Paul Bolno: Sorry, last question, with the GSK component. So as much as I’d love to be able to speak for a partner. I think, we — as we shared today, I think it’s safe to say there is an extraordinary amount of momentum. It was evident. Anybody who attended bio, we were on — we were the participants with GSK as they were sharing kind of where they see the translational potential of their genetics investment and have really been highlighted by them as the opportunity that we provide with a multimodal platform. I think, editing is one. Importantly, as GSK looks across their universe, it’s not just constrained to loss of function and editing. There are a lot of really prominent programs in silencing and we are partnering with them across silencing using siRNA and the data — some of that basement for that data we shared last year as well as on the editing approach.
And so really being able to think about diseases holistically where we’re not constrained to either an upregulation, gain of function disease or loss of function that can really pick the best tool for the right job. So I think there’s a lot of momentum on the collaboration across programs, and we’re excited to continue to provide updates where we can.
Operator: Our next call comes from the line of Eun Yang from Jefferies.
Eun Yang: Thank you. I have a couple of questions on RNA editing. So obviously, you have the most advanced program in RNA editing, but others are doing as well. So could you comment on your technology, how you are better positioned for RNA editing compared to others, although there is not a lot of data out there from others? So that’s question number one. And second question is on AATD program. And obviously, you’re going to be talking more about this at the R&D Day. But as you are very close to filing a CTA, when do you think we will be able to see the data? And in terms of serum AAT protein levels, you have shown remarkable increases in rodents. But how should we think about in humans? And how many fold of increase would be your objective?
Paul Bolno: Great. We’ll unpack that a lot. I think, if we — we’ll start with the front and then I’ll move to your final question, which is really the translation of why are we leading AATD — in editing, how that translate into AATD and ultimately, what do we expect to see in the translation in human? I think, you said a lot at the beginning when you said why are we ahead of others? And I think you really brought up the important piece, which is there’s not a lot of data out there from others. And I think, what I’d like to say is this doesn’t come from an immediacy of saying, well, we’re interested in RNA editing. It comes from over a decade of investments in building an oligonucleotide chemistry engine. And what we are poised to do when we entered in the editing space was really bring together this convergence of best-in-class nucleic acid chemistry.
The how? How do you create guidance trends that better interact with enzymes? How do we design that to more specifically and enhance the opportunity set that we have with ADAR? And ultimately, how does that translate both in our non-human primate studies in the liver using GalNAc, in the liver not using GalNAc and in other tissues beyond the liver? And I think, if we get to the root of really how we approach things, it is a chemistry-driven exercise. And I think in doing so, we’ve been able to get to short oligonucleotides that are long that don’t require a delivery vehicle. So we’re not constrained to lipid nanoparticles that I do think ultimately complicate delivery and accessibility. That’s important because I think a lot of times for those not familiar with LNP, the chemistry that one would use to be compatible with an LNP versus what we can do to not be in one, means that we can focus on really enhancing not just potency, but stability and durability.
So as we think about optimizing pharmacology as we transition programs, I think of it not as a science experiment on editing, but ultimately translating into a human therapeutic means we are thinking about what’s going to be a best-in-class therapeutic, infrequent subcutaneous administration; being something that’s potent and durable in terms of patients and safe as we think about being able to avoid off-target editing, bystander editing and reversibility. So as we put that whole profile together, we’ve built this program systematically from the beginning, off of our chemistry engine in a data-driven way through our publication and presentation, and have built that up-through model. Now what’s important in translation, you said, okay, well, how do we know this is going to translate, what’s giving us confidence is.
I think the data to-date, and I appreciate your recognition that, that data is consequential that we’ve seen approach production. By starting in GalNAc, what we’re able to see is there is good precedence for that translation of thinking about human hepatocytes into rodent studies into humans, given that we’ve got a delivery agent in GalNAc. And so, we’re going to learn a lot about the translational pharmacology both on the disease and the new modality via that translation. And that’s going to teach us obviously, a lot about preclinical modeling as we think about subsequent programs, as they go forward. The benefit of all of that is, we can look to other modalities in their translation to be able to start predicting where we think doses need to be as we get into the clinic.
