Progressive decline in tacrolimus clearance after renal transplantation is partially explained by decreasing CYP3A4 activity and increasing haematocrit

Tacrolimus Dose Requirement in De Novo Adult Kidney Transplant Patients Treated With Adoport® Can Be Anticipated

All the factors potentially influencing tacrolimus dose requirement and combinations thereof have never been thoroughly investigated, precluding accurate prediction of tacrolimus starting dose. This prospective, non-interventional, multicenter study in de novo adult kidney transplant recipients over the first year after transplantation aimed to investigate the factors influencing tacrolimus dose-standardized trough blood concentration (C0/D) over the first week post-transplant (D4-D7, primary objective), D8-M3 and M3-M12 (secondary objectives). Statistical analysis employed mixed linear models with repeated measures. Eighteen sites enrolled 440 patients and followed them up for 9.5 ± 4.1 months. Age at baseline (p = 0.0144), end-stage renal disease (p = 0.0092), CYP3A phenotype (p < 0.0001), dyslipidemia at baseline (p = 0.0031), hematocrit (p = 0.0026), total bilirubin (p = 0.0261) and plasma creatinine (p = 0.0484) independently increased with log(C0/D) over D4-D7, explaining together 72.3% of the interindividual variability, and representing a robust model to estimate tacrolimus initial dose. Donor age and CYP3A phenotype were also influential over D8-M3 and M3-12, in addition to recipient age. Corticosteroids, diabetes at baseline, and ASAT yielded inconstant results between D8-M3 and M3-M12. We found no ethnicity effect when CYP3A phenotype was accounted for, and no food effect. Intra-individual variability over M3-M12 was moderate, and significantly lower in patients with chronic hepatic disorder (p = 0.0196) or cancer (p = 0.0132).

Physiologically-based pharmacokinetic modelling to investigate the effect of CYP3A4/3A5 maturation on tacrolimus pharmacokinetics in paediatric HSCT patients

Tacrolimus (FK506) is a cornerstone of GVHD-prophylaxis treatment in paediatrics undergoing haematopoietic stem cell transplantation (HSCT). However, due to concerns about highly inter/intra-individual variability, precision dosing of FK506 is crucial. Cytochrome P450 (CYP) 3A4 and 3A5 are considered important sources of FK506 pharmacokinetic variability. Nevertheless, the impact of age-related maturation in hepatic and intestinal CYP3A4/3A5 enzymes remains unknown in paediatric HSCT patients. Physiologically-based pharmacokinetic (PBPK) models were developed and verified in adult volunteers and adult HSCT patients using GastroPlus™ (version 9.0), and then extrapolated to paediatric HSCT patients, taking into account the maturation of CYP3A4 and CYP3A5. Default CYP3A4 and CYP3A5 ontogeny profiles were updated based on the latest reports. The paediatric PBPK model was evaluated with independent data collected from Sun Yat-sen Memorial Hospital (86 paediatric HSCT patients, 1 to 16 -year-old). Simulations were performed to evaluate a reported FK506 dosing regimen in infants and children with different CYP3A5 genotypes. Extensive PBPK model validation indicated good predictability, with the predicted/observed (P/O) ratios within the range of 0.80-fold to 1.25-fold. Blood tacrolimus concentration-time curves were comparable between the real and virtual patients. Simulations showed that the higher levels of tacrolimus in 9-month-old to 3-year-old infants were mainly attributed to the CYP3A4/3A5 ontogeny profiles, which resulted in lower clearance and higher exposure relative to dose. The oral dosage of 0.1 mg/kg/day (q12 h) is considered appropriate for paediatric HSCT patients 9 months to 15 years of age with CYP3A5 *1/*1 genotypes. Lower doses were required for paediatric HSCT patients with CYP3A5 *1/*3 (0.08 mg/kg/day, q12h) or CYP3A5 *3/*3 genotypes (0.07 mg/kg/day, q12h), and analyses demonstrated 12.5-20 % decreases in ≤3-year-old patients. The study highlights the feasibility of PBPK modelling to explore age-related enzyme maturation in infants and children (≤3-year-old) undergoing HSCT and emphasizes the need to include hepatic and gut CYP3A4/3A5 maturation parameters.

Tacrolimus-why pharmacokinetics matter in the clinic

The calcineurin inhibitor (CNI) Tacrolimus (Tac) is the most prescribed immunosuppressant drug after solid organ transplantation. After renal transplantation (RTx) approximately 95% of recipients are discharged with a Tac-based immunosuppressive regime. Despite the high immunosuppressive efficacy, its adverse effects, narrow therapeutic window and high intra- and interpatient variability (IPV) in pharmacokinetics require therapeutic drug monitoring (TDM), which makes treatment with Tac a major challenge for physicians. The C/D ratio (full blood trough level normalized by daily dose) is able to classify patients receiving Tac into two major metabolism groups, which were significantly associated with the clinical outcomes of patients after renal or liver transplantation. Therefore, the C/D ratio is a simple but effective tool to identify patients at risk of an unfavorable outcome. This review highlights the challenges of Tac-based immunosuppressive therapy faced by transplant physicians in their daily routine, the underlying causes and pharmacokinetics (including genetics, interactions, and differences between available Tac formulations), and the latest data on potential solutions to optimize treatment of high-risk patients.

Pharmacokinetic Evaluation of Tacrolimus in Chinese Adult Patients during the Early Stages Post-Lung Transplantation

BACKGROUND

Although tacrolimus has been widely used in patients undergoing lung transplantation, few studies have reported the pharmacokinetics of tacrolimus in Chinese patients after lung transplantation. Thus, we aimed to investigate the pharmacokinetics and influential factors in this patient cohort in the early stage after lung transplantation.

METHODS

We enrolled 14 adult lung transplant recipients who were treated with tacrolimus and then intensively collected blood samples within a 12-h dosing interval. The pharmacokinetic parameters of tacrolimus were calculated using non-compartmental analysis, and the influence of pathophysiological characteristics and CYP3A5*3 and CYP3A4*1G genotypes on the pharmacokinetics of tacrolimus was assessed. Using linear regression analysis, we investigated the correlation between tacrolimus concentration at different sampling points and measured the area under the time-concentration curve (AUC_0-12h).

RESULTS

Geometric mean of apparent clearance (CL/F) was 18.13 ± 1.65 L/h in non-CYP3A5*3/*3 carriers, five times higher than that in CYP3A5*3/*3 carriers (p < 0.001). Furthermore, the tacrolimus concentration 4 h after administration had the strongest correlation with AUC_0-12h (R² = 0.979).

CONCLUSION

The pharmacokinetics of tacrolimus varied largely between patients during the early stage post-transplantation, which could be partially explained by CYP3A5*3 genetic polymorphisms.

Comparison of Tacrolimus Intra-Patient Variability during 6-12 Months after Kidney Transplantation between CYP3A5 Expressers and Nonexpressers

A high intra-patient variability (IPV) of tacrolimus exposure is associated with poor long-term kidney transplantation outcomes. To assess the influence of cytochrome P450 (CYP) 3A5 genetic polymorphisms on tacrolimus IPV, 188 clinically stable kidney transplant recipients, who had received an immediate-release tacrolimus-based immunosuppressive regimen, were enrolled in this retrospective cohort study. Genotyping of CYP3A5*3 (rs776746) was performed and 110 (58.5%) were identified as CYP3A5 expressers and 78 (41.5%) as nonexpressers. Whole blood tacrolimus concentrations were analyzed by chemiluminescent microparticle immunoassay. Dose-adjusted trough tacrolimus concentrations (C0/D) measured at months 6, 9, and 12 were used to determine IPV. There were no significant differences in the IPV estimated by the coefficient of variation, the IPV calculated by mean absolute deviation method, and the proportions of recipients with the IPV estimated by the coefficient of variation of 30% or more between CYP3A5 expressers and nonexpressers (p = 0.613, 0.686, and 0.954, respectively). Tacrolimus C0/D in CYP3A5 expressers was approximately half of those in nonexpressers, overall (p < 0.001). In both CYP3A5 expressers and nonexpressers, tacrolimus C0/D increased gradually from month 6 to month 12 (p = 0.021). There was no evidence that the CYP3A5 polymorphisms significantly influence tacrolimus IPV during the 6 to 12 months after kidney transplantation.

Impact of POR*28 Variant on Tacrolimus Pharmacokinetics in Kidney Transplant Patients with Different CYP3A5 Genotypes

BACKGROUND

The introduction of tacrolimus (TAC) in clinical practice was essential to the establishment of transplantation as therapy for patients with chronic renal disease. However, the higher interindividual variation of TAC metabolism has been an important limiting factor for its clinical use. Although the relationship between CYP3A5 polymorphisms and TAC pharmacokinetics (PK) is well established, the effects of other genetic variants on TAC metabolism, such as POR*28, still remain uncertain.

OBJECTIVE

To evaluate the impact of POR variants on TAC PK in renal transplant patients with different CYP3A5 genotypes (expressers and non-expressers).

METHODS

A total of 115 patients were included in this study. Genomic DNA was isolated from peripheral blood, and the real-time PCR technique was used to analyze the polymorphism POR rs1057868; C>T.

RESULTS

During the initial post-transplant period, variant allele carriers (*1/*28 and *28/*28) showed a lower TAC dose requirement than POR wild homozygotes (*1/*1). Regarding the influence of the different polymorphisms of POR within the CYP3A5 expresser and non-expresser groups, no differences were observed in any of the PK parameters analyzed during 12 months after transplantation.

CONCLUSION

In the studied population, the variant allelic POR*28 was significantly associated with lower TAC dose requirements and higher Co/D ratio in the first-month post-transplant. However, the effects of this polymorphism on the CYP3A5 enzyme activity were not observed.

CYP3A-status is associated with blood concentration and dose-requirement of tacrolimus in heart transplant recipients

High inter-individual variability in tacrolimus clearance is attributed to genetic polymorphisms of CYP3A enzymes. However, due to CYP3A phenoconversion induced by non-genetic factors, continuous changes in tacrolimus-metabolizing capacity entail frequent dose-refinement for optimal immunosuppression. In heart transplant recipients, the contribution of patients' CYP3A-status (CYP3A5 genotype and CYP3A4 expression) to tacrolimus blood concentration and dose-requirement was evaluated in the early and late post-operative period. In low CYP3A4 expressers carrying CYP3A5*3/*3, the dose-corrected tacrolimus level was significantly higher than in normal CYP3A4 expressers or in those with CYP3A5*1. Modification of the initial tacrolimus dose was required for all patients: dose reduction by 20% for low CYP3A4 expressers, a 40% increase for normal expressers and a 2.4-fold increase for CYP3A5*1 carriers. The perioperative high-dose corticosteroid therapy was assumed to ameliorate the low initial tacrolimus-metabolizing capacity during the first month. The fluctuation of CYP3A4 expression and tacrolimus blood concentration (C₀/D) was found to be associated with tapering and cessation of corticosteroid in CYP3A5 non-expressers, but not in those carrying CYP3A5*1. Although monitoring of tacrolimus blood concentration cannot be omitted, assaying recipients' CYP3A-status can guide optimization of the initial tacrolimus dose, and can facilitate personalized tacrolimus therapy during steroid withdrawal in the late post-operative period.

Tacrolimus Dose-Conversion Ratios Based on Switching of Formulations for Patients with Solid Organ Transplants

Background

Tacrolimus may be administered during hospitalization as an IV formulation or oral suspension. However, literature suggesting appropriate ratios for conversion from these formulations to capsules is limited.

Objective

To evaluate conversion ratios after a switch in formulation of tacrolimus for solid-organ transplant recipients.

Methods

This single-centre observational longitudinal study involved hospitalized patients who underwent a switch in formulation of tacrolimus according to 1 of 3 possible scenarios: IV to oral suspension, IV to capsule, or oral suspension to capsule. Data were collected from the earliest accessible electronic file (January 2009) to January 1, 2019. Conversion ratios were calculated for each of the 3 groups using data for blood concentrations and doses before and after the switch. The calculated ratios were then compared with recommended conversion ratios: 1:5 (i.e., 1 mg of IV tacrolimus is converted to 5 mg of oral tacrolimus, expressed as "5") for either of the switches involving an IV formulation and 1:1 (i.e., same amount, expressed as "1") for the switch from oral formulation to capsules.

Results

For the group who underwent switching from the IV formulation to oral suspension, the mean calculated conversion ratio was 3.04, which was significantly different from the recommended ratio of 5. For the group who underwent switching from the IV formulation to capsules, the calculated conversion ratio was 5.18, which was not significantly different from the recommended ratio of 5. For the group who underwent switching from oral suspension to capsules, the calculated conversion ratio was 1.17, which was not significantly different from the recommended ratio of 1.

Conclusion

In this small retrospective study of tacrolimus therapy, the calculated conversion ratio was significantly different from the recommended ratio for patients who were switched from IV administration to oral suspension, but not for those switched from IV administration or oral suspension to capsules. Therapeutic drug monitoring therefore appears indispensable, regardless of conversion ratios.

Influential Factors and Efficacy Analysis of Tacrolimus Concentration After Allogeneic Hematopoietic Stem Cell Transplantation in Children with β-Thalassemia Major

Purpose

To analyze factors influencing tacrolimus (TAC) trough concentration (C₀) in β-thalassemia major (β-TM) pediatric patients after allogeneic hematopoietic stem cell transplantation (Allo-HSCT) and to investigate the effects of genotype polymorphism and drug-drug interactions on TAC trough concentration in children with β-TM. Furthermore, to analyze the correlation between TAC C₀ and efficacy and adverse reactions.

Patients and Methods

Prospectively collection of demographic information and details of combined treatment of patients with β-TM receiving HSCT, and genotypes of CYP3A4, CYP3A5, and ABCB1 (rs1045642, rs1128503, rs2032582) were obtained for each patient. Univariate analysis and multiple linear regression analysis were used to investigate influencing factors on TAC C₀. The impact of different genotypes and the co-administration of azole antifungal drugs on β-TM patients receiving TAC were evaluated, together with the correlation between acute graft-versus-host disease (aGVHD), infection, and liver injury of TAC C₀.

Results

A total of 46 patients with 587 concentration data were included. The multiple linear regression results showed that the patient's sex, weight, postoperative time, hemoglobin, platelet count, serum cystatin C, and combined voriconazole were independent influencing factors of the infusion trough concentration/daily dose, C₀/D_iv. Age, body surface area, postoperative time, co-administration of voriconazole, and CYP3A4*18B are independent influencing factors of C₀/D_po. Group comparisons showed that voriconazole can affect TAC C₀ administered intravenously (IV) and orally in β-TM pediatric patients, while patient genotype can affect TAC C₀ during oral administration. TAC C₀ does not correlate with aGVHD or liver injury, but infection may be associated with TAC C₀.

Conclusion

The concentration of TAC should be closely monitored when co-administered with voriconazole. It is worth considering that the influence of genotype on the trough concentration of oral TAC and individualized drug administration warrant investigation. Finally, this study indicated that C₀ is not suitable as an indicator of the efficacy of TAC.

Unbound Plasma, Total Plasma, and Whole-Blood Tacrolimus Pharmacokinetics Early After Thoracic Organ Transplantation

BACKGROUND AND OBJECTIVE

Therapeutic drug monitoring of tacrolimus whole-blood concentrations is standard care in thoracic organ transplantation. Nevertheless, toxicity may appear with alleged therapeutic concentrations possibly related to variability in unbound concentrations. However, pharmacokinetic data on unbound concentrations are not available. The objective of this study was to quantify the pharmacokinetics of whole-blood, total, and unbound plasma tacrolimus in patients early after heart and lung transplantation.

METHODS

Twelve-hour tacrolimus whole-blood, total, and unbound plasma concentrations of 30 thoracic organ recipients were analyzed with high-performance liquid chromatography-tandem mass spectrometry directly after transplantation. Pharmacokinetic modeling was performed using non-linear mixed-effects modeling.

RESULTS

Plasma concentration was < 1% of the whole-blood concentration. Maximum binding capacity of erythrocytes was directly proportional to hematocrit and estimated at 2700 pg/mL (95% confidence interval 1750-3835) with a dissociation constant of 0.142 pg/mL (95% confidence interval 0.087-0.195). The inter-individual variability in the binding constants was considerable (27% maximum binding capacity, and 29% for the linear binding constant of plasma).

CONCLUSIONS

Tacrolimus association with erythrocytes was high and suggested a non-linear distribution at high concentrations. Monitoring hematocrit-corrected whole-blood tacrolimus concentrations might improve clinical outcomes in clinically unstable thoracic organ transplants.

CLINICAL TRIAL REGISTRATION

NTR 3912/EudraCT 2012-001909-24.

Determination of tacrolimus, three mono-demethylated metabolites and a M1 tautomer in human whole blood by liquid chromatography - tandem mass spectrometry

The immunosuppressant tacrolimus is the primary drug used in kidney transplantation to prevent organ rejection. A sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed to measure tacrolimus and its three known mono-demethylated metabolites 13-O-desmethyl tacrolimus (M1), 31-O-desmethyl tacrolimus (M2), 15-O-desmethyl tacrolimus (M3). By generating the metabolites to use as standards after incubation of tacrolimus with rat liver microsomes, we discovered multiple M1 peaks which we identified as two tautomers of M1. The M1 tautomer II was also successfully validated in this method. The separation and purification of the metabolites and tautomers were performed by semi-preparative liquid chromatography with UV-detection, while confirmation was done by UPLC-MS/MS and Nuclear Magnetic Resonance. For quantification an easy sample preparation was performed with zinc sulfate and acetonitrile as cell lyses and precipitation. Detection was performed in positive electrospray ionization. By better characterization of the metabolites and the tautomers, we could possibly explain insight into the clinical condition and thus adjust the immunosuppressant therapy individually per patient. Calibration curves were linear for all compounds. Precision was assessed according to the NCCLS EP5-T guideline, being below 15 % and mean recoveries were between 93 and 110 % for tacrolimus, its three metabolites and the M1 tautomer II. The validated method was successfully applied in a cohort of 20 patients after kidney transplantation.

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.

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.

Model-Informed Precision Dosing of Everolimus: External Validation in Adult Renal Transplant Recipients

BACKGROUND AND OBJECTIVE

The immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring, typically based on whole-blood trough concentrations (C₀). Unfortunately, therapeutic drug monitoring target attainment rates are often unsatisfactory and patients with on-target exposure may still develop organ rejection. As everolimus displays erythrocyte partitioning, haematocrit-normalised whole-blood exposure has been suggested as a more informative therapeutic drug monitoring marker. Furthermore, model-informed precision dosing has introduced options for more sophisticated dose adaptation. We have previously developed a mechanistic population pharmacokinetic model, which described everolimus plasma pharmacokinetics and enabled estimation of haematocrit-normalised whole-blood exposure. Here, we externally evaluated this model for its utility for model-informed precision dosing.

METHODS

The retrospective dataset included 4123 pharmacokinetic observations from routine clinical therapeutic drug monitoring in 173 renal transplant recipients. Model appropriateness was confirmed with a visual predictive check. A fit-for-purpose analysis was conducted to evaluate whether the model accurately and precisely predicted a future C₀ or area under the concentration-time curve (AUC) from prior pharmacokinetic observations. Bias and imprecision were expressed as the mean percentage prediction error (MPPE) and mean absolute percentage prediction error (MAPE), stratified on 6 months post-transplant. Additionally, we compared dose adaptation recommendations of conventional C₀-based therapeutic drug monitoring and C₀- or AUC-based model-informed precision dosing, and assessed the percentage of differences between observed and haematocrit-normalised C₀ (∆C₀) and AUC (∆AUC) exceeding ± 20%.

RESULTS

The model showed adequate accuracy and precision for C₀ and AUC prediction at ≤ 6 months (MPPE_C0: 8.1 ± 2.5%, MAPE_C0: 26.8 ± 2.1%; MPPE_AUC: - 9.7 ± 5.1%, MAPE_AUC: 13.3 ± 3.9%) and > 6 months post-transplant (MPPE_C0: 4.7 ± 2.0%, MAPE_C0: 25.4 ± 1.4%; MPPE_AUC: - 0.13 ± 4.8%, MAPE_AUC: 13.3 ± 2.8%). On average, dose adaptation recommendations derived from C₀-based and AUC-based model-informed precision dosing were 2.91 ± 0.01% and 13.7 ± 0.18% lower than for conventional C₀-based therapeutic drug monitoring at ≤ 6 months, and 0.93 ± 0.01% and 3.14 ± 0.04% lower at > 6 months post-transplant. The ∆C₀ and ∆AUC exceeded ± 20% on 13.6% and 14.3% of occasions, respectively.

CONCLUSIONS

We demonstrated that our population pharmacokinetic model was able to accurately and precisely predict future everolimus exposure from prior pharmacokinetic measurements. In addition, we illustrated the potential added value of performing everolimus therapeutic drug monitoring with haematocrit-normalised whole-blood concentrations. Our results provide reassurance to implement this methodology in clinical practice for further evaluation.

Population Pharmacokinetic Modeling of Cyclosporine Among Malaysian Renal Transplant Patients: An Evaluation of Methods to Handle Missing Doses in Conventional Drug-Monitoring Data

Cyclosporine is a primary drug in transplant immunosuppression regimens. It has a narrow therapeutic index and variable pharmacokinetic behavior. This study aimed to develop a population pharmacokinetic model of cyclosporine in Malaysian renal transplant recipients as well as to evaluate the performances of different methodsfor handling missing doses. A total of 2804 concentrationts predose and 2 hours after doses were collected retrospectively from 113 renal transplant patients on cyclosporine in Penang General Hospital. Model structure and pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling software. Missing doses were handled using different methods to evaluate their performance. Covariate analysis was performed using stepwise forward addition (P < .05) followed by backward elimination (P < .001). Prediction-corrected visual predictive check and sampling-importance resampling methods were used to validate the final model. A 1-compartment model with first-order absorption and elimination best fitted the data. All methods to handle missing doses performed well with the missing dose method being superior to other methods and thus was applied in the final model. Cyclosporine clearance (CL/F) was estimated as 15.1 L/h, and volume of distribution (V/F) was 108 L. Postoperative time, sex, and calcium channel blockers were identified as significant covariates on CL/F, whereas sex and cholesterol level were identified as significant covariates on V/F. This is the first population pharmacokinetic model developed in Malaysian renal transplant patients using a large sample with an evaluation of different methods to handle missing doses in less informative conventional therapeutic drug-monitoring data.

A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation

Background and Objective

Given that a high intrapatient variability (IPV) of tacrolimus whole blood concentration increases the risk for a poor kidney transplant outcome, some experts advocate routine IPV monitoring for detection of high-risk patients. However, attempts to estimate the variance of tacrolimus trough concentrations (TTC) are limited by the need for patients to receive a fixed dose over time and/or the use of linear statistical models. A goal of this study is to overcome the current limitations through the novel application of statistical methodology generalizing the relationship between TTC and dose through the use of nonparametric functional regression modeling.

Methods

With TTC as a response and dose as a covariate, the model employs an unknown bivariate function, allowing for the potentially complex, nonlinear relationship between the two parameters. A dose-adjusted variance of TTC is then derived based on standard functional principal component analysis (FPCA). To assess the model, it was compared against an FPCA-based model and linear mixed-effects models using prediction error, bias, and coverage probabilities for simulated data as well as phase III data from the Astellas new drug application studies for extended-release tacrolimus.

Results

Our numerical investigation indicates that the new model better predicts dose-adjusted TTCs compared with the prediction of linear mixed effects models. Estimated coverage probabilities also indicate that the new model accurately accounts for the variance of TTC during the periods of large fluctuation in dose, whereas the linear mixed effects model consistently underestimates the coverage probabilities because of the inaccurate characterization of TTC fluctuation.

Conclusion

This is the first known application of a functional regression model to assess complex relationships between TTC and dose in a real clinical setting. This new method has applicability in future clinical trials including real-world data sets due to flexibility of the nonparametric modeling approach.

Electronic supplementary material

The online version of this article (10.1007/s40268-019-0271-2) contains supplementary material, which is available to authorized users.

Comparison of the effect of direct-acting antiviral with and without ribavirin on cyclosporine and tacrolimus clearance values: results from the ANRS CO23 CUPILT cohort

PURPOSE

Direct-acting antiviral agents have demonstrated their efficacy in treating HCV recurrence after liver transplantation and particularly the sofosbuvir/daclatasvir combination. Pharmacokinetic data on both calcineurin inhibitors and direct-acting antiviral exposure in liver transplant recipients remain sparse.

METHODS

Patients were enrolled from the ANRS CO23 CUPILT cohort. All patients treated with sofosbuvir/daclatasvir with or without ribavirin were included in this study when blood samples were available to estimate the clearance of immunosuppressive therapy before direct-acting antiviral initiation and during follow-up. Apparent tacrolimus and cyclosporine clearances were estimated from trough concentrations measured using validated quality control assays.

RESULTS

Sixty-seven mainly male patients (79%) were included, with a mean age of 57 years and mean MELD score of 8.2; 50 were on tacrolimus, 17 on cyclosporine. Ribavirin was combined with sofosbuvir/daclatasvir in 52% of patients. Cyclosporine clearance remained unchanged as well as tacrolimus clearance under the ribavirin-free regimen. Tacrolimus clearance increased 4 weeks after direct-acting antivirals and ribavirin initiation versus baseline (geometric mean ratio 1.81; 90% CI 1.30-2.52). Patients under ribavirin had a significantly higher fibrosis stage (> 2) (p = 0.02) and lower haemoglobin during direct-acting antiviral treatment (p = 0.02) which impacted tacrolimus measurements. Direct-acting antiviral exposure was within the expected range.

CONCLUSION

Our study demonstrated that liver transplant patients with a recurrence of hepatitis C who are initiating ribavirin combined with a sofosbuvir-daclatasvir direct-acting antiviral regimen may be at risk of lower tacrolimus concentrations because of probable ribavirin-induced anaemia and higher fibrosis score, although there are no effects on cyclosporine levels.

TRIAL REGISTRATION

NCT01944527.

Assessment of tacrolimus intrapatient variability in stable adherent transplant recipients: Establishing baseline values

The purpose of this study was to determine the intrapatient (within the same patient) variability of tacrolimus in adherent patients. Daily tacrolimus trough levels were obtained at home using dried blood spot technology in kidney and liver transplant recipients. Patients were randomized to receive 3 formulations of tacrolimus, each for two 1-week periods. Adherence was monitored by patient diary, pill counts, and use of the Medication Event Monitoring System (MEMS). Variability was quantified as the coefficient of variation (CV). Comparison of CV between groups was by independent t test or one-way ANOVA as appropriate. The population was found to be adherent with a rate of 99.9% with a mean interval between the evening and morning dose of tacrolimus of 11.86 hours. The median CV for the entire population was 15.2% (range 4.8%-110%). There were no differences in CV by allograft type or tacrolimus formulation. The multivariate analysis did not identify any demographic characteristics associated with a CV > 30%. In a highly adherent population, tacrolimus did not display high intrapatient variability. Given the association between IPV and poor allograft outcomes, future studies are needed to quantitate the influence of adherence and establish target IPV goals.

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 Theoretical Physiologically-Based Pharmacokinetic Approach to Ascertain Covariates Explaining the Large Interpatient Variability in Tacrolimus Disposition

Physiologically‐based pharmacokinetic (PBPK) modeling allows assessment of the covariates contributing to the large pharmacokinetic (PK) variability of tacrolimus; these include multiple physiological and biochemical differences among patients. A PBPK model of tacrolimus was developed, including a virtual population with physiological parameter distributions reflecting renal transplant patients. The ratios of predicted to observed dose‐normalized maximum plasma concentration (C_max), 0–12‐hour area under the concentration–time curve (AUC_0–12 hour), and trough plasma concentration (C_trough) ranged from 0.92‐fold to 1.15‐fold, indicating good predictive performance. The model quantitatively indicated the impact of cytochrome P450 (CYP)3A4 abundance, hematocrit, and serum albumin levels, in addition to CYP3A5 genotype status, on tacrolimus PK and associated variability. Age‐dependent change in tacrolimus trough concentration in pediatric patients was mainly attributed to the CYP3A ontogeny profile. This study demonstrates the utility of PBPK modeling as a tool for mechanistic and quantitative assessment of the impact of patient physiological differences on observed large PK variability.

4β-Hydroxycholesterol as an Endogenous Biomarker for CYP3A Activity: Literature Review and Critical Evaluation

A number of cytochrome P450 (CYP)3A phenotyping probes have been used to characterize the drug interaction potential of new molecular entities; of these, midazolam has emerged as the gold standard. Recently, plasma 4β-hydroxycholesterol (4β-OHC), the metabolite of CYP3A-mediated cholesterol metabolism, has been championed as an endogenous biomarker for CYP3A, particularly during chronic conditions where CYP3A activity is altered by disease and in long-term treatment studies where midazolam administration is not optimal. Multiple studies in humans have shown that 4β-OHC can qualitatively differentiate among weak, moderate, and potent CYP3A induction when an inducer, typically rifampin, is administered for up to 2 weeks. Conversely, longer durations of CYP3A inhibitor administration (≥1 month) appear to be necessary to differentiate among weak, moderate, and potent CYP3A inhibitors. A number of studies have reported statistically significant linear relationships between 4β-OHC plasma concentrations (and 4β-OHC:cholesterol ratios) and midazolam clearance. However, sufficiently powered studies assessing the ability of 4β-OHC or 4β-OHC:cholesterol ratios to measure CYP3A activity (ie, predictive performance) have not been conducted to date. Additional limitations associated with 4β-OHC phenotyping include inability to detect acute changes in CYP3A activity, uncertainty with regard to its intestinal formation, ambiguity surrounding the role of CYP3A5 in its metabolism, and lack of clarity regarding the role of transporters in its disposition. As such, the data do not support the use of 4β-OHC or 4β-OHC:cholesterol ratios as an endogenous biomarker for CYP3A activity.

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.

Impact of POR and CYP3A5 Polymorphisms on Trough Concentration to Dose Ratio of Tacrolimus in the Early Post-operative Period Following Kidney Transplantation

BACKGROUND

Tacrolimus, a critical dose drug, is widely used in transplantation. Knowing the contribution of genetic factors, which significantly influence tacrolimus variability, is beneficial in the personalization of its starting dose. The significant impact of CYP3A5*3 polymorphisms on tacrolimus exposure has been reported. Conflicting results of the additional influence of POR*28 polymorphisms on tacrolimus pharmacokinetic interindividual variability have been observed among different populations. The objective of this study was to explore the interaction between POR*28 and CYP3A5*3 polymorphisms and their main effects on tacrolimus trough concentration to dose ratios on day 7 after kidney transplantation.

METHODS

Two hundred sixteen adult kidney transplant recipients participated in this retrospective study. All participants received a twice daily tacrolimus regimen. Blood samples and data were collected on day 7 after transplantation. A 2-way analysis of covariance was performed. Tested covariates were age, hemoglobin, serum albumin, and prednisolone dose.

RESULTS

A 2 × 2 analysis of covariance revealed that the interaction between CYP3A5 polymorphisms (CYP3A5 expresser and CYP3A5 nonexpresser) and POR polymorphisms (POR*28 carrier and POR*28 noncarrier) was not significant (F(1, 209) = 2.473, P = 0.117, (Equation is included in full-text article.)= 0.012). The predicted main effect of CYP3A5 and POR polymorphisms was significant (F(1, 209) = 105.565, P < 0.001, (Equation is included in full-text article.)= 0.336 and F(1, 209) = 4.007, P = 0.047, (Equation is included in full-text article.)= 0.019, respectively). Hemoglobin, age, and steroid dose influenced log C0/dose of tacrolimus (F(1, 209) = 20.612, P < 0.001, (Equation is included in full-text article.)= 0.090; F(1, 209) = 14.360, P < 0.001, (Equation is included in full-text article.)= 0.064; and F(1, 209) = 5.512, P = 0.020, (Equation is included in full-text article.)= 0.026, respectively).

CONCLUSIONS

After adjusting for the influences of hemoglobin, age, and prednisolone dose, significant impacts of the CYP3A5 and POR polymorphisms on tacrolimus exposure were found. The effect of POR*28 and CYP3A5*3 polymorphisms during the very early period after kidney transplantation is independent of each other.

Tacrolimus population pharmacokinetic models according to CYP3A5/CYP3A4/POR genotypes in Chinese Han renal transplant patients

Aim: To develop a population pharmacokinetic (PK) model of tacrolimus in Chinese Han renal transplant population and establish the influence of different covariates (especially different CYP3A5/3A4/POR genotype) on PK properties. Materials & methods: Trough tacrolimus concentrations, clinical characteristics and CYP3A5/CYP3A4/POR genotypes were collected from 141 adult renal transplant recipients after transplantation. The population PK analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 3.4.2. Results: Tacrolimus PK profiles exhibited high interpatient variability. A two compartment model with first-order input and elimination described the tacrolimus PK profiles in the studied population. Among the genotypes, only CYP3A5 genotype was confirmed to have clinical significance. Conclusion: Our final model confirmed that CYP3A5*3 plays a more significant role in tacrolimus PK and could affect the blood concentrations and CL/F (clearance rate/bioavailbility). This model is expected to help to improve individualized tacrolimus dosing.

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.

Prediction of Tacrolimus Exposure by CYP3A5 Genotype and Exposure of Co-Administered Everolimus in Japanese Renal Transplant Recipients

While tacrolimus and everolimus have common metabolic pathways through CYP3A4/5, tacrolimus is metabolized solely by CYP3A4 in recipients with the CYP3A5*3/*3. The purpose of this study was to evaluate how the area under the blood concentration-time curves (AUC) of tacrolimus could be predicted based on CYP3A5 genotype and the AUC of everolimus in renal transplant patients taking both drugs. The dose-adjusted AUC (AUC/D) of tacrolimus and everolimus were calculated at one month and one year after transplantation. Significant correlations between the AUC/D of tacrolimus and everolimus were found for patients with the CYP3A5*1 allele or CYP3A5*3/*3 at both one month and one year. At both stages, the determination coefficients were higher and the slopes of regression equations were larger for patients with CYP3A5*3/*3 compared to the CYP3A5*1 allele. A good correlation between single doses of tacrolimus and everolimus was found for CYP3A5*3/*3 patients at 1 year after transplantation (r = 0.794, p < 0.001). The variability of the AUC_0–24/D of tacrolimus for each CYP3A5 genotype could be predicted based on the AUC_0–12/D of everolimus. Clinicians may be able to comprehensively carry out the dose adjustments of tacrolimus and everolimus based on relationship with AUCs of both drugs in each CYP3A5 genotype.

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.

Diagnosis and treatment of pulmonary cavity after liver transplantation

BACKGROUND

Although the outcomes have improved in the current era, pulmonary infection remains a significant post-transplant complication in liver transplant (LT) recipients. Pulmonary infection with cavity formation often leads to higher mortality rates after LT. We wished to investigate the diagnosis and treatment of pulmonary cavity (PC) formation after LT.

METHODS

We evaluated (retrospectively) five cases of PC formation, shown on CT scans of the chest after LT, by analyzing imaging features, diagnosis, treatment, liver function, and the concentration changes and efficacy of immunosuppressants.

RESULTS

According to the results from the CT scan, serum Aspergillus galactomannan (GM) assay, the purified protein derivative (PPD) skin test, and the sputum smears and blood culture, three cases were diagnosed with Aspergillus infection, and the other two cases were diagnosed with Mycobacterium tuberculosis infection. Liver function and FK506 concentration were monitored during treatment. Antibiotics used for treatment of Aspergillus and Mycobacterium tuberculosis infections affected liver function and FK506 concentration. However, after adjustment of drug doses, antibiotic treatment was tolerated in all patients. Four cases were cured, but 1 patient died of Aspergillus infection.

CONCLUSIONS

Distinguishing between Aspergillus infection and Mycobacterium tuberculosis infection for PCs after liver transplantation (LT) using a CT scan is difficult. The diagnosis can be confirmed using clinical characteristics, sputum culture, GM assay, PPD, and sputum smears. Early diagnosis and treatment could lead to a better prognosis.

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.

Fexofenadine, a Putative In Vivo P-glycoprotein Probe, Fails to Predict Clearance of the Substrate Tacrolimus in Renal Recipients

Whether the combined use of probe drugs for CYP3A4 and P-glycoprotein can clarify the relative contribution of these proteins to pharmacokinetic variability of a dual substrate like tacrolimus has never been assessed. Seventy renal recipients underwent simultaneous 8-h pharmacokinetic profiles for tacrolimus, the CYP3A4 probe midazolam, and the putative P-glycoprotein probe fexofenadine. Patients were genotyped for polymorphisms in CYP3A5, CYP3A4, ABCB1, ABCC2 and SLCO2B1, -1B1, and 1B3. Carriers of the ABCB1 2677G>A polymorphism displayed lower fexofenadine C_max (-66%; P = 0.012) and a trend toward higher clearance (+157%; P = 0.078). Predictors of tacrolimus clearance were CYP3A5 genotype, midazolam clearance, hematocrit, weight, and age (R² = 0.61). Fexofenadine pharmacokinetic parameters were not predictive of tacrolimus clearance. In conclusion, fexofenadine pharmacokinetics varied considerably between renal recipients but most of this variability remained unexplained, with only minor effects of genetic polymorphisms. Fexofenadine cannot be used to assess in vivo CYP3A4-P-glycoprotein interplay in tacrolimus-treated renal recipients.

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.

Influence of tacrolimus metabolism rate on renal function after solid organ transplantation

The calcineurin inhibitor (CNI) tacrolimus (TAC) is an integral part of the immunosuppressive regimen after solid organ transplantation. Although TAC is very effective in prevention of acute rejection episodes, its highly variable pharmacokinetic and narrow therapeutic window require frequent monitoring of drug levels and dose adjustments. TAC can cause CNI nephrotoxicity even at low blood trough levels (4-6 ng/mL). Thus, other factors besides the TAC trough level might contribute to CNI-related kidney injury. Unfortunately, TAC pharmacokinetic is determined by a whole bunch of parameters. However, for daily clinical routine a simple application strategy is needed. To address this problem, we and others have evaluated a simple calculation method in which the TAC blood trough concentration (C) is divided by the daily dose (D). Fast TAC metabolism (C/D ratio < 1.05) was identified as a potential risk factor for an inferior kidney function after transplantation. In this regard, we recently showed a strong association between fast TAC metabolism and CNI nephrotoxicity as well as BKV infection. Therefore, the TAC C/D ratio may assist transplant clinicians in a simple way to individualize the immunosuppressive regimen.

Pharmacogenetics of immunosuppressants: State of the art and clinical implementation - recommendations from the French National Network of Pharmacogenetics (RNPGx)

Therapeutic drug monitoring is already widely used for immunosuppressive drugs due to their narrow therapeutic index. This article summarizes evidence reported in the literature regarding the pharmacogenetics of (i) immunosuppressive drugs used in transplantation and (ii) azathioprine used in chronic inflammatory bowel disease. The conditions of use of currently available major pharmacogenetic tests are detailed and recommendations are provided based on a scale established by the RNPGx scoring tests as "essential", "advisable" and "potentially useful". Other applications for which the level of evidence is still debated are also discussed.

High Intrapatient Variability of Tacrolimus Concentrations Predicts Accelerated Progression of Chronic Histologic Lesions in Renal Recipients

High intrapatient variability (IPV) of tacrolimus concentrations is increasingly recognized as a predictor of poor outcome in solid organ recipients. How it relates to evolution of histology has not been explored. We analyzed tacrolimus IPV using the coefficient of variability (CV) from months 6-12 after transplantation in a cohort of 220 renal recipients for whom paired protocol biopsies at 3 mo and 2 years were available. Recipients in the highest CV tertile had an increased risk of moderate to severe fibrosis and tubular atrophy by 2 years compared with the low-IPV tertile (odds ratio [OR] 2.47, 95% confidence interval [CI] 1.09-5.60, p = 0.031; and OR 2.40, 95% CI 1.03-5.60, p = 0.043, respectively). Other predictors were donor age, severity of chronic lesions at 3 mo, and presence of borderline or subclinical rejection at 3 mo. Chronicity score increased significantly more in the high CV tertile group than in the middle and low tertiles (mean increase 1.97 ± 2.03 vs. 1.18 ± 2.44 and 1.12 ± 1.80, respectively; p < 0.05). CV did not predict evolution of renal function, which did not deteriorate within the 2-year follow-up period. These results indicate that high IPV is related to accelerated progression of chronic histologic lesions before any evidence of renal dysfunction.

Effect of ABCB1 diplotype on tacrolimus disposition in renal recipients depends on CYP3A5 and CYP3A4 genotype

The relevance of most genetic polymorphisms beyond CYP3A5*1 on tacrolimus disposition remains unclear. We constructed a predictive mixed model for tacrolimus dose-corrected trough concentration (C₀/dose) at months 3, 12 and 24 after transplantation in a retrospective cohort of 766 predominantly Causasian adult renal recipients (n=2042 trough concentrations). All patients were genotyped for 32 single-nucleotide polymorphisms with a proven or possible relevance to tacrolimus disposition based on the previous studies. Of these, ABCB1, ABCC2, OATP1B1, COMT, FMO, PPARA and APOA5 were analyzed as (functional) diplotype groups. Predictors of C₀/dose were CYP3A5*1, hematocrit, age, CYP3A4*22, use of concomitant CYP3A4 inhibitor or inducer, ALT, estimated glomerular filtration rate, tacrolimus formulation (once vs twice daily), ABCB1 diplotype and time after transplantation. The effect of ABCB1 diplotype was small but strongly accentuated in CYP3A4*22 carriers and non-existent in CYP3A5 expressors. ABCC2 diplotype had a limited effect on C₀/dose that was only statistically significant in CYP3A5 non-expressors.