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

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 R² of 0.98 and an mean absolute error (MAE) of 0.12. These predictive models were further utilized to optimize ALGEC formulations to achieve targeted viscosity ranges. Constructs were bioprinted using a random and an optimized composition, demonstrating the effectiveness of model-driven bioink optimization. These findings enhance tissue engineering by improving bioink printability, leading to structurally stable bioprinted constructs for regenerative medicine applications.