Evidence map›Paper›PMID 29246855›Full record

ReviewAdvanced drug delivery reviews2018

Technological strategies to estimate and control diffusive passage times through the mucus barrier in mucosal drug delivery.

Jay M Newby, Ian Seim, Martin Lysy, Yun Ling, Justin Huckaby, Samuel K Lai, M Gregory Forest

Open access · bronzeAbstract readReview
In one paragraph

Review in Advanced drug delivery reviews, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
1.5field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 59 citations in OpenAlex.

  1. Article
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  3. Review
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  16. Review
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  19. Review
  20. ENHANCED ELECTRODIFFUSIVE TRANSPORT ACROSS A MUCUS LAYER.SIAM journal on applied mathematics · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Jay M NewbyDepartment of Mathematics and Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States.
Ian SeimDepartment of Mathematics and Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States.
Martin LysyDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, United States.
Yun LingDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, United States.
Justin HuckabyDivision of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States.
Samuel K LaiDivision of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States; UNC-NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States.
M Gregory ForestDepartment of Mathematics and Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States; UNC-NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States. Electronic address: forest@unc.edu.
Applied Physical Sciences (United States) · USUniversity of North Carolina at Chapel Hill · USUNC/NCSU Joint Department of Biomedical Engineering · US

Funding

Big Data to Knowledge Training ProgramT32LM012420 · NLM · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FOREST, MARK GREGORY, KOSOROK, MICHAEL R · 2016 to 2019
$1.1M
Big Data to Knowledge Training ProgramT32CA201159 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FOREST, MARK GREGORY, KOSOROK, MICHAEL R · 2015 to 2015
$316k
Development of novel sperm-binding antibodiesR56HD095629 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LAI, SAMUEL · 2017 to 2017
$307k
Artificial neural networks for high performance, fully automated particle tracking analysis even at low signal-to-noise regimesR41GM123897 · NIGMS · AI TRACKING SOLUTIONS, LLC · PI LAI, SAMUEL · 2017 to 2017
$225k
NCI NIH HHS T32 CA201159NICHD NIH HHS R56 HD095629NIGMS NIH HHS R41 GM123897NLM NIH HHS T32 LM012420
6 · The paper itself

Abstract

In mucosal drug delivery, two design goals are desirable: 1) insure drug passage through the mucosal barrier to the epithelium prior to drug removal from the respective organ via mucus clearance; and 2) design carrier particles to achieve a prescribed arrival time and drug uptake schedule at the epithelium. Both goals are achievable if one can control "one-sided" diffusive passage times of drug carrier particles: from deposition at the mucus interface, through the mucosal barrier, to the epithelium. The passage time distribution must be, with high confidence, shorter than the timescales of mucus clearance to maximize drug uptake. For 100nm and smaller drug-loaded nanoparticulates, as well as pure drug powders or drug solutions, diffusion is normal (i.e., Brownian) and rapid, easily passing through the mucosal barrier prior to clearance. Major challenges in quantitative control over mucosal drug delivery lie with larger drug-loaded nanoparticulates that are comparable to or larger than the pores within the mucus gel network, for which diffusion is not simple Brownian motion and typically much less rapid; in these scenarios, a timescale competition ensues between particle passage through the mucus barrier and mucus clearance from the organ. In the lung, as a primary example, coordinated cilia and air drag continuously transport mucus toward the trachea, where mucus and trapped cargo are swallowed into the digestive tract. Mucus clearance times in lung airways range from minutes to hours or significantly longer depending on deposition in the upper, middle, lower airways and on lung health, giving a wide time window for drug-loaded particle design to achieve controlled delivery to the epithelium. We review the physical and chemical factors (of both particles and mucus) that dictate particle diffusion in mucus, and the technological strategies (theoretical and experimental) required to achieve the design goals. First we describe an idealized scenario - a homogeneous viscous fluid of uniform depth with a particle undergoing passive normal diffusion - where the theory of Brownian motion affords the ability to rigorously specify particle size distributions to meet a prescribed, one-sided, diffusive passage time distribution. Furthermore, we describe how the theory of Brownian motion provides the scaling of one-sided diffusive passage times with respect to mucus viscosity and layer depth, and under reasonable caveats, one can also prescribe passage time scaling due to heterogeneity in viscosity and layer depth. Small-molecule drugs and muco-inert, drug-loaded carrier particles 100nm and smaller fall into this class of rigorously controllable passage times for drug delivery. Second we describe the prevalent scenarios in which drug-loaded carrier particles in mucus violate simple Brownian motion, instead exhibiting anomalous sub-diffusion, for which all theoretical control over diffusive passage times is lost, and experiments are prohibitive if not impossible to measure one-sided passage times. We then discuss strategies to overcome these roadblocks, requiring new particle-tracking experiments and emerging advances in theory and computation of anomalous, sub-diffusive processes that are necessary to predict and control one-sided particle passage times from deposition at the mucosal interface to epithelial uptake. We highlight progress to date, remaining hurdles, and prospects for achieving the two design goals for 200nm and larger, drug-loaded, non-dissolving, nanoparticulates.

Indexed as

Drug Delivery SystemsHumansMucusNanoparticlesTime FactorsViscosityMucosal drug-loaded particle deliveryOne-sided diffusive passage timesParticle tracking experiments

Identifiers

PMID29246855
PMCPMC5809312
OpenAlexW2773161490

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.