As humans, we all want the same thing, a life that's full of good experiences, more time with family, with friends, more time to love,
but sometimes genetic illness can cut that short or really, for all of us at some point, our body breaks down.
And our bodies are genetic machines. For many diseases, the cause of the disease is a mutation in the genome.
Gene therapy is a vision that many have had for decades, more than 50 years.
The power of genetic technology is that once you get inside of cells with a DNA molecule, that molecule can stay there for the lifetime of the cell.
So it's the potential for a one-time treatment for a disease where you wouldn't otherwise be able to reach the cells and solve for the root cause of the disease.
Today though, for the most part, the genome you're born with is the genome you die with.
Access to this molecular level is out of reach for almost all of us.
We've tried many different things, but have really struggled to be able to get enough of the genetic payload into the cells where they're gonna be effective as a therapeutic.
And it's getting inside of the cells that has really been a challenge for many, many years.
I'm Eric Kelsic, CEO and co-founder at Dyno Therapeutics.
For the past 10 years, I've been working to solve the grand challenge of gene delivery.
How are we gonna make gene therapy a mainstream kind of medicine?
We need to solve these grand challenges like delivery, being able to deliver a therapeutic payload to every organ or every cell where there might be some benefit to patient health.
To do that, we're engineering protein shells derived from viruses.
Capsids are the protein shells of adeno-associated virus.
AAVs, adeno-associated virus, is a parasite of other viruses.
AAV naturally isn't known to cause any disease.
The reason why AAV gets a lot of attention is because it's one of the smallest viruses, and that enables it to get into many places all across the body where we need to deliver a therapeutic DNA.
We still don't know a lot about how it functions naturally.
That said, we don't need to understand everything about how the virus works in order to adapt it as a therapeutic technology, and that's our focus at Dyno, engineering the capsid sequence to make capsids a better delivery vehicle for gene therapies.
What's amazing about capsids is they're evolved in nature to do so many different things.
So they can go through your body, through the blood, find a cell, enter the cell, and then be released into the cell cytoplasm, get into the nucleus through the nuclear pore, break open the capsid and release the genome, and that's where it expresses.
So for a gene therapy, going from the blood, into cells, into the cytoplasm, into the nucleus, and then expressing the genetic payload, that's entirely the goal.
And when therapeutic genes are expressed in the nucleus, they can be treating those cells for a patient's entire lifetime.
As a one-time treatment, it can be an effective cure.
However, natural capsids, they're not efficient enough for most therapeutic purposes.
So for the past 28 years, protein engineers have been working to modify the capsid to make it better as a therapeutic protein, applying a technique called directed evolution.
Directed evolution is evolution like occurs in nature but for a goal that we choose,
and the most common approach there had been to randomly change the capsid sequence to make very large libraries, millions or even billions of different capsid sequence molecules.
With a very low quality library, but a very large one, you have a chance of getting a good hit, but it's like a needle in a haystack.
And the reason is because the capsid has many different functions, and if you break even one of them, then, as a therapeutic, it's essentially useless.
Roughly 80% of the single changes that you could make to the capsid break the most essential function, which is the assembly and packaging of the genome.
What that means is that, if by chance you make any mutation, four out of five times, it's gonna break the function.
And that's a problem for engineering because, to get improved function, we're gonna need to make multiple changes, maybe even hundreds of changes.
So if every time you make a change, the viability drops down, it's really hard to have a library of changes that are going to do a chance of finding an improved capsid.
And that's just the basics. You need to be able to produce and purify that capsid at scale.
It needs to be stable at low temperature or frozen, but even when it's in your body, which is a relatively high temperature, it also needs to get into the right cells.
For example, there's a lot of unmet need for gene therapy in the brain because it's very difficult to get therapeutic proteins or other molecules across the blood-brain barrier.
At a high dose, you might be able to get into .1% or maybe a little bit more of the neurons in the brain. That's not enough to treat many diseases.
And in addition to that, most of the capsid delivers its payload to the liver.
And at a high enough dose, that can also become toxic.
We need to improve the efficiency of delivery to the target cell.
Over decades of trying this approach, we just didn't get enough improved variants or variants that were optimized for all the different functions that were needed to make them effective as gene therapies.
I had seen that there was a new wave of technologies coming with the potential to change the way that we engineer proteins completely.
It starts with this DNA multiplexing technology.
So we have an idea of an experiment we want to run, testing many different capsids.
They might be designed to bind to a certain receptor or they might be designed in a neighborhood of sequence space that before we found is promising.
And we came up with a way of building very large libraries of capsids in which the sequence was programmed, meaning we had designed it on a computer, synthesized that DNA, and then cloned it into the capsid, so this could be injected in a few mLs.
The best way we have to make a prediction about what's gonna be safe and effective for humans is to do an animal experiment.
We do most of our screening in non-human primates, especially in cynomolgus monkeys.
That's one reason why we developed this technology because their lives are also very precious.
We wanna get as much information as we can from even one experiment.
In this case, we're measuring maybe a hundred or two hundred thousand, sometimes even a million different capsid sequences in that one animal.
We'll get all these tissues back from our animal experiments, and then we wanna learn as much as we can from that experiment, meaning look at every organ.
Where did the capsids go or where did they not go?
Extract the nucleic acids, purify the DNA, purify the RNA.
You can then work all the way back to what was the capsid sequence that this molecule corresponds to?
Is there more or less of that in the library?
And if there's more, that might mean that it was functionally improved for delivery.
If there's less, it might mean that there was a problem and it was broken.
We do this across all of our library.
At Dyno today we've got petabytes of data from the DNA sequencing.
I had always thought that proteins, they're too complex for us to understand as humans, certainly too complex for me to understand.
When you look at a string of 735 letters, it's really hard to notice all the differences.
But with all that data, what I could see, even myself, was there's a lot of patterns in that data, patterns about which amino acids work at each position.
My thought was that if I could recognize those patterns and the data set is so vast, there's probably a lot more information in them as well.
That's actually the perfect type of problem for a machine learning model.
We can use AI to automate the analysis of all that data and to find even more nuanced patterns to maximize the chances of success, the expected value of finding an improved variant.
We call that AI-guided design.
But once we have that data and we've trained models on it, we can now query those models billions and billions of times.
So our ability to scale the computational work is even higher than the molecular side.
We can't possibly test everything in an experiment.
But with machine learning, we can test many different sequences in silico, meaning on a computer, and the models will tell us which ones they think are better,
or we might try many models, tens or hundreds of different models that each have a different insight.
And we compare the opinions of all those different experts to choose the ones that we're very confident are worth investing in as we bring them forward into the next experiment.
So it's this iterative cycle of making libraries in DNA, measuring their properties, then building models to analyze and understand those properties.
Then querying the models to know where are the most promising regions of sequence space that we should go next.
And then going back to design a new library, turning that into DNA.
We're using a lot of technology, but there's always human judgment at some point before we do another round of experiments.
I think that, over time, what we wanna do is put humans at an even higher point of leverage so that they're able to use their exceptional judgment and shift some of the more routine tasks to AI agents or even just to simple scripts that run on the computer.
Being able to collaborate with AIs more effectively is where we'd like to go so that we can, for example, give instructions to the AI to automate how we analyze the library or how we design it and get back the answers that we expect.
We wanna get the results as fast as we can.
Patients are waiting for better medicines.
We wanna make sure that, if there's anything wrong, we catch it quickly, and for that, we need a human in the loop.
The power of genetic technology is that once you get inside of cells with a DNA molecule, that molecule can stay there for the lifetime of the cell.
So for example, in the neurons where they're not dividing, getting the right therapeutic DNA sequence into the neuron can be effectively a cure for a patient's entire lifetime.
That's the reason why at Dyno, and myself personally, and many of us are so excited about the potential of gene therapy.
A good example of this would be Zolgensma, which is now an approved medicine and was really a breakthrough drug.
SMA, spinal muscular atrophy, was the leading cause of death from a genetic disease in children prior to this treatment.
The problem is that the SMN1 gene is not functional in patients.
This disease, prior to gene therapy, was always fatal at a very young age.
Children would die usually around two or three years old.
With Zolgensma though, if children are treated very early, in the first few weeks of life, say, the gene therapy can restore the function of that gene.
It can, with a one-time treatment, completely cure the disease.
And it's an example of the amazing potential of gene therapy.
What's unfortunate is that there's, today, just a handful of FDA-approved gene therapies, but there's thousands of genetic diseases that we know about, 7,000 or more.
And for most of them, we have no good treatment options available.
What we want to be able to do with our capsid engineering work, by solving deliveries, make it easier to get into all the cells that will enable us to then apply the knowledge we have from genome sequencing and from systems biology to develop therapies that are gonna treat the underlying cause of those diseases.
Today, most of our attention, most of the industry's attention is focused on a smaller number of diseases, diseases that could benefit more patients, and where the markets are large enough to justify commercial investment.
There's also a long tail of rare and ultra-rare diseases where there might only be 10 or even a single patient in the entire world who has a certain disease.
Today, gene therapies are very expensive.
A single dose might cost millions of dollars.
Our goal is to bring the cost of delivery down to zero, or very, very close to it.
To do that, we also need to enable there to be many more genetic medicines so that there's good competition between developers.
We can also look to other industries where there's been really dramatic changes in the cost efficiencies and the scale economies over time.
One of them is in semiconductors, as we've been able to dramatically improve the number of transistors that you can put on a chip.
Other areas like solar where we'd been able to bring the cost down more than 200 fold over five decades and increase deployment over 100,000 times.
These phenomena are all called Wright's Law, which is basically that with every doubling of production, there's a percentage decrease in the cost.
And in gene therapy, I think there will be something similar.
So we can go from a gene therapy that might cost hundreds of thousands of dollars down to something that costs $10,000 or even $1,000 to develop.
To the point where rare diseases or ultra-rare diseases, non-profit efforts could be fully funding the treatments for patients.
Obviously, there's a lot of things we need to do in order to achieve that.
Solving delivery is just one of them.
The therapies are complex and we don't understand exactly how to design them in a way they're gonna work in humans,
but AI may be part of that solution because if an AI could design a therapy just for one patient and customize it to their genome sequence, customize it to their goals, that could be done on-demand,
and that AI could even chart out how to develop the therapy, how to produce it, how to test it, how to ensure that it's safe.
This could be done in a massively scalable way.
And I think that's the path that we can use to solve for the long tail of disease and to help patients who, today, we understand their genetics. We know what the problem is. We even, in many cases, know how to design a therapy that could help them,
but we need to be able to bring that to the patient directly, and AI is a way that they can get the benefit from all this innovation in a way that's economically affordable.
As there's more gene therapies, one thing that we may want to do is to be able to reset or remove prior gene therapies.
It's far away because it's not the urgent priority today.
But for a future with genetic agency where patients are making the best choice for them to live a healthy life, they may want to be able to upgrade their therapy in time.
This ability to reset would give them that potential.
For example, if there's a new approach that's even more effective, a patient wouldn't think twice about taking that now, knowing that they could remove it in the future and replace it with a better therapy that might come along in 10 or 20 years.
That makes gene therapy a much more routine decision.
What that means is that we can think about genetic technologies less as really a part of us, but just something that we choose to use in the same way that I might wear one set of clothes a day or a different set next year, but that's not really part of who I am.
And I think about that very differently than today how I think about my genome, which has always been a part of me.
And up until very recently, I thought I would die with the same genome that I was born with.
Because of the genetic technologies, I think we're gonna no longer associate the genetics that we have with who we are, and it's more a decision for who we want to be or what we want to become,
and you'll be able to have much more control over that so you can live the very best possible life.