Engineering the tumor microenvironment through organoid on chip guided nanomedicine for precision oncology

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.

Authors

Institutions

Publication Details

Journal
Discover Oncology
Published
2026-09-25
DOI
https://doi.org/10.1007/s12672-026-05993-z
Primary Topic
3D Printing in Biomedical Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Engineering the tumor microenvironment through organoid on chip guided nanomedicine for precision oncology

Pradeep Goswami, Sonakshi Antal, Rehab Alanazi, Muneeb Kosvi et al.
Discover Oncology
3D Printing in Biomedical Research
article

Engineering the tumor microenvironment through organoid on chip guided nanomedicine for precision oncology

Pradeep Goswami, Sonakshi Antal, Rehab Alanazi, Muneeb Kosvi, Rohit Saroha, Rimpy, Tushar Anshu, Arunprasad VK, Megha Garg, Vikas Kumar Pandey, Rajni Tanwar
article en

Abstract

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.

Discover Oncology
Northern Border University (SA), Teerthanker Mahaveer University (IN), Shri Venkateshwara University (IN), Guru Jambheshwar University of Science and Technology (IN), Institute of Management Technology (IN), Kurukshetra University (IN), Symbiosis International University (IN), Narayan Medical College and Hospital (IN)
Good health and well-being
Openalex Percentile: Top 21%
3D Printing in Biomedical Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.