Strong validation of Evaxion’s AI-Immunology™ platform in multiple clinical trials
- Data from three separate clinical trials documents the strong ability of the AI-Immunology™ platform to select clinically relevant vaccine targets
- Strongest outcomes are generated by the top-ranked AI-Immunology™ vaccine targets, thereby linking AI-vaccine design with real-world evidence
- This underscores the platform’s unique potential in selecting effective vaccine targets
- The ongoing generation of relevant clinical data allows for continuous refinement of AI-Immunology™ thereby broadening the commercial potential of the platform
COPENHAGEN, Denmark, October 9, 2024 – Evaxion Biotech A/S (NASDAQ: EVAX) (“Evaxion”), a clinical-stage TechBio company specializing in developing AI-Immunology™ powered vaccines, has now evidence from three different clinical trials validating the vaccine target predictions of its AI platform.
Evaxion has developed three personalized cancer vaccine candidates with the use of its AI-Immunology™ platform. Two of these – EVX-01 and EVX-02 – have completed clinical phase 1 testing and EVX-01 is currently in phase 2, while the third – EVX-03 – is an IND-ready vaccine candidate.
The AI-Immunology™ platform identifies cancer vaccine targets, so called neoantigens, and assigns a prediction score reflecting the likelihood of each vaccine target to elicit a specific immune response against the tumor. In all three clinical trials, a statistically significant correlation was observed between the AI-Immunology™ prediction scores and the immunological responses measured in the patients, with the most robust responses being generated by vaccine targets with the highest AI-Immunology™ prediction scores.
Further, in the one trial where it was assessed, a statistically significant correlation was demonstrated between AI-Immunology™ prediction scores and Progression Free Survival (PFS) in patients, underscoring the platform's ability to identify vaccine targets with meaningful clinical impact.
“We are proud to have collected such strong clinical data documenting the unique predictive capabilities of the AI-Immunology™ platform across several clinical trials. 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™,” says Christian Kanstrup, CEO of Evaxion.
About AI-Immunology™
AI-Immunology™ is a scalable and adaptable artificial intelligence technology platform at the forefront of vaccine discovery for infectious diseases and cancers. By integrating the collective power of proprietary AI models PIONEER™, EDEN™, RAVEN™, and ObsERV™, the platform can model the complexity of the patient’s immune system. AI-Immunology™ advanced computational modeling swiftly and uniquely identifies, predicts, and designs vaccine candidates, revolutionizing the landscape of immunotherapy by offering a holistic and personalized approach to combat fast-evolving pathogens and malignant cells.
Contact information
Evaxion Biotech A/S
Mads Kronborg
Vice President, Investor Relations & Communication
About EVAXION
Evaxion Biotech A/S is a pioneering TechBio company based upon its AI platform, AI-Immunology™. Evaxion’s proprietary and scalable AI prediction models harness the power of artificial intelligence to decode the human immune system and develop novel immunotherapies for cancer, bacterial diseases, and viral infections. Based upon AI-Immunology™, Evaxion has developed a clinical-stage oncology pipeline of novel personalized vaccines and a preclinical infectious disease pipeline in bacterial and viral diseases with high unmet medical needs. Evaxion is committed to transforming patients’ lives by providing innovative and targeted treatment options. For more information about Evaxion and its groundbreaking AI-Immunology™ platform and vaccine pipeline, please .
Forward-looking statement
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