Delivering Large Molecules To The CNS, Overcoming The Blood-Brain Barrier
By Tyler Menichiello, contributing editor
The blood-brain barrier (BBB) is a semi-permeable membrane that prevents the majority of small-molecule drugs and all large-molecule drugs from entering the brain. As important as it is, it presents a real challenge in terms of delivering biopharmaceuticals to the brain to treat disease. That’s why Aliada Therapeutics is working to overcome this delivery challenge with its MODEL platform. This molecular delivery technology is the cornerstone of the company’s lead candidate, ALIA-1758, for Alzheimer’s disease. To learn more about ALIA-1758 and the story behind MODEL, I met with Aliada CSO, John Dunlop, Ph.D.
**The following Q&A has been edited for length and clarity.**
Please Explain Aliada’s MODEL platform
What we call the “workhorse” component of the delivery module platform is a single-chain FV portion of an antibody — the part that typically binds to an antigen. These are normally very unstable protein domains, but we’ve used proprietary technology to “staple” that single-chain FV which transforms it into a highly stable moiety. And, because it’s a relatively small antibody fragment, we can attach it to antibodies in a relatively simple way. In the case of our lead program, a molecule we call ALIA-1758, we simply attach the single-chain FV to the C terminus of the FC region of the antibody. We can make that in a single cell line, so it’s actually a very well-behaved process. Right now, we’re at about three grams per liter yield in our cell production, which we’re going to continue to optimize as we do more CMC work in the coming years to scale up production.
So, that’s one component of the delivery MODEL platform — the workhorse. Another that we use quite often is a mono-IgG. This is where you simply have an antibody with a single Fab arm, and then we have the binder to the BBB transcytosis receptor on the Fab arm. There, we’re starting to use these scaffolds to make antibody-oligonucleotide conjugates. We’re hopeful that we can use our delivery modules to deliver RNA therapeutics to the brain peripherally rather than resorting to invasive intrathecal bolus administration, which is currently the standard practice.
What came first: ALIA-1758 or the MODEL platform?
They were developed together inside of Janssen. The biotherapeutics team was working to optimize the MODULAR delivery platform, and at the same time, they were working closely with the neuroscience group to generate the molecule that we now call ALIA-1758 — an antibody that targets amyloid beta for the treatment of Alzheimer's disease. It targets an epitope on amyloid beta that’s referred to as a pyroglutamate amyloid beta, an aggregation-prone form of the protein that’s highly expressed in plaques in human Alzheimer’s brains.
There’s another molecule that’s been developed by Eli Lilly called donanemab that also attaches to this epitope. ALIA-1758 was designed to build on learnings from donanemab, and it has a higher affinity for pyroglutamate amyloid beta. We've also attached our delivery module to that antibody as well, so we can get more of it into the brain than we know donanemab can. Donanemab is also restricted to IV administration, which means that people have go to IV infusion centers. Our goal is to enable once-monthly subcutaneous administration. So, I think we have a really big advantage that could give us a best-in-class molecule.
How de-risked was this technology when you licensed it from Janssen, and what made it attractive to you?
It was quite de-risked in the sense that the Janssen team had done a lot of their own internal validation data — both in rodent models and in non-human primates to show improved delivery with the delivery modules attached relative to what we call a “naked” antibody. And then, as we brought the technology into Aliada, we were able to replicate all the same data that Janssen had generated. It’s always comforting when you can replicate data and see exactly the same thing. We’ve gone on to extend that — not only working with antibodies, but with different types of cargo and learning the rules for how we can best optimize the platform for each.
So that was some of the de-risking. And for me, what was most compelling was just seeing the data compared between a naked antibody and some of our MODEL-enabled antibodies. Seeing that it really increased brain exposure, to me, was very powerful and suggested that this platform could be very valuable and unlock huge potential for patients in a number of different areas.
This challenge of BBB delivery for large molecules is one we've been wrestling with for a long time. You can see the way biologics have transformed the treatment of other diseases in immunology and oncology, so if we could provide that same solution to neuroscience, I think we could really bring a tremendous value to patients, and that's really exciting.
How does the manufacturing approach differ by indication or large molecule?
There are two examples of how we would format manufacturing differently. One, in the case of ALIA-1758 and other antibody-targeted therapeutics, is we code for and generate the antibody in a single cell. On the other hand, when we talk about oligonucleotide-based therapeutics, we would essentially make our antibody scaffold and do a conjugation process to attach the oligonucleotides.
Right now, we’re exploring a couple different ways to conjugate these oligonucleotides through different types of chemistries. One is stochastic cysteine conjugation, which takes advantage of the cysteine residues on the FC region of the antibody. Then we’re also exploring more site-specific conjugation approaches that give a little more control over the conjugation to the antibody scaffold, and I think that nicely reflects some of the modular components and fits nicely with the MODEL platform.
What has been Aliada’s biggest learning thus far?
I think the one thing we’ve learned is these delivery modules could also be of some value in targeting therapeutics to other organs, not just the brain. But, as a small company, we want to stay very focused initially, so that's why we've really focused our efforts around neurology, where there's a huge unmet medical need. We think we have some really good, exciting targets that — if we can get good molecules and improve brain delivery — we can have some impact in. I think that's been a good learning for us — to stay focused while also recognizing that as Aliada matures and grows as a company, we can start considering other applications.