Targeting Airway Remodeling in Severe Asthma: Is There a Window of Opportunity for Biologic Therapy Predicting Effects Using Causal Artificial Intelligence?

Vollständiger Abstract

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ABSTRACT Airway remodeling is increasingly recognized as a major determinant of asthma progression, fixed airflow limitation, and long‐term morbidity, particularly in severe disease. Although biologic therapies have transformed outcomes by reducing exacerbations and systemic corticosteroid exposure, their potential to modify structural airway trajectories—and whether a time‐sensitive “window of opportunity” exists—remains uncertain. Here, we provide a progressive landscape integrating mechanistic remodeling pathways with measurable structural readouts (biopsy‐derived indices and quantitative imaging) and emerging digital biomarkers derived from connected respiratory technologies. We propose an operational framework linking mechanism → biomarker → remodeling readout → timing decision, and we outline a 1‐, 3‐, and 5‐year research roadmap in which advanced artificial intelligence (AI) methods (multimodal learning, causal inference, federated learning, and digital‐twin architectures) evolve in parallel with wearable and smart‐inhaler ecosystems. This landscape aims to standardize endpoints, sharpen trial design, and accelerate a shift from symptom control toward credible disease modification in severe asthma.