Orakl Oncology, a Paris-based startup, has raised a $34M Series A to predict how cancer drugs will perform in clinical trials before those trials deliver results.
Norrsken VC and Singular co-led the round. The company grows copies of patients' tumors in the lab, tests drugs on them, and uses AI to predict trial results. It plans to expand into lung and gastric cancer.
The logic is straightforward. A drug tested on tissue from real patients may reveal more about a trial's likely outcome than a generic lab model does. If the forecasts hold, a drugmaker could drop a weak candidate sooner or see which kinds of patients are most likely to respond. Clinical trials are slow and expensive, and many drug candidates fail in them, so even a modest improvement in the odds would be worth paying for.
The evidence is the open question. Orakl has not published accuracy data, so there is no public measure of how often its predictions match what later happens in a trial. The meaningful test is a forecast made before a trial reads out and then checked against the result.
Most attention in AI and drug development goes to finding new molecules. Orakl is working on a later question: whether a candidate that already exists will work in patients. That is where much of the cost of a failure lands, which explains why investors are willing to fund the attempt before the numbers are public.