4D bioprinting in advanced cancer model: technologies, drug delivery, and translational challenges

Recent advances in 3D and 4D bioprinting have enabled the development of physiologically relevant cancer models that better mimic the spatial and temporal complexity of tumor biology. 4D bioprinting integrates intelligent biomaterials, stimuli-responsive bioinks, and computational design to produce constructs capable of planned, time-dependent modifications in structure or function after printing. Such dynamic features could be useful for modeling tumor microenvironment (TME) evolution, cancer progression, therapeutic responses, and spatio-temporally controlled drug delivery. Prior reviews predominantly address 4D printing technologies, stimuli-responsive materials, or general tissue-engineering applications. This review proposes an oncology-centered framework to critically discuss whether temporal actuation grants measurable biological and predictive advantages over traditional static 3D cancer models. Special focus is placed on distinguishing dynamically changing 4D cancer constructs from advanced 3D systems with restricted stimulus-responsive behavior and on relating dynamic material behavior to TME remodeling, invasion, drug response, and therapeutic delivery. The review also incorporates AI-based analytics (Artificial Intelligence), microfluidics, computational modeling, and quantitative benchmarking into a framework for predictive validation. Finally, the translational readiness is critically assessed for reproducibility, standardization, scalability, biosafety, and regulatory and clinical applicability. We identify critical gaps in the evidence and propose a biology-to-translation paradigm that integrates dynamic biofabrication, tumor biology, quantitative validation, predictive drug-response assessment, and translational concerns. Current 4D-bioprinted cancer models are at Technology Readiness Levels 3-5 (experimental proof-of-concept to active preclinical validation), and no 4D-bioprinted cancer therapy has yet entered the clinical trial stage. Realization of predictive, clinically relevant oncology platforms will require significant advances in standardization, scalable manufacturing, and regulatory clarity.