aceApplication of Machine Learning Models to Predict Tigecycline Exposure in Critically Ill Patients Using Different Limited Sampling Strategies
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, an…
An LSTM model trained on 98 critically ill patients estimated tigecycline 24-hour drug exposure accurately, and a single trough-sample version cut the sampling burden for therapeutic drug monitoring.
Results come from internal validation on a single pharmacokinetic dataset of 98 patients, and the single-sample model was less accurate than the three-point model.
Evidence
- Peer-reviewedDrug Design, Development and Therapy2026-10-14
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Truvace Impact Record TRV-2026-2061, v1: “Application of Machine Learning Models to Predict Tigecycline Exposure in Critically Ill Patients Using Different Limited Sampling Strategies.” Truvace, 2026-10-21. /record/TRV-2026-2061 (accessed at citation time). sha256 76144ef81fa5774f…
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