Evidence map›Paper›PMID 31835682›Full record

ArticlePharmaceutics2019

3D-Printed Solid Dispersion Drug Products.

Suet Li Chew, Laura Modica de Mohac, Bahijja Tolulope Raimi-Abraham

Open access · goldAbstract read
In one paragraph

Article in Pharmaceutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 26 citations in OpenAlex.

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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

3 authors at 1 institution in 3 countries.

Suet Li ChewDrug Delivery Group, Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK.
Laura Modica de MohacDrug Delivery Group, Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK.
Bahijja Tolulope Raimi-AbrahamDrug Delivery Group, Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK.ORCID 0000-0002-5330-3967
King's College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the well-known advantages of additive manufacturing methods such as three-dimensional (3D) printing in drug delivery, it is disappointing that only one product has been successful in achieving regulatory approval in the past few years. Further research and development is required in this area to introduce more 3D printed products into the market. Our study investigates the potential of fixed dose combination solid dispersion drug products generated via 3D printing. Two model drugs-fluorescein sodium (FS) and 5-aminosalicyclic acid (5-ASA)-were impregnated onto a polyvinyl alcohol (PVA) filament, and the influence of solvent choice in optimal drug loading as well as other influences such as the physicochemical and mechanical properties of the resultant filaments were investigated prior to development of the resultant drug products. Key outcomes of this work included the improvement of filament drug loading by one- to threefold due to solvent choice on the basis of its polarity and the generation of a 3D-printed product confirmed to be a solid dispersion fixed dose combination with the two model drugs exhibiting favourable in vitro dissolution characteristics.

Indexed as

3D printingadditive manufacturingamorphous solid dispersionfixed dose combinationpoor solubility

Identifiers

PMID31835682
PMCPMC6956082
OpenAlexW2995408762

What OpenQuestion holds

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LicenceCC BY
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.