Data-Driven Optimization of Bioink Formulations for Extrusion-Based Bioprinting: A Predictive Modeling Approach

Area of Science:

• Biomaterials Science
• Tissue Engineering
• Rheology

Background:

• Extrusion-based bioprinting is crucial for fabricating complex tissue constructs.
• Bioink rheology, especially viscosity, dictates printability and structural integrity.
• Understanding bioink composition-viscosity relationships is vital for successful bioprinting.

Purpose of the Study:

• To investigate the rheological behavior of novel ALGEC bioinks (Alginate, Gelatin, TEMPO-oxidized nanofibrillated cellulose).
• To develop predictive models for bioink viscosity based on composition and shear rate.
• To optimize ALGEC formulations for enhanced bioprinting performance.

Main Methods:

• Prepared ALGEC bioinks with varying concentrations of alginate, gelatin, and TO-NFC.
• Measured viscosity across a range of shear rates (0.1–100 s⁻¹).
• Developed and validated polynomial fit and multiple regression models to predict viscosity.

Main Results:

• The best predictive model achieved an R² of 0.98 and MAE of 0.12.
• Optimized ALGEC formulations demonstrated improved printability and structural stability.
• Model-driven optimization successfully guided bioink formulation for targeted viscosity.

Conclusions:

• Predictive rheological models are effective for optimizing bioink formulations in tissue engineering.
• Optimized ALGEC bioinks enhance the printability and stability of bioprinted constructs.
• This approach advances regenerative medicine by improving biofabrication processes.