ReviewFuture journal of pharmaceutical sciences2022
3D printing of pharmaceuticals: approach from bench scale to commercial development.
Review in Future journal of pharmaceutical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Translation of pharmaceutical 3D printing to clinical point-of-care and industrial manufacturing.Drug delivery and translational research · 2026Review
- The emergence of advanced technologies in the pharmacy profession and the need for education: The case of point-of-care sensing systems and 3D printing of pharmaceuticals.BioImpacts : BI · 2026Review
- Comparative Formulation and Physicochemical Evaluation of Orodispersible Films Fabricated via Pneumatic and Syringe-Based 3D Printing.Pharmaceutical research · 2025Article
- Toward a harmonized regulatory framework for 3D-printed pharmaceutical products: the role of critical feedstock materials and process parameters.Drug delivery and translational research · 2025Review
- Trend of pharmaceuticals 3D printing in the Middle East and North Africa (MENA) region: An overview, regulatory perspective and future outlook.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The three-dimensional (3D) printing is paradigm shift in the healthcare sector. 3D printing is platform technologies in which complex products are developed with less number of additives. The easy development process gives edge over the conventional methods. Every individual needs specific dose treatment. 'One size fits all' is the current traditional approach that can shift to more individual specific in 3D printing. The present review aims to cover different perspectives regarding selection of drug, polymer and technological aspects for 3D printing. With respect to clinical practice, regulatory issue and industrial potential are also discussed in this paper. Main body: The individualization of medicines with patient centric dosage form will become reality in upcoming future. It provides individual's need of dose by considering genetic profile, physiology and diseased condition. The tailormade dosages with unique drug loading and release profile of different geometrical shapes and sizes can easily deliver therapeutic dose. The technology can fulfill growing demand of efficiency in the dose accuracy for the patient oriented sectors like pediatric, geriatric and also easy to comply with cGMP requirements of regulated market. The clinical practice can focus on prescribing each individual's necessity of dose. Conclusion: In the year 2015, FDA approved first 3D printed drug product, which is initiator in the new phase of manufacturing of pharmaceuticals. The tailormade formulations can be made in future for personalized medications. Regulatory approval from agencies can bring the 3DP product into the market. In the future, formulators can bring different sector-specific products for personalized need through 3DP pharmaceutical product. Graphical Abstract:
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Registered trials
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.