How your body could outlive the genome you were born with

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 I've really struggled to be able to get enough of the genetic

payload into the cells where they're going to be effective as a therapeutic. And it's getting

inside the cells that has really been a challenge for many, many years. I'm Eric Kelsic, CEO and

co-founder at Diner Therapeutics. For the past 10 years, I've been working to solve the grand

challenge of gene delivery. How are we going to make gene therapy a mainstream kind of medicine

We need to solve these ground 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

AV naturally isn't known to cause any disease. The reason why AV 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 of 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 it 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 essentially useless Roughly 80 of the single changes that you can 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 going to break the function. And that's a problem for engineering because to get improved

function, we're going to 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 give you 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 0.1% 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 was 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 mils

The best way we have to make a prediction about what's going to be safe and effective for humans is to do an animal experiment

We do most of our screening in non-human primates, especially in cinemogous monkeys

That's one reason why we developed this technology, because their lives are also very precious

We want to get as much information as we can from even one experiment

In this case, we're measuring maybe 100 or 200,000, sometimes even a million different caps and sequences in that one animal

We'll get all these tissues back from our animal experiments, and then we want to 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, say 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

and then 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 actually the perfect type of problem for a machine learning model You can use AI to automate the ysis 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

With machine learning, we can test many different sequences in silico, meaning my 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 NESS experiment. So it's this iterative cycle of making libraries in DNA

measuring their properties, then building models to yze 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 want to 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 yze the library or how we design it

and get back the answers that we expect. We want to get the results as fast as we can

Patients are waiting for better medicines. We want to 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 Dino 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 Zogansman though, if children are treated

very early and 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 caps and 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 going to treat the underlying cause of those diseases

Today most of our attention and most of the industry 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 also a long tail of rare and ultra 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. We're very, very close to it

To do that, we also need to enable there to be many more genetic medicines, so 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 the 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 have 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, nonprofit 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. Solve and delivery is just one of them

The therapies are complex, and we don't understand exactly how to design them

in the way they're going to 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 as 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 today 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 going to 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