Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

Bone tissue engineering has emerged as a promising strategy to overcome the limitations of current treatments for bone-related disorders, but the trade-off between mechanical properties and bioactivity remains a concern for many polymeric materials. To address this need, novel polymeric blends of poly-L-lactic acid (PLLA), polycaprolactone (PCL) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) have been explored. Blend filaments comprising PLLA/PCL/PHBV at a ratio of 90/5/5 wt% have been prepared using twin-screw extrusion. The PLLA/PCL/PHBV blends were enriched with nano-hydroxyapatite (nano-HA) and strontium-substituted nano-HA (Sr-nano-HA) to produce composite filaments. Three-dimensional scaffolds were printed by fused deposition modelling from PLLA/PCL/PHBV blend and composite filaments and evaluated mechanically and biologically for their capacity to support bone formation in vitro. The composite scaffolds had a mean porosity of 40%, mean pores of 800 µm, and an average compressive modulus of 32 MPa. Polymer blend and enriched scaffolds supported cell attachment and proliferation. The alkaline phosphatase activity and calcium production were significantly higher in composite scaffolds compared to the blends. These findings demonstrate that thermoplastic polyesters (PLLA and PCL) can be combined with polymers produced via a bacterial route (PHBV) to produce polymer blends with excellent biocompatibility, providing additional options for polymer blend optimization. The enrichment of the blend with nano-HA and Sr-nano-HA powders enhanced the osteogenic potential in vitro.

Tissue Engineering (TE) represents a promising solution to fabricate engineered constructs able to restore tissue damage after implantation. In the classic TE approach, biomaterials are used alongside growth factors to create a scaffolding structure that supports cells during the construct maturation. A current challenge in TE is the creation of engineered constructs able to mimic the complex microenvironment found in the natural tissue, so as to promote and guide cell migration, proliferation, and differentiation. In this context, the introduction inside the scaffold of molecularly imprinted polymers (MIPs)—synthetic receptors able to reversibly bind to biomolecules—holds great promise to enhance the scaffold-cell interaction. In this review, we analyze the main strategies that have been used for MIP design and fabrication with a particular focus on biomedical research. Furthermore, to highlight the potential of MIPs for scaffold-based TE, we present recent examples on how MIPs have been used in TE to introduce biophysical cues as well as for drug delivery and sequestering.

This work presents a computational model to study the degradation behavior of polyester-based three-dimensional (3D) functionalized scaffolds for bone regeneration. As a case study, we investigated the behavior of a 3D-printed scaffold presenting a functionalized surface with ICOS-Fc, a bioactive protein able to stimulate bone regeneration and healing, inhibiting osteoclast activity. The aim of the model was to optimize the scaffold design to control its degradation and thus the release of grafted protein over time and space. Two different scenarios were considered: (i) a scaffold without macroporosity presenting a functionalized external surface; and (ii) a scaffold presenting an internal functionalized macroporous architecture with open channels to locally deliver the degradation products.

The application of mechanical stimulation on bone tissue engineering constructs aims to mimic the native dynamic nature of bone. Although many attempts have been made to evaluate the effect of applied mechanical stimuli on osteogenic differentiation, the conditions that govern this process have not yet been fully explored. In this study, pre-osteoblastic cells were seeded on PLLA/PCL/PHBV (90/5/5 wt.%) polymeric blend scaffolds. The constructs were subjected every day to cyclic uniaxial compression for 40 min at a displacement of 400 μm, using three frequency values, 0.5, 1, and 1.5 Hz, for up to 21 days, and their osteogenic response was compared to that of static cultures. Finite element simulation was performed to validate the scaffold design and the loading direction, and to assure that cells inside the scaffolds would be subjected to significant levels of strain during stimulation. None of the applied loading conditions negatively affected the cell viability. The alkaline phosphatase activity data indicated significantly higher values at all dynamic conditions compared to the static ones at day 7, with the highest response being observed at 0.5 Hz. Collagen and calcium production were significantly increased compared to static controls. These results indicate that all of the examined frequencies substantially promoted the osteogenic capacity.