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

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Commercially available bioinks and state-of-the-art lab-made formulations for bone tissue engineering: A comprehensive review

Bioprinting and bioinks are two of the game changers in bone tissue engineering. This review presents different bioprinting technologies including extrusion-based, inkjet-based, laser-assisted, light-based, and hybrid technologies with their own strengths and weaknesses. This review will aid researchers in the selection and assessment of the bioink; the discussion ranges from commercially available bioinks to custom lab-made formulations mainly based on natural polymers, such as agarose, alginate, gelatin, collagen, and chitosan, designed for bone tissue engineering. The review is centered on technological advancements and increasing clinical demand within the rapidly growing bioprinting market. From this point of view, 4D, 5D, and 6D printing technologies promise a future where unprecedented levels of innovation will be involved in fabrication processes leading to more dynamic multifunctionalities of bioprinted constructs. Further advances in bioprinting technology, such as hybrid bioprinting methods are covered, with the promise to meet personalized medicine goals while advancing patient outcomes for bone tissues engineering applications.

3D Printing of a Biomimetic Myotendinous Junction Assisted by Artificial Intelligence

The myotendinous junction (MTJ) facilitates force transmission between muscle and tendon to produce joint movement. The complex microarchitecture and regional mechanical heterogeneity of the myotendinous junction pose major challenges in creating this interface in vitro. Engineering this junction in vitro is challenging due to substantial fabrication difficulties in creating scaffolds with intricate microarchitecture and stiffness heterogeneity to mimic the native muscle-tendon interface. To address the current challenges in creating the MTJ in vitro, digital light processing (DLP)-based 3D printing was used to fabricate poly(glycerol sebacate)acrylate (PGSA)-based muscle-tendon scaffolds with physiologically informed microstructure and mechanical properties. Local mechanical properties in various regions of the scaffold were tuned by adjusting the exposure time and light intensity used during the continuous DLP-based 3D printing process to match the mechanical properties present in distinct regions of native muscle-tendon tissue using printing parameters defined by an artificial intelligence-trained algorithm. To evaluate how the presence of zonal stiffness regions can affect the phenotype of a 3D-printed MTJ in vitro model, three 3D-printed PGSA-based scaffold conditions were investigated: (1) a scaffold with muscle-informed mechanical properties in its entirety without zonal stiffness regions, (2) a scaffold with one end possessing native muscle stiffness and the other end possessing native tendon stiffness, and (3) a scaffold with three distinct regions whose stiffness values correspond to those of muscle on one end of the scaffold, MTJ in the middle junction of the scaffold, and tendon on the other end of the scaffold. The scaffold containing regional mechanical heterogeneity most similar to the native MTJ (condition 3) was found to enhance the expression of MTJ-related markers compared to those without the presence of zonal stiffness regions. Overall, the DLP-based 3D printing platform and biomaterial system developed in this study could serve as a useful tool for mimicking the complexity of the native MTJ, which possesses inherent geometric and mechanical heterogeneity.

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.