Evidence map›Paper›PMID 39640773›Full record

ArticleHeliyon2024

Experimental and modeling approaches to determine drug diffusion coefficients in artificial mucus.

Ashley C Wynne, Brandon S Abbott, Reza Niazi, Kayla Foley, Keisha B Walters

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Ashley C WynneSealed Air Corporation, 2405 Cascade Pointe Blvd, Charlotte, NC, 28208, USA.
Brandon S AbbottSchool of Chemical, Biological and Materials Engineering, The University of Oklahoma, Norman, OK, 73019, USA.
Reza NiaziDepartment of Mathematics, The University of Oklahoma, Norman, OK, 73019, USA.
Kayla FoleyRalph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville, AR, 72701, USA.
Keisha B WaltersRalph E. Martin Department of Chemical Engineering, University of Arkansas, Fayetteville, AR, 72701, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Asthma, usually characterized by inflammation and mucus accumulation, causes restricted airflow and impaired lung function. The physiological and biochemical characteristics of mucus pose a strong barrier for drugs administered orally or via the pulmonary route for asthma treatment. In this study, two drugs commonly employed in the treatment of asthma, theophylline and albuterol, were placed in contact with an artificial mucus layer, measuring their interface concentrations, and modeling the concentration profiles to determine their diffusion coefficients. To monitor the diffusion process, the upper surface of a mucus layer was placed in contact with the drug solutions and the lower mucus surface was in contact with a zinc selenide crystal to allow for time-resolved Fourier transform infrared spectroscopy (FTIR) measurements. FTIR spectra were collected at constant time intervals and monitored for quantitative changes in spectral peaks corresponding to functional groups specific to each of these drugs. Changes in peak heights were correlated to concentration via Beer's Law. Fick's 2nd Law of Diffusion was used along with Crank's trigonometric series solution for a planar semi-infinite sheet to analyze the concentration data and determine diffusion coefficients. Using this method, fitting the experimental data resulted in diffusivity coefficients of

Indexed as

AlbuterolArtificial mucusDiffusion coefficientFourier transform infrared spectroscopyTheophylline

Identifiers

PMID39640773
PMCPMC11619999

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Registered trials

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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.