This research was aimed at the development and validation of new in vitro methods capable of predicting in vivo drug deposition from dry powder inhalers, DPIs, in lung-normal human adults. Three physical models of the mouth, throat and upper airways, MT-TB, were designed and validated using the anatomical literature. Small, medium and large versions were constructed to cover approximately 95% of the variation seen in normal adult humans of both genders. The models were housed in an artificial thorax and used for in vitro testing of drug deposition from Budelin Novolizer DPIs using a breath simulator to mimic inhalation profiles reported in clinical trials of deposition from the same inhaler. Testing in the model triplet produced results for in vitro total lung deposition (TLD) consistent with the complete range of drug deposition results reported in vivo. The effect of variables such as in vitro flow rate were also predictive of in vivo deposition. To further assess the method’s robustness, in vitro drug deposition from 5 marketed DPIs was assessed in the “medium” MT-TB model. With the exception of Relenza Diskhaler, mean values for %TLD+SD differed by only < 2% from their literature in vivo. The relationship between inhaler orientation and in vitro regional airway deposition was determined. Aerosol drug deposition was found to depend on the angle at which an inhaler is inserted into the mouth although the results for MT deposition were dependent on both the product and the formulation being delivered. In the clinic, inhalation profiles were collected from 20 healthy inhaler naïve volunteers (10M, 10F) before and after they received formal inhalation training in the use of a DPI. Statistically significant improvements in Peak Inhalation Flow Rate (PIFR) and Inhalation Volume (V) were observed following formalized training. The shapes of the average inhalation profiles recorded in the clinic were found to be comparable to the simulated profiles used in the in vitro deposition studies described above. In conclusion, novel in vitro test methods are described that accurately predict both the average and range of aerosol airway drug deposition seen from DPIs in the clinic.

Clean air is a global health concern. Each year more than seven million people across the globe perish from breathing poor quality air. Development of high efficiency particulate air (HEPA) filters demonstrate an effort to mitigate dangerous aerosol hazards at the point of production. The nuclear power industry installs HEPA filters as a final line of containment of hazardous particles. Advancement air filtration technology is paramount to achieving global clean air. An exploration of analytical, experimental, computational, and machine learning models is presented in this dissertation to advance the science of air filtration technology. This dissertation studies, develops, and analyzes alternative air filtration materials and methods of analysis that optimize filtration efficiency and reduce resistance to air flow. Alternative nonwoven filter materials are considered for use in HEPA filtration. A detailed review of natural and synthetic fibers is presented to compare mechanical, thermal, and chemical properties of fibers to desirable characteristics for air filtration media. An experimental effort is undertaken to produce and evaluate new nanofibrous air filtration materials through electrospinning. Electrospun and stabilized nanofibrous media are visually analyzed through optical imaging and tested for filtration efficiency and air flow resistance. The single fiber efficiency (SFE) analytical model is applied to air filtration media for the prediction of filtration efficiency and air flow resistance. Digital twin replicas of nonwoven nanofibrous media are created using computer scripting and commercial digital geometry software. Digital twin filters are visually compared to melt-blown and electrospun filters. Scanning electron microscopy images are evaluated using a machine learning model. A convolutional neural network is presented as a method to analyze complex geometry. Digital replication of air filtration media enables coordination among experimental, analytical, machine learning, and computational air filtration models. The value of using synthetic data to train and evaluate computational and machine learning models is demonstrated through prediction of air filtration performance, and comparison to analytical results. This dissertation concludes with discussion on potential opportunities and future work needed in the continued effort to advance clean air technologies for the mitigation of a global health and safety challenge.