3D printed scaffolds with random microarchitecture for bone tissue engineering applications: Manufacturing and characterization. (March 2020) - Pecci, Raffaella; Baiguera, Silvia; Ioppolo, Pietro; Bedini, Rossella; Del Gaudio, Costantino
Abstract: Additive manufacturing for tissue engineering applications offers the possibility to design scaffolds characterized by a fine and detailed microarchitecture. Several fabrication technologies are currently available which allow to prepare tailored structures with a large selection of materials for restoring and healing tissues. However, 3D printed scaffolds are generally collected by assembling repetitive geometrical units or reproducing specific patterns in the layering direction, leading to a highly ordered architecture that does not mimic the morphology of the natural extracellular matrix (ECM), one of the main goals to be reached for an effective therapeutic approach. It is usually stated in the tissue engineering field that a scaffold has to be considered a temporary ECM, resembling all the peculiar features as close as possible and, in this regard, an ordered microstructure cannot be usually observed within biological tissues and organs. With the aim to overcame this limitation and offer a potential approach for bone tissue applications, the present study proposes a design methodology to fabricate 3D printed scaffolds characterized by a random microarchitecture which can be repeatedly reproduced thanks to the intrinsic controllable process of additive manufacturing. In this framework, four different models in polylactic acid were fabricated by means of fused deposition modelling, including a three-dimensional random distribution of spherical pores of 400, 500, and 600 μm for the first three cases, and a randomly varied distribution in the range 400–600 μm for the fourth case. A detailed assessment by means of microcomputed tomography and mechanical evaluation was then carried out in order to fully analyse the resulting scaffolds, providing both morphological and quantitative data.