Novel Approach Methodologies in Modeling Complex Bioaerosol Exposure in Asthma and Allergic Rhinitis Under Climate Change.

The undeniable impact of climate change and air pollution on respiratory health has led to increasing cases of asthma, allergic rhinitis and other chronic non-communicable immune-mediated upper and lower airway diseases. Natural bioaerosols, such as pollen and fungi, are essential atmospheric components undergoing significant structural and functional changes due to industrial pollution and atmospheric warming. Pollutants like particulate matter(PMx), polycyclic aromatic hydrocarbons(PAHs), nitrogen dioxide(NO), sulfur dioxide(SO) and carbon monoxide(CO) modify the surface and biological properties of atmospheric bioaerosols such as pollen and fungi, enhancing their allergenic potentials. As a result, sensitized individuals face heightened risks of asthma exacerbation, and these alterations likely contribute to the rise in frequency and severity of allergic diseases. NAMs, such as precision-cut lung slices(PCLS), air-liquid interface(ALI) cultures and lung-on-a-chip models, along with the integration of data from these innovative models with computational models, provide better insights into how environmental factors influence asthma and allergic diseases compared to traditional models. These systems simulate the interaction between pollutants and the respiratory system with higher precision, helping to better understand the health implications of bioaerosol exposure. Additionally, NAMs improve preclinical study outcomes by offering higher throughput, reduced costs and greater reproducibility, enhancing the translation of data into clinical applications. This review critically evaluates the potential of NAMs in researching airway diseases, with a focus on allergy and asthma. It highlights their advantages in studying the increasingly complex structures of bioaerosols under conditions of environmental pollution and climate change, while also addressing the existing gaps, challenges and limitations of these models.