Machine learning combined with population pharmacokinetics: a hybrid model for predicting the plasma concentration of linezolid in critically ill pediatric patients.

Objective: Linezolid is a crucial agent for treating drug-resistant Gram-positive bacterial infections in critically ill pediatric patients. However, its pharmacokinetics exhibit high inter-individual variability, making standard dosing regimens susceptible to underexposure or overexposure. This study aimed to explore the integration of population pharmacokinetics (PopPK) and machine learning (ML) algorithms to build a best-performing model for predicting individual linezolid plasma concentrations in critically ill children, thereby guiding personalized dosing. Methods: Based on data from a retrospective cohort of 145 critically ill pediatric patients (213 samples), 32 features (including PK parameters) and the target variable (linezolid plasma concentration) were included. A PopPK model was established, and Monte Carlo simulations were conducted to optimize dosing regimens. After systematically evaluating 7 ML algorithms, the best predictive model was identified. Its interpretation was then performed with SHapley Additive exPlanations (SHAP). Results: A one-compartment model with first-order elimination adequately described the pharmacokinetics of linezolid. Monte Carlo simulations for minimum inhibitory concentration (MIC) values between 0.5 and 2 mg/L, most renal function levels achieved a probability of target attainment (PTA) > 90% through dose adjustments. When MIC reached 4 mg/L, a PTA > 90% was achieved only in patients with severe renal impairment using 20 mg/kg q8h, yet with high safety risk, indicating the need for alternative antimicrobial agents. The LightGBM algorithm was best-performing among the seven algorithms evaluated, with testing set R Conclusion: This exploratory study integrated PK parameters into ML models for predicting linezolid plasma concentrations in critically ill pediatric patients, providing preliminary insights for future research as a proof of concept. However, the reported metrics could not support clinical deployment without leakage-free re-estimation using time-constrained or sequential validation.