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.