Engineering Lung-on-a-chip microdevices for respiratory disease modelling and drug testing: A fit-for-purpose framework for design and translational validation

Abstract

Lung-on-a-chip (LoAC) technology has emerged as a human-relevant microphysiological approach for respiratory disease modelling and preclinical drug evaluation. However, substantial variation in device architecture, membrane properties, fluidic conditions, mechanical actuation, cellular composition, sensing and manufacturing limits cross-platform comparison and translational confidence. This structured narrative review examines LoAC systems from a fit-for-purpose engineering perspective, emphasising how quantitative design parameters influence biological performance within defined contexts of use. Recent platforms demonstrate application-dependent trade-offs in membrane and interface design, flow and shear conditions, breathing-related strain, cellular complexity, analytical accessibility, scalability and reproducibility. Evidence from cancer, inhalation toxicology, infection and radiation-injury models further shows that engineering choices can alter barrier function, inflammatory responses, cellular differentiation and therapeutic sensitivity rather than merely improve physiological resemblance. To support practical assessment of translational readiness, we propose an evidence-gated framework comprising engineering verification, biological qualification, disease or pharmacological validation, human concordance, and deployment and regulatory readiness. Importantly, physiological resemblance is distinguished from demonstrated concordance with patient-derived or clinical data and, where required by the context of use, from clinically anchored predictive performance for therapeutic or toxicological outcomes. Translation will require predefined context-of-use (CoU) criteria, quantitative engineering specifications, appropriate reference comparators, clinically anchored benchmarking, quality-controlled manufacturing, standardised reporting and inter-laboratory reproducibility. Prioritising validated, fit-for-purpose performance over maximal complexity may provide a more credible pathway for advancing LoAC platforms toward reliable respiratory research, drug development and regulatory decision-support applications.