COMPUTER-AIDED SCAFFOLD DESIGN OPTIMIZATION TOWARDS ENHANCED BONE REGENERATION

Large bone defects remain a clinical challenge, with a gold standard treatment - autologous bone graft - that presents many drawbacks. Design optimized scaffolds appear as a promising alternative [1]; however, bone scaffold design remains a trial and error approach where some specific properties (porosity, mechanical properties, etc) are individually optimized. Thus, the aim of this study was to develop a computer-aided scaffold design optimisation framework towards enhanced bone regeneration, taking into account the dynamics of the bone regeneration process. A computer model of scaffold-guided bone regeneration was developed and tested against different experimental setups that have used different scaffold designs for large bone defect healing in large animal models [2,3]. The model takes into account the scaffold design (architecture and material properties) as well as its interaction with different cellular processes (e.g. migration). Computer model predictions of bone tissue formation within the scaffold pores were compared against in vivo experimental data. The validated model was then used to develop a computer framework that allow us to optimize the scaffold design with the objective of achieving maximum bone regeneration. The computer model of scaffold-supported bone regeneration is able to explain experimental observations of bone tissue formation within a honeycomb titanium scaffold and a strut-based PCL-βTCP scaffold. In both experimental settings, scaffold surface guidance was predicted to play a key role on the regulation of cellular activity. Computer-aided optimization resulted in a scaffold design which was predicted to achieve almost complete bone regeneration within a large bone defect. We have developed a framework that allows 1) to investigate the mechanisms behind scaffold-supported bone regeneration and 2) to optimize the scaffold design to achieve maximum bone regeneration. Although, the computer model of bone regeneration has shown promising results in different experimental settings, further testing of the model and validation against experimental data is needed to ensure model robustness. The optimization framework allows shape optimization of specific scaffold designs. Future studies will focus on the validation of the optimization framework.