean IPV calculated from adjusted Tac levels for 6-12 months (IPV6-12) and 6-24 months (IPV6-24) after KT were 20.64% ± 11.68% and 23.53% ± 10.39%, respectively. Twenty-six patients (11%) showed late rejection and/or DSA occurrence, and had significantly higher IPV6-24 (29.42% ± 13.78%) than others (22.77% ± 9.64%; P = 0.02). There was no difference in IPV6-12 (24.31% ± 14.98% versus 20.17% ± 10.90%; P = 0.18). IPV6-12 and IPV6-24 were comparable in patients who did and did not receive CYPinh. When using mean IPV6-24 as a cutoff, patients with higher IPV6-24 had a higher probability of developing DSA and/or late rejection (P = 0.048).

CONCLUSIONS

Tac IPV6-24 was higher and more significantly associated with DSA development and/or late rejection than Tac IPV6-12, independent of Tac trough level. This is the first study to demonstrate the impact of high IPV on DSA development in Asian patients, and that Tac IPV is comparable between patients with and without CYPinh.

A Population Pharmacokinetic Model Does Not Predict the Optimal Starting Dose of Tacrolimus in Pediatric Renal Transplant Recipients in a Prospective Study: Lessons Learned and Model Improvement

Background and Objective

Bodyweight-based dosing of tacrolimus is considered standard care. Currently, at first steady state, a third of pediatric kidney transplant recipients has a tacrolimus pre-dose concentration within the target range. We investigated whether adaptation of the starting dose according to a validated dosing algorithm could increase this proportion.

Methods

This was a multi-center, single-arm, prospective trial with a planned interim analysis after 16 patients, in which the tacrolimus starting dose was based on bodyweight, cytochrome P450 3A5 genotype, and donor status (living vs. deceased donor).

Results

At the interim analysis, 31% of children had a tacrolimus pre-dose concentration within the target range. As the original dosing algorithm was poorly predictive of tacrolimus exposure, the clinical trial was terminated prematurely. Next, the original model was improved by including the data of the children included in this trial, thereby doubling the number of children in the model building cohort. Data were best described with a two-compartment model with inter-individual variability, allometric scaling, and inter-occasion variability on clearance. Cytochrome P450 3A5 genotype, hematocrit, and creatinine influenced the tacrolimus clearance. A new starting dose model was developed in which the cytochrome P450 3A5 genotype was incorporated. Both models were successfully internally and externally validated.

Conclusions

The weight-normalized starting dose of tacrolimus should be higher in patients with a lower bodyweight and in those who are cytochrome P450 3A5 expressers.

Electronic supplementary material

The online version of this article (10.1007/s40262-019-00831-8) contains supplementary material, which is available to authorized users.

Can We Predict Individual Concentrations of Tacrolimus After Liver Transplantation? Application and Tweaking of a Published Population Pharmacokinetic Model in Clinical Practice

BACKGROUND

Various population pharmacokinetic models have been developed to describe the pharmacokinetics of tacrolimus in adult liver transplantation. However, their extrapolated predictive performance remains unclear in clinical practice. The purpose of this study was to predict concentrations using a selected literature model and to improve these predictions by tweaking the model with a subset of the target population.

METHODS

A literature review was conducted to select an adequate population pharmacokinetic model (L). Pharmacokinetic data from therapeutic drug monitoring of tacrolimus in liver-transplanted adults were retrospectively collected. A subset of these data (70%) was exploited to tweak the L-model using the $PRIOR subroutine of the NONMEM software, with 2 strategies to weight the prior information: full informative (F) and optimized (O). An external evaluation was performed on the remaining data; bias and imprecision were evaluated for predictions a priori and Bayesian forecasting.

RESULTS

Seventy-nine patients (851 concentrations) were enrolled in the study. The predictive performance of L-model was insufficient for a priori predictions, whereas it was acceptable with Bayesian forecasting, from the third prediction (ie, with ≥2 previously observed concentrations), corresponding to 1 week after transplantation. Overall, the tweaked models showed a better predictive ability than the L-model. The bias of a priori predictions was -41% with the literature model versus -28.5% and -8.73% with tweaked F and O models, respectively. The imprecision was 45.4% with the literature model versus 38.0% and 39.2% with tweaked F and O models, respectively. For Bayesian predictions, whatever the forecasting state, the tweaked models tend to obtain better results.

CONCLUSIONS

A pharmacokinetic model can be used, and to improve the predictive performance, tweaking the literature model with the $PRIOR approach allows to obtain better predictions.

Precision Dosing for Tacrolimus Using Genotypes and Clinical Factors in Kidney Transplant Recipients of European Ancestry

Genetic variation in the CYP3A4 and CYP3A5 (CYP3A4/5) genes, which encode the key enzymes in tacrolimus metabolism, is associated with tacrolimus clearance and dose requirements. Tacrolimus has a narrow therapeutic index with high intra- and intersubject variability, in part because of genetic variation. High tacrolimus clearance and low trough concentration are associated with a greater risk for rejection, whereas high troughs are associated with calcineurin-induced toxicity. The objective of this study was to develop a model of tacrolimus clearance with a dosing equation accounting for genotypes and clinical factors in adult kidney transplant recipients of European ancestry that could preemptively guide dosing. Recipients receiving immediate-release tacrolimus for maintenance immunosuppression from 2 multicenter studies were included. Participants in the GEN03 study were used for tacrolimus model development (n = 608 recipients) and was validated by prediction performance in the DeKAF Genomics study (n = 1361 recipients). Nonlinear mixed-effects modeling was used to develop the apparent oral tacrolimus clearance (CL/F) model. CYP3A4/5 genotypes and clinical covariates were tested for their influence on CL/F. The predictive performance of the model was determined by assessing the bias (median prediction error [ME] and median percentage error [MPE]) and the precision (root median squared error [RMSE]) of the model. CYP3A5*3, CYP3A4*22, corticosteroids, calcium channel blocker and antiviral drug use, age, and diabetes significantly contributed to the interindividual variability of oral tacrolimus apparent clearance. The bias (ME, MPE) and precision (RMSE) of the final model was good, 0.49 ng/mL, 6.5%, and 3.09 ng/mL, respectively. Prospective testing of this equation is warranted.

Tacrolimus Measured in Capillary Volumetric Microsamples in Pediatric Patients-A Cross-Validation Study

BACKGROUND

Therapeutic drug monitoring of tacrolimus (Tac) is mandatory in solid organ transplant (SOT) recipients. Finger-prick microsampling is more flexible and tolerable during the therapeutic drug monitoring of tacrolimus and has been shown to be applicable in adult SOT recipients. In this study, a previously validated method applying volumetric absorptive microsampling (VAMS) to measure Tac in adults was cross-validated in a pediatric population.

METHODS

Patients with SOT scheduled for standard posttransplant follow-up visits were recruited. Blood samples were obtained by trained phlebotomists using standard venipuncture and capillary microsampling, before the morning dose of Tac as well as 2 and 5 hours after dosing. Tac concentrations were quantified using liquid chromatography-tandem mass spectrometry. Concordance between Tac concentrations obtained with venipuncture and VAMS was evaluated using Passing-Bablok regression, calculation of absolute and relative differences, and percentage of samples within ±20% and ±30% difference.

RESULTS

A total of 39 SOT patients aged 4-18 years (22 male) were included. The median (range) predose venous blood concentration was 4.8 (2.6-13.6) mcg/L, with a difference between VAMS and venous blood samples of -0.2 ± 0.7 mcg/L. The relative mean difference was -1.3% [95% confidence interval (CI), -5.9% to 3.4%]. Ninety-two percent and 97% of the sample pairs demonstrated differences within ±20% and ±30%, respectively. Postdose (2 hours and/or 5 hours, n = 17) median concentration in venous blood was 7.9 (4.8-19.2) mcg/L. The difference between VAMS and venous blood samples was 0.1 ± 1.0 mcg/L, with a relative mean difference of -2.5% (95% confidence interval, -8.8% to 3.8%). Eighty-eight percent of the postdose sample pairs were within ±20% difference, and all were within ±30% difference.

CONCLUSIONS

Tac concentrations can be accurately measured using VAMS technology in pediatric SOT recipients. This makes home-based Tac monitoring feasible in the pediatric population.

Quantitation of Tacrolimus in Human Whole Blood Samples Using the MITRA Microsampling Device

BACKGROUND

The calcineurin inhibitor tacrolimus is a narrow therapeutic index medication, which requires therapeutic drug monitoring to optimize dose on the basis of systemic exposure. MITRA microsampling offers a minimally invasive approach for the collection of capillary blood samples from a fingerprick as an alternative to conventional venous blood sampling for quantitation of tacrolimus concentrations.

METHODS

A bioanalytical method for the quantitation of tacrolimus in human whole blood samples collected on MITRA tips was developed, using liquid-liquid extraction followed by liquid chromatography with tandem mass spectrometry detection. Validation experiments were performed according to the current Food and Drug Administration and European Medicines Agency guidelines on validation of bioanalytical methods. Validation criteria included assay specificity and sensitivity, interference, carryover, accuracy, precision, dilution integrity, matrix effect, extraction recovery, effect of hematocrit and hyperlipidemia, and stability.

RESULTS

All assay validation results were within the required acceptance criteria, indicating a precise and accurate tacrolimus quantitation method. The validated assay range was 1.00-50.0 ng/mL. No interference, carryover or matrix effect was observed. Extraction recovery was acceptable across the assay range. Samples were stable for up to 96 days at -20°C and 20°C, and 28 days at 40°C. Hematocrit, hyperlipidemia, and lot-to-lot differences in the nominal absorption volume of the 10-μL MITRA tips were shown not to influence tacrolimus quantitation by this assay method.

CONCLUSIONS

The bioanalytical method validated in this study is appropriate and practical for the quantitation of tacrolimus in human whole blood samples collected using the MITRA microsampling device.

Etiologies and Outcomes Associated With Tacrolimus Levels Out of a Typical Range That Lead to High Variability in Kidney Transplant Recipients

BACKGROUND

High tacrolimus intrapatient variability (tac IPV) is associated with poor outcomes in kidney transplantation, including rejection, donor-specific antibodies, and graft loss. A common cause of high tac IPV is related to patient nonadherence, but this is yet to be conclusively demonstrated.

METHODS

This was a longitudinal cohort study comprising adult kidney recipients, who received transplants between 2015 and 2017, with follow-ups through February 2020. The goal of this study was to identify the most common etiologies of tac levels outside the typical range, which lead to high tac IPV, and assess the etiology-specific associations between high tac IPV and graft outcomes. Multivariate Cox regression was used to assess time-to-event analyses.

RESULTS

In total, 537 adult kidney recipients were included; 145 (27%) were identified as having a high tac IPV (>40%) 3-102 months post-transplant. Common etiologies of tac levels significantly outside the standard goal range (6-12 ng/mL) leading to high tac IPV included patient nonadherence (20%), infections (19%), tac-related toxicities (17%), and undocumented issues (27%). In multivariable Cox modeling, those with high tac IPV because of nonadherence had a 3.5 times higher risk of late acute rejection (P = 0.019) and 2.2 times higher risk of late graft loss (P = 0.044). No other etiologies in the typical tac level range were significantly associated with either acute rejection or graft loss.

CONCLUSIONS

Although high tac IPV has many causes, only high tac IPV caused by nonadherence is consistently associated with poor allograft outcomes.

Validation of a Capillary Dry Blood Sample MITRA-Based Assay for the Quantitative Determination of Systemic Tacrolimus Concentrations in Transplant Recipients

BACKGROUND

Tacrolimus is a narrow therapeutic index medication, which requires therapeutic drug monitoring to optimize dosing based on systemic exposure. MITRA microsampling offers a convenient, minimally invasive approach for the collection of capillary blood samples from a finger prick versus conventional venous blood sampling for quantitation of tacrolimus blood concentrations. However, the suitability of MITRA microsampling for the determination of tacrolimus concentrations requires assessment in clinical settings.

METHODS

Paired venous (2 mL) and capillary (10 μL) blood samples were collected pre-tacrolimus dose and 1 and 3 hours postdose during routine outpatient visits from stable adult liver or kidney transplant patients receiving prolonged-release tacrolimus. Tacrolimus concentrations were determined by liquid chromatography-tandem mass spectrometry, and the concentrations obtained by the 2 sampling methods were compared by linear regression and Bland-Altman agreement analyses.

RESULTS

Samples were available for 82 transplant recipients (kidney, n = 41; liver, n = 41). A high correlation was observed between tacrolimus concentrations in capillary and venous blood samples (Pearson correlation coefficient, 0.97; Lin concordance coefficient, 0.87; slope of the fitted line, >1.0). Tacrolimus concentrations in capillary samples were 22.5% higher on average than in the corresponding venous blood samples (95% limits of agreement, 0.5%-44.6%). Similar results were observed in both transplant subgroups.

CONCLUSIONS

MITRA finger prick sampling provides a convenient alternative to venipuncture for therapeutic drug monitoring in transplant recipients maintained on prolonged-release tacrolimus. When using the finger prick MITRA method, the positive bias in tacrolimus concentrations observed with this technique, when compared with venipuncture, needs to be taken into consideration.

A Systematic Review and Meta-Analysis of Pharmacogenetic Studies in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is an important global public health problem due to its high prevalence and morbidity. Although the treatment of nephrology patients has changed considerably, ineffectiveness and side effects of medications represent a major issue. In an effort to elucidate the contribution of genetic variants located in several genes in the response to treatment of patients with CKD, we performed a systematic review and meta-analysis of all available pharmacogenetics studies. The association between genotype distribution and response to medication was examined using the dominant, recessive, and additive inheritance models. Subgroup analysis based on ethnicity was also performed. In total, 29 studies were included in the meta-analysis, which examined the association of 11 genes (16 polymorphisms) with the response to treatment regarding CKD. Among the 29 studies, 18 studies included patients with renal transplantation, 8 involved patients with nephrotic syndrome, and 3 studies included patients with lupus nephritis. The present meta-analysis provides strong evidence for the contribution of variants harbored in the ABCB1, IL-10, ITPA, MIF, and TNF genes that creates some genetic predisposition that reduces effectiveness or is associated with adverse events of medications used in CKD.

The impact of cytochrome P450 3A genetic polymorphisms on tacrolimus pharmacokinetics in ulcerative colitis patients

Tacrolimus (Tac) is an effective remission inducer of refractory ulcerative colitis (UC). Gene polymorphisms result in interindividual variability in Tac pharmacokinetics. In this study, we aimed to examine the relationships between gene polymorphisms and the metabolism, pharmacokinetics, and therapeutic effects of Tac in patients with UC. Forty-five patients with moderate-to-severe refractory UC treated with Tac were retrospectively enrolled. Genotyping for cytochrome P450 (CYP) 3A4*1G, CYP3A5*3, CYP2C19*2, CYP2C19*3, nuclear receptor subfamily 1 group I member 2 (NR1I2)–25385C>T, ATP-binding cassette subfamily C member 2 (ABCC2)–24C>T, ABCC2 1249G>A, and ABCC2 3972C>T was performed. Concentration/dose (C/D) ratio, clinical therapeutic effects, and adverse events were evaluated. The C/D ratio of Tac in UC patients with the CYP3A4*1G allele was statistically lower than in those with the CYP3A4*1/*1 allele (P = 0.005) and significantly lower in patients with CYP3A5*3/*3 than in those with CYP3A5*1 (P < 0.001). Among patients with the CYP3A4*1G allele, the C/D ratio was significantly lower in patients with CYP3A5*1 than in those with CYP3A5*3/*3 (P = 0.001). Patients with the NR1I2–25385C/C genotype presented significantly more overall adverse events than those with the C/T or T/T genotype (P = 0.03). Although CYP3A4*1G and CYP3A5*3 polymorphisms were related to Tac pharmacokinetics, CYP3A5 presented a stronger effect than CYP3A4. The NR1I2–25385C/C genotype was related to the overall adverse events. The evaluation of these polymorphisms could be useful in the treatment of UC with Tac.

Evaluation of Bayesian Forecasting Methods for Prediction of Tacrolimus Exposure Using Samples Taken on Two Occasions in Adult Kidney Transplant Recipients

BACKGROUND

Bayesian forecasting-based limited sampling strategies (LSSs) for tacrolimus have not been evaluated for the prediction of subsequent tacrolimus exposure. This study examined the predictive performance of Bayesian forecasting programs/services for the estimation of future tacrolimus area under the curve (AUC) from 0 to 12 hours (AUC0-12) in kidney transplant recipients.

METHODS

Tacrolimus concentrations were measured in 20 adult kidney transplant recipients, 1 month post-transplant, on 2 occasions one week apart. Twelve samples were taken predose and 13 samples were taken postdose at the specified times on the first and second sampling occasions, respectively. The predicted AUC0-12 (AUCpredicted) was estimated using Bayesian forecasting programs/services and data from both sampling occasions for each patient and compared with the fully measured AUC0-12 (AUCmeasured) calculated using the linear trapezoidal rule on the second sampling occasion. The bias (median percentage prediction error [MPPE]) and imprecision (median absolute prediction error [MAPE]) were determined.

RESULTS

Three programs/services were evaluated using different LSSs (C0; C0, C1, C3; C0, C1, C2, C4; and all available concentrations). MPPE and MAPE for the prediction of fully measured AUC0-12 were <15% for each program/service (with the exclusion of when only C0 was used), when using estimated AUC from data on the same (second) occasion. The MPPE and MAPE for the prediction of a future fully measured AUC0-12 were <15% for 2 programs/services (and for the third when participants who had a tacrolimus dose change between sampling days were excluded), when the occasion 1-AUCpredicted, using C0, C1, and C3, was compared with the occasion 2-AUCmeasured.

CONCLUSIONS

All 3 Bayesian forecasting programs/services evaluated had acceptable bias and imprecision for predicting a future AUC0-12, using tacrolimus concentrations at C0, C1, and C3, and could be used for the accurate prediction of tacrolimus exposure in adult kidney transplant recipients.

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.

Lack of Improvement in Insulin Sensitivity After Pancreas Transplantation in Recipients With a High Level of Calcineurin Inhibitors

OBJECTIVES

This study aimed to assess posttransplant changes in insulin sensitivity and β-cell function of pancreas transplant recipients according to the type of diabetes mellitus (DM) and the pretransplant insulin sensitivity measured by the Matsuda Index (MI).

METHODS

We analyzed 60 patients who underwent pancreas transplantation and oral glucose tolerance test pretransplant and at 1 month posttransplant.

RESULTS

At 1 month posttransplant, insulin sensitivity did not show significant improvement; particularly, the MI was significantly lower after transplant in recipients with type 1 DM (T1DM) and those with pretransplant MI of 5 or greater. β-cell function was significantly improved after transplant in all recipients regardless of the type of DM and pretransplant MI values. Glucose control was significantly improved in recipients with T1DM and in all recipients regardless of the pretransplant MI values. Additional oral glucose tolerance test at 1 year posttransplant revealed that insulin sensitivity remained unimproved and β-cell function was higher compared with pretransplant. Glucose control had partially reverted to pretransplant levels in recipients with T1DM and those with pretransplant MI of 5 or greater.

CONCLUSIONS

Unlike β-cell function and glucose control, insulin sensitivity did not significantly improve until posttransplant 1 year after pancreas transplantation regardless of the type of DM or the degree of pretransplant insulin sensitivity.

Volumetric absorptive microsampling for the quantification of tacrolimus in capillary blood by high performance liquid chromatography-tandem mass spectrometry

Volumetric absorptive microsampling (VAMS) is an innovative alternative strategy to venipuncture for monitoring tacrolimus levels in transplant recipients. In this study, we aimed to validate a new high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for quantifying tacrolimus in blood collected by VAMS. Tacrolimus was extracted from dried blood tips in an original process involving sonication, protein precipitation and salting out. The assay was validated in accordance with EMA and IATDMCT guidelines. For clinical validation, the tacrolimus concentrations measured in liquid venous whole blood (with the reference method) were compared with those measured in capillary whole blood collected simultaneously with VAMS by a nurse. The assay was then used to monitor tacrolimus exposure in transplant recipients. The method was linear, sensitive and fast. Within-day and between-day precisions and overall bias were within ±15%. No significant hematocrit effect was observed. The matrix effect was negligible and recovery exceeded 80% for every concentration and hematocrit levels. Tacrolimus was stable in blood collected by VAMS for 1 week at room temperature, 48 h at 60 °C and 4 °C and 1 month at -80 °C. Clinical validation (n = 42 paired samples) demonstrated a strong correlation between the two methods (r = 0.97 Pearson correlation). Bland-Altman analysis revealed that more than 90% of the differences between VAMS and liquid blood paired concentrations were within the ±20% acceptable range. The method had a satisfactory analytical performance and fulfilled clinical requirements. This minimally invasive VAMS-based assay appears reliable for the determination of tacrolimus levels in blood from transplanted patients.

Comparison of the Impact of Pharmacogenetic Variability on the PK of Slow Release and Immediate Release Tacrolimus Formulations

Tacrolimus-modified release formulations allow for once-daily dosing, and adherence is better compared to the twice-daily immediate release formulation. When patients are switched from one formulation to another, variable changes in drug concentrations are observed. Current data suggest that the changes in drug exposure are larger in patients who express the CYP3A5 enzyme (CYP3A5 *1/*3 or *1/*1) compared to nonexpressers (CYP3A5*3/*3). Possibly, these differences are due to the fact that in the upper region of the small intestine CYP3A activity is higher, and that this expression of CYP3A decreases towards the more distal parts of the gut. Modified release formulations may therefore be subject to a less presystemic metabolism. However, the full implications of pharmacogenetic variants affecting the expression and function of drug transporters in the gut wall and of enzymes involved in phase I and phase II metabolism on the different formulations are incompletely understood, and additional studies are required. Conclusions: In all patients in whom the formulation of tacrolimus is changed, drug levels need to be checked to avoid clinically relevant under- or overexposure. In patients with the CYP3A5 expresser genotype, this recommendation is even more important, as changes in drug exposure can be expected.

Combined Effect of Inter- and Intrapatient Variability in Tacrolimus Exposure on Graft Impairment Within a 3-Year Period Following Kidney Transplantation: A Single-Center Experience

BACKGROUND AND OBJECTIVE

Tacrolimus is a cornerstone of the most immunosuppressive protocols after kidney transplantation, but its use is complicated by notable interpatient and intrapatient variability (IPV). The goal of this study was to evaluate whether or not tacrolimus IPV, or average dose-adjusted trough concentration (C₀/D), during 6-12 months post-transplantation might have contributed to graft function decline in a 3-year period following kidney transplantation. After primary evaluation of individual effects of tacrolimus IPV and C₀/D, the study aimed to estimate the combined effect of tacrolimus IPV and C₀/D on composite endpoint (consisting of graft failure, chronic allograft dysfunction, chronic rejection, and doubling of serum creatinine concentration) in the period between 13 and 36 months after kidney transplantation. In addition, the goal was to analyze the impact of genetics on interpatient variability in tacrolimus exposure in the early and late post-transplantation periods.

METHODS

The study enrolled 104 Caucasian patients and included 2541 patient examinations up to 36 months after kidney transplantation. All patients were genotyped on CYP3A5 6986A>G and ABCB1 3435C>T gene polymorphism. Patients were divided into groups based on the tacrolimus IPV tertiles and the median value of average C₀/D during 6-12 months post-transplantation.

RESULTS

The results showed a more pronounced decline in estimated glomerular filtration rate values within the high IPV tertile group (p = 0.018), as well as within the low C₀/D group (p = 0.013) in a 3-year period after kidney transplantation. The carriers of CYP3A5*1/*3 genotype had lower C₀/D compared to the CYP3A5*3/*3 carriers during the entire study period, while the results for ABCB1 were inconsistent when considering tacrolimus C₀/D. Patients with high IPV/low C₀/D had significantly reduced graft survival compared to the other tacrolimus IPV/C₀/D combination groups (i.e., high IPV/high C₀/D, low IPV/low C₀/D, low IPV/high C₀/D) with the hazard ratio of 3.14 in Cox analysis for reaching the composite endpoint.

CONCLUSION

The findings of this study suggest that combined assessment of tacrolimus IPV and tacrolimus C₀/D may categorize patients towards risk of graft deterioration in the long-term post-transplantation period.

Effects of red blood cell concentrate transfusion on blood tacrolimus concentration

Background Elevated blood concentration of tacrolimus is frequently observed following transfusion of red blood cell concentrate in patients after allogeneic hematopoietic stem cell transplantation. Objective The aim of this retrospective study was to clarify the effects of transfusion of red blood cell concentrate on the blood concentration of tacrolimus. Setting Chiba University Hospital in Japan. Method Fifty-two patients (aged 0-65 years) receiving both tacrolimus and transfusion after allogeneic hematopoietic stem cell transplantation were enrolled. The ratio of measurement after transfusion to measurement before transfusion was calculated for hematocrit and blood concentration/dose ratio of tacrolimus (termed the hematocrit ratio and the tacrolimus ratio, respectively). Main outcome measure Change in blood concentration/dose ratio of tacrolimus and variable factors associated with variation in tacrolimus ratio. Results The blood concentration/dose ratio of tacrolimus was increased after transfusion compared with before transfusion (p < 0.001). A statistically significant correlation was seen between the hematocrit ratio and tacrolimus ratio (r = 0.32, p < 0.001). Hematocrit ratio, age or body surface area, and difference in aspartate aminotransferase level before and after transfusion were associated with the variation in tacrolimus ratio. There was no correlation between tacrolimus ratio and change in serum creatinine or potassium level in the short term. Conclusion Change in the blood concentration/dose ratio of tacrolimus was associated with change in the hematocrit ratio after transfusion, and more attention is required for children or patients with small body surface area. Dose adjustment of tacrolimus is required if the blood concentration of tacrolimus is much higher than the target concentration.

Donor CYP3A5 Gene Polymorphism Alone Cannot Predict Tacrolimus Intrarenal Concentration in Renal Transplant Recipients

CYP3A5 gene polymorphism in recipients plays an important role in tacrolimus blood pharmacokinetics after renal transplantation. Even though CYP3A5 protein is expressed in renal tubular cells, little is known about the influence on the tacrolimus intrarenal exposure and hence graft outcome. The aim of our study was to investigate how the tacrolimus intrarenal concentration (C_tissue) could be predicted based on donor CYP3A5 gene polymorphism in renal transplant recipients. A total of 52 Japanese renal transplant patients receiving tacrolimus were enrolled in this study. Seventy-four renal biopsy specimens were obtained at 3 months and 1 year after transplantation to determine the donor CYP3A5 polymorphism and measure the C_tissue by liquid chromatography-tandem mass spectrometry (LC-MS-MS). The tacrolimus C_tissue ranged from 52 to 399 pg/mg tissue (n = 74) and was weak but significantly correlated with tacrolimus trough concentration (C₀) at 3 months after transplantation (Spearman, r = 0.3560, p = 0.0096). No significant relationship was observed between the donor CYP3A5 gene polymorphism and C_tissue or C_tissue/C₀. These data showed that the tacrolimus systemic level has an impact on tacrolimus renal accumulation after renal transplantation. However, donor CYP3A5 gene polymorphism alone cannot be used to predict tacrolimus intrarenal exposure_. This study may be valuable for exploring tacrolimus renal metabolism and toxicology mechanism in renal transplant recipients.

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform

Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.

Conversion From a Twice-Daily to a Once-Daily Tacrolimus Formulation in Kidney Transplant Recipients

BACKGROUND

The aim of the study was to assess bioavailability aspects of tacrolimus formulations during conversion from twice-daily (TAC BID) to once-daily (TAC OD) formulation in 89 stable kidney transplant recipients.

MATERIALS AND METHODS

The study included 89 stable kidney transplant recipients transplanted between 1998 and 2008 (37 female, 52 male, aged 46.0 ± 12.4 years) and followed for 10 years. For a comprehensive comparison of the different tacrolimus formulations, dose-normalized trough levels (ng/mL/mg total daily dose, C/D ratio) and their variability were studied for 10 consecutive visits before and 6 months after conversion.

RESULTS

The mean trough level decreased significantly 14 days after conversion (16%, 5.77 ± 1.94 [5.6, 4.5-6.5] ng/mL, P < .001). There was no significant difference between the tacrolimus trough levels before and 3 months after conversion (6.92 ± 1.89 [6.8, 5.9-8.0] ng/mL, P = .548). The tacrolimus daily dose 3 months after conversion (4.56 ± 1.81 [4.5, 3.5-5.5] mg/d) was significantly higher than the dose before conversion (4.16 ± 1.80 [4.0, 3.0-5.0] mg/d, P = .006). The post-conversion mean TAC trough level (10 measures) (6.6 [6.2-7.0] ng/mL) was similar to preconversion level (6.8 [5.6-7.9] ng/mL, P = .203). C/D ratio as well as C/D intrapatient variability (CV%) did not change during conversion (C/D 1.68 [1.36-2.53] vs 1.74 [1.41 vs 2.31], P = .075; CV% 19.5 [16.4-26.6] vs 24.4 [17.5-28.3], P = .114).

CONCLUSIONS

Conversion from TAC BID to TAC OD is associated with a significant increase in tacrolimus dose during the first 3 months. In a long-term observation both formulations present similar dose-normalized trough levels and variability.

Pharmacogenetic-Whole blood and intracellular pharmacokinetic-Pharmacodynamic (PG-PK2-PD) relationship of tacrolimus in liver transplant recipients

Tacrolimus (TAC) is the cornerstone of immunosuppressive therapy in liver transplantation. This study aimed at elucidating the interplay between pharmacogenetic determinants of TAC whole blood and intracellular exposures as well as the pharmacokinetic-pharmacodynamic relationship of TAC in both compartments. Complete pharmacokinetic profiles (Predose, and 20 min, 40 min, 1h, 2h, 3h, 4h, 6h, 8h, 12h post drug intake) of twice daily TAC in whole blood and peripheral blood mononuclear cells (PBMC) were collected in 32 liver transplanted patients in the first ten days post transplantation. A non-parametric population pharmacokinetic model was applied to explore TAC pharmacokinetics in blood and PBMC. Concurrently, calcineurin activity was measured in PBMC. Influence of donor and recipient genetic polymorphisms of ABCB1, CYP3A4 and CYP3A5 on TAC exposure was assessed. Recipient ABCB1 polymorphisms 1199G>A could influence TAC whole blood and intracellular exposure (p<0.05). No association was found between CYP3A4 or CYP3A5 genotypes and TAC whole blood or intracellular concentrations. Finally, intra-PBMC calcineurin activity appeared incompletely inhibited by TAC and less than 50% of patients were expected to achieve intracellular IC₅₀ concentration (100 pg/millions of cells) at therapeutic whole blood concentration (i.e.: 4–10 ng/mL). Together, these data suggest that personalized medicine regarding TAC therapy might be optimized by ABCB1 pharmacogenetic biomarkers and by monitoring intracellular concentration whereas the relationship between intracellular TAC exposure and pharmacodynamics biomarkers more specific than calcineurin activity should be further investigated.

The association between trough blood concentration and systemic exposure of tacrolimus: Comparison between once-daily (Advagraf®) and twice-daily (Prograf®) formulation in de novo kidney transplant recipients

Available data of early conversion from twice-daily tacrolimus (TAC-BID) to once-daily tacrolimus (TAC-OD) in de novo kidney transplant (KT) recipients are limited. We conducted a prospective study of early conversion to TAC-OD in de novo KT recipients. Eligible patients were enrolled to receive TAC-BID (Prograf®) and then converted to TAC-OD (Advagraf®) by 1:1 ratio, approximately 14 days after KT (range 9-22). Blood samples were investigated for pharmacokinetic parameters before and 7-14 days after the conversion. Fifteen patients were included and provided AUC_0-24 of 202.9 ± 44.4 ng h/mL for TAC-BID (pre-conversion) and 193.0 ± 63.4 ng h/mL for TAC-OD (post-conversion) (p = 0.41). Mean trough blood concentration (C_min) of TAC-BID and TAC-OD was 6.4 ± 1.4 ng/mL and 4.9 ± 1.6 ng/mL (p = 0.01). Correlation coefficient (r) between C_min and AUC_0-24 of TAC-BID and TAC-OD were 0.620 and 0.875. Additional analysis found that patients with a drop of C_min > 30% had a significant lower AUC_0-24 after conversion. Renal function remains stable. We conclude that early conversion to TAC-OD is safe and well tolerated with an indifferent systemic exposure. However, patients with a drop of C_min > 30% after conversion to TAC-OD will require additional dose adjustment.

Early Tacrolimus Concentrations After Lung Transplant Are Predicted by Combined Clinical and Genetic Factors and Associated With Acute Kidney Injury

Tacrolimus exhibits unpredictable pharmacokinetics (PKs) after lung transplant, partly explained by cytochrome P450 (CYP)-enzyme polymorphisms. However, whether exposure variability during the immediate postoperative period affects outcomes is unknown, and pharmacogenetic dosing may be limited by residual PK variability. We estimated adjusted associations between early postoperative tacrolimus concentrations and acute kidney injury (AKI) and acute cellular rejection (ACR), and identified clinical and pharmacogenetic factors that explain postoperative tacrolimus concentration variability in 484 lung transplant patients. Increasing tacrolimus concentration was associated with higher AKI risk (hazard ratio (HR) 1.54; 95% confidence interval (CI) 1.20-1.96 per 5-mg/dL); and increasing AKI severity (odds ratio 1.29; 95% CI 1.04-1.60 per 5-mg/dL), but not ACR (HR 1.02; 95% CI 0.73-1.42). A model with clinical and pharmacogenetic factors explained 42% of concentration variance compared with 19% for pharmacogenetic factors only. Early tacrolimus exposure was independently associated with AKI after lung transplantation, but not ACR. Clinical factors accounted for substantial residual tacrolimus concentration variability not explained by CYP-enzyme polymorphisms.

Delivery of FK506-loaded PLGA nanoparticles prolongs cardiac allograft survival

In this study, FK506-loaded poly(lactide-co-glycolide) nanoparticles (PLGA-FK506-NPs) were developed using an O/W emulsion solvent evaporation method. The PLGA-FK506-NPs were observed to be monodispersed and spherical by transmission and scanning electron microscopy. The mean size and zeta potential measured by dynamic light scattering were 110 ± 1.3 nm and -20.56 ± 3.65 mV, respectively. The FK506 entrapment and loading efficiency were 94.46 ± 1.88% and 5.38 ± 0.24%, respectively. Moreover, a pharmacokinetics study revealed that the PLGA-FK506-NPs behaved significantly different than free FK506 by exhibiting a higher area under curve (1.69-fold), higher mean residence time (1.29-fold), slower clearance and longer elimination half-life. Notably, the concentrations of FK506 in the spleen and mesenteric lymph nodes of the PLGA-FK506-NP group were 3.1-fold and 2.9-fold higher than those of the free FK506 group. Furthermore, the immunosuppressive efficacy was evaluated in a rat heterotopic heart transplantation model, and the results showed that PLGA-FK506-NP treatment could successfully alleviate acute rejection and prolong allograft survival compared with the free FK506 treatment (mean survival time, 17.1 ± 2.0 versus 13.3 ± 1.7 days). In conclusion, PLGA-FK506-NPs are a promising formulation for spleen and lymph node delivery and have potential use in the treatment of cardiac allograft acute rejection.

Clinically useful limited sampling strategy to estimate area under the concentration-time curve of once-daily tacrolimus in adult Japanese kidney transplant recipients

BACKGROUND

An extended-release, once-daily, oral formulation of tacrolimus is currently used after kidney transplantation as a substitute for the conventional twice-daily formulation. The purpose of this study was to provide a limited sampling strategy with minimum and optimum sampling points to predict the tacrolimus area under the concentration-time curve (AUC) after administration of once-daily tacrolimus in de novo adult kidney transplant patients.

METHODS

A total of 36 adult Japanese kidney transplant patients receiving once-daily tacrolimus were included: 31 were allocated to a study group to develop limited sampling strategy (LSS) model equations based on multiple stepwise linear regression analysis, and 5 were allocated to a validation group to estimate the precision of the LSS equations developed by the study group. Twelve-hour AUC (AUC0-12) was calculated by the trapezoidal rule, and the relationship between individual concentration points and AUC0-12 were determined by multiple linear regression analysis. The coefficient of determination (R2) was used to assess the goodness-of-fit of the regression models. Three error indices (mean error, mean absolute error, and root mean squared prediction error) were calculated to evaluate predictive bias, accuracy, and precision, respectively. Quality of the statistical models was compared with Akaike's information criterion (AIC).

RESULTS

A four-point model using C0, C2, C4 and C6 gave the best fit to predict AUC0-12 (R2 = 0.978). In the three- and two-point models, the best fits were at time points C2, C4, and C6 (R2 = 0.973), and C2 and C6 (R2 = 0.962), respectively. All three models reliably estimated tacrolimus AUC0-12, consistent with evaluations by the three error indices and Akaike's information criterion. Practically, the two-point model with C2 and C6 was considered to be the best combination, providing a highly accurate prediction and the lowest blood sampling frequency.

CONCLUSIONS

The two-point model with C2 and C6 may be valuable in reducing the burden on patients, as well as medical costs, for once-daily tacrolimus monitoring.