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 Anti-PD1 therapy to see if it can accurately reconstruct patients' responses.
Study Design
Sensitive Patients
Responsive to Anti-PD1 baseline
Resistant Patients
Non-responsive to Anti-PD1 baseline
TASK: Virtual Clinical Trials to predict responses at molecular level and phenotypic responses of real patients to virtual anti-PD1 treatment.
Validation
Per-Patient Responsiveness to Anti-PD1
TRAILBLAZER reconstructed the responsiveness distribution of 10 unseen patients. Responsive patients (P1–P5) score consistently above 0.5; non-responsive patients (P6–P10) score below.
Drug Discovery
Combinatorial Anti-PD1 Treatment Predictions
TRAILBLAZER performs a zero-shot virtual drug screen by ranking treatments most likely to lead to desirable α-PD-1. Accuracy measures corroboration of top predictions against published clinical literature, evaluated on unseen breast cancer patients.
Predictions validated against published clinical literature on treatments augmenting immune checkpoint inhibitor response. CELLFLOW is the leading neural network benchmark trained on the same data foundation.
In Silico Discovery
Patient-Specific Treatment Rankings
Starting from each patient's non-responsive phenotype, TRAILBLAZER independently ranks candidate treatments by their predicted ability to shift the immune landscape toward α-PD1 responsiveness. The model re-discovers the clinically validated α-PD-1/IL-15 combination at the correct patient-specific rank — and proposes 10 novel first-in-class candidates (ANU-01–10) now in active validation. Hover any cell to highlight all patients sharing that treatment.
For simulating individual patient responses
Match rate with clinical outcomes
Compared to state-of-the-art
Identified for further study
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.
Saved per approved drug for every 10% improvement in predictive positive value of clinical trials.
Current in vitro and in vivo pre-clinical predictive positive value — the benchmark TRAILBLAZER is built to surpass.