Evaxion Biotech has announced plans to develop customised cancer vaccines by targeting a category of AI-discovered tumour antigens known as ERVs.
ERVs, which are ancient virus remnants dormant in human genomes, are often overexpressed in cancer cells but not in healthy tissue.
The company's AI platform, AI-Immunology, is pivotal in discovering these new types of cancer vaccine targets.
The technology enables the creation of customised and precision vaccine solutions, which are particularly promising for patients who are presently unresponsive to standard cancer immunotherapies.
The AI-Immunology powered vaccine concept also offers potential treatment solutions for these patients.
Evaxion’s scalable AI prediction models utilise AI for decoding the human immune system and are instrumental in developing new immunotherapies for cancer, as well as bacterial and viral infections.
Using AI-Immunology, the company has developed a clinical-stage oncology pipeline of new personalised vaccines and a preclinical infectious disease pipeline addressing ailments with high unmet medical needs.
The preclinical activities have commenced, with proof-of-concept data anticipated in the second half of 2024.
Computational modelling of this scalable and adaptable AI technology platform rapidly detects, forecasts and creates vaccine candidates, offering a tailored approach to fight fast-evolving pathogens and malignant cells.
Evaxion chief scientific officer Birgitte Rønø stated: “With our intensified focus on ERV cancer vaccines, we aim to expedite the development process to deliver treatment solutions to cancer patients who, until now, have been deemed unresponsive to immunotherapy.
“The ERV cancer vaccine targets represent a promising breakthrough that could significantly broaden the horizons of cancer vaccine applicability and marks a significant step forward in our commitment to improving healthcare through innovative and AI-powered approaches. We are already seeing significant interest in ERV-based vaccines and look forward to further underpinning the significant potential by these proof-of-concept data.”
