Advanced In Silico, In Vitro, and In Vivo Methods for Pulmonary Healthcare and Occupational Exposure Risk Assessment

Dear Colleagues,

The interaction between respirable aerosolized particulate matter (e.g., aerosolized medications for inhalation therapy, airborne transmissible viruses, and e-cigarettes) and the human respiratory system is a complex process that spans multiple spatial and temporal scales. This Special Issue seeks to gather cutting-edge research integrating state-of-the-art in silico, in vitro, and in vivo methodologies to comprehensively understand lung aerosol dynamics. By elucidating the intricate interplay of particle transport, deposition, and interactions with subject-specific and disease-specific respiratory system environments, this integration can revolutionize pulmonary healthcare and occupational exposure risk assessment, e.g., innovation in lung disease diagnosis, inhalation therapy, and toxic aerosol exposure risk mitigation.  

This Special Issue (SI) on “Advanced In Silico, In Vitro and In Vivo Methods for Pulmonary Healthcare and Occupational Exposure Risk Assessment”, is dedicated to advancing our understanding of multiscale lung aerosol dynamics through cutting-edge synergistic methods. Studies highlighting the clinical translation of multiscale aerosol dynamics, aiding in drug delivery optimization, inhalation therapy advancements, personalized treatment strategies, exposure risk assessments and preventions associated with environmental pollutants, occupational hazards, and aerosol-based diseases are all considered relevant to this SI. Topics of interest include, but are not limited to:

1. In Silico Methods

• To Health Endpoints: Developments in the coupling of computational fluid dynamics (CFD) simulations with physiologically based pharmacokinetic/toxicokinetic/pharmacodynamic (PBPK/TK/PD) models to capture the complete journey of aerosol particles from inhalation to lung deposition, systemic translocation, and potential body responses;
• Advanced Air–Mucus–Particle Flow Dynamics: Innovative integration of multiple computational methods with CFD that account for complex, physiologically realistic airway kinematics, air–mucus transport dynamics, and particle–particle interactions, enhancing the accuracy of predicting inhaled aerosol transport dynamics in disease-specific lung environments;
• Artificial Intelligence (AI) Integration: AI utilization, including machine learning and deep learning approaches, to analyze large lung aerosol dynamics datasets, extract meaningful patterns, and predict personalized responses to inhaled aerosols, leading to innovations in lung disease diagnosis and therapy, as well as exposure risk mitigation.

2. In Vitro Methods

• More Physiologically Realistic 3D Airway Case Models: Showcase advanced in vitro lung models that authentically mimic airway elasticities and accurately replicate small airways beyond generation 9 (G9), revolutionizing our understanding of aerosol dynamics, deposition, and interactions for groundbreaking advancements in pulmonary healthcare and exposure risk assessment on a disease-specific level;
• Microfluidic Lung-on-a-Chip Models: Development and utilization of microfluidic platforms that mimic the physiological conditions of the lung, allowing for controlled aerosol exposure experiments, real-time monitoring of particle transport, and assessment of cellular responses to different aerosolized substances.

3. In Vivo Methods

• ­Refined Animal Models for Aerosol Studies: Refined animal models that replicate human respiratory physiology and responses to inhaled therapeutic or toxic aerosols. These models could be used to investigate aerosolized medication delivery, disease progression, and potential adverse effects associated with exposure to airborne pollutants;
• Imaging Techniques for Localized Lung Dosimetry Visualization: Advanced imaging modalities to visualize aerosol deposition patterns and interactions within the lung in vivo, especially for clinical and animal studies. This can provide valuable insights into regional lung function and disease-specific responses, and insightful in vivo data for model validations.

We welcome you to join us on this compelling expedition, which promises to amplify the horizons of pulmonary healthcare and render exposure risk assessment and mitigation more perceptive and precise.

Dr. Yu Feng
Dr. Xiaole Chen
Guest Editors

Manuscript Submission Information

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• multiscale lung aerosol dynamics simulation
• in silico pulmonary healthcare and occupational exposure risk assessment
• physiologically realistic in vitro models
• advanced imaging for aerosol dynamics
• animal models for aerosol studies