Iterative Engineering and Optimization Strategies for Maximizing LNP-mediated Gene Editing in the Liver

I’m Tyler McCullough, and I’ll be sharing how ElevateBio develops iterative drug substance optimization, proprietary LNP delivery, and cross-modality gene editing expertise to improve in vivo liver editing performance. This expertise can help partners advance more potent editor candidates. At ElevateBio, really, we focus on creating advanced genomic medicines from discovery all the way to manufacturing. So obviously, the goal with these editing modalities is to get as good a potency as we can, so we can move forward with a drug product that could potentially be used as a therapeutic.

We obviously have this different stack of modalities, have newer ones like large insertion, but for the point of this presentation, we’re just focusing on nuclease base editing and RT editing.

So I did want to take one second just to recognize that here at Elevate, we have this workflow of how we essentially get from our database of editors all the way up to potential therapeutic going in vivo. And really, the benefit of this is that we have many going in parallel, and so this allows us to feed forward learnings from parallel tracks.

And so that’s what we’re focusing on today.

Briefly, this has got us to our current state of potency with our simplest form of editing, which is our nuclease editing, when this is almost saturating editing as low as 0.1 mg per kg, which is a relatively low dose in these mice. And we can show this through editing and other orthogonal assays like the pseudoglycolate over here and get more into those details in the presentation itself.

So really what’s gotten us to these levels of potency is working on different drug substance optimizations. And so in the case of our proprietary LNP formulations, this is the mRNA and the guide RNA. And so we can do different modification schemes across both of these RNA species that are going into our LNPs, and they can lead to different increases in potency.

So that’s shown here for both of these different RNA modification schemes.

It’s these different modifications, among other engineering efforts that we’ve done, that has led us to potencies that allow us to do other editing modalities, one such as reverse transcriptase editing or RT editing, which we have some data shown here with pretty robust in vivo potency.

This is a difficult one to get good potency in vivo, so we’re really proud of this second attempt we had here, getting up to around 30% editing, again, for RT.
And then we also have A base editing, just another modality that can change a single A to G, and we’ve shown this in a different model as compared to nucleases.
This here is PCSK9, but again, getting relatively low doses, such as 0.3 mg per kg, and getting editing almost saturating up to 50%. So it’s through all these mouse studies and these different modalities that we’ve really had many gains in our potency. We started at a modest high dose, and we’ve worked our way down to a low dose now, getting saturated editing with these low doses, and this is what has enabled our NHP-ready gene editors. And so this is what we’ve put into NHP now.

This study design had multiple doses, but the long story short is that we were able to go up to 2 mg per kg and get robust editing, shown in orange, as well as a reduction in the target protein we’re trying to reduce by A base editing. And so there’s still opportunity to increase potency, even with this NHP study. So we’ve obviously done a lot of effort at Elevate, doing different strategies for protein engineering, like deep mutational scanning, directed evolution, or rational design, among others, to make better variants. And this has led to quick turnarounds with in vitro studies that give us higher potency next-level editors or next-generation editors, and that’s what’s shown towards the end of the presentation here.

So in the end, what we’re able to show is that we can do iterative engineering and optimization on both drug substance and the proteins they’re encoding, and this has led to some great potency and tolerability for NHP editing, as well as short turnaround times to even further generations of our editors. For partners, this platform creates a faster path to optimized in vivo liver editing by connecting drug substance design, guide chemistry, LNP formulation, protein engineering, and preclinical validation in one iterative system.