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Evaxion's AI-Immunology Platform Validated Across Multiple Clinical Trials

• Evaxion Biotech's AI-Immunology™ platform demonstrates strong ability to select clinically relevant vaccine targets across three clinical trials. • Top-ranked AI-Immunology™ vaccine targets correlate with the strongest clinical outcomes, linking AI-driven design with real-world evidence. • The platform's predictive precision improved significantly, with 79% of predicted targets in the EVX-01 phase 2 trial triggering a tumor-specific immune response. • Clinical data enables continuous refinement of AI-Immunology™, broadening the platform's commercial potential in personalized cancer vaccine development.

Evaxion Biotech A/S has announced compelling data from three separate clinical trials, validating the predictive capabilities of its AI-Immunology™ platform in selecting clinically relevant vaccine targets. The data underscore the platform's potential in designing effective personalized cancer vaccines.

AI-Driven Vaccine Target Selection

The AI-Immunology™ platform identifies cancer vaccine targets, known as neoantigens, and assigns a prediction score reflecting the likelihood of each target to elicit an immune response against the tumor. Across all three clinical trials, a statistically significant correlation was observed between the AI-Immunology™ prediction scores and the immunological responses measured in patients. The most robust responses were consistently generated by vaccine targets with the highest AI-Immunology™ prediction scores.
In one trial, a statistically significant correlation was demonstrated between AI-Immunology™ prediction scores and Progression Free Survival (PFS) in patients, highlighting the platform's ability to identify vaccine targets with meaningful clinical impact.

Enhanced Precision with Iterative Learning

Recent data further confirms a significant improvement in the predictive power of Evaxion's AI-Immunology™ platform. In the ongoing phase 2 trial with the personalized cancer vaccine EVX-01, 79% of the AI-Immunology™ predicted vaccine targets triggered a tumor-specific immune response. This is a notable increase from the 58% observed in the EVX-01 phase 1 trial.
This improvement results from iterative learning loops, optimization based on clinical data, and the integration of novel bioinformatic and machine learning methodologies.

Clinical Pipeline and Future Directions

Evaxion has developed three personalized cancer vaccine candidates using its AI-Immunology™ platform. EVX-01 and EVX-02 have completed clinical phase 1 testing, with EVX-01 currently in phase 2. The third candidate, EVX-03, is an IND-ready vaccine candidate designed for the treatment of advanced solid cancers and is a DNA-based immunotherapy.
"We are proud to have collected such strong clinical data documenting the unique predictive capabilities of the AI-Immunology™ platform across several clinical trials," said Christian Kanstrup, CEO of Evaxion. "The correlation between the platform’s prediction score and clinical outcome is unique and provides very solid guidance for the further development of personalized cancer vaccines based upon AI-Immunology™. Generating highly relevant clinical data is very important for our ability to apply constant learning loops, allowing for the continuous development of AI-Immunology™."
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Reference News

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Recent clinical data confirms significantly improved predictive power of Evaxion's AI ...
globenewswire.com · Oct 3, 2024

Evaxion Biotech's AI-Immunology™ platform predicts 79% of vaccine targets triggering a tumor-specific immune response in...

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Evaxion's AI-Immunology platform shines in multiple clinical trials - biopharma-reporter.com
biopharma-reporter.com · Oct 10, 2024

Evaxion Biotech's AI-Immunology platform, which identifies neoantigens for cancer vaccines, showed strong correlation be...

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Strong validation of Evaxion's AI-Immunology™ platform in multiple clinical trials
globenewswire.com · Oct 9, 2024

Evaxion Biotech's AI-Immunology™ platform validated in three clinical trials, showing strong correlation between AI-pred...

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