Evidence map›Paper›PMID 36559151›Full record

ArticlePharmaceutics2022

Optimisation of a High-Throughput Model for Mucus Permeation and Nanoparticle Discrimination Using Biosimilar Mucus.

Leah Wright, Timothy J Barnes, Paul Joyce, Clive A Prestidge

Abstract read
In one paragraph

Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Leah WrightUniSA: Clinical and Health Sciences, University of South Australia, Adelaide 5000, Australia.
Timothy J BarnesUniSA: Clinical and Health Sciences, University of South Australia, Adelaide 5000, Australia.ORCID 0000-0001-7367-3925
Paul JoyceUniSA: Clinical and Health Sciences, University of South Australia, Adelaide 5000, Australia.ORCID 0000-0003-3619-7901
Clive A PrestidgeUniSA: Clinical and Health Sciences, University of South Australia, Adelaide 5000, Australia.ORCID 0000-0001-5401-7535

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput permeation models are essential in drug development for timely screening of new drug and formulation candidates. Nevertheless, many current permeability assays fail to account for the presence of the gastrointestinal mucus layer. In this study, an optimised high-throughput mucus permeation model was developed employing a highly biorelevant mucus mimic. While mucus permeation is primarily conducted in a simple mucin solution, the complex chemistry, nanostructure and rheology of mucus is more accurately modelled by a synthetic biosimilar mucus (BSM) employing additional protein, lipid and rheology-modifying polymer components. Utilising BSM, equivalent permeation of various molecular weight fluorescein isothiocyanate-dextrans were observed, compared with native porcine jejunal mucus, confirming replication of the natural mucus permeation barrier. Furthermore, utilising synthetic BSM facilitated the analysis of free protein permeation which could not be quantified in native mucus due to concurrent proteolytic degradation. Additionally, BSM could differentiate between the permeation of poly (lactic-co-glycolic) acid nanoparticles (PLGA-NP) with varying surface chemistries (cationic, anionic and PEGylated), PEG coating density and size, which could not be achieved by a 5% mucin solution. This work confirms the importance of utilising highly biorelevant mucus mimics in permeation studies, and further development will provide an optimal method for high-throughput mucus permeation analysis.

Indexed as

biorelevantmucopermeationmucusnanoparticlesPLGAtranswell

Identifiers

PMID36559151
PMCPMC9782027

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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.