Evidence map›Paper›PMID 36839860›Full record

ArticlePharmaceutics2023

Production of Bi-Compartmental Tablets by FDM 3D Printing for the Withdrawal of Diazepam.

Joana Macedo, Rita Marques, Chris Vervaet, João F Pinto

Open access · goldAbstract read
In one paragraph

Article in Pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

4 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
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 at 2 institutions in 2 countries.

Joana MacedoiMed.ULisboa, Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.ORCID 0000-0002-3211-8316
Rita MarquesiMed.ULisboa, Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.
Chris VervaetLaboratory of Pharmaceutical Technology, Ghent University, 9000 Ghent, Belgium.ORCID 0000-0002-1291-6054
João F PintoiMed.ULisboa, Faculdade de Farmácia, Universidade de Lisboa, 1649-003 Lisboa, Portugal.ORCID 0000-0001-6499-310X
University of Lisbon · PTGhent University · BE

Funding

Fundação para a Ciência e Tecnologia PTDC/CTM-CTM/30949/2017Fundação para a Ciência e Tecnologia SFRH/BD/125212/2016
6 · The paper itself

Abstract

Diazepam (DZP) is a long-acting benzodiazepine to treat anxiety or acute alcohol withdrawal. Although this class of drugs should be taken for a short period of time, many patients take them for longer than recommended, which has been linked to an increased risk of dementia and dependence. The present work aimed at using the dual-nozzle system of fused deposition modeling (FDM) 3D printers to prepare tablets with gradual doses of DZP with constant mass and size. Placebo and DZP-loaded filaments were prepared by hot-melt extrusion and used to print the bi-compartmental tablets. Thermal processing allowed the conversion of crystalline DZP to its amorphous counterpart. Tablets with different DZP contents were effectively printed with a mass, thickness and diameter average of 111.6 mg, 3.1 mm, and 6.4 mm, respectively. Microscopic data showed good adhesion between the different layers in the printed tablets. The desired drug contents were successfully achieved and were within the acceptance criteria (European Pharmacopeia). The combination of a placebo and drug-loaded extrudates proved to be beneficial in the production of tablets by FDM for patients in need of drug withdrawal.

Indexed as

3D printingbenzodiazepinesbi-compartmental tabletdiazepamfused deposition modellingPVAwithdrawal

Identifiers

PMID36839860
PMCPMC9960133
OpenAlexW4319317020

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.