Data-Driven Optimization of Bioink Formulations for Extrusion-Based Bioprinting: A Predictive Modeling Approach
Sarah R., Rohauer R., Schimmelpfennig K., Limon SM., Lewis CL., Habib A.
Laboratory Study, published in J Manuf Sci Eng (2025) — 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
- Laboratory Study
- Journal
- J Manuf Sci Eng (2025)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 41112526
- PMCID
- PMC12533942
- DOI
- 10.1115/1.4069041
Abstract (original English)
The field of tissue engineering has significantly advanced with the development of extrusion-based bioprinting. This technique utilizes shear forces to generate filaments for fabricating intricate structures. The printability and structural integrity of bioprinted constructs rely heavily on the rheological properties of bioinks, particularly viscosity, which varies with the shear rate for non-Newtonian materials. Since the shear rate at the nozzle tip fluctuates during extrusion, it is essential to understand how bioink composition influences this behavior. This study investigates the rheological behavior of ALGEC bioinks, a novel formulation composed of ALginate, GElatin, and 2,2,6,6-Tetramethylpiperidine 1-oxyl (TEMPO)-oxidized nanofibrillated cellulose (TO-NFC). The bioinks were prepared with varying concentrations: alginate (0-5.25%), gelatin (0-5.25%), and TO-NFC (0-1.5%), with a maximum total solid content of 8%. Viscosity was conducted over shear rates ranging from 0.1 to 100 s -1 , with 252 viscosity data points used 80% for training and 20% for validation. To predict viscosity, polynomial fit and interaction-based multiple regression models were developed. Experimental data were used to estimate viscosity based on bioink composition and shear rate, with the best-performing model achieving an R 2 of 0.98 and an mean absolute error (MAE) of 0.12. These predictive models
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
How we grade evidenceBrowse all related research
Filter the research library by this study's title keywords, author, or publication year.