How it works
Today the search happens at the bench
Promoter development usually starts with a literature search. A team picks a few familiar
promoters, clones them into vectors, and tests them in whatever cell models are available.
If expression is too weak, too strong, or not specific enough, the team picks another set of
sequences and begins again. Every cycle costs design, synthesis, cloning, vector production,
cell assays and analysis — and animal studies can still show that a sequence which looked
promising in vitro behaves differently in vivo.
Promoter Atlas moves the broad search into software. Our models evaluate
thousands of natural and designed promoters before synthesis, so you enter the laboratory
with a focused set of candidates already prioritised for the expression profile you need.
Conventional development
7–12 months
3–5 Design-Build-Test-Learn cycles · ~8–10 weeks each · ≈$2–7M in direct R&D
→
With Promoter Atlas
2–3 months
One focused validation cycle · ≈$0.5–1.5M under the same assumptions
These are our current planning assumptions for a typical programme, not a quotation. Reaching
a lead cassette four to eight months earlier may preserve tens of millions of dollars in
programme value, depending on burn rate and development stage.
You still need a laboratory to validate the final therapeutic cassette. You do not need many
laboratory design cycles to discover that a promoter is broadly active, too weak, or active
in the wrong cells.
What this changes
Fewer build-test cycles
Candidate generation and prioritisation move from repeated experimental cycles to an in silico search completed before synthesis.
Less spend before synthesis
Weak, non-specific and otherwise unsuitable candidates are removed before you spend on synthesis, cloning, vector production and animal studies.
Selected for low off-target activity
Candidates are selected for strong target-cell expression and low off-target activity — the right amount of protein in the right cells.
Evaluated against human contexts
Promoters can be evaluated against both preclinical and human requirements, so you can prioritise shared sequences or matched pairs early.