Complex or not too complex? One size does not fit all in next generation microphysiological systems.

iScience

Regenerative Medicine Technologies Lab, Laboratories for Translational Research, Ente Ospedaliero Cantonale, via Chiesa 5, 6500 Bellinzona, Switzerland.

Published: March 2024

AI Article Synopsis

  • - Researchers are developing alternative models like microphysiological systems to address shortcomings in existing models, with early efforts focusing on simpler 2.5D systems that replicate blood vessels.
  • - Recent advancements have led to complex systems, such as multi-organ on chip technologies that connect 3D tissues, but these complexities bring challenges regarding reproducibility and data analysis.
  • - To effectively use these microphysiological systems in research or industry, it's crucial to adjust their complexity based on the intended application and to integrate findings from different model types to enhance predictive capabilities.

Article Abstract

In the attempt to overcome the increasingly recognized shortcomings of existing and models, researchers have started to implement alternative models, including microphysiological systems. First examples were represented by 2.5D systems, such as microfluidic channels covered by cell monolayers as blood vessel replicates. In recent years, increasingly complex microphysiological systems have been developed, up to multi-organ on chip systems, connecting different 3D tissues in the same device. However, such an increase in model complexity raises several questions about their exploitation and implementation into industrial and clinical applications, ranging from how to improve their reproducibility, robustness, and reliability to how to meaningfully and efficiently analyze the huge amount of heterogeneous datasets emerging from these devices. Considering the multitude of envisaged applications for microphysiological systems, it appears now necessary to tailor their complexity on the intended purpose, being academic or industrial, and possibly combine results deriving from differently complex stages to increase their predictive power.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904982PMC
http://dx.doi.org/10.1016/j.isci.2024.109199DOI Listing

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