Machine Learning Prediction of Unbound Ceftriaxone Concentrations in Children: Capturing Developmental Changes in Protein Binding for Precision Dosing

of 0.87. Using only routine variables, including total ceftriaxone concentration, serum albumin, weight, and age, the model accurately estimated unbound ceftriaxone concentrations across a wide range of pediatric patients (0-12 years). Real-world validation further confirmed its robustness, yielding a mean absolute percentage error (MAPE) of 28.9% and outperforming both reference equations, with reductions in MAPE of 14.2% and 14.7% relative to the empirical In vivo and disease-adapted equations, respectively. Application of the final model to a virtual pediatric population aged 0-12 years revealed a nonlinear age-dependent protein binding pattern, characterized by low and variable binding in neonates, rapid maturation during early infancy, and stabilization after 2 years of age. By incorporating these maturational effects, the ML model provides a practical tool for predicting unbound drug exposure, facilitating individualized dosing and precision medicine in pediatric patients.