Dynamic Culture Improves the Predictive Power of Bronchial and Alveolar Airway Models of SARS-CoV-2 Infection

Human in vitro lung models represent advanced tools for studying respiratory infections, particularly those caused by emerging respiratory pathogens. Despite scientific advances, vaccine and therapeutics pre-clinical development has yet to fully adopt human-relevant testing platforms due in part to a lack of validation. In this study, we characterised how static and dynamic flow culture conditions influence microphysiological systems (MPS) generated using primary bronchial and alveolar epithelial cells. We assessed epithelial structure, functional differentiation, and infection dynamics. This study represents the first direct comparison of how dynamic flow and endothelial co-culture influence viral tropism, replication kinetics, and host responses across anatomically distinct regions of the respiratory tract in vitro. Dynamic flow promoted formation of more physiologically relevant tissue architecture, pseudostratified bronchial epithelium and alveolar sac-like structures, with enhanced epithelial differentiation and retention of region-specific cell phenotypes at the transcriptomic level. Both static and dynamic flow models demonstrated responsiveness to inflammatory stimuli (poly(I:C), LPS), producing distinct, tissue-specific cytokine profiles and supporting infection with multiple SARS-CoV- 2 variants. Differences in infection efficiency, viral replication, and host gene expression were observed between variants, with dynamic flow models offering enhanced sensitivity and resolution. In alveolar tissues, dynamic flow increased infection efficiency and reduced variability, enabling more robust and consistent transcriptional responses. This facilitated the identification of interferon signalling pathways as key targets of the host response. Among the variants tested, Delta induced the most extensive tissue damage and strongest transcriptional response, whereas Omicron BA.5 exhibited greater infectivity in alveolar models compared to earlier variants. Our findings demonstrate that dynamic flow MPS more closely replicate human lung tissue architecture and cellular diversity, while also enhancing the predictive power and clinical relevance of airway models for ex vivo studies of SARS-CoV-2 infection. These improvements strengthen the reliability of data generated for the study of host-pathogen interaction studies and support the use of dynamic systems for evaluating novel anti-infectives, immunomodulators, and functional characterisation of immune sera generated by next-generation vaccines. Collectively, our results highlight the value of integrating dynamic in vitro models into preclinical pipelines for emerging respiratory pathogens.