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 viability, proliferation, and functional activity in three-dimensional culture. We compared regression models, multiple linear regression, lasso, ridge, elastic net, and support vector regression (SVR) using cross-validated RMSE and R 2 . 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 −1 , 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.