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A microphysiological system (MPS) is an in vitro culture technology that reproduces the physiological microenvironment and functionality of humans and is expected to be applied for drug screening. In this study, we developed an MPS for the structured culture of human iPSC-derived sensory neurons and then predicted drug-induced neurotoxicity by morphological deep learning. Using human iPSC-derived sensory neurons, after the administration of representative anti-cancer drugs, the toxic effects on soma and axons were evaluated by an AI model with neurite images. Significant toxicity was detected in positive drugs and could be classified by different effects on soma or axons, suggesting that the current method provides an effective evaluation of chemotherapy-induced peripheral neuropathy. The results of neurofilament light chain expression changes in the MPS device also agreed with clinical reports. Therefore, the present MPS combined with morphological deep learning is a useful platform for in vitro peripheral neurotoxicity assessment.

Intestinal absorption is a complex process involving the permeability of the epithelial barrier, efflux transporter activity, and intestinal metabolism. Identifying the key factors that govern intestinal absorption for each investigational drug is crucial. To assess and predict intestinal absorption in humans, it is necessary to leverage appropriate in vitro systems. Traditionally, Caco-2 monolayer systems and intestinal Ussing chamber studies have been considered the ‘gold standard’ for studying intestinal absorption. However, these methods have limitations that hinder their universal use in drug discovery and development. Recently, there has been an increasing number of reports on complex in vitro models (CIVMs) using human intestinal organoids derived from intestinal tissue specimens or iPSC-derived enterocytes plated on 2D or 3D in microphysiological systems. These CIVMs provide a more physiologically relevant representation of key ADME-related proteins compared to conventional in vitro methods. They hold great promise for use in drug discovery and development due to their ability to replicate the expressions and functions of these proteins. This review highlights recent advances in gut CIVMs employing intestinal organoid model systems compared to conventional methods. It is important to note that each CIVM should be tailored to the investigational drug properties and research questions at hand.

A microphysiological system (MPS) is an in vitro culture technology that reproduces the physiological microenvironment and functionality of humans and is expected to be applied for drug screening. In this study, we developed an MPS for the structured culture of human iPSC-derived sensory neurons and then predicted drug-induced neurotoxicity by morphological deep learning. Using human iPSC-derived sensory neurons, after the administration of representative anti-cancer drugs, the toxic effects on soma and axons were evaluated by an AI model with neurite images. Significant toxicity was detected in positive drugs and could be classified by different effects on soma or axons, suggesting that the current method provides an effective evaluation of chemotherapy-induced peripheral neuropathy. The results of neurofilament light chain expression changes in the MPS device also agreed with clinical reports. Therefore, the present MPS combined with morphological deep learning is a useful platform for in vitro peripheral neurotoxicity assessment.

Osteoarthritis (OA) is a painful and disabling joint disease affecting millions worldwide. The lack of clinically relevant models limits our ability to predict therapeutic outcomes prior to clinical trials, where most drugs fail. Therefore, there is a need for a model that accurately recapitulates the whole-joint disease nature of OA in humans. Emerging microphysiological systems provide a new opportunity. We recently established a miniature knee joint system, known as the miniJoint, in which human bone-marrow-derived mesenchymal stem cells (hBMSCs) were used to create an osteochondral complex, synovial-like fibrous tissue, and adipose tissue analogs. In this study, we explored the potential of the miniJoint in developing novel treatments for OA by testing the hypothesis that co-treatment with anti-inflammation and chondroinducing agents can suppress joint inflammation and associated cartilage degradation. Specifically, we created a “synovitis”-relevant OA model in the miniJoint by treating synovial-like tissues with interleukin-1β (IL-1β), and then a combined treatment of oligodeoxynucleotides (ODNs) suppressing the nuclear factor kappa beta (NF-κB) genetic pathway and bone morphogenic protein-7 (BMP-7) was introduced. The combined treatment with BMP-7 and ODNs reduced inflammation in the synovial-like fibrous tissue and showed an increase in glycosaminoglycan formation in the cartilage portion of the osteochondral complex. For the first time, this study demonstrated the potential of the miniJoint in developing disease-modifying OA drugs. The therapeutic efficacy of co-treatment with NF-κB ODNs and BMP-7 can be further validated in future clinical studies.

We guide the use of organ-on-chip technology in tissue engineering applications. Organ-on-chip technology is a form of microengineered cell culture platform that elaborates the in-vivo like organ or tissue microenvironments. The organ-on-chip platform consists of microfluidic channels, cell culture chambers, and stimulus sources that emulate the in-vivo microenvironment. These platforms are typically engraved into an oxygen-permeable transparent material. Fabrication of these materials requires the use of microfabrication strategies, including soft lithography, 3D printing, and injection molding. Here we provide an overview of what is an organ-on-chip platform, where it can be used, what it is composed of, how it can be fabricated, and how it can be operated. In connection with this topic, we also introduce an overview of the recent applications, where different organs are modeled on the microscale using this technology.

Human adipose-derived stromal/stem cells (hASC) are widely used for in vitro modeling of physiologically relevant human adipose tissue. These models are useful for the development of tissue constructs for soft tissue regeneration and 3-dimensional (3D) microphysiological systems (MPS) for drug discovery. In this systematic review, we report on the current state of hASC culture and assessment methods for adipose tissue engineering using 3D MPS. Our search efforts resulted in the identification of 184 independent records, of which 27 were determined to be most relevant to the goals of the present review. Our results demonstrate a lack of consensus on methods for hASC culture and assessment for the production of physiologically relevant in vitro models of human adipose tissue. Few studies have assessed the impact of different 3D culture conditions on hASC adipogenesis. Additionally, there has been a limited use of assays for characterizing the functionality of adipose tissue in vitro. Results from this study suggest the need for more standardized culture methods and further analysis on in vitro tissue functionality. These will be necessary to validate the utility of 3D MPS as an in vitro model to reduce, refine, and replace in vivo experiments in the drug discovery regulatory process.

Osteoarthritis (OA) is a common joint disorder with a significant economic and healthcare impact. The knee joint is composed of cartilage and the adjoining bone, a synovial capsule, the infrapatellar fat pad (IPFP), and other connective tissues such as tendons and ligaments. Adipose tissue has recently been highlighted as a major contributor to OA through strong inflammation mediating effects. In this study, methacrylated gelatin (GelMA) constructs seeded with adipose tissue-derived mesenchymal stem cells (ASCs) and cultured in a 3D printed bioreactor were investigated for use in microphysiological systems to model adipose tissue in the knee joint. Four patient-derived ASC populations were seeded at a density of 20 million cells/mL in GelMA. Live/Dead and boron-dipyrromethene/4′,6-diamidino-2-phenylindole (BODIPY/DAPI) staining of cells within the constructs demonstrated robust cell viability after 28 days in a growth (control) medium, and robust cell viability and lipid accumulation in adipogenic differentiation medium. qPCR gene expression analysis and protein analysis demonstrated an upregulated expression of key adipogenesis-associated genes. Overall, these data indicate that ASCs retain their adipogenic potential when seeded within GelMA hydrogels and cultured within perfusion bioreactors, and thus can be used in a 3D organ-on-a-chip system to study the role of the IPFP in the pathobiology of the knee OA.

Several anticancer drugs used in cancer therapy induce chemotherapy-induced peripheral neuropathy (CIPN), leading to dose reduction or therapy cessation. Consequently, there is a demand for an in vitro assessment method to predict CIPN and mechanisms of action (MoA) in drug candidate compounds. In this study, a method assessing the toxic effects of anticancer drugs on soma and axons using deep learning image analysis is developed, culturing primary rat dorsal root ganglion neurons with a microphysiological system (MPS) that separates soma from neural processes and training two artificial intelligence (AI) models on soma and axonal area images. Exposing the control compound DMSO, negative compound sucrose, and known CIPN-causing drugs (paclitaxel, vincristine, oxaliplatin, suramin, bortezomib) for 24 h, results show the somatic area-learning AI detected significant cytotoxicity for paclitaxel (* p < 0.05) and oxaliplatin (* p < 0.05). In addition, axonal area-learning AI detected significant axonopathy with paclitaxel (* p < 0.05) and vincristine (* p < 0.05). Combining these models, we detected significant toxicity in all CIPN-causing drugs (** p < 0.01) and could classify anticancer drugs based on their different MoA on neurons, suggesting that the combination of MPS-based culture segregating soma and axonal areas and AI image analysis of each area provides an effective evaluation method to predict CIPN from low concentrations and infer the MoA.

The human intestine is a dynamic organ where the complex host-microbe interactions that orchestrate intestinal homeostasis occur. Major contributing factors associated with intestinal health and diseases include metabolically-active gut microbiota, intestinal epithelium, immune components, and rhythmical bowel movement known as peristalsis. Human intestinal disease models have been developed; however, a considerable number of existing models often fail to reproducibly predict human intestinal pathophysiology in response to biological and chemical perturbations or clinical interventions. Intestinal organoid models have provided promising cytodifferentiation and regeneration, but the lack of luminal flow and physical bowel movements seriously hamper mimicking complex host-microbe crosstalk. Here, we discuss recent advances of human intestinal microphysiological systems, such as the biomimetic human “Gut-on-a-Chip” that can employ key intestinal components, such as villus epithelium, gut microbiota, and immune components under peristalsis-like motions and flow, to reconstitute the transmural 3D lumen-capillary tissue interface. By encompassing cutting-edge tools in microfluidics, tissue engineering, and clinical microbiology, gut-on-a-chip has been leveraged not only to recapitulate organ-level intestinal functions, but also emulate the pathophysiology of intestinal disorders, such as chronic inflammation. Finally, we provide potential perspectives of the next generation microphysiological systems as a personalized platform to validate the efficacy, safety, metabolism, and therapeutic responses of new drug compounds in the preclinical stage.