Mycophenolic Acid Exposure Prediction Using Machine Learning. Issue 2 (6th April 2021)

• Record Type: Journal Article
• Authors: Woillard, Jean‐Baptiste; Labriffe, Marc; Debord, Jean; Marquet, Pierre
• Is Part Of: Clinical pharmacology & therapeutics. Volume 110:Issue 2(2021)
• Page Start: 370
• Page End: 379
• Publication Date: 2021-04-06
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.2216
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : Therapeutic 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 (AUC0–12 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 AUC0–12 h and can be used for routine exposure estimation and dose adjustment and will soon be implemented in a dedicated web interface.