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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·kg 1 ·12 h -1 for the CYP3A5 expressor)., Conclusion: The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients., (© 2023 British Pharmacological Society.)

Background: The Immunosuppressant Bayesian Dose Adjustment web site aids clinicians and pharmacologists involved in the care of transplant recipients; it proposes dose adjustments based on the estimated area under the concentration-time curve (AUCs). Three concentrations (T 20 min , T 1 h , and T 3 h ) are sufficient to estimate mycophenolic acid (MPA) AUC 0-12 h in pediatric kidney transplant recipients. This study investigates mycophenolate mofetil (MMF) doses and MPA AUC values in pediatric kidney transplant recipients, and target exposure attainment when the proposed doses were followed, through a large-scale analysis of the data set collated since the inception of the Immunosuppressant Bayesian Dose Adjustment web site., Methods: In this study, 4051 MMF dose adjustment requests, corresponding to 1051 patients aged 0-18 years, were retrospectively analyzed. AUC calculations were performed in the back office of the Immunosuppressant Bayesian Dose Adjustment using published Bayesian and population pharmacokinetic models., Results: The first AUC request was posted >12 months posttransplantation for 41% of patients. Overall, only 50% had the first MPA AUC 0-12 h within the recommended 30-60 mg.h/L range. When the proposed dose was not followed, the proportion of patients with an AUC in the therapeutic range for MMF with cyclosporine or tacrolimus at the subsequent request was lower (40% and 45%, respectively) than when it was followed (58% and 60%, respectively): P = 0.08 and 0.006, respectively. Furthermore, 3 months posttransplantation, the dispersion of AUC values was often lower at the second visit when the proposed doses were followed, namely, P = 0.03, 0.003, and 0.07 in the 4 months-1 year, and beyond 1 year with <6-month or >6-month periods between both visits, respectively., Conclusions: Owing to extreme interindividual variability in MPA exposure, MMF dose adjustment is necessary; it is efficient at reducing such variability when based on MPA AUC., Competing Interests: Conflicts of Interest and Source of funding: None declared., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: Tacrolimus is an immunosuppressant widely used in transplantations requiring mandatory concentration-controlled dosing to prevent acute rejection or adverse effects, including new-onset diabetes mellitus (NODM). However, no relationship between NODM and tacrolimus exposure has been established. This study aimed to evaluate the relationship between cumulative tacrolimus exposure and NODM occurrence., Methods: A total of 452 kidney transplant patients were included in this study. Sixteen patients developed NODM during the first 3 months after transplant. We considered all tacrolimus concentration (C0) values collected until the diagnosis of NODM in these patients and until 3 months after transplant in the others. New tacrolimus cumulative exposure metrics were derived from the time profile of the tacrolimus morning predose concentration, C0: the percentage of C0 values > cutoff, the average of C0 values above the cutoff, and the percentage of the area under C0 versus time curve, AUCC0, above the cutoff. The cutoff chosen was 15 ng/mL, corresponding to the higher end of the therapeutic range for the early post-transplant period. The influence of these metrics on NODM and other clinical and biological characteristics was investigated using the Cox models., Results: The percentage of C0 > 15 mcg/L was statistically different between patients with and without NODM (P = 0.01). Only these tacrolimus C0-derived metrics were significantly associated with an increased risk of NODM [HR: 1.73 (1.43-2.10, P < 0.001)]., Conclusion: This study shows that tacrolimus concentrations >15 mcg/L affect the incidence of NODM., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (= "true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose ® -tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE., Methods: Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients., Results: Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = - 1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = - 1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = - 3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = - 3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points., Conclusion: The MARS ML models developed using "true" MeltDose ® -tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The aim of this work was to evaluate, in a large data set of renal transplant recipients, the intraindividual variability of the area under the curve (AUC)/predose concentration (C0) ratio in comparison with that of AUC, C0, AUC/dose, and C0/dose., Methods: Patients with at least 2 tacrolimus AUC estimation requests were extracted from the Immunosuppressant Bayesian dose Adjustment website, and relative variations between 2 consecutive visits for the different metrics were calculated and compared., Results: Data from 1325 patients on tacrolimus (3827 measured C0 and estimated AUC) showed that the lowest mean relative variation between 2 consecutives visits was for the AUC/C0 ratio (95% confidence interval [CI] relative fold change = -43% to 44% for AUC/C0; 95% CI, -77% to 72% for AUC; 95% CI, -82% to 98% for AUC/dose; 95% CI, -81% to 80% for C0 and 95% CI, -94% to 117% for C0/dose. The correlation between 2 consecutive requests, whether close or far apart, was also best for the AUC/C0 ratio ( r  = 0.33 and r  = 0.34, respectively) in comparison with C0 ( r  = 0.21 and r  = 0.22, respectively) and AUC ( r  = 0.19 and 0.28, respectively). Regression analysis between AUC0-24 and C0 showed that for some patients, the usual C0 targets translated into some very unusual AUC values. As the AUC/C0 ratio is quite stable during large periods, individualized C0 targets can be derived from the AUC targets, and an algorithm that estimates the individualized C0 was developed for situations in which prior AUC estimates are available or not., Conclusions: In this study, we confirmed in a large data set that the AUC/C0 ratio yields low intraindividual variability, whereas C0 shows the largest, and we propose to calculate individualized C0 targets based on this ratio., Competing Interests: The authors declare no funding or conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Everolimus is an immunosuppressant with a small therapeutic index and large between-patient variability. The area under the concentration versus time curve (AUC) is the best marker of exposure but measuring it requires collecting many blood samples. The objective of this study was to train machine learning (ML) algorithms using pharmacokinetic (PK) profiles from kidney transplant recipients, simulated profiles, or both types, and compare their performance for everolimus AUC 0-12h estimation using a limited number of predictors, as compared to an independent set of full PK profiles from patients, as well as to the corresponding maximum a posteriori Bayesian estimates (MAP-BE). XGBoost was first trained on 508 patient interdose AUCs estimated using MAP-BE, and then on 500-10,000 rich interdose PK profiles simulated using previously published population PK parameters. The predictors used were: predose, ~1 h, and ~2 h whole blood concentrations, differences between these concentrations, relative deviations from theoretical sampling times, morning dose, patient age, and time elapsed since transplantation. The best results were obtained with XGBoost trained on 5016 simulated profiles. AUC estimation achieved in an external dataset of 114 full-PK profiles was excellent (root mean squared error [RMSE] = 10.8 μg*h/L) and slightly better than MAP-BE (RMSE = 11.9 μg*h/L). Using more profiles (n = 10,035) did not improve the ML algorithm performance. The contribution of mixing patient and simulated profiles was significant only when they were in balanced numbers, with ~500 for each (RMSE = 12.5 μg*h/L), compared with patient data alone (RMSE = 18.0 μg*h/L)., (© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Aims: Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogeneous patients. A Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) was derived and evaluated in external patients., Methods: Full pharmacokinetic data (7-12 samples) from 172 patients receiving iohexol for measurement of their GFR (unstable and stable ICU patients, liver failure patients and kidney transplant patients) were split into development (n = 136) and validation (n = 36) datasets. A PopPK model was developed in Monolix and was used to develop MAP-BE based on LSS. Its performance for GFR estimation was evaluated in the validation set., Results: A two-compartment model with first-order elimination best described the data. The final model included the type of patients on volume of distribution (Vd), clearance and intercompartmental constants, serum creatinine on clearance and body weight on Vd. The best LSS included samples at 0.1-1-9 h exhibiting a relative mean prediction error (MPE) (RMSE) = -3.7% (14.3%) and better performance than the Bröchner-Mortensen formula (-3.0%/17%). Split by type of patients, the highest interindividual variability and imprecision was observed in unstable ICU patients (MPE (RMSE) = 3.7% (18.8%)) while the best performances were obtained for renal transplant patients (MPE (RMSE) = 1.0% (5.8%)). All LSS that included samples before 9 hours for the third sample were associated with an increased imprecision., Conclusion: A single MAP-BE of iohexol based on a three-sample LSS for four heterogeneous populations was developed and allowed accurate estimation of GFR in kidney transplant patients, slightly biased in stable ICU patients and slightly imprecise in unstable ICU patients., (© 2022 British Pharmacological Society.)

Background: Immunosuppressant Bayesian Dose Adjustment (ISBA) is an online expert system that estimates the area under the curve (AUC) of immunosuppressive drugs through pharmacokinetic modelling and Bayesian estimation to propose dose adjustments to reach predefined exposure targets. The ISBA database was retrospectively analyzed to describe tacrolimus pharmacokinetics and exposure, evaluate the efficiency of ISBA dose recommendations, and propose tacrolimus AUC0-12h target ranges for pediatric renal allograft recipients treated with immediate release tacrolimus., Methods: The database included 1935 tacrolimus dose adjustment requests from 419 patients <19 years old who were treated with immediate-release tacrolimus and followed in 21 French hospitals. The tacrolimus exposure evolution with patient age and posttransplantation time, the correlation between trough tacrolimus concentration (C0) and AUC0-12h at different periods posttransplantation, and the efficiency of dose recommendations to avoid underexposure and overexposure and to decrease between-patient AUC variability were investigated., Results: Tacrolimus AUC showed large between-patient variability (CV% = 40%) but moderate within-patient variability (median = 24.3% over a 3-month period). Dose-standardized exposure but not the AUC/C0 ratio significantly decreased with time posttransplantation and patient age. We derived AUC0-12h ranges from the consensual C0 ranges using linear regression equations. When the ISBA recommended dose was applied, the AUC distribution was narrower and a significantly higher proportion was within the targets (P < 0.0001)., Conclusions: ISBA efficiently reduced tacrolimus underexposure and overexposure. The AUC0-12h target ranges for pediatric patients derived from the database were similar to those previously reported for adults. Estimating the AUC/C0 ratio could help determine personalized C0 targets., Competing Interests: P. Marquet received funding from Sandoz and Chiesi as a consultant as well as to support his laboratory research. J.-B. Woillard received speaker fees from Chiesi and Gilead and was invited to presentations sponsored by Chiesi, Gilead, VIIV, Astellas, and Abbvie. The remaining authors declare no conflict of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Tacrolimus has a narrow therapeutic range and requires dose adjustment, usually based on the trough blood concentration but preferably on the area under the concentration-time curve over 12 h post-dose (AUC 0-12h ). The single-arm, multicentre, clinical study IMPAKT aimed: (i) to develop, in de novo kidney transplant recipients, pharmacokinetic models and maximum a-posteriori Bayesian estimators for a generic, immediate-release, oral formulation of tacrolimus to estimate tacrolimus AUC 0-12h at different post-transplant periods using a limited sampling strategy, and considering the CYP3A5*3 polymorphism as a covariate and (ii) to compare the performance of these Bayesian estimators to those previously developed for the original formulation., Methods: Thirty patients were enrolled and 29 provided nine blood samples over 9 h at day 7 and months 1 and 3 post-transplant. Tacrolimus blood profiles measured with liquid chromatography-tandem mass spectrometry were modelled using one-compartment, double gamma absorption, linear elimination models developed in-house. Different limited sampling strategies of three time-points within 4 h post-dose were tested for the maximum a-posteriori Bayesian estimator of tacrolimus AUC 0-12h . The models and estimators were validated internally and their performance compared to that of models previously developed for the original formulation., Results: The concentration-time curves, AUC 0-12h /dose and trough blood concentration/dose exhibited wide inter-individual variability. The covariate-free pharmacokinetic models developed for the three post-transplant periods closely fitted the individual profiles. Maximum a-posteriori Bayesian estimators based on three different limited sampling strategies and no covariate yielded accurate AUC 0-12h estimates, including for the five cytochrome P450 3A5 expressers and for the four patients without corticosteroids. The 0-1 h-3 h strategy finally chosen had very low bias (- 4.0 to - 2.5%) and imprecision (root mean square error 5.5-9.2%). The maximum a-posteriori Bayesian estimators previously developed for the reference product fitted the generic profiles with similar performance., Conclusions: We developed original pharmacokinetic models and accurate maximum a-posteriori Bayesian estimators to estimate patient exposure and adjust the dose of generic tacrolimus, and confirmed that the robust tools previously developed for the original formulation can be applied to this generic.

Therapeutic drug monitoring (TDM) is mandatory for the immunosuppressive drug tacrolimus (Tac). For clinical applicability, TDM is performed using morning trough concentrations. With recent developments making tacrolimus concentration determination possible in capillary microsamples and Bayesian estimator predicted area under the concentration curve (AUC), AUC-guided TDM may now be clinically applicable. Tac circadian variation has, however, been reported, with lower systemic exposure following the evening dose. The aim of the present study was to investigate tacrolimus pharmacokinetic (PK) after morning and evening administrations of twice-daily tacrolimus in a real-life setting without restrictions regarding food and concomitant drug timing. Two 12 hour tacrolimus investigations were performed; after the morning dose and the following evening dose, respectively, in 31 renal transplant recipients early after transplantation both in a fasting-state and under real-life nonfasting conditions (14 patients repeated the investigation). We observed circadian variation under fasting-conditions: 45% higher peak-concentration and 20% higher AUC following the morning dose. In the real-life nonfasting setting, the PK-profiles were flat but comparable after the morning and evening doses, showing slower absorption rate and lower AUC compared with the fasting-state. Limited sampling strategies using concentrations at 0, 1, and 3 hours predicted AUC after fasting morning administration, and samples obtained at 1, 3, and 6 hours predicted AUC for the other conditions (evening and real-life nonfasting). In conclusion, circadian variation of tacrolimus is present when performed in patients who are in the fasting-state, whereas flatter PK-profiles and no circadian variation was present in a real-life, nonfasting setting., (© 2020 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

This work investigated, in two large cohorts of French renal transplants treated with tacrolimus, the influence of donor and recipient ABCB1, CYP3A4, and CYP3A5 genotypes on the risk of allograft loss. A discovery and a replication population of 330 and 369 adult renal transplant patients, each from a different transplantation center and all receiving a tacrolimus-based immunosuppressive regimen, were retrospectively genotyped. The influence of genetic factors and other known risk factors on allograft loss was investigated using multivariate Cox proportional hazard analyses. The existence of previous transplantations (per unit HR = 1.89 [1.10-3.26] P = .0216) and the donor ABCB1 c.1199GA/AA genotype (GA/AAvs GG: HR = 3.22 [1.14-9.09], P = .0288) were associated with an increased risk of allograft loss in the discovery cohort and with graft loss due to humoral rejection in the replication cohort (per unit HR = 2.26 [1.34-3.81], P = .00229; GA/AAvs GG HR = 3.42 [1.28-9.16], P = .0142). Genotyping the donor for the ABCB1 c.1199 G>A (exon 11, rs2229109) allele may be of interest before prescribing tacrolimus to the recipient, although this polymorphism is rather rare and its effect may be limited to certain mechanisms of graft loss., (© 2018 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background and Objective: Intracellular exposure of everolimus may be a better marker of therapeutic effect than trough whole blood concentrations. We aimed to develop pharmacokinetic population models and Bayesian estimators based on a limited sampling strategy for estimation of dose interval exposures of everolimus in whole blood and peripheral blood mononuclear cells (PBMCs) in renal transplant recipients., Methods: Full whole blood and PBMC concentration-time profiles of everolimus were obtained from 12 stable renal transplant recipients on two different occasions, 4 weeks apart. The dataset was treated as 24 individual profiles and split into a development dataset (n = 20) and a validation dataset (n = 4). The pharmacokinetic model was developed using non-parametric modeling and its performances and those of the derived Bayesian estimator were evaluated in the validation set., Results: A structural two-compartment model with first-order elimination and two absorption phases described by a sum of two gamma distributions were developed. None of the tested covariates (age, sex, albumin, hematocrit, fat-free mass and genetic variants such as CYP3A5*1, ABCB1 haplotype, PPARA*42, PPARA*48, and POR*28) were retained in the final model. A limited sampling schedule of two whole blood samples at 0 and 1.5 h and one PBMC sample at 1.5 h post dose provided accurate estimates of the area under the plasma concentration-time curve (AUC) in comparison with the trapezoidal reference AUC (relative bias ± standard deviation = - 3.9 ± 10.6 and 4.1 ± 12.3% for whole blood and PBMC concentrations, respectively)., Conclusion: The developed model allows simultaneous and accurate prediction of everolimus exposure in whole blood and PBMCs, and supplies a base for a feasible exploration of the relationships between intracellular exposure and therapeutic effects in prospective trials.

Background: Immunosuppressant Bayesian dose adjustment (ISBA) is an online expert system, routinely used by approximately 140 transplantation centers in the world for the dose adjustment of immunosuppressive drugs in transplant patients. This system determines the area under the curve (AUC) of the drug by pharmacokinetic modeling and Bayesian estimation. The purpose of this study was to analyze tacrolimus exposure after administration of its modified-release formulation (Advagraf) in kidney allograft recipients, to optimize its therapeutic drug monitoring., Methods: This is a retrospective study of exposure indices measured locally [trough tacrolimus concentration (C0), C0/dose] or estimated through ISBA (AUC, AUC/dose, AUC/C0), of their evolution over posttransplantation time, and of the correlations between them., Results: A total of 922 requests posted by 28 different centers for routine Advagraf adjustment in 530 different patients treated with Advagraf were studied. The exposure to, and dose requirement of, tacrolimus significantly increased across the first posttransplant months before reaching steady state. The AUC:C0 ratio (on which C0 monitoring is implicitly based) was stable across the different posttransplant periods, although with high interindividual variability. C0-AUC correlation was stronger in the late than in the early posttransplant period (r = 0.75 versus 0.63; P = 0.0075). Using the regression equations obtained, AUC ranges corresponding to different applicable C0 target ranges were calculated to guide dose adjustment. When one of the doses recommended was administered, the following AUC was significantly more often in the predicted target ranges (P < 0.0001)., Conclusions: This study improves our knowledge of Advagraf pharmacokinetic variability and relations between exposure indices and the scientific background of the expert service provided through the ISBA Web site.

Background: Several studies found differences in tacrolimus whole blood trough levels (C0) or area-under-the curve (AUC) between the twice-daily (Tac-BID) and once-daily (Tac-OD) formulations given to kidney transplant recipients at equal doses. As C0 is widely used as a surrogate of the AUC for individual dose adjustment, this study investigated the correlation and proportionality between C0 and the 24h-AUC, depending on the formulation, time post-transplantation, pharmacogenetics traits and other individual characteristics., Methods: 45 adult kidney transplant recipients were randomized to receive either Tac OD or Tac BID. On days 8±1 (D8) and 90±3 (month 3, M3), blood samples were collected over 24h in both groups. Tacrolimus concentrations were determined using HPLC-MS/MS and common CYP3A5, CYP3A4 and ABCB1 genotypes characterized using allelic discrimination assays. Tacrolimus population pharmacokinetics was studied in the two patient groups using the Iterative Two Stage (ITS) technique, considering a one-compartment model with two gamma laws to describe the absorption phase. Bayesian estimation based on the C0, C1h and C3h concentrations was employed to estimate individual Tac AUC 0-12h and AUC 12-24h (for Tac BID), or AUC 0-24h (for Tac OD). Multiple linear regression was used to evaluate the influence of Tac formulation, post-transplantation period, recipient gender, existing glucose metabolism disorders, and CYP3A5, CYP3A4 and ABCB1 genotypes on C0, AUC 0-24h and the AUC-to-trough concentration ratios., Results: The Full Analysis Set comprised 22 patients on Tac OD and 20 on Tac BID. Tac exposure indices as well as their time evolution were similar in the two groups. Multi-linear modeling analysis showed that the Tac dose was higher with Tac-OD than Tac-BID, on D8 than at M3 and in CYP3A5 expressors (p<0.0001 for all). No such influence was found on C0 or C24h, while the AUC 0-24h was significantly higher on D8 than at M3. The AUC 0-24h /C0 ratio was not affected by the drug formulation and the polymorphisms studied, but it was significantly lower on D8 than at M3 (p=7.8×10 -5 ). In contrast, both the post-transplantation period (p=1.53×10 -4 ), and CYP3A5 expression (p=0.003) had a significant influence on the AUC 0-24h /C24h ratio, explaining 19% and 12% of its variability, respectively. Consistently, for both Tac formulations, the AUC 0-24h was better correlated with C24h than C0, and for Tac-BID the AUC 0-12h was better correlated with C12h than C0., Conclusions: This study confirms that the precisely timed 12h- or 24h-post-dose blood concentration (as opposed to the vaguely defined 'trough level') is a convenient surrogate of the 24h-AUC of tacrolimus for the two TAC formulations over the first 3 months post-transplantation. Still, for a given C24h value, AUC 0-24h was higher on D8 and in CYP3A5 expressors. Bayesian estimation of AUC 0-12h for TAC BID and AUC 0-24h for TAC OD is feasible using only 3 time points within the first 3h, thus giving access to the actual overall exposure., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background and Objectives: A new once-daily formulation of tacrolimus (Envarsus ® ) has recently been developed, with alleged different pharmacokinetics from previous tacrolimus formulations. The objectives of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies for Envarsus ® in kidney and liver transplant recipients., Materials and Methods: Full tacrolimus concentration-time profiles (13 samples) were drawn from 57 liver (113 profiles) and 49 kidney (97 profiles) graft recipients transplanted for at least 6 months and switched from Prograf ® to Envarsus ® . The two databases were split into a development (75%) and a validation (25%) dataset. Pharmacokinetic models characterised by a single compartment with first-order elimination and absorption in two phases described by a sum of two gamma distributions were developed using non-parametric (Pmetrics) and parametric (ITSIM) approaches in parallel. The best limited sampling strategy for each patient group was determined using the multiple model optimal algorithm. The performance of the models and derived Bayesian estimators was evaluated in the validation set., Results: The best limited sampling strategy was 0, 8 and 12 h post-dose, leading to a relative bias ± standard deviation (root-mean-square error) between observed and modelled inter-dose area under the curve in the validation dataset of: 0.32 ± 6.86% (6.87%) for ITSIM and 3.4 ± 13.4% (13.2%) for Pmetrics in kidney transplantation; and 0.89 ± 7.32% (7.38%) for ITSIM and -2.62 ± 8.65% (8.89%) for Pmetrics in liver transplantation., Conclusion: Population pharmacokinetic models and Bayesian estimators for Envarsus ® in kidney and liver transplantation were developed and are now available online for area under the curve-based tacrolimus dose adjustment.

Objectives: The aim of this study was to develop a pharmacokinetics model allowing the description of the evolution of tacrolimus exposure in kidney transplant patients over the first months after transplant, using trough concentrations of routinely collected blood., Materials and Methods: The authors performed a retrospective analysis of trough concentration data collected from adult kidney transplant recipients (from 2008 to 2013). The total data set was divided into a building data set, used to build the structural model, and a validation data set, used to validate the structural model. (C0 = 133; 26 patients). A pharmacokinetics analysis was carried out by applying a nonparametric adaptive grid approach. The structural model parameters were tacrolimus clearance and volume of distribution., Results: In patients in the building set group, estimated clearance was 3.6 ± 0.57 L/h and estimated volume of distribution was 9.9 ± 1.14 L. No covariate was significantly associated with tacrolimus clearance or volume of distribution. The model adequately described tacrolimus dose-normalized trough concentration evolution after transplant (the plot of individual model predicted versus observed concentrations resulted in r = 0.84). The prediction performance in the validation group yielded 2.3% mean prediction error and 21.4% root mean squared error., Conclusions: This model could be highly useful in the optimization of tacrolimus prescription at any posttransplant time in kidney transplant patients.

Aim: To investigate the potential influence of variants in genes involved in the calcineurin pathway on the efficacy and toxicity of calcineurin inhibitors in renal transplantation., Materials & Methods: Twenty-three polymorphisms in thirteen genes were tested in 381 renal transplant recipients receiving ciclosporin (n = 221) or tacrolimus (n = 160) and mycophenolate mofetil. Data were collected prospectively over the first year post-transplantation., Results: Multivariate survival analyses revealed no genetic associations with biopsy proven acute graft rejection and serious infections. Donor-recipient Cytomegalovirus mismatch was the only variable associated with serious infection., Conclusion: This large exploratory study casts doubts on the potential interest of genetic biomarkers related to CNI pharmacodynamics but associations with other phenotypes in transplantation deserve further studies.

Objectives: Ganciclovir is the most widely used treatment for cytomegalovirus infections. However, neutropenia is a frequent associated adverse effect leading to a decrease in the ganciclovir dose or discontinuation of the therapy, thereby favouring viral resistance. In the present study, the objectives were: (i) to describe the pharmacokinetics of blood and intracellular ganciclovir and its metabolites; and (ii) to explore the relationship between exposure to ganciclovir and/or its metabolites and evolution of the neutrophil count under treatment., Methods: Pharmacokinetic profiles (pre-dose and 1, 2, 3 and 5 h after dosing) of ganciclovir and its metabolites were measured in 22 adult renal transplant patients and further modelled by a non-parametric approach (PMetrics(®)). The relationship between exposure indices to ganciclovir and the slope of the neutrophil count was investigated using multiple linear regression., Results: A four-compartment open model was able to accurately describe ganciclovir and its intracellular forms. A significant association was found between intracellular ganciclovir triphosphate concentrations (AUC0-5) and the decrease in neutrophil count over the first 3 months of treatment (β= -0.0019 ± 5 × 10(-4); P < 0.01)., Conclusions: In this population of renal transplant patients, the decrease in neutrophil count, used as a surrogate marker of haematological toxicity, was associated with ganciclovir triphosphate accumulation in blood cells. Further studies are needed to test this biomarker as a predictive factor for toxicity., (© The Author 2015. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Objectives: Different associations between single nucleotide polymorphisms (SNPs) in cellular target, metabolism enzymes or transport proteins, and biopsy-proven acute rejection (BPAR) or adverse events have been reported in transplant patients receiving mycophenolate mofetil. This work aimed to study these in patients on enteric-coated mycophenolate sodium (EC-MPS)., Patients and Methods: The study included 189 renal transplant patients from the DOMINOS trial. Fifteen SNPs in IMPDH2, IMPDH1, ABCC2, SLCO1B3, UGT1A8, UGT1A9, UGT2B7, CYP2C8, HUS1, and IL12A were genotyped in all patients. Associations between SNPs and the first event of BPAR or diarrhea were investigated using multivariate logistic regressions. Associations between SNPs and leukopenia or anemia at nine different visits between days 0 and 190 after transplantation were studied using time-dependent Cox proportional hazards regression models., Results: Multivariate analyses showed that the CYP2C8 rs11572076 wild-type genotype was associated significantly with a lower risk of leukopenia [GG vs. GA: hazard ratio (95% confidence interval) 0.14 (0.03, 0.59), P=0.00783]. Higher EC-MPS doses and the UGT2B7 c.-840 G>A variant allele were associated with an increased risk of anemia [EC-MPS per unit dose increase: 1.004 (1.003, 1.005), P<0.0001; UGT2B7 GA vs. AA: 1.65 (1.12, 2.43), P=0.01043; GG vs. AA: 1.88 (1.23, 2.88), P=0.00343]. However, no significant association was found between any of the SNPs studied and diarrhea or BPAR., Conclusion: Two pharmacogenetic associations reported previously with mycophenolate mofetil were found in a population of 189 renal transplant patients treated with EC-MPS., Competing Interests: Jean-Baptiste Woillard, Nicolas Picard, Antoine Thierry have no conflict of interest. Pierre Marquet is a consultant for Roche France and received research grants from Roche and Novartis. Guy Touchard received a travel grant from Novartis.

Background: Recent studies have identified new candidate polymorphisms in the genes related to CYP3A activity or calcineurin inhibitor dose requirements in kidney transplant recipients. These genes and polymorphisms are CYP3A4 (cytochrome P450, family 3, subfamily A, polypeptide 4) (rs35599367-C>T; *22); POR [P450 (cytochrome) oxidoreductase] (rs1057868-C>T; *28); and PPARA (peroxisome proliferator-activated receptor alpha) (rs4253728-G>A). We investigated the impact of these polymorphisms on sirolimus (SRL) in vitro hepatic metabolism, SRL trough concentrations (C0), and SRL adverse events in kidney transplant recipients., Methods: The clinical study included 113 stable kidney transplant patients switched from a calcineurin inhibitor to SRL (SRL C0 measured at 1, 3, and 6 months thereafter). We investigated SRL metabolism in vitro using human liver microsomes derived from individual donors (n = 31). Microsomes and patients were genotyped by use of Taqman® allelic discrimination assays. The effects of polymorphisms and covariates were studied using multilinear regression imbedded in linear mixed-effect models or logistic regressions., Results: In vitro, the CYP3A4*22 allele resulted in approximately 20% lower metabolic rates of SRL (P = 0.0411). No significant association was found between CYP3A4, CYP3A5, or PPARA genotypes and SRL dose, C0, or C0/dose in kidney transplant patients. POR*28 was associated with a minor but significant decrease in SRL log-transformed C0 [CT/TT vs CC, β = -0.15 (0.05); P = 0.0197] but this did not have any impact on the dose administered, which limited the relevance of the finding. After adjustment for nongenetic covariates and correction for false discovery finding, none of the single-nucleotide polymorphisms tested showed significant association with SRL adverse events., Conclusions: These recently described polymorphisms do not seem to substantially influence the pharmacokinetics of SRL or the occurrence of SRL adverse events in kidney transplant recipients.

Objectives: Since 2007, a number of transplantation centers have been routinely using an expert system for tacrolimus (TAC) dose adjustment in kidney allograft recipients, based on PK modeling and Bayesian estimation for area-under-the-curve (AUC) determination. This has allowed the setting up of a large database of TAC pharmacokinetic profiles and AUC values, a part of which was analyzed here., Methods: We retrospectively studied 2030 requests posted by 21 different centers for routine TAC dose adjustment in 1000 different adult renal transplant patients (not enrolled in any kind of concentration-controlled clinical trial). For each request, the following information was obtained: time elapsed since transplantation, TAC daily dose, calculated AUC, and trough concentration (C0)., Results: The dose-standardized exposure to TAC significantly and progressively increased in the months after transplantation: from month (M) 1 to M9 C0/dose increased from 2.33 to 3.44 mcg·L(1)·mg(1) and AUC/dose from 43.1 to 64.2 mcg·h(1)·L(1)·mg(1), respectively. On the contrary, in patients beyond the first year whose C0 or AUC was in the target range, the odds of remaining in this range were high for a long time period, suggesting a low intrapatient variability in the stable phase. Regression analyses showed that the correlation between C0 and AUC was better in the first 3-month period (r(2) = 0.76) than later on (r(2) ≤ 0.67). Using the regression equations obtained, AUC ranges corresponding to different applicable C0 targets were calculated., Conclusions: From a large number of kidney graft recipients, we have estimated the relationships between C0 and AUC, modeled the evolution of TAC exposure with time and defined AUC targets that could be useful to lead further controlled-concentration trials and improve routine TAC therapeutic drug monitoring.

Background: The mammalian target of rapamycin (m-TOR) inhibitor sirolimus is an immunosuppressive drug used in kidney transplantation. m-TOR binds with Raptor and phosphorylates p70S6 kinase, a protein involved in numerous cell signalling pathways. We examined the association of candidate polymorphisms in m-TOR, Raptor and p70S6K, sirolimus dose and exposure, and other time-independent as well as time-dependent covariates, with sirolimus-induced adverse events in kidney transplant recipients., Methods: This study included a first group of 113 patients, switched from a calcineurin inhibitor to sirolimus, and a validation group of 66 patients from another clinical trial, with the same immunosuppressive regimen. The effects of gene polymorphisms and covariates on the total cholesterol, LDL cholesterol, triglycerides, haemoglobin, cutaneous adverse events, oedemas and infections were studied using multilinear regression, or logistic regression imbedded in linear mixed-effect models., Results: An m-TOR variant haplotype was significantly associated with a decrease in haemoglobin levels in the two populations of patients (discovery group: β=-0.82 g/dl, P=0.0076; validation group: β=-1.58 g/dl, P=0.0308). Increased sirolimus trough levels were significantly associated with increased total cholesterol levels (discovery group: β=0.02 g/l, P<0.0001; validation group: β=0.02 g/l, P=0.0002) and triglyceride levels (discovery group: β=0.02 g/l, P=0.0059; validation group: β=0.05 g/l, P=0.0370). Sirolimus trough levels were also associated with an increased risk for cutaneous adverse events [odds ratio=1.97, 95% confidence interval (1.32-1.94), P=0.0009] and oedemas [odds ratio=1.16, 95% confidence interval (1.03-1.30), P=0.01342] in the discovery group, but this was not confirmed in the validation group., Conclusion: These results provide evidence of an association between an m-TOR haplotype and a decrease in haemoglobin in renal transplant recipients.

Aim: To investigate the differences in the pharmacokinetics of Prograf and the prolonged release formulation Advagraf and to develop a Bayesian estimator to estimate tacrolimus inter-dose area under the curve (AUC) in renal transplant patients receiving either Prograf or Advagraf., Methods: Tacrolimus concentration-time profiles were collected, in adult renal transplant recipients, at weeks 1 and 2, and at months 1, 3 and 6 post-transplantation from 32 Prograf treated patients, and one profile was collected from 41 Advagraf patients more than 12 months post-transplantation. Population pharmacokinetic (popPK) parameters were estimated using nonmem. In a second step, the popPK model was used to develop a single Bayesian estimator for the two tacrolimus formulations., Results: A two-compartment model with Erlang absorption (n= 3) and first-order elimination best described the data. In Advagraf patients, a bimodal distribution was observed for the absorption rate constant (K(tr) ): one group with a K(tr) similar to that of Prograf treated patients and the other group with a slower absorption. A mixture model for K(tr) was tested to describe this bimodal distribution. However, the data were best described by the nonmixture model including covariates (cytochrome P450 3A5, haematocrit and drug formulation). Using this model and tacrolimus concentrations measured at 0, 1 and 3h post-dose, the Bayesian estimator could estimate tacrolimus AUC accurately (bias = 0.1%) and with good precision (8.6%)., Conclusions: The single Bayesian estimator developed yields good predictive performance for estimation of individual tacrolimus inter-dose AUC in Prograf and Advagraf treated patients and is suitable for clinical practice., (© 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.)

Background and Objectives: Advagraf is a new extended-release once-daily formulation of tacrolimus, a potent immunosuppressant widely used in renal transplantation. The aims of his study were (i) to develop a population pharmacokinetic model for once-daily tacrolimus in adult renal transplant patients; and (ii) to develop a Bayesian estimator able to reliably estimate individual pharmacokinetic parameters and exposure indices., Methods: Full pharmacokinetic profiles obtained from 41 adult renal transplant patients who had been switched from ciclosporin to a single daily dose of the new once-daily tacrolimus formulation for more than 6 months were analysed. Tacrolimus concentrations were measured using validated turbulent flow chromatography-tandem mass spectrometry methods. Population parameters were computed using nonlinear mixed-effect modelling software (NONMEM Version VI). The patients were randomly divided into (i) a model-building test group (n = 29); and (ii) a validation group (n = 12). Population pharmacokinetic analysis was performed to estimate the effects on tacrolimus pharmacokinetics of demographic characteristics (sex, bodyweight, age), drug interaction with prednisolone, laboratory test results (the haematocrit, haemaglobin level and serum creatinine level) and cytochrome P450 (CYP) 3A5 (CYP3A5) genetic polymorphism. The population pharmacokinetic model was further refined by taking into account all of the data from the 41 patients, and the final model was validated using a bootstrap and a visual predictive check. For Bayesian estimation, the best limited-sampling strategy was determined on the basis of the D-optimality criterion and validation performed in the validation group., Results: The trapezoidal area under the whole-blood concentration time curve from 0 to 24 hours (AUC(24)) of tacrolimus varied by up to 50% for the same trough concentration value. The pharmacokinetics of once-daily tacrolimus were well described by a two-compartment model combined with an Erlang distribution to describe the absorption phase. The CYP3A5 genotype was the only covariate retained in the final model. The apparent clearance of tacrolimus was 2-fold higher in expressers (with the CYP3A5*1/*1 and CYP3A5*1/*3 genotypes) than in non-expressers (with the CYP3A5*3/*3 genotype). This factor explained around 25% of the interindividual variability in the apparent clearance. A posteriori Bayesian estimation allowed accurate prediction of the AUC(24) of once-daily tacrolimus, using just three sampling times (0, 1 and 3 hours post-dose) with a nonsignificant mean bias of 0.7% (range 16-20%) and good precision (root mean square error 9%)., Conclusions: Population pharmacokinetic analysis of once-daily tacrolimus in renal transplant recipients resulted in identification of the CYP3A5*1/*3 genotype as a significant covariate on the apparent clearance of tacrolimus, and the design of an accurate maximum a posteriori Bayesian estimator based on three blood concentration measurements and this covariate. Such a tool could be helpful for comparing different exposure indices or different target levels. It could contribute to improvement of the efficacy and tolerability of once-daily tacrolimus in some patients.

Aim: In renal transplant patients given mycophenolate mofetil (MMF), we investigated the relationship between the digestive adverse events and polymorphisms in the UGT genes involved in mycophenolic acid (MPA) intestinal metabolism and biliary excretion of its phase II metabolites., Methods: Clinical data and DNA from 256 patients transplanted between 1996 and 2006 and given MMF with cyclosporin (CsA, n = 185), tacrolimus (TAC, n = 49) or sirolimus (SIR, n = 22), were retrospectively analysed. The relationships between diarrhoea and polymorphisms in UGT1A8 (2; 518C>G, 3; 830G>A), UGT1A7 (622C>T), UGT1A9 (-275T>A), UGT2B7 (-840G>A) and ABCC2 (-24C>T, 3972C>T) or the co-administered immunosuppressant were investigated using the Cox proportional hazard model., Results: Multivariate analysis showed that patients on TAC or SIR had a 2.8 higher risk of diarrhoea than patients on CsA (HR = 2.809; 95%CI (1.730, 4.545); P < 0.0001) and that non-carriers of the UGT1A8 2 allele (CC518 genotype) had a higher risk of diarrhoea than carriers (C518G and 518GG genotypes) (HR = 1.876; 95%CI (1.109, 3.175); P = 0.0192). When patients were divided according to the immunosuppressive co-treatment, a significant effect of UGT1A8 2 was found in those co-treated with CsA (HR = 2.414; 95%CI (1.089, 5.354); P = 0.0301) but not TAC or SIR (P = 0.4331)., Conclusion: These results suggest that a possible inhibition of biliary excretion of MPA metabolites by CsA and a decreased intestinal production of these metabolites in UGT1A8 2 carriers may be protective factors against MMF-induced diarrhoea.

LCP-tacrolimus displays enhanced oral bioavailability compared to immediate-release (IR-) tacrolimus. The ENVARSWITCH study aimed to compare tacrolimus AUC0-24 h in stable kidney (KTR) and liver transplant recipients (LTR) on IR-tacrolimus converted to LCPtacrolimus, in order to re-evaluate the 1:0.7 dose ratio recommended in the context of a switch and the efficiency of the subsequent dose adjustment. Tacrolimus AUC0-24 h was obtained by Bayesian estimation based on three concentrations measured in dried blood spots before (V2), after the switch (V3), and after LCP-tacrolimus dose adjustment intended to reach the pre-switch AUC0-24 h (V4). AUC0-24 h estimates and distributions were compared using the bioequivalence rule for narrow therapeutic range drugs (Westlake 90% CI within 0.90-1.11). Fifty-three KTR and 48 LTR completed the study with no major deviation. AUC0-24 h bioequivalence was met in the entire population and in KTR between V2 and V4 and between V2 and V3. In LTR, the Westlake 90% CI was close to the acceptance limits between V2 and V4 (90% CI = [0.96-1.14]) and between V2 and V3 (90% CI = [0.96-1.15]). The 1:0.7 dose ratio is convenient for KTR but may be adjusted individually for LTR. The combination of DBS and Bayesian estimation for tacrolimus dose adjustment may help with reaching appropriate exposure to tacrolimus rapidly after a switch. [ABSTRACT FROM AUTHOR]

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

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 PmetricsR. 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% IC95%[20-26%], 41% IC95%[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. [ABSTRACT FROM AUTHOR]

Every transplant patient will, at least occasionally, miss immunosuppressive drug doses or take them outside the prescribed times. This study aims at quantifying the impact of poor execution on tacrolimus exposure in renal transplant patients. Validated pharmacokinetic tools applied in clinical setting were used to simulate the steady-state pharmacokinetic profiles of the drug when given as the immediate-release formulation to renal transplant patients, being CYP3A5 expressors or not, and who have reached either a standard or a minimized exposure. Situations of interruption due to a missed or delayed dose were simulated and the impact on drug exposure was explored. In case of a missed dose, it was observed that: (i) a single forgotten dose can greatly impact exposure: up to 49% decrease for tacrolimus trough concentration and 70% for AUC 0 -12 h in patients with the highest clearance values; (ii) patients with a minimized exposure are the most affected by a missed dose; and (iii) a dose of 1.5 times the usual dose may be recommended after a total dose oversight. Considering that intra-patient exposure variability is a predictive factor of poor graft outcome, these modeling results may serve as recommendations for patients, both preventively and in response to their questions. [ABSTRACT FROM AUTHOR]

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Mycophenolate mofetil (MMF), the most widely used drug in allograft transplantation, is subject to hepatic and intestinal glucuronidation and entero-hepatic cycling. • Diarrhoea is its most frequent adverse event leading to non-compliance, treatment interruption and ultimately to an increased rate of acute rejection. • Cyclosporin reduces the biliary excretion of mycophenolate metabolites, presumably by inhibiting the efflux transporter MRP2. • When combined with MMF, cyclosporin reduces the incidence of diarrhoea, suggesting the role played by biliary excretion of mycophenolate glucuronides in this adverse event. WHAT THIS STUDY ADDS • In a long-term cohort of renal transplant patients on MMF, the two factors significantly associated with a reduced incidence of diarrhoea were the co-medication with cyclosporin (as opposed to tacrolimus or sirolimus) and the *2 variant allele of the intestinal UGT1A8. • Polymorphisms in the other UDP-glucuronosyl-transferases and MRP2 were not significant. AIM In renal transplant patients given mycophenolate mofetil (MMF), we investigated the relationship between the digestive adverse events and polymorphisms in the UGT genes involved in mycophenolic acid (MPA) intestinal metabolism and biliary excretion of its phase II metabolites. METHODS Clinical data and DNA from 256 patients transplanted between 1996 and 2006 and given MMF with cyclosporin (CsA, n = 185), tacrolimus (TAC, n = 49) or sirolimus (SIR, n = 22), were retrospectively analysed. The relationships between diarrhoea and polymorphisms in UGT1A8 (*2; 518C>G, *3; 830G>A), UGT1A7 (622C>T), UGT1A9 (−275T>A), UGT2B7 (−840G>A) and ABCC2 (−24C>T, 3972C>T) or the co-administered immunosuppressant were investigated using the Cox proportional hazard model. RESULTS Multivariate analysis showed that patients on TAC or SIR had a 2.8 higher risk of diarrhoea than patients on CsA (HR = 2.809; 95%CI (1.730, 4.545); P < 0.0001) and that non-carriers of the UGT1A8*2 allele (CC518 genotype) had a higher risk of diarrhoea than carriers (C518G and 518GG genotypes) (HR = 1.876; 95%CI (1.109, 3.175); P = 0.0192). When patients were divided according to the immunosuppressive co-treatment, a significant effect of UGT1A8* 2 was found in those co-treated with CsA (HR = 2.414; 95%CI (1.089, 5.354); P = 0.0301) but not TAC or SIR ( P = 0.4331). CONCLUSION These results suggest that a possible inhibition of biliary excretion of MPA metabolites by CsA and a decreased intestinal production of these metabolites in UGT1A8* 2 carriers may be protective factors against MMF-induced diarrhoea. [ABSTRACT FROM AUTHOR]

Ganciclovir (GCV) is the cornerstone of cytomegalovirus prevention and treatment in transplant patients. It is associated with problematic adverse hematological effects in this population of immunosuppressed patients, which may lead to dose reduction thus favoring resistance. GCV crosses the membranes of cells, is activated by phosphorylation, and then stops the replication of viral DNA. Its intracellular accumulation might favor host DNA polymerase inhibition, hence toxicity. Following this hypothesis, we investigated the association between a selected panel of membrane transporter polymorphisms and the evolution of neutrophil counts in n = 174 renal transplant recipients. An independent population of n = 96 renal transplants served as a replication and experiments using HEK293T-transfected cells were performed to validate the clinical findings. In both cohorts, we found a variant in ABCC4 (rs11568658) associated with decreased neutrophil counts following valganciclovir (GCV prodrug) administration (exploratory cohort: β ± SD = −0.68 ± 0.28, p = 0.029; replication cohort: β ± SD = −0.84 ± 0.29, p = 0.0078). MRP4-expressing cells showed decreased GCV accumulation as compared to negative control cells (transfected with an empty vector) (−61%; p < 0.0001). The efflux process was almost abolished in cells expressing MRP4 rs11568658 variant protein. Molecular dynamic simulations of GCV membrane crossing showed a preferred location of the drug just beneath the polar head group region, which supports its interaction with efflux transporters. [ABSTRACT FROM AUTHOR]