Multimodal Three-Dimensional Printing for Micro-Modulation of Scaffold Stiffness Through Machine Learning

Facile and rapid fabrication of a novel 3D-printable, visible light-crosslinkable and bioactive polythiourethane for large-to-massive rotator cuff tendon repair

Facile and rapid 3D fabrication of strong, bioactive materials can address challenges that impede repair of large-to-massive rotator cuff tears including personalized grafts, limited mechanical support, and inadequate tissue regeneration. Herein, we developed a facile and rapid methodology that generates visible light-crosslinkable polythiourethane (PHT) pre-polymer resin (∼30 min at room temperature), yielding 3D-printable scaffolds with tendon-like mechanical attributes capable of delivering tenogenic bioactive factors. Ex vivo characterization confirmed successful fabrication, robust human supraspinatus tendon (SST)-like tensile properties (strength: 23 MPa, modulus: 459 MPa, at least 10,000 physiological loading cycles without failure), excellent suture retention (8.62-fold lower than acellular dermal matrix (ADM)-based clinical graft), slow degradation, and controlled release of fibroblast growth factor-2 (FGF-2) and transforming growth factor-β3 (TGF-β3). In vitro studies showed cytocompatibility and growth factor-mediated tenogenic-like differentiation of mesenchymal stem cells. In vivo studies demonstrated biocompatibility (3-week mouse subcutaneous implantation) and ability of growth factor-containing scaffolds to notably regenerate at least 1-cm of tendon with native-like biomechanical attributes as uninjured shoulder (8-week, large-to-massive 1-cm gap rabbit rotator cuff injury). This study demonstrates use of a 3D-printable, strong, and bioactive material to provide mechanical support and pro-regenerative cues for challenging injuries such as large-to-massive rotator cuff tears.

Enhancing quality control in bioprinting through machine learning

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.