Genflow Biosciences PLC (LSE:GENF, OTCQB:GENFF) CEO Dr Eric Leire talked with Proactive about the company’s new strategic partnership with US-based Heureka Labs, a spinoff of Duke University, to enhance its gene therapy research using AI technology.
Leire said, “Every biotech in gene therapy should have an AI component. If you don't have that, you miss a critical tool to unlock value for your shareholders.” He stressed the need for a multi-omics approach—genomic, transcriptomic, and proteomic—to better understand gene expression and drug response, which traditional analysis methods struggle to fully capture.
The partnership with Heureka Labs, which integrates deep AI and biological expertise, will initially support Genflow’s lead candidate, GF-1002, a SIRT-6 gene therapy targeting MASH (metabolic dysfunction-associated steatohepatitis). AI will help the company analyse complex preclinical data, including insights from nearly 700 mice tested, to understand mechanisms of action and identify likely responders, crucial in discussions with regulators such as the FDA and EMA.
Leire also emphasised that the collaboration is scalable across Genflow’s pipeline, helping unlock synergies between programs in liver, muscle, and eye diseases.
Proactive: Eric, very good to speak with you. You've announced a strategic partnership with Heureka Labs in the US to use their AI platform. Tell us more about Heureka Labs and what this collaboration will entail?
Proactive: Heureka Labs is a spinoff of Duke University. We have a long relationship with Duke University through Professor Dr Matthew Hirschey. What's very interesting in Heureka Labs is that they have really focused not only on AI, but on biology—to really understand biology. Now having AI is a collaboration between AI and biology. It's not an advantage for biotech, it's a necessity. So we are quite happy with this relationship with Heureka Labs—major players in AI and biology.
Proactive: So how will you use their AI technology to enhance your own research, Eric?
Dr Eric Leire: AI now has the potential to play a critical role in analyzing increasingly complex and large genomic data. If I come back to biology—not that long ago—we sequenced the genome. We understood the gene, the DNA. We thought we had all the solutions, that we understood all the biology. And then we realized it was more complex. You had to understand the entire sequence. It’s because DNA—a gene in itself—does nothing. It has to go on to code for protein. So you have to look at the DNA, then look at the mRNA, and then at the protein. That’s what we call a multi-omics approach: genomic, transcriptomic, proteomic. And the amount of data is huge.
In addition to that complexity, we now understand that it’s impossible to grasp the function of a gene in isolation. Gene expression is—or rather, it’s regulated—among all the genes. This is a very intricate landscape of gene expression and regulation.
At Genflow, we generate a lot of data through our preclinical program. Almost 700 mice—we did RNA sequencing, we did genomic, we did proteomic. When we don’t use AI, we understand maybe 10% of the message, and 90% of the patterns go unidentified. AI helps us to do that. It helps us identify mechanisms of action—which is very important in our relationship with the regulators. It allows us to fine-tune our approach in terms of interaction with other genes. It also helps us identify the patients most likely to respond.
You know that when you receive a drug, on average it might work at, say, 30% efficacy. That means nothing. As an individual, that’s just the average for a large population. In reality, there are people who don’t respond to a drug, and people who do. Now, most of the regulators want to see some biological tests—biomarkers—that allow us to identify the most likely responders, so people don’t get gene therapy if they’re not likely to respond. AI can play a major role in that. So it’s crucial—and it has the potential to unlock significant value for Genflow.
Proactive: So Eric it will be initially supporting your lead candidate. That's GF-1002. That's the SIRT-6 gene therapy targeting MASH (metabolic dysfunction-associated steatohepatitis). Take us through the first steps.
Dr Eric Leire: Yes. MASH is important because we have discussions with the regulators, and all this in-depth analysis of the preclinical results for MASH will be extremely useful in our discussions with the regulators—FDA and the European Agency.
But more importantly, it's a tool we need. And that's why we have this master service agreement. It's a partnership with Heureka. And the size of Genflow and the size of Heureka allow us to have this kind of partnership. It will be used for all the programs and the synergies between the programs, because the mechanism of action we discover through AI, through the liver program, can be used for the eye, can be used for the muscle.
That's why you see, for example, Sam Altman from OpenAI investing a major amount of money in biotech companies like Retro Biosciences. Retro Biosciences is the biotech company of Sam Altman, if you will. But it's not the only one. Every biotech in gene therapy should have an AI component. If you don't have that, you miss a critical tool to unlock value for your shareholders.
Proactive: Eric, I hope you'll keep us posted on progress with that program. Thank you very much for speaking with us today.