“Translational Models for Acute Asthma: Critical Evaluation of In Vivo, In Vitro, and Emerging Technologies in Drug Discovery”

OBJECTIVES: To critically evaluate conventional and emerging preclinical methodologies for screening novel asthma therapeutics; to compare their translational relevance; and to propose an integrated framework aimed at improving precision, safety, and phenotype-targeted efficacy in drug development. DATASOURCES: A review of peer-reviewed literature was conducted using PubMed. Sources included studies on enzymatic and receptor-binding assays, in vitro cellular platforms, allergen-induced animal models, and recent innovations such as 3D airway organoids, lung-on-a-chip microfluidics, and omics-guided biomarker discovery. STUDYSELECTIONS: Included studies focused on (i) molecular validation of drug targets, (ii) mechanistic and high-throughput screening in cellular systems, (iii) in vivo replication of asthma pathophysiology in animal models, or (iv) technologies enhancing preclinical predictability. RESULTS: Conventional screening assays provide molecular validation but lack complexity. In vitro systems allow mechanistic insight and scalability, yet do not fully reproduce airway interactions. Allergen-induced animal models replicate inflammation and hyperresponsiveness but fail to capture human heterogeneity or acute exacerbations, a major contributor to morbidity and mortality. Acute asthma remains particularly difficult to model, limiting evaluation of therapies aimed at severe attacks. Emerging approaches, including organoids, lung-on-a-chip, and omics-based biomarkers, offer improved physiological relevance, dynamic assessment of inflammatory responses, and greater potential to model acute disease states. CONCLUSIONS: Integrating conventional and innovative models may accelerate the development of safer, more effective, and phenotype-specific asthma therapies. Approaches that incorporate both chronic disease and acute exacerbations could reduce steroid resistance, improve emergency care, and enhance translational success across diverse populations.