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      Artificial confocal microscopy for deep label-free imaging

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          Most cited references46

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          ImageNet: A large-scale hierarchical image database

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            Is Open Access

            Three-Dimensional in Vitro Cell Culture Models in Drug Discovery and Drug Repositioning

            Drug development is a lengthy and costly process that proceeds through several stages from target identification to lead discovery and optimization, preclinical validation and clinical trials culminating in approval for clinical use. An important step in this process is high-throughput screening (HTS) of small compound libraries for lead identification. Currently, the majority of cell-based HTS is being carried out on cultured cells propagated in two-dimensions (2D) on plastic surfaces optimized for tissue culture. At the same time, compelling evidence suggests that cells cultured in these non-physiological conditions are not representative of cells residing in the complex microenvironment of a tissue. This discrepancy is thought to be a significant contributor to the high failure rate in drug discovery, where only a low percentage of drugs investigated ever make it through the gamut of testing and approval to the market. Thus, three-dimensional (3D) cell culture technologies that more closely resemble in vivo cell environments are now being pursued with intensity as they are expected to accommodate better precision in drug discovery. Here we will review common approaches to 3D culture, discuss the significance of 3D cultures in drug resistance and drug repositioning and address some of the challenges of applying 3D cell cultures to high-throughput drug discovery.
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              Quantitative phase imaging in biomedicine

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                Author and article information

                Contributors
                Journal
                Nature Photonics
                Nat. Photon.
                Springer Science and Business Media LLC
                1749-4885
                1749-4893
                January 12 2023
                Article
                10.1038/s41566-022-01140-6
                37143962
                11a492da-4329-4f40-adb3-eb5066038cbb
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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