RNA drugs moved fast during the pandemic, but designing them is still a slow and manual process. Small sequence choices can break translation, shorten half-life, trip innate immunity, or miss the right tissue. Teams run long assay cycles to tune those trade-offs, and too many molecules underperform in vivo.
Today Therna Biosciences is emerging from stealth to change that with an AI platform that treats RNA like software. To achieve that mission, the company raised $10 million in seed funding from top investors including AIX Ventures, Pear VC, and Fusion Fund.
The pitch: a lab-in-the-loop workflow that learns how sequence drives function, then designs programmable RNA with better translation, durability, immune evasion, and tissue-specific expression, the four dials that matter most for real drugs.
“RNA-based medicine is poised to significantly improve the way we fight disease,” says Krish Ramadurai, life sciences partner at AIX Ventures. “Therna’s lab-in-the-loop approach and proprietary functional RNA models give the company a clear edge to engineer better RNA medicines, faster.” It’s a tidy summary of the strategy: pair wet-lab data with generative models and iterate quickly until the platform learns stable rules, not just one-off recipes.
Why RNA still resists scale
Most RNA pipelines look like this: pick a target, sketch a sequence, choose modifications and a delivery vehicle, run assays, then try to fix whatever failed, often immunogenicity or poor expression in the tissue that matters. Each loop can take months, and the interplay between immunogenicity, translation dynamics, stability, and tropism is messy. Generic machine-learning models don’t help much because they aren’t trained on the right functional data or they ignore the biological context that makes or breaks a therapeutic.
“AI is reaching an inflection point in its application to biological design,” said Nazli Azimi, Ph.D., co-founder and CEO of Therna. “At Therna, we have created a platform that radically accelerates the development of RNA medicines, enabling a lab-in-the-loop cycle that produces better molecules, with fewer iterations. Our deep expertise in drug development and clinical strategy gives us a clear path to translate these discoveries into meaningful clinical impact.”
World class founding team
Therna was co-founded by Nazli Azimi, Ph.D., a serial biotech entrepreneur with deep development experience, and Hani Goodarzi, Ph.D., a UCSF and Arc Institute scientist with a decade-plus in RNA biology and generative methods. Around them is a group of researchers and advisors drawn from the NIH, Princeton, Illumina, AbbVie, and Flagship Pioneering. =
The investors see a market opening. RNA has moved beyond vaccines into rare disease, oncology, and ex vivo cell engineering, but many programs stall on the same knobs Therna wants to tune. If Therna can consistently shorten design-to-data cycles and reduce in vivo surprises, it earns a seat at the table across modalities.
Therna is part of a broader shift: use AI not as a veneer, but as the engine of design, welded to experiments that update the model fast. If Therna can turn RNA into something programmable, with predictable control over translation, durability, immune visibility, and tissue expression, it could make development faster, cheaper, and less brittle. That’s the kind of leverage the field needs.
For now, the company has cash, a defined platform, early target areas, and an investor group aligned on pace. The next year will show whether Therna’s lab-in-the-loop loop produces candidates that hold up beyond the bench, and whether “programmable RNA” becomes a reality.
