Optimization of drug diffusion in drug-eluting stents for coronary artery based on deep reinforcement learning

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Half-embedded: 6.99% increase in therapeutic concentration; Fully embedded: 8.14% improvement in uniformity; Non-embedded: 2.04% and 6.30% increases in concentration and uniformity respectively

• Primary Outcome: Optimization of drug concentration and uniformity in therapeutic domain while controlling non-treatment domain levels

• Nnt Or Sensitivity: Not applicable - computational optimization study

• Confidence Interval: Not reported

Clinical Application

Requires significant computational infrastructure and clinical validation studies before implementation Would need TGA approval for any modified stent designs; could inform future PBS-listed DES development if clinically validated Patients requiring drug-eluting stents for coronary artery disease, pending clinical validation

Abstract

Drug-eluting stents (DES) are extensively used to treat coronary artery disease, and improving their therapeutic efficacy remains a long-standing research objective. Existing optimization strategies for DES drug delivery lack adaptive, learning-based frameworks capable of simultaneously enhancing drug concentration and uniformity in the therapeutic domain while regulating levels in the non-treatment domain, and show limited capacity to address complex, nonlinear optimization problems. This study applies a deep reinforcement learning (DRL)-based framework to optimize drug diffusion in DES. By integrating neural networks with the proximal policy optimization (PPO) algorithm, the framework enables closed-loop, feedback-driven regulation of drug diffusion in real time and demonstrates robustness to stochastic disturbances and parametric variations. The method was systematically evaluated across three stent embedding configurations: half-embedded, fully embedded, and non-embedded. Results show the DRL agent successfully balances the three therapeutic objectives and adopts distinct control strategies for different configurations. All configurations exhibited improved overall performance. For the half-embedded configuration, the DRL policy increased therapeutic domain concentration by 6.99%, accompanied by a slight rise in the non-treatment domain and a minor decrease in uniformity within the therapeutic domain. Under the fully embedded configuration, concentrations in both domains remained essentially unchanged, whereas uniformity in the therapeutic domain improved by 8.14%. For the non-embedded configuration, both therapeutic domain concentration and uniformity increased (by 2.04% and 6.30%, respectively), with negligible change in the non-treatment domain. These results demonstrate the feasibility of DRL for adaptive DES optimization and highlight its potential for patient-specific applications and guidance in clinical selection of drug-embedding configurations.

References

1. 1.Lou, Z., Zhu, J., Wang, Y., Si, X., Wang, W., & Zhang, X. (2026). Optimization of drug diffusion in drug-eluting stents for coronary artery based on deep reinforcement learning. International Journal of Pharmaceutics, 126733. https://doi.org/10.1016/j.ijpharm.2026.126733