Radical Numerics has announced that its Omnii genome model can rank neoantigens and write an mRNA cassette for personalized cancer vaccines, marking a step toward AI‑driven immunotherapy.

Radical Numerics announced that its Omnii genome‑model platform can now rank neoantigens and automatically generate the mRNA cassette needed for personalized cancer vaccines, marking a notable advance in AI‑driven immunotherapy.

How Omnii Works

Omnii leverages deep‑learning models trained on large cancer genomics datasets to predict which tumor‑specific mutations are most likely to provoke a robust T‑cell response. The system then assembles a synthetic mRNA sequence that encodes the selected neoantigens in a single cassette.

From Prediction to Production

The workflow begins with a patient’s tumor sequencing data, which Omnii ingests and evaluates in minutes. After ranking candidate neoantigens, the platform writes a codon‑optimized mRNA construct, ready for in‑vitro transcription and formulation into a vaccine.

  • Rapid neoantigen ranking using AI models
  • Automated mRNA cassette design with codon optimization
  • Export of production‑ready sequences for GMP manufacturing

Implications for Personalized Cancer Therapy

By automating the most time‑consuming steps of personalized vaccine development, Omnii could shorten the interval between tumor biopsy and vaccine administration, potentially improving clinical outcomes for patients with aggressive malignancies.

The company plans to validate the platform in collaboration with academic and industry partners, aiming for early‑phase clinical trials within the next year.

For more details, see RuntimeWire coverage of Radical Numerics’ Omnii platform.