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Abstract Three-dimensional (3D) bioprinting has emerged as a powerful engineering approach for various tissue engineering applications, particularly for the development of 3D cellular structures with unique mechanical and/or biological properties. For the jammed gelatin microgel-gelatin solution composite bioink, comprising a discrete phase of microgels (enzymatically gelled gelatin microgels) and a cross-linkable continuous gelatin precursor solution-based phase containing transglutaminase (TG), its rheological properties and printability change gradually due to the TG enzyme-induced cross-linking process. The objective of this study is to establish a direct mapping between the printability of the gelatin microgel-gelatin solution based cross-linkable composite bioink and the TG concentration and cross-linking time, respectively. Due to the inclusion of TG in the composite bioink, the bioink starts cross-linking once prepared and is usually prepared right before a printing process. Herein, the bioink printability is evaluated based on the three metrics: injectability, feature formability, and process-induced cell injury. In this study, the rheological properties such as the storage modulus and viscosity have been first systematically investigated and predicted at different TG concentrations and times during the cross-linking process using the first-order cross-linking kinetics model. The storage modulus and viscosity have been satisfactorily modeled as exponential functions of the TG concentration and time with an experimentally calibrated cross-linking kinetic rate constant. Furthermore, the injectability, feature formability, and process-induced cell injury have been successfully correlated to the TG concentration and cross-linking time via the storage modulus, viscosity, and/or process-induced shear stress. By combing the good injectability, good feature formability, and satisfactory cell viability zones, a good printability zone (1.65, 0.61, and 0.31 h for the composite bioinks with 1.00, 2.00, and 4.00% w/v TG, respectively) has been established during the printing of mouse fibroblast-based 2% gelatin B microgel-3% gelatin B solution composite bioink. This printability zone approach can be extended to the use of other cross-linkable bioinks for bioprinting applications.
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This content will become publicly available on January 1, 2026
Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting
The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2–5.25%), gelatin (2–5.25%), and TEMPO-Nano fibrillated cellulose (0.5–1%) at shear rates from 0.1 to 100 s−1. Our study analyzed 169 rheological measurements using 80% training and 20% validation data. The results, based on the coefficient of determination (R2) and mean absolute error (MAE), showed that the RF algorithm-based model performed best: [(R2, MAE) RF = (0.99, 0.09), (R2, MAE) PF = (0.95, 0.28), (R2, MAE) DT = (0.98, 0.13)]. These predictive models serve as valuable tools for bioink formulation optimization, allowing researchers to determine effective viscosities without extensive experimental trials to accelerate tissue engineering.
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- Award ID(s):
- 1757371
- PAR ID:
- 10581580
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Gels
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2310-2861
- Page Range / eLocation ID:
- 45
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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