Three-Dimensional-Bioprinted Liver Chips and Challenges

Skin-on-a-chip technologies towards clinical translation and commercialization

Skin is the largest organ of the human body which plays a critical role in thermoregulation, metabolism (e.g. synthesis of vitamin D), and protection of other organs from environmental threats, such as infections, microorganisms, ultraviolet radiation, and physical damage. Even though skin diseases are considered to be less fatal, the ubiquity of skin diseases and irritation caused by them highlights the importance of skin studies. Furthermore, skin is a promising means for transdermal drug delivery, which requires a thorough understanding of human skin structure. Current animal andin vitrotwo/three-dimensional skin models provide a platform for disease studies and drug testing, whereas they face challenges in the complete recapitulation of the dynamic and complex structure of actual skin tissue. One of the most effective methods for testing pharmaceuticals and modeling skin diseases are skin-on-a-chip (SoC) platforms. SoC technologies provide a non-invasive approach for examining 3D skin layers and artificially creating disease models in order to develop diagnostic or therapeutic methods. In addition, SoC models enable dynamic perfusion of culture medium with nutrients and facilitate the continuous removal of cellular waste to further mimic thein vivocondition. Here, the article reviews the most recent advances in the design and applications of SoC platforms for disease modeling as well as the analysis of drugs and cosmetics. By examining the contributions of different patents to the physiological relevance of skin models, the review underscores the significant shift towards more ethical and efficient alternatives to animal testing. Furthermore, it explores the market dynamics ofin vitroskin models and organ-on-a-chip platforms, discussing the impact of legislative changes and market demand on the development and adoption of these advanced research tools. This article also identifies the existing obstacles that hinder the advancement of SoC platforms, proposing directions for future improvements, particularly focusing on the journey towards clinical adoption.

Additive manufacturing and three-dimensional printing in obstetrics and gynecology: a comprehensive review

Three-dimensional (3D) printing, also known as additive manufacturing, is a technology used to create complex 3D structures out of a digital model that can be almost any shape. Additive manufacturing allows the creation of customized, finely detailed constructs. Improvements in 3D printing, increased 3D printer availability, decreasing costs, development of biomaterials, and improved cell culture techniques have enabled complex, novel, and customized medical applications to develop. There have been rapid development and utilization of 3D printing technologies in orthopedics, dentistry, urology, reconstructive surgery, and other health care areas. Obstetrics and Gynecology (OBGYN) is an emerging application field for 3D printing. This technology can be utilized in OBGYN for preventive medicine, early diagnosis, and timely treatment of women-and-fetus-specific health issues. Moreover, 3D printed simulations of surgical procedures enable the training of physicians according to the needs of any given procedure. Herein, we summarize the technology and materials behind additive manufacturing and review the most recent advancements in the application of 3D printing in OBGYN studies, such as diagnosis, surgical planning, training, simulation, and customized prosthesis. Furthermore, we aim to give a future perspective on the integration of 3D printing and OBGYN applications and to provide insight into the potential applications.

Peptide-dendrimer-reinforced bioinks for 3D bioprinting of heterogeneous and biomimetic in vitro models

Despite 3D bioprinting having emerged as an advanced method for fabricating complex in vitro models, developing suitable bioinks that fulfill the opposing requirements for the biofabrication window still remains challenging. Although naturally derived hydrogels can better mimic the extracellular matrix (ECM) of numerous tissues, their weak mechanical properties usually result in architecturally simple shapes and patchy functions of in vitro models. Here, this limitation is addressed by a peptide-dendrimer-reinforced bioink (HC-PDN) which contained the peptide-dendrimer branched PEG with end-grafted norbornene (PDN) and the cysteamine-modified HA (HC). The extensive introduction of ethylene end-groups facilitates the grafting of sufficient moieties and enhances thiol-ene-induced crosslinking, making HC-PDN exhibits improved mechanical and rheological properties, as well as a significant reduction in reactive oxygen species (ROS) accumulation than that of methacrylated hyaluronic acid (HAMA). In addition, HC-PDN can be applied for the bioprinting of numerous complex structures with superior shape fidelity and soft matrix microenvironment. A heterogeneous and biomimetic hepatic tissue is concretely constructed in this work. The HepG2-C3As, LX-2s, and EA.hy.926s utilized with HC-PDN and assisted GelMA bioinks closely resemble the parenchymal and non-parenchymal counterparts of the native liver. The bioprinted models show the endothelium barrier function, hepatic functions, as well as increased activity of drug-metabolizing enzymes, which are essential functions of liver tissue in vivo. All these properties make HC-PDN a promising bioink to open numerous opportunities for in vitro model biofabrication. STATEMENT OF SIGNIFICANCE: In this manuscript, we introduced a peptide dendrimer system, which belongs to the family of hyperbranched 3D nanosized macromolecules that exhibit high molecular structure regularity and various biological advantages. Specifically, norbornene-modified peptide dendrimer was grafted onto PEG, and hyaluronic acid (HA) was selected as a base material for bioink formulation because it is a component of the ECM. Peptide dendrimers confer the following advantages to bioinks: (a) Geometric symmetry can facilitate construction of bioinks with homogeneous networks; (b) abundant surface functional groups allow for abundant crosslinking points; (c) the biological origin can promote biocompatibility. This study shows conceptualization to application of a peptide-dendrimer bioink to extend the Biofabrication Window of natural bioinks and will expand use of 3D bioprinting of in vitro models.

Development of an Induced Pluripotent Stem Cell-Based Liver-on-a-Chip Assessed with an Alzheimer's Disease Drug

Liver-related drug metabolism is a key aspect of pharmacokinetics and possible toxicity. From this perspective, the availability of advanced in vitro models for drug testing is still an open need, also to the end of reducing the burden of in vivo experiments. In this scenario, organ-on-a-chip is gaining attention as it couples a state-of-the art in vitro approach to the recapitulation of key in vivo physiological features such as fluidodynamics and a tri-dimensional cytoarchitecture. We implemented a novel liver-on-a-chip (LoC) device based on an innovative dynamic device (MINERVA 2.0) where functional hepatocytes (iHep) have been encapsulated into a 3D hydrogel matrix interfaced through a porous membrane with endothelial cells (iEndo)]. Both lines were derived from human-induced pluripotent stem cells (iPSCs), and the LoC was functionally assessed with donepezil, a drug approved for Alzheimer's disease therapy. The presence of iEndo and a 3D microenvironment enhanced the expression of liver-specific physiologic functions as in iHep, after 7 day perfusion, we noticed an increase of albumin, urea production, and cytochrome CYP3A4 expression compared to the iHep static culture. In particular, for donepezil kinetics, a computational fluid dynamic study conducted to assess the amount of donepezil diffused into the LoC indicated that the molecule should be able to pass through the iEndo and reach the target iHep construct. Then, we performed experiments of donepezil kinetics that confirmed the numerical simulations. Overall, our iPSC-based LoC reproduced the in vivo physiological microenvironment of the liver and was suitable for potential hepatotoxic screening studies.

Bioprinting on Organ-on-Chip: Development and Applications

Organs-on-chips (OoCs) are microfluidic devices that contain bioengineered tissues or parts of natural tissues or organs and can mimic the crucial structures and functions of living organisms. They are designed to control and maintain the cell- and tissue-specific microenvironment while also providing detailed feedback about the activities that are taking place. Bioprinting is an emerging technology for constructing artificial tissues or organ constructs by combining state-of-the-art 3D printing methods with biomaterials. The utilization of 3D bioprinting and cells patterning in OoC technologies reinforces the creation of more complex structures that can imitate the functions of a living organism in a more precise way. Here, we summarize the current 3D bioprinting techniques and we focus on the advantages of 3D bioprinting compared to traditional cell seeding in addition to the methods, materials, and applications of 3D bioprinting in the development of OoC microsystems.

Toward 3D-Bioprinted Models of the Liver to Boost Drug Development

The main problems in drug development are connected to enormous costs related to the paltry success rate. The current situation empowered the development of high-throughput and reliable instruments, in addition to the current golden standards, able to predict the failures in the early preclinical phase. Being hepatotoxicity responsible for the failure of 30% of clinical trials, and the 21% of withdrawal of marketed drugs, the development of complex in vitro models (CIVMs) of liver is currently one of the hottest topics in the field. Among the different fabrication techniques, 3D-bioprinting is emerging as a powerful ally for their production, allowing the manufacture of three-dimensional constructs characterized by computer-controlled and customized geometry, and inter-batches reproducibility. Thanks to these, it is possible to rapidly produce tailored cell-laden constructs, to be cultured within static and dynamic systems, thus reaching a further degree of personalization when designing in vitro models. This review highlights and prioritizes the most recent advances related to the development of CIVMs of the hepatic environment to be specifically applied to pharmaceutical research, with a special focus on 3D-bioprinting, since the liver is primarily involved in the metabolism of drugs.

Shape Fidelity Evaluation of Alginate-Based Hydrogels through Extrusion-Based Bioprinting

Extrusion-based 3D bioprinting is a promising technique for fabricating multi-layered, complex biostructures, as it enables multi-material dispersion of bioinks with a straightforward procedure (particularly for users with limited additive manufacturing skills). Nonetheless, this method faces challenges in retaining the shape fidelity of the 3D-bioprinted structure, i.e., the collapse of filament (bioink) due to gravity and/or spreading of the bioink owing to the low viscosity, ultimately complicating the fabrication of multi-layered designs that can maintain the desired pore structure. While low viscosity is required to ensure a continuous flow of material (without clogging), a bioink should be viscous enough to retain its shape post-printing, highlighting the importance of bioink properties optimization. Here, two quantitative analyses are performed to evaluate shape fidelity. First, the filament collapse deformation is evaluated by printing different concentrations of alginate and its crosslinker (calcium chloride) by a co-axial nozzle over a platform to observe the overhanging deformation over time at two different ambient temperatures. In addition, a mathematical model is developed to estimate Young’s modulus and filament collapse over time. Second, the printability of alginate is improved by optimizing gelatin concentrations and analyzing the pore size area. In addition, the biocompatibility of proposed bioinks is evaluated with a cell viability test. The proposed bioink (3% w/v gelatin in 4% alginate) yielded a 98% normalized pore number (high shape fidelity) while maintaining >90% cell viability five days after being bioprinted. Integration of quantitative analysis/simulations and 3D printing facilitate the determination of the optimum composition and concentration of different elements of a bioink to prevent filament collapse or bioink spreading (post-printing), ultimately resulting in high shape fidelity (i.e., retaining the shape) and printing quality.

Machine learning-enabled optimization of extrusion-based 3D printing

Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.