A mechanobiological computer optimization framework to design scaffolds to enhance bone regeneration

Area of Science:

• Biomaterials Engineering
• Regenerative Medicine
• Computational Modeling

Background:

• Large bone defects pose significant clinical challenges.
• 3D printed scaffolds offer a promising solution, but their design is complex due to numerous variables affecting bone regeneration.
• Existing scaffold optimization focuses on immediate post-implantation mechanical conditions, neglecting the dynamic healing process.

Purpose of the Study:

• To develop and validate a computational framework for optimizing 3D printed scaffold designs to promote long-term bone regeneration outcomes.
• To integrate a mechanobiological bone regeneration model with a surrogate healing outcome model and an optimization algorithm.
• To predict and enhance the final regenerated bone volume within scaffolds.

Main Methods:

• A computational framework combining a validated mechanobiological bone regeneration model, a surrogate bone healing outcome model, and an optimization algorithm was developed.
• The framework optimizes scaffold design based on predicted regenerated bone volume.
• The framework's efficacy was demonstrated through the optimization of a cylindrical scaffold for a critical-size tibia defect in a large animal model.

Main Results:

• The computational framework successfully predicted long-term bone healing outcomes.
• Optimization using the framework led to improved scaffold designs for promoting bone regeneration.
• The study verified the framework's capability in a clinically relevant large animal model.

Conclusions:

• The proposed computational framework enables the optimization of 3D printed scaffold designs for enhanced long-term bone regeneration.
• This novel approach offers new possibilities for sustainable strategies in scaffold design for bone defect treatment.
• The ability to predict long-term healing outcomes represents a significant advancement in regenerative medicine.