# A Novel Aerosolization and Inhalation Platform for the Pulmonary Delivery of Anti-inflammatory Agents to Distal Airways for the Enhanced Pain Management in Chronic Obstructive Pulmonary Disease (COPD)

> **NIH NIH R43** · SCIENTIFIC HORIZONS CONSULTING LLC · 2024 · $274,875

## Abstract

Project Summary/Abstract
Treating inflammation in deep lung would help manage pain in chronic obstructive pulmonary disease (COPD),
as pain in COPD patients is often associated with the prolonged induction of painful stimuli from the hard-to-
reach inflammations in distal airways. Current guidelines recommend pulmonary delivery of anti-inflammatory
agents to treat COPD inflammations. However, existing nebulizer and inhalation technologies suffer from several
disadvantages including low pulmonary delivery efficiencies, hard-to-use hand-breath coordination, and the lack
of a point-of-care monitoring and dose control system of inhaled medicine for individualized treatment.
We have developed an inhalation device prototype for dose-controlled pulmonary delivery of medicines into the
deep lung. The device utilizes a novel atomizer with a “bottom-up” aerosolization technology that generates
aerosols with optimized particle sizes. Preliminary data shows that anti-inflammatory drugs (ibuprofen) can be
aerosolized with optimized particle sizes (MMAD < 2 µm) for delivery to small distal airways. No detectable
impurities were found in aerosolized molecules and deposited aerosols yielded a significant reduction of pro-
inflammatory mucin secretion based on a 3D respiratory cell model (EpiAirway). The inhalation device contains
a built-in chip and algorithms that monitor and provide real-time consumption dosage to users and authorized
physicians. This technology platform has the potential to shift the paradigm from traditional nebulizer and inhaler
strategies for the inflammation and pain treatment in COPD and other inflammatory respiratory diseases.
In Aim 1, we plan to investigate the feasibility of delivering two categories of small anti-inflammatory molecules,
corticosteroids and non-steroidal anti-inflammatory drugs (NSAIDs), to distal airways using our novel inhalation
device prototype. Our investigation will involve developing formulations that are compatible with the device and
conducting rigorous gravimetric and chemical analyses to quantify the dosage, while establishing the acute
toxicity profile of the aerosolized molecules. We will also systematically adjust the simulated inhalation regimes
based on our collected data to verify the dosage algorithm and estimate the optimal dosage. Lastly, we will
employ established dosimetry models to estimate pulmonary deposition status in simulated human respiratory
tracts, providing insights into the therapeutic potential of our proposed inhalation device prototype.
In Aim 2, we plan to leverage an established inflamed multicellular model to characterize the anti-inflammatory
efficacies. Specifically, we will utilize a 3D respiratory tissue culture model, EpiAlveolar, co-cultured with
monocyte-derived macrophages (MDMs) to mimic the inflammation phenotypes in distal airways of COPD
patients. We will assess the anti-inflammatory reactions, including cytokine and gene expression, with additional
endpoints includi...

## Key facts

- **NIH application ID:** 10819739
- **Project number:** 1R43HL172316-01
- **Recipient organization:** SCIENTIFIC HORIZONS CONSULTING LLC
- **Principal Investigator:** Xiang Gao
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $274,875
- **Award type:** 1
- **Project period:** 2024-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10819739

## Citation

> US National Institutes of Health, RePORTER application 10819739, A Novel Aerosolization and Inhalation Platform for the Pulmonary Delivery of Anti-inflammatory Agents to Distal Airways for the Enhanced Pain Management in Chronic Obstructive Pulmonary Disease (COPD) (1R43HL172316-01). Retrieved via AI Analytics 2026-07-25 from https://api.ai-analytics.org/grant/nih/10819739. Licensed CC0.

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