Three-dimensional (3D) printing technologies have become an attractive manufacturing process to fabricate scaffolds in tissue engineering. Recent research has focused on the fabrication of alginate complex shaped structures that closely mimic biological organs or tissues. Alginates can be effectively manufactured into porous three-dimensional networks for tissue engineering applications. However, the structure, mechanical properties, and shape fidelity of 3D-printed alginate hydrogels used for preparing tissue-engineered scaffolds is difficult to control. In this work, the use of alginate/gelatin hydrogels reinforced with TiO₂ and β-tricalcium phosphate was studied to tailor the mechanical properties of 3D-printed hydrogels. The hydrogels reinforced with TiO₂ and β-TCP showed enhanced mechanical properties up to 20 MPa of elastic modulus. Furthermore, the pores of the crosslinked printed structures were measured with an average pore size of 200 μm. Additionally, it was found that as more layers of the design were printed, there was an increase of the line width of the bottom layers due to its viscous deformation. Shrinkage of the design when the hydrogel is crosslinked and freeze dried was also measured and found to be up to 27% from the printed design. Overall, the proposed approach enabled fabrication of 3D-printed alginate scaffolds with adequate physical properties for tissue engineering applications.

In this work we present a novel algorithm for generating in-silico biomimetic models of a cortical bone microstructure towards manufacturing biomimetic bone via additive manufacturing. The software provides a tool for physicians or biomedical engineers to develop models of cortical bone that include the inherent complexity of the microstructure. The correspondence of the produced virtual prototypes with natural bone tissue was assessed experimentally employing Digital Light Processing (DLP) of a thermoset polymer resin to recreate healthy and osteoporotic bone tissue microstructure. The proposed tool was successfully implemented to develop cortical bone structure based on osteon density, cement line thickness, and the Haversian and Volkmann channels to produce a user-designated bone porosity that matches within values reported from literature for these types of tissues. Characterization of the specimens using a Scanning Electron Microscopy with Focused Ion Beam (SEM/FIB) and Computer Tomography (CT) revealed that the manufacturability of intricated virtual prototype is possible for scaled-up versions of the tissue. Modeling based on the density, inclination and size range of the osteon and Haversian and Volkmann´s canals granted the development of a dynamic in-silico porosity (13.37–21.49%) that matches with models of healthy and osteoporotic bone. Correspondence of the designed porosity with the manufactured assessment (5.79–16.16%) shows that the introduced methodology is a step towards the development of more refined and lifelike porous structures such as cortical bone. Further research is required for validation of the proposed methodology model of the real bone tissue and as a patient-specific customization tool of synthetic bone.

Biocompatible and biodegradable membrane treatments for regeneration of bone are nowadays a promising solution in the medical field. Bioresorbable polymers are extensively used in membrane elaboration, where polycaprolactone (PCL) is used as base polymer. The goal of this work was to improve electrospun membranes’ biocompatibility and antibacterial properties by adding micro- and nanoparticles such as Ag, TiO₂ and Na₂Ti₆O₁₃. Micro/nanofiber morphologies of the obtained membranes were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, differential scanning calorimetry, scanning electron microscopy, energy-dispersive X-ray spectroscopy and a tensile test. Also, for this study optical microscopy was used to observe DAPI-stained cells. Membranes of the different systems were electrospun to an average diameter of 1.02–1.76 μm. To evaluate the biological properties, cell viability was studied by growing NIH/3T3 cells on the microfibers. PCL/TiO₂ strength was enhanced from 0.6 MPa to 6.3 MPa in comparison with PCL without particles. Antibacterial activity was observed in PCL/TiO₂ and PCL/Na₂Ti₆O₁₃ electrospun membranes using Staphylococcus aureus bacteria. Bioactivity of the membranes was confirmed with simulated body fluid (SBF) treatment. From this study, the ceramic particles TiO₂ and Na₂Ti₆O₁₃, combined with a PCL matrix with micro/nanoparticles, enhanced cell proliferation, adhesion and antibacterial properties. The electrospun composite with Na₂Ti₆O₁₃ can be considered viable for tissue regenerative processes.

Recent developments in additive manufacturing (AM) have led to significant opportunities in the design and fabrication of implantable medical devices due to the advantages that AM offers compared to conventional manufacturing, such as high customizability, the ability to fabricate highly complex shapes, good dimensional accuracy, a clean build environment, and reduced material usage. The study of structural design optimization (SDO) involves techniques such as Topology Optimization (TO), Shape Optimization (SHO), and Size Optimization (SO) that determine specific parameters to achieve the best measurable performance in a defined design space under a given set of loads and constraints. Integration of SDO techniques with AM leads to utmost benefits in designing and fabricating optimized implantable medical devices with enhanced functional performance. Research and development of various lattice structures represents a powerful method for unleashing the full potential of additive manufacturing (AM) technologies in creating medical implants with improved surface roughness, biocompatibility, and mechanical properties. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) in structural optimization has expanded opportunities to improve device performance, adaptability, and durability. The review is meticulously divided into two main sections, reflecting the predictability of the implant’s internal structure: (a) unpredictable interior topology, which explores topology-based optimization techniques, and (b) predictable inner topology, concentrating on lattice structures. The analysis of the reviewed literature highlights a common focus on addressing issues such as stress shielding, osseointegration enhancement, customization to individual needs, programmable functionalities, and weight reduction in implant designs. It emphasizes significant advances in reducing stress shielding effects, promoting osseointegration, and facilitating personalized implant creation. The review provides a detailed classification of optimization methods, with each approach scrutinized for its unique contribution to overcoming specific challenges in medical implant design, thus leading to more advanced, effective, and patient-oriented implantable devices.