UPDATE | June 26, 2026
Data-centric organoid services for predictive drug response and next-generation oncology research
This whitepaper explores how Samsung Organoids combines GxP-aligned patient-derived organoid screening with high-content screening (HCS), multi-omics analysis, and clinical data to generate clinically relevant, data-centric insights that support translational oncology research and drug development.
Key highlights include:
GxP-aligned organoid screening integrated with high-content screening (HCS), multi-omics analysis, and clinical data
Human-relevant translational models for evaluating drug response, resistance, and tumor heterogeneity
Functional drug-response data connected with genomic, transcriptomic, and clinical data
Clinically relevant data delivered through an integrated screening workflow in approximately five weeks
Process development and GMP manufacturing conducted within a single site to support early clinical development
Discover how data-centric organoid services can support more predictive oncology research and accelerate the transition from translational insights to clinical development.
This whitepaper explores how Samsung Organoids combines GxP-aligned patient-derived organoid screening with high-content screening (HCS), multi-omics analysis, and clinical data to generate clinically relevant, data-centric insights that support translational oncology research and drug development.
Key highlights include:
- GxP-aligned organoid screening integrated with high-content screening (HCS), multi-omics analysis, and clinical data
- Human-relevant translational models for evaluating drug response, resistance, and tumor heterogeneity
- Functional drug-response data connected with genomic, transcriptomic, and clinical data
- Clinically relevant data delivered through an integrated screening workflow in approximately five weeks
- Process development and GMP manufacturing conducted within a single site to support early clinical development
Discover how data-centric organoid services can support more predictive oncology research and accelerate the transition from translational insights to clinical development.
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