Grassi L, Crabbé A. Recreating chronic respiratory infections
in vitro using physiologically relevant models.
Eur Respir Rev 2024;
33:240062. [PMID:
39142711 PMCID:
PMC11322828 DOI:
10.1183/16000617.0062-2024]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/18/2024] [Indexed: 08/16/2024] Open
Abstract
Despite the need for effective treatments against chronic respiratory infections (often caused by pathogenic biofilms), only a few new antimicrobials have been introduced to the market in recent decades. Although different factors impede the successful advancement of antimicrobial candidates from the bench to the clinic, a major driver is the use of poorly predictive model systems in preclinical research. To bridge this translational gap, significant efforts have been made to develop physiologically relevant models capable of recapitulating the key aspects of the airway microenvironment that are known to influence infection dynamics and antimicrobial activity in vivo In this review, we provide an overview of state-of-the-art cell culture platforms and ex vivo models that have been used to model chronic (biofilm-associated) airway infections, including air-liquid interfaces, three-dimensional cultures obtained with rotating-wall vessel bioreactors, lung-on-a-chips and ex vivo pig lungs. Our focus is on highlighting the advantages of these infection models over standard (abiotic) biofilm methods by describing studies that have benefited from these platforms to investigate chronic bacterial infections and explore novel antibiofilm strategies. Furthermore, we discuss the challenges that still need to be overcome to ensure the widespread application of in vivo-like infection models in antimicrobial drug development, suggesting possible directions for future research. Bearing in mind that no single model is able to faithfully capture the full complexity of the (infected) airways, we emphasise the importance of informed model selection in order to generate clinically relevant experimental data.
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