Tacrolimus pharmacodynamics and pharmacogenetics along the calcineurin pathway in human lymphocytes.

BACKGROUND: Although therapeutic drug monitoring has improved the clinical use of immunosuppressive drugs, there is still interpatient variability in efficacy and toxicity that pharmacodynamic monitoring may help to reduce. To select the best biomarkers of tacrolimus pharmacodynamics, we explored the strength and variability of signal transduction and the influence of polymorphisms along the calcineurin pathway. METHODS: Peripheral blood mononuclear cells from 35 healthy volunteers were incubated with tacrolimus (0.1-50 ng/mL) and stimulated ex vivo. Inhibition of NFAT1 (nuclear factor of activated T cells 1) translocation to the nucleus and intracellular expression of interleukin-2 in CD4(+) and CD8(+) T cells and the surface activation marker CD25 in CD3(+) cells were measured by flow cytometry. We sequenced the promoter regions of immunophilins and calcineurin subunits and characterized selected single nucleotide polymorphisms in the genes of the calcineurin pathway with allelic discrimination assays. RESULTS: All responses closely fitted an I/Imax sigmoid model. Large interindividual variability (n = 30) in I0 and IC50 was found for all biomarkers. Moreover, strong and statistically significant associations were found between tacrolimus pharmacodynamic parameters and polymorphisms in the genes coding cyclophilin A, the calcineurin catalytic subunit α isoenzyme, and CD25. CONCLUSIONS: This study demonstrates the consistency and large interindividual variability of signal transduction along the calcineurin pathway, as well as the strong influence of pharmacogenetic polymorphisms in the calcineurin cascade on both the physiological activity of this route and tacrolimus pharmacodynamics.Agencia Nacional de Investigación e InnovaciónUnidda de Biología Molecular, Facultad de Química, UdelarService de Coopération Sientífique et d´Action Culturelle de l´Ambassade de France en UruguayU1248 INSERM, IPPRITT (Individual Profiling and Preventions of Risks with Immunosuppressive Therapies and Transplantation) Université de Limoges, Franc

Identification of Factors Affecting Tacrolimus Trough Levels in Latin American Pediatric Liver Transplant Patients

Tacrolimus is the cornerstone in pediatric liver transplant immunosuppression. Despite close monitoring, fluctuations in tacrolimus blood levels affect safety and efficacy of immunosuppressive treatments. Identifying the factors related to the variability in tacrolimus exposure may be helpful in tailoring the dose. The aim of the present study was to characterize the clinical, pharmacological, and genetic variables associated with systemic tacrolimus exposure in pediatric liver transplant patients. De novo transplant patients with a survival of more than 1 month were considered for inclusion and were genotyped for cytochrome P450 3A5 (CYP3A5). Peritransplant clinical factors and laboratory covariates were recorded retrospectively between 1 month and 2 years after transplant, including alanine aminotransferase (ALT), aspartate aminotransferase, hematocrit, and tacrolimus predose steady-state blood concentrations collected 12 hours after tacrolimus dosing. A linear mixed effect (LME) model was used to assess the association of these factors and the log-transformed tacrolimus dose-normalized trough concentration (logC0/D) levels. Bootstrapping was used to internally validate the final model. External validation was performed in an independent group of patients who matched the original population. The developed LME model described that logC0/D increases with increases in time after transplant (β = 0.019, 95% confidence interval [CI], 0.010-0.028) and ALT values (β = 0.00030, 95% CI, 0.00002-0.00056), whereas logC0/D is significantly lower in graft CYP3A5 expressers compared with nonexpressers (β = −0.349, 95% CI, −0.631 to −0.062). In conclusion, donor CYP3A5 genotype, time after transplant, and ALT values are associated with tacrolimus disposition between 1 month and 2 years after transplant. A better understanding of tacrolimus exposure is essential to minimize the occurrence of an out-of-range therapeutic window that may lead to adverse drug reactions or acute rejection.Fil: Riva, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Woillard, Jean Baptiste. Inserm; FranciaFil: Distefano, Maximiliano. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Moragas, Matías. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Dip, Marcelo Fabian. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Halac, Esteban Tomas. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Cáceres Guido, Paulo. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Licciardone, Nieves. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Mangano, Andrea María Mercedes. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bosaleh, Andrea. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: de Davila, María Teresa. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Schaiquevich, Paula Susana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; ArgentinaFil: Imventarza, Oscar Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentin

Pharmacogénétique et transplantation rénale (approche méthodologique et exemples d'applications)

En pharmacogénétique (PG), une approche par haplotype permet de déceler des effets ou des interactions indétectables par l'étude de Single Nucleotide Polymorphism ;SNP. Ce travail illustre l'intérêt de l'approche par haplotypes dans une thématique concernant l'utilisation des immunosuppresseurs en transplantation rénale : 1) étude de l'influence des haplotypes du gène ABCB1 du donneur sur la perte du greffon chez des patients greffés rénaux traités par Ciclosporine. Une analyse de Cox réalisée sur 259 patients greffés rénaux et les 227 donneurs associés, par SNP, puis par haplotype montre un risque de perte du greffon environ 2,5 fois plus important pour les donneurs porteurs de l'haplotype TTT par rapport aux donneurs CGC (HR : 2,34,IC95%[1,26-4,35],p=0,007). 2) influence des haplotypes du gène ABCC2 sur l'exposition à l'acide mycophénolique (MPA) et ses métabolites. Une analyse quantitative réalisée sur 50 patients traités par MPA montre une augmentation de l'AUC/dose de MPA chez les patients porteurs de l'haplotype CGC par rapport aux porteurs d'un haplotype différent. Cette étude a permis de révéler des effets non visibles lors d'une approche par SNPTOULOUSE3-BU Santé-Centrale (315552105) / SudocSudocFranceF

Pharmacogénétique des immunosuppresseurs en transplantation rénale (étude d'association entre polymorphismes et effets indésirables, modélisation pharmacogénétique et pharmacogénétique/pharmacocinétique)

De nombreuses études pharmacogénétiques (PG) ont été réalisées sur les immunosuppresseurs (IS). Cependant, beaucoup se sont intéressées à leur pharmacocinétique (PK) alors que le plus souvent le Suivi Thérapeutique Pharmacologique permet de compenser des différences d exposition d origine génétique. Peu d études PG ont été réalisées sur les effets indésirables des IS, parfois indépendants des concentrations sanguines d IS. Dans une première partie, après un bref rappel méthodologique, les résultats d études d association PG-effets indésirables des IS sont présentés. L étude réalisée sur la perte du greffon rénal a permis de mettre en évidence l effet majeur des polymorphismes de la P-glycoprotéine (P-gp) du donneur et laisse penser qu une diminution d activité de la P-gp pourrait aboutir à long terme à une majoration de la toxicité de la ciclosporine par accumulation intracellulaire. Le travail réalisé sur les effets indésirables digestifs du mycophénolate mofétil (MMF) a montré une diminution du risque de diarrhée chez les patients porteurs de l allèle UGT1A8*2qui diminue l activité cette enzyme intestinale. Enfin, l étude des polymorphismes des principales protéines de la voie m-TOR fait ressortir une diminution de la concentration d hémoglobine chez les patients porteurs d un haplotype particulier de la m-TOR. Dans une deuxième partie, l intégration de données génétiques aux outils d individualisation thérapeutique a été étudiée avec un travail de modélisation de population de la PK de l Advagraf® et du Prograf® confirmant le rôle du polymorphisme CYP3A5 sur la PK du tacrolimus et permettant le développement d un estimateur bayesien intégrant ce polymorphisme.LIMOGES-BU Médecine pharmacie (870852108) / SudocSudocFranceF

Tacrolimus Exposure Prediction Using Machine Learning

International audienceThe aim of this work is to estimate the area‐under the blood concentration curve of tacrolimus (TAC) following b.i.d. or q.d. dosing in organ transplant patients, using Xgboost machine learning (ML) models. A total of 4,997 and 1,452 TAC interdose area under the curves (AUCs) from patients on b.i.d. and q.d. TAC, sent to our Immunosuppressant Bayesian Dose Adjustment expert system ( www.pharmaco.chu‐limoges.fr/ ) for AUC estimation and dose recommendation based on TAC concentrations measured at least at 3 sampling times (predose, ~ 1 and 3 hours after dosing) were used to develop 4 ML models based on 2 or 3 concentrations. For each model, data splitting was performed to obtain a training set (75%) and a test set (25%). The Xgboost models in the training set with the lowest root mean square error (RMSE) in a 10‐fold cross‐validation experiment were evaluated in the test set and in 6 independent full‐pharmacokinetic (PK) datasets from renal, liver, and heart transplant patients. ML models based on two or three concentrations, differences between these concentrations, relative deviations from theoretical times of sampling, and four covariates (dose, type of transplantation, age, and time between transplantation and sampling) yielded excellent AUC estimation performance in the test datasets (relative bias < 5% and relative RMSE < 10%) and better performance than maximum a posteriori Bayesian estimation in the six independent full‐PK datasets. The Xgboost ML models described allow accurate estimation of TAC interdose AUC and can be used for routine TAC exposure estimation and dose adjustment. They will soon be implemented in a dedicated web interface

Mycophenolic Acid Exposure Prediction Using Machine Learning

International audienceTherapeutic drug monitoring of mycophenolic acid (MPA) based on area under the curve (AUC) is well‐established and machine learning (ML) approaches could help to estimate AUC. The aim of this work is to estimate the AUC of MPA in organ transplant patients using extreme gradient boosting (Xgboost R package) ML models. A total of 12,877 MPA AUC from 0 to 12 hours (AUC 0–12 h ) requests from 6,884 patients sent to our Immunosuppressant Bayesian Dose Adjustment expert system ( https://abis.chu‐limoges.fr ) for AUC estimation and dose recommendation based on MPA concentrations measured at least at three sampling times (~ 20 minutes, 1 and 3 hours after dosing) were used to develop two ML models based on two or three concentrations. Data were split into a training set (75%) and a test set (25%) and the Xgboost models in the training set with the lowest root mean squared error (RMSE) in a 10‐fold cross‐validation experiment were evaluated in the test set and in 4 independent full‐pharmacokinetic (PK) datasets from renal or heart transplant recipients. ML models based on two or three concentrations, differences between these concentrations, relative deviations from theoretical times of sampling, presence of a delayed absorption peak, and five covariates (dose, type of transplantation, associated immunosuppressant, age, and time between transplantation and sampling) yielded accurate AUC estimation performances in the test datasets (relative bias < 5% and relative RMSE < 20%) and better performance than MAP Bayesian estimation in the four independent full‐PK datasets. The Xgboost ML models described allow accurate estimation of MPA AUC 0–12 h and can be used for routine exposure estimation and dose adjustment and will soon be implemented in a dedicated web interface

Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: The example of tacrolimus

International audienceWe previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Bayesian estimation (MAP-BE). However, the major limitation in the development of such ML algorithms is the limited availability of large databases of concentration vs. time profiles for such drugs. The objectives of this study were: (i) to develop a Xgboost model to estimate tacrolimus inter-dose AUC based on concentration-time profiles obtained from a literature population pharmacokinetic (POPPK) model using Monte Carlo simulation; and (ii) to compare its performance with that of MAP-BE in external datasets of rich concentration-time profiles. The population parameters of a previously published PK model were used in the mrgsolve R package to simulate 9000 rich interdose tacrolimus profiles (one concentration simulated every 30 min) at steady-state. Data splitting was performed to obtain a training set (75%) and a test set (25%). Xgboost algorithms able to estimate tacrolimus AUC based on 2 or 3 concentrations were developed in the training set and the model with the lowest RMSE in a tenfold cross-validation experiment was evaluated in the test set, as well as in 4 independent, rich PK datasets from transplant patients. ML algorithms based on 2 or 3 concentrations and a few covariates yielded excellent AUC estimation in the external validation datasets (relative bias < 5% and relative RMSE < 10%), comparable to those obtained with MAP-BE. In conclusion, Xgboost machine learning models trained on concentration-time profiles simulated using literature POPPK models allow accurate tacrolimus AUC estimation based on sparse concentration data. This study paves the way to the development of artificial intelligence at the service of precision therapeutic drug monitoring in different therapeutic areas

A Method for Evaluating Robustness of Limited Sampling Strategies—Exemplified by Serum Iohexol Clearance for Determination of Measured Glomerular Filtration Rate

In combination with Bayesian estimates based on a population pharmacokinetic model, limited sampling strategies (LSS) may reduce the number of samples required for individual pharmacokinetic parameter estimations. Such strategies reduce the burden when assessing the area under the concentration versus time curves (AUC) in therapeutic drug monitoring. However, it is not uncommon for the actual sample time to deviate from the optimal one. In this work, we evaluate the robustness of parameter estimations to such deviations in an LSS. A previously developed 4-point LSS for estimation of serum iohexol clearance (i.e., dose/AUC) was used to exemplify the effect of sample time deviations. Two parallel strategies were used: (a) shifting the exact sampling time by an empirical amount of time for each of the four individual sample points, and (b) introducing a random error across all sample points. The investigated iohexol LSS appeared robust to deviations from optimal sample times, both across individual and multiple sample points. The proportion of individuals with a relative error greater than 15% (P15) was 5.3% in the reference run with optimally timed sampling, which increased to a maximum of 8.3% following the introduction of random error in sample time across all four time points. We propose to apply the present method for the validation of LSS developed for clinical use

Population Pharmacokinetics and Bayesian Estimators for Refined Dose Adjustment of a New Tacrolimus Formulation in Kidney and Liver Transplant Patients

International audienceBACKGROUND 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

Lessons from routine dose adjustment of tacrolimus in renal transplant patients based on global exposure.

International audienceOBJECTIVES: 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