ArticlePharmaceutical research2024
Development of Mathematical Function Control-Based 3D Printed Tablets and Effect on Drug Release.
Article in Pharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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
5 citing papers in PubMed.
- From Digital Design to Dosage Performance: Material-Process-Geometry Interactions in 3D-Printed Oral Tablets.AAPS PharmSciTech · 2026Review
- Quality by Design Approach for Hot-Melt Extrusion Coupled Fused Deposition Modeling (HME-FDM) 3D Printing: A Systematic Review.Pharmaceutics · 2026Review
- Prediction of Solvent Penetration Rate-Limited Release of Drug from Amorphous Solid Dispersion Discs of Various Geometries.Molecular pharmaceutics · 2026Article
- Self-Emulsifying Drug Delivery Systems (SEDDS): Transition from Liquid to Solid-A Comprehensive Review of Formulation, Characterization, Applications, and Future Trends.Pharmaceutics · 2025Review
- Personalized Medicine Through Semisolid-Extrusion Based 3D Printing: Dual-Drug Loaded Gummies for Enhanced Patient Compliance.Pharmaceutical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purposeThe application of 3D printing technology in drug delivery is often limited by the challenges of achieving precise control over drug release profiles. The goal of this study was to apply surface equations to construct 3D printed tablet models, adjust the functional parameters to obtain multiple tablet models and to correlate the model parameters with the in vitro drug release behavior.
methodsThis study reports the development of 3D-printed tablets using surface geometries controlled by mathematical functions to modulate drug release. Utilizing fused deposition modeling (FDM) coupled with hot-melt extrusion (HME) technology, personalized drug delivery systems were produced using thermoplastic polymers. Different tablet shapes (T1-T5) were produced by varying the depth of the parabolic surface (b = 4, 2, 0, -2, -4 mm) to assess the impact of surface curvature on drug dissolution.
resultsThe T5 formulation, with the greatest surface curvature, demonstrated the fastest drug release, achieving complete release within 4 h. In contrast, T1 and T2 tablets exhibited a slower release over approximately 6 h. The correlation between surface area and drug release rate was confirmed, supporting the predictions of the Noyes-Whitney equation. Differential Scanning Calorimetry (DSC) and Scanning Electron Microscope (SEM) analyses verified the uniform dispersion of acetaminophen and the consistency of the internal structures, respectively.
conclusionsThe precise control of tablet surface geometry effectively tailored drug release profiles, enhancing patient compliance and treatment efficacy. This novel approach offers significant advancements in personalized medicine by providing a highly reproducible and adaptable platform for optimizing drug delivery.
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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.