AI-guided biomaterials and biofabrication strategies for enhanced tumor extracellular matrix mimicry
Jin Yan, Min Hu, Peiyuan Zhao, Chunling Zhang, Jiayi Lin, Yiwen Shen, Hongzhuan Chen, Weidong Zhang, Min Tang, Xin Luan
Abstract
The extracellular matrix (ECM) of tumors plays a pivotal role in cancer progression, influencing tumor growth, migration, invasion, and resistance to treatment. Recent advancements in biomaterials and biofabrication technologies have enabled the simulation of certain ECM features using natural and synthetic biomaterials and methods like 3D bioprinting and microfluidics. However, existing approaches remain limited in capturing the ECM's inherent complexity and dynamic behavior. Artificial intelligence (AI) addresses these limitations by enhancing precision and adaptability across three stages of ECM modeling: pre-process, in-process, and post-process. During the pre-process stage, AI facilitates biomaterials design through predictive modeling and initial design option exploration, leading to tailored material properties. In the in-process stage, AI enables real-time monitoring and optimization of biofabrication methods, including precise control over microspheres, 3D bioprinting, and microfluidic processes, thus ensuring accurate replication of tumor ECM structures and properties. Finally, in the post-process stage, AI facilitates high-throughput analysis of ECM datasets, linking biophysical traits to tumor behavior. By integrating AI across all stages of biomaterials and biofabrication workflows, this review underscores the enhanced accuracy, efficiency, and dynamism achievable in tumor ECM models.