Adaptive PK/PD model optimization: a comparative analysis of bounded optimization methods for individual BIS prediction.

This study investigates the feasibility of adaptive optimization in pharmacokinetic/pharmacodynamic (PK/PD) models for predicting individual Bispectral Index (BIS) values during sedation and anesthesia by refining established models—specifically the Schnider, Marsh, Schuttler, Eleveld model for propofol and Minto model for remifentanil — using bounded optimization techniques. We adapt these population-based models through cumulative intraoperative BIS data to derive an effective set of PK/PD parameters for improved patient-specific BIS prediction. Comparative analyses using datasets from sedation and general anesthesia demonstrate that wider parameter bounding constraints improve model performance, as shown by higher Pearson correlations, and lower prediction errors, while maintaining computational efficiency. Our experiment results showed that Sequential Least Squares Quadratic Programming (SLSQP) and Least Squares methods excel in accuracy and computational efficiency, having the highest Pearson correlation of 0.56 and lowest root mean square error of 10.3. Real-time updates to PK/PD models using continuous BIS data may improve patient-specific BIS prediction during sedation and general anesthesia. These findings support the feasibility of adaptive calibration of population PK/PD models as a component of future model-based anesthetic management. Individualized dosing strategies could support closed-loop anesthesia systems and advance personalized medicine in perioperative care.