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.

The aim of this study was to develop maximum a posteriori probability (MAP) Bayesian estimators of mycophenolic acid (MPA) pharmacokinetics (PK) capable of accurately estimating the MPA interdose AUC in renal transplant patients using a limited number of blood samples. The individual MPA plasma concentration-time profiles of 44 adult kidney transplant recipients were retrospectively studied: in 24 de novo transplant patients, 2 profiles were obtained on day 7 and day 30 after transplantation, and in 20 stable transplant patients, 1 profile was obtained in the stable period (>3 months). MPA was assayed by liquid chromatography-mass spectrometry. Concentration data were fitted using previously designed PK models, including 1 or 2Gamma-distribution to describe the absorption rate. MAP-Bayesian estimations were performed using an in-house program. For each posttransplantation period, the limited sampling strategies (LSS) providing either the best determination coefficient or the lowest bias for AUC estimates with respect to trapezoidal AUCs were selected and compared with respect to the percentage of "clinically acceptable" AUC estimates (ie, within -20% to +20% of the true value) they yielded. A common LSS (blood samples collected at T20 min, T1 h, and T3 h postdosing), convenient for all 3 periods, was also selected and validated: bias (RMSE%) values were -5.7% (20.5%), -8.2% (14.4%), and +0.4% (12.0%) on D7, D30, and for >M3 with respect to the reference values obtained using the trapezoidal rule, respectively. For the first time, MAP-Bayesian estimators of MPA systemic exposure at different posttransplantation periods (early as well as later periods) could be designed. They have since been used for MPA dose adaptation in concentration-controlled studies as well as for MPA therapeutic drug monitoring in clinical practice.

A specific pharmacokinetic model was built to accurately fit MPA blood concentration-time profiles after MMF oral dosing in SLE patients, which allowed development of an accurate Bayesian estimator of MPA exposure that should allow MMF monitoring based on the AUC(12) in these patients. The predictive value of targeting one specific or different AUC values on patients' outcome using this estimator in SLE will need to be evaluated.

The 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 (http://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.