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Organoid-on-Chip Technologies in Precision Oncology: Bridging Patient-Specific Tumor Biology and Physiologically Relevant Drug Screening

Sep 2026 · Organoids · 0 citations · 143 references

TL;DR

This review synthesizes the biological rationale, technical principles, and translational applications of PDO–OoC integration, with an emphasis on recent clinical validation studies, AI integration, and post-FDA Modernization Act 2.0 regulatory evolution.

Abstract

The inadequacy of traditional preclinical oncology models, specifically two-dimensional (2D) monolayer cultures and murine in vivo systems, in predicting human drug responses has led to the development of patient-derived organoids (PDOs) and microfluidic organ-on-chip (OoC) technologies. These innovations represent significant recent methodological advancements in the field of cancer research. This review synthesizes the biological rationale, technical principles, and translational applications of PDO–OoC integration, with an emphasis on recent clinical validation studies, AI integration, and post-FDA Modernization Act 2.0 regulatory evolution—areas that have not been comprehensively addressed in prior reviews. We examined the predictive limitations of 2D models, organoid generation, and ToC engineering principles. The synergistic integration of organoids into chip-based systems, extended into multi-organ “Body-on-a-Chip” architectures, is presented as a unifying framework that combines patient-specific biological fidelity with dynamic microenvironmental control. We further reviewed the research applications and early clinical validation studies of high-throughput drug screening, immuno-oncology modeling, and patient-specific drug response prediction across multiple tumor types. Clinical validation studies have reported moderate correlations (r ~ 0.4–0.6) between organoid responses and outcomes, indicating partial predictive capacity. Despite this progress, clinical translation remains constrained by standardization and reproducibility deficits, biomaterial limitations (e.g., PDMS drug absorption and Matrigel batch variability), and regulatory ambiguities within the evolving FDA Modernization Act 2.0. Finally, we discuss the emerging integration of artificial intelligence, including transfer learning-based drug response prediction and real-time organoid avatar systems in clinical trials, as a pathway toward closed-loop individualized functional precision oncology. Organoid and tumor-on-chip platforms have advanced toward clinical utility, although barriers remain.

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