Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Logan Kaler, Katherine Joyner, View ORCID ProfileGregg A. Duncan
doi: https://doi.org/10.1101/2022.03.04.483046
Logan Kaler
1Biophysics Program, University of Maryland, College Park, MD 20742, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Katherine Joyner
2Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gregg A. Duncan
1Biophysics Program, University of Maryland, College Park, MD 20742, USA
2Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gregg A. Duncan
  • For correspondence: gaduncan@umd.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

Diffusion and transport of nanomaterials through mucus, is of critical importance to many basic and applied areas of research such as drug delivery and infectious disease. However, it is often challenging to interpret the dynamics of nanoparticles within the mucus gel due to its inherently heterogeneous microstructure and biochemistry. In this study, we measured the diffusion of densely PEGylated nanoparticles (NP) in human airway mucus ex vivo using multiple particle tracking and utilized machine learning to classify NP movement as either traditional Brownian motion (BM) or one of two different models of anomalous diffusion, fractional Brownian motion (FBM) and continuous time random walk (CTRW). Specifically, we employed a physics-based neural network model to predict the modes of diffusion experienced by individual NP in human airway mucus. We observed rapidly diffusing NP primarily exhibit BM whereas CTRW and FBM exhibited lower diffusion rates. Given the use of muco-inert nanoparticles, the observed transition from diffusive (BM) to sub-diffusive (CTRW/FBM) motion is likely a result of patient-to-patient variation in mucus network pore size. Using mathematic models that account for the mode of NP diffusion, we predicted the percentage of nanoparticles that would cross the mucus barrier over time in human airway mucus with varied total solids concentration. We also applied this approach to explore the transport modes and predicted fate of influenza A virus within human mucus. These results provide new tools to evaluate the extent of synthetic and viral nanoparticle penetration through mucus in the lung and other tissues.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted March 06, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier
Logan Kaler, Katherine Joyner, Gregg A. Duncan
bioRxiv 2022.03.04.483046; doi: https://doi.org/10.1101/2022.03.04.483046
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier
Logan Kaler, Katherine Joyner, Gregg A. Duncan
bioRxiv 2022.03.04.483046; doi: https://doi.org/10.1101/2022.03.04.483046

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioengineering
Subject Areas
All Articles
  • Animal Behavior and Cognition (3609)
  • Biochemistry (7584)
  • Bioengineering (5533)
  • Bioinformatics (20816)
  • Biophysics (10341)
  • Cancer Biology (7992)
  • Cell Biology (11651)
  • Clinical Trials (138)
  • Developmental Biology (6616)
  • Ecology (10222)
  • Epidemiology (2065)
  • Evolutionary Biology (13639)
  • Genetics (9553)
  • Genomics (12856)
  • Immunology (7928)
  • Microbiology (19561)
  • Molecular Biology (7673)
  • Neuroscience (42165)
  • Paleontology (308)
  • Pathology (1259)
  • Pharmacology and Toxicology (2204)
  • Physiology (3271)
  • Plant Biology (7052)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1953)
  • Systems Biology (5431)
  • Zoology (1119)