A Comparison of AI and Population PK Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records.

Population pharmacokinetic (PK) models are commonly used to predict drug concentrations, but artificial intelligence (AI) models have gained interest due to their ability to identify complex patterns without requiring mathematical assumptions. This study compares the predictive performance of AI and population PK models using therapeutic drug monitoring (TDM) records of four antiepileptic drugs (AEDs): carbamazepine (CBZ), phenobarbital (PHB), phenytoin (PHE), and valproic acid (VPA). Additionally, we analyzed key covariates influencing drug concentration predicting using the most accurate model. We extracted concentration data for CBZ, PHB, PHE, and VPA from TDM reports at Seoul National University Hospital (2010-2021), along with patient diagnoses and lab results. The predictive performances of 10 AI models, including ensemble and deep learning models, were compared with published population PK models. The predictive performance of AI models generally exceeded that of population PK models. The best-performing AI models, such as Adaboost, eXtreme Gradient Boosting, and Random Forest, had lower root mean squared error values for CBZ, PHB, PHE, and VPA (2.71, 27.45, 4.15, and 13.68 μg/mL, respectively) compared to population PK models (3.09, 26.04, 16.12, and 25.02 μg/mL). The most influential covariate was time after last drug administration. AI models, particularly ensemble methods, showed strong predictive performance and may support individualized AED dosing, improving therapeutic outcomes while minimizing adverse effects.