A high-throughput biomimetic bone-on-a-chip platform with artificial intelligence-assisted image analysis for osteoporosis drug testing

From Organ-on-a-Chip to Human-on-a-Chip: A Review of Research Progress and Latest Applications

Organ-on-a-Chip (OOC) technology, which emulates the physiological environment and functionality of human organs on a microfluidic chip, is undergoing significant technological advancements. Despite its rapid evolution, this technology is also facing notable challenges, such as the lack of vascularization, the development of multiorgan-on-a-chip systems, and the replication of the human body on a single chip. The progress of microfluidic technology has played a crucial role in steering OOC toward mimicking the human microenvironment, including vascularization, microenvironment replication, and the development of multiorgan microphysiological systems. Additionally, advancements in detection, analysis, and organoid imaging technologies have enhanced the functionality and efficiency of Organs-on-Chips (OOCs). In particular, the integration of artificial intelligence has revolutionized organoid imaging, significantly enhancing high-throughput drug screening. Consequently, this review covers the research progress of OOC toward Human-on-a-chip, the integration of sensors in OOCs, and the latest applications of organoid imaging technologies in the biomedical field.

A comprehensive review and advanced biomolecule-based therapies for osteoporosis

BACKGROUND

The prevalence of osteoporosis (OP) on a global scale is significantly elevated that causes life threatening issues. The potential of groundbreaking biomolecular therapeutics in the field of OP is highly encouraging. The administration of biomolecular agents has the potential to mitigate the process of bone demineralization while concurrently augmenting the regenerative capacity of bone tissue, thereby facilitating a personalized therapeutic approach. Biomolecules-based therapies showed promising results in term of bone mass protection and restoration in OP.

AIM OF REVIEW

We summarized the recent biomolecular therapies with notable progress in clinical, demonstrating the potential to transform illness management. These treatments frequently utilize different biomolecule based strategies. Biomolecular therapeutics has a targeted character, which results in heightened specificity and less off-target effects, ultimately leading to increased patient outcomes. These aspects have the capacity to greatly enhance the management of OP, thus resulting in a major enhancement in the quality of life encountered by individuals affected by this condition.

Organs in orbit: how tissue chip technology benefits from microgravity, a perspective

Tissue chips have become one of the most potent research tools in the biomedical field. In contrast to conventional research methods, such as 2D cell culture and animal models, tissue chips more directly represent human physiological systems. This allows researchers to study therapeutic outcomes to a high degree of similarity to actual human subjects. Additionally, as rocket technology has advanced and become more accessible, researchers are using the unique properties offered by microgravity to meet specific challenges of modeling tissues on Earth; these include large organoids with sophisticated structures and models to better study aging and disease. This perspective explores the manufacturing and research applications of microgravity tissue chip technology, specifically investigating the musculoskeletal, cardiovascular, and nervous systems.

A well plate-based GelMA photo-crosslinking system with tunable hydrogel mechanical properties to regulate the PTH-mediated osteogenic fate

Versatile and efficient regulation of the mechanical properties of the extracellular matrix is crucial not only for understanding the dynamic changes in biological systems, but also for obtaining precise and effective cellular responses in drug testing. In this study, we developed a well plate-based hydrogel photo-crosslinking system to effectively control the mechanical properties of hydrogels and perform high-throughput assays. We improved cell biocompatibility by using gelatin methacryloyl (GelMA) with a visible light photo-crosslinking method. Multiple cell-laden GelMA hydrogels were simultaneously and uniformly created using multi-arrayed 520 nm light-emitting diodes in a well plate format. The elastic modulus of the hydrogels can be widely adjusted (0.5-30 kPa) using a photo-crosslinking system capable of independently controlling the light intensity or exposure time for multiple samples. We demonstrate the feasibility of our system by observing enhanced bone differentiation of human mesenchymal stem cells (hMSCs) cultured on stiffer hydrogels. Additionally, we observed that the osteogenic fate of hMSCs, affected by the different mechanical properties of the gel, was regulated by parathyroid hormone (PTH). Notably, in response to PTH, hMSCs in a high-stiffness microenvironment upregulate osteogenic differentiation while exhibiting increased proliferation in a low-stiffness microenvironment. Overall, the developed system enables the generation of multiple cell-laden three-dimensional cell culture models with diverse mechanical properties and holds significant potential for expansion into drug testing.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

A long-term storable gel-laden chip composite built in a multi-well plate enablingin situcell encapsulation for high-throughput liver model

Hydrogels are widely used as scaffold materials for constructingin vitrothree-dimensional microphysiological systems. However, their high sensitivity to various external cues hinders the development of hydrogel-laden, microscale, and high-throughput chips. Here, we have developed a long-term storable gel-laden chip composite built in a multi-well plate, which enablesin situcell encapsulation and facilitates high-throughput analysis. Through optimized chemical crosslinking and freeze-drying method (C/FD), we have achieved a high-quality of gel-laden chip composite with excellent transparency, uniform porosity, and appropriate swelling and mechanical characteristics. Besides collagen, decellularized extracellular matrix with tissue-specific biochemical compound has been applied as chip composite. As a ready-to-use platform,in situcell encapsulation within the gel has been achieved through capillary force generated during gel reswelling. The liver-mimetic chip composite, comprising HepG2 cells or primary hepatocytes, has demonstrated favorable hepatic functionality and high sensitivity in drug testing. The developed fabrication process with improved stability of gels and storability allows chip composites to be stored at a wide range of temperatures for up to 28 d without any deformation, demonstrating off-the-shelf products. Consequently, this provides an exceptionally simple and long-term storable platform that can be utilized for an efficient tissue-specific modeling and various biomedical applications.

Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis

Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of anticancer drug treatment varies significantly depending on individual patients and the extent of drug resistance. In this study, we developed a validation system based on an organ-on-a-chip integrated with artificial intelligence technologies to predict resistance to anticancer drugs in bladder cancer. As a proof-of-concept, we utilized the gemcitabine-resistant bladder cancer cell line T24 with four distinct levels of drug resistance (parental, early, intermediate, and late). These cells were co-cultured with endothelial cells in a 3D microfluidic chip. A dataset comprising 2,674 cell images from the chips was analyzed using a convolutional neural network (CNN) to distinguish the extent of drug resistance among the four cell groups. The CNN achieved 95.2% accuracy upon employing data augmentation and a step decay learning rate with an initial value of 0.001. The average diagnostic sensitivity and specificity were 90.5% and 96.8%, respectively, and all area under the curve (AUC) values were over 0.988. Our proposed method demonstrated excellent performance in accurately identifying the extent of drug resistance, which can assist in the prediction of drug responses and in determining the appropriate treatment for bladder cancer patients.

Microphysiological Constructs and Systems: Biofabrication Tactics, Biomimetic Evaluation Approaches, and Biomedical Applications

In recent decades, microphysiological constructs and systems (MPCs and MPSs) have undergone significant development, ranging from self-organized organoids to high-throughput organ-on-a-chip platforms. Advances in biomaterials, bioinks, 3D bioprinting, micro/nanofabrication, and sensor technologies have contributed to diverse and innovative biofabrication tactics. MPCs and MPSs, particularly tissue chips relevant to absorption, distribution, metabolism, excretion, and toxicity, have demonstrated potential as precise, efficient, and economical alternatives to animal models for drug discovery and personalized medicine. However, current approaches mainly focus on the in vitro recapitulation of the human anatomical structure and physiological-biochemical indices at a single or a few simple levels. This review highlights the recent remarkable progress in MPC and MPS models and their applications. The challenges that must be addressed to assess the reliability, quantify the techniques, and utilize the fidelity of the models are also discussed.

Application of Artificial Intelligence at All Stages of Bone Tissue Engineering

The development of artificial intelligence (AI) has revolutionized medical care in recent years and plays a vital role in a number of areas, such as diagnostics and forecasting. In this review, we discuss the most promising areas of AI application to the field of bone tissue engineering and prosthetics, which can drastically benefit from AI-assisted optimization and patient personalization of implants and scaffolds in ways ranging from visualization and real-time monitoring to the implantation cases prediction, thereby leveraging the compromise between specific architecture decisions, material choice, and synthesis procedure. With the emphasized crucial role of accuracy and robustness of developed AI algorithms, especially in bone tissue engineering, it was shown that rigorous validation and testing, demanding large datasets and extensive clinical trials, are essential, and we discuss how through developing multidisciplinary cooperation among biology, chemistry with materials science, and AI, these challenges can be addressed.

Injection of a PMMA-doped MSC spheroid gel for the treatment of painful osteoporotic vertebral compression fractures

We aimed to develop a biocompatible treatment to overcome the limitations of polymethyl methacrylate (PMMA) vertebroplasty for osteoporotic compression fracture patients. We synthesized an injectable hydrogel containing PMMA. Mesenchymal stem cell (MSC) spheroids were included in the injectable PMMA-doped gel (= PMMA-doped spheroid gel). In vitro, the osteogenic/anti-inflammatory effects of the embedded spheroids were investigated by the quantitative real-time polymerase chain reaction method. In vivo, we used ovariectomy (OVX)-induced osteoporotic rats with injured femurs to investigate the pain-relief effects. The OVX rats were divided into four groups according to the materials injected (non, PMMA, PMMA gel, and PMMA-spheroid gel) into the lesion. The immunofluorescence (IF) intensity levels of painful markers in dorsal root ganglia (DRG) were measured. In vitro, a volumetric ratio of the gel of 8 (gel):2 (PMMA) was non-cytotoxic for MSCs and promoted the expression of osteogenic/anti-inflammatory markers. In vivo, the values of several bone parameters in the PMMA-doped spheroid gel group showed remarkable increases compared to those in the PMMA group. In addition, the IF intensity levels of the painful markers were noticeably decreased in the PMMA-spheroid gel group. We, therefore, suggest that this treatment can be useful for osteoporotic vertebral compression fracture patients.

In situbiosensing technologies for an organ-on-a-chip

Thein vitrosimulation of organs resolves the accuracy, ethical, and cost challenges accompanyingin vivoexperiments. Organoids and organs-on-chips have been developed to model thein vitro, real-time biological and physiological features of organs. Numerous studies have deployed these systems to assess thein vitro, real-time responses of an organ to external stimuli. Particularly, organs-on-chips can be most efficiently employed in pharmaceutical drug development to predict the responses of organs before approving such drugs. Furthermore, multi-organ-on-a-chip systems facilitate the close representations of thein vivoenvironment. In this review, we discuss the biosensing technology that facilitates thein situ, real-time measurements of organ responses as readouts on organ-on-a-chip systems, including multi-organ models. Notably, a human-on-a-chip system integrated with automated multi-sensing will be established by further advancing the development of chips, as well as their assessment techniques.

Organ-on-a-chip meets artificial intelligence in drug evaluation

Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.

Osteogenesis and osteoclastogenesis on a chip: Engineering a self-assembling 3D coculture

Healthy bone is maintained by the process of bone remodeling. An unbalance in this process can lead to pathologies such as osteoporosis which are often studied with animal models. However, data from animals have limited power in predicting the results that will be obtained in human clinical trials. In search for alternatives to animal models, human in vitro models are emerging as they address the principle of reduction, refinement, and replacement of animal experiments (3Rs). At the moment, no complete in vitro model for bone-remodeling exists. Microfluidic chips offer great possibilities, particularly because of the dynamic culture options, which are crucial for in vitro bone formation. In this study, a scaffold free, fully human, 3D microfluidic coculture model of bone remodeling is presented. A bone-on-a-chip coculture system was developed in which human mesenchymal stromal cells differentiated into the osteoblastic lineage and self-assembled into scaffold free bone-like tissues with the shape and dimensions of human trabeculae. Human monocytes were able to attach to these tissues and to fuse into multinucleated osteoclast-like cells, establishing the coculture. Computational modeling was used to determine the fluid flow induced shear stress and strain in the formed tissue. Furthermore, a set-up was developed allowing for long-term (35 days) on-chip cell culture with benefits including continuous fluid-flow, low bubble formation risk, easy culture medium exchange inside the incubator and live cell imaging options. This on-chip coculture is a crucial advance towards developing in vitro bone remodeling models to facilitate drug testing.

Bone-on-a-Chip: Biomimetic Models Based on Microfluidic Technologies for Biomedical Applications

With the increasing importance of preclinical evaluation of newly developed drugs or treatments, in vitro organ or disease models are necessary. Although various organ-specific on-chip (organ-on-a-chip, or OOC) systems have been developed as emerging in vitro models, bone-on-a-chip (BOC) systems that recapitulate the bone microenvironment have been less developed or reviewed compared with other OOCs. The bone is one of the most dynamic organs and undergoes continuous remodeling throughout its lifetime. The aging population is growing worldwide, and healthcare costs are rising rapidly. Since in vitro BOC models that recapitulate native bone niches and pathological features can be important for studying the underlying mechanism of orthopedic diseases and predicting drug responses in preclinical trials instead of in animals, the development of biomimetic BOCs with high efficiency and fidelity will be accelerated further. Here, we review recently engineered BOCs developed using various microfluidic technologies and investigate their use to model the bone microenvironment. We have also explored various biomimetic strategies based on biological, geometrical, and biomechanical cues for biomedical applications of BOCs. Finally, we addressed the limitations and challenging issues of current BOCs that should be overcome to obtain more acceptable BOCs in the biomedical and pharmaceutical industries.

Three-Dimensional Bioprinting Applications for Bone Tissue Engineering

The skeletal system is a key support structure within the body. Bones have unique abilities to grow and regenerate after injury. Some injuries or degeneration of the tissues cannot rebound and must be repaired by the implantation of foreign objects following injury or disease. This process is invasive and does not always improve the quality of life of the patient. New techniques have arisen that can improve bone replacement or repair. 3D bioprinting employs a printer capable of printing biological materials in multiple directions. 3D bioprinting potentially requires multiple steps and additional support structures, which may include the use of hydrogels for scaffolding. In this review, we discuss normal bone physiology and pathophysiology and how bioprinting can be adapted to further the field of bone tissue engineering.

Construction of Bone Hypoxic Microenvironment Based on Bone-on-a-Chip Platforms

The normal physiological activities and functions of bone cells cannot be separated from the balance of the oxygenation level, and the physiological activities of bone cells are different under different oxygenation levels. At present, in vitro cell cultures are generally performed in a normoxic environment, and the partial pressure of oxygen of a conventional incubator is generally set at 141 mmHg (18.6%, close to the 20.1% oxygen in ambient air). This value is higher than the mean value of the oxygen partial pressure in human bone tissue. Additionally, the further away from the endosteal sinusoids, the lower the oxygen content. It follows that the construction of a hypoxic microenvironment is the key point of in vitro experimental investigation. However, current methods of cellular research cannot realize precise control of oxygenation levels at the microscale, and the development of microfluidic platforms can overcome the inherent limitations of these methods. In addition to discussing the characteristics of the hypoxic microenvironment in bone tissue, this review will discuss various methods of constructing oxygen gradients in vitro and measuring oxygen tension from the microscale based on microfluidic technology. This integration of advantages and disadvantages to perfect the experimental study will help us to study the physiological responses of cells under more physiological-relevant conditions and provide a new strategy for future research on various in vitro cell biomedicines.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.