Latest / Core Memory / Gene Therapies That Work? - EP 86 Adrian Veres

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Gene Therapies That Work? - EP 86 Adrian Veres · Core Memory

• 0:00
  SPEAKER_00: All right, we're talking gene therapies today.
• 0:03
  SPEAKER_00: It has long been this much hyped,
• 0:06
  SPEAKER_00: much heralded technology from biotech land where you send a therapy into your body.
• 0:13
  SPEAKER_00: It is much smarter than your typical therapies.
• 0:17
  SPEAKER_00: It's going in, fixing your DNA for what ails you.
• 0:21
  SPEAKER_00: The massive problem with gene therapy tech is that it has been Very expensive, hard to produce.
• 0:30
  SPEAKER_00: It's been hard to send into the right places in the body.
• 0:34
  SPEAKER_00: Often these gene therapies get all sucked up in the liver and don't go where we need them to go.
• 0:40
  SPEAKER_00: Our guest today is Adrian Veresh.
• 0:43
  SPEAKER_00: He is the co-founder and chief science officer at Dyno Therapeutics.
• 0:49
  SPEAKER_00: They have been working on making A better gene therapy for a long time.
• 0:54
  SPEAKER_00: They were kind of doing this AI bio thing way before it was cool.
• 0:59
  SPEAKER_00: They've been using AI, machine learning technology to reshape viruses.
• 1:06
  SPEAKER_00: Viruses are often how these gene therapies are kind of built and put into the body.
• 1:12
  SPEAKER_00: So they've been trying to create...
• 1:15
  SPEAKER_00: A modified virus,
• 1:17
  SPEAKER_00: call it a platform,
• 1:18
  SPEAKER_00: a new way to deliver these therapies that is better,
• 1:22
  SPEAKER_00: smarter, and faster than what's come before.
• 1:25
  SPEAKER_00: They are a little different than many biotech companies.
• 1:28
  SPEAKER_00: They're not trying to make just one specific cure for a number of diseases.
• 1:34
  SPEAKER_00: They're trying to be what people call like a platform play.
• 1:38
  SPEAKER_00: They want to sell their technology to the likes of Roche and Novartis
• 1:42
  SPEAKER_00: and other companies,
• 1:43
  SPEAKER_00: some of whom are already Their partners,
• 1:47
  SPEAKER_00: Adrian, is going to give us the basics on how gene therapy tech works.
• 1:52
  SPEAKER_00: He's going to make you smarter on all this stuff.
• 1:55
  SPEAKER_00: It's just fascinating.
• 1:57
  SPEAKER_00: Technology is a tremendous listen, which is what I always say, but this is true.
• 2:03
  SPEAKER_00: I am Ashlee Vance.
• 2:04
  SPEAKER_00: This is Core Memory.
• 2:05
  SPEAKER_00: Here we go.
• 2:21
  SPEAKER_00: Adrian, thank you so much for coming.
• 2:24
  SPEAKER_01: Excited to be here.
• 2:25
  SPEAKER_00: I'm excited too.
• 2:27
  SPEAKER_00: I always try to do my own research,
• 2:29
  SPEAKER_00: reading things,
• 2:30
  SPEAKER_00: but then it is the age of AI,
• 2:32
  SPEAKER_00: so I always type the person's name in, and then I'm like, what would you ask them?
• 2:37
  SPEAKER_00: And at least OpenAI is a big fan of yours, and it tells me I have a coup.
• 2:43
  SPEAKER_00: It said, you're clearly...
• 2:45
  SPEAKER_00: One of the more accomplished young scientists in the gene therapy world
• 2:49
  SPEAKER_00: and have stayed almost entirely out of the press and
• 2:51
  SPEAKER_00: that I'm breaking new ground here by getting you on.
• 2:56
  SPEAKER_00: Let's do it.
• 3:00
  SPEAKER_00: I feel like we're going to be talking about your company, Dyno, and gene therapy.
• 3:07
  SPEAKER_00: I always feel like this is a difficult subject.
• 3:11
  SPEAKER_00: I think people hear about gene therapies in the news, even...
• 3:15
  SPEAKER_00: Me, as someone who covers it off and on,
• 3:18
  SPEAKER_00: I always have trouble understanding where we're at with this technology,
• 3:22
  SPEAKER_00: sort of how it works.
• 3:23
  SPEAKER_00: I mean, I think for the average person,
• 3:28
  SPEAKER_00: you think you're getting some injection and it's rewiring all of your DNA.
• 3:32
  SPEAKER_00: And then we hear about these things, but then they don't seem to be...
• 3:37
  SPEAKER_00: Out in the wild a lot.
• 3:39
  SPEAKER_00: And, and so, yeah, I mean,
• 3:41
  SPEAKER_00: the, I always like to just explain the basics of some of what's going on here.
• 3:46
  SPEAKER_00: Um, does that seem like a decent place to start?
• 3:50
  SPEAKER_01: Absolutely.
• 3:51
  SPEAKER_01: And I think the other thing to realize is I think I think we're going to hear
• 3:55
  SPEAKER_01: a lot more and more about gene therapy.
• 3:57
  SPEAKER_01: I think we've been in the background phase.
• 3:59
  SPEAKER_01: And if like what we and everyone else is working on,
• 4:02
  SPEAKER_01: what's going to happen is we're going to be hearing more and more about it
• 4:06
  SPEAKER_01: because it's just going to be a bigger and bigger part of medicine.
• 4:08
  SPEAKER_01: So it's a great time to get the basics in.
• 4:11
  SPEAKER_00: I feel like that's the thing.
• 4:12
  SPEAKER_00: It's just stuff you hear about in the news for so many years.
• 4:15
  SPEAKER_00: And it obviously would come with all this promise if we have a brand,
• 4:19
  SPEAKER_00: you know, a whole new field of incredible...
• 4:21
  SPEAKER_00: Quite personalized therapies for people.
• 4:24
  SPEAKER_00: Um, and then it's just,
• 4:25
  SPEAKER_00: it always feels like a little bit of a letdown because they're incredibly expensive.
• 4:29
  SPEAKER_00: They're rare.
• 4:31
  SPEAKER_00: They seem to have bad consequences sometimes.
• 4:34
  SPEAKER_00: And so, yeah, anyway,
• 4:35
  SPEAKER_00: that's where, so,
• 4:36
  SPEAKER_00: so let's,
• 4:38
  SPEAKER_00: let's start here as one, one item, which I think kind of gets at what you're saying.
• 4:43
  SPEAKER_00: The federal government recently.
• 4:47
  SPEAKER_00: Seem to kind of like open its mind towards gene therapies,
• 4:50
  SPEAKER_00: especially in the case of rare diseases.
• 4:53
  SPEAKER_00: And so, so for,
• 4:54
  SPEAKER_00: for a very long time,
• 4:55
  SPEAKER_00: these have been people who are this,
• 4:56
  SPEAKER_00: this difficult population where, um,
• 4:59
  SPEAKER_00: it's just not usually cost effective for a big pharma company to develop a drug
• 5:04
  SPEAKER_00: for someone with this rare genetic disease.
• 5:08
  SPEAKER_00: And so they get kind of neglected.
• 5:09
  SPEAKER_00: It turns out for some of these diseases,
• 5:11
  SPEAKER_00: we call them rare, is actually quite a decent number of people.
• 5:14
  SPEAKER_00: And if you add up all the rare diseases, it's a very large population of people.
• 5:19
  SPEAKER_00: And so, you know,
• 5:19
  SPEAKER_00: it's a difficult circumstance to,
• 5:21
  SPEAKER_00: as I read about it really quickly,
• 5:23
  SPEAKER_00: but it seems like this particular administration is saying,
• 5:28
  SPEAKER_00: We've kind of come far enough with gene therapy technology where if you're
• 5:33
  SPEAKER_00: in quite dire circumstances and there's enough evidence that something might work,
• 5:41
  SPEAKER_00: we're going to be more lenient in letting you try this therapy without having to go
• 5:45
  SPEAKER_00: through all the usual trials.
• 5:47
  SPEAKER_00: Is that sort of where we're at and is that indicative of something new?
• 5:51
  SPEAKER_01: That's part of it.
• 5:52
  SPEAKER_01: I think that's tying into...
• 5:55
  SPEAKER_01: We're seeing a lot more new ideas on the regulatory side as well as
• 6:00
  SPEAKER_01: on how people are framing what is a gene therapy.
• 6:05
  SPEAKER_01: Is it more of a medicine?
• 6:07
  SPEAKER_01: Is it more similar to a pill that you just take once?
• 6:10
  SPEAKER_01: Or is it more like a surgery where it's like a bigger intervention that you do once
• 6:15
  SPEAKER_01: with a very specific intent that's actually different from person to person?
• 6:18
  SPEAKER_01: And a lot of these ideas are Are pretty new.
• 6:21
  SPEAKER_01: The reason we're also talking about them is the moment feels right.
• 6:24
  SPEAKER_01: Like the convergence of technologies that's enabling this is just getting better and better.
• 6:30
  SPEAKER_01: And it's helping,
• 6:30
  SPEAKER_01: you know,
• 6:32
  SPEAKER_01: what's going to happen now is a lot of the challenges that you mentioned,
• 6:36
  SPEAKER_01: like cost, safety, how widely applicable it can be.
• 6:42
  SPEAKER_01: All of these are really technological challenges at the core root, right?
• 6:45
  SPEAKER_01: There's like really nothing that says, laws of nature don't say we can't do this.
• 6:49
  SPEAKER_01: We just have to find a way to do it.
• 6:51
  SPEAKER_01: And, you know,
• 6:52
  SPEAKER_01: I feel that the progress we've been making as a field in
• 6:56
  SPEAKER_01: the past year has just been really incredible and it is going to continue.
• 7:00
  SPEAKER_01: And, you know,
• 7:01
  SPEAKER_01: that's why I think we're going to hear,
• 7:03
  SPEAKER_01: you'll be hearing more and more about gene therapies.
• 7:05
  SPEAKER_00: Okay, well, yeah, so let's bounce off that.
• 7:07
  SPEAKER_00: I mean, why don't we, let's define a gene therapy.
• 7:10
  SPEAKER_00: Can you help walk us through the different forms that are available and then some
• 7:14
  SPEAKER_00: of what you're seeing that's starting to change?
• 7:16
  SPEAKER_01: Yeah, I think I...
• 7:17
  SPEAKER_01: At the simplest level,
• 7:19
  SPEAKER_01: what a gene therapy is, is acting at the genetic level to improve health.
• 7:27
  SPEAKER_01: That's really what a gene therapy is.
• 7:28
  SPEAKER_01: The point is you're going to intervene at the genetic level to have the impact
• 7:34
  SPEAKER_01: that you want to have.
• 7:36
  SPEAKER_01: The contrast with this is If you take a small molecule drug,
• 7:44
  SPEAKER_01: you can choose to act at many different levels of the biology.
• 7:48
  SPEAKER_01: You could act at the symptom.
• 7:50
  SPEAKER_01: You could act at the very last thing that's giving you a certain feeling of pain, for example.
• 7:55
  SPEAKER_01: You can try to suppress that.
• 7:57
  SPEAKER_01: Part of gene therapy is as a framing, it's restricted to where you're doing this intervention.
• 8:03
  SPEAKER_01: And what's helpful to know is we've been wanting to do this for a very long time.
• 8:08
  SPEAKER_01: The reason for it is In understanding disease,
• 8:11
  SPEAKER_01: if you go back about 50 years,
• 8:13
  SPEAKER_01: we quickly realized that there's a lot of diseases that we could tie back where
• 8:17
  SPEAKER_01: that disease is at the genetic level.
• 8:19
  SPEAKER_01: Meaning once we started And we understood that there's this thing called DNA.
• 8:23
  SPEAKER_01: It's this like stretch of letters.
• 8:27
  SPEAKER_01: And we started asking, like, can we find, you know, there's this terrible disease that exists.
• 8:33
  SPEAKER_01: Can we understand its cause, like tied back to the letters that have changed?
• 8:37
  SPEAKER_01: And once we started doing that, the answer was like, yes, we can.
• 8:41
  SPEAKER_01: It used to be really difficult.
• 8:42
  SPEAKER_01: Now it's gotten dramatically easier because of technology.
• 8:46
  SPEAKER_01: But very quickly,
• 8:47
  SPEAKER_01: we were able to point to, hey, there's this gene that That causes this disease.
• 8:51
  SPEAKER_01: In fact, there's many genes that are named in that way, right?
• 8:53
  SPEAKER_01: Like the cystic fibrosis, like implicated gene is really called CFTR.
• 8:58
  SPEAKER_01: The CF stands for cystic fibrosis.
• 9:00
  SPEAKER_01: And we've been wanting to,
• 9:01
  SPEAKER_01: you know, as soon as we knew that, the next step was like, okay, can we fix it?
• 9:05
  SPEAKER_00: Can we just turn it off or, yeah.
• 9:07
  SPEAKER_01: Yeah, like, there's a mistake.
• 9:09
  SPEAKER_01: Can you just go in and fix it?
• 9:10
  SPEAKER_01: And that journey...
• 9:12
  SPEAKER_01: It's been going on for decades now.
• 9:15
  SPEAKER_01: Because it turns out that it wasn't so easy to just will it into action.
• 9:19
  SPEAKER_01: There's a lot of components that we needed to bring into place.
• 9:21
  SPEAKER_01: Even actually just the changing of the letters of the DNA.
• 9:25
  SPEAKER_01: That's like the promise of CRISPR, which everybody's heard about CRISPR.
• 9:29
  SPEAKER_01: Why was it such a big promise,
• 9:32
  SPEAKER_01: such a big thing in our field and continues to be is because it brought in
• 9:36
  SPEAKER_01: the ability to go make those changes.
• 9:38
  SPEAKER_01: But then another problem was,
• 9:39
  SPEAKER_01: say you're actually able to Poke at the DNA,
• 9:42
  SPEAKER_01: like change the actual letters with some tools, some technology like CRISPR.
• 9:48
  SPEAKER_01: Well, you have to get the CRISPR in the cell at the right place for it to act.
• 9:53
  SPEAKER_01: And that's another big challenge.
• 9:54
  SPEAKER_01: And actually probably the bigger challenge that's held back gene therapy is
• 9:57
  SPEAKER_01: the ability to do delivery effectively.
• 10:01
  SPEAKER_01: Go in and actually there's a bunch of different ways you can intervene on DNA
• 10:07
  SPEAKER_01: and other components that are close to DNA.
• 10:10
  SPEAKER_01: But actually getting the machines in,
• 10:12
  SPEAKER_01: in the right cells, safely, cheaply, that's been a big challenge.
• 10:16
  SPEAKER_01: And that's actually the challenge that like we at Dyno,
• 10:20
  SPEAKER_01: we founded the company to focus exclusively on that challenge.
• 10:22
  SPEAKER_01: That's what we focused it.
• 10:23
  SPEAKER_00: To work on that.
• 10:24
  SPEAKER_00: And so like when we talk about small molecules,
• 10:26
  SPEAKER_00: like a pill that we take, I mean,
• 10:28
  SPEAKER_00: is it fair to kind of talk about that more as like,
• 10:31
  SPEAKER_00: Kind of mitigating a problem,
• 10:33
  SPEAKER_00: but it's not fully turned...
• 10:35
  SPEAKER_00: Something's not turned off or turned on like you want it to be.
• 10:38
  SPEAKER_00: You're always still dealing with this condition in some way.
• 10:41
  SPEAKER_01: I think that's a bit unfair to amazing medicine.
• 10:46
  SPEAKER_01: Like I think the reality is the small molecules give you more degree of freedom
• 10:52
  SPEAKER_01: in terms of where you can go.
• 10:54
  SPEAKER_01: And you can use that to have a causal intervention.
• 10:57
  SPEAKER_01: Like if there's a way to...
• 11:01
  SPEAKER_01: Do the intervention.
• 11:04
  SPEAKER_01: Small molecules can do it on certain contexts.
• 11:07
  SPEAKER_01: The problem is that it's just a very hard hacking problem.
• 11:11
  SPEAKER_01: It's like, if maybe a programming analogy might help, you can't actually change the source code.
• 11:20
  SPEAKER_01: And sometimes there's just an actual problem in the source code.
• 11:23
  SPEAKER_01: Or even if the problem isn't in the source code in the first place,
• 11:26
  SPEAKER_01: if you could go in and change the source code,
• 11:29
  SPEAKER_01: That'd just be a very direct way to fix it.
• 11:31
  SPEAKER_01: And instead,
• 11:31
  SPEAKER_01: with small molecules,
• 11:32
  SPEAKER_01: sometimes you have to find some other way of like, oh, this thing is happening here.
• 11:36
  SPEAKER_01: So if I poke it here, you have a direct impact.
• 11:39
  SPEAKER_01: And genetic medicines complement other traditional medicines,
• 11:45
  SPEAKER_01: but they offer a different angle of attack,
• 11:47
  SPEAKER_01: a different point of leverage that's very, very high.
• 11:50
  SPEAKER_01: Because once you change the source code,
• 11:51
  SPEAKER_01: the The premise is you should be able to make a change that lasts forever for
• 11:56
  SPEAKER_01: a very long time.
• 11:57
  SPEAKER_01: And that's the potential of, that's what's such a big potential and why the field has continued.
• 12:03
  SPEAKER_01: And in the face of many, many challenges, just kept at it decade after decade.
• 12:10
  SPEAKER_00: And over these couple decades, I mean, I hear all the time about monogenetic conditions.
• 12:16
  SPEAKER_00: The rare diseases that we're talking about,
• 12:17
  SPEAKER_00: often they fall into this group where it's a single gene that is causing a lot
• 12:24
  SPEAKER_00: of mayhem and it's disrupting a very important process.
• 12:29
  SPEAKER_00: As time has gone on, well, obviously there's other conditions that have many genes at work.
• 12:35
  SPEAKER_00: And when we're talking about gene therapies broadly, are we...
• 12:39
  SPEAKER_00: In this moment in time,
• 12:41
  SPEAKER_00: just very, very focused still on these single gene problems, or is there a thought?
• 12:48
  SPEAKER_00: I could never tell if once we get to the multi-gene level is just too complicated
• 12:52
  SPEAKER_00: to even begin to address that.
• 12:55
  SPEAKER_01: I think...
• 12:57
  SPEAKER_01: I think it's a matter of the order of events.
• 13:03
  SPEAKER_01: It's very natural that where there is a single gene,
• 13:07
  SPEAKER_01: the other thing that's very helpful in a monogenic disease is you understand
• 13:11
  SPEAKER_01: the causal biology.
• 13:13
  SPEAKER_01: Like you really know that if you have all the evidence that if you go in
• 13:17
  SPEAKER_01: and make this change, you're going to have the impact you want to have.
• 13:21
  SPEAKER_01: And that's actually just really valuable in biology because often in biology,
• 13:27
  SPEAKER_01: the trick is in biology is you can have a story that's like really beautiful.
• 13:31
  SPEAKER_01: It makes a lot of sense.
• 13:31
  SPEAKER_01: There's like a very clear argument.
• 13:34
  SPEAKER_01: And then we've seen time and time again that we actually test the argument,
• 13:37
  SPEAKER_01: you're missing some component in the story.
• 13:40
  SPEAKER_01: And so it doesn't work out.
• 13:41
  SPEAKER_01: For monogenic diseases, that's so crystal clear.
• 13:44
  SPEAKER_01: So that's why the focus is there.
• 13:46
  SPEAKER_01: But nobody in the gene therapy field is saying like, cool,
• 13:49
  SPEAKER_01: we're gonna do gene,
• 13:51
  SPEAKER_01: like single gene diseases, and then we'll have to find something else.
• 13:54
  SPEAKER_01: I think we're actually all excited about the potential to intervene at
• 13:59
  SPEAKER_01: the circuit level,
• 14:00
  SPEAKER_01: like to have It intervenes on a combination of things
• 14:04
  SPEAKER_01: to really shape cellular behavior.
• 14:06
  SPEAKER_01: And, you know,
• 14:07
  SPEAKER_01: that's absolutely coming pretty soon,
• 14:11
  SPEAKER_01: actually,
• 14:11
  SPEAKER_01: given where the technology is, the pace of technological development right now.
• 14:15
  SPEAKER_00: And so Dyno is focused on viruses, viral vector delivery.
• 14:21
  SPEAKER_00: What are the methods at our disposal in 2026 if I want to deliver a gene therapy?
• 14:28
  SPEAKER_00: What are my options?
• 14:29
  SPEAKER_01: There's a couple, yeah, there's a couple options.
• 14:33
  SPEAKER_01: The challenge,
• 14:34
  SPEAKER_01: ultimately the challenge,
• 14:35
  SPEAKER_01: it's helpful to understand the challenge is you have a piece of DNA or a piece
• 14:40
  SPEAKER_01: of RNA.
• 14:40
  SPEAKER_01: You have to get it inside of a cell.
• 14:43
  SPEAKER_01: And there's really like two ways to do this.
• 14:47
  SPEAKER_01: One is you have to put it in some sort of box that's going to get into the cells.
• 14:52
  SPEAKER_01: And that box can be like chemically formulated.
• 14:56
  SPEAKER_01: This is something like a lipid nanoparticle where you Just mix
• 14:59
  SPEAKER_01: a few components around and depending on the conditions you can achieve that goal.
• 15:05
  SPEAKER_01: Then there's another set of things which is what we normally do in biology is rather
• 15:11
  SPEAKER_01: than invent new things,
• 15:12
  SPEAKER_01: we look at what nature's made and we like borrow and steal it.
• 15:16
  SPEAKER_01: And viruses do this by default.
• 15:18
  SPEAKER_01: There's many different viruses that do it and you can take them to do this delivery for you.
• 15:25
  SPEAKER_01: Better yet is you can actually,
• 15:26
  SPEAKER_01: which is what we do is we engineer The system
• 15:32
  SPEAKER_01: to actually make it do not just what it was naturally evolved to do,
• 15:38
  SPEAKER_01: but we design it towards a specific purpose and really turn it
• 15:41
  SPEAKER_01: into something we picked up from nature into a technology that's going to be able
• 15:46
  SPEAKER_01: to give you the characteristics you want for any disease that you might want
• 15:50
  SPEAKER_01: to be treating.
• 15:51
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  SPEAKER_00: Okay, and then tell me how badly,
• 17:01
  SPEAKER_00: I butcher this,
• 17:02
  SPEAKER_00: but so viruses have this kind of outer shell called a capsid, I think.
• 17:08
  SPEAKER_00: I'll go.
• 17:09
  SPEAKER_00: Some viruses do, but yes, they
• 17:10
  SPEAKER_01: all do in some way.
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  SPEAKER_01: Okay, okay.
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  SPEAKER_00: So far, so good.
• 17:14
  SPEAKER_00: And so nature is very good at this.
• 17:16
  SPEAKER_00: Like you said, I mean, we look to nature.
• 17:17
  SPEAKER_00: Obviously,
• 17:17
  SPEAKER_00: we all know once you get a virus,
• 17:19
  SPEAKER_00: it gets in there and it goes to work pretty quickly.
• 17:22
  SPEAKER_00: Quickly sometimes doing what it's trying to do.
• 17:25
  SPEAKER_00: And so you want to create a virus that's not just doing what it normally wants to do.
• 17:31
  SPEAKER_00: You want to reprogram it to do what we want out of it, but to kind of use its mechanism.
• 17:37
  SPEAKER_00: And so there's this outer layer that it seems like we can manipulate and try
• 17:42
  SPEAKER_00: to get our Are the genetic code that we're,
• 17:46
  SPEAKER_00: the tweaks that we're trying to make into this outer layer and
• 17:48
  SPEAKER_00: then have this system feed into your body?
• 17:51
  SPEAKER_01: Yeah.
• 17:51
  SPEAKER_01: So let's talk about that.
• 17:53
  SPEAKER_01: There's a couple of interesting things.
• 17:54
  SPEAKER_01: I think it's actually fair to, these aren't viruses anymore.
• 17:59
  SPEAKER_01: Once you start using them as, you know, you take out just the shell.
• 18:03
  SPEAKER_01: Like we've borrowed a component from a virus,
• 18:05
  SPEAKER_01: but it's helpful to know like,
• 18:07
  SPEAKER_01: like fundamentally what makes a virus a virus is it can go in.
• 18:12
  SPEAKER_01: It's a self-replicating machine.
• 18:14
  SPEAKER_01: It's kind of one of the minimal.
• 18:15
  SPEAKER_01: It's not the most minimal,
• 18:17
  SPEAKER_01: that's probably prions,
• 18:18
  SPEAKER_01: but it's one of the smallest self-replicating bits of biology out there.
• 18:22
  SPEAKER_01: And its goal is to self-replicate.
• 18:25
  SPEAKER_01: Well, we don't have that goal, right?
• 18:27
  SPEAKER_01: In its process of self-replication,
• 18:29
  SPEAKER_01: it has to deliver itself somewhere where it can take over the cell
• 18:34
  SPEAKER_01: for its own purposes of replication.
• 18:36
  SPEAKER_01: We're borrowing just the delivery components, so the capsid.
• 18:39
  SPEAKER_01: So that's actually the component that we're engineering.
• 18:41
  SPEAKER_01: We're engineering the capsid.
• 18:44
  SPEAKER_01: And you're also hinting at something interesting, which is quite a bit of viruses.
• 18:48
  SPEAKER_01: We know many of them,
• 18:49
  SPEAKER_01: but we actually know a tiny fraction of what's out there in people,
• 18:54
  SPEAKER_01: also in the animal kingdom overall.
• 18:59
  SPEAKER_01: But what the field has done is actually there's a virus that Has piqued a lot
• 19:04
  SPEAKER_01: of curiosity because it just seems to,
• 19:07
  SPEAKER_01: it's just kind of not a very good virus fundamentally.
• 19:10
  SPEAKER_01: In fact, it can't even replicate on its own.
• 19:12
  SPEAKER_01: Like it actually needs another virus around to be able to replicate.
• 19:17
  SPEAKER_01: And it seemed to be that it's very simple.
• 19:20
  SPEAKER_01: So it's protein only.
• 19:22
  SPEAKER_01: And it's...
• 19:24
  SPEAKER_01: Doesn't cause much of a reaction to the body.
• 19:27
  SPEAKER_01: And also it doesn't cause disease.
• 19:28
  SPEAKER_01: So if you have, if you are infected by that virus, you actually never really know.
• 19:34
  SPEAKER_01: And so that virus is called adeno-associated virus.
• 19:38
  SPEAKER_01: It's AAV.
• 19:39
  SPEAKER_01: And...
• 19:41
  SPEAKER_01: Because of these set of properties,
• 19:42
  SPEAKER_01: it was just very intriguing for the field for decades,
• 19:44
  SPEAKER_01: being like, wow,
• 19:44
  SPEAKER_01: if we had to take over the components and use this to deliver gene therapies,
• 19:50
  SPEAKER_01: this is a great starting point.
• 19:52
  SPEAKER_01: And that's the story.
• 19:54
  SPEAKER_01: Those are the giants whose shoulders we're on,
• 19:57
  SPEAKER_01: is this progress of finding this one,
• 20:00
  SPEAKER_01: testing this hypothesis, building it and building it.
• 20:03
  SPEAKER_01: And what's happened in the last few years is a convergence of technologies,
• 20:06
  SPEAKER_01: including AI and This ability to explore the sequence space in new ways than before
• 20:15
  SPEAKER_01: that have converged,
• 20:16
  SPEAKER_01: both in the lab and in AI,
• 20:17
  SPEAKER_01: and enabling us to really just customize these capsid shells
• 20:21
  SPEAKER_01: to do exactly what it is we want them.
• 20:23
  SPEAKER_01: And, you know, that's been our focus.
• 20:25
  SPEAKER_00: Do we know why this particular virus, like what is its purpose then in the world?
• 20:32
  SPEAKER_01: It's hard to speculate on these things.
• 20:34
  SPEAKER_01: You know, by default, it's...
• 20:38
  SPEAKER_01: If I had to speculate, I'd just say, like most viruses, they want to replicate.
• 20:42
  SPEAKER_01: That's it.
• 20:42
  SPEAKER_01: Their purpose is to self-replicate and continue.
• 20:44
  SPEAKER_00: But it seems like it's bad at replicating?
• 20:47
  SPEAKER_01: I mean, it's making do.
• 20:48
  SPEAKER_01: It doesn't have to be the best.
• 20:51
  SPEAKER_01: If it weren't able to do it at all, it wouldn't exist.
• 20:54
  SPEAKER_01: So clearly it's good enough.
• 20:56
  SPEAKER_01: And that's good enough for me.
• 20:58
  SPEAKER_01: Interesting thing is the,
• 20:59
  SPEAKER_01: you know,
• 21:00
  SPEAKER_01: it's got this funny name, adeno-associated, because people were studying adenovirus.
• 21:03
  SPEAKER_01: And then when they were looking like in electron microscopes,
• 21:06
  SPEAKER_01: they saw like, wow, there's this other thing that sometimes pops up.
• 21:11
  SPEAKER_01: It looks like a virus.
• 21:13
  SPEAKER_01: Could it be?
• 21:13
  SPEAKER_01: And it took a while to figure out like, yes, it actually is.
• 21:16
  SPEAKER_01: And it's interesting in its own right.
• 21:18
  SPEAKER_01: Yeah.
• 21:19
  SPEAKER_00: And so when you guys are,
• 21:20
  SPEAKER_00: when you're talking about changing the shell, I mean, what are you doing?
• 21:24
  SPEAKER_00: What is actually happening?
• 21:26
  SPEAKER_00: And then what do those changes allow you to do?
• 21:29
  SPEAKER_01: Maybe it's helpful to describe what this is,
• 21:31
  SPEAKER_01: because it's actually, it sounds complicated, but it isn't.
• 21:35
  SPEAKER_01: You can picture something very, very simply.
• 21:38
  SPEAKER_01: So...
• 21:39
  SPEAKER_01: This capsid is, it's helpful to picture like a, I said a box that undersells it a little.
• 21:47
  SPEAKER_01: Picture a really nice like origami box.
• 21:51
  SPEAKER_01: And what you want to picture is this like icosahedron thing and there's one piece
• 21:57
  SPEAKER_01: that is repeated 60 times.
• 22:00
  SPEAKER_01: That piece is actually very small.
• 22:02
  SPEAKER_01: So that's a protein.
• 22:05
  SPEAKER_01: And that piece is defined by about 700 amino acids, which are the letters that define proteins.
• 22:12
  SPEAKER_01: So it's just a string.
• 22:13
  SPEAKER_01: It's really quite literally just a string of these 700 amino acids.
• 22:18
  SPEAKER_01: Now, those amino acids are incredible, right?
• 22:21
  SPEAKER_01: Because those 700 amino acids are able to together create this
• 22:28
  SPEAKER_01: icosahedral You know,
• 22:30
  SPEAKER_01: origami box.
• 22:32
  SPEAKER_01: And then that box is able to, you can put DNA in that box.
• 22:37
  SPEAKER_01: So when we do the engineering, what we're doing simply is really just changing the letters.
• 22:43
  SPEAKER_01: Of those 700 letters,
• 22:45
  SPEAKER_01: if we find the right change, well,
• 22:47
  SPEAKER_01: we can directly,
• 22:48
  SPEAKER_01: you know,
• 22:49
  SPEAKER_01: that can make it,
• 22:49
  SPEAKER_01: that can change it if we want it to go to the brain,
• 22:51
  SPEAKER_01: because we want to make gene therapies for a neurological disease,
• 22:54
  SPEAKER_01: to find the right set of changes.
• 22:57
  SPEAKER_01: You can direct it there.
• 22:59
  SPEAKER_01: And now there's a lot of very interesting science for how we do that.
• 23:02
  SPEAKER_01: But that's the core concept of the problem we're out to solve.
• 23:06
  SPEAKER_00: And nature has had millions and millions of years to work on this.
• 23:13
  SPEAKER_00: But the thought is that we can make these adjustments and still be...
• 23:19
  SPEAKER_00: You know, good at creating a change like even one-upping nature here.
• 23:26
  SPEAKER_01: So there's a couple challenges, right?
• 23:28
  SPEAKER_01: One is nature was never optimizing for our own goal, right?
• 23:34
  SPEAKER_01: It wasn't trying to make...
• 23:38
  SPEAKER_01: An amazing delivery vehicle for brain gene therapies or muscle gene therapies.
• 23:44
  SPEAKER_01: I mean, I guess we are now,
• 23:45
  SPEAKER_01: so nature is finding a way in a complicated path,
• 23:48
  SPEAKER_01: but the actual evolutionary process at the viral level wasn't aiming to create that.
• 23:54
  SPEAKER_01: It's obvious that once we set a new objective, we're going to be able to do the engineering.
• 23:59
  SPEAKER_01: Now, that engineering, though,
• 24:00
  SPEAKER_01: is pretty hard because what's happened is if you look at nature and it's a very,
• 24:06
  SPEAKER_01: very finely crafted machine, that's that little protein shell.
• 24:10
  SPEAKER_01: And we've actually run this experiment of what happens if you...
• 24:15
  SPEAKER_01: Take those 700 letters and make for every one amino acid.
• 24:19
  SPEAKER_01: There's 19 others.
• 24:20
  SPEAKER_01: You can make the change to any other amino acid.
• 24:23
  SPEAKER_01: And we've done this experiment.
• 24:25
  SPEAKER_01: This is work done in the church lab at Harvard prior to the creation of the company
• 24:28
  SPEAKER_01: where we made every change.
• 24:30
  SPEAKER_01: And quickly that explains to you why the problem is hard.
• 24:33
  SPEAKER_01: When you do that process, about 70% of the changes just break the shell.
• 24:39
  SPEAKER_01: So it tells you that if you're going to be changing it,
• 24:41
  SPEAKER_01: you really have to understand, okay,
• 24:44
  SPEAKER_01: if I'm making a change here,
• 24:46
  SPEAKER_01: got to make another change somewhere else to make up for it and keep exploring
• 24:50
  SPEAKER_01: the space.
• 24:50
  SPEAKER_01: That's one piece.
• 24:52
  SPEAKER_01: To your question of also how come there's room to do the engineering,
• 24:57
  SPEAKER_01: one thing that's really cool is if you look at existing AAVs in nature,
• 25:02
  SPEAKER_01: there's a few that are very different from each other.
• 25:04
  SPEAKER_01: So you can pick out two things that are clearly AAVs,
• 25:07
  SPEAKER_01: like they make the same type of Protein, origami shell.
• 25:10
  SPEAKER_01: But they differ in like half the positions.
• 25:14
  SPEAKER_01: And that tells you that actually there's room to engineer.
• 25:16
  SPEAKER_01: There's not one solution.
• 25:17
  SPEAKER_01: There's many possible solutions.
• 25:18
  SPEAKER_01: So if we explore that space effectively, we can get there.
• 25:21
  SPEAKER_01: And then the other big piece of good news that we found was once you get that data,
• 25:26
  SPEAKER_01: so for example,
• 25:26
  SPEAKER_01: once you make every single change and find the good ones,
• 25:29
  SPEAKER_01: you start doing combinations of changes.
• 25:32
  SPEAKER_01: One thing we noticed, and this was before...
• 25:35
  SPEAKER_01: The AI movement that's happening now, this was around 2017, 2018, we noticed that actually
• 25:42
  SPEAKER_01: Machine learning techniques are exquisitely good at picking out patterns
• 25:46
  SPEAKER_01: and allowing you to get traction on this engineering problem much better
• 25:49
  SPEAKER_01: than people, much more aggressive, but also just able to pick out amazing patterns.
• 25:55
  SPEAKER_01: And that was the core bet of our company was let's apply these technologies
• 25:59
  SPEAKER_01: of let's measure many changes all at once,
• 26:02
  SPEAKER_01: which was the sort of traditional church lab type of technology.
• 26:06
  SPEAKER_01: And then let's combine this by applying machine learning and AI to Take
• 26:11
  SPEAKER_01: the information from all of those changes and put it together towards
• 26:14
  SPEAKER_01: a very specific practical objective and put it together and see what happens.
• 26:20
  SPEAKER_01: And now we're a few years into that journey and the results are really exciting
• 26:23
  SPEAKER_01: to us in terms of the implications of the impact of this output.
• 26:27
  SPEAKER_00: Yeah.
• 26:28
  SPEAKER_00: And I wanted to kind of come back to that a little later in some ways,
• 26:31
  SPEAKER_00: because you, you guys did start, you, the company was founded in 2018, I think.
• 26:36
  SPEAKER_00: And, and so, you know, that's like transformers are just being written about in a paper.
• 26:41
  SPEAKER_00: I mean, that was way before this big AI revolution.
• 26:44
  SPEAKER_00: It's way before.
• 26:47
  SPEAKER_00: Eight million,
• 26:47
  SPEAKER_00: it feels like AI biotech startups were peppering me with we're using AI
• 26:53
  SPEAKER_00: to redesign proteins and all this stuff.
• 26:55
  SPEAKER_00: You guys clearly were kind of on the cutting edge of all this.
• 27:00
  SPEAKER_00: And maybe we could describe what I was reading about Dino.
• 27:05
  SPEAKER_00: I mean, the way it's presented in the literature is that you're,
• 27:11
  SPEAKER_00: for this captive,
• 27:12
  SPEAKER_00: this outer shell that you're,
• 27:15
  SPEAKER_00: Using machine learning to try millions and millions of different combinations
• 27:20
  SPEAKER_00: of these, what,
• 27:20
  SPEAKER_00: of these amino acids,
• 27:21
  SPEAKER_00: of these proteins,
• 27:23
  SPEAKER_00: and then try to get to a point where you have the most promising ones,
• 27:27
  SPEAKER_00: and then I don't know if you test on animals or not, I presume.
• 27:32
  SPEAKER_00: You probably test on mice and things like that along the way.
• 27:35
  SPEAKER_00: And then,
• 27:35
  SPEAKER_00: so you're You're starting with these millions of things,
• 27:38
  SPEAKER_00: machine learning's kind of helping winnow it down,
• 27:41
  SPEAKER_00: and then you get to sort of animal testing,
• 27:44
  SPEAKER_00: and then you move forward with the most promising ones.
• 27:46
  SPEAKER_00: Is that the general idea?
• 27:47
  SPEAKER_01: You have the right pieces, but let me put them in the right order.
• 27:51
  SPEAKER_01: One thing that the field of gene therapy learned pretty early on was,
• 27:56
  SPEAKER_01: especially for delivery, it's really valuable to get...
• 28:01
  SPEAKER_01: to the ground truth as much as possible.
• 28:04
  SPEAKER_01: And this means working in animals from the beginning,
• 28:08
  SPEAKER_01: especially in the most relevant animal species, for example, like primates.
• 28:13
  SPEAKER_01: And the focus there is also actually learning, like these are challenging experiments.
• 28:18
  SPEAKER_01: These are,
• 28:19
  SPEAKER_01: you know,
• 28:19
  SPEAKER_01: ethically weighty experiments,
• 28:21
  SPEAKER_01: making sure we learn the most from each one of these that we have to do.
• 28:24
  SPEAKER_01: And what we're able to do is we have the ability to Make a collection
• 28:31
  SPEAKER_01: of these different capsids.
• 28:33
  SPEAKER_01: Call it about a million.
• 28:35
  SPEAKER_01: It's not that large of a number, but I'll get into why that's actually better in many ways.
• 28:42
  SPEAKER_01: It's actually, we chose that number because then we can get measurements on each one.
• 28:46
  SPEAKER_01: And we can get that in vivo.
• 28:47
  SPEAKER_01: So in one experiment, you can measure...
• 28:50
  SPEAKER_01: For this, you know, half a million to a million capsids, how many went to the brain?
• 28:56
  SPEAKER_01: How many went to the liver?
• 28:57
  SPEAKER_01: How many went to the muscle?
• 28:59
  SPEAKER_01: How many went to all the different organs?
• 29:01
  SPEAKER_01: And allow you to see how are you changing from the natural propensity of the capsid, which...
• 29:05
  SPEAKER_00: Okay, so you put many in at the same time.
• 29:08
  SPEAKER_00: Yes.
• 29:08
  SPEAKER_00: Okay.
• 29:08
  SPEAKER_00: Yes.
• 29:09
  SPEAKER_00: And then there's markers on there so you can track them.
• 29:11
  SPEAKER_01: Yeah.
• 29:12
  SPEAKER_01: So the way to think about this is since you have that protein shell and inside
• 29:16
  SPEAKER_01: of it is DNA,
• 29:17
  SPEAKER_01: Every time we make a change on the outside of the protein,
• 29:22
  SPEAKER_01: which is what we're trying to do,
• 29:23
  SPEAKER_01: we record the nature of that change on the piece of DNA that goes inside the shell.
• 29:29
  SPEAKER_01: It's using a barcode.
• 29:31
  SPEAKER_01: We're essentially encrypting it inside the box.
• 29:34
  SPEAKER_01: But then what that allows you to do is when you actually, you can mix several of these together.
• 29:39
  SPEAKER_01: They're labeled.
• 29:40
  SPEAKER_01: By the piece of DNA that's inside the shell.
• 29:43
  SPEAKER_01: And when you put that in an animal and it goes to the brain,
• 29:46
  SPEAKER_01: if you then in the brain take out the DNA and say like,
• 29:49
  SPEAKER_01: all right, what genes have been delivered here by AAV?
• 29:53
  SPEAKER_01: And then you read the barcodes and then you can say, okay, so which capsid did that?
• 29:57
  SPEAKER_01: That's a new, the natural one didn't do this or did it very poorly.
• 30:02
  SPEAKER_01: What about which of the new ones did it?
• 30:04
  SPEAKER_01: And that gives you exactly the type of data that you want to understand.
• 30:09
  SPEAKER_01: Now the problem is the first time you do it, it just doesn't work very well.
• 30:12
  SPEAKER_01: Like you barely see any signal, right?
• 30:15
  SPEAKER_01: Like you'll see a wisp of like,
• 30:17
  SPEAKER_01: wow, maybe it went a little away from the liver and a little into the brain.
• 30:22
  SPEAKER_01: But as you iterate,
• 30:24
  SPEAKER_01: as you take that data,
• 30:26
  SPEAKER_01: feed it into machine learning algorithms and ask it, okay,
• 30:29
  SPEAKER_01: learn from this, find the combinations which are gonna be most promising in silico.
• 30:37
  SPEAKER_01: When you're doing this design, you also get to specify exactly your wishlist.
• 30:40
  SPEAKER_01: Like you can say,
• 30:41
  SPEAKER_01: go here,
• 30:42
  SPEAKER_01: go here,
• 30:42
  SPEAKER_01: don't go here,
• 30:43
  SPEAKER_01: don't have these features,
• 30:45
  SPEAKER_01: be easy to make so that this doesn't cost a ton of money at the end.
• 30:48
  SPEAKER_01: Like you can actually specify all these features and have
• 30:51
  SPEAKER_01: the machine learning compute stack just like run through billions and billions
• 30:56
  SPEAKER_01: of possibilities and then come down to,
• 30:58
  SPEAKER_01: all right, the next million you should test is this one.
• 31:01
  SPEAKER_01: And then we do the experiment again.
• 31:03
  SPEAKER_01: And again and again.
• 31:05
  SPEAKER_01: And that's the approach that we've been taking and we found to be very fruitful.
• 31:10
  SPEAKER_00: Are the markers, do they fluoresce?
• 31:13
  SPEAKER_00: Or how do you find that barcode?
• 31:15
  SPEAKER_01: So the barcode is a piece of DNA.
• 31:18
  SPEAKER_01: And because it's a piece of DNA, the two things are going to happen.
• 31:22
  SPEAKER_01: And we actually measure both because we care a lot about this data.
• 31:25
  SPEAKER_01: DNA gets expressed into RNA.
• 31:28
  SPEAKER_01: That's how genes turn from their DNA...
• 31:31
  SPEAKER_01: Information into a protein that can do stuff.
• 31:34
  SPEAKER_01: And we can, and both DNA and RNA are information carrying molecules.
• 31:38
  SPEAKER_01: Like their literal purpose in the cell is to just ferry information around,
• 31:41
  SPEAKER_01: which means they're actually kind of easy to read and write.
• 31:44
  SPEAKER_01: So we just read them off with sequencing.
• 31:46
  SPEAKER_01: We just sequence it.
• 31:47
  SPEAKER_01: Okay.
• 31:47
  SPEAKER_01: And we do this in a pooled fashion.
• 31:49
  SPEAKER_01: And because everything is barcoded,
• 31:50
  SPEAKER_01: when we see a certain sequence,
• 31:52
  SPEAKER_01: we know that that came from a certain virus we designed in a certain way.
• 31:56
  SPEAKER_01: And then we can use benefit from that information.
• 31:59
  SPEAKER_00: So you give an animal an injection?
• 32:01
  SPEAKER_00: Yes.
• 32:02
  SPEAKER_00: And then it acts for a while, and then you just take a blood sample or a tissue sample?
• 32:06
  SPEAKER_00: So we take out tissue samples of every tissue
• 32:10
  SPEAKER_01: so that we can learn as much as
• 32:11
  SPEAKER_00: possible.
• 32:11
  SPEAKER_00: And see what happens where.
• 32:13
  SPEAKER_01: Exactly.
• 32:13
  SPEAKER_01: Sequence each of those tissues.
• 32:16
  SPEAKER_01: And then ultimately this just becomes this like giant table.
• 32:19
  SPEAKER_01: I see.
• 32:20
  SPEAKER_01: Like literal, you could open it in Excel.
• 32:22
  SPEAKER_01: That's like every tissue,
• 32:23
  SPEAKER_01: every virus you tried,
• 32:25
  SPEAKER_01: Did it, you know, go, is it more than the baseline or less than the baseline?
• 32:29
  SPEAKER_01: And then from that measurement, you can look at it.
• 32:31
  SPEAKER_01: Actually, sometimes you see like, wow, that's really promising.
• 32:34
  SPEAKER_01: We should just validate that one because it's, you know, maybe it does what we need it to do.
• 32:41
  SPEAKER_01: Or at least it looks like a step in the right direction.
• 32:43
  SPEAKER_01: We can confirm that.
• 32:45
  SPEAKER_01: But then you can then also repeat this process.
• 32:48
  SPEAKER_00: All right, pod friends.
• 32:49
  SPEAKER_00: I am here to tell you about SendCutSend.
• 32:52
  SPEAKER_00: They are a manufacturing company.
• 32:55
  SPEAKER_00: Phenomenon.
• 32:55
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• 33:01
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• 33:11
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• 33:19
  SPEAKER_00: We love SendCutSend.
• 33:20
  SPEAKER_00: Go visit them today.
• 33:23
  SPEAKER_00: And you mentioned the liver pretty early on, which is an issue here, right?
• 33:28
  SPEAKER_00: Because we've seen that gene therapies tend to, the liver likes to absorb things.
• 33:33
  SPEAKER_00: And so it's picking these things up.
• 33:35
  SPEAKER_00: And this has been a problem, right?
• 33:37
  SPEAKER_00: Is you want to create something to help a condition in the central nervous system,
• 33:42
  SPEAKER_00: in the eye,
• 33:43
  SPEAKER_00: in the brain,
• 33:44
  SPEAKER_00: and the liver is gobbling everything up and then it's creating toxicity issues and,
• 33:49
  SPEAKER_00: and I mean, that's part of this quest, right?
• 33:51
  SPEAKER_00: Is to get this out of the liver and to the spot where you want it to go.
• 33:55
  SPEAKER_00: Nailed it, yes.
• 33:57
  SPEAKER_00: And I see on your website, I mean, you guys really emphasize this a lot.
• 34:02
  SPEAKER_00: Because this has been the central knock on delivery is just that we come up
• 34:06
  SPEAKER_00: with all these great ideas and the liver sucks everything up and kind of ruins
• 34:10
  SPEAKER_00: the party.
• 34:10
  SPEAKER_01: It's more than sucks everything up.
• 34:11
  SPEAKER_01: In some cases,
• 34:12
  SPEAKER_01: the viruses are homing there because that's where they're going
• 34:15
  SPEAKER_01: to do their replication.
• 34:17
  SPEAKER_01: And so if you take the delivery component from that virus, it's not surprising.
• 34:21
  SPEAKER_01: It's going to keep going to the liver.
• 34:23
  SPEAKER_01: That's one piece of it.
• 34:24
  SPEAKER_01: The other piece of it is it's helpful to understand...
• 34:28
  SPEAKER_01: Why are we seeing the toxicities that we're seeing?
• 34:31
  SPEAKER_01: The reason for it is actually if you take just the capsid from a natural virus,
• 34:36
  SPEAKER_01: it almost looks like it should work.
• 34:39
  SPEAKER_01: And then when you try it, you're like, wow, this thing's actually quite safe.
• 34:42
  SPEAKER_01: You can put in a lot,
• 34:44
  SPEAKER_01: like a huge number compared to almost any other virus before
• 34:49
  SPEAKER_01: the immune system has even almost any reaction.
• 34:53
  SPEAKER_01: And then so what we do is we just turn this crank because For example,
• 34:57
  SPEAKER_01: if you're making a muscle gene therapy with an existing capsid,
• 35:00
  SPEAKER_01: it goes there a little.
• 35:01
  SPEAKER_01: So if you increase the dose enough,
• 35:04
  SPEAKER_01: you're going to get enough delivery,
• 35:06
  SPEAKER_01: but you're faced with this trade-off of,
• 35:09
  SPEAKER_01: You can't go as high of a dose as you would need,
• 35:12
  SPEAKER_01: but you want to go high enough to have a shot at having a strong impact.
• 35:17
  SPEAKER_01: But then you're kind of, we call this the therapeutic index, like it's just very narrow.
• 35:23
  SPEAKER_01: And that's why we're so focused on, well,
• 35:24
  SPEAKER_01: if we can just massively detarget the liver,
• 35:27
  SPEAKER_01: it's going to Widen completely the therapeutic index.
• 35:32
  SPEAKER_01: But that's also a place where, you know, we could do just that.
• 35:35
  SPEAKER_01: That would be great.
• 35:36
  SPEAKER_01: But actually,
• 35:37
  SPEAKER_01: you know,
• 35:37
  SPEAKER_01: we're more ambitious than that because we can detarget the liver to help with safety
• 35:41
  SPEAKER_01: and at the same time just like make it go more to the place it needs to go.
• 35:44
  SPEAKER_00: Okay.
• 35:45
  SPEAKER_00: Okay.
• 35:45
  SPEAKER_00: And then on you guys' website, you know, I noticed you were calling out.
• 35:50
  SPEAKER_00: Promising work around muscles, around the eye, around the brain and central nervous system.
• 35:56
  SPEAKER_00: They kind of get lumped together sometimes.
• 35:58
  SPEAKER_00: I'd love to break down for people still how this works
• 36:04
  SPEAKER_00: when it's doing what it's supposed to be doing.
• 36:06
  SPEAKER_00: I don't know if we want to go through all three or should we pick one of these?
• 36:09
  SPEAKER_00: Let's pick one.
• 36:10
  SPEAKER_00: Let's pick the brain.
• 36:10
  SPEAKER_00: Okay.
• 36:10
  SPEAKER_00: So let's do the brain and let's kind of describe.
• 36:13
  SPEAKER_00: So you've done all this work.
• 36:15
  SPEAKER_00: Um, to, to make this capsid the way you want and you're, you're getting past the liver.
• 36:21
  SPEAKER_00: And then, so what are we doing in the brain?
• 36:23
  SPEAKER_01: So you've got this protein shell with a piece of DNA in it.
• 36:29
  SPEAKER_01: It's going in the blood.
• 36:30
  SPEAKER_01: You inject it in the blood and it's just moving around in the blood.
• 36:34
  SPEAKER_01: The first challenge you have,
• 36:35
  SPEAKER_01: you face is the, the brain actually, nature's designed it quite well.
• 36:40
  SPEAKER_01: One of the things it has is it's this thing called a blood brain barrier.
• 36:43
  SPEAKER_01: Think of it as just a bouncer.
• 36:45
  SPEAKER_01: That's like literally deciding like, oh, you want to get in the brain.
• 36:49
  SPEAKER_01: You have to have very specific things to let it.
• 36:52
  SPEAKER_00: This is the ultimate VIP.
• 36:54
  SPEAKER_00: Exactly.
• 36:54
  SPEAKER_01: But we can...
• 36:57
  SPEAKER_01: We can learn about that bouncer and we can allow our protein shell to make it
• 37:01
  SPEAKER_01: through that first barrier.
• 37:04
  SPEAKER_01: That's actually one of the big challenges in the brain, the first one.
• 37:07
  SPEAKER_01: Now you're through the blood-brain barrier, there's a neuron sitting there.
• 37:11
  SPEAKER_01: So the virus just touches the cell.
• 37:14
  SPEAKER_01: And I'm going to have to use somewhat vague words
• 37:18
  SPEAKER_01: because we barely understand how this actually works in practice at
• 37:21
  SPEAKER_01: the molecular level, but we have hypotheses.
• 37:24
  SPEAKER_01: And it's going to start making it inside the cell.
• 37:27
  SPEAKER_01: And then the virus is,
• 37:28
  SPEAKER_01: actually the cell is gonna start detecting,
• 37:30
  SPEAKER_01: like this is kind of weird, I should just put it in the recycling bin.
• 37:34
  SPEAKER_01: And when that process starts to happen,
• 37:36
  SPEAKER_01: the capsid knows, okay,
• 37:38
  SPEAKER_01: it's actually time for me to,
• 37:40
  SPEAKER_01: if I don't do anything,
• 37:41
  SPEAKER_01: I'm gonna end up in a recycling bin, so it's time for me to get out.
• 37:44
  SPEAKER_01: It sort of wriggles out of the state that it's in,
• 37:50
  SPEAKER_01: and then has and finds a way to make it to the nucleus,
• 37:53
  SPEAKER_01: where the DNA of the cell resides.
• 37:56
  SPEAKER_01: Once it's there, the job of the delivery platform is accomplished.
• 38:00
  SPEAKER_01: You've now delivered a new piece of DNA inside of a neuron.
• 38:04
  SPEAKER_01: And, you know, I've described one neuron with one protein, but one capsid.
• 38:08
  SPEAKER_01: Obviously, you're doing this with billions of it all over the brain.
• 38:11
  SPEAKER_01: That's the goal.
• 38:12
  SPEAKER_01: And now what can happen?
• 38:14
  SPEAKER_01: Now it's up to you.
• 38:15
  SPEAKER_01: If you are making a drug, you can now do any number of things.
• 38:18
  SPEAKER_01: For example,
• 38:19
  SPEAKER_01: that piece of DNA could express a CRISPR system that goes in and like fixes
• 38:25
  SPEAKER_01: a specific genetic disease.
• 38:27
  SPEAKER_01: Yeah.
• 38:28
  SPEAKER_01: Maybe you don't even need to do that.
• 38:29
  SPEAKER_01: Maybe the gene is just broken and missing.
• 38:31
  SPEAKER_01: So all you need to do is you can just express the gene directly.
• 38:34
  SPEAKER_01: And that's it.
• 38:35
  SPEAKER_01: That gives you a path to curing a specific disease.
• 38:39
  SPEAKER_01: And this also highlights actually, you know, I'm not describing to you a drug.
• 38:44
  SPEAKER_01: I'm not actually telling you I'm going to go in and express that one gene to cure
• 38:48
  SPEAKER_01: that one disease.
• 38:50
  SPEAKER_01: That's actually because that's not what we're doing.
• 38:51
  SPEAKER_01: We are not building drugs, which is different for our field.
• 38:55
  SPEAKER_01: We're building this delivery technology.
• 38:57
  SPEAKER_01: And then working with many partners who say,
• 39:00
  SPEAKER_01: I need delivery technology and I have a very Really good thesis for how I'm going
• 39:06
  SPEAKER_01: to put in a payload inside of this capsid and go and intervene causally on
• 39:10
  SPEAKER_01: a disease.
• 39:12
  SPEAKER_01: And, you know, we're doing that together.
• 39:14
  SPEAKER_01: And that type of partnership has also been like just really,
• 39:16
  SPEAKER_01: really fun in the story of our company.
• 39:18
  SPEAKER_00: Yeah.
• 39:19
  SPEAKER_00: And just going back to the neuron for one second.
• 39:20
  SPEAKER_00: I mean, so once you've made these changes, the neuron is doing what?
• 39:23
  SPEAKER_00: It's either producing more or less of something essentially?
• 39:27
  SPEAKER_01: So it has to start by producing, you know, it has that piece of DNA.
• 39:34
  SPEAKER_01: It has to start by turning that into RNA.
• 39:36
  SPEAKER_01: And this is where things get kind of cool.
• 39:38
  SPEAKER_01: And let's find the right altitude.
• 39:42
  SPEAKER_01: But that piece of RNA can do a number of things.
• 39:44
  SPEAKER_01: The piece of RNA itself could go in and make changes to other pieces of RNA.
• 39:49
  SPEAKER_01: So the simplest is it could go and shut off another piece of RNA.
• 39:53
  SPEAKER_01: So that enables you to turn a gene off,
• 39:55
  SPEAKER_01: which for many different diseases, that's That's a great starting point.
• 39:59
  SPEAKER_01: Let's do that.
• 40:00
  SPEAKER_01: Actually, there's pieces of RNA which now are more complicated and can do interesting things.
• 40:05
  SPEAKER_01: They can actually edit the RNA directly.
• 40:08
  SPEAKER_01: And that's allowing you to now change the behavior of another gene in
• 40:12
  SPEAKER_01: a way that's going to help improve health.
• 40:14
  SPEAKER_01: That's very direct.
• 40:15
  SPEAKER_01: So that's one way.
• 40:16
  SPEAKER_01: And this is even just RNA.
• 40:17
  SPEAKER_01: Actually, these are maybe the more sophisticated...
• 40:21
  SPEAKER_01: For the connoisseur approaches.
• 40:23
  SPEAKER_01: The simplest one to think about is, you go from DNA, you go from RNA, then you make a protein.
• 40:29
  SPEAKER_01: And now that protein, maybe the natural protein is missing in some way.
• 40:34
  SPEAKER_01: So you just have to replace it.
• 40:36
  SPEAKER_01: And there's many gene therapies that have that approach.
• 40:39
  SPEAKER_01: In some cases, that protein itself is then gonna go do something more complicated.
• 40:43
  SPEAKER_01: So for example,
• 40:44
  SPEAKER_01: if that protein is a gene editor itself,
• 40:47
  SPEAKER_01: then that protein is gonna go and find the genome.
• 40:50
  SPEAKER_01: Make that change.
• 40:51
  SPEAKER_01: And you can now kind of see like what the potential of genetic medicine is, right?
• 40:56
  SPEAKER_01: Like you have all of these different ways to intervene
• 40:59
  SPEAKER_01: on this like central repository of information that is the DNA and RNA of the cell.
• 41:04
  SPEAKER_01: And ultimately it's such a point of high leverage to go after disease.
• 41:10
  SPEAKER_00: And like you mentioned, I mean, so if we're talking muscle, eye, brain,
• 41:16
  SPEAKER_00: You guys,
• 41:17
  SPEAKER_00: normally I hop on some biotech company's website and they have their pipeline
• 41:20
  SPEAKER_00: of their drugs and which stage of a trial they're at.
• 41:23
  SPEAKER_00: When I was looking at you guys,
• 41:25
  SPEAKER_00: like you mentioned, you have this list of pharma partners that you're with.
• 41:29
  SPEAKER_00: So they are licensing or creating some deal with you around this delivery mechanism.
• 41:35
  SPEAKER_00: And then they're testing whatever they want on the eye, the brain, their particular device.
• 41:41
  SPEAKER_00: Whatever they've discovered, their gene edit that they want to push in there.
• 41:46
  SPEAKER_01: Exactly right.
• 41:46
  SPEAKER_01: And it's not just pharma.
• 41:48
  SPEAKER_01: It's not just the largest companies,
• 41:50
  SPEAKER_01: although they are very interested in genetic medicines,
• 41:53
  SPEAKER_01: and that's an amazing moment there.
• 41:55
  SPEAKER_01: It's also biotechs at every stage.
• 41:57
  SPEAKER_01: It's also increasingly nonprofits or families who are trying to make a drug in an N
• 42:03
  SPEAKER_01: of one setting.
• 42:04
  SPEAKER_01: And that's really speaking to the moment that the field is having
• 42:09
  SPEAKER_01: through this convergence of technologies of if you can bring in,
• 42:14
  SPEAKER_01: solve one component really well, It's going to unlock everything else.
• 42:19
  SPEAKER_01: I also want to tag that ultimately, if,
• 42:21
  SPEAKER_01: you know,
• 42:22
  SPEAKER_01: if in your intro,
• 42:23
  SPEAKER_01: you're hinting at, man, progress in generalism has been kind of slow.
• 42:27
  SPEAKER_01: If you had to blame it all on one piece, it's probably delivery.
• 42:30
  SPEAKER_00: Well, okay.
• 42:31
  SPEAKER_00: I was just about to ask you.
• 42:31
  SPEAKER_00: Okay.
• 42:32
  SPEAKER_00: I feel like we've set a framework,
• 42:33
  SPEAKER_00: you know, to have everybody up to speed on the basics here, what you guys do.
• 42:38
  SPEAKER_00: And so, yeah, this is where I wanted to open up is I just have a bunch of questions.
• 42:41
  SPEAKER_00: I mean, I had Jake B.
• 42:43
  SPEAKER_00: Craft from Strand Therapeutics on my podcast.
• 42:45
  SPEAKER_00: I had Jennifer Doudna.
• 42:46
  SPEAKER_00: On, you know, both of whom were talking about this delivery problem.
• 42:50
  SPEAKER_00: And so, okay, like 20 something years ago, 20 what?
• 42:53
  SPEAKER_00: Yeah, 26-ish years ago, we sequenced genome.
• 42:58
  SPEAKER_00: Almost immediately, everyone's like, oh, we're going to cure everything.
• 43:01
  SPEAKER_00: This is going to be pretty easy.
• 43:02
  SPEAKER_00: It turns out to be really hard.
• 43:04
  SPEAKER_00: Some years pass, we get CRISPR, and it's like, okay, well, now we have this great tool.
• 43:09
  SPEAKER_00: To make all these, these edits and, and everybody gets all excited again.
• 43:13
  SPEAKER_00: And then it turns out, okay, this is hard.
• 43:15
  SPEAKER_00: Sometimes when we make these edits, we're really ripping up the DNA in aggressive ways.
• 43:20
  SPEAKER_00: We can't do this on as big a chunks of DNA as we want.
• 43:23
  SPEAKER_00: And we need to be more precise with how we go about this.
• 43:27
  SPEAKER_00: And so, I mean, this has been, what happened?
• 43:30
  SPEAKER_00: Like, like,
• 43:32
  SPEAKER_00: was this over enthusiasm or like,
• 43:35
  SPEAKER_00: were people just not Paying enough attention to this delivery issue or this is just
• 43:41
  SPEAKER_00: the nature of how technology is developed and you got to do all these basic steps
• 43:44
  SPEAKER_00: and then focus on this end game?
• 43:49
  SPEAKER_01: I think it's...
• 43:50
  SPEAKER_00: Like, do we fuck up
• 43:53
  SPEAKER_01: or... No, no, not at all.
• 43:54
  SPEAKER_01: I think all of these things,
• 43:56
  SPEAKER_01: it's very hard sometimes to know,
• 43:59
  SPEAKER_01: especially in biology, where these systems are so complicated...
• 44:03
  SPEAKER_01: It's sometimes hard to know what is like really hard versus,
• 44:08
  SPEAKER_01: actually this is easy, you just need the right solution.
• 44:10
  SPEAKER_01: Like once you actually put the right solution in place, like bam, you just sort of solve it.
• 44:14
  SPEAKER_01: So it's sometimes hard to know like which of these combinations of things and
• 44:18
  SPEAKER_01: where do you wanna go.
• 44:19
  SPEAKER_01: For a very long time,
• 44:21
  SPEAKER_01: actually like changing DNA in
• 44:24
  SPEAKER_01: a programmable fashion Was this like impossibly complicated problem, right?
• 44:29
  SPEAKER_01: It was, it felt like, how could we do this?
• 44:34
  SPEAKER_01: There's very few machines, like there's actually very few ways to copy from nature.
• 44:37
  SPEAKER_01: That's our first trick is like, can we copy from nature if it's doing this somewhere else?
• 44:42
  SPEAKER_01: And actually programmable changes the DNA,
• 44:46
  SPEAKER_01: not something if you, Like, look, in most of nature, you don't see that.
• 44:49
  SPEAKER_01: And it took some incredible scientists to find and be actually, wait,
• 44:54
  SPEAKER_01: this actually has the features,
• 44:55
  SPEAKER_01: like some very strange system that actually is doing this for a reason
• 44:59
  SPEAKER_01: to protect itself from viruses, funny enough.
• 45:03
  SPEAKER_01: We can borrow this.
• 45:05
  SPEAKER_01: And then suddenly that problem went from top of the list of, wow,
• 45:09
  SPEAKER_01: we really need to sort this out to like,
• 45:11
  SPEAKER_01: we have traction, now we need to engineer it.
• 45:14
  SPEAKER_01: Delivery,
• 45:15
  SPEAKER_01: maybe part of it is we felt like we had more of a handle on it from the get-go
• 45:19
  SPEAKER_01: because for many years,
• 45:20
  SPEAKER_01: actually,
• 45:21
  SPEAKER_01: we'd picked up these viruses from nature,
• 45:23
  SPEAKER_01: we were going somewhere, it was going pretty well, it was pretty safe.
• 45:26
  SPEAKER_01: And then we also upped the ante of like, where do we actually need to go, right?
• 45:30
  SPEAKER_01: Part of the challenge is once you realize like, oh no, to have impact, it's not sufficient.
• 45:36
  SPEAKER_01: If all disease that we needed to treat was in the liver, I mean, we'd be almost done, right?
• 45:42
  SPEAKER_01: But that's the challenge is actually, we want to go in many different places.
• 45:45
  SPEAKER_01: We're going to go there really effectively.
• 45:47
  SPEAKER_01: So we just got to have these, you know, put these pieces together.
• 45:50
  SPEAKER_00: Okay.
• 45:50
  SPEAKER_00: And then the, on the,
• 45:53
  SPEAKER_00: when you guys,
• 45:54
  SPEAKER_00: so when you guys are calling out muscle,
• 45:56
  SPEAKER_00: central nervous system, I, is there a reason it's in those buckets?
• 46:01
  SPEAKER_00: Like, like if you figure this out, why can't we be delivering this to all the parts of the body?
• 46:06
  SPEAKER_01: That we want to get everywhere.
• 46:08
  SPEAKER_01: We absolutely want to get everywhere.
• 46:10
  SPEAKER_01: The reason we started there is it felt strategically to us and really to the field
• 46:17
  SPEAKER_01: that this is a place where you have a ton of unmet need and probably no other path
• 46:23
  SPEAKER_01: to get there.
• 46:24
  SPEAKER_01: For example, on our list, we used to have liver.
• 46:27
  SPEAKER_01: All NMPs just got Pretty good.
• 46:30
  SPEAKER_01: But actually to getting to CNS or to like very effectively getting to every muscle,
• 46:35
  SPEAKER_01: we think using AV capsids is ultimately by far the front runner.
• 46:41
  SPEAKER_01: And with the engineering approaches that we have,
• 46:43
  SPEAKER_01: it's starting to look like we have the right solutions in hand and we have momentum
• 46:48
  SPEAKER_01: to make them even better.
• 46:49
  SPEAKER_01: So it's an exciting time for that reason.
• 46:51
  SPEAKER_00: Okay.
• 46:51
  SPEAKER_00: And I don't want to put, uh,
• 46:53
  SPEAKER_00: words in his mouth because you don't understand this field well enough,
• 46:57
  SPEAKER_00: but I feel like when I talk to Jake Becraft,
• 46:59
  SPEAKER_00: you know,
• 46:59
  SPEAKER_00: and other people, they always tell me the virus stuff is just really expensive.
• 47:04
  SPEAKER_00: It's just,
• 47:04
  SPEAKER_00: it's never going to be the path because it's,
• 47:07
  SPEAKER_00: when you see how much like these,
• 47:08
  SPEAKER_00: these one-off gene therapies cost,
• 47:10
  SPEAKER_00: it's millions and millions and millions of dollars.
• 47:12
  SPEAKER_00: And they say it doesn't get like that much better.
• 47:15
  SPEAKER_00: Even for broader conditions,
• 47:16
  SPEAKER_00: and I guess Strand has committed to lipid nanoparticles like you're talking about,
• 47:21
  SPEAKER_00: mRNA being delivered, you know, as a much cheaper mechanism.
• 47:26
  SPEAKER_00: Is there something,
• 47:27
  SPEAKER_00: first of all, like,
• 47:28
  SPEAKER_00: you know,
• 47:29
  SPEAKER_00: I don't know,
• 47:29
  SPEAKER_00: I guess address that,
• 47:30
  SPEAKER_00: but is there something you guys have done that makes yours somehow cheaper
• 47:34
  SPEAKER_00: than what other people have done with viruses?
• 47:36
  SPEAKER_01: Absolutely.
• 47:36
  SPEAKER_01: So as you, there's two challenges really.
• 47:40
  SPEAKER_01: One is, well,
• 47:41
  SPEAKER_01: first of all,
• 47:42
  SPEAKER_01: I just want to say on that perspective,
• 47:44
  SPEAKER_01: I think it's always a tough bet to bet against engineering technology.
• 47:49
  SPEAKER_01: I think there's a lot of things that a lot of people say, it's like, that's not gonna happen.
• 47:53
  SPEAKER_01: And I'd say like, let's try it out and let's see what plays out.
• 47:59
  SPEAKER_01: I think part of it is just developing the right technology.
• 48:02
  SPEAKER_01: There's some incredible companies that are saying,
• 48:05
  SPEAKER_01: For example,
• 48:06
  SPEAKER_01: manufacturing even natural AVs,
• 48:09
  SPEAKER_01: before we engineered them to make them easy to manufacture,
• 48:12
  SPEAKER_01: manufacturing natural AVs is a pretty expensive process.
• 48:15
  SPEAKER_01: If you break down the process of why it's hard, there's a bunch of different reasons.
• 48:18
  SPEAKER_01: There's a very creative startup in San Diego that's saying, what if we just made this in plants?
• 48:24
  SPEAKER_01: Because it turns out it's just expensive to grow human cells in giant containers
• 48:29
  SPEAKER_01: that you have to stir and prevent from being infected.
• 48:31
  SPEAKER_01: But you know one thing we're like amazing at is just growing.
• 48:33
  SPEAKER_01: Have you ever seen a tobacco field?
• 48:35
  SPEAKER_01: Like that's a lot of biomass.
• 48:37
  SPEAKER_01: And can we move the engineering there?
• 48:39
  SPEAKER_00: It's making the AAV sequence in a plant?
• 48:43
  SPEAKER_01: Yeah, that's exactly what they're working on.
• 48:44
  SPEAKER_01: And it sounds as crazy as that.
• 48:47
  SPEAKER_01: Who is that?
• 48:48
  SPEAKER_01: Circium.
• 48:49
  SPEAKER_00: Circium, okay.
• 48:50
  SPEAKER_01: And they're...
• 48:53
  SPEAKER_01: That type of approach is exactly the type of technology on locks that we can bring.
• 48:58
  SPEAKER_01: It also comes down to the community aspect of there's a few different pieces
• 49:01
  SPEAKER_01: of technology,
• 49:02
  SPEAKER_01: and in each one, including on lowering cost, folks are working on doing this.
• 49:07
  SPEAKER_01: Maybe you're saying, actually, that sounds...
• 49:11
  SPEAKER_01: Making avian plant, wow, that sounds too sci-fi.
• 49:14
  SPEAKER_01: Can you just automate better?
• 49:15
  SPEAKER_01: Make better bioreactors and just use AI to guide you through
• 49:20
  SPEAKER_01: the manufacturing process so it becomes cheaper.
• 49:22
  SPEAKER_01: So I think we're already seeing huge unlocks on the cost.
• 49:26
  SPEAKER_01: In the manufacturing side of things.
• 49:28
  SPEAKER_01: Let's actually talk about the capsid for a second.
• 49:31
  SPEAKER_01: One thing is imminently engineerable is if you make the capsid 10 times better,
• 49:36
  SPEAKER_01: you can lower the dose 10 times.
• 49:37
  SPEAKER_01: So that's it.
• 49:38
  SPEAKER_01: You've got a direct cost unlock there by just making the capsid itself more efficient.
• 49:43
  SPEAKER_01: Like if you make it go to the brain more than the liver,
• 49:46
  SPEAKER_01: the ones that were previously going to the liver were wasted.
• 49:48
  SPEAKER_01: So you had to make up for it by adding more.
• 49:50
  SPEAKER_01: Now you can just make, you know, Cost savings right there.
• 49:55
  SPEAKER_01: And then the next step is the actual process of manufacturing,
• 49:58
  SPEAKER_01: the way this nanomachine assembles is also engineerable.
• 50:03
  SPEAKER_01: And we can focus on that too to drive down costs.
• 50:07
  SPEAKER_01: So I think ultimately for cost, the perspective here is it's kind of like solar panels.
• 50:12
  SPEAKER_01: Used to be super expensive and people were saying it's not going to scale.
• 50:16
  SPEAKER_01: And we started doing more and more and the cost went down and down.
• 50:19
  SPEAKER_01: I think that same trajectory is going to happen to genetic medicines, including delivered by AV.
• 50:23
  SPEAKER_00: So do you think, are we, you know, that solar process was a multi-decade journey?
• 50:28
  SPEAKER_00: Like, and I,
• 50:29
  SPEAKER_00: maybe it's impossible for you to speak on behalf of your partners in all this,
• 50:33
  SPEAKER_00: but you know,
• 50:34
  SPEAKER_00: if I was getting your therapy,
• 50:36
  SPEAKER_00: a therapy based on your underlying mechanism,
• 50:39
  SPEAKER_00: your platform today, and what, I don't know, let's
• 50:42
  SPEAKER_01: pick the brain again.
• 50:44
  SPEAKER_00: You know, I,
• 50:45
  SPEAKER_00: one of your partners has come up with this great,
• 50:47
  SPEAKER_00: this great Drug for the brain and it's going to be delivered with your mechanism,
• 50:52
  SPEAKER_00: would this be today like a multi-million dollar type thing?
• 50:56
  SPEAKER_01: No, far from it.
• 50:57
  SPEAKER_01: Okay.
• 50:57
  SPEAKER_01: I think we're looking at costs that are closer to the thousands or low tens
• 51:02
  SPEAKER_01: of thousands, depending on the dose and the approach.
• 51:05
  SPEAKER_01: And even that's like going down.
• 51:07
  SPEAKER_01: Okay.
• 51:08
  SPEAKER_01: So already we've dialed in, I mean,
• 51:11
  SPEAKER_01: in the past,
• 51:12
  SPEAKER_01: less than the past few years, dialed down a 10X drop and it's going to continue.
• 51:17
  SPEAKER_01: So I think...
• 51:19
  SPEAKER_01: The reason for it is kind of obvious is people looked at it and saw it's going to be
• 51:22
  SPEAKER_01: a huge problem.
• 51:23
  SPEAKER_01: If it costs a million dollars per to make this drug,
• 51:26
  SPEAKER_01: the impact of this drug is going to be tiny.
• 51:29
  SPEAKER_01: And, you know,
• 51:30
  SPEAKER_01: we've put our technological and engineering capabilities to lower that cost
• 51:34
  SPEAKER_01: and it's worked.
• 51:36
  SPEAKER_01: So it's advancing.
• 51:37
  SPEAKER_00: All right.
• 51:37
  SPEAKER_00: This will be my nastiest question.
• 51:40
  SPEAKER_00: You know, you guys, in some ways, you were so far ahead of the game.
• 51:46
  SPEAKER_00: And where we are now,
• 51:47
  SPEAKER_00: I just wonder if you were almost too specific,
• 51:50
  SPEAKER_00: because if I talk to a company like, say,
• 51:52
  SPEAKER_00: Chai today,
• 51:53
  SPEAKER_00: they'll tell me,
• 51:54
  SPEAKER_00: we're not just doing proteins and amino acids designs around viruses.
• 51:59
  SPEAKER_00: We can do anything, you know?
• 52:00
  SPEAKER_00: And you look at, I don't know, just some of the work Google DeepMind puts out.
• 52:04
  SPEAKER_00: I mean, it seems quite broad in what they're attacking.
• 52:08
  SPEAKER_00: If I open my inbox today,
• 52:10
  SPEAKER_00: I guarantee you I would be being pitched by a biotech company
• 52:13
  SPEAKER_00: that tells me they're using AI to design all kinds of stuff.
• 52:17
  SPEAKER_00: And so, you know, George Church, who is the professor that you worked under the most?
• 52:22
  SPEAKER_01: I worked with Doug Melton.
• 52:23
  SPEAKER_00: Doug, right.
• 52:23
  SPEAKER_01: Making beta cells in vitro.
• 52:24
  SPEAKER_01: Different cells.
• 52:28
  SPEAKER_00: Pioneers in their field at the cutting edge.
• 52:31
  SPEAKER_00: And then it feels like the world has kind of like caught up in some ways to
• 52:34
  SPEAKER_00: where you guys were thinking.
• 52:36
  SPEAKER_00: Yeah.
• 52:37
  SPEAKER_00: So did you, are you, is this a curse or a blessing?
• 52:40
  SPEAKER_00: Were you, were you too narrow?
• 52:42
  SPEAKER_00: Maybe.
• 52:44
  SPEAKER_01: You're observing something great.
• 52:45
  SPEAKER_01: And actually there's two, two important things here.
• 52:49
  SPEAKER_01: One is, I saw this elsewhere,
• 52:52
  SPEAKER_01: so I'll borrow their phrase,
• 52:53
  SPEAKER_01: but say it,
• 52:54
  SPEAKER_01: you know, show it in vivo or it didn't happen in biology is like really important.
• 52:59
  SPEAKER_01: Like I think actual proof.
• 53:00
  SPEAKER_00: So in the body, not just in the spreadsheet.
• 53:02
  SPEAKER_00: Like in the body, not just in simulation is really key.
• 53:05
  SPEAKER_01: So that's one piece where I'd say,
• 53:07
  SPEAKER_01: once you do that test,
• 53:07
  SPEAKER_01: you actually see much more clearly what works and what doesn't.
• 53:10
  SPEAKER_01: The other piece is we noticed this too.
• 53:12
  SPEAKER_01: Meaning we noticed that,
• 53:14
  SPEAKER_01: As we got better and better at engineering capsids,
• 53:17
  SPEAKER_01: we realized that everything we learned on the machine learning side could be applied
• 53:21
  SPEAKER_01: to proteins more generally.
• 53:23
  SPEAKER_01: And it's also a place where we're starting to share that technology.
• 53:26
  SPEAKER_01: So we're presenting our foundation scale models for broad protein engineering
• 53:33
  SPEAKER_01: that have come from our experience of building capsids.
• 53:36
  SPEAKER_01: The other thing that's cool with capsids is These are really hard to make.
• 53:41
  SPEAKER_01: So in a way it's like,
• 53:42
  SPEAKER_01: if you're going to the gym and doing the hardest possible workout,
• 53:46
  SPEAKER_01: that's gonna help you for the like general problem.
• 53:51
  SPEAKER_01: And that's the experience we've brought in.
• 53:54
  SPEAKER_01: We're also excited to share our technology,
• 53:57
  SPEAKER_01: both on the models that do the protein engineering,
• 54:01
  SPEAKER_01: as well as the approaches to use those models effectively,
• 54:04
  SPEAKER_01: not just have a thing that only machine learning wizard scientists can use,
• 54:10
  SPEAKER_01: but actually make it very easy for any scientist to deploy this technology
• 54:14
  SPEAKER_01: at scale confidently.
• 54:16
  SPEAKER_01: We've benefited from many,
• 54:17
  SPEAKER_01: many reps of doing this ourselves,
• 54:19
  SPEAKER_01: where we Don't just ship the model,
• 54:22
  SPEAKER_01: but ship the model, use it,
• 54:24
  SPEAKER_01: put the sequences,
• 54:24
  SPEAKER_01: test it in vivo,
• 54:26
  SPEAKER_01: see what works,
• 54:26
  SPEAKER_01: be disappointed about all the stuff that didn't work,
• 54:29
  SPEAKER_01: learn from that lesson, and repeat that.
• 54:31
  SPEAKER_01: So I think being early is never a curse.
• 54:35
  SPEAKER_01: But you've got to keep your mind open to how can you have impact from what you've learned.
• 54:40
  SPEAKER_00: How will this play out when...
• 54:43
  SPEAKER_00: We see the work that Google DeepMind has done in science.
• 54:46
  SPEAKER_00: I mean, they've won like a Nobel Prize,
• 54:47
  SPEAKER_00: you know,
• 54:48
  SPEAKER_00: and then we see OpenAI,
• 54:51
  SPEAKER_00: Anthropic,
• 54:52
  SPEAKER_00: these giant companies with billions of dollars behind them and not infinite compute
• 54:57
  SPEAKER_00: because it's being dragged in all sorts of places,
• 54:59
  SPEAKER_00: but the world's biggest computers.
• 55:01
  SPEAKER_00: And they've gotten interested in biotech,
• 55:03
  SPEAKER_00: obviously,
• 55:04
  SPEAKER_00: sometimes through a partnership and then sometimes trying
• 55:06
  SPEAKER_00: to do their own internal work.
• 55:09
  SPEAKER_00: If I think about fields like brain research and things like that,
• 55:13
  SPEAKER_00: I'll talk to brain researchers who are doing Connectome stuff,
• 55:16
  SPEAKER_00: trying to build these maps.
• 55:17
  SPEAKER_00: And, you know,
• 55:17
  SPEAKER_00: they have to, like,
• 55:18
  SPEAKER_00: to do the work that they want to do,
• 55:19
  SPEAKER_00: they have to fight for time on these Google supercomputers.
• 55:22
  SPEAKER_00: And it's like,
• 55:22
  SPEAKER_00: have you buttered up the person at Google enough to sort of be put at the front of
• 55:29
  SPEAKER_00: the line for a startup?
• 55:32
  SPEAKER_00: I mean, you know,
• 55:33
  SPEAKER_00: obviously I can see a world where a focused startup is going to make more progress
• 55:38
  SPEAKER_00: than some broad Some broad company that this is not their sole business and area
• 55:44
  SPEAKER_00: of attention.
• 55:44
  SPEAKER_00: On the other hand,
• 55:45
  SPEAKER_00: it just seems like, man,
• 55:46
  SPEAKER_00: we get to some point where the foundational model companies have so much resource
• 55:53
  SPEAKER_00: in their model and their compute that if they,
• 55:56
  SPEAKER_00: yeah, I mean, do they become competition here?
• 56:00
  SPEAKER_01: Maybe.
• 56:00
  SPEAKER_01: The way I'd frame it is...
• 56:03
  SPEAKER_01: There's two things to say here.
• 56:05
  SPEAKER_01: One is access to compute is key.
• 56:08
  SPEAKER_01: And we've mentioned our pharma collaborations.
• 56:12
  SPEAKER_01: We've also had the privilege and the honor to be partnered with NVIDIA
• 56:15
  SPEAKER_01: as we've been doing this work.
• 56:17
  SPEAKER_01: And that's been a Fantastic collaboration to push us and work with,
• 56:24
  SPEAKER_01: collaborate at the very leading edge.
• 56:26
  SPEAKER_01: So we absolutely want to stay there because it's critical.
• 56:28
  SPEAKER_01: I think there's three things.
• 56:30
  SPEAKER_01: To make AI work, you really need a combination of three things.
• 56:33
  SPEAKER_01: You need compute, you need algorithms, you need data.
• 56:36
  SPEAKER_01: Data is a place where if you don't have the right data,
• 56:38
  SPEAKER_01: it's just really hard to intuit the shape of the problem,
• 56:41
  SPEAKER_01: especially when you're working with completely new...
• 56:45
  SPEAKER_01: New problems or new questions and delivery in humans or in primates is a problem
• 56:52
  SPEAKER_01: where you have to go make the data to apply it.
• 56:55
  SPEAKER_01: And it's also not data that's publicly available.
• 56:57
  SPEAKER_01: So we've created that own data set.
• 57:00
  SPEAKER_01: The other part that to me is exciting about all this progress and the scale
• 57:05
  SPEAKER_01: of people wanting to contribute to this is, we're doing this for our mission, right?
• 57:10
  SPEAKER_01: Like we're doing this to make Genetic medicines happen.
• 57:14
  SPEAKER_01: We're calling this concept genetic agency.
• 57:16
  SPEAKER_01: Like we want to be in a place where you can And that's a really meaningful problem.
• 57:21
  SPEAKER_01: It's going to take a lot of effort and applying the combined effort of all
• 57:24
  SPEAKER_01: of these individuals,
• 57:25
  SPEAKER_01: organizations, now increasingly AIs, we're going to need all of that.
• 57:28
  SPEAKER_01: It's just not a problem that
• 57:45
  SPEAKER_01: We're going to look around and be like, wow, that was easy to solve.
• 57:49
  SPEAKER_01: And so the more we increase the scale of the resources that are deployed to this,
• 57:53
  SPEAKER_01: I think that's all good news.
• 57:54
  SPEAKER_00: Okay.
• 57:55
  SPEAKER_00: And a bit on that point, I mean,
• 57:56
  SPEAKER_00: I asked you on the cost element, but,
• 57:59
  SPEAKER_00: and again,
• 57:59
  SPEAKER_00: maybe it's hard because you can't talk about what your partners are doing
• 58:02
  SPEAKER_00: or you don't want to speak on their behalf,
• 58:04
  SPEAKER_00: but this initial batch of work that's being done with your technology,
• 58:09
  SPEAKER_00: is that going to be
• 58:13
  SPEAKER_00: Will the therapies that result from that in the near term be for limited,
• 58:18
  SPEAKER_00: more on the line of the rare disease type populations,
• 58:22
  SPEAKER_00: or are some of your partners going after quite broad diseases in
• 58:26
  SPEAKER_00: the general population?
• 58:27
  SPEAKER_01: Like you guessed, I can't comment on any specific, but I can tell you that the answer is yes.
• 58:33
  SPEAKER_01: It applies to each one.
• 58:35
  SPEAKER_01: It's also part of it is we have several partners that have different strategies.
• 58:38
  SPEAKER_01: Some partners for...
• 58:41
  SPEAKER_01: If you're a foundation focused on one disease,
• 58:44
  SPEAKER_01: they're gonna be focused on that one disease and it's gonna be as rare or common
• 58:47
  SPEAKER_01: as they want it to be because that's their mission.
• 58:50
  SPEAKER_01: Then there's organizations that are working across several diseases and we're happy
• 58:54
  SPEAKER_01: to support that.
• 58:55
  SPEAKER_01: And that comes down to why we made the company the way we wanted to do it.
• 59:00
  SPEAKER_01: We wanted to,
• 59:02
  SPEAKER_01: as we created these capsids that unlocked possibilities,
• 59:06
  SPEAKER_01: We wanted to make sure that the impact wasn't limited by
• 59:10
  SPEAKER_01: the drugs we could make using that one capsid.
• 59:13
  SPEAKER_01: But instead,
• 59:13
  SPEAKER_01: the impact could be maximized by everyone being able to,
• 59:18
  SPEAKER_01: who wanted the technology, could access it and use it and build their programs.
• 59:22
  SPEAKER_01: You know,
• 59:22
  SPEAKER_01: it's kind of this full court press on disease using genetic medicines with,
• 59:27
  SPEAKER_01: you know, highly engineered delivery capabilities.
• 59:30
  SPEAKER_00: Okay.
• 59:31
  SPEAKER_00: Okay.
• 59:32
  SPEAKER_00: All right.
• 59:32
  SPEAKER_00: We're going to broaden out for a second because I have like more general questions for you.
• 59:36
  SPEAKER_00: I mean, one we've been dancing around or we've addressed it somewhat,
• 59:39
  SPEAKER_00: but just to put a fine point on it.
• 59:44
  SPEAKER_00: For 20 years,
• 59:45
  SPEAKER_00: I have been told that biology,
• 59:47
  SPEAKER_00: we've gotten to the point where biology is like engineering,
• 59:50
  SPEAKER_00: and we've just gone through many reasons why it doesn't work.
• 59:54
  SPEAKER_00: Does not seem exactly like computer programming.
• 59:57
  SPEAKER_00: I mean, is this ultimately like a false pro I felt like 20 years ago,
• 1:00:03
  SPEAKER_00: synthetic biology became this term that started to enter the world.
• 1:00:08
  SPEAKER_00: And, and for the reasons I outlined, we sequenced the whole genome.
• 1:00:11
  SPEAKER_00: It's like, okay, this is just, this is, this is code.
• 1:00:14
  SPEAKER_00: This is math.
• 1:00:15
  SPEAKER_00: This is all stuff we know well.
• 1:00:18
  SPEAKER_00: Um, I think your background, you're a physicist and a chemistry undergrad at Harvard.
• 1:00:23
  SPEAKER_00: I mean, so you're kind of approaching this from the brain of somebody who's wants
• 1:00:28
  SPEAKER_00: to engineer the world and deal with these concrete things that we can manipulate.
• 1:00:33
  SPEAKER_00: I just, and I don't feel totally let down.
• 1:00:36
  SPEAKER_00: I love this stuff.
• 1:00:37
  SPEAKER_00: I cover it all the time.
• 1:00:38
  SPEAKER_00: I know things take a long time to develop, but like, have we been sold a false bill of goods?
• 1:00:44
  SPEAKER_00: Will biology always be so much more complicated than code in a software program?
• 1:00:52
  SPEAKER_01: It's funny because I want to say yes, but I worried you'd be disappointed.
• 1:00:55
  SPEAKER_01: To me, that's a good thing.
• 1:00:56
  SPEAKER_00: That's a good thing.
• 1:00:57
  SPEAKER_00: Okay.
• 1:00:57
  SPEAKER_01: But let me explain that.
• 1:00:59
  SPEAKER_01: I think unpacking part of it is,
• 1:01:01
  SPEAKER_01: I think the challenge with biology is this multi-scale computational process.
• 1:01:10
  SPEAKER_01: And part of it is we have this desire to be like, cool, okay, we understand one of the layers.
• 1:01:19
  SPEAKER_01: That's it.
• 1:01:20
  SPEAKER_01: We're good.
• 1:01:21
  SPEAKER_01: Because if you write code or if you actually engineer a system,
• 1:01:24
  SPEAKER_01: if you're going to have multiple scales,
• 1:01:27
  SPEAKER_01: you actually want to have very elegant abstractions that separate them so
• 1:01:31
  SPEAKER_01: that you can operate on one independent from the other.
• 1:01:34
  SPEAKER_01: Biology just doesn't play with those rules.
• 1:01:37
  SPEAKER_01: It's just squishy.
• 1:01:38
  SPEAKER_01: And it's allowed to just make shortcuts between the scales and how things impact.
• 1:01:43
  SPEAKER_01: And that's the main challenge.
• 1:01:45
  SPEAKER_01: It's actually very hard for us to keep track of a lot of that.
• 1:01:49
  SPEAKER_01: That said,
• 1:01:50
  SPEAKER_01: every time we make improvements on the ability to measure the biology,
• 1:01:55
  SPEAKER_01: the ability to make sense of what we measure,
• 1:01:58
  SPEAKER_01: the ability to interrogate it in new ways,
• 1:01:59
  SPEAKER_01: we actually unlock more of that bigger picture.
• 1:02:02
  SPEAKER_01: There's probably always going to be surprises.
• 1:02:04
  SPEAKER_01: Like even in the far future, we're going to look at something.
• 1:02:07
  SPEAKER_01: We're going to find some bug somewhere that does something.
• 1:02:10
  SPEAKER_01: We're like, wow, we'd never thought of this.
• 1:02:13
  SPEAKER_01: But I think that's what is framing the problem that's hard.
• 1:02:17
  SPEAKER_01: That's also...
• 1:02:18
  SPEAKER_01: Why it's beautiful.
• 1:02:20
  SPEAKER_01: There's this complexity that's just such a puzzle and it is so satisfying as a scientist.
• 1:02:26
  SPEAKER_01: And for a while we were starting Dyno as a company,
• 1:02:29
  SPEAKER_01: it was just so fun pitching machine learning people to own this problem,
• 1:02:32
  SPEAKER_01: being like, hey, 700 letters goes everywhere in the body.
• 1:02:37
  SPEAKER_01: Like that's it.
• 1:02:38
  SPEAKER_01: And now there's like a question mark in between, help us puzzle the question mark.
• 1:02:42
  SPEAKER_01: And you can see people be like, oh, wow.
• 1:02:45
  SPEAKER_01: So it's coming.
• 1:02:47
  SPEAKER_01: Will it ever be completely computable?
• 1:02:49
  SPEAKER_01: I, I don't think so.
• 1:02:51
  SPEAKER_01: Um, but I think that's great.
• 1:02:53
  SPEAKER_00: Yeah.
• 1:02:53
  SPEAKER_00: I just, you know, I just get, I get hopeful and then I get frustrated.
• 1:02:58
  SPEAKER_00: It's like, if I cover IPSCs, I cover MRNA, what you guys are doing, I get so excited.
• 1:03:04
  SPEAKER_00: And then if I talk to each person in these various fields,
• 1:03:08
  SPEAKER_00: you know, the,
• 1:03:09
  SPEAKER_00: the IPSC people will tell me,
• 1:03:11
  SPEAKER_00: no, you know, like 10 years, we got this thing working for sure.
• 1:03:14
  SPEAKER_00: Got to reprogram our organs back to their state.
• 1:03:17
  SPEAKER_00: And then I go talk to my other very smart,
• 1:03:19
  SPEAKER_00: rational friend and they're like, no, whole field's bullshit.
• 1:03:22
  SPEAKER_00: You know, it's not going to work.
• 1:03:24
  SPEAKER_00: I always feel like there's,
• 1:03:25
  SPEAKER_00: there's a rug that gets pulled out from under us on, on each one of these areas.
• 1:03:29
  SPEAKER_00: And that, um, I mean, I got to synthetic biology when I first started to hear about it.
• 1:03:34
  SPEAKER_00: I was like, this makes sense.
• 1:03:36
  SPEAKER_00: This is blowing my mind.
• 1:03:37
  SPEAKER_00: How amazing.
• 1:03:38
  SPEAKER_00: And then we have done amazing things.
• 1:03:40
  SPEAKER_00: But yeah, it feels just like a brutal, brutal slog.
• 1:03:44
  SPEAKER_00: And I think the way it gets presented to the public sometimes
• 1:03:48
  SPEAKER_00: by people like me is like,
• 1:03:50
  SPEAKER_00: you know, Somebody is going to unlock this universal key that cures all illness.
• 1:03:55
  SPEAKER_00: And it just seems like...
• 1:03:58
  SPEAKER_00: Because it does get presented as...
• 1:04:00
  SPEAKER_00: DNA gets presented as software that we can just fix.
• 1:04:03
  SPEAKER_01: And it just seems so difficult.
• 1:04:05
  SPEAKER_01: I think what's helpful there is I...
• 1:04:08
  SPEAKER_01: I understand the disappointment,
• 1:04:10
  SPEAKER_01: and I think it kind of comes down to what's different between biology
• 1:04:16
  SPEAKER_01: and other forms of technology.
• 1:04:19
  SPEAKER_01: And the difference in my mind is biology is underpinning medicine.
• 1:04:26
  SPEAKER_01: And with medicine comes just very real stuff, disease of yourself, disease of loved ones.
• 1:04:33
  SPEAKER_01: And with that comes a desire for like,
• 1:04:36
  SPEAKER_01: you know,
• 1:04:37
  SPEAKER_01: you have this like iPhone in your pocket that's this like incredible piece
• 1:04:40
  SPEAKER_01: of technology.
• 1:04:41
  SPEAKER_01: And then you might have a family member with a disease that we've been studying
• 1:04:44
  SPEAKER_01: for 50 years and there's Nothing you can do and it's like hard
• 1:04:49
  SPEAKER_01: to believe what's going on there.
• 1:04:51
  SPEAKER_01: And I think that's one of the elements where if that makes people feel impatient,
• 1:04:58
  SPEAKER_01: I think that's actually, that makes sense.
• 1:05:00
  SPEAKER_01: That's normal.
• 1:05:02
  SPEAKER_01: We feel that.
• 1:05:03
  SPEAKER_01: That's a totally reasonable feeling to have in that position.
• 1:05:07
  SPEAKER_01: So I think part of it is we're holding biology to like a different standard
• 1:05:11
  SPEAKER_01: than other technologies in that sense.
• 1:05:13
  SPEAKER_01: And I think maybe we should.
• 1:05:16
  SPEAKER_01: But I also think that if someone's saying like, oh,
• 1:05:18
  SPEAKER_01: all of this stuff is just like vaporware,
• 1:05:22
  SPEAKER_01: You know, wet vaporware, that's also wrong.
• 1:05:25
  SPEAKER_01: Like that's also just an overly skeptical view.
• 1:05:27
  SPEAKER_01: And the nice thing there is we're seeing the proof in the progress in the clinical trials.
• 1:05:32
  SPEAKER_01: Those are an extremely high test.
• 1:05:34
  SPEAKER_01: There's no, you can't fake it.
• 1:05:36
  SPEAKER_01: And you actually do the trial and you see, does it work or does it not?
• 1:05:41
  SPEAKER_01: And the picture is gonna be really clear,
• 1:05:43
  SPEAKER_01: but we're seeing,
• 1:05:44
  SPEAKER_01: you know, the work I used to work on making beta cells in vitro to cure type one.
• 1:05:50
  SPEAKER_01: It took years to get that into people.
• 1:05:52
  SPEAKER_01: But then once it wasn't a person and there's just,
• 1:05:55
  SPEAKER_01: you know,
• 1:05:55
  SPEAKER_01: people going around who've received these cells that have like all been made in
• 1:05:58
  SPEAKER_01: the lab, but they just don't have to inject insulin every day anymore.
• 1:06:03
  SPEAKER_01: You know, it's not perfect yet.
• 1:06:04
  SPEAKER_01: They have to suppress their immune system for the privilege of not injecting insulin.
• 1:06:08
  SPEAKER_01: But for a bunch of people, that tradeoff makes perfect sense.
• 1:06:10
  SPEAKER_01: You know, that's incredible.
• 1:06:11
  SPEAKER_01: And that progress is absolutely real.
• 1:06:14
  SPEAKER_00: Okay.
• 1:06:15
  SPEAKER_00: You what?
• 1:06:16
  SPEAKER_00: You grew up in Romania?
• 1:06:17
  SPEAKER_01: I grew up in Canada.
• 1:06:18
  SPEAKER_00: In Canada.
• 1:06:18
  SPEAKER_00: I was born in Romania.
• 1:06:19
  SPEAKER_00: Okay.
• 1:06:19
  SPEAKER_01: Grew up in Montreal.
• 1:06:20
  SPEAKER_01: Great city.
• 1:06:20
  SPEAKER_00: So your parents are Romanian?
• 1:06:22
  SPEAKER_00: Yes.
• 1:06:22
  SPEAKER_00: Are they academics?
• 1:06:24
  SPEAKER_01: Mix.
• 1:06:25
  SPEAKER_01: Dad's a physicist.
• 1:06:26
  SPEAKER_01: Mom's a programmer.
• 1:06:27
  SPEAKER_00: Okay.
• 1:06:28
  SPEAKER_00: Okay.
• 1:06:28
  SPEAKER_01: Yeah.
• 1:06:29
  SPEAKER_00: I mean, your academic credentials are quite extraordinary.
• 1:06:34
  SPEAKER_00: All these honors and everything at Harvard.
• 1:06:36
  SPEAKER_00: I mean, you did undergrad and grad at Harvard.
• 1:06:39
  SPEAKER_00: And so what?
• 1:06:40
  SPEAKER_00: You were... Did you...
• 1:06:42
  SPEAKER_00: I assume you did quite well in your teenage years at school.
• 1:06:46
  SPEAKER_01: I did well in school.
• 1:06:47
  SPEAKER_01: I think the thing that was really special for me before college was,
• 1:06:51
  SPEAKER_01: and I got very lucky about this,
• 1:06:54
  SPEAKER_01: is I was a very serious nerd participating in science fair competitions.
• 1:06:59
  SPEAKER_01: And that was really fun.
• 1:07:01
  SPEAKER_01: That was just such a permission to play.
• 1:07:02
  SPEAKER_00: Science fair kids always do well, man.
• 1:07:05
  SPEAKER_01: You just get that bug at that age, and it was hard to let go.
• 1:07:10
  SPEAKER_00: No, but like through my career reporting,
• 1:07:11
  SPEAKER_00: it's one of the, it's a,
• 1:07:13
  SPEAKER_00: it's a high signal,
• 1:07:14
  SPEAKER_00: you know,
• 1:07:14
  SPEAKER_00: somebody who does well in these international,
• 1:07:17
  SPEAKER_00: well, even national science fairs tend, they tend to, tend to do quite well in life.
• 1:07:21
  SPEAKER_01: I mean, for me, the, the,
• 1:07:23
  SPEAKER_01: it's almost stronger than that is like, for me,
• 1:07:26
  SPEAKER_01: going back to the first science fair I participated in,
• 1:07:29
  SPEAKER_01: that's where I like bumped into biology.
• 1:07:30
  SPEAKER_00: Okay.
• 1:07:31
  SPEAKER_01: And that's where like it started.
• 1:07:32
  SPEAKER_01: That's what I was going to ask.
• 1:07:33
  SPEAKER_00: Yeah.
• 1:07:34
  SPEAKER_00: Is it like,
• 1:07:34
  SPEAKER_00: if you were,
• 1:07:35
  SPEAKER_00: if you were starting over again,
• 1:07:37
  SPEAKER_00: given everything you know about AI and Many of the things we talked about,
• 1:07:42
  SPEAKER_00: you know,
• 1:07:43
  SPEAKER_00: if you had to pick physics,
• 1:07:45
  SPEAKER_00: chemistry, biology, energy, I mean, would you still pick biology now?
• 1:07:52
  SPEAKER_00: Is there any element to this where you see what the AIs are doing and you're like,
• 1:07:56
  SPEAKER_00: oh, they're going to do a lot of the work from here on out.
• 1:08:00
  SPEAKER_00: Maybe this will be less exciting for me.
• 1:08:03
  SPEAKER_01: I think my feeling about what's gonna happen there is as the power of AIs grows
• 1:08:11
  SPEAKER_01: and they can help us do more and more things,
• 1:08:15
  SPEAKER_01: it's gonna become, like purpose is gonna become increasingly more important.
• 1:08:20
  SPEAKER_01: Like if you can just apply this thing to like solve anything,
• 1:08:24
  SPEAKER_01: applying it on the things that matter most.
• 1:08:27
  SPEAKER_01: And that's a bit of a personal decision of like, what do you feel matters, right?
• 1:08:32
  SPEAKER_01: Is it something beautiful?
• 1:08:34
  SPEAKER_01: Is it creating beauty, which is really what art is about?
• 1:08:38
  SPEAKER_01: Is it understanding how the world works, which is what science is about?
• 1:08:42
  SPEAKER_01: Doing this with more and more AIs just sounds perfect.
• 1:08:45
  SPEAKER_01: And I think for everyone in the field,
• 1:08:48
  SPEAKER_01: we're going to get to continue doing it with increasingly better collaborators,
• 1:08:54
  SPEAKER_01: increasingly focused on the things that actually only we can do or we have
• 1:09:00
  SPEAKER_01: the strongest leverage.
• 1:09:01
  SPEAKER_01: That sounds fun in the years to come.
• 1:09:03
  SPEAKER_00: So if we sit around,
• 1:09:04
  SPEAKER_00: and you're going to sleep at night,
• 1:09:06
  SPEAKER_00: and this is the most optimistic case scenario, because you have this window.
• 1:09:15
  SPEAKER_00: Into, into all this technology and where it's heading.
• 1:09:17
  SPEAKER_00: I mean, do you,
• 1:09:18
  SPEAKER_00: do you sit around and you're like,
• 1:09:19
  SPEAKER_00: fuck in 10 years, we've just like cured every disease.
• 1:09:24
  SPEAKER_00: I can see that very much on the horizon.
• 1:09:26
  SPEAKER_00: Or are you still more like,
• 1:09:28
  SPEAKER_00: Ooh, this is, this is, this is a 40, 50, 60, 70, a hundred year journey.
• 1:09:32
  SPEAKER_00: We're still on.
• 1:09:34
  SPEAKER_00: And I mean, every disease is a lot.
• 1:09:35
  SPEAKER_00: You don't have to like take that verbatim,
• 1:09:38
  SPEAKER_00: but I mean, you know, like, do you sit back and you're like, man, the world.
• 1:09:41
  SPEAKER_00: In 10 years, people have no idea what's coming for them.
• 1:09:44
  SPEAKER_00: It's going to be a huge change in health.
• 1:09:47
  SPEAKER_01: You know, a bunch of my SF friends keep selling me on like, this is how you feel.
• 1:09:51
  SPEAKER_01: And it's just kind of not how I view it.
• 1:09:53
  SPEAKER_01: I think, I mean, I do hope that in 10 years, like...
• 1:09:57
  SPEAKER_01: We are ripping through diseases in terms of places where we can have impact and,
• 1:10:02
  SPEAKER_01: and we can just put together pieces of technology to achieve the impact that we want
• 1:10:05
  SPEAKER_01: to have.
• 1:10:06
  SPEAKER_00: Well, you're saying hope,
• 1:10:07
  SPEAKER_00: but like you think that, like,
• 1:10:10
  SPEAKER_00: is there, what's,
• 1:10:11
  SPEAKER_00: what's the low hanging fruit list of the diseases that fall into that 10 years?
• 1:10:16
  SPEAKER_01: I think if we start listing monogenic diseases,
• 1:10:20
  SPEAKER_01: you know,
• 1:10:21
  SPEAKER_01: things like Huntington's disease, ALS, you know, SMA is already on the list.
• 1:10:27
  SPEAKER_01: Like we've already, spinal muscular atrophy.
• 1:10:31
  SPEAKER_01: So this was one of the first gene therapy with one of the first AV gene therapies,
• 1:10:35
  SPEAKER_01: Olgensma was approved for SMA.
• 1:10:38
  SPEAKER_01: And some of the kids that received that therapy are now entering their teenage years soon.
• 1:10:43
  SPEAKER_01: And there's maybe a,
• 1:10:44
  SPEAKER_01: actually we're gonna get to learn like,
• 1:10:46
  SPEAKER_01: Is the function of that gene even necessary once you're done growing?
• 1:10:49
  SPEAKER_01: And we will learn that information soon.
• 1:10:52
  SPEAKER_01: So that list is gonna grow,
• 1:10:53
  SPEAKER_01: but also diseases like Parkinson's, which are not anywhere near
• 1:11:02
  SPEAKER_01: rare disease.
• 1:11:03
  SPEAKER_01: It's one of the most common diseases, but it has single gene handles that we can pull on.
• 1:11:10
  SPEAKER_01: And there's quite a few approaches that are deemed
• 1:11:13
  SPEAKER_01: The trials are starting soon is how I would describe it.
• 1:11:16
  SPEAKER_01: So in 10 years, we'll know the answers, which of those work.
• 1:11:21
  SPEAKER_01: And at that point, we'll be in a deployment phase for this low-hanging fruit.
• 1:11:26
  SPEAKER_01: We're in a scale and deploy and also looking to bring in more pieces,
• 1:11:34
  SPEAKER_01: bring more diseases, more areas of opportunity to the application
• 1:11:41
  SPEAKER_01: I think the other part here is as we grow, you know, there's like aging broadly.
• 1:11:46
  SPEAKER_01: There's a place where a combination of different genetic interventions should help,
• 1:11:51
  SPEAKER_01: like will help.
• 1:11:52
  SPEAKER_01: Question is just let's find the right ones.
• 1:11:54
  SPEAKER_01: And, you know, that's, we're absolutely going to be in that direction.
• 1:11:57
  SPEAKER_00: Like how into the aging stuff are you?
• 1:11:59
  SPEAKER_00: Is there anything in particular that excites you?
• 1:12:02
  SPEAKER_01: I think I'm a bit more myself as a pessimist there,
• 1:12:06
  SPEAKER_01: just honestly, partially because of the time I trained as a physician.
• 1:12:12
  SPEAKER_01: I never practiced, but that was...
• 1:12:17
  SPEAKER_01: A very grounding experience on like what can and can't happen.
• 1:12:21
  SPEAKER_01: So it's,
• 1:12:21
  SPEAKER_01: it's maybe bringing a certain pessimism to me in terms of, of,
• 1:12:28
  SPEAKER_01: you know,
• 1:12:28
  SPEAKER_01: what are the,
• 1:12:29
  SPEAKER_01: you know,
• 1:12:29
  SPEAKER_01: if you look at the max age or like how healthy you are until you die, right?
• 1:12:34
  SPEAKER_01: Like understanding these perspectives for me kind of is very grounded in the reality
• 1:12:39
  SPEAKER_01: of just meeting many patients through medical training.
• 1:12:43
  SPEAKER_01: Um,
• 1:12:44
  SPEAKER_01: I am quite hopeful that increasing health span by quite a bit is absolutely possible
• 1:12:50
  SPEAKER_01: as we understand the drivers of this health span.
• 1:12:53
  SPEAKER_01: And I think it's amazing the focus we're bringing and also the will we have
• 1:12:59
  SPEAKER_01: to test both hypotheses.
• 1:13:00
  SPEAKER_01: I think to me, that's amazing.
• 1:13:03
  SPEAKER_01: And I'm really glad we're doing this investment in terms of what it's gonna take
• 1:13:07
  SPEAKER_01: to increase the maximal age of a human.
• 1:13:11
  SPEAKER_01: That's going to be tough.
• 1:13:13
  SPEAKER_01: Part of me feels like I just don't see as much the proof of concept unlock.
• 1:13:19
  SPEAKER_01: I'm more in that camp of I don't see the reason to know that we have the pieces
• 1:13:24
  SPEAKER_01: that creates this unlock.
• 1:13:26
  SPEAKER_01: But at the same time,
• 1:13:28
  SPEAKER_01: if there's many,
• 1:13:29
  SPEAKER_01: many different diseases we can stack and fix,
• 1:13:32
  SPEAKER_01: that's going to have a direct impact on healthspan as well.
• 1:13:35
  SPEAKER_00: I've got Brian Johnson's olive oil up there.
• 1:13:38
  SPEAKER_00: We can have a shot.
• 1:13:39
  SPEAKER_00: Amazing.
• 1:13:41
  SPEAKER_00: Well, you were making fun of the San Francisco, your San Francisco friends.
• 1:13:46
  SPEAKER_00: I go back and forth between Cambridge and San Francisco,
• 1:13:50
  SPEAKER_00: and I obviously see what you're talking about.
• 1:13:53
  SPEAKER_00: I mean, there are two distinct flavors to these communities.
• 1:13:56
  SPEAKER_00: You know, one, when I go to Boston and Cambridge, just absurd.
• 1:14:02
  SPEAKER_00: Everyone I meet is an MD, PhD, or a PhD, PhD.
• 1:14:06
  SPEAKER_00: And, but, you know, and just...
• 1:14:09
  SPEAKER_00: At the cutting edge of incredible breakthroughs.
• 1:14:12
  SPEAKER_00: I mean, the Church Lab,
• 1:14:14
  SPEAKER_00: Boyden Lab,
• 1:14:15
  SPEAKER_00: you know,
• 1:14:15
  SPEAKER_00: I feel like they're just knocking out all these people
• 1:14:18
  SPEAKER_00: that end up making fantastic startups and everything.
• 1:14:21
  SPEAKER_00: But, you know,
• 1:14:21
  SPEAKER_00: there is this more conservative,
• 1:14:23
  SPEAKER_00: traditional, slower moving sort of vibe there out here.
• 1:14:28
  SPEAKER_00: It's a bit reckless at times.
• 1:14:30
  SPEAKER_00: I just keep running into 20-year-olds who have a bunch of mice in their lab
• 1:14:35
  SPEAKER_00: and they'll just come out and be like, I've cured insomnia, you know?
• 1:14:38
  SPEAKER_00: And it's like, have you?
• 1:14:41
  SPEAKER_00: But so, yeah, I mean,
• 1:14:43
  SPEAKER_00: do you take pleasure in both communities or do you find San Francisco just over
• 1:14:49
  SPEAKER_00: the top?
• 1:14:50
  SPEAKER_01: I was rubbing my friends though, but I think the trick is find the best of both worlds.
• 1:14:56
  SPEAKER_01: And that's really fun as an approach, I think.
• 1:15:02
  SPEAKER_01: Even in the work that we do,
• 1:15:03
  SPEAKER_01: like at a practical level,
• 1:15:06
  SPEAKER_01: combining bio and AI,
• 1:15:07
  SPEAKER_01: you want to try to play to the strengths of both rather than be constrained by
• 1:15:11
  SPEAKER_01: the relative weaknesses of both.
• 1:15:14
  SPEAKER_01: That's essential.
• 1:15:14
  SPEAKER_01: But yeah, it's fun to see the different vibes in the different communities.
• 1:15:19
  SPEAKER_00: You don't yearn to move the company to the Bay Area?
• 1:15:22
  SPEAKER_01: No, we're not.
• 1:15:24
  SPEAKER_01: No?
• 1:15:24
  SPEAKER_01: No near-term plans.
• 1:15:25
  SPEAKER_01: We're very happy where we are.
• 1:15:26
  SPEAKER_01: We have an amazing space.
• 1:15:27
  SPEAKER_01: And look to us,
• 1:15:29
  SPEAKER_01: also the focus has been We're just so blessed by this incredible team of really,
• 1:15:36
  SPEAKER_01: really creative, really committed scientists.
• 1:15:40
  SPEAKER_01: And the same way that Silicon Valley is this for tech in the world,
• 1:15:45
  SPEAKER_01: this is what Boston is for biotech.
• 1:15:47
  SPEAKER_01: And so we want to continue to benefit from that and continue tapping that.
• 1:15:51
  SPEAKER_01: But we do have some remote folks across the country, including NSF and playing to win everybody.
• 1:15:59
  SPEAKER_00: Erne Marogi,
• 1:16:00
  SPEAKER_00: who writes for Core Memory,
• 1:16:01
  SPEAKER_00: has even hosted a Core Memory podcast, is studying to be a doctor like you.
• 1:16:08
  SPEAKER_00: Was he a co-founder?
• 1:16:10
  SPEAKER_00: He was one of our early employees.
• 1:16:11
  SPEAKER_00: Early employees.
• 1:16:14
  SPEAKER_00: Should we shame or praise Erne?
• 1:16:17
  SPEAKER_01: I'll praise.
• 1:16:18
  SPEAKER_01: I'll praise for Erne.
• 1:16:21
  SPEAKER_00: I think we're going to start like a Ask the Doctor Q&A series
• 1:16:26
  SPEAKER_00: where people can like send in questions to Aaron A to be answered.
• 1:16:31
  SPEAKER_00: Do you have a favorite?
• 1:16:34
  SPEAKER_00: I'm trying to humiliate him here.
• 1:16:37
  SPEAKER_01: Trying to think, you know, what can I do?
• 1:16:39
  SPEAKER_01: Can I share right here?
• 1:16:40
  SPEAKER_01: No, but Aaron A, I mean,
• 1:16:41
  SPEAKER_01: I think part of what actually probably makes him effective for Core Memory
• 1:16:49
  SPEAKER_01: Is I think he sees what things are, like sees them as they are and also is willing to speak up.
• 1:16:55
  SPEAKER_00: Yeah.
• 1:16:55
  SPEAKER_01: Which when you're making a company is, You know, sometimes it's, like, annoying, right?
• 1:17:01
  SPEAKER_01: Like, you're like, oh, this person speaks up in this way.
• 1:17:03
  SPEAKER_01: She's willing to be critical.
• 1:17:05
  SPEAKER_01: Yeah.
• 1:17:06
  SPEAKER_01: But then you actually sit down and you're like,
• 1:17:08
  SPEAKER_01: oh, yeah, that's, you know, there's something here.
• 1:17:10
  SPEAKER_01: And then I think actually getting good at that is key.
• 1:17:12
  SPEAKER_01: So, you know, Irene,
• 1:17:14
  SPEAKER_01: having taught us this loop and having set us this loop of,
• 1:17:18
  SPEAKER_01: you know,
• 1:17:18
  SPEAKER_01: should have a culture where if something's not how it should be,
• 1:17:21
  SPEAKER_01: you should speak up so we can...
• 1:17:23
  SPEAKER_01: Change it, make it better.
• 1:17:24
  SPEAKER_01: That's been a great impact beyond the scientific contributions more directly.
• 1:17:29
  SPEAKER_00: He was a coward.
• 1:17:30
  SPEAKER_00: He had to go get his medical degree.
• 1:17:32
  SPEAKER_01: I had a lot of fun myself being like, hey, just finish my medical degree.
• 1:17:38
  SPEAKER_01: Here's some thoughts I have as someone who realized I'd have
• 1:17:44
  SPEAKER_01: a bigger impact not practicing myself.
• 1:17:47
  SPEAKER_01: Okay.
• 1:17:48
  SPEAKER_00: He did send me a couple questions to ask you,
• 1:17:50
  SPEAKER_00: one of which is low-hanging fruit, but let's do it anyway.
• 1:17:55
  SPEAKER_00: Peptides.
• 1:17:57
  SPEAKER_00: All the rage.
• 1:17:59
  SPEAKER_00: Should people be playing with their own peptide stack?
• 1:18:05
  SPEAKER_01: Let's put it this way.
• 1:18:06
  SPEAKER_01: I'm not playing with my own peptide stack.
• 1:18:08
  SPEAKER_01: I think what people should do is, you know, up to...
• 1:18:13
  SPEAKER_01: There is a framing where as long as it's understood that it's safe...
• 1:18:18
  SPEAKER_01: You know, let people do how they please.
• 1:18:20
  SPEAKER_01: But I think the question is for, you know, depending on the peptide, like, is it safe or not?
• 1:18:25
  SPEAKER_00: Yeah, the question's where, like,
• 1:18:27
  SPEAKER_00: we seem to be tilting to quite a large,
• 1:18:30
  SPEAKER_00: ever-growing number of people experimenting with things they're buying off
• 1:18:34
  SPEAKER_00: the internet,
• 1:18:35
  SPEAKER_00: which I guess, like, the weightlifting community has been doing for decades.
• 1:18:38
  SPEAKER_00: And this is just kind of becoming mainstream in some ways.
• 1:18:41
  SPEAKER_01: I think there are some,
• 1:18:43
  SPEAKER_01: you know,
• 1:18:43
  SPEAKER_01: what's giving me pause for myself,
• 1:18:45
  SPEAKER_01: anyways,
• 1:18:45
  SPEAKER_01: is the There are some places where there's interesting surprises
• 1:18:50
  SPEAKER_01: when you study something for a long time that you don't understand.
• 1:18:54
  SPEAKER_01: And I think the trick is when you're making a drug,
• 1:18:57
  SPEAKER_01: the trade-offs is often very immediate and worth it.
• 1:19:00
  SPEAKER_01: So you are willing to tolerate some side effect because there's a very clear, like,
• 1:19:07
  SPEAKER_01: So that you're willing to make that trade.
• 1:19:09
  SPEAKER_01: I think the risk here is that there might be times when we realize over time,
• 1:19:12
  SPEAKER_01: like, oh, this had a side effect.
• 1:19:15
  SPEAKER_01: And we might also realize, like,
• 1:19:17
  SPEAKER_01: actually, we don't understand the benefit or the benefit's not nearly as clear.
• 1:19:20
  SPEAKER_01: And then people might end up kind of disappointed because they're going
• 1:19:24
  SPEAKER_01: to have certainty on the side effect, but not on, did it do anything?
• 1:19:27
  SPEAKER_01: Yeah.
• 1:19:28
  SPEAKER_01: They might, you know, would have done things differently if they knew that information.
• 1:19:34
  SPEAKER_01: At the same time, I think, you know, if we're looking at the peptides, like the GLP-1s, right?
• 1:19:39
  SPEAKER_01: Like the fact that they're blurring this line of medicine into consumer product, that's great.
• 1:19:47
  SPEAKER_01: I think that's amazing from the perspective of not tying into this thing
• 1:19:53
  SPEAKER_01: of like medicine is this You know,
• 1:19:56
  SPEAKER_01: a thing where a small cast of a pointed few can tell you what you should
• 1:20:02
  SPEAKER_01: and shouldn't do in some conditions.
• 1:20:04
  SPEAKER_01: But, you know,
• 1:20:05
  SPEAKER_01: you as a person can look at information and understand what something is and decide
• 1:20:09
  SPEAKER_01: if it's right for you.
• 1:20:10
  SPEAKER_01: And that's that transition.
• 1:20:11
  SPEAKER_01: But, you know, it's helpful to point out GLP-1s have been studied for a very long time.
• 1:20:17
  SPEAKER_01: And again, the impact is so clear.
• 1:20:20
  SPEAKER_01: So it makes the tradeoffs easy.
• 1:20:22
  SPEAKER_00: Okay.
• 1:20:23
  SPEAKER_00: I asked this next question, um, I always go to China at some point in the podcast.
• 1:20:28
  SPEAKER_00: China.
• 1:20:30
  SPEAKER_00: And I hope people understand that I love China.
• 1:20:34
  SPEAKER_00: I love going there.
• 1:20:35
  SPEAKER_00: I am not like some jingoistic, crazy American, you know.
• 1:20:42
  SPEAKER_00: Anyway, these questions do not come from that place.
• 1:20:44
  SPEAKER_00: I just generally think people are not paying enough attention to the Right at
• 1:20:51
  SPEAKER_00: which China is making progress in, in science and all sorts of fields.
• 1:20:56
  SPEAKER_00: I just, I feel like,
• 1:20:58
  SPEAKER_00: I feel like most Americans are kind of sitting around expecting we're going
• 1:21:01
  SPEAKER_00: to dominate all these things for a long time.
• 1:21:04
  SPEAKER_00: And, and, and I'm not sure that that's the case.
• 1:21:06
  SPEAKER_00: What's, what's like the.
• 1:21:08
  SPEAKER_00: So China can run trials faster, cheaper than the United States.
• 1:21:12
  SPEAKER_00: It is catching up, if not on par with us in many areas of cutting edge science at this point.
• 1:21:20
  SPEAKER_00: You know, I see a world where these technologies are getting powerful.
• 1:21:26
  SPEAKER_00: And if we are creating,
• 1:21:28
  SPEAKER_00: I don't want to call it like super humans,
• 1:21:30
  SPEAKER_00: but if humans are starting to become healthier, gaining...
• 1:21:35
  SPEAKER_00: Functions we didn't expect.
• 1:21:37
  SPEAKER_00: I kind of fully expect most of these things to maybe turn up in China first.
• 1:21:41
  SPEAKER_00: What's the pro case for American science?
• 1:21:45
  SPEAKER_00: What's the best thing we have going for us?
• 1:21:48
  SPEAKER_00: Or answer that question in any U.S. or China sort of frame you prefer.
• 1:21:56
  SPEAKER_01: Yeah, I think to me, I agree, the rise of
• 1:22:03
  SPEAKER_01: Like Chinese biomedical science has been amazing.
• 1:22:05
  SPEAKER_01: And it's really interesting to see, I think.
• 1:22:08
  SPEAKER_00: I have friends,
• 1:22:10
  SPEAKER_00: you know,
• 1:22:10
  SPEAKER_00: who have very good jobs,
• 1:22:13
  SPEAKER_00: like professorships at the University of Michigan, um,
• 1:22:19
  SPEAKER_00: Who are not, unlike tech in some ways, are not like being actively recruited home.
• 1:22:24
  SPEAKER_00: They're just choosing to just like, go move back to China.
• 1:22:27
  SPEAKER_00: Yeah, you know, yeah.
• 1:22:29
  SPEAKER_01: I can see why.
• 1:22:29
  SPEAKER_01: And I feel previously,
• 1:22:30
  SPEAKER_01: if you looked at 20 years ago,
• 1:22:31
  SPEAKER_01: it was obvious that if you wanted to be cutting edge research,
• 1:22:33
  SPEAKER_01: you should be in America for biotech.
• 1:22:37
  SPEAKER_01: There's parity or depending on the field, there's just key differences.
• 1:22:43
  SPEAKER_01: And I think you're right to point out that there's a real risk that we've been at
• 1:22:50
  SPEAKER_01: the forefront for so long that we sort of assume that this is just our place
• 1:22:55
  SPEAKER_01: to be rather than a place where we need to Keep innovating.
• 1:23:02
  SPEAKER_01: The hope for American science here is if you look at other historic times,
• 1:23:06
  SPEAKER_01: like in rocket science,
• 1:23:08
  SPEAKER_01: of times when there was great power competition,
• 1:23:10
  SPEAKER_01: where the point was the technological race, we know we can do it.
• 1:23:16
  SPEAKER_01: I think we're finding the will to do it, which is exciting.
• 1:23:21
  SPEAKER_01: And ultimately, I think if competition makes us accelerate, that's awesome.
• 1:23:26
  SPEAKER_01: That's Again, these are such hard problems, which is a clear value.
• 1:23:30
  SPEAKER_01: If we hit them, we should do it.
• 1:23:33
  SPEAKER_01: I think there is a risk, which is that different countries do have somewhat different diseases.
• 1:23:39
  SPEAKER_01: What's most common can be related to genetics,
• 1:23:43
  SPEAKER_01: can be related to the food you eat, the society you have.
• 1:23:47
  SPEAKER_01: And so there's a real risk that if we,
• 1:23:50
  SPEAKER_01: right now as Americans,
• 1:23:51
  SPEAKER_01: we more or less get to prioritize the list of diseases based on Where we want
• 1:23:57
  SPEAKER_01: to apply this focus or we do this like jointly with Europe.
• 1:24:00
  SPEAKER_01: We have a responsibility to do this for the whole world,
• 1:24:02
  SPEAKER_01: but there's a risk that we need to,
• 1:24:04
  SPEAKER_01: you know,
• 1:24:05
  SPEAKER_01: want to continue being able to allocate and know that we're applying
• 1:24:09
  SPEAKER_01: the best technology in the right places.
• 1:24:11
  SPEAKER_01: I do think the changes that are coming,
• 1:24:13
  SPEAKER_01: for example, from a regulatory perspective are key, right?
• 1:24:16
  SPEAKER_01: It's like a wake up call to be, you know, this is just done faster and simpler here.
• 1:24:21
  SPEAKER_01: So why are we doing it in this, You know, cumbersome way.
• 1:24:25
  SPEAKER_01: But at the same time, if you look at, for example, the story of maybe KJ, right?
• 1:24:31
  SPEAKER_01: Like we still have the goods.
• 1:24:33
  SPEAKER_01: We can run the race.
• 1:24:35
  SPEAKER_01: And, you know, the wake-up call from that perspective, I think is good news.
• 1:24:41
  SPEAKER_01: And, you know, let's find the will to speed up.
• 1:24:46
  SPEAKER_00: Yeah, I would...
• 1:24:48
  SPEAKER_00: I'm long American, ingenuity, creativity, work ethic.
• 1:24:53
  SPEAKER_00: I'm short on, I see some will.
• 1:24:57
  SPEAKER_00: I don't see massive,
• 1:25:00
  SPEAKER_00: I don't think people are paying enough attention to sort of the scale
• 1:25:05
  SPEAKER_00: of what we're competing against.
• 1:25:07
  SPEAKER_01: I think in biotech, it's probably a place where people see it, feel it, get it.
• 1:25:14
  SPEAKER_00: Like most people do not really, I mean, again, I, God,
• 1:25:17
  SPEAKER_00: I hope this doesn't come off as like really crass and like nationalistic
• 1:25:22
  SPEAKER_00: or something.
• 1:25:23
  SPEAKER_00: Like most of the,
• 1:25:25
  SPEAKER_00: at least 50%,
• 1:25:27
  SPEAKER_00: if not more of the people who are like postdocs working in
• 1:25:30
  SPEAKER_00: a lab are Chinese immigrants, right?
• 1:25:33
  SPEAKER_00: Working at U.S. labs.
• 1:25:35
  SPEAKER_00: Is that fair?
• 1:25:36
  SPEAKER_01: I don't know if it's up to 50%.
• 1:25:38
  SPEAKER_01: It's a lot, right?
• 1:25:38
  SPEAKER_01: It's a big number, but I think more generally, right?
• 1:25:40
  SPEAKER_01: Like myself as a Canadian, I, I love Montreal, amazing city.
• 1:25:47
  SPEAKER_01: Would have loved to stay in Montreal if I could work at the absolute cutting edge
• 1:25:52
  SPEAKER_01: of biology, which is what I wanted to do.
• 1:25:54
  SPEAKER_01: But it was clear that coming to America is the right thing to do.
• 1:25:57
  SPEAKER_01: I think that's just been the case for a long time.
• 1:26:01
  SPEAKER_01: And I think the fact that that is shifting is a bit new.
• 1:26:05
  SPEAKER_01: And we'll want to respond to that and continue to benefit from this massive influx
• 1:26:10
  SPEAKER_01: of Some of the most committed and talented people.
• 1:26:15
  SPEAKER_00: That was sort of my point,
• 1:26:16
  SPEAKER_00: is like,
• 1:26:16
  SPEAKER_00: at this particular moment in time,
• 1:26:18
  SPEAKER_00: maybe we don't want to scare off, like,
• 1:26:20
  SPEAKER_00: a bunch of brilliant Chinese researchers and make it really hard for them
• 1:26:25
  SPEAKER_00: to come here and work in these labs, you know?
• 1:26:27
  SPEAKER_01: Yeah, I mean,
• 1:26:28
  SPEAKER_01: at the same time,
• 1:26:28
  SPEAKER_01: there's,
• 1:26:29
  SPEAKER_01: you know,
• 1:26:30
  SPEAKER_01: America's got so many good things going for it, down to its, like, principles.
• 1:26:37
  SPEAKER_01: You know, got to play to those advantages.
• 1:26:39
  SPEAKER_01: That's going to remain true, so.
• 1:26:40
  SPEAKER_00: All right.
• 1:26:42
  SPEAKER_00: Making me feel a little better, maybe.
• 1:26:44
  SPEAKER_00: I don't know.
• 1:26:45
  SPEAKER_00: The cool thing about biology is usually whoever's solving these puzzles,
• 1:26:50
  SPEAKER_00: this will be dispersed throughout the world.
• 1:26:54
  SPEAKER_00: So that part seems that good.
• 1:26:57
  SPEAKER_00: I asked you all of my biggest questions and I've taken up quite a bit of your time.
• 1:27:03
  SPEAKER_00: Is there anything in particular you want to talk about that I didn't hit?
• 1:27:07
  SPEAKER_01: To me, it's this...
• 1:27:10
  SPEAKER_01: We've talked about it,
• 1:27:11
  SPEAKER_01: but maybe it's helpful to put the pieces together of this moment that's coming
• 1:27:16
  SPEAKER_01: in genetic medicine.
• 1:27:18
  SPEAKER_01: To us, when a moment is happening,
• 1:27:22
  SPEAKER_01: we've found it's helpful to label a thing, because then you can notice it.
• 1:27:26
  SPEAKER_01: To us, we've found the best label is this concept of genetic agency.
• 1:27:32
  SPEAKER_01: And to me, I think that's tying together all of this story.
• 1:27:35
  SPEAKER_01: So let me just define what is genetic agency.
• 1:27:38
  SPEAKER_01: To us, it's the ability of an individual to act at a genetic level to live a healthier life.
• 1:27:45
  SPEAKER_01: Right at the beginning,
• 1:27:46
  SPEAKER_01: we talked about genetic medicines as being the operant that does the acting.
• 1:27:51
  SPEAKER_01: Thinking about genetic agency as this, like, now you're, like, someone's ability to do this.
• 1:27:57
  SPEAKER_01: And I think part of it is there's,
• 1:27:59
  SPEAKER_01: this is really like a movement that's building in the field between the ask
• 1:28:04
  SPEAKER_01: for people who want to have this,
• 1:28:05
  SPEAKER_01: who look at the technology and say,
• 1:28:08
  SPEAKER_01: Seems like we can put the pieces together and can you do it faster?
• 1:28:11
  SPEAKER_01: And like creating the pressure for that and this various systems also saying like,
• 1:28:15
  SPEAKER_01: wait, this is true.
• 1:28:16
  SPEAKER_01: We actually need to rise to the occasion.
• 1:28:18
  SPEAKER_00: You're talking about somebody who's ill?
• 1:28:20
  SPEAKER_01: Yes, or a family member.
• 1:28:21
  SPEAKER_00: Okay.
• 1:28:21
  SPEAKER_01: Yes.
• 1:28:22
  SPEAKER_00: And you're saying,
• 1:28:23
  SPEAKER_00: you're saying, Okay, well, okay,
• 1:28:26
  SPEAKER_00: going back to where we started, I mean,
• 1:28:27
  SPEAKER_00: obviously the U.S.
• 1:28:28
  SPEAKER_00: government is addressing this some way for these very rare cases,
• 1:28:31
  SPEAKER_00: but I mean,
• 1:28:31
  SPEAKER_00: what I feel like you're maybe saying is something even broader,
• 1:28:34
  SPEAKER_00: which is like the tools are getting quite good and this applies to a broader group
• 1:28:43
  SPEAKER_00: of people who,
• 1:28:44
  SPEAKER_00: if they're willing to kind of, you know, be on the cutting edge, could be helped.
• 1:28:49
  SPEAKER_00: Is that?
• 1:28:49
  SPEAKER_01: Yes, and the proof of the principle is there too.
• 1:28:52
  SPEAKER_01: Like people are looking at the baby KJ story.
• 1:28:54
  SPEAKER_00: Yeah.
• 1:28:55
  SPEAKER_01: And saying, can we do it again?
• 1:28:58
  SPEAKER_00: Yeah.
• 1:29:17
  SPEAKER_01: Community of like technology builders.
• 1:29:20
  SPEAKER_01: AI has a role to play here.
• 1:29:22
  SPEAKER_01: It's probably one of the like purest applications of...
• 1:29:25
  SPEAKER_01: You know, obviously unalloyed good that AI can play is like furthering this future.
• 1:29:31
  SPEAKER_01: So the folks who are building AI want to enable that, want to apply it in that way.
• 1:29:35
  SPEAKER_01: So I think that that moment is happening and it's really exciting to see
• 1:29:43
  SPEAKER_01: for me personally to being a part of,
• 1:29:44
  SPEAKER_01: but also to invite like others who want to be a part of this.
• 1:29:47
  SPEAKER_01: There's just many angles by which folks can contribute to this mission and movement.
• 1:29:54
  SPEAKER_01: So we're excited for that.
• 1:29:56
  SPEAKER_00: Okay.
• 1:29:56
  SPEAKER_00: I'm going to get one final thought from you on that.
• 1:29:58
  SPEAKER_00: If anyone, the story of baby KJ is fascinating.
• 1:30:01
  SPEAKER_00: It's this child who had a rare genetic disease and we,
• 1:30:05
  SPEAKER_00: in very short order,
• 1:30:06
  SPEAKER_00: were able to create a therapy which needed to be very fast because his development
• 1:30:11
  SPEAKER_00: as a kid was going to be stunted lifelong.
• 1:30:15
  SPEAKER_00: And then it was also a debilitating illness and it was miraculous.
• 1:30:20
  SPEAKER_00: A bunch of scientists all got together.
• 1:30:23
  SPEAKER_00: It kind of proved out how far we've come with these gene therapy technologies.
• 1:30:27
  SPEAKER_00: If you want to learn more, Aaron A. did write a story for us on Core Memory.
• 1:30:30
  SPEAKER_00: He also did a podcast,
• 1:30:32
  SPEAKER_00: and we also talked about it, Jennifer Doudna, if you want to check it out.
• 1:30:34
  SPEAKER_00: But, so like what you're saying, I mean,
• 1:30:36
  SPEAKER_00: I'm just trying to wrap my head around it fully,
• 1:30:37
  SPEAKER_00: is because those are such isolated,
• 1:30:42
  SPEAKER_00: specific cases, and you seem to be saying just that...
• 1:30:49
  SPEAKER_00: What, that that's just exciting that we've been able to do that and we've shown
• 1:30:52
  SPEAKER_00: that this stuff works and there's speed and now the government's adjusting and
• 1:30:56
  SPEAKER_00: that maybe even though we're starting with rare disease,
• 1:31:00
  SPEAKER_00: just that people should be,
• 1:31:02
  SPEAKER_00: they can contribute to this but also be cognizant
• 1:31:06
  SPEAKER_00: of technology that's like available.
• 1:31:09
  SPEAKER_01: Yeah, I'm saying it's a herald of a time to come.
• 1:31:11
  SPEAKER_01: Okay.
• 1:31:13
  SPEAKER_01: Notice, like pay attention to the movement, get curious.
• 1:31:16
  SPEAKER_01: That's part of the beauty of the movement is it's also just like really interesting
• 1:31:21
  SPEAKER_01: for many different reasons.
• 1:31:23
  SPEAKER_01: So approaching it with curiosity is probably the Everybody can do that.
• 1:31:28
  SPEAKER_01: And then also,
• 1:31:30
  SPEAKER_01: in some cases,
• 1:31:31
  SPEAKER_01: approaching it with,
• 1:31:33
  SPEAKER_01: you know,
• 1:31:33
  SPEAKER_01: more than curiosity with agency in terms of,
• 1:31:37
  SPEAKER_01: you know,
• 1:31:37
  SPEAKER_01: if I want to get involved in this mission, there's many different ways to do it.
• 1:31:42
  SPEAKER_01: And, you know, we, again,
• 1:31:44
  SPEAKER_01: just the problem is so hard that anybody,
• 1:31:46
  SPEAKER_01: someone raises their hand and says, yeah, I want to participate.
• 1:31:50
  SPEAKER_01: How can I?
• 1:31:51
  SPEAKER_01: There's There's probably a way to find.
• 1:31:53
  SPEAKER_01: Now it's just a matter of finding that point of leverage and coming into
• 1:31:59
  SPEAKER_01: the genetic agency family.
• 1:32:01
  SPEAKER_00: Okay.
• 1:32:02
  SPEAKER_00: Okay.
• 1:32:02
  SPEAKER_00: Last question and then I'll let you go.
• 1:32:04
  SPEAKER_00: And it's along those lines.
• 1:32:05
  SPEAKER_00: If you were 18 years old today,
• 1:32:06
  SPEAKER_00: would you become, given what you just said, would you become a MD?
• 1:32:11
  SPEAKER_00: Would you become a computational biologist?
• 1:32:13
  SPEAKER_00: Would you become a biologist?
• 1:32:14
  SPEAKER_00: Would you become a computer scientist?
• 1:32:16
  SPEAKER_00: Yeah.
• 1:32:18
  SPEAKER_00: I guess we need all of them, but what would be, knowing what you know now?
• 1:32:23
  SPEAKER_00: Biologist.
• 1:32:24
  SPEAKER_01: Remove the computational part is...
• 1:32:27
  SPEAKER_01: Probably going to be more direct,
• 1:32:30
  SPEAKER_01: like you'll be able to collaborate with amazing computational AI biologists as well.
• 1:32:37
  SPEAKER_01: But I would focus on the biology just because it's so interesting and
• 1:32:43
  SPEAKER_01: so powerful once you understand the interesting bits.
• 1:32:47
  SPEAKER_01: It's going to be still,
• 1:32:51
  SPEAKER_01: I'm confident that for the rest of our lives,
• 1:32:52
  SPEAKER_01: it's going to be a thing we talk about,
• 1:32:54
  SPEAKER_01: a thing we marvel at, and a thing we just get a ton of value from.
• 1:32:58
  SPEAKER_00: Okay.
• 1:32:58
  SPEAKER_00: That was specifically for my son, uh, my oldest son, who I want to become a biologist.
• 1:33:04
  SPEAKER_00: It may or may, he may or may not listen to this podcast.
• 1:33:07
  SPEAKER_01: He should come see Dino a happy
• 1:33:09
  SPEAKER_00: I'm trying, man.
• 1:33:10
  SPEAKER_00: I'm trying.
• 1:33:11
  SPEAKER_00: He told me he wanted to be a computational biologist when he was like 12,
• 1:33:14
  SPEAKER_00: and I haven't been able to let this go,
• 1:33:16
  SPEAKER_00: even though I think his interest may have shifted.
• 1:33:19
  SPEAKER_00: But that's what I would do if I was his age.
• 1:33:23
  SPEAKER_00: Adrian, thank you so much for coming on.
• 1:33:26
  SPEAKER_00: Really appreciate it.
• 1:33:27
  SPEAKER_00: Everyone can go to Dino.com.
• 1:33:30
  SPEAKER_00: Was it DinoBio?
• 1:33:31
  SPEAKER_01: It's DinoTX.com.
• 1:33:32
  SPEAKER_00: Okay.
• 1:33:33
  SPEAKER_00: And there's all kinds of cool information and get a better sense of some
• 1:33:37
  SPEAKER_00: the things we're talking about.
• 1:33:38
  SPEAKER_00: And thank you for trekking all the way out of here.
• 1:33:41
  SPEAKER_00: Thank you.
• 1:33:44
  SPEAKER_00: The Core Memory Podcast is hosted by me,
• 1:33:46
  SPEAKER_00: Ashlee Vance, and or Kylie Robison, or both of us together.
• 1:33:51
  SPEAKER_00: It is produced by me and David Nicholson.
• 1:33:55
  SPEAKER_00: Our theme song is by James Mercer and John Sortland.
• 1:33:59
  SPEAKER_00: Thank you most of all to everybody for listening or watching.
• 1:34:03
  SPEAKER_00: We love you.
• 1:34:04
  SPEAKER_00: Please leave us a like, a review, a subscribe, all those tremendous things.
• 1:34:09
  SPEAKER_00: Thank you, and we'll see you again.