3D Ex Vivo Patient Tissue Platform
Evaluate oncology drugs in patient tumors with preserved native TME
A 3D high content imaging-based screening assay for evaluating monotherapy and combination drug responses in patient tumors with endogenous immune cell populations intact.
Patient-relevant translational systems that better mimic the heterogeneity and molecular/genetic complexity of human tumors to:
- Understand drug effects on native TME
- Gain more accurate insights beyond general cell viability (i.e. CTG)
- Evaluate immuno-oncology (I/O) drugs including immune checkpoint inhibitors (ICI) with endogenous immune cells
- Make better informed decisions about progressing into the clinic with more data
A Unique 3D Ex Vivo Patient Tissue Platform
- The most patient-relevant ex vivo system available
- Derived from fresh patient tumor samples processed within 24 hours of receipt
- Preserves native TME with endogenous immune cells, fibroblasts, and other stromal components
- Patient-specific plate: 50-300 patient tumor tissues directly seeded in hydrogel matrix in 384-well format
- Drug effects including tumor killing and immune cell proliferation are measured by phenotypic high content imaging (HCI) analysis
Preserving Patient Tumor Biology
Ex vivo testing protocols established for a wide range of solid tumors representing patient tumor biology
Key advantages:
- Physiologically Relevant 3D models
Leveraging patient- and PDX-derived tissue organoid models matched to patient data, 3D spheroids from CDX and PDX material, and patient samples - High-Throughput, Imaging-Based platform
Automated high content microscopy used to image 3D cultures grown in 384-well plates to enable efficient combination and dosing regimen evaluations - 3D Phenotypic High Content Image Analysis
Image analysis with proprietary software developed to measure phenotypic changes induced by small molecules and new therapeutic modalities in 3D - Reliable, Reproducible Data
Tumor killing and immune cell proliferation are accurately measured via phenotypic analysis to support important R&D decisions
