Drug Design Development and Therapy
Application of Machine Learning Models to Predict Tigecycline Exposure in Critically Ill Patients Using Different Limited Sampling Strategies
By Anran Dai, Hao Liu, Bo Yang and 9 others.
Abstract
Background
The high variability in the pharmacokinetics of tigecycline may impact therapeutic efficacy, particularly in critically ill patients. This study introduces machine learning (ML) methods, aiming to clarify their effectiveness in individualized drug exposure prediction and determine the optimal modeling strategy.
Methods
The least absolute shrinkage and selection operator (LASSO) method was used to screen the covariates. Four ML models, random forest, extreme gradient boosting, multilayer perceptron, and long short-term memory network (LSTM), were trained and tested using 751 concentrations from 98 patients in the pharmacokinetic dataset to predict the area under the steady-state plasma concentration-time curve within 24 hours (AUC 0-24h ) of tigecycline. Furthermore, on the internal test set, we evaluated the accuracy of the published tigecycline Population Pharmacokinetics (PopPK) models.