ArticlePharmaceutical research2020
CFD Guided Optimization of Nose-to-Lung Aerosol Delivery in Adults: Effects of Inhalation Waveforms and Synchronized Aerosol Delivery.
Article in Pharmaceutical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 33 citations in OpenAlex.
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Development of CPAP Overlay Interfaces for Efficient Administration of Aerosol Surfactant Therapy to Preterm Infants.AAPS PharmSciTech · 2025Article
- Leveraging Numerical Simulation Technology to Advance Drug Preparation: A Comprehensive Review of Application Scenarios and Cases.Pharmaceutics · 2024Review
- Development of an effective two-equation turbulence modeling approach for simulating aerosol deposition across a range of turbulence levels.Journal of aerosol science · 2024Article
- In Vitro Evaluation of Nebulized Pharmaceutical Aerosol Delivery to the Lungs Using a New Heated Dryer System (HDS).AAPS PharmSciTech · 2022Article
- Formulation of Solid Lipid Nanoparticles Loaded with Nociceptin/Orphanin FQ (N/OFQ) and Characterization in a Murine Model of Airway Hyperresponsiveness.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Aerosol delivery through high-flow nasal therapy: Technical issues and clinical benefits.Frontiers in medicine · 2022Review
- Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach.Journal of hazardous materials · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe objective of this study was to optimize nose-to-lung aerosol delivery in an adult upper airway model using computational fluid dynamics (CFD) simulations in order to guide subsequent human subject aerosol delivery experiments.
methodsA CFD model was developed that included a new high-flow nasal cannula (HFNC) and pharmaceutical aerosol delivery unit, nasal cannula interface, and adult upper airway geometry. Aerosol deposition predictions in the system were validated with existing and new experimental results. The validated CFD model was then used to explore aerosol delivery parameters related to synchronizing aerosol generation with inhalation and inhalation flow rate.
resultsThe low volume of the new HFNC unit minimized aerosol transit time (0.2 s) and aerosol bolus spread (0.1 s) enabling effective synchronization of aerosol generation with inhalation. For aerosol delivery correctly synchronized with inhalation, a small particle excipient-enhanced growth delivery strategy reduced nasal cannula and nasal depositional losses each by an order of magnitude and enabled ~80% of the nebulized dose to reach the lungs. Surprisingly, nasal deposition was not sensitive to inhalation flow rate due to use of a nasal cannula interface with co-flow inhaled air and the small initial particle size.
conclusionsThe combination of correct aerosol synchronization and small particle size enabled high efficiency nose-to-lung aerosol delivery in adults, which was not sensitive to inhalation flow rate.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.