Paired CIN cell lines: Our biology team has built unique and proprietary paired cell lines to accurately model different types of CIN. This provides a way to test our targets and compounds in CIN-high versus CIN-low in-vitro systems.
CIN CRISPR screens: We run novel CIN-based screens to identify the most optimal genetic targets. Using synthetic lethal principles across broad CRISPR screens, we have discovered multiple genes essential in CIN-high cells, forming the basis of an extensive pipeline.
Organoid systems: We are proud to partner with Cornell to leverage innovative 3D cell cultures, known as organoids. Organoids can more effectively model tumor growth and response than 2D cell lines, resulting in significantly improved simulations of patient response.
2D and 3D design tools: Our chemistry team has made significant investments in property-based analytics that drive hypothesis-driven drug design. This ensures our future drugs have the best molecular and cellular characteristics, which we believe will translate into better patient response.
Biological and ADME assays: We have built unique, robust and translatable assays to minimize the burdensome design-make-test-analyze cycle for faster results. This deep understanding of the biological and metabolic underpinnings of our medicines will translate into better clinical results.
Structure-activity analysis: Our in-house biochemistry and medicinal chemistry expertise allows us to quantitatively map the relationship of structural parameters to PK/PD hypotheses and human PK and dose. This leads to the most optimal dosing regimens in our clinical trials.
Merged data analytics: We continue to build a wealth of CIN-specific data through our efforts in imaging and genomics. By merging this proprietary dataset with existing publicly available datasets (e.g. Broad Institute DepMap or TCGA) the team generates insights that integrate with our biological research to enhance our target ID and patient selection.
CIN computational screens: Our growing data science team uses state-of-the-art computational techniques to mine merged proprietary and public databases. This complements our biological screens to identify promising CIN-specific targets.
CIN imaging and genomic metrics: Through our collaboration with Microsoft, we have developed a unique way to measure CIN by leveraging machine learning imaging technology applied to routine H&Es. This, paired with novel genomic CIN-metrics, will contribute to more accurate patient selection.