Pulmonary Nanoparticle Drug Delivery Strategies

Abstract

Pulmonary nanoparticle drug delivery has emerged as a promising strategy for treating respiratory diseases by enabling localized drug deposition while minimizing systemic toxicity. Despite substantial advances in nanoparticle engineering, current delivery systems often overlook patient-specific physiological variability, resulting in inconsistent therapeutic outcomes. This manuscript addresses this limitation by proposing an artificial intelligence-driven framework that predicts pulmonary nanoparticle deposition using and formulation characteristics. Unlike previous studies that primarily optimize nanoparticle composition, the proposed research integrates machine learning with digital respiratory profiling to enable individualized therapeutic planning. A hybrid ensemble learning architecture is conceptually developed to estimate deposition efficiency across different pulmonary regions. The framework combines inhalation parameters, particle characteristics, and airway morphology to improve prediction accuracy. The proposed methodology establishes a digital health perspective for intelligent pulmonary drug delivery. This research provides a foundation for future AI-assisted precision nanomedicine capable of supporting personalized respiratory treatment