Tumor Microenvironment (TME) is a major determinant of tumor progression, immune evasion, metastasis, and therapeutic resistance, yet its complexity remains inadequately represented by conventional preclinical models. Two-dimensional cell cultures and animal models often fail to reproduce the cellular heterogeneity, biomechanical forces, vascular architecture, and dynamic biochemical gradients of human tumors, limiting their predictive value for clinical translation. Patients derived organoids (PDOs) and organ-on-chip (OoC) technology has recently been developed to create biomimetic platforms retaining patient-specific tumor characteristics and reproducing physiologically relevant tumor microenvironments by the control of tumor microenvironment perfusion. The review highlights the potential of using the organoid-on-chip approach to combine with nanomedicine for the next generation of precision oncology approaches to engineering the TME. We emphasize the engineering principles of organoid-on-chip platforms such as shear stress, oxygen and nutrient gradients, extracellular matrix remodeling, and multicellular interactions, which are all important factors in regulating the behavior of tumors. Special focus is on the mechanism of transport of nanoparticles, cellular uptake, intracellular trafficking, endosomal escape, and drug release in TME, which are relevant to acidic pH, hypoxia, ROS, enzymes, glutathione and ATP. The review also explores the current strategies for TME engineering which include extracellular matrix remodeling, immune reprogramming, vascular normalization, metabolic modulation, and biomechanical regulation. Additionally, recent innovations combining AI, digital twin, multi-omics, spatial transcriptomics, single-cell sequencing, and high-content imaging with organoid-on-a-chip platforms are presented as promising solutions for designing nanoparticles for better therapeutic efficacy, predicting responses to drugs, and personalized drug screening. Organoid-on-chip-guided nanomedicine holds great promise as a clinically relevant and effective platform for predictive cancer modeling, for rapid drug development, and for personalized precision oncology.
No takes yet. Share an insight, caveat, or question.
Antal et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: