Unidirectional Fiber Composite Materials for Bone Tissue Engineering: A Systematic Review of the Literature and Prediction of Effective Mechanical Properties via Supervised Machine Learning

This paper presents a comprehensive review of current advances and challenges in the field of bone tissue engineering. A systematic review of the literature was conducted to identify recent developments in biomaterials, scaffold design, cell sources, and growth factors for bone tissue engineering applications. Based on this review, an experimental proposal is presented for the development of porous composite biomaterials that may enhance bone regeneration, which consist of hybrid amyloid/spidroin fibers combined with a bioactive ceramic matrix. An iterative design process of modeling and simulation, production, and characterization of both the fibers and the composite material is proposed. A modeling and simulation approach is also presented for unidirectional fiber composite biomaterials using 2-point correlation functions, finite element simulations, and machine learning. This approach was demonstrated to enable the efficient and accurate prediction of the effective Young’s modulus of candidate composite biomaterials, which can inform the design of optimized materials for bone tissue engineering applications. The proposed experimental and simulation approaches have the potential to address current challenges and lead to the development of novel composite biomaterials that can augment the current technologies in the field of bone tissue engineering.