Immunomodulation of
Tumor Micro-environment
Only 1 in 4 patients respond to Anti-PD1 therapies like Keytruda. We need to find treatments that shift the patient's phenotype from 'Non-Responsive' to 'Responsive'.
Generating Patient Digital Twins
We generated digital twins for 10 unseen cancer patients—5 sensitive and 5 resistant to Anti-PD1 therapy.
We then tasked TRAILBLAZER to simulate billions of treatment permutations to find a combination that would overcome Anti-PD1 resistance.
Study Design
Sensitive Patients
Responsive to Anti-PD1 baseline
Resistant Patients
Non-responsive to Anti-PD1 baseline
TRAILBLAZER simulated combinations to predict phenotype-shifting treatments.
Validated First-in-Class Treatments
TRAILBLAZER achieved 83% accuracy in predicting patient response, compared to 50% for CellFlow.
TRAILBLAZER Accuracy
CellFlow Baseline
Top Augmenting Treatments
Match rate with clinical outcomes
Identified for further study
Compared to traditional methods
What This Means for Patients
By accurately predicting which combination therapies can convert non-responders into responders, TRAILBLAZER enables personalized treatment strategies that maximize patient outcomes.
This approach reduces trial-and-error in clinical settings and accelerates the path to effective immunotherapy combinations.