AI-driven multimaterial top-down digital light processing for cell-laden multilayered vascular-like tubular constructs

Replicating the multiscale and multimaterial architecture of native vascular networks remains a major challenge in tissue bioprinting and regenerative medicine. Conventional fabrication methods struggle to reproduce the structural and functional complexity of vascularized tissues, limiting the development of physiologically relevant constructs. In this work, we build upon our previously developed AI-driven multimaterial digital light processing (DLP) bioprinting platform and its computational optimization framework to demonstrate its application to the fabrication of complex, cell-laden vascular constructs. The platform enables seamless integration of up to four distinct bioresins within a single construct while maintaining high structural fidelity. The AI-based optimization framework dynamically adjusts layer thickness during printing by considering resin viscosity, curing kinetics, interfacial adhesion, and printer response dynamics, enabling stable transitions between materials and improved vertical resolution. The main advance presented here is the application of this multimaterial switching strategy to the fabrication of hollow, multilayered vascular-like channels with spatially defined cellular organization. To demonstrate its potential, a multilayered vascular channel was fabricated using three bioinks designed to mimic the structure of arterial vessel walls. From the lumen outward, the construct incorporated endothelial cells (HUVECs), human aortic smooth muscle cells (HAoSMCs), and human dermal fibroblasts (HDFs), corresponding to the principal cellular layers of native blood vessels. This work demonstrates the potential of AI-assisted multimaterial DLP bioprinting for generating biomimetic vascular architectures and provides a foundation for the development of more physiologically relevant vascularized tissue models and patient-specific regenerative medicine applications.