Abstract:Here,
we have developed and evaluated a microfluidic-based human
blood–brain-barrier (μBBB) platform that models and predicts
brain tissue uptake of small molecule drugs and nanoparticles (NPs)
targeting the central nervous system. By using a photocrosslinkable
copolymer that was prepared from monomers containing benzophenone
and N-hydroxysuccinimide ester functional groups,
we were able to evenly coat and functionalize μBBB chip channels in situ, providing a covalently attached homogenous layer
of extracellul… Show more
“…By combining polymer chemistry and OoC technology, we recently reported a blood–brain barrier (BBB)-on-a-chip with simulated BBB function. We successfully evaluated the permeability of small-molecule drugs and monitored the endocytosis and transcytosis of nanomaterials in the endothelium [ 32 , 33 ]. Furthermore, collaboration between research institutions and pharmaceutical companies has brought OoCs to a practical stage.…”
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.
“…By combining polymer chemistry and OoC technology, we recently reported a blood–brain barrier (BBB)-on-a-chip with simulated BBB function. We successfully evaluated the permeability of small-molecule drugs and monitored the endocytosis and transcytosis of nanomaterials in the endothelium [ 32 , 33 ]. Furthermore, collaboration between research institutions and pharmaceutical companies has brought OoCs to a practical stage.…”
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.
“… Figure 4 Dynamic BBB models (A) Photograph (top), schematic illustration (bottom left), and immunofluorescence micrographs (bottom right) of a 2D BBB model ( Park et al., 2019 ). (B) Top view (top) and cross section (bottom) of 2D BBB chip composed three channels ( Peng et al., 2020 ). (C) Biofabrication process of a hollowed structure using a rod (top), and bright-field image of bibranched lumen and multibranched (tertiary branches) with phalloidin staining (bottom), with a scale bar of 150 μm ( Jimenez-Torres et al., 2016 ).…”
“…These models can be very useful considering the complexity of the human brain, which makes it difficult to study the CNS in animal models or 2D cultures [ 13 ].The blood-brain barrier plays a vital role in homeostasis and it is the main limitation for the administration of drugs in the brain [ 109 ]. The development of integrated systems reproducing the barrier properties and modelling inter-individual variation [ 2 , 15 , 74 ] makes it possible to assess whether a drug designed to treat a neuro-related disease can actually reach its therapeutic target [ 13 , 78 ].…”
Research on alternatives to the use of animal models and cell cultures has led to the creation of organ-on-a-chip systems, in which organs and their physiological reactions to the presence of external stimuli are simulated. These systems could even replace the use of human beings as subjects for the study of drugs in clinical phases and have an impact on personalized therapies. Organ-on-a-chip technology present higher potential than traditional cell cultures for an appropriate prediction of functional impairments, appearance of adverse effects, the pharmacokinetic and toxicological profile and the efficacy of a drug. This potential is given by the possibility of placing different cell lines in a three-dimensional-arranged polymer piece and simulating and controlling specific conditions. Thus, the normal functioning of an organ, tissue, barrier, or physiological phenomenon can be simulated, as well as the interrelation between different systems. Furthermore, this alternative allows the study of physiological and pathophysiological processes. Its design combines different disciplines such as materials engineering, cell cultures, microfluidics and physiology, among others. This work presents the main considerations of OoC systems, the materials, methods and cell lines used for their design, and the conditions required for their proper functioning. Examples of applications and main challenges for the development of more robust systems are shown. This non-systematic review is intended to be a reference framework that facilitates research focused on the development of new OoC systems, as well as their use as alternatives in pharmacological, pharmacokinetic and toxicological studies.
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