Tacrolimus exposure prediction using machine learning

The aim of this work is to estimate the area-under the blood concentration curve of tacrolimus following twice-a-day (BID) or once-a-day (QD) dosing in organ transplant patients, using Xgboost machine learning (ML) models. A total of 4997 and 1452 tacrolimus inter-dose AUCs from patients on BID and QD tacrolimus, sent to our ISBA expert system (www.pharmaco.chu-limoges.fr/) for AUC estimation and dose recommendation based on tacrolimus concentrations measured at least at 3 sampling times (predose, approx. 1 and 3h after dosing) were used to develop four 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 RMSE in a ten-fold cross-validation experiment were evaluated in the test set and in 6 independent full-pk datasets from renal, liver and heart transplant patients. ML models based on 2 or 3 concentrations, differences between these concentrations, relative deviations from theoretical times of sampling and 4 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 MAP Bayesian estimation in the 6 independent full-pk datasets. The Xgboost ML models described allow accurate estimation of tacrolimus interdose AUC and can be used for routine tacrolimus exposure estimation and dose adjustment. They will soon be implemented in a dedicated web interface.