Drug release analysis and optimization for drug-eluting stents

Area of Science:

• Biomedical Engineering
• Materials Science
• Pharmacology

Background:

• Drug-eluting stents (DES) are crucial for treating arterial diseases.
• Optimizing drug release from DES requires understanding complex mechanics, fluid dynamics, and mass transfer.
• Current methods for analyzing DES drug release are computationally intensive.

Purpose of the Study:

• To develop an efficient computational framework for drug release analysis and optimization in DES.
• To investigate the impact of coating parameters on drug release kinetics and arterial wall penetration.
• To enhance the therapeutic efficacy of DES through optimized design.

Main Methods:

• Finite Element Method (FEM) for simulating drug release dynamics within arterial vessels.
• Kriging surrogate modeling to approximate the relationship between coating parameters and drug distribution.
• Adaptive optimization strategy employing Expected Improvement (EI) for efficient design space exploration.
• Selection of diffusion coefficients and coating thickness as key design variables.

Main Results:

• The Kriging surrogate model effectively replaces computationally expensive FEM reanalysis in the optimization loop.
• The adaptive optimization approach, guided by EI, balances local and global search to identify optimal designs.
• FEM analysis revealed the significant influence of coating diffusivity and thickness on drug release profiles.
• Optimized DES designs demonstrated improved drug deposition and penetration into arterial walls.

Conclusions:

• The proposed adaptive optimization method using Kriging surrogate models offers an efficient approach for DES design.
• Optimized coating parameters significantly enhance the localized delivery and therapeutic effect of drugs from DES.
• This study provides a valuable computational tool for advancing the design and efficacy of drug-eluting stents.