3D Ex Vivo Patient Tissue Platform

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