A Reinforcement Learning (RL)-Motivated Simulation Framework for Evaluating Vancomycin Dosing Strategies

Achieving and maintaining the therapeutic range in vancomycin treatment is important for optimal outcomes. While guidelines and best practices based on empirical studies exist, the theoretical best dosing strategies under various conditions remain illusive. We developed an RL-based simulation framework using a deep learning two-compartment pharmacokinetic model (PK-RNN-2CM) and introduced the area under the time-concentration curve (AUC) reward score, which translates clinical guidelines into an RL reward. Ground truth time-concentration curves were generated from patient-specific data, and simulated curves were produced under different dosing strategies with optional noise perturbations to mimic real-world settings. Evaluation metrics included 24-hour AUC assessments and RMSE. Results indicated that while the low-dosing AUC target (low-doser) and the high-dosing AUC target (high-doser) performed comparably in noise-free conditions, the low-doser achieved slightly higher AUC reward scores under noisy conditions, whereas the high-doser exhibited greater stability. This framework opens new approaches for optimizing vancomycin dosing.