Evidence map›Paper›PMID 40136868›Full record

ArticleGels (Basel, Switzerland)2025

Ibuprofen-Loaded, Nanocellulose-Based Buccal Films: The Development and Evaluation of Promising Drug Delivery Systems for Special Populations.

Katarina Bolko Seljak, Blaž Grilc, Mirjana Gašperlin, Mirjam Gosenca Matjaž

Abstract read
In one paragraph

Article in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Katarina Bolko SeljakFaculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia.
Blaž GrilcFaculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia.ORCID 0000-0002-4791-6740
Mirjana GašperlinFaculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia.
Mirjam Gosenca MatjažFaculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia.

Funding

The Slovenian Research and Innovation Agency P1-0189
6 · The paper itself

Abstract

The objective of this work was to investigate the use of nanocrystalline cellulose (NCC) as a drug-delivery excipient for buccal films. Gel-like dispersions were created by blending either gel or powder NCC (gNCC or pNCC) with natural polymers (alginate, pectin, or chitosan) in water, with glycerol serving as a plasticiser. Ibuprofen (IBU) as an active pharmaceutical ingredient (API) was dissolved in a self-microemulsifying drug delivery system (SMEDDS) to improve its solubility prior to its addition to gel-like dispersions. Dispersions were dried, and resulting films were cut to 3 cm × 1.5 cm size, appropriate for buccal delivery. Rheological measurements revealed that shorter, thinner, and less crystalline nanocellulose fibres are more favourable for stronger gel properties. While overall, weaker gel structure prior to film casting also resulted in shorter disintegration time, this was not the case for NCC-chitosan films; here, the low solubility of chitosan in neutral media proved to be the main obstacle. Nevertheless, the prolonged disintegration of NCC-chitosan films did not impact the dissolution of IBU, as these films exhibited the fastest dissolution rate, followed by NCC-pectin and NCC-alginate. Furthermore, NCC properties significantly influenced the dissolution behaviour of the chitosan formulations, with gNCC favouring faster IBU release due to weaker gel formation prior to film casting.

Indexed as

buccal filmsdrug deliveryhydrogelsibuprofenmicroemulsionmucoadhesionnanocrystalline celluloseSMEDDS

Identifiers

PMID40136868
PMCPMC11942018

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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.