Research Projects

The proposed project addresses a critical global health issue by improving therapeutic outcomes for patients with respiratory infections such as COPD, ARDS, asthma, and viral pneumonia. These infections typically follow a known path, entering through the upper airways and progressing into the lungs, often damaging surfactant-producing alveolar type-II cells and leading to conditions like ARDS. Early intervention in the upper airway is essential to prevent disease progression. To this end, the project investigates two complementary drug delivery strategies: (i) direct liquid drug instillation using the surfactant replacement therapy (SRT) and (ii) aerosolized drug delivery via inhalers. Both approaches are evaluated through a comprehensive computational framework that simulates realistic pulmonary environments across different disease conditions. The SRT-based direct drug delivery can neutralize the virus and restore surfactant deficiency caused by infection. Although SRT is effective in neonates, its application in adults has seen limited success due to the larger and more complex airway structure, which requires higher fluid volumes and faces challenges such as uneven film formation, airway closure, and poor drug retention. This proposal presents a high-resolution multiphase model using the phase-field method to address these issues. The solver can model critical phenomena such as moving contact lines, interfacial tension effects, Marangoni stresses, viscoelastic mucosal film dynamics and airflow instability in flexible adult airway geometries. Further, achieving a targeted delivery of aerosolized drug particles is key to inhaler-based patient-specific drug delivery success. The airways physiology, breathing pattern, particle injection rate, size, and shape govern particle depositions. To accurately capture this complex deposition dynamic, we propose a combined Lagrangian (Discrete Phase Model or DPM), and Eulerian Wall Film (EWF) framework. The proposed model can not just capture the pinpointed locations of particle depositions, but can also predict the post-deposition dynamics of liquid particles. Popularly used computational models in this field are inadequate to capture post-deposition dynamics. The proposed model will be a high-fidelity computational tool to optimize inhaler-based drug delivery. Both models will be built using CT-scan-derived airway geometries to ensure anatomical fidelity. The framework can handle pathological changes such as airway blockage, excess mucus, or constriction, making it appropriate for targeted treatment planning. The project's core research objectives are: (1) identify the causes of surfactant therapy failure in adults, including fluid dynamics and film non-uniformity; (2) develop and validate the multiphase CFD model for SRT; (3) optimize inhaler-based delivery using the coupled DPM-EWF model; and (4) develop a handy AI based direct drug delivery module for the medical practitioners. This work is novel not only for its technical depth but also for its broader impact. The project bridges a gap in respiratory drug delivery research by delivering a validated, patient-specific, and physiologically accurate in-silico model. The model could serve as a clinical decision-support tool, helping personalize treatments and optimize drug dosing based on individual anatomy and disease condition. It may also reduce the need for extensive experimental trials by offering a cost-effective, ethical alternative for preclinical testing. In the broader context, the proposed model could contribute to pandemic preparedness by supporting rapid testing of therapeutic strategies and drug formulations against airborne viruses. In conclusion, this project presents a forward-looking computational initiative that combines innovative modeling with fundamental science and practical application. It will improve healthcare delivery, reduce treatment costs, and respond to future pandemic needs.