03/25/2024
By Danielle Fretwell

The Francis College of Engineering, Department of BMEBT, invites you to attend a Doctoral Dissertation defense by Mohamed Ali Talaat Mohamed Zakaria entitled "Experimental and Computational Evaluation of Inhalation Dosimetry of Metered Dose Inhalers in Human Lungs."

Candidate Name: Mohamed Ali Talaat Mohamed Zakaria
Degree: Doctoral
Defense Date: Monday, April 8, 2024
Time: 1 to 2 p.m.
Location: Falmouth 302M

Committee:

  • Advisor Jinxiang Xi, Associate Professor, Biomedical Engineering, University of Massachusetts Lowell
  • Walfre Franco, Interim Department Chair, Associate Professor, Biomedical Engineering, UML
  • Ramaswamy Nagarajan, Distinguished University Professor, Co-Director of HEROES, PERC, UML

Brief Abstract:

The burden of respiratory illnesses globally, including asthma, COPD, and the recent impacts of pandemics, underscores the urgent need for advancements in therapeutic approaches. Central to effective treatment is the administration of aerosol drugs through devices like metered-dose inhalers (MDIs), where success critically hinges on accurate drug deposition within the lung's targeted areas. Traditional methodologies for evaluating drug delivery efficacy, such as in vivo visualization and clinical trials, are marred by limitations including prohibitive costs, radiation exposure risks, and ethical dilemmas. This study endeavors to transcend these barriers through an integrated computational and experimental paradigm aimed at refining the precision of inhalation dosimetry for MDIs.

At the heart of our research is the employment of high-speed imaging, Particle Image Velocimetry (PIVlab), and cutting-edge Computational Fluid Dynamics (CFD) tools, including ANSYS Fluent. These technologies facilitate a detailed simulation of lung dynamics and aerosol distribution patterns, offering insights previously unattainable with traditional methods. Our approach is distinguished by the development of intricate lung CFD models that meticulously capture droplet-fluid interactions and consider the myriad factors influencing aerosol behavior within the pulmonary environment, such as droplet evaporation, bidirectional interactions, and modifications induced by various lung diseases.

Furthermore, this research introduces a pioneering application of Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) models to classify the severity of lung diseases based on the characteristics of exhaled flows. This novel methodology not only augments the predictive accuracy of disease diagnostics but also integrates seamlessly with our advanced dosimetry models to tailor drug delivery to individual patient needs, thus embodying the essence of personalized medicine.

Our findings hold the promise of revolutionizing the landscape of respiratory drug delivery and diagnostics. By enhancing the precision with which medications are administered and enabling the non-invasive assessment of lung disease severity, we pave the way for more effective treatment regimens, reduced side effects, and improved patient outcomes. The implications of our work extend beyond the immediate advancements in inhaler technology and diagnostic tools; they herald a new era in clinical trials and regulatory processes, characterized by greater efficiency and a deeper understanding of drug-pulmonary interactions.

In conclusion, this thesis represents a comprehensive effort to redefine the standards of care for patients with respiratory illnesses. Through a symbiosis of advanced computational simulations and experimental validation, we provide a robust framework for the next generation of inhalation drug delivery systems and diagnostic methodologies. This research not only contributes significantly to the field of respiratory medicine but also exemplifies the transformative potential of interdisciplinary collaboration in addressing complex health challenges.