Digital Twin Model Optimizes Cell-Seeding for Nerve Tissue Engineering, Enhancing Angiogenesis Factors

Millions worldwide suffer from peripheral nerve injuries, leading to significant functional deficits. Current gold-standard treatments like autologous nerve grafting are limited by donor site morbidity, while artificial nerve conduits have yet to match their outcomes. A critical gap exists in optimizing cell-seeding strategies for engineered neural tissues, which traditionally relies on time-consuming and resource-intensive in vivo testing. This research addresses this by proposing a novel, cost-effective computational approach to accelerate the design and optimization of regenerative strategies, specifically focusing on factors crucial for angiogenesis.

Researchers developed a mathematical cell-solute model to predict optimal cell-seeding strategies for hydrogels in silico. This model was informed and validated through in vitro experiments. Promising designs identified by the model were then manufactured using 3D-printed moulds. The regenerative potential of these designs was subsequently evaluated in vivo in a nerve repair model. The primary focus of this evaluation was the impact of different cell-seeding strategies on vascular endothelial growth factor (VEGF) secretion and gradient generation, both critical elements for regenerative angiogenesis in early nerve repair.

The study successfully established a digital twin framework for nerve tissue engineering, integrating mathematical cell-solute modeling with in vitro and in vivo validation. This approach identified promising cell-seeding strategies in silico at significantly reduced cost and time compared to traditional in vivo optimization. The 3D-printed moulds successfully manufactured these designs for subsequent validation. Crucially, the model focused on optimizing parameters for vascular endothelial growth factor (VEGF) secretion and gradient generation, which are critical for regenerative angiogenesis in early nerve repair. The in vivo phase served to evaluate the regenerative potential of these digitally-designed strategies, providing a robust first proof-of-concept for this integrated approach. This methodology offers a novel pathway to accelerate the development of advanced nerve repair solutions. > This work provides a first proof-of-concept of a digital twin for nerve tissue engineering, using in silico, in vitro, and in vivo repair models to bridge the gap in optimization.