Next-Generation Cancer Models: Integrating 3D Bioprinting and Organ-on-Chip Systems for Precision Oncology
DhruvaKumar Chatarajupalli
Health data science, Saint Louis University, Missouri, USA.
Yaswanth Reddy Gogireddy
Health data science, Saint Louis University, Missouri, USA.
Seetharam Gude
*
Research and Development, Aurobindo Pharma Limited, Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: This review aims to provide a comprehensive overview of recent advancements in 3D bioprinting integrated with organ-on-a-chip technologies for developing next-generation cancer-on-a-chip systems. The objective is to highlight how these hybrid platforms replicate the tumor microenvironment (TME), enhance physiological relevance, and improve the predictive power of preclinical cancer research.
Methodology: A thorough literature review was conducted focusing on key technological domains including 3D bioprinting strategies, microfluidic design, biomaterial and bioink innovations, and tumor–stroma co-culture methods. Comparative analysis of traditional 2D cultures, spheroids, organoids, and advanced cancer-on-a-chip constructs was performed to assess their biological relevance. Special emphasis was placed on evaluating bioink selection, vascularization approaches, ECM-mimetic materials, patient-derived models, and the integration of biosensing and artificial intelligence for real-time monitoring.
Results: Findings show that integrating additive manufacturing with microfluidics enables the fabrication of physiomimetic tumor constructs featuring spatially organized cell populations, perfusable vascular networks, and controllable biochemical gradients. These systems successfully recapitulate critical TME characteristics, including hypoxia, cellular heterogeneity, and stromal interactions. Innovations in patient-derived bioinks and multi-nozzle bioprinting have enhanced architectural complexity, personalization, and scalability. Additionally, incorporating biosensors and AI-driven analytics strengthens drug-response monitoring, enabling more accurate mechanistic studies and high-throughput drug screening compared with conventional in vitro models.
Conclusion: 3D-bioprinted cancer-on-a-chip platforms represent a transformative paradigm in tumor modeling by bridging the gap between traditional in vitro assays and animal models. Despite existing challenges particularly in vascularization, standardization, and maintaining long-term viability ongoing advances in biomaterials, automation, and immunocompetent systems continue to elevate their translational potential. These next-generation platforms hold significant promise for accelerating drug discovery and supporting precision oncology.
Keywords: 3D bioprinting, precision oncology, tumor microenvironment, microfluidic platforms