Guidelines for establishing a 3-D printing biofabrication laboratory

Biofabrication approaches and regulatory framework of metastatic tumor-on-a-chip models for precision oncology

The complexity of the tumor microenvironment (TME) together with the development of the metastatic process are the main reasons for the failure of conventional anticancer treatment. In recent years, there is an increasing need to advance toward advanced in vitro models of cancer mimicking TME and simulating metastasis to understand the associated mechanisms that are still unknown, and to be able to develop personalized therapy. In this review, the commonly used alternatives and latest advances in biofabrication of tumor‐on‐chips, which allow the generation of the most sophisticated and optimized models for recapitulating the tumor process, are presented. In addition, the advances that have allowed these new models in the area of metastasis, cancer stem cells, and angiogenesis are summarized, as well as the recent integration of multiorgan‐on‐a‐chip systems to recapitulate natural metastasis and pharmacological screening against it. We also analyze, for the first time in the literature, the normative and regulatory framework in which these models could potentially be found, as well as the requirements and processes that must be fulfilled to be commercially implemented as in vitro study model. Moreover, we are focused on the possible regulatory pathways for their clinical application in precision medicine and decision making through the generation of personalized models with patient samples. In conclusion, this review highlights the synergistic combination of three‐dimensional bioprinting systems with the novel tumor/metastasis/multiorgan‐on‐a‐chip systems to generate models for both basic research and clinical applications to have devices useful for personalized oncology.

Ultrasound Imaging Offers Promising Alternative to Create 3-D Models for Personalised Auricular Implants

Three-dimensional imaging and advanced manufacturing are being applied in health care research to create novel diagnostic and surgical planning methods, as well as personalised treatments and implants. For ear reconstruction, where a cartilage-shaped implant is embedded underneath the skin to re-create shape and form, volumetric imaging and segmentation processing to capture patient anatomy are particularly challenging. Here, we introduce 3-D ultrasound (US) as an available option for imaging the external ear and underlying auricular cartilage structure, and compare it with computed tomography (CT) and magnetic resonance imaging (MRI) against micro-CT (µCT) as a high-resolution reference (gold standard). US images were segmented to create 3-D models of the auricular cartilage and compared against models generated from µCT to assess accuracy. We found that CT was significantly less accurate than the other methods (root mean square [RMS]: 1.30 ± 0.5 mm) and had the least contrast between tissues. There was no significant difference between MRI (RMS: 0.69 ± 0.2 mm) and US (0.55 ± 0.1 mm). US was also the least expensive imaging method at half the cost of MRI. These results unveil a novel use of ultrasound imaging that has not been presented before, as well as support its more widespread use in biofabrication as a low-cost imaging technique to create patient-specific 3D models and implants.

Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.

Tissue-Specific Decellularized Extracellular Matrix Bioinks for Musculoskeletal Tissue Regeneration and Modeling Using 3D Bioprinting Technology

The musculoskeletal system is a vital body system that protects internal organs, supports locomotion, and maintains homeostatic function. Unfortunately, musculoskeletal disorders are the leading cause of disability worldwide. Although implant surgeries using autografts, allografts, and xenografts have been conducted, several adverse effects, including donor site morbidity and immunoreaction, exist. To overcome these limitations, various biomedical engineering approaches have been proposed based on an understanding of the complexity of human musculoskeletal tissue. In this review, the leading edge of musculoskeletal tissue engineering using 3D bioprinting technology and musculoskeletal tissue-derived decellularized extracellular matrix bioink is described. In particular, studies on in vivo regeneration and in vitro modeling of musculoskeletal tissue have been focused on. Lastly, the current breakthroughs, limitations, and future perspectives are described.

Regenerative medicine clinical readiness

Regenerative medicine, poised to transform 21st century healthcare, has aspired to enrich care options by bringing cures to patients in need. Science-driven responsible and regulated translation of innovative technology has enabled the launch of previously unimaginable care pathways adopted prudently for select serious diseases and disabilities. The collective resolve to advance the design, manufacture and validity of affordable regenerative solutions aims to democratize such health benefits for all. The objective of this Review is to outline the framework and prerequisites that underpin clinical readiness of regenerative care. Integrated research and development, specialized workforce education and accessible evidence-based practice implementation are at the core of realizing an equitable regenerative medicine vision.