Machine Learning Prediction and Validation of Plasma Concentration-Time Profiles

Recent research has increasingly focused on using machine learning for covariate selection in population pharmacokinetics (PPK) analysis. However, few studies have explored the prediction of plasma concentration profiles of drugs using nonlinear mixed-effect models combined with machine learning. This gap includes limited validation of prediction accuracy and applicability to diverse patient populations and dosing conditions. This study addresses these gaps by using remifentanil as a model drug and applying machine learning models to predict plasma concentration profiles based on virtual and real-world data. We created various training data sets for the virtual data by clustering based on the size and diversity of the test data set. Our results demonstrated high prediction accuracy for virtual and real-world data sets using Random Forest models. These results suggest that machine learning models are effective for large-scale data sets and real-world data with variable dosing times and amounts per patient. Considering the efficiency of machine learning, it offers a fit-for-purpose approach alongside traditional PPK methods, potentially enhancing future pharmacokinetic and pharmacodynamic studies.