The Dutch biotech ProQR Therapeutics is turning to Ginkgo Bioworks’ autonomous lab technology as it tries to scale AI-enabled discovery around its Axiomer RNA editing platform.
The companies said April 8 that ProQR will use Ginkgo’s Nebula autonomous lab, a 50-plus-instrument setup, to generate high-throughput experimental data intended to improve the scale and speed of discovery on Axiomer. ProQR also used the announcement to unveil an AI advisory board and say it expects its first AI-discovered development candidate to enter the clinic in 2026, with a clinical trial application anticipated in mid-2026 and initial clinical data expected by year-end.
Over the last 18 months we have developed an AI model that leads to significant acceleration and improvement of editing oligonucleotides in drug discovery, —ProQR founder and CEO Daniel A. de Boer.
De Boer added that the Ginkgo deal gives ProQR access to “their state-of-the-art autonomous lab enabling high throughput data generation to further scale up our AI-enabled drug discovery.”
Axiomer is ProQR’s RNA editing platform, which uses editing oligonucleotides to recruit endogenous ADAR enzymes for targeted single-nucleotide changes in RNA. The company’s pitch is that better and faster data generation can sharpen the predictive performance of its discovery models and help move candidates forward more efficiently.
In a separate interview with R&D World, Ginkgo CEO Jason Kelly described the ProQR deal as the sort of collaboration that sits outside the standard CRO model. “There aren’t really good CROs for doing [work like] this.,” Kelly said. “CROs mostly orbit around the most common assays people would do,” he said. “You can outsource an assay, you can outsource a tox study, you can outsource some antibody binding, the most down-the-middle, repeatable stuff from the most common modalities.”
That leaves a gap between fixed outsourced services and fully internal lab work, he argued. For Ginkgo, the opportunity is to offer something more flexible than a standardized assay service while still using automation to drive scale. “What we’re doing with our cloud lab at Ginkgo is offering more variable experiments,” Kelly said. “Oh, you want these particular experiments? We can run those at high throughput for you and just send you back the data.”
That flexibility is central to how Kelly frames the bigger automation challenge in drug discovery. “I actually think the artisanal nature, doing many different protocols, is necessary,” he said. “You actually need to be artisanal to discover drugs. I just think you can now automate artisanal.”
In ProQR’s case, that means using Ginkgo’s lab to feed experimental data into an AI system that the RNA editing company says it has been building for roughly the past year and a half. “They have an AI model they want to generate this data for,” Kelly said. “We’re going to use the automation to make the data generation.”
ProQR belongs to a relatively small cohort of biotech companies trying to rethink development systemically rather than just optimize a single asset. “There are a few other companies orbiting around the question: is there just a better way to do this stuff, independent of any one candidate, just systematically?” Kelly asks.
Alongside the Ginkgo partnership, ProQR said it has formed an AI advisory board that includes leaders from Owkin, NVIDIA, Hugging Face, Leiden University, HCVC and Kimia Therapeutics.
The deal also includes a strategic equity investment from Ginkgo, which Kelly described in practical terms. “I believe in their view on things. I’m excited about the company. It makes sense for us to have equity in the company,” he said.
