Iteration of Tumor Organoids in Drug Development: Simplification and Integration
Abstract
1. Introduction
2. Minus in Organoid Culture
3. Computational Approaches in Organoids: Drug Screening and Multi-Omics
3.1. Organoid Activity Assessment Through Image Recognition
3.2. Prediction of Drug Response Based on Big Data Models
3.3. AI Enhances Multi-Omics Analysis in Organoid Research
4. Automation and High Throughput Enable Organoids to Be Used in Disease Treatment
4.1. Development of Integrated Systems for Organoid Fabrication
4.2. Precision-Manufactured Models and Automated Detection Platforms
5. Immune Co-Culture Systems for Organoids
5.1. Classical Organoid-Immune Co-Culture Technologies
5.2. Advanced Organoid-Immune Co-Culture Technologies
5.3. Applications in Clinical Translation
6. Vascularizing Organoids
6.1. Stem Cell Co-Differentiation
6.2. Mixed Cell Co-Culture
6.3. Host-Derived Vascularization
6.4. Decellularization-Recellularization
6.5. Applications of Vascularized Organoids
7. Application of Organoid-Related Technologies in Drug Therapy Development
7.1. Application of Computational Approaches in Drug Screening and Efficacy Prediction
7.2. Automation and High-Throughput Technologies Accelerate Drug Development Efficiency
7.3. Immune Co-Culture Systems Facilitate the Development of Immunotherapeutic Drugs
7.4. Vascularized Organoid Models Optimize Drug Delivery and Toxicity Assessment
8. After the Hype: The Challenges and Future of Organoids
8.1. Strengths
8.2. Weaknesses
8.3. Opportunities
8.4. Threats
8.5. The “Plus and Minus”: Internal Refinement and External Enhancement
9. Discussion and Perspectives
10. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
2D | Two-dimensional |
3D | Three-dimensional |
AI | Artificial Intelligence |
ALI | Air–Liquid Interface |
ASC | Adult Stem Cell |
BBB | Blood–Brain Barrier |
BME | Basement Membrane Extract |
CAR-T | Chimeric Antigen Receptor T-cell |
CAF | Cancer-Associated Fibroblast |
CRCO | Colorectal Cancer Organoids |
DC | Dendritic Cell |
DL | Deep Learning |
EC | Endothelial Cell |
ECM | Extracellular Matrix |
FDA | Food and Drug Administration |
HCC | Hepatocellular Carcinoma |
HUVEC | Human Umbilical Vein Endothelial Cell |
hPSC | Human Pluripotent Stem Cell |
iPSC | Induced Pluripotent Stem Cell |
LCA | Lung Cancer Assembloid |
ML | Machine Learning |
NK | Natural Killer |
OoC | Organ-on-a-Chip |
PBMC | Peripheral Blood Mononuclear Cell |
PDO | Patient-Derived Organoid |
PDX | Patient-Derived Xenograft |
ReBiA | Robotic-Enabled Biological Automation |
TAM | Tumor-Associated Macrophage |
TIL | Tumor-Infiltrating Lymphocyte |
TME | Tumor Microenvironment |
VEGF | Vascular Endothelial Growth Factor |
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Category | Aspect | Key Characteristics | References |
---|---|---|---|
Strengths | Biological fidelity | Retains 3D architecture, cellular heterogeneity, and native tumor molecular profiles Facilitating more clinically relevant drug screening | [6,43,130] |
Technological integration | AI drug sensitivity modeling Automated platforms for reproducibility or scalability | [43,44,56,62] | |
Immunotherapy applications | Preserves TILs, supports CAR-T/NK interaction studies Optimizing immune cell therapies | [79,131] | |
Vascularization strategies | Stem cell co-differentiation and bioengineered scaffold Enabling perfusable vascular networks and enhancing TME mimicry | [94,108] | |
Weaknesses | Technical limitations | Batch variability and incomplete stromal/immune microenvironment replication | [132] |
Resource constraints | High costs in AI, multi-omics, and robotic automation | [48,62] | |
Functional deficiencies | Lack of critical vasculature, neural networks, and long-term immune viability Limiting organoid application scope | [54,79,106] | |
Opportunities | Technological convergence | Microfluidics, robotics, AI integration Large-scale drug screening/therapeutic testing | [61,118] |
Clinical translation | Biobanking and co-clinical trials Opportunities for biomarker validation and personalized drug screening | [41,133] | |
Advanced modeling platforms | Vascularized multi-organ chips, accurate metastasis/BBB modeling | [110,112] | |
Regulatory advancement | Protocol standardization: accelerating FDA/EMA approval Ensuring faster organoid clinical adoption | ||
Threats | Technical barriers | Robotic precision and multi-omics integration challenges Hindering reproducibility/scalability | [48,70] |
Ethical/legal concerns | Stem cell ethics, scalability, and IP disputes impeding adoption/innovation | [104,108] | |
Predictive validity challenges | Organoid-clinical outcome discrepancies undermining clinician confidence | [83,134] | |
Competitive technologies | The rise in OoC and PDX, potential competition for organoids | [109,135] |
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Zhao, R.; Feng, Q.; Xia, Y.; Liao, L.; Xie, S. Iteration of Tumor Organoids in Drug Development: Simplification and Integration. Pharmaceuticals 2025, 18, 1540. https://doi.org/10.3390/ph18101540
Zhao R, Feng Q, Xia Y, Liao L, Xie S. Iteration of Tumor Organoids in Drug Development: Simplification and Integration. Pharmaceuticals. 2025; 18(10):1540. https://doi.org/10.3390/ph18101540
Chicago/Turabian StyleZhao, Rui, Qiushi Feng, Yangyang Xia, Lingzi Liao, and Shang Xie. 2025. "Iteration of Tumor Organoids in Drug Development: Simplification and Integration" Pharmaceuticals 18, no. 10: 1540. https://doi.org/10.3390/ph18101540
APA StyleZhao, R., Feng, Q., Xia, Y., Liao, L., & Xie, S. (2025). Iteration of Tumor Organoids in Drug Development: Simplification and Integration. Pharmaceuticals, 18(10), 1540. https://doi.org/10.3390/ph18101540