Complex in vitro models of the tissue microenvironment, termed microphysiological systems, have enormous potential to transform the process of discovering drugs and disease mechanisms. Such a paradigm shift is urgently needed in acute respiratory distress syndrome (ARDS), an acute lung condition with no successful therapies and a 40% mortality rate. Here, we consider how microphysiological systems could improve understanding of biological mechanisms driving ARDS and ultimately improve the success of therapies in clinical trials. We first discuss how microphysiological systems could explain the biological mechanisms underlying the segregation of ARDS patients into two clinically distinct phenotypes. Then, we contend that ARDS-mimetic microphysiological systems should recapitulate three critical aspects of the distal airway microenvironment, namely, mechanical force, inflammation, and fibrosis, and we review models that incorporate each of these aspects. Finally, we recognize the substantial challenges associated with combining inflammation, fibrosis, and/or mechanical force in microphysiological systems. Nevertheless, complex in vitro models are a novel paradigm for studying ARDS, and they could ultimately improve patient care.

High‐throughput tissue barrier models can yield critical insights on how barrier function responds to therapeutics, pathogens, and toxins. However, such models often emphasize multiplexing capability at the expense of physiologic relevance. Particularly, the distal lung's air–blood barrier is typically modeled with epithelial cell monoculture, neglecting the substantial contribution of endothelial cell feedback in the coordination of barrier function. An obstacle to establishing high‐throughput coculture models relevant to the epithelium/endothelium interface is the requirement for underside cell seeding, which is difficult to miniaturize and automate. Therefore, this paper describes a scalable, low‐cost seeding method that eliminates inversion by optimizing medium density to float cells so they attach under the membrane. This method generates a 96‐well model of the distal lung epithelium–endothelium barrier with serum‐free, glucocorticoid‐free air–liquid differentiation. The polarized epithelial–endothelial coculture exhibits mature barrier function, appropriate intercellular junction staining, and epithelial‐to‐endothelial transmission of inflammatory stimuli such as polyinosine:polycytidylic acid (poly(I:C)). Further, exposure to influenza A virus PR8 and human beta‐coronavirus OC43 initiates a dose‐dependent inflammatory response that propagates from the epithelium to endothelium. While this model focuses on the air–blood barrier, the underside seeding method is generalizable to various coculture tissue models for scalable, physiologic screening.

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Each year, hundreds of millions of individuals are affected by respiratory disease leading to approximately 4 million deaths. Most respiratory pathologies involve substantially dysregulated immune processes that either fail to resolve the underlying process or actively exacerbate the disease. Therefore, clinicians have long considered immune-modulating corticosteroids (CSs), particularly glucocorticoids (GCs), as a critical tool for management of a wide spectrum of respiratory conditions. However, the complex interplay between effectiveness, risks and side effects can lead to different results, depending on the disease in consideration. In this comprehensive review, we present a summary of the bench and the bedside evidence regarding GC treatment in a spectrum of respiratory illnesses. We first describe here the experimental evidence of GC effects in the distal airways and/or parenchyma, both in vitro and in disease-specific animal studies, then we evaluate the recent clinical evidence regarding GC treatment in over 20 respiratory pathologies. Overall, CS remain a critical tool in the management of respiratory illness, but their benefits are dependent on the underlying pathology and should be weighed against patient-specific risks.

The mistakes and corrections are as follows:• In the Figure 3A caption, "n = 3 technical replicates per MOI." should read "n = 3 technical replicates per MOI (24h); n = 2 technical replicates per MOI (48h and 72h)." • In Figure 3A, the y-axis units "μg/cm 2 " should read "pg/cm 2 ". • In the Statistical Analysis section, it was originally reported that Figure 3A data were analyzed with 1-way ANOVA. This should read "2-way ANOVA".The overall results of the study and implications of the paper remain the same.