Environmental cellular reprogramming (ENCER) cancer models for your drug and indication
Treatment resistance is responsible for 90% of cancer deaths, however, existing pre-clinical models do not produce the data necessary to understand and prevent it.
Our ENCER models mimic treatment course, environment, and duration to create bespoke cancer models that represent outcomes across natural patient diversity.
Model
We work with you to define ENCER parameters, including cancer type, tumor microenvironment, drug dosing parameters, and standard of care control.
Reprogram
Cancer models are reprogrammed in our proprietary ResCu device by modulating the tumor microenvironment and drug exposure, ensuring that heterogeneity is preserved and sub-cellular populations develop resistance mechanisms found in patient subpopulations.
Map
Using longitudinal multi-omic data from single cell sequencing, we identify mechanisms that drive resistance and map their interactions to create complex resistance networks.
Applications
Capture up to 92% of clinically-identified resistance mechanisms in vitro
ENCER cancer models generate human-relevant changes in the genome, transcriptome, epigenome, proteome, lipidome, and metabolome that other methods can’t.
CRISPR excels at artificially engineering biology and it fails to accurately simulate nuanced natural processes like resistance pathways.
Sustained artificial stimulation of signaling pathways to develop 3D structures eliminates the nuance of naturally-evolving resistance.
Mouse signaling factors trigger different responses than human signal factoring, skewing natural cell response and biology.
Our models mimic treatment course and duration within a human-relevant environment, enabling resistance to evolve like it would within a patient.



Become a resistance fighter
At resistanceBio, our goal is to stop treatment resistance before it happens. Come join us to create state-of-the-art models and push the next generation of cancer therapies forward.
Get answers with ENCER
Join top five biopharmas closing the gap between their pre-clinical and clinical data