Bridging rheology and bioprinting: A predictive framework for safe and precise extrusion bioprinting parameters

Extrusion‐based bioprinting has become increasingly common due to its high cell viability, compatibility with diverse crosslinking mechanisms, and ability to print at high cell densities with minimal cellular damage. However, most fabrication workflows still rely on trial-and-error optimization, leading to time-consuming, increased material costs, and reduced reproducibility. Here, we introduce a simple and yet robust mathematical model that predicts the extrusion pressure required for printing directly from rheological data, enabling the construction of a pressure-based printability window that reduces optimization time and resource use. The predicted pressures were further used to estimate wall shear stress, providing a pre-print assessment of safe-to-print conditions. The model achieved a 5–15% relative error compared with experimentally adjusted printing pressures across multiple nozzle types. All bioprinted constructs maintained >80% cell viability, and the predicted shear stress remained below reported thresholds for fibroblast safety, confirming the model’s reliability for guiding extrusion bioprinting.

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Publication Details

Journal
Bioprinting
Published
2026-03-27
DOI
https://doi.org/10.1016/j.bprint.2026.e00480
Primary Topic
3D Printing in Biomedical Research
Type
article
Field-Weighted Citation Impact
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article

Bridging rheology and bioprinting: A predictive framework for safe and precise extrusion bioprinting parameters

Vijay Singh Parihar, Minna Kellomäki, Hatai Jongprasitkul, Sanna Turunen et al.
Bioprinting
3D Printing in Biomedical Research
article

Bridging rheology and bioprinting: A predictive framework for safe and precise extrusion bioprinting parameters

Vijay Singh Parihar, Minna Kellomäki, Hatai Jongprasitkul, Sanna Turunen, Ana Marina Ferreira, David A. Fulton
article en

Abstract

Extrusion‐based bioprinting has become increasingly common due to its high cell viability, compatibility with diverse crosslinking mechanisms, and ability to print at high cell densities with minimal cellular damage. However, most fabrication workflows still rely on trial-and-error optimization, leading to time-consuming, increased material costs, and reduced reproducibility. Here, we introduce a simple and yet robust mathematical model that predicts the extrusion pressure required for printing directly from rheological data, enabling the construction of a pressure-based printability window that reduces optimization time and resource use. The predicted pressures were further used to estimate wall shear stress, providing a pre-print assessment of safe-to-print conditions. The model achieved a 5–15% relative error compared with experimentally adjusted printing pressures across multiple nozzle types. All bioprinted constructs maintained >80% cell viability, and the predicted shear stress remained below reported thresholds for fibroblast safety, confirming the model’s reliability for guiding extrusion bioprinting.

BioprintingVol. 56
University of Turku (FI), Turku University of Applied Sciences (FI), Tampere University (FI), Newcastle University (GB)
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Openalex Percentile: Top 6%
3D Printing in Biomedical Research
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