Early-stage material properties as predictors of neural bioink performance during extrusion 3D bioprinting

Abstract Bioink formulation plays a central role in determining the physical and biological performance of bioprinted tissue constructs. While compositional tuning has traditionally guided bioink development, a more mechanistic understanding of how material properties influence cellular behaviour remains underexplored. Here, we hypothesized that early-stage physicochemical properties, particularly rheological, printability, and swelling/degradation characteristics, can predict long-term biological outcomes. We systematically characterized the mechanical behaviour of fibrin–alginate-based bioink formulations and assessed their influence on neural progenitor cell (NPC) viability, proliferation, and functional activity in 3D culture. We compared regression models, Multiple Linear Regression, Lasso, Ridge, Elastic Net, and Support Vector Regression (SVR) using cross-validated RMSE and R². Performance was endpoint-dependent, but SVR provided the most consistent overall trade-off across outputs in this small, noisy dataset (best in 83% of features). External validation on chemically distinct bioinks revealed material-dependent transfer, robust for Chitosan- and reduced for Cellulose- and Pluronic- based bioinks in selected readouts, thereby defining practical generalization limits. Finally, multi-objective optimization identified an optimal candidate (fibrin 20 mg/mL, alginate 1%), and experimental validation confirmed neuronal marker expression and extensive neurite outgrowth. Together, these results establish a rheology-informed, data-driven framework to prioritize bioink formulations, map cross-material predictability, and reduce empirical trial-and-error in neural biofabrication.