Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Recent Developments in Bio-Ink Formulations Using Marine-Derived Biomaterials for Three-Dimensional (3D) Bioprinting

3D bioprinting is a disruptive, computer-aided, and additive manufacturing technology that allows the obtention, layer-by-layer, of 3D complex structures. This technology is believed to offer tremendous opportunities in several fields including biomedical, pharmaceutical, and food industries. Several bioprinting processes and bio-ink materials have emerged recently. However, there is still a pressing need to develop low-cost sustainable bio-ink materials with superior qualities (excellent mechanical, viscoelastic and thermal properties, biocompatibility, and biodegradability). Marine-derived biomaterials, including polysaccharides and proteins, represent a viable and renewable source for bio-ink formulations. Therefore, the focus of this review centers around the use of marine-derived biomaterials in the formulations of bio-ink. It starts with a general overview of 3D bioprinting processes followed by a description of the most commonly used marine-derived biomaterials for 3D bioprinting, with a special attention paid to chitosan, glycosaminoglycans, alginate, carrageenan, collagen, and gelatin. The challenges facing the application of marine-derived biomaterials in 3D bioprinting within the biomedical and pharmaceutical fields along with future directions are also discussed.

Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian optimization models

The fields of regenerative medicine and cancer modeling have witnessed tremendous growth in the application of 3D bioprinting. Maintaining high cell viability throughout the bioprinting process is crucial for the success of this technology, as it directly affects the accuracy of the 3D bioprinted models, the validity of experimental results, and the discovery of new therapeutic approaches. Therefore, optimizing bioprinting conditions, which include numerous variables influencing cell viability during and after the procedure, is of utmost importance to achieve desirable results. So far, these optimizations have been accomplished primarily through trial and error and repeating multiple time-consuming and costly experiments. To address this challenge, we initiated the process by creating a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed machine learning models to predict cell viability based on different bioprinting variables. The trained neural network yielded regressionR2value of 0.71 and classification accuracy of 0.86. Compared to models that have been developed so far, the performance of our models is superior and shows great prediction results. The study further introduces a novel optimization strategy that employs the Bayesian optimization model in combination with the developed regression neural network to determine the optimal combination of the selected bioprinting parameters to maximize cell viability and eliminate trial-and-error experiments. Finally, we experimentally validated the optimization model's performance.

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.

Hydrogel Extrusion Speed Measurements for the Optimization of Bioprinting Parameters

Three-dimensional (3D) bioprinting is the use of computer-controlled transfer processes for assembling bioinks (cell clusters or materials loaded with cells) into structures of prescribed 3D organization. The correct bioprinting parameters ensure a fast and accurate bioink deposition without exposing the cells to harsh conditions. This study seeks to optimize pneumatic extrusion-based bioprinting based on hydrogel flow rate and extrusion speed measurements. We measured the rate of the hydrogel flow through a cylindrical nozzle and used non-Newtonian hydrodynamics to fit the results. From the videos of free-hanging hydrogel strands delivered from a stationary print head, we inferred the extrusion speed, defined as the speed of advancement of newly formed strands. Then, we relied on volume conservation to evaluate the extrudate swell ratio. The theoretical analysis enabled us to compute the extrusion speed for pressures not tested experimentally as well as the printing speed needed to deposit hydrogel filaments of a given diameter. Finally, the proposed methodology was tested experimentally by analyzing the morphology of triple-layered square-grid hydrogel constructs printed at various applied pressures while the printing speeds matched the corresponding extrusion speeds. Taken together, the results of this study suggest that preliminary measurements and theoretical analyses can simplify the search for the optimal bioprinting parameters.

Application of artificial intelligence in 3D printing physical organ models

Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact humanity. 3D printing of patient-specific organ models is expected to replace animal carcasses, providing scenarios that simulate the surgical environment for preoperative training and educating patients to propose effective solutions. Due to the complexity of 3D printing manufacturing, it is still used on a small scale in clinical practice, and there are problems such as the low resolution of obtaining MRI/CT images, long consumption time, and insufficient realism. AI has been effectively used in 3D printing as a powerful problem-solving tool. This paper introduces 3D printed organ models, focusing on the idea of AI application in 3D printed manufacturing of organ models. Finally, the potential application of AI to 3D-printed organ models is discussed. Based on the synergy between AI and 3D printing that will benefit organ model manufacturing and facilitate clinical preoperative training in the medical field, the use of AI in 3D-printed organ model making is expected to become a reality.

Organ bioprinting: progress, challenges and outlook

Bioprinting, as a groundbreaking technology, enables the fabrication of biomimetic tissues and organs with highly complex structures, multiple cell types, mechanical heterogeneity, and diverse functional gradients. With the growing demand for organ transplantation and the limited number of organ donors, bioprinting holds great promise for addressing the organ shortage by manufacturing completely functional organs. While the bioprinting of complete organs remains a distant goal, there has been considerable progress in the development of bioprinted transplantable tissues and organs for regenerative medicine. This review article recapitulates the current achievements of organ 3D bioprinting, primarily encompassing five important organs in the human body (i.e., the heart, kidneys, liver, pancreas, and lungs). Challenges from cellular techniques, biomanufacturing technologies, and organ maturation techniques are also deliberated for the broad application of organ bioprinting. In addition, the integration of bioprinting with other cutting-edge technologies including machine learning, organoids, and microfluidics is envisioned, which strives to offer the reader the prospect of bioprinting in constructing functional organs.

Bioprinting: a focus on improving bioink printability and cell performance based on different process parameters

Three dimensional (3D) bioprinting is an emerging biofabrication technique that shows great potential in the field of tissue engineering, regenerative medicine and advanced drug delivery. Despite the current advancement of bioprinting technology, it faces several obstacles such as the challenge of optimizing the printing resolution of 3D constructs while retaining cell viability before, during, and after bioprinting. Therefore, it is of great significance to fully understand factors that influence the shape fidelity of printed structures and the performance of cells encapsulated in bioinks. This review presents a comprehensive analysis of bioprinting process parameters that influence bioink printability and cell performance, including bioink properties (composition, concentration, and component ratio), printing speed and pressure, nozzle charateristics (size, length, and geometry), and crosslinking parameters (crosslinker types, concentration, and crosslinking time). Key examples are provided to analyze how these parameters could be tailored to achieve the optimal printing resolution as well as cell performance. Finally, future prospects of bioprinting technology, including correlating process parameters to particular cell types with predefined applications, applying statistical analysis and artificial intelligence (AI)/machine learning (ML) technique in parameter screening, and optimizing 4D bioprinting process parameters, are highlighted.

Vascularization in Bioartificial Parenchymal Tissue: Bioink and Bioprinting Strategies

Among advanced therapy medicinal products, tissue-engineered products have the potential to address the current critical shortage of donor organs and provide future alternative options in organ replacement therapy. The clinically available tissue-engineered products comprise bradytrophic tissue such as skin, cornea, and cartilage. A sufficient macro- and microvascular network to support the viability and function of effector cells has been identified as one of the main challenges in developing bioartificial parenchymal tissue. Three-dimensional bioprinting is an emerging technology that might overcome this challenge by precise spatial bioink deposition for the generation of a predefined architecture. Bioinks are printing substrates that may contain cells, matrix compounds, and signaling molecules within support materials such as hydrogels. Bioinks can provide cues to promote vascularization, including proangiogenic signaling molecules and cocultured cells. Both of these strategies are reported to enhance vascularization. We review pre-, intra-, and postprinting strategies such as bioink composition, bioprinting platforms, and material deposition strategies for building vascularized tissue. In addition, bioconvergence approaches such as computer simulation and artificial intelligence can support current experimental designs. Imaging-derived vascular trees can serve as blueprints. While acknowledging that a lack of structured evidence inhibits further meta-analysis, this review discusses an end-to-end process for the fabrication of vascularized, parenchymal tissue.

A focused review on three-dimensional bioprinting technology for artificial organ fabrication

Three-dimensional (3D) bioprinting technology has attracted a great deal of interest because it can be easily adapted to many industries and research sectors, such as biomedical, manufacturing, education, and engineering. Specifically, 3D bioprinting has provided significant advances in the medical industry, since such technology has led to significant breakthroughs in the synthesis of biomaterials, cells, and accompanying elements to produce composite living tissues. 3D bioprinting technology could lead to the immense capability of replacing damaged or injured tissues or organs with newly dispensed cell biomaterials and functional tissues. Several types of bioprinting technology and different bio-inks can be used to replicate cells and generate supporting units as complex 3D living tissues. Bioprinting techniques have undergone great advancements in the field of regenerative medicine to provide 3D printed models for numerous artificial organs and transplantable tissues. This review paper aims to provide an overview of 3D-bioprinting technologies by elucidating the current advancements, recent progress, opportunities, and applications in this field. It highlights the most recent advancements in 3D-bioprinting technology, particularly in the area of artificial organ development and cancer research. Additionally, the paper speculates on the future progress in 3D-bioprinting as a versatile foundation for several biomedical applications.

Printability and Cell Viability in Extrusion-Based Bioprinting from Experimental, Computational, and Machine Learning Views

Extrusion bioprinting is an emerging technology to apply biomaterials precisely with living cells (referred to as bioink) layer by layer to create three-dimensional (3D) functional constructs for tissue engineering. Printability and cell viability are two critical issues in the extrusion bioprinting process; printability refers to the capacity to form and maintain reproducible 3D structure and cell viability characterizes the amount or percentage of survival cells during printing. Research reveals that both printability and cell viability can be affected by various parameters associated with the construct design, bioinks, and bioprinting process. This paper briefly reviews the literature with the aim to identify the affecting parameters and highlight the methods or strategies for rigorously determining or optimizing them for improved printability and cell viability. This paper presents the review and discussion mainly from experimental, computational, and machine learning (ML) views, given their promising in this field. It is envisioned that ML will be a powerful tool to advance bioprinting for tissue engineering.

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.