Bayesian optimization-enabled efficient embedded ink writing of human tissue analogs

Embedded ink writing has been extensively applied in recent years for various biomedical applications. Despite its outstanding ability to create complex structures, the challenge of optimizing multiple factors has hampered further utilization of this three-dimensional bioprinting strategy. In this work, we experimentally summarized the coupling effects of ink viscosity, support bath rheological properties, and key printing parameters on filament formation. Based on the gathered data, Bayesian optimization is used to establish a filament prediction platform, which can accurately estimate the rheology of support baths for printing alginate-based ink under the given conditions. Additionally, the platform is used to predict the optimal parameters for printing with chitosan ink. Two representative eye-relevant tissues are successfully fabricated using the predictions of the platform. The insights from this study lay the foundation for embedded ink writing strategies that can guide support bath design and identify optimal printing parameters, aiding efficient reconstruction of human tissues and organs in the future.