Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review

Argus M Sun1,2, Tyler Hoffman1, Bao Q Luu3

  • 1Department of Bioengineering, Samueli School of Engineering, University of California - Los Angeles, 420 Westwood Plaza 5121 Engineering V University of California, Los Angeles, CA 90095-1600 USA.

Insights

Developing advanced in vitro lung models, including microphysiological systems (MPS), is crucial for accelerating the discovery of effective therapeutics for coronavirus disease 2019 (COVID-19) and other infectious diseases.

Area of Science:

  • Biomedical Engineering
  • Infectious Disease Research
  • Drug Development

Background:

  • The COVID-19 pandemic highlights the urgent need for rapid therapeutic development.
  • Traditional drug development is slow and resource-intensive, often hampered by preclinical failures.
  • The lung's complex 3D structure is vital for respiratory function and is targeted by SARS-CoV-2.

Purpose of the Study:

  • To review in vitro lung models for studying COVID-19 pathology.
  • To assess the suitability of microphysiological systems (MPS) for drug screening.
  • To explore integration of MPS with high-throughput screening and AI for accelerated drug discovery.

Main Methods:

  • Review of current in vitro lung models: Transwell, organoids, and MPS.
  • Analysis of models relevant to COVID-19 pathology (inflammation, edema, coagulation, immune function).
  • Consideration of practical design and fabrication aspects of MPS.

Main Results:

  • In vitro lung models, particularly MPS, can recapitulate lung tissue structure and cellular interactions.
  • Existing models show relevance for studying COVID-19 mechanisms.
  • MPS offer potential for mechanistic studies and drug screening.

Conclusions:

  • Refined in vitro lung MPS are essential for efficient COVID-19 therapeutic development.
  • Integration with advanced technologies like AI can further accelerate drug discovery.
  • Lung MPS are valuable tools for both single- and multi-organ disease modeling.