Biomaterial Strategies for Three-Dimensional Bioprinting and Drug Delivery Application
Phan TNL., Truong TT., Vo TH., Pham VH., Nguyen TX., Duong TKN.
Narrative Review, published in Materials (Basel) (2026) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Narrative Review
- Journal
- Materials (Basel) (2026)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 42279839
- PMCID
- PMC13257692
- DOI
- 10.3390/ma19112186
Abstract (original English)
Three-dimensional (3D) bioprinting has rapidly evolved into a controlling platform for the fabrication of patient-specific biomedical implants, with growing importance in advanced drug delivery systems. Beyond structural tissue engineering, bioprinted constructs now function as programmable therapeutic depots capable of localized, sustained, and stimuli-responsive drug release. This review focuses on recent biomaterial design strategies that enable precise control over drug encapsulation, retention, and release kinetics within 3D bioprinted architectures. The physicochemical and mechanical properties of bioinks, including crosslinking density, porosity, degradation behavior, viscoelasticity, and swelling characteristics, directly influence drug loading efficiency and release dynamics under physiological conditions. The rational tuning of these parameters allows the development of constructs that provide spatially controlled and temporally regulated therapeutic delivery. Recent advances in predictive modeling, such as finite element modeling (FEM), data-driven machine learning approaches, and ML, have significantly improved the ability to correlate material composition, printing parameters, and structural geometry with drug diffusion and degradation-mediated release mechanisms. These tools facilitate the optimization of printing variables including extrusion pressure, nozzle dia
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is a narrative review: it collects no new patient data and does not systematically appraise evidence quality.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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