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| J Korean Med Assoc > Volume 68(10); 2025 > Article |
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Funding
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (RS-2024-00441103). This research was supported by a grant of Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS-2024-00512240).
| Model type | Advantages | Limitations | Implications for clinical translation | Reference |
|---|---|---|---|---|
| 2D cell culture | - Simple and cost-effective | - Lacks 3D tissue architecture | - Limited predictive value | [7] |
| - High-throughput screening | - Poor cell-cell/ECM interaction | - Poor correlation with in vivo human outcomes | ||
| - Non-physiological drug response | ||||
| 3D spheroid culture | - Better mimic of cell-cell interactions | - No perfusable vasculature | - Incomplete modeling of tissue-level functions | [8,9] |
| - Some structural complexity | - Hypoxic core/necrosis | - Limited reproducibility | ||
| - Batch-to-batch variability | ||||
| Patient-derived xenograft | - Maintains patient-specific tumor genetics | - Interspecies differences in stroma/immune system | - Limited scalability | [10] |
| - Useful for oncology studies | - Long setup time | - Ethical concerns and high cost | ||
| - Low throughput | ||||
| Animal models | - Systemic physiology present | - Species-specific differences in immune/metabolic pathways | - Inadequate for modeling human-specific pathophysiology and drug metabolism | [12,13] |
| - Regulatory familiarity | - Ethical and reproducibility issues |
| P-MPS type | Source material | Key features | Common applications | Clinical utility | Reference |
|---|---|---|---|---|---|
| Patient-derived cells | Tumor tissues, surgical specimens, blood-derived cells | High genetic fidelity, preserved tumor microenvironment, inter-patient heterogeneity | Cancer drug screening, tumor-immune modeling, patient-specific assays | Drug efficacy prediction, biomarker discovery, personalized treatment profiling | [44,45] |
| Patient-derived organoids | Patient tissue biopsies or stem-cell-derived organoids | 3D tissue architecture, disease-specific phenotypes, dynamic perfusion via MPS | Organoid-on-a-chip for colorectal, pancreatic, lung, and breast cancers | Therapy stratification, treatment response prediction, tumor behavior analysis | [46–48] |
| iPSCs | Patient somatic cells reprogrammed to iPSCs and differentiated | Lineage-specific differentiation, developmental modeling, genetic disease relevance | BBB modeling, cardiac/pancreatic/retinal MPS, rare disease modeling | Modeling of genetic disorders, systemic disease modeling, longitudinal analysis | [49–51] |
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