By Anna Rose Welch, Director, Cell & Gene Collaborative
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This article is part three of a four-part series unpacking four of my biggest takeaways from the mRNA Therapeutics Summit, which took place in Boston on July 27 & 28, 2022.
As I outlined in my previous articles (part one and part two), companies in the mRNA space need to get comfortable with pursuing science and collaboration outside of their own corporate walls and department silos. Seeing as this is also an intensely scientific industry — one that demands parallel corporate advancement in lockstep with the fast and furious science — the best-practice of “thinking outside the box” must also be applied to some of the more scientific challenges the industry faces. It goes without saying that every sector of the mRNA space craves — and deserves — immediate attention and evolution. However, it was especially clear throughout the conversations at the mRNA Therapeutics Summit that, to have a future with mRNA therapeutics, there must be advancements and new ideas in the delivery arena.
Over the past few years of successful mRNA vaccine scale-up, the LNP has emerged as the darling of mRNA delivery. However, the journey from the first approval of Onpattro (an LNP-delivered siRNA for a patient population of only 5000) to billions of vaccine doses was hardly an organic process for LNPs, despite how quickly we achieved it. (Hats off to the supply chain folks — this was a big part of the headache. Not all of us can be Pfizer and pivot to making our cationic lipids in-house.)
Despite just how far we’ve come, we have only just begun to scratch the surface of mRNA delivery. Many of us automatically turn our attention solely to the delivery vehicle — predominantly an LNP. But during a panel discussion dedicated specifically to delivery, each speaker was adamant that better potency and targeting is not just reliant on a better LNP. Rather, we need to get better at thinking about the mRNA drug substance — or your API — and the LNP as a dynamic duo that can and will do their best work together. (Partnership in the mRNA space takes many forms…)
There are a few critical areas to consider here. In addition to the delicate balance of assembling your raw materials into an LNP, the overall purity of the mRNA (or “cargo”), the number of/ specific modifications made, and how the LNP and cargo are mixed together into the drug product are all critical parts of the delivery puzzle. This could mean altering the mRNAs UTRs and codons or employing an entirely different modality of RNA. (Circular DNA got quite a bit of hype at this conference.)
On the chemical modification side of things, I was struck by a comment that, of all the hundreds of modifications we may be able to explore, the two approved mRNA vaccines only have one chemical modification to boast — and that’s the well-known modification to uridine. Now, to be fair, each modification raises questions we cannot yet answer. As one speaker added, we remain unsure of the effect more modifications to the payload itself will have and/or if the ribosomes will go on strike, raising flags proclaiming that they “cannot compute.” Much of the decision around modality and delivery vehicle will also depend on the tissue being targeted and the desired persistence and durability. Scalability may also be a critical consideration, depending on the complexity of the payload.
Of course, we can’t discuss delivery and better targeting without touching upon the topic ‘du jour:’ “AI” (if you’re an investor, one panelist joked), or “machine learning” for those working in computational design. Such technologies are being employed today to identify the best delivery vehicles and most optimized sequences for the desired expression, duration, and stability profiles. Like everything else in the mRNA sector, however, the technology — and how we use it — has a long way to go. As several panelists shared, the current generation of technology has yet to convince the industry that it can/will arrive at the best structure to reach a specific type/types of cells.
Even more important to note, the current generations of technology we’re talking about are far from the nifty, alluring contraptions we’d see in Hollywood. In fact, one speaker put it best: 99 percent of machine learning is data curation, which is decidedly unsexy and unglamorous work. (No offense to data scientists…) Computational design tools are a necessity to advance and speed up the science in your labs — especially on the nonviral delivery vehicle side of things. However, it’s important companies realize that off-the-shelf data curation solutions are non-existent today. In turn, a large part of investing in ML requires painstaking database construction and greater investment/hiring in data science. Only once this infrastructure exists can a company take full advantage of ML’s opportunities/benefits — especially in the fledgling realm of mRNA/RNA nonviral delivery.
If you like what you're reading, why stop now? Continue on to part 4!