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The Company Trying to Reprogram Aging · Core Memory

0:00
To make a medicine for you,
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I don't want to turn you into a bag of stem cells.
0:03
SPEAKER_01: That would actually be really bad.
0:04
SPEAKER_01: What I want to do is take your old skin cells,
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SPEAKER_01: your old liver cells,
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SPEAKER_01: your old immune cells,
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SPEAKER_01: and turn them into young versions of themselves.
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SPEAKER_01: So can I decouple cell age and type in the other direction?
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SPEAKER_01: Can I reprogram cell age but preserve cell type?
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SPEAKER_01: And so we're searching for those groups of factors,
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SPEAKER_01: similar to the factors Yamanaka found,
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SPEAKER_01: that might be able to reprogram age independently.
0:34
SPEAKER_02: Welcome one and all.
0:36
SPEAKER_02: This is Ashley.
0:37
SPEAKER_02: I am in our Still in Progress podcast studio,
0:41
SPEAKER_02: and we have a slightly different podcast for you today.
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We're going to be interviewing Jacob Kimmel,
0:47
the co-founder and CEO of New Limit.
0:49
I've got to tell you a little bit more about the show because
0:54
it's different.
0:55
SPEAKER_02: But before that,
0:56
SPEAKER_02: I would urge you all,
0:57
SPEAKER_02: please go to Core Memory,
0:59
read the story we put up on Friday about Brad Smith and Tiffany
1:03
Smith and the Smith family.
1:05
SPEAKER_02: Brad has ALS.
1:06
SPEAKER_02: He's the third person to receive the Neuralink implant.
1:10
SPEAKER_02: Me and our team spent...
1:13
the last few months hanging out with brad i was reporting the
1:16
story we've filmed a
1:18
mini documentary which you can see on core memory or our
1:22
youtube channel about them
1:25
you can read the story see the film the the film's going viral
1:28
on youtube right now
1:29
SPEAKER_02: which is great come join everyone else and subscribe um we could
1:35
SPEAKER_02: Use the help, and it would be appreciated.
1:38
SPEAKER_02: More importantly, it's a really moving story, I think.
1:42
SPEAKER_02: Tiffany, the wife of his family, is incredible.
1:44
SPEAKER_02: The kids are incredible.
1:45
SPEAKER_02: Brad's incredible.
1:46
SPEAKER_02: I think you'll all enjoy it.
1:49
SPEAKER_02: And now, for this episode, it's a little different.
1:53
SPEAKER_02: than we usually do.
1:54
SPEAKER_02: We've been filming with New Limit as well.
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SPEAKER_02: It's a biotech company.
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SPEAKER_02: They are in this field of IPSC technology-induced pluripotent stem cells.
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SPEAKER_02: I talk to you guys about them all the time.
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SPEAKER_02: It's the most exciting area of biotech for me.
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SPEAKER_02: We have an episode coming on New Limit in a few weeks, but...
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SPEAKER_02: Just today,
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SPEAKER_02: the company has announced that it raised $130 million from Nat Friedman,
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SPEAKER_02: Daniel Gross,
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SPEAKER_02: Kleiner Perkins,
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SPEAKER_02: Human Capital,
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SPEAKER_02: and Coastal Ventures.
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SPEAKER_02: And so, obviously, they're going to be pushing on with their efforts.
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SPEAKER_02: Our interview with,
2:32
SPEAKER_02: oh,
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SPEAKER_02: also,
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SPEAKER_02: they raised more money from Patrick and John Collison,
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SPEAKER_02: Joe Lonsdale,
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SPEAKER_02: Fred Ursum,
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other rich people,
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Josh Kushner that I,
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um,
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it's kind of a who's who of,
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of tech investors.
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SPEAKER_02: So company's doing well.
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SPEAKER_02: Jacob is one of the most eloquent.
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SPEAKER_02: And I think,
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SPEAKER_02: um,
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SPEAKER_02: just gives really great explanations of how this technology works,
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SPEAKER_02: what new limits trying to do.
3:01
SPEAKER_02: So I think you'll really enjoy this episode.
3:03
SPEAKER_02: And then in a few weeks,
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SPEAKER_02: we'll have a core memory show for you guys on the site and,
3:08
SPEAKER_02: and on YouTube.
3:10
SPEAKER_02: Um,
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SPEAKER_02: With that, we will go to Jacob in a minute.
3:15
SPEAKER_02: Please, I know it's cheesy, but like and subscribe to all our stuff.
3:20
SPEAKER_02: It really does help.
3:21
SPEAKER_02: And this episode is sponsored, again, by E1 Ventures.
3:25
SPEAKER_02: They're a venture capital firm based in the Bay Area.
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SPEAKER_02: They fund all kinds of deep tech stuff.
3:32
SPEAKER_02: Just wonderful, kind people that I've gotten to know.
3:35
SPEAKER_02: a bit from doing the show.
3:38
SPEAKER_02: And thank you to E1 Ventures.
3:41
SPEAKER_02: And here is Jacob.
3:46
SPEAKER_01: My name is Jacob Kimmel.
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SPEAKER_01: I'm one of the co-founders and the president here at New Limit.
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SPEAKER_01: So I run the firm day-to-day and lead our research programs.
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SPEAKER_02: I've been here once before.
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SPEAKER_02: It was probably like, I don't know, I want to say like 18 months ago.
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For people who haven't heard of you guys or learned about this technology,
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SPEAKER_02: can you give us the overview of what you're trying to do?
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SPEAKER_01: Of course.
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SPEAKER_01: This is what I love to do more than anything.
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SPEAKER_01: So we work on a new technology called epigenetic reprogramming,
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and we're trying to make new types of medicines based on this
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tool that will allow
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us to treat age-related diseases and eventually the aging that
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occurs in every
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SPEAKER_01: single person.
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SPEAKER_01: So epigenetic reprogramming is a very weighty set of syllables,
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SPEAKER_01: but it describes a pretty simple concept.
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SPEAKER_01: So all of the cells in your body have the exact same DNA code,
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SPEAKER_01: and yet they do very different things,
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SPEAKER_01: which might seem simple on the surface,
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SPEAKER_01: but is actually kind of profound when you realize it.
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Your tongue and your eyeball,
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the same code,
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and yet they have very different jobs.
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Turns out that's controlled by a set of chemical modifications
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on top of the DNA.
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SPEAKER_01: which basically tell each cell which genes to use and when.
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As you age,
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unfortunately,
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those chemical marks get messed up,
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for lack of a better term.
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And so the tools we're using actually try and rewrite that code
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back to the way it
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SPEAKER_01: was when you were young.
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So your old cells use the right genes at the right time and
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ideally behave a bit
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SPEAKER_01: more like their younger selves,
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SPEAKER_01: allowing you to treat diseases that would otherwise be intractable and to
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SPEAKER_01: eventually address the challenges that emerge as each of us get older.
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SPEAKER_02: So we've got...
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SPEAKER_02: neurons, which are brain cells.
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SPEAKER_02: We've got kidney cells, all the cells.
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SPEAKER_02: Obviously,
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SPEAKER_02: when we're,
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SPEAKER_02: call it 18 to 25,
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in this optimal state and these cells degrade over time and
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just can't do whatever
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SPEAKER_02: they used to do as efficiently.
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SPEAKER_01: Yeah,
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and we think one of the main reasons for this is that the genes
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are actually still
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SPEAKER_01: all there,
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SPEAKER_01: which is kind of profound to think about again.
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It's like all of the instructions the cell needed to perform
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its job when you were young,
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SPEAKER_01: when it was working well,
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SPEAKER_01: they're still preserved.
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SPEAKER_01: They're still in that cell, modulo a few details.
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SPEAKER_01: And yet they're not using the instructions the right way anymore.
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SPEAKER_01: And so we think that really by re-imprinting these epigenetic marks,
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allowing them to use the right genes the way they did when you
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were 18 to 25,
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SPEAKER_01: right in your prime,
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SPEAKER_01: then even in your latter decades,
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SPEAKER_01: you could actually restore some of that youthful function you've lost.
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SPEAKER_02: This is based off...
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SPEAKER_02: what is it, roughly 12 to 15-year-old?
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SPEAKER_02: Science is the foundation of this.
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SPEAKER_02: There was a Japanese researcher named Yamanaka.
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He discovered these four proteins that turned genes on and off
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and seemed to be
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SPEAKER_02: able to reboot cells,
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SPEAKER_02: right?
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SPEAKER_01: Yeah, exactly.
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SPEAKER_01: So there's actually even science going back even earlier.
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We knew that we could reprogram an old cell back to a young
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state and give rise to
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SPEAKER_01: an animal with a normal lifespan ahead of it.
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SPEAKER_01: So this actually goes back to some early technology around cloning.
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SPEAKER_01: So a lot of people have heard of Dolly, the sheep.
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SPEAKER_01: Cloning actually was much broader than just Dolly.
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SPEAKER_01: Even just Dolly had a sister no one knows the name of.
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SPEAKER_01: Her name's Polly.
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SPEAKER_01: People forgot about it.
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SPEAKER_00: Yeah, poor Polly.
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SPEAKER_01: But so even with that technology,
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which you can think of as like a really complicated way to do
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this science of reprogramming,
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SPEAKER_01: to take the epigenome and change it back to a youthful state,
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SPEAKER_01: we knew you could take a cell from an old animal,
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SPEAKER_01: reprogram it back to a youthful state,
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and then give rise to a whole other animal with a normal life
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ahead of it.
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And there's even a group in Japan that used that cloning
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technology to clone the
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SPEAKER_01: same animal effectively 13 times in a row.
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SPEAKER_01: And so you could take an old cell,
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SPEAKER_01: reprogram it back,
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SPEAKER_01: give rise to a young animal,
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SPEAKER_01: wait for that animal to get old,
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SPEAKER_01: reprogram it back,
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SPEAKER_01: and do that 13 times.
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SPEAKER_01: Now mice live two years, so that experiment takes about 20.
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SPEAKER_01: And if aging were somehow immutable,
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SPEAKER_01: if there was some sort of damage occurring that was totally irreversible,
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you'd expect that last mouse to be pretty messed up and not
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live a normal life.
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SPEAKER_01: But instead,
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SPEAKER_01: what we were able to demonstrate as a community,
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SPEAKER_01: I should say,
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SPEAKER_01: not we here at New Limit,
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SPEAKER_01: was that just through reprogramming,
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SPEAKER_01: that animal could live a totally normal lifespan.
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SPEAKER_02: It's rebooting.
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It's...
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SPEAKER_01: It's rebooting.
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SPEAKER_01: It's restarting at sort of the same epigenetic state,
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the same use of its genetic instructions that you did when you
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were young.
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SPEAKER_01: So all of that technology is actually pretty old,
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SPEAKER_01: but it's not something you could imagine turning into medicines.
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You know,
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it's probably too complicated to do something like cloning as a
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therapy for people.
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SPEAKER_01: So just as you referenced...
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There's some science about 15 years ago from a remarkable
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scientist named Shinya Yamanaka,
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where he discovered that whole complex process could be
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achieved just by turning on
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SPEAKER_01: a set of four genes at the same time.
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SPEAKER_01: And so these genes are now known eponymously as the Yamanaka factors.
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And each one of them is what's called— And he won the Nobel
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Prize in 2012.
8:01
SPEAKER_00: You know,
8:01
SPEAKER_01: one of the quickest Nobel Prizes,
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SPEAKER_01: I might add,
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SPEAKER_01: because it's so profound,
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SPEAKER_01: such an advance in the field.
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I think the prior belief was that this would just be way more
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complicated than that,
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SPEAKER_01: that there are tens of thousands of genes in the genome,
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SPEAKER_01: millions of molecular interactions.
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There's no way we could reset a cell from its developed adult
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age state back to
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this youthful embryonic one in a really simple activation of
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just four genes.
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SPEAKER_01: And so his result,
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SPEAKER_01: I think,
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really broke down a lot of the cognitive biases and prior
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beliefs the field had.
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SPEAKER_01: And so those genes are really special.
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SPEAKER_01: They're called transcription factors.
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And I like to think of them as sort of like orchestra
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conductors in the genome.
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They don't really do much themselves,
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kind of like an orchestra conductor.
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SPEAKER_01: Maybe not the person you want playing first chair violin.
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SPEAKER_01: But they can point at other genes effectively and tell them when to play,
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SPEAKER_01: when to turn on,
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SPEAKER_01: when to turn off.
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SPEAKER_01: And so just by activating a few of these genes,
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you can actually control much broader programs in the genome
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and rewrite those
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instructions that determine which genes are being used by which
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cells at which times.
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SPEAKER_02: And it's wild that even in the mouse experiment you were talking about,
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it's not like it's like we found some magic cell that's being
9:06
at the heart of doing
9:07
SPEAKER_02: these different generations.
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SPEAKER_02: You can take like a skin cell.
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SPEAKER_01: Skin cell, blood cell, almost any cell.
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SPEAKER_01: We think you can create a whole animal with a normal life.
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SPEAKER_02: Like reboot, take that skin cell back to...
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SPEAKER_02: It's got a stem cell state and then reboot a whole animal.
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SPEAKER_02: It's wild.
9:23
SPEAKER_01: Yeah.
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It's like truly remarkable that all of this biology works as
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well as it does.
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I think what makes it somewhat more intuitive is to remember
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that this is also just
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SPEAKER_01: how reproduction works.
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SPEAKER_01: You know,
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SPEAKER_01: regardless of the age of two parents,
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SPEAKER_01: when they have a child,
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SPEAKER_01: the child's age starts at zero.
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And so if you have a kid at 20 and a kid at 40,
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both of those kids start as infants.
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SPEAKER_01: And one of them is not plus 20 years.
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Maybe even a little silly to talk about because it's so
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intuitive to all of us.
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No,
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I feel like we take it for granted until I started getting into
9:51
this field.
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SPEAKER_02: I'd never actually even thought about it.
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SPEAKER_02: You're just like, oh, two old people make this totally young.
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SPEAKER_01: Make a young kid.
9:56
SPEAKER_01: Yeah, of course that works, right?
9:57
SPEAKER_01: Like we all understand that.
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And there was a belief in science for a while that maybe there
10:02
was something about
10:02
SPEAKER_01: reproduction that was really special.
10:04
Maybe like the cells involved in reproduction just like never
10:07
aged and they were
10:07
SPEAKER_01: protected in some way.
10:09
There's a fancy name for this called the disposable soma theory
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that only your
10:12
SPEAKER_01: reproduction matters and the rest of your body can get old.
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SPEAKER_01: And I think what Yamanaka really showed is that that's not true,
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SPEAKER_01: that you can actually execute the same reprogramming event,
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the same reinvigoration of that youthful cell state using just
10:25
a few genes at a
10:26
SPEAKER_01: given time.
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SPEAKER_01: And so that's really what's been inspiring here to us is to ask,
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SPEAKER_01: can we now take that biology and try and make medicines with it?
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SPEAKER_01: Maybe not directly those four genes,
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SPEAKER_01: but can we figure out ways to use small numbers of factors like that,
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SPEAKER_01: small enough numbers we can even imagine making medicines for them,
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SPEAKER_01: and achieve really dramatic changes in cell function,
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SPEAKER_01: the same way Yamanaka was able to achieve with those four factors.
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SPEAKER_02: And just to underscore that one point, I mean...
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SPEAKER_02: I think people hear about longevity.
10:53
SPEAKER_02: I mean,
10:53
SPEAKER_02: you know,
10:54
SPEAKER_02: all this stuff is hard,
10:56
SPEAKER_02: takes many years,
10:56
but like there is this fundamental thing that we know is that
11:00
somewhere in nature,
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SPEAKER_02: this is happening because life is rebooting.
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SPEAKER_02: I mean,
11:04
SPEAKER_02: there must be,
11:05
SPEAKER_02: there is,
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SPEAKER_02: I don't know if we can guarantee we can replicate this recipe,
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SPEAKER_02: but we know something is possible because it happens all the time.
11:12
SPEAKER_01: Yeah, I think it's a really important point.
11:14
I think a lot of people have the intuition that aging has to
11:17
happen or is in some
11:19
SPEAKER_01: way necessary for biology.
11:21
SPEAKER_01: It's like physics where all organisms must get older.
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SPEAKER_01: And as you highlighted, humans haven't aged into extinction yet, so...
11:28
So we know somewhere in this process there is a rejuvenation
11:31
event occurring.
11:32
And so whether we can make a medicine from it is a totally
11:34
different question.
11:35
SPEAKER_01: And we hope to one day answer that in the affirmative.
11:37
SPEAKER_01: But we at least know it's possible.
11:39
You know,
11:39
in physics,
11:39
you'd call this,
11:40
or mathematics,
11:40
you'd call it an existence proof.
11:42
We know what we're trying to do is not entirely crazy,
11:45
maybe just crazy enough.
11:47
SPEAKER_02: And there's a few things I want to dig into.
11:51
SPEAKER_02: When Yamanaka factors arrived,
11:53
SPEAKER_02: it was first used to take these older cells,
11:57
SPEAKER_02: whether it's a skin cell,
11:58
SPEAKER_02: a liver cell,
11:58
SPEAKER_02: a heart cell,
12:00
SPEAKER_02: back to the stem cell state,
12:02
SPEAKER_02: which is this blank slate where you're building from there.
12:05
SPEAKER_02: So for the first time, we had these...
12:07
SPEAKER_02: Instead of harvesting stem cells in these controversial ways,
12:10
you have this lab-grown stem cell that then you could do all
12:13
kinds of interesting
12:14
SPEAKER_02: things with.
12:15
SPEAKER_02: But there's a whole other track to this,
12:17
SPEAKER_02: which is you take that liver cell or heart cell,
12:20
SPEAKER_02: and instead of rebooting all the way to the blanks,
12:23
SPEAKER_02: The blank slate,
12:24
SPEAKER_02: the blank state,
12:25
SPEAKER_02: you want to keep it a liver or a heart cell,
12:28
SPEAKER_02: keep its identity,
12:29
SPEAKER_02: but you want the young platonic version of that cell.
12:35
SPEAKER_02: And that's really where you guys are focused.
12:37
SPEAKER_01: Yeah.
12:38
SPEAKER_01: Absolutely.
12:38
SPEAKER_01: That's really our goal is to be able to say,
12:40
Yamanaka showed you can reprogram a cell's age while changing
12:44
its cell type at the
12:44
SPEAKER_01: same time.
12:45
So I take an old cell from an old mouse on Death's Door,
12:47
turn it into a young stem cell.
12:49
SPEAKER_01: So it's both young.
12:50
SPEAKER_01: That's one big difference.
12:51
SPEAKER_01: The age changed and it's now a stem cell.
12:52
SPEAKER_01: So the type changed at the same time.
12:54
So there's also some pretty amazing science where other folks
12:56
have shown you can
12:57
SPEAKER_01: take an old skin cell and turn it directly into a neuron,
13:00
SPEAKER_01: which just kind of blows my mind every time.
13:01
You can take a skin cell and turn it into a neuron with a big
13:04
axon firing action potentials,
13:06
SPEAKER_01: like this crazy dramatic change in what a cell does.
13:08
SPEAKER_01: And remarkably, that neuron actually looks old still.
13:11
SPEAKER_01: And so you can change the cell's type without changing its age.
13:14
SPEAKER_01: So New Limit's basically trying to ask that question in the other way,
13:17
SPEAKER_01: which is,
13:17
SPEAKER_01: you know,
13:17
SPEAKER_01: so somebody's shown you can break apart cell age and type reprogramming.
13:20
SPEAKER_01: Can we reprogram age without reprogramming type?
13:23
To make a medicine for you,
13:24
I don't want to turn you into a bag of stem cells.
13:26
SPEAKER_01: That would actually be really bad.
13:27
SPEAKER_01: What I want to do is take your old skin cells,
13:29
SPEAKER_01: your old liver cells,
13:30
SPEAKER_01: your old immune cells,
13:31
SPEAKER_01: and turn them into young versions of themselves.
13:33
SPEAKER_01: So can I decouple cell age and type in the other direction?
13:36
SPEAKER_01: Can I reprogram cell age but preserve cell type?
13:39
SPEAKER_01: And so we're searching for those groups of factors,
13:41
SPEAKER_01: similar to the factors Yamanaka found,
13:42
SPEAKER_01: that might be able to reprogram age independently.
13:45
SPEAKER_02: And it's weird to me,
13:46
SPEAKER_02: just as an outsider covering this,
13:48
SPEAKER_02: so Yamanaka finds these four transcription factors,
13:51
SPEAKER_02: this very precise combination of four things,
13:54
SPEAKER_02: basically proteins,
13:56
SPEAKER_02: and it has this effect,
13:57
SPEAKER_02: and then...
13:59
most for whatever it is,
14:00
12,
14:01
15 years,
14:02
it's people experimenting with those same four things.
14:05
You guys are looking to see if there's other transcription factors,
14:10
other recipes we can use.
14:12
SPEAKER_02: And I was just curious.
14:13
SPEAKER_02: I mean, why did people get stuck on these four?
14:17
I would have thought like you immediately start exploring to
14:21
find all these other
14:22
things that may have these magical properties or seemingly
14:25
magical properties.
14:26
SPEAKER_01: Yeah, you're entirely right.
14:27
SPEAKER_01: It's kind of shocking.
14:29
SPEAKER_01: I think of epi reprogramming,
14:30
the ability to turn on these combinations of transcription
14:33
factors and see changes
14:34
SPEAKER_01: in cell state,
14:35
SPEAKER_01: be that cell age or cell type.
14:36
It's really the most powerful change in cell function we've
14:39
ever been able to evoke in biology.
14:42
SPEAKER_01: I used to work on small molecule and antibody types of medicines.
14:45
SPEAKER_01: And honestly,
14:45
it makes those seem a little bit trivial when you think about
14:48
the complexity of
14:49
SPEAKER_01: what you're achieving.
14:50
SPEAKER_01: And so other groups have looked for new combinations of factors.
14:53
SPEAKER_01: That's how we got amazing results.
14:54
SPEAKER_01: Like, oh, I can turn a skin cell directly into a neuron.
14:57
SPEAKER_01: Mariusz Wernig, not far down the road at Stanford, discovered that.
15:00
SPEAKER_01: Others have found converting between other cell types.
15:03
SPEAKER_01: But for reprogramming cell age,
15:04
SPEAKER_01: I think for a long time,
15:06
it was difficult to even think about how you would find another
15:09
set of factors.
15:09
SPEAKER_01: The tools didn't really exist.
15:11
SPEAKER_01: When Yamanaka found his first four factors,
15:13
one of the reasons he was able to discover those was because he
15:16
was changing the
15:16
SPEAKER_01: cell type at the same time.
15:18
SPEAKER_01: And so it was really easy to know when you would achieve the goal.
15:20
You could just look at the cells under a microscope,
15:22
and you could see visually.
15:23
SPEAKER_01: These are a very different cell type than they started out as.
15:26
SPEAKER_02: Go ahead.
15:27
SPEAKER_02: Well, but what was he even doing?
15:29
Because there's a very long list of transcription factors that
15:35
are possible.
15:36
SPEAKER_02: How many are there?
15:37
SPEAKER_02: There's...
15:37
SPEAKER_01: Yeah, there's about 2,000.
15:38
SPEAKER_01: This is debate.
15:39
SPEAKER_01: If you get a bunch of transcription factor biologists into a room,
15:41
SPEAKER_01: they'll get really heated about whether there's 1,200 or 2,000,
15:44
SPEAKER_01: but somewhere in between.
15:45
SPEAKER_02: Roughly.
15:45
And then he hits on these four,
15:48
and he hit on these four relatively quickly.
15:51
SPEAKER_02: Yeah.
15:53
Were they somewhat obvious that these would be the four you
15:56
might want to try for
15:57
SPEAKER_02: this kind of thing?
15:58
SPEAKER_01: I don't think it was obvious at all.
15:59
SPEAKER_01: I mean, at the time, Shinya Yamanaka didn't even have a laboratory.
16:03
SPEAKER_01: He was running a core facility.
16:05
SPEAKER_01: He was doing this work in a way where I don't think he had the levels of
16:08
SPEAKER_01: institutional support one would expect.
16:10
SPEAKER_01: I mean,
16:10
this was really like the motivation of him and his group
16:13
pushing out on a limb,
16:14
SPEAKER_01: trying something very few others were.
16:16
But the way he was able to actually get that to work was he had
16:18
a pretty simple hypothesis.
16:20
SPEAKER_01: He looked at embryonic stem cells,
16:21
so the stem cells we know that start in embryos and eventually
16:24
form whole organisms,
16:25
SPEAKER_01: so they can become any cell type in the body.
16:26
And he measured which transcription factors are really highly
16:30
active in those embryonic cells.
16:31
SPEAKER_00: I see it.
16:32
SPEAKER_01: Then he measured some non-embryonic cells,
16:33
SPEAKER_01: some adult somatic cells,
16:34
SPEAKER_01: and said,
16:35
SPEAKER_01: which of those transcription factors are gone?
16:37
SPEAKER_01: Maybe if I turn those light switches back on,
16:39
SPEAKER_01: these cells will turn back into the embryonic state.
16:41
SPEAKER_01: Maybe these are the control mechanisms.
16:43
SPEAKER_01: And so then what he did was he just tried random combinations of them.
16:47
SPEAKER_01: So he actually selected a group of,
16:48
SPEAKER_01: I believe it's just 24 factors,
16:50
SPEAKER_01: and then tried as many combos as he could,
16:52
SPEAKER_01: and lucked into this combination of four.
16:55
SPEAKER_01: And it turns out it's not unique.
16:56
SPEAKER_01: Actually, other combos work too.
16:58
So you can make these stem cell-like reprogramming events using
17:01
a number of different sets.
17:02
SPEAKER_01: That's sort of how he stumbled upon it.
17:04
And I think one key insight there is like the reason he was
17:06
able to test so many
17:07
SPEAKER_01: things was that his assay,
17:09
SPEAKER_01: his way to measure if it worked,
17:10
SPEAKER_01: was really,
17:11
SPEAKER_01: really simple.
17:11
SPEAKER_01: Look under a microscope.
17:13
SPEAKER_01: Are these cells,
17:13
SPEAKER_01: did they go from these fibroblast-like sort of skin cell-like state,
17:16
SPEAKER_01: which you can see visually,
17:18
SPEAKER_01: to a stem cell-like state?
17:19
where they grow really fast and they're really small and they
17:21
clump up together.
17:22
SPEAKER_01: And so you don't need fancy equipment to measure this.
17:24
SPEAKER_01: You can just look under a microscope and know.
17:27
And I think that's why finding these factors that reprogram age
17:29
alone has been so tricky.
17:31
SPEAKER_01: Until recently,
17:32
SPEAKER_01: the difference between an old and a young liver cell,
17:35
SPEAKER_01: an old and a young skin cell,
17:36
SPEAKER_01: is not as apparent with really simple tools.
17:39
SPEAKER_01: You can't just look under a microscope and know.
17:41
SPEAKER_01: And so we needed technologies like the ones we employ here at New Limit,
17:43
where we can measure every gene that's being used in an old and
17:47
a young cell at the
17:48
SPEAKER_01: level of each individual cell.
17:49
SPEAKER_01: This is called single-cell genomics.
17:51
SPEAKER_01: In order to be able to even ask if the experiment worked,
17:54
SPEAKER_01: if I test a set of factors,
17:55
SPEAKER_01: did I even make the old ones look younger?
17:57
We needed tools that were high enough resolution that had the
18:00
complexity to
18:00
SPEAKER_01: actually enable us to answer it.
18:02
SPEAKER_02: So if it's a liver cell...
18:04
SPEAKER_02: Is it clearing toxins as efficiently in a younger liver cell?
18:10
SPEAKER_02: Is it clearing toxins more efficiently than an older one?
18:12
SPEAKER_02: Is it processing something like alcohol faster,
18:15
SPEAKER_02: like as it did in your college days kind of thing?
18:18
SPEAKER_00: I mean,
18:19
you have to find these clues of what's getting turned on and
18:22
what makes a younger
18:24
SPEAKER_02: cell have these qualities that are beneficial and useful.
18:27
SPEAKER_02: Yeah.
18:27
SPEAKER_01: Yeah, absolutely.
18:28
So in addition to just measuring what we call internally like a
18:30
looks like assay,
18:31
SPEAKER_01: you know,
18:31
we're not trying to ask right away whether it functions like a
18:34
young liver cell,
18:35
SPEAKER_01: like you're highlighting processes,
18:36
SPEAKER_01: alcohol,
18:37
SPEAKER_01: caffeine,
18:38
SPEAKER_01: regenerates,
18:39
SPEAKER_01: doesn't die when you drink,
18:40
SPEAKER_01: that sort of thing.
18:42
SPEAKER_01: Those have to be sort of designed for each cell type individually.
18:44
SPEAKER_01: And building those assays is really challenging as well.
18:47
SPEAKER_01: Before we even get there,
18:48
SPEAKER_01: We're just asking at the level of the genes a cell is using,
18:51
SPEAKER_01: does it kind of look like a younger version of itself?
18:53
SPEAKER_01: Does it use the genes a young cell would?
18:55
And for us,
18:55
we hope that's a clue that maybe it'll act like a young cell too.
18:58
SPEAKER_01: And even those acts like assays,
19:00
SPEAKER_01: you might expect naively,
19:02
SPEAKER_01: as I did,
19:02
SPEAKER_01: that a lot of the stuff is just known.
19:04
SPEAKER_01: That,
19:04
SPEAKER_01: you know,
19:04
maybe you can just go and like go to your favorite search
19:06
engine or talk to your
19:07
SPEAKER_01: favorite set of weights and they'll tell you,
19:09
SPEAKER_01: ah,
19:09
these are the four things you go in the lab and you measure and
19:11
you know if the
19:12
SPEAKER_01: cell is old or young.
19:13
It turns out a lot of that foundational science just hasn't
19:16
been done at a very rigorous level.
19:17
SPEAKER_01: And so we also had to build a lot of those assays,
19:20
SPEAKER_01: these measurement systems to know,
19:22
SPEAKER_01: is this old liver cell acting like a young one?
19:24
SPEAKER_01: Does it process alcohol the way a young one would?
19:27
Does it resist some of those damages from things like a
19:29
high-fat diet the way a
19:30
SPEAKER_01: young cell would?
19:31
SPEAKER_01: And so standing up each of those assays is likewise really challenging.
19:34
SPEAKER_01: Okay.
19:35
SPEAKER_01: And I think in doing those,
19:36
even if we were able to build all those assays without the
19:39
looks like assay,
19:40
SPEAKER_01: you'd never be able to do enough of them to find new factors.
19:43
So to sort of bring it back to your question of like,
19:45
why did it take so long?
19:46
SPEAKER_01: It's been 15 years.
19:48
SPEAKER_01: I think that's really the answer.
19:49
It's like those sort of acts like assays,
19:51
you know,
19:52
does it process alcohol?
19:53
SPEAKER_01: Does it regenerate?
19:54
We could build those 10 years ago,
19:55
but we can't really do that many of them.
19:57
You need some middle step where you go from this crazy number
20:00
of factors to that smaller number.
20:02
And for us,
20:03
that was sort of this notion of,
20:04
I can measure all the genes the cells are using.
20:06
And that gives me a decent idea of which ones are even worth
20:09
testing with an
20:10
SPEAKER_01: alcohol diet or something like that,
20:12
SPEAKER_01: which is much harder to scale up to very large numbers.
20:14
SPEAKER_02: Okay, let's dig into that.
20:16
SPEAKER_02: I mean, let's assume this 2000 transcription...
20:21
SPEAKER_02: pool is roughly correct.
20:23
SPEAKER_02: And so you're hunting for this combination of these many things.
20:27
SPEAKER_02: I mean,
20:28
SPEAKER_02: you have to find maybe two,
20:29
SPEAKER_02: three,
20:29
SPEAKER_02: four,
20:29
SPEAKER_02: five of those that when are together are doing something.
20:32
SPEAKER_02: So that's already like mathematically complex.
20:35
SPEAKER_02: And then you've got all these downstream effects that you're
20:38
SPEAKER_02: checking for.
20:39
SPEAKER_02: And so this is, yeah, this is mathematically a large computational field.
20:45
SPEAKER_01: It turns out it's about 10 to the 16 possible combinations.
20:48
SPEAKER_00: Very large.
20:49
SPEAKER_01: Yeah, very large.
20:50
SPEAKER_01: That number doesn't mean anything to human beings.
20:52
SPEAKER_01: It's just like, oh, it's like some scientific notation thing.
20:54
So to give you a sense,
20:55
there's about 10 to the 11,
20:56
10 to the 12 stars in the Milky Way.
20:58
So we're talking about like 10,000 galaxies worth of stars of experiments.
21:02
SPEAKER_01: You just can't do all those experiments.
21:03
SPEAKER_01: It doesn't matter how smart you are.
21:05
SPEAKER_01: You're never going to be able to test all those things.
21:06
SPEAKER_02: And this is, in many ways, the heart of the company as well.
21:10
SPEAKER_02: So we have this wet lab type science.
21:13
SPEAKER_02: We have this biological stuff that's going on.
21:15
SPEAKER_02: And then you're bringing this computation to bear, not just
21:20
SPEAKER_02: new tools like single cell, being able to look at single cell genomics.
21:26
SPEAKER_02: But then we've had an AI revolution.
21:28
SPEAKER_02: We've had just raw sort of computing horsepower being added to all this.
21:32
SPEAKER_02: But I mean,
21:33
that was kind of the thesis of the company was let's build that
21:37
model that goes and
21:38
SPEAKER_02: finds these things both computationally and biologically.
21:42
SPEAKER_01: Absolutely.
21:43
So just as you alluded to,
21:44
I'm really proud of how many experiments we can do.
21:46
SPEAKER_01: I think we can do way more than anyone else really in this field.
21:49
But the number of experiments we can do is still something like
21:52
11 or 12 orders of
21:53
magnitude short of what we would need to do to just search
21:56
everything exhaustively.
21:57
And so we've also got to be really clever about picking which
22:00
questions do we even
22:01
SPEAKER_01: ask before we start trying to get an answer in the lab.
22:04
And I think 10 years ago,
22:06
this wouldn't have been a very tractable problem.
22:08
SPEAKER_01: But because of this AI revolution that's come to pass,
22:11
SPEAKER_01: now if we're able to gather a sample of that data,
22:13
SPEAKER_01: we can run a large number of experiments,
22:15
SPEAKER_01: but not nearly enough.
22:16
We can train AI models that are able to predict what might
22:18
happen for a new combo
22:20
SPEAKER_01: of transcription factors that we've never tested before in the lab.
22:23
SPEAKER_01: And that model can make a guess.
22:24
SPEAKER_01: Will this make the old cells look younger?
22:27
SPEAKER_01: And for us, those models don't even need to be perfect.
22:30
SPEAKER_01: We're not trying to get the model to answer the question necessarily.
22:33
We're trying to get it to tell us if the question is even
22:36
interesting to go ask.
22:38
SPEAKER_01: Yeah, like limit the scope of what we're going to do.
22:41
SPEAKER_01: And I really think this is going to apply not just to our work,
22:43
SPEAKER_01: but to broad areas of the biological sciences.
22:46
SPEAKER_01: This is a pretty common problem.
22:47
There's just way more things to test than you're ever going to
22:49
be able to do in the real world.
22:51
SPEAKER_01: And so these sorts of in silico models,
22:53
SPEAKER_01: I think,
22:53
are going to more and more come to dominate how we discover
22:56
drugs in the future.
22:57
And this is really what I think is one of the most exciting and
23:00
high impact
23:00
SPEAKER_01: applications of those tools,
23:02
SPEAKER_01: because these are such potent pieces of biology.
23:05
SPEAKER_01: If we're able to build models,
23:06
SPEAKER_01: which I'd like to brag that I think we have today,
23:09
SPEAKER_01: that are able to make those predictions in a reasonable way,
23:11
dramatically increases our chances of actually finding those
23:15
magic combinations
23:16
SPEAKER_01: that have the effects we're looking for.
23:17
SPEAKER_01: Because we spend more time testing the hypotheses that are reasonable
23:21
SPEAKER_01: and less time-testing hypotheses that are very unlikely to work.
23:24
SPEAKER_02: And I don't know if this is apples to apples,
23:26
SPEAKER_02: but if I'm thinking of a large language model,
23:29
SPEAKER_02: it's sucked in trillions of words from the Internet,
23:33
and then you're applying transformers onto these things and
23:35
getting this token
23:37
SPEAKER_02: predictor of what the next word would be.
23:40
SPEAKER_02: Like in this case, are you...
23:43
SPEAKER_02: You're feeding the model all the 2000-something transcription factors.
23:48
SPEAKER_02: You're showing it what has happened,
23:52
SPEAKER_02: data tied to,
23:53
SPEAKER_02: like,
23:53
SPEAKER_02: what's happened with Yamanaka factors,
23:55
SPEAKER_02: data that's tied to your experiments.
23:56
SPEAKER_02: I mean, I don't even know what you actually feed.
23:58
SPEAKER_01: Yeah, it's simpler than you'd think.
24:00
SPEAKER_01: So I'll start in the simplest version,
24:02
SPEAKER_01: and then I'll sort of back out and get a little more complicated.
24:04
But I think your analogy to large language models is actually a
24:07
pretty apt one,
24:08
SPEAKER_01: because those play a role in how we solve this problem too.
24:11
So the main idea is if we can show a model some mathematical
24:14
representation of
24:15
SPEAKER_01: transcription factors,
24:16
SPEAKER_01: so some set of numbers,
24:17
SPEAKER_01: and let's just go on the journey for a minute,
24:20
where we pretend those numbers represent the biology of the
24:22
transcription factor.
24:24
Can I go from sets of those transcription factor
24:26
representations and then some
24:27
SPEAKER_01: representation of what an old cell looks like?
24:29
You kind of get to know where you're starting to know what
24:32
reprogramming would do,
24:33
SPEAKER_01: where it's going to take you.
24:33
SPEAKER_01: But I don't know the starting place.
24:37
SPEAKER_02: You're mathematically coding.
24:38
SPEAKER_01: You're mathematically representing both of these things.
24:40
SPEAKER_01: So I want to take some notion of what's the transcription factor,
24:44
SPEAKER_01: represent that as a set of numbers a machine learning model can see.
24:47
SPEAKER_01: Not so dissimilar from how a large language model looks at text.
24:49
SPEAKER_01: Sure.
24:50
SPEAKER_01: And then it does what's called tokenization.
24:51
SPEAKER_01: It turns that into a set of numbers the model can understand.
24:54
We sort of need to do the same thing for transcription factors
24:56
and old cells.
24:58
SPEAKER_01: And then maybe by way of analogy to a large language model,
25:00
SPEAKER_01: you can show the model these representations of the TFs,
25:03
SPEAKER_01: the representations of the old cell.
25:04
And the same way a large language model completes a sentence
25:06
with missing words,
25:07
SPEAKER_01: we can ask,
25:08
SPEAKER_01: what would the outcome of this experiment be?
25:10
SPEAKER_01: Complete the missing word here being the outcome of what would occur.
25:14
And so the way we're training these models is that we've got
25:17
those representations.
25:18
We've got the many sets of transcription factors we've tested
25:20
where we sort of know the answer.
25:21
SPEAKER_01: We know what that missing word looks like.
25:23
SPEAKER_01: Did this make the old cell look young?
25:25
SPEAKER_01: Did it make it look older?
25:26
SPEAKER_01: Did it do nothing at all?
25:27
And we train the model to complete that and say,
25:29
ah,
25:29
okay,
25:30
these made the cell look younger.
25:31
SPEAKER_01: These made it look older.
25:32
SPEAKER_01: Now,
25:33
SPEAKER_01: your analogy,
25:33
SPEAKER_01: again,
25:34
to LLMs is not too far off because where we actually get those
25:37
numbers that
25:38
represent transcription factors is really using a lot of that
25:41
same technology.
25:42
So we can use things like language models trained on proteins
25:45
and language models
25:46
SPEAKER_01: trained on DNA.
25:47
And so we can show those models the protein sequence of each
25:50
transcription factor
25:51
and show it the DNA sequences the transcription factor binds to
25:55
and then extract
25:55
SPEAKER_01: from that what's called an embedding.
25:57
SPEAKER_01: So this is the same way that in a large language model,
25:59
SPEAKER_01: you might take two documents,
26:00
SPEAKER_01: you turn those into a set of numbers,
26:02
SPEAKER_01: and then in that embedding space,
26:03
SPEAKER_01: you can ask,
26:04
SPEAKER_01: are these documents about similar things or different things?
26:07
SPEAKER_01: Well,
26:07
SPEAKER_01: we can do that for transcription factors and then say,
26:09
SPEAKER_01: are these two transcription factors similar or different?
26:12
SPEAKER_01: So maybe if our model has only seen some subset of the combinations,
26:16
SPEAKER_01: it hasn't seen this unknown combination,
26:17
SPEAKER_01: but it's seen one that has pretty similar transcription factors in it,
26:20
SPEAKER_01: it can make an educated guess about what might happen.
26:23
And so because we're starting from those foundational models of
26:26
protein language
26:27
SPEAKER_01: and DNA language,
26:28
SPEAKER_01: our models aren't like learning all of biology from scratch.
26:31
SPEAKER_01: They start from a pretty smart place.
26:32
The same way that people would take a large language model and
26:34
maybe fine-tune it
26:35
SPEAKER_01: for a task they're working on,
26:37
we're taking these protein and DNA language models and using
26:40
that knowledge to
26:41
SPEAKER_01: really jumpstart our own work.
26:43
SPEAKER_01: Okay.
26:43
SPEAKER_02: And then...
26:45
SPEAKER_02: You guys gave an update recently.
26:47
SPEAKER_02: I'm going to mess up some of the numbers, but you'll help fix them.
26:49
I mean,
26:52
as far as I remember,
26:54
you've been working on this technology for a couple years.
26:56
In the last six months to a year,
26:58
you felt like the model's humming along in a better state.
27:03
And you'd found 100,
27:06
was it 100 transcription factors you thought were promising?
27:09
SPEAKER_01: 100 sets.
27:09
SPEAKER_02: 100 combos.
27:10
SPEAKER_02: 100 combos.
27:11
SPEAKER_02: And so you've been doing this work.
27:14
SPEAKER_02: You're searching this huge space,
27:15
SPEAKER_02: and now you've found 100 combinations that are promising to go look at.
27:22
SPEAKER_02: Is that the right?
27:23
SPEAKER_01: Yeah, absolutely.
27:24
SPEAKER_01: So we found right now,
27:25
SPEAKER_01: a little over 100,
27:26
SPEAKER_01: the number's a little out of date,
27:28
combinations of transcription factors where when we put them in
27:30
old cells,
27:31
SPEAKER_01: and these are real old cells from real old people.
27:33
So it's in some ways simpler than a lot of the science you
27:36
might hear about.
27:36
These aren't like engineered cancer cell lines with gosh knows
27:39
how many chromosomes.
27:41
SPEAKER_01: They're not mouse cells.
27:42
SPEAKER_01: They're real cells from old humans.
27:43
SPEAKER_01: It's just like old blood, right?
27:44
SPEAKER_01: It's old blood cells, old liver cells from old people and young people.
27:47
SPEAKER_01: And we can put the transcription factor combos in those old cells.
27:51
And more than 100 of them make the old cells express genes that
27:54
are much more
27:55
SPEAKER_01: similar to a younger state.
27:56
And so the way we phrase this internally to keep it simple is
27:59
they make an old cell
28:00
SPEAKER_01: look younger based on its gene expression.
28:02
So we don't know for every one of those sets yet whether it
28:04
makes the old cell act younger,
28:06
whether it can actually function better in a way that would
28:07
help you help treat a disease,
28:09
SPEAKER_01: help you just feel better when you're generally older.
28:11
SPEAKER_01: We don't know that for every set yet.
28:13
SPEAKER_01: But for a number of them, we do.
28:15
SPEAKER_01: And so we actually have four now,
28:16
SPEAKER_01: four sets,
28:17
where we can put those sets into old liver cells in old animals
28:21
and even old human
28:22
SPEAKER_01: liver cells.
28:23
and see them function better so they can regenerate better or
28:26
they can resist
28:27
SPEAKER_01: injury more effectively.
28:28
SPEAKER_01: So example,
28:28
SPEAKER_01: in liver cells,
28:30
SPEAKER_01: one of the things that changes with age is when you drink alcohol,
28:32
SPEAKER_01: old liver cells get way more damage than young cells do.
28:35
SPEAKER_01: It's so dramatic, you don't even need statistics.
28:37
SPEAKER_01: You can just look at the chart and know there's a difference.
28:40
Many of us probably believe that because we've run the
28:41
experiment in our own lives.
28:42
So some personal distributed replication,
28:45
I should say,
28:45
has been performed in this case.
28:48
And we can put some of these sets of transcription factors in
28:50
and dramatically
28:51
reduce the amount of damage these cells experience when they
28:54
actually consume alcohol.
28:55
So these are like actual mice getting- These are actual mice
28:58
that get drunk,
28:58
yeah.
29:01
SPEAKER_02: What do you give them?
29:02
SPEAKER_02: Just-
29:03
SPEAKER_01: It's surprisingly hard to get a mouse drunk.
29:05
SPEAKER_01: As one of our SAB members has told me, they evolved to eat garbage.
29:08
SPEAKER_01: And so they actually drink an alcohol-based diet.
29:11
So it's kind of like drinking beer as their main calorie source
29:15
for a couple weeks.
29:16
SPEAKER_01: And what that leads to is some damage in their livers.
29:19
And they're actually remarkably resistant to being what you and
29:22
I would recognize as inebriated.
29:24
SPEAKER_01: We can look at a few later.
29:25
SPEAKER_02: Yes, I'm excited.
29:27
SPEAKER_02: They get a shot?
29:28
SPEAKER_01: No, so they actually, they just, oh, so to get the transcription factors?
29:31
SPEAKER_01: Yeah, yeah.
29:32
SPEAKER_01: So we just give them a shot intravenously.
29:33
So it's the equivalent of like an IV for a human you would get
29:36
in the hospital.
29:37
And that's how these medicines would eventually make their way
29:39
to humans as well,
29:40
SPEAKER_01: is this would be something you got as an IV when you went for infusion.
29:43
SPEAKER_01: So very similar to some existing medicines that patients already get.
29:47
SPEAKER_01: The medicines we use are called lipid nanoparticles, carbor mRNA.
29:52
SPEAKER_01: So basically a lipid nanoparticles, really fancy phrase for a fat bubble.
29:55
If you've ever taken olive oil with some water in a tube and
29:58
you like shake it up,
29:58
you'll notice there's little bubbles of fat with little dots of
30:00
water inside.
30:01
SPEAKER_01: We do the same thing for these RNAs.
30:03
SPEAKER_01: And RNAs sit between DNA and protein.
30:06
So they're sort of the intermediator between actually going
30:08
from code in your
30:09
SPEAKER_01: genome to having proteins that do a job in your cell.
30:12
SPEAKER_01: And so we can deliver those RNAs, which encode the transcription factor.
30:15
And we just wrap them up in a fat bubble,
30:17
and then your liver cells suck them up.
30:18
SPEAKER_01: It's actually what the liver cells evolved to do.
30:20
SPEAKER_01: So when you eat a cheeseburger,
30:21
SPEAKER_01: you get fat in your blood,
30:22
SPEAKER_01: your liver pulls the fat out of your blood.
30:24
SPEAKER_01: So it's kind of like a Trojan horse.
30:25
Like we're tricking the liver cell into thinking this is just a
30:28
normal fat bubble.
30:29
SPEAKER_01: And actually,
30:30
it's a normal fat bubble carrying a couple mRNAs that might
30:33
make that liver cell younger.
30:34
SPEAKER_02: And how long does it take for the mouse to start rebooting a bit?
30:39
SPEAKER_02: Yeah, like until we see differences?
30:40
SPEAKER_02: Yeah, yeah.
30:40
SPEAKER_01: Hours.
30:41
SPEAKER_01: Hours.
30:41
Yeah,
30:42
we're talking,
30:42
I think the shortest timeline we've ever measured is like 12 hours.
30:45
SPEAKER_01: But you start to see benefits quite rapidly.
30:47
SPEAKER_02: And the liver is like actually...
30:51
SPEAKER_02: The cells in the liver are rebooting on their own,
30:55
SPEAKER_02: or it's just growing a batch of newer,
30:58
SPEAKER_02: better liver cells?
30:59
SPEAKER_02: It's definitely the former.
31:00
SPEAKER_01: So what we're seeing is that the cells that are already there,
31:02
SPEAKER_01: the cells you already have in your body,
31:04
SPEAKER_01: they have all the right code,
31:06
SPEAKER_01: right?
31:06
SPEAKER_01: The DNA is still there.
31:07
SPEAKER_01: They're just using the wrong genes.
31:08
SPEAKER_01: And so those cells start to have their epigenomes changed.
31:11
SPEAKER_01: So rather than using the wrong genes like an old cell does,
31:14
SPEAKER_01: they have the epigenetic marks placed on it,
31:16
SPEAKER_01: the little bookmarks on your genome that say,
31:17
SPEAKER_01: use these genes,
31:18
SPEAKER_01: don't use that one.
31:19
SPEAKER_01: And so we don't have to make all new cells.
31:21
SPEAKER_01: It's like you still have all the code.
31:22
SPEAKER_01: You've got all the cells.
31:23
SPEAKER_01: They're just kind of like misbehaving.
31:24
SPEAKER_01: And so we can make them behave much better.
31:26
SPEAKER_01: And so they're able to use the right genes at the right time.
31:28
SPEAKER_01: You don't have to grow a whole new liver.
31:29
SPEAKER_02: That's what I mean.
31:30
SPEAKER_02: I know we talked about it, but this is helping wrap my head.
31:33
SPEAKER_02: I mean, so the cells are just sitting there.
31:34
SPEAKER_02: And over time, it's just programming.
31:37
SPEAKER_02: It just starts to...
31:39
break down and you're just reminding it to go flip some other
31:42
switches back on.
31:43
You're just kind of reminding it the way you might have to
31:45
remind yourself every
31:46
SPEAKER_01: once in a while with your to-do list,
31:47
SPEAKER_01: what's a high priority,
31:48
SPEAKER_01: what's not.
31:49
SPEAKER_01: And so you don't have to introduce anything crazy.
31:51
SPEAKER_01: You're not taking new cells and putting them in the organism.
31:54
You're just taking the cells that are there that aren't doing
31:56
their job anymore and
31:57
SPEAKER_01: trying to remind them,
31:58
SPEAKER_01: so to speak,
31:59
SPEAKER_01: in an anthropomorphic way,
32:00
SPEAKER_01: which genes to use.
32:01
SPEAKER_01: And that's really what those transcription factors are doing,
32:03
SPEAKER_01: is going in and placing little reminders on your DNA and saying,
32:07
SPEAKER_01: use these genes,
32:07
SPEAKER_01: don't use those ones.
32:08
SPEAKER_02: I know there's a lot of theories about this, but I don't know.
32:11
SPEAKER_02: Maybe I'm after what your theory is.
32:15
Are those genes getting turned down over time because somewhere
32:19
in evolution we
32:20
SPEAKER_02: found it was like calorically expensive or something to have those on?
32:25
Or is this just this like slow degradation process and just the
32:29
coding just gets
32:30
SPEAKER_02: off kilter as time goes on?
32:33
SPEAKER_02: Or what's your guess as to why this happens in the first place?
32:36
SPEAKER_01: Yeah.
32:37
SPEAKER_01: It's basically unknowable, I think.
32:39
SPEAKER_01: Whenever biologists get together and start talking about evolution,
32:41
it's like time to go to the bar because like we can't answer
32:44
any of these questions.
32:45
SPEAKER_01: My own hypothesis is it's probably the latter.
32:48
I think if you imagine like how did we end up with the genomes
32:50
we have today,
32:51
SPEAKER_01: you know,
32:52
SPEAKER_01: sort of taking the selfish gene theory.
32:54
basically all of the code in your DNA was evolved so that that
32:58
DNA would be passed
32:59
SPEAKER_01: on more aggressively.
33:01
And so anything that occurs in your life after the period of
33:05
max fertility,
33:06
SPEAKER_01: it basically doesn't have any selective pressure.
33:08
Like there's no feedback tuning your genome to be more
33:12
effective after you've had kids.
33:14
It's like after you've had all your kids and primordial human
33:17
beings are running
33:18
SPEAKER_01: around on the savanna,
33:19
SPEAKER_01: after you've done your childbearing,
33:21
Basically,
33:22
nothing that happens to you is getting feedback into the genome.
33:25
SPEAKER_01: There are hypotheses against this.
33:26
SPEAKER_01: There are things called like kin selection,
33:27
SPEAKER_01: like,
33:28
SPEAKER_01: oh,
33:28
SPEAKER_01: well,
33:28
maybe having the older people in the community age faster had
33:31
some benefit.
33:32
But I think you have to start twisting yourself into kind of
33:35
funny arguments to believe it.
33:36
SPEAKER_01: And so I sort of like the simple hypothesis,
33:38
which is just like your genome optimized for you to have a lot
33:41
of kids really young.
33:42
SPEAKER_01: And everything that happens after that is just unoptimized.
33:45
And so one framing I like to take on it is when you're a
33:48
biologist and you're
33:49
SPEAKER_01: trying to improve a system,
33:50
trying to improve a system that evolution already had a couple
33:53
billion years to
33:54
SPEAKER_01: iterate on is really,
33:55
SPEAKER_01: really hard.
33:56
SPEAKER_01: You usually got pretty close to an optimal.
33:58
Trying to improve a system that evolution didn't bother to
34:01
optimize at all
34:03
SPEAKER_01: is a much easier thing to spend your time on.
34:05
SPEAKER_01: Because you might not even have to do anything too sophisticated.
34:08
Even simple changes that just didn't have any selective
34:11
pressure could improve it.
34:12
I think this is where some of the results in the field are
34:14
really striking.
34:15
SPEAKER_01: You know, one result a lot of people point to in the field of aging.
34:18
SPEAKER_01: Came from an old colleague of mine,
34:19
SPEAKER_01: a woman named Cynthia Kenyon,
34:21
where she demonstrated you can break one gene in a worm and
34:24
make it live two to
34:25
SPEAKER_01: three times longer.
34:26
SPEAKER_01: If evolution had optimized for lifespan, like that shouldn't work.
34:30
It sort of tells you right off the bat that if something so
34:32
simple can dramatically
34:33
SPEAKER_01: expand lifespan,
34:34
SPEAKER_01: that lifespan really wasn't optimized to begin with.
34:37
SPEAKER_01: And why don't we all just break this gene?
34:38
SPEAKER_01: It turns out for like various complicated reasons,
34:41
SPEAKER_01: you know,
34:41
SPEAKER_01: that gene does something different in worms than it does in humans.
34:44
SPEAKER_01: It's,
34:44
SPEAKER_01: you know,
34:44
SPEAKER_01: in humans has some other negative side effects,
34:46
like making you a little bit more rotund and a little bit less
34:49
muscular and other
34:50
SPEAKER_01: things you wouldn't want to intervene on.
34:52
SPEAKER_00: But you live twice as long?
34:53
SPEAKER_01: It also doesn't extend lifespan as much in mammals, like mice or humans.
34:56
So again,
34:57
it sort of does a different thing in worms than maybe it does
34:59
in humans.
35:00
But that's sort of just one example of the fact that if
35:03
evolution had optimized to
35:04
SPEAKER_01: make organisms live for a long time,
35:07
SPEAKER_01: it shouldn't be so easy.
35:08
SPEAKER_01: You shouldn't be able to find these quick wins.
35:10
SPEAKER_02: Yeah, so it seems like there's low-hanging fruit.
35:12
SPEAKER_02: I know with the transcription factors, I mean, one issue has been...
35:18
SPEAKER_02: As you're trying to go to reboot to that younger state,
35:21
SPEAKER_02: it's like,
35:22
can you get the cell to that younger place without going too
35:25
far and going back to
35:26
SPEAKER_02: the blank slate?
35:27
And then there's worries that obviously this is happening
35:31
inside your body,
35:33
SPEAKER_02: and so you don't want something going wrong.
35:36
SPEAKER_02: You might go younger,
35:37
and then this process of how you get a runaway process that's
35:41
making tumors or
35:42
SPEAKER_02: something horrible.
35:43
SPEAKER_02: Is that still...
35:47
SPEAKER_02: Even on the liver,
35:48
do you guys feel like now you have actually figured some of
35:51
those bits and pieces out,
35:53
SPEAKER_02: or there's just tons more work to do?
35:54
SPEAKER_01: Yeah.
35:55
SPEAKER_01: I don't want to claim we've got it entirely solved,
35:57
SPEAKER_01: but I do think everything you said is totally valid.
36:00
SPEAKER_01: Someone might listen to us chat and say,
36:02
SPEAKER_01: well,
36:03
SPEAKER_01: if the Amanaka factors can make an old cell young,
36:05
SPEAKER_01: can't you just dial it in?
36:07
SPEAKER_01: Can't you get the right dose?
36:09
SPEAKER_01: There are folks who believe that.
36:10
SPEAKER_01: We tend to think that
36:12
It's going to be very challenging to use some of those existing
36:15
factors and find a
36:16
way to reprogram your cells just enough without abolishing
36:19
their cell type.
36:19
SPEAKER_01: And exactly as you said,
36:21
basically sending it to cell too far back and then creating a
36:24
tumor in the body.
36:25
And so what we're really focused on from even the beginning of
36:28
our discovery
36:28
process is trying to find sets of factors that have much lower
36:32
risk of changing a
36:33
SPEAKER_01: cell's type,
36:34
SPEAKER_01: which would lead to something dangerous like you're describing,
36:36
SPEAKER_01: like forming a tumor.
36:37
So when we perform our early screens,
36:39
we test what these TFs do in cells.
36:41
SPEAKER_01: We measure all the genes they're using.
36:43
SPEAKER_01: We look at not only does it make this cell look younger,
36:45
SPEAKER_01: we also measure does this cell still look like the right type of cell?
36:48
SPEAKER_01: If I start with a liver cell,
36:49
SPEAKER_01: ideally when my younger cell comes out of the other end,
36:51
SPEAKER_01: it's still a younger liver cell.
36:53
SPEAKER_01: It doesn't look like something else.
36:55
SPEAKER_01: And so we can see that even early on, and we filter stuff out.
36:57
As an example,
36:58
the Yamanaka factors make cells look not at all like the cells
37:01
they started with.
37:02
SPEAKER_01: We filter stuff out really early based on that sort of risk.
37:05
And then we can go downstream in animal models and test these
37:08
things directly.
37:09
So you can ask,
37:10
you know,
37:10
does this make cells proliferate uncontrollably like a tumor would?
37:13
SPEAKER_01: And, you know, hopefully check those TFs off.
37:16
You can then also do things like turn the TFs on for much
37:19
longer than you probably
37:20
SPEAKER_01: would in a medicinal context and just make sure nothing bad happens.
37:23
SPEAKER_01: To give you an example,
37:24
SPEAKER_01: you know,
37:24
SPEAKER_01: in medicine,
37:25
SPEAKER_01: we often talk about therapeutic index,
37:27
the difference between a dose that helps you and a dose that
37:29
might be harmful.
37:30
SPEAKER_01: So even medicines that are over the counter have a therapeutic index.
37:33
SPEAKER_01: You take too many Tylenol and you destroy your liver, right?
37:35
SPEAKER_01: So,
37:35
SPEAKER_01: you know,
37:36
we understand that you can find safe doses and then also have
37:39
too much of a good
37:40
SPEAKER_01: thing be a bad thing.
37:41
SPEAKER_01: And so for the Yamanaka factors,
37:42
the difference between the safe dose and the beneficial dose is
37:45
less than twofold.
37:46
SPEAKER_01: So it's like, take one Tylenol, your headache goes away.
37:48
SPEAKER_01: Take 1.4 Tylenol, and you die.
37:50
So it's probably not going to be the right direction to go for
37:53
every cell type.
37:55
What we're hoping for is that the therapeutic indexes we can
37:57
find are really,
37:58
SPEAKER_01: really big,
37:58
SPEAKER_01: so that the dose we're giving is so safe,
38:01
SPEAKER_01: the chance anything negative occurs downstream is really,
38:03
SPEAKER_01: really low.
38:04
SPEAKER_01: And thankfully, we get to stand on the shoulders of giants here.
38:08
SPEAKER_01: The FDA has been thinking about this problem for a long time.
38:11
SPEAKER_01: It's not a risk just for reprogramming.
38:13
SPEAKER_01: It's a risk for lots of different types of drugs you put in your body.
38:16
Maybe they could cause cancers,
38:17
do something bad that you didn't understand.
38:19
SPEAKER_01: And so there are tools you can use to ask,
38:21
SPEAKER_01: you know,
38:21
SPEAKER_01: for example,
38:22
SPEAKER_01: if I put this in animals and I basically look at them for a long time,
38:25
SPEAKER_01: can I measure if they don't get tumors?
38:27
And we now have a lot of data on sort of what sorts of models
38:30
are predictive of
38:30
SPEAKER_01: what will happen in a person.
38:32
And we can leverage a lot of that existing safety knowledge to
38:34
make sure that our
38:35
SPEAKER_01: medicines are safe too.
38:37
SPEAKER_02: Some of the work you guys do,
38:38
SPEAKER_02: the first time I came here,
38:40
SPEAKER_02: it's kind of cool,
38:41
SPEAKER_02: the starting point.
38:41
SPEAKER_02: I mean,
38:42
SPEAKER_02: you just had,
38:43
SPEAKER_02: we talked about it a little bit,
38:44
SPEAKER_02: but you had these,
38:45
SPEAKER_02: you've got bags of old blood.
38:47
SPEAKER_02: You've got bags of young blood, right?
38:50
SPEAKER_02: And then you're trying to turn one thing into the other.
38:54
And so you use those cells to run the experiments on the old
38:59
blood and then see if
39:00
SPEAKER_02: it starts to resemble what's in the good stuff.
39:03
SPEAKER_01: Yeah, yeah, exactly.
39:04
SPEAKER_01: I mean, so we've tried to keep this like pretty simple from the day one.
39:08
SPEAKER_01: You know, I think in biology often doing the right experiments is hard.
39:12
SPEAKER_01: And so we often allow ourselves some approximations.
39:15
As an example,
39:16
cells from real people,
39:17
they don't grow crazy well in plastic dishes.
39:20
SPEAKER_01: They evolve to grow inside a human body.
39:22
And so everything's easier if you're willing to just do your
39:24
experiments in cancer cells.
39:25
SPEAKER_01: Those guys will grow on anything.
39:27
SPEAKER_01: Yeah.
39:27
You know,
39:28
and so folks will often make those sorts of approximations
39:31
early on.
39:32
But we worry that ultimately what we're trying to do is make
39:35
medicines for patients.
39:36
SPEAKER_01: We're not just trying to write a paper like that's great,
39:38
SPEAKER_01: too,
39:38
SPEAKER_01: but we're really trying to make medicines.
39:40
And so we want to start from the earliest days doing all of our
39:43
experiments in real
39:44
SPEAKER_01: human cells,
39:45
SPEAKER_01: hoping that they'll translate to human cells inside of a patient.
39:48
We've even started now doing some of our earliest screens in
39:51
vivo so we can make,
39:52
SPEAKER_01: for example,
39:53
SPEAKER_01: mice that have mostly human livers.
39:55
SPEAKER_01: And so it's not a perfect representation of what will happen in a person,
39:58
SPEAKER_01: but it's a human cell inside a working organism.
40:01
SPEAKER_01: It's probably about as close as you can get before you get to the clinic.
40:04
SPEAKER_01: And so we're willing to bite the bullet on doing a bunch of hard stuff,
40:07
SPEAKER_01: like getting hundreds of bags of blood and isolating all the cells.
40:10
SPEAKER_01: It's like certainly arduous.
40:11
SPEAKER_01: I've been on calls at like 2 a.m.
40:13
trying to get like hepatocytes sent to new limits so that we
40:15
can get the right
40:16
SPEAKER_01: hepatocytes are Greek for liver cell.
40:18
SPEAKER_01: So we can get the right liver cells at the right time,
40:20
SPEAKER_01: you know,
40:21
dealing with a bunch of these just like logistical and
40:22
operational issues.
40:23
We're willing to bite the bullet on that because we think at
40:25
the end of the day,
40:26
it means the results of our experiments are much more likely to
40:29
actually be meaningful,
40:30
SPEAKER_01: to actually translate into people.
40:31
I heard you talking about this mouse with a human liver on this presentation.
40:37
SPEAKER_02: I mean,
40:37
how do you put human liver cells inside of a mouse and
40:43
actually— It's so crazy this works.
40:44
SPEAKER_01: It's so crazy this works.
40:45
SPEAKER_01: So we can't take credit for the idea that this works.
40:47
SPEAKER_01: I can tell you a little bit about what we've innovated on.
40:50
But these mice were actually invented by a member of our
40:53
scientific advisory board
40:54
SPEAKER_01: called Marcus Grompe.
40:55
And so basically the trick is that there are these diseases
40:59
which cause the hepatocytes,
41:00
SPEAKER_01: the liver cells,
41:01
SPEAKER_01: to be sick.
41:02
SPEAKER_01: And so they don't work very well,
41:04
SPEAKER_01: and they actually start to die if they don't get a certain medicine.
41:07
SPEAKER_01: And so these are diseases that happen in real people,
41:09
SPEAKER_01: but you can also engineer that disease into a mouse.
41:11
SPEAKER_01: And so now we've got a mouse which doesn't have an immune system.
41:14
So you can transplant in human cells and they don't get
41:16
rejected the way that,
41:17
SPEAKER_01: you know,
41:17
SPEAKER_01: if you transplant in the wrong blood type into a human,
41:19
SPEAKER_01: it gets rejected.
41:20
SPEAKER_01: So we just got rid of the immune system in these mice mostly.
41:22
SPEAKER_01: So we don't have to worry about that.
41:23
SPEAKER_01: And then the mouse liver cells are sick.
41:25
And so all you need to do really is inject them with a dose of
41:28
human liver cells
41:29
SPEAKER_01: that don't have that genetic disease.
41:31
And then you remove a drug you were giving the mice that helped
41:33
their sick liver
41:34
SPEAKER_01: cells stay healthy.
41:35
SPEAKER_01: So now the sick liver cells start to die off.
41:37
SPEAKER_01: They start to get sick.
41:38
SPEAKER_01: The human liver cells don't have that genetic disease.
41:42
SPEAKER_01: And so they start to out-compete the mouse.
41:44
SPEAKER_01: And over time,
41:45
you're really pushing each of these human liver cells and
41:47
you're asking the question,
41:48
SPEAKER_01: how well can you regenerate in this sick environment?
41:51
SPEAKER_01: Kind of resembles like a disease in a human.
41:53
SPEAKER_01: The rest of the liver is sick.
41:54
SPEAKER_01: Their cells are dying off.
41:55
Maybe these cells are seeing damage and they might want to die themselves.
41:58
SPEAKER_01: And you're asking not only can you not die,
42:00
SPEAKER_01: but can you repair the damage that's occurring elsewhere?
42:03
SPEAKER_01: Can you make more liver when you need it?
42:05
SPEAKER_01: And so over the course of a few months,
42:07
what you end up with is a mouse that has very few mouse liver
42:09
cells left and mostly
42:11
SPEAKER_01: human liver cells.
42:12
SPEAKER_01: That's kind of crazy to think about,
42:13
SPEAKER_01: but you're holding a mouse that has basically a human tissue in it.
42:15
SPEAKER_01: So it's not a perfect model.
42:17
SPEAKER_01: Again, these mice aren't that big.
42:18
SPEAKER_01: They weigh about 30 grams.
42:19
So I'm not going to claim it predicts exactly what will happen
42:21
in a human in the clinic,
42:23
but a lot closer probably than what happens in a normal mouse
42:25
and probably about as
42:26
SPEAKER_01: close as you can get before you get into human beings.
42:29
SPEAKER_02: That is weird.
42:30
SPEAKER_02: And the liver, sorry, it's probably a
42:32
SPEAKER_02: No.
42:33
SPEAKER_02: Really stupid question.
42:34
SPEAKER_02: The liver doesn't try to grow.
42:36
SPEAKER_01: No, it's crazy that this works, right?
42:37
SPEAKER_01: It's not a stupid question at all.
42:39
So the liver has all sorts of internal signaling that basically
42:42
tell it how big to get.
42:43
SPEAKER_01: It's kind of a crazy biology problem when you think about it.
42:46
SPEAKER_01: Like, why don't my organs grow too big?
42:47
SPEAKER_01: Like, how do they know?
42:48
SPEAKER_01: How does my heart know what size to be?
42:49
SPEAKER_01: How does my liver know what size to be?
42:51
SPEAKER_01: Turns out there's all these signaling mechanisms.
42:53
SPEAKER_01: And a lot of them are simpler than you'd think.
42:54
SPEAKER_01: They're like mechanical.
42:55
When the liver starts to press up against other tissues,
42:58
it says,
42:58
ah,
42:58
okay,
42:59
I'm the right size.
43:00
SPEAKER_01: And that tells the cells to stop dividing.
43:02
SPEAKER_01: And all of that circuitry,
43:03
SPEAKER_01: all of those genetic signals,
43:05
SPEAKER_01: are actually conserved across a lot of species.
43:07
SPEAKER_01: So the human cells inside a mouse,
43:09
SPEAKER_01: when they fill up the right amount of liver space,
43:11
SPEAKER_01: the mouse body says,
43:13
SPEAKER_01: OK,
43:13
SPEAKER_01: liver's big enough.
43:14
SPEAKER_01: Don't grow anymore.
43:15
And so these mice can actually live reasonably long periods of
43:17
time with human
43:18
SPEAKER_01: liver cells in them.
43:19
SPEAKER_01: And they function just fine.
43:20
SPEAKER_01: OK.
43:21
SPEAKER_01: So what we've been working on,
43:22
and this is sort of where we started to introduce innovations
43:24
on top of what our
43:25
SPEAKER_01: SAB member Marcus invented,
43:26
is we've started working on actually building those livers with
43:29
mixes of young and
43:30
SPEAKER_01: old cells.
43:31
Rather than like one donor liver at a time,
43:34
we mix a bunch of them together.
43:35
And then we can use genomics to actually ask what happened in
43:38
the liver cells of
43:39
SPEAKER_01: each individual person.
43:40
So rather than testing our medicines effectively in one human
43:43
being who might be
43:44
SPEAKER_01: weird and idiosyncratic in their own way—
43:46
SPEAKER_01: We can mix cells from a bunch of people together and say,
43:48
SPEAKER_01: okay,
43:49
SPEAKER_01: which interventions that we try actually work in everybody?
43:51
SPEAKER_01: Because what we're trying to do is make medicines for everybody,
43:53
SPEAKER_01: not like donor number six,
43:55
SPEAKER_01: which is what a lot of firms end up doing.
43:57
And so we've also found ways to take that system and put combos
44:01
of transcription
44:02
SPEAKER_01: factors into the hepatocytes.
44:03
SPEAKER_01: So now we end up with this mouse whose liver is not only human,
44:06
but all of its hepatocytes have different combos of
44:08
transcription factors.
44:09
SPEAKER_01: And we can turn those on and off,
44:11
SPEAKER_01: mimicking what happens with a drug,
44:13
SPEAKER_01: and ask,
44:13
SPEAKER_01: at the end of the day,
44:14
SPEAKER_01: did we make those cells look younger?
44:15
SPEAKER_01: Do they express the right genes?
44:17
SPEAKER_01: And more importantly,
44:18
did they regenerate and actually outcompete some of their
44:20
brethren and build a
44:22
SPEAKER_01: bigger part of this new human liver inside the mouse?
44:24
Which we hope is associated with their ability to regenerate
44:26
and function in a
44:27
SPEAKER_01: human being who might have a disease or just normal aging.
44:31
SPEAKER_02: I give you guys credit because I feel like you always...
44:35
SPEAKER_02: explain where you're at in fairly reserved, clear terms.
44:39
SPEAKER_02: You're not promising, over-promising to me.
44:42
I think a lot of times when I hear about some of the science in
44:47
this field,
44:47
SPEAKER_02: it's always with mice.
44:49
SPEAKER_02: It's always in the liver because the liver seems to regenerate.
44:52
SPEAKER_02: It seems to absorb all this stuff a bit easier.
44:56
SPEAKER_02: Won't there be some
44:57
SPEAKER_02: Critics are,
44:59
SPEAKER_02: and I know it's early days and you have to start somewhere,
45:01
SPEAKER_02: but yeah,
45:02
SPEAKER_02: wouldn't there be like,
45:02
SPEAKER_02: okay,
45:03
SPEAKER_02: all your exciting,
45:04
SPEAKER_02: not all your exciting stuff,
45:05
SPEAKER_02: but some of your most exciting stuff,
45:06
SPEAKER_02: it's in a liver,
45:07
SPEAKER_02: it's in mice,
45:07
SPEAKER_02: this is like where everybody goes,
45:10
SPEAKER_02: how excited should we actually be about the results you're seeing?
45:14
Yeah,
45:15
well,
45:15
you know,
45:15
for obvious reasons,
45:16
like I'm stoked on what we're doing.
45:18
SPEAKER_01: But I think it's a totally fair critique.
45:19
SPEAKER_01: You know, people hear we're trying to make you live longer.
45:22
And then they get like 10 minutes into us talking and it's like,
45:24
oh,
45:25
it's all about livers.
45:26
SPEAKER_01: It's all about these like tiny little 30 gram animals.
45:28
SPEAKER_01: Like, how much does this actually matter?
45:30
The way we thought about this at New Limit is that you need to
45:32
start somewhere.
45:33
You know,
45:34
I really admire firms that have the ability to stage their
45:37
ambitions over time.
45:38
And so we think internally about,
45:40
you know,
45:40
what's like the Tesla Roadster?
45:42
The first thing you can build that really works before you can
45:45
scale out to
45:45
SPEAKER_01: eventually have the Model Y,
45:47
SPEAKER_01: the most popular car in the world.
45:49
SPEAKER_01: And so here for us,
45:50
SPEAKER_01: we think that for a bunch of reasons like you highlighted,
45:52
SPEAKER_01: you can get these mRNAs into the liver really easily.
45:56
SPEAKER_01: And the fact that we know the liver is important.
45:58
SPEAKER_01: We know if you make it younger that it'll have benefits for people.
46:02
SPEAKER_01: We started there, but that's not the limit of our ambition.
46:04
SPEAKER_01: We eventually want to extend out into other cell types.
46:07
SPEAKER_01: So we also work on T cells.
46:08
SPEAKER_01: We talked about isolating cells from the blood earlier.
46:10
SPEAKER_01: We're hoping to fix your immune system so we can get an infection.
46:13
SPEAKER_01: When you're old,
46:13
you can actually fight it off the way you do when you're young
46:15
so that when you're
46:16
SPEAKER_01: 75 and get sick from your grandkids,
46:17
SPEAKER_01: it's kind of annoying rather than like sending you to the hospital,
46:20
SPEAKER_01: which is unfortunately what will happen today.
46:22
And eventually we want to be working on many other cell types
46:24
in the body.
46:25
SPEAKER_01: But you have to do this in a stage-wise process.
46:27
We think you really are going to need these specific
46:29
reprogramming medicines for
46:31
SPEAKER_01: different cell types in your body.
46:32
SPEAKER_01: There's not going to be just a single pill probably that you can take.
46:35
SPEAKER_01: We'd be very excited if that were the case.
46:37
SPEAKER_01: You could get like one single set of factors that works everywhere.
46:40
SPEAKER_01: But we think you're going to need to do it in a specific way.
46:42
And so the stage-wise process,
46:43
I think,
46:44
is the right way to roll this out.
46:46
SPEAKER_01: We're not going to go to Mars tomorrow.
46:47
You have to first reach orbit sort of metaphor that I like to
46:49
use internally.
46:51
We think where we're at today is that the results we have in
46:54
animals using
46:55
reprogramming are as strong as what some companies have used to
46:58
go into the clinic
46:59
SPEAKER_01: and actually run human trials.
47:00
We're not going to do that next year,
47:02
and I don't want to overstate or overpromise there.
47:04
SPEAKER_01: But just to give you a sense of scale,
47:06
these are the sorts of data that people think are about as good
47:09
as you need to be
47:09
SPEAKER_01: able to run an experiment in human beings.
47:12
SPEAKER_01: On the liver and the T-cell?
47:14
SPEAKER_01: On the liver right now is where we're most advanced.
47:16
SPEAKER_01: It's on the liver.
47:17
We've got results that really are the same sorts of results
47:20
people have triggered
47:20
SPEAKER_01: clinical trials on.
47:22
SPEAKER_01: And so we think it's still very early days.
47:24
SPEAKER_01: But for a drug development program, this is pretty promising.
47:28
SPEAKER_01: It's as fast as I've seen a program move.
47:30
SPEAKER_01: We started working on the liver.
47:32
SPEAKER_01: nine, 10 months ago now.
47:33
SPEAKER_01: And already,
47:34
we're at that position because of some of the technology we
47:37
built in T cells,
47:38
SPEAKER_01: enabling us to move really quickly across cell types.
47:40
SPEAKER_01: So I think this is still a really exciting time.
47:43
SPEAKER_01: Yes, drug discovery is a long road.
47:45
But I think we're in a place where anyone listening to us chat
47:48
right now could
47:49
SPEAKER_01: anticipate that in some number of years,
47:51
SPEAKER_01: you're going to see these reprogramming medicines in people.
47:54
SPEAKER_01: And some number of years after that,
47:55
you're going to see them actually deploying out not just into
47:58
patients who are very sick,
47:59
SPEAKER_01: but eventually people who are healthier and healthier,
48:02
SPEAKER_01: but who want to stay healthy,
48:03
SPEAKER_01: who don't want to be degrading as they age.
48:06
SPEAKER_01: And so it'll take a while.
48:07
SPEAKER_01: This is unfortunately not quite like software,
48:09
SPEAKER_01: but I don't think it's pure science fiction anymore.
48:11
I do think we're going to start seeing clinical results in a
48:13
number of years down the line.
48:15
SPEAKER_02: So, I mean, in some sense, you answer the question completely.
48:19
I just,
48:19
I feel like sometimes I hear,
48:21
it's like,
48:22
ah,
48:22
and it's working in the liver.
48:24
SPEAKER_02: The second we go to,
48:26
SPEAKER_02: if you wanted to deal with something like dementia or Alzheimer's,
48:29
SPEAKER_02: the second we're trying to,
48:30
SPEAKER_02: Rebooters and the neuron,
48:31
all this stuff that we're so excited about that worked in the
48:34
liver is just not applicable.
48:36
SPEAKER_02: You know,
48:37
SPEAKER_02: so you're on this journey,
48:38
SPEAKER_02: but it's like,
48:39
SPEAKER_02: can this stuff even translate to the other bag of things?
48:44
SPEAKER_02: Yeah.
48:44
SPEAKER_01: Yeah.
48:45
SPEAKER_01: I mean, we don't know today, and I don't want to overpromise there.
48:47
SPEAKER_01: Like,
48:47
we don't know if the types of approaches we found can allow us
48:51
to discover sets of
48:52
SPEAKER_01: transcription factors that work in every cell type.
48:55
SPEAKER_01: But we have the ambition to go out and find out.
48:58
And I think we know from the Yamanaka factor reprogramming case
49:02
that in principle,
49:03
SPEAKER_01: it can work everywhere.
49:04
SPEAKER_01: Whether we can turn that into a medicine is a whole other question,
49:06
SPEAKER_01: and we don't know the answer today.
49:08
SPEAKER_01: But in principle,
49:08
SPEAKER_01: the existence proof is there,
49:10
SPEAKER_01: which is not something you can say about every therapeutic program,
49:13
SPEAKER_01: that you know there's a possible hit in your data set somewhere.
49:17
So I think even if we just fix the liver and even if we just
49:20
fix the immune system,
49:22
SPEAKER_01: No one knows for sure,
49:23
SPEAKER_01: but my own personal bet is that still buys you multiple years of healthy,
49:27
SPEAKER_01: active human life.
49:29
SPEAKER_01: And I can't really imagine a product more valuable than that,
49:32
SPEAKER_01: even if it just works in the liver and the immune system.
49:35
SPEAKER_01: What's more valuable than more time to spend with your loved ones,
49:38
SPEAKER_01: more time to spend on the things you love,
49:40
SPEAKER_01: honing your craft?
49:41
deciding which hiking trail to go on rather than which nursing
49:43
home to end up in.
49:45
SPEAKER_01: So we're really excited, even if that's the first place it works.
49:48
But we do have the ambition that eventually we do want to
49:50
address almost every cell
49:51
SPEAKER_01: type in the body as well.
49:53
SPEAKER_01: But it's still early days.
49:54
I don't want to overpromise and say,
49:56
like,
49:56
oh,
49:56
yeah,
49:56
for sure it's going to work in Alzheimer's.
49:58
SPEAKER_01: Don't think anyone knows today, but we want to be the company to try.
50:01
SPEAKER_02: Fantastic.
50:03
SPEAKER_02: Because this technology is so exciting, a ton of money poured into this.
50:07
SPEAKER_02: There's you guys, there's Retro, there's Altos Labs, there's others.
50:10
SPEAKER_02: But even just among those three,
50:12
I'm probably talking about billions of dollars in funding has
50:17
been poured into this.
50:18
SPEAKER_02: So many of the world's leading scientists who work on this stuff.
50:22
SPEAKER_02: So all that is great.
50:23
SPEAKER_02: It's kind of moved from academia in the industry.
50:26
SPEAKER_02: Things are going to go faster.
50:28
SPEAKER_02: It is also,
50:29
SPEAKER_02: there is an argument to be made that maybe you guys are publishing less,
50:34
SPEAKER_02: you're sharing information less.
50:36
SPEAKER_02: Alters Labs in particular is very secretive.
50:38
SPEAKER_02: This is such a prized technology.
50:41
SPEAKER_02: Wouldn't it be more wonderful if all of this was out in the open?
50:46
SPEAKER_02: I mean, this strikes me as one of the things I'm most...
50:49
SPEAKER_02: interested and excited about because it could be just,
50:53
SPEAKER_02: it's like antibiotics level of- Yeah,
50:56
SPEAKER_01: that's entirely the ambition.
50:58
SPEAKER_01: It's like antibiotics level improvement to human health.
51:00
SPEAKER_02: Right.
51:00
SPEAKER_02: So yeah,
51:01
is there a downside that all you guys have come into this and
51:05
sucked up all the
51:06
SPEAKER_02: brains into your research caves?
51:08
SPEAKER_01: Yeah, well, I hope we're not in a cave anymore.
51:11
SPEAKER_01: Ashley's referring to our first office, which looked like a cave.
51:15
SPEAKER_01: I don't think there's really a downside.
51:17
SPEAKER_01: I think there's still a lot of attention in academia.
51:21
SPEAKER_01: And if anything,
51:22
SPEAKER_01: I think that this particular problem,
51:23
SPEAKER_01: this particular science,
51:24
still gets less attention than it deserves in terms of the
51:26
potential impact.
51:27
SPEAKER_01: It does.
51:27
SPEAKER_02: It's crazy to me.
51:30
SPEAKER_02: I looked on the New York Times.
51:31
SPEAKER_02: I don't want to...
51:32
SPEAKER_02: totally libel myself or whatever I mean.
51:35
SPEAKER_02: But I think when I looked, it was like Yamanaka won the Nobel Prize.
51:39
SPEAKER_02: I think since then,
51:40
there was one other story that mentioned IPSCs and Yamanaka
51:45
factors over years.
51:47
SPEAKER_02: And then this is bizarre to me, because this seems like the
51:51
SPEAKER_02: potentially the most exciting thing.
51:53
SPEAKER_01: It's one of the most exciting things happening in,
51:55
SPEAKER_01: like,
51:55
SPEAKER_01: the natural sciences,
51:56
SPEAKER_01: not just biology,
51:57
SPEAKER_01: to my mind.
51:58
SPEAKER_01: I mean, talk in my own book, but, like, I really believe that.
52:00
SPEAKER_01: You know, New York Times can be no core memory.
52:02
SPEAKER_01: We can't all be at the same level of scientific activity.
52:05
SPEAKER_02: We have gone, in our early days, we've gone heavy IPSC.
52:09
SPEAKER_02: I can stand behind that.
52:10
SPEAKER_01: Yeah.
52:11
SPEAKER_01: I appreciate it.
52:13
SPEAKER_01: Yeah, I don't think the science gets enough attention already.
52:16
And so I think the fact that we've started to see some level of
52:19
investment in the
52:19
SPEAKER_01: science is on the industrial side is actually really a net boon.
52:23
I think what you're going to end up with is because of this
52:25
industrial investment,
52:26
SPEAKER_01: you'll see more academics working on this problem on net,
52:29
SPEAKER_01: not less.
52:30
SPEAKER_01: I think the ecosystem needs both pieces.
52:32
SPEAKER_01: You know,
52:32
there are questions that you can answer in an academic setting
52:34
on very long time horizons,
52:36
SPEAKER_01: really low chances of success,
52:37
SPEAKER_01: but we can't really do in a company.
52:39
SPEAKER_01: It doesn't make sense as a business.
52:40
SPEAKER_01: And so that ecosystem has lots of interaction and cross-pollination.
52:44
And I think seeing that there are places where talented people
52:47
who have worked on
52:47
SPEAKER_01: the science can go after their academic trajectory.
52:50
Seeing that there's a hope that if you discover something
52:52
really exciting in the
52:53
SPEAKER_01: academic world,
52:53
there's a pathway for it to actually reach humans through some
52:56
of these great organizations,
52:57
SPEAKER_01: you know,
52:57
SPEAKER_01: not only ourselves,
52:58
SPEAKER_01: but some of the other companies that we admire as well,
53:00
SPEAKER_01: like Retro and Altos.
53:02
SPEAKER_01: Then I think that leads to more attention, not less.
53:04
SPEAKER_01: And I think really,
53:05
I think about growing sort of the size of the pie of people
53:08
paying attention to
53:09
SPEAKER_01: this and way less about like dividing up the slices.
53:11
SPEAKER_01: Because right now the pie is just microscopic.
53:13
SPEAKER_01: And like, I would rather not talk about how many microns your slice is.
53:17
SPEAKER_01: I'd rather be arguing about how do we get our pie to the size of meters.
53:20
SPEAKER_01: So that's sort of how I think about it.
53:22
SPEAKER_01: And ultimately,
53:24
SPEAKER_01: the types of problems we're trying to address making medicines,
53:27
SPEAKER_01: they can really only be done in industry.
53:29
SPEAKER_01: And I think the history of this plays out across therapeutic modalities.
53:32
SPEAKER_01: Like you look at the history of biotech,
53:34
SPEAKER_01: you know,
53:34
SPEAKER_01: recombinant DNA was invented at UCSF and Stanford,
53:37
SPEAKER_01: but it was actually pioneered and turned into medicines at Genentech.
53:40
SPEAKER_01: That's not an accident,
53:42
SPEAKER_01: and that's been repeated with cell therapy,
53:43
SPEAKER_01: repeated with monoclonal antibodies.
53:45
You really need the focus and the concentration of an
53:49
industrial effort with,
53:50
SPEAKER_01: I think,
53:50
SPEAKER_01: a for-profit motive is really important,
53:52
and able to actually translate this and turn it into a product
53:55
that people can have
53:56
SPEAKER_01: in their own lives.
53:57
SPEAKER_01: That's ultimately what matters.
53:58
I see business as the vehicle by which you turn ideas into
54:02
benefit for real people.
54:03
And I don't think we've discovered as a human civilization
54:06
really a better
54:06
mechanism of that than a company where you get a lot of smart
54:09
people who wouldn't
54:10
SPEAKER_01: otherwise organize together to do more together than they could alone.
54:14
SPEAKER_02: Even like among my...
54:16
SPEAKER_02: longevity chums,
54:18
SPEAKER_02: which I have many and I think are pretty...
54:20
SPEAKER_01: Why am I not on the invite list,
54:22
SPEAKER_01: man?
54:22
SPEAKER_01: No, no, you're in it.
54:23
SPEAKER_01: Come on, get me on the text thread.
54:24
SPEAKER_02: You're in it, you're in it.
54:25
SPEAKER_02: I just meet all this group of people I've always,
54:28
SPEAKER_02: you know,
54:29
SPEAKER_02: trying to learn from and understand this world.
54:31
SPEAKER_02: You know,
54:33
SPEAKER_02: so I would characterize this as pretty quite rational,
54:38
SPEAKER_02: science-forward people,
54:40
SPEAKER_02: not the kind of fringe end of longevity,
54:43
SPEAKER_02: like people I trust and...
54:45
You know,
54:46
half the people are people like you,
54:47
and they're super into this.
54:48
SPEAKER_02: And then the other half are just like,
54:50
SPEAKER_02: this is never,
54:51
SPEAKER_02: with transcription value,
54:52
SPEAKER_02: this is never going to work.
54:54
SPEAKER_02: And they're really down on the technology.
54:56
SPEAKER_02: Yeah, I mean, so you might, I'm sure you face that.
55:00
SPEAKER_02: And obviously you've decided we're going to see if this works or not.
55:04
SPEAKER_02: But what is the,
55:06
SPEAKER_02: there is a case to be made among some people that there's like,
55:09
SPEAKER_02: we'll never get this recipe quite right.
55:12
SPEAKER_01: Yeah, yeah.
55:13
SPEAKER_01: If I could do my best,
55:13
SPEAKER_01: like,
55:14
SPEAKER_01: you know,
55:14
SPEAKER_01: Berkeley rationalist steel man argument,
55:17
SPEAKER_01: like,
55:17
SPEAKER_01: let me,
55:18
SPEAKER_01: like,
55:18
SPEAKER_01: indict,
55:18
SPEAKER_01: like,
55:19
SPEAKER_01: why what we're doing might not be perfect.
55:21
I think a lot of the arguments I hear are something like,
55:24
biology is really complicated.
55:26
SPEAKER_01: There are lots of molecules running around in a cell,
55:29
SPEAKER_01: lots of challenges that can emerge with age.
55:31
Things like mutations in the DNA aren't going to be fixed just
55:34
by changing the epigenome.
55:35
That really tells you which part of the code to use if the
55:37
code's messed up.
55:38
SPEAKER_01: It doesn't fix that.
55:39
SPEAKER_01: And so,
55:40
SPEAKER_01: you know,
55:40
we're not going to get to living forever just with epigenetic
55:43
reprogramming alone.
55:45
SPEAKER_01: And I basically agree with that.
55:46
I don't think we're going to be able to live forever with
55:48
epigenetic reprogramming alone.
55:50
SPEAKER_01: But taking the median lifespan from, you know, 76 in the U.S.
55:53
SPEAKER_01: to 95, eventually to 110—we know thousands of people live to 110.
55:58
That's not impossible—is a very audacious goal that I would
56:01
love to be part of
56:02
SPEAKER_01: actually realizing in the world—
56:03
And I don't think saying that epigenetic reprogramming is not
56:06
the perfect answer to
56:07
all of aging means that it is not going to be one of the
56:10
highest impact levers that
56:12
SPEAKER_01: we can actually access.
56:13
And I would argue probably the highest impact mechanism that we
56:16
could work on at this time.
56:18
SPEAKER_01: So I definitely hear some of those arguments and I think they're valid.
56:20
But again,
56:21
I think this comes to the sort of the stepwise staging argument.
56:23
SPEAKER_01: If you spend your entire time
56:25
SPEAKER_01: on whiteboards trying to find what the perfect plan is that takes you to
56:30
SPEAKER_01: immortality from today,
56:31
SPEAKER_01: and you don't do anything until you find that perfect plan?
56:35
SPEAKER_01: Well,
56:36
people have been whiteboarding around for a few hundred years
56:38
and haven't found the
56:39
SPEAKER_01: perfect plan yet.
56:40
SPEAKER_01: I don't think it's coming.
56:41
I really believe in sort of a bias for action that produces new
56:44
information in the world.
56:46
And I think the path we're on has the potential to add decades
56:49
of healthy life to every person.
56:51
SPEAKER_01: And for us at New Limit, we're wonderfully excited by that mission.
56:54
We think not only would this be one of the most important
56:57
moments in the history of humanity,
56:59
SPEAKER_01: on part with the invention of antibiotics.
57:00
SPEAKER_01: You know,
57:00
we went from an average lifespan of around 45 a couple hundred
57:03
years ago to an
57:04
SPEAKER_01: average lifespan of 76.
57:05
SPEAKER_01: We like forget.
57:06
SPEAKER_01: This is like the miracle of the 20th century.
57:07
SPEAKER_01: I'd argue as important as any other technology of that era.
57:10
And I think we've got the chance here with happy reprogramming
57:13
to do that again.
57:14
SPEAKER_01: And so while,
57:15
SPEAKER_01: yes,
57:16
SPEAKER_01: it may not be the solution to all problems,
57:18
SPEAKER_01: it's a big enough solution that it's worth working on.
57:21
SPEAKER_02: Yeah.
57:22
SPEAKER_02: I'm sure there's different idealized,
57:24
I guess the idealized form is maybe you pop a pill and you pop
57:27
that pill once and
57:29
SPEAKER_02: you're rebooted across your whole body.
57:31
SPEAKER_02: I know, you know, that's far off.
57:33
SPEAKER_02: And so in a,
57:35
SPEAKER_02: in a semi-idealized form near term,
57:37
SPEAKER_02: say it's like five,
57:38
SPEAKER_02: 10,
57:39
SPEAKER_02: 15 years out and things are working really well,
57:43
SPEAKER_02: people would,
57:44
SPEAKER_02: they would what?
57:44
SPEAKER_02: They would show up for like an IV and have this,
57:48
SPEAKER_02: a little bit of a cocktail that would reboot multiple kinds of cells.
57:51
And then they would have to,
57:52
this isn't a one-time thing,
57:54
like a pure vaccine,
57:56
right?
57:56
SPEAKER_02: At least in the early days,
57:57
SPEAKER_02: like you would show up every few months,
58:00
SPEAKER_02: every couple of years to get this reboot through this cell cocktail.
58:05
SPEAKER_01: Yeah, yeah.
58:06
SPEAKER_01: You're asking exactly the right question,
58:08
SPEAKER_01: which in some ways is like,
58:09
SPEAKER_01: okay,
58:09
SPEAKER_01: let's imagine we even find the factors that make an old cell look young.
58:12
SPEAKER_01: That's awesome.
58:13
SPEAKER_01: But it's not sufficient to make a medicine.
58:15
We can imagine a version where it makes an old cell look and
58:17
act young for 10 minutes.
58:19
In which case,
58:19
if you need an IV,
58:20
it's actually not that useful of a medicine.
58:22
SPEAKER_01: And so you need to actually be a durable effect.
58:25
SPEAKER_01: So I'll start first with like, what do we know is possible?
58:27
SPEAKER_01: And what do we have today?
58:28
SPEAKER_01: And how do we get to maybe spanning the gap between those?
58:32
SPEAKER_01: We know epigenetic reprogramming can last decades.
58:35
The most extreme form of that is that you were once a bunch of
58:37
embryonic stem cells
58:38
SPEAKER_01: that became other types of cells,
58:40
and your eyeballs don't spontaneously forget that they're
58:42
eyeballs and become a pancreas.
58:43
And so we know these epigenetic marks can last over,
58:46
in principle,
58:47
your entire lifespan.
58:48
SPEAKER_01: We also know that from other groups,
58:50
SPEAKER_01: you can go in and make targeted epigenetic reprogramming events,
58:53
SPEAKER_01: you know,
58:54
SPEAKER_01: change the epigenetics around a single locus in the genome.
58:57
SPEAKER_01: And that can last multiple years in a non-human primate.
59:00
SPEAKER_01: So those experiments have been done.
59:01
SPEAKER_01: That's an existence proof exists.
59:03
SPEAKER_01: We also know in the laboratory,
59:05
you can go and make some of those specific edits and then make
59:07
cells divide
59:09
SPEAKER_01: hundreds of times,
59:10
kind of simulating like multiple lifespans worth of divisions
59:13
where they have to
59:13
SPEAKER_01: copy this mark over and over again.
59:16
SPEAKER_01: And it's still durable.
59:17
So I think the upper bound on what's possible is basically you
59:20
could have,
59:21
SPEAKER_01: in principle,
59:22
SPEAKER_01: a one-time dose that resets your epigenome back to a youthful state.
59:26
SPEAKER_01: We don't have that today.
59:27
SPEAKER_01: That's just like, in principle, it's possible.
59:29
So what we've shown today is that these effects can last in
59:31
animals and human cells
59:33
SPEAKER_01: growing inside mice and humanized livers.
59:36
SPEAKER_01: These effects can last at least a few weeks.
59:38
And already,
59:38
I think,
59:39
for certain types of patients,
59:40
that's actually enough.
59:41
There are patients who every three weeks go in and get an IV
59:44
for an LNP RNA medicine,
59:45
SPEAKER_01: and that's been going on for about a decade now.
59:47
SPEAKER_01: So we think even that duration is tractable for a medicine.
59:50
But certainly for a broad population,
59:52
what you'd want to do is extend that durability.
59:53
SPEAKER_01: First to months, ideally you'd have like a six-month dosing first.
59:57
And there are patients who get a medicine like that called
59:58
Inclusiran as well.
59:59
Every six months you get an IV,
1:00:01
keeps your cholesterol lower for that particular medicine.
1:00:04
SPEAKER_01: Ideally, you then move out to years.
1:00:06
SPEAKER_01: Maybe that number of years gets quite large,
1:00:08
and this becomes something that you do every once in a while as
1:00:10
part of your
1:00:10
SPEAKER_01: regular checkup.
1:00:12
SPEAKER_01: Ideally,
1:00:12
SPEAKER_01: it's not something you're worried about all the time,
1:00:14
SPEAKER_01: but once every couple of years,
1:00:15
SPEAKER_01: you go in for your physical,
1:00:16
SPEAKER_01: and you get your IV of your reprogramming medicine,
1:00:18
SPEAKER_01: and you don't worry about it again for quite a while.
1:00:21
You're so eloquent and well-versed on both the biology and the computation.
1:00:26
SPEAKER_02: What's your background to get to this place, and what drew you into this?
1:00:31
SPEAKER_01: Sure, so I did an undergrad degree in effectively molecular biology.
1:00:35
SPEAKER_01: And then I did a PhD at UCSF right up the road in San Francisco.
1:00:39
SPEAKER_01: So technically my PhD is in developmental biology,
1:00:42
SPEAKER_01: but I'll jokingly say,
1:00:43
SPEAKER_01: don't ask me to look at an embryo because I know less than most would.
1:00:47
I really did my PhD at sort of the intersection of computer
1:00:50
science and molecular biology.
1:00:51
SPEAKER_01: And so I like to tell people,
1:00:52
SPEAKER_01: you know,
1:00:53
I try and look for problems where by being a dilettante in both
1:00:55
of those fields,
1:00:56
SPEAKER_01: you can make progress as opposed to just being an expert in one.
1:01:00
SPEAKER_01: And so I really have been working on aging my entire career.
1:01:04
My sort of story of like why I work on biology is I originally
1:01:07
wanted to be a
1:01:08
SPEAKER_01: physicist when I was a kid.
1:01:09
SPEAKER_01: I really thought Richard Feynman was like the coolest guy alive.
1:01:12
SPEAKER_01: And then I realized that when I read all these physics books,
1:01:14
SPEAKER_01: all the dates of the important discoveries were like 100 years ago.
1:01:17
SPEAKER_01: And then they just kind of stopped.
1:01:19
SPEAKER_01: And I was like, wait a second.
1:01:20
SPEAKER_01: It seems like maybe progress in this field isn't going quite as quickly.
1:01:24
SPEAKER_01: And by contrast,
1:01:25
SPEAKER_01: I was reading a biology textbook one day,
1:01:27
SPEAKER_01: and the beginning of the book starts like you'd expect.
1:01:29
SPEAKER_01: It's like a chapter about how life starts.
1:01:31
SPEAKER_01: Like, here's how a cell becomes an organism.
1:01:33
SPEAKER_01: And I was like 15,
1:01:34
SPEAKER_01: so I did not understand all of the details,
1:01:37
SPEAKER_01: but I understood there were a lot of details there.
1:01:39
SPEAKER_01: And I kind of naturally expected I'd get to the end.
1:01:41
And I was like,
1:01:42
okay,
1:01:42
this is where the answer is about why everyone gets old and dies.
1:01:45
SPEAKER_01: And there's just no chapter on how aging works.
1:01:47
SPEAKER_01: And I thought originally, like, this textbook must be bad.
1:01:49
SPEAKER_01: Let me go find another textbook.
1:01:51
SPEAKER_01: And I realized there's never a chapter on how aging works.
1:01:54
And I just became fascinated with this originally as sort of
1:01:57
just a really profound,
1:01:59
SPEAKER_01: important problem in the natural sciences,
1:02:01
SPEAKER_01: sort of on par with,
1:02:02
SPEAKER_01: like,
1:02:02
SPEAKER_01: what is matter composed of?
1:02:04
SPEAKER_01: How does energy emerge?
1:02:05
SPEAKER_01: Why do biological systems inevitably break down?
1:02:08
SPEAKER_01: And then as I had matured and grew as a scientist,
1:02:11
SPEAKER_01: realized if you could answer this question,
1:02:13
SPEAKER_01: it's not just a curiosity.
1:02:15
If you could make even a marginal impact on the rate of aging
1:02:17
in people or reverse
1:02:18
SPEAKER_01: it to some degree,
1:02:20
it would really be the largest impact we could have in all of
1:02:22
medicine in terms of
1:02:23
SPEAKER_01: adding happy,
1:02:24
SPEAKER_01: healthy years to individuals.
1:02:25
And if you think about what are the most valuable products one
1:02:29
could produce,
1:02:29
SPEAKER_01: I really do think it's time.
1:02:31
It's more years to do the things you want to do,
1:02:33
more years with the people you care about.
1:02:34
SPEAKER_01: You would trade nothing for that at the end of your life.
1:02:38
SPEAKER_01: And so I've always been fascinated with this.
1:02:40
And so,
1:02:40
you know,
1:02:40
as a graduate student,
1:02:41
I worked on trying to study how muscle stem cells aged.
1:02:44
Why,
1:02:45
no matter how many bicep curls do I do when I'm 75,
1:02:47
can I not get jacked?
1:02:49
SPEAKER_01: That was sort of the question in my mind.
1:02:50
SPEAKER_01: Unless you're Brian Johnson.
1:02:51
Unless you're Brian Johnson,
1:02:52
in which case you might still get jacked when you're 75.
1:02:56
And so I worked on basically trying to address that question by
1:02:59
building new tools
1:02:59
SPEAKER_01: to measure how these cells behaved.
1:03:01
At first,
1:03:01
I was really trying to apply modern deep learning and computer
1:03:04
vision to microscopy.
1:03:06
SPEAKER_01: That's sort of how I got into the machine learning field.
1:03:08
SPEAKER_01: This is now like 10 years ago, right when things were kicking off.
1:03:11
SPEAKER_01: This is like right after AlexNet.
1:03:14
SPEAKER_01: And then I went on to a company called Calico,
1:03:16
SPEAKER_01: which is owned by Alphabet,
1:03:17
SPEAKER_01: run by a really legendary CEO named Art Levinson,
1:03:19
one of just like the most intelligent people I've ever had the
1:03:22
pleasure of working for.
1:03:23
SPEAKER_01: Yeah, he's very good genetic.
1:03:24
SPEAKER_01: Yeah, he was CEO at Genentech.
1:03:26
SPEAKER_01: He was one of the early employees.
1:03:27
SPEAKER_01: He's chairman at Apple.
1:03:28
You know,
1:03:29
again,
1:03:29
guy's like truly a genius,
1:03:31
somebody that I really admire and look up to.
1:03:34
Had the pleasure to work there for a number of years across
1:03:37
some of the drug
1:03:37
development side and then later ran a group in the aging
1:03:40
biology division and so
1:03:42
SPEAKER_01: worked on epigenetic reprogramming there as well.
1:03:44
So again,
1:03:45
I've sort of traveled this path of always trying to understand
1:03:48
how does aging work?
1:03:48
SPEAKER_01: How can we ideally intervene upon it?
1:03:50
And then because I'm not an expert in either molecular biology
1:03:53
or computer science,
1:03:54
trying to find those problems at the intersection where being
1:03:57
good enough at both
1:03:58
SPEAKER_01: is sufficient to really make progress.
1:03:59
SPEAKER_01: Yeah.
1:04:00
SPEAKER_02: Okay.
1:04:00
SPEAKER_02: So, but you're not...
1:04:02
SPEAKER_02: You mentioned before,
1:04:03
it's only in the last 10 years that you thought some of this
1:04:06
was possible,
1:04:07
SPEAKER_02: maybe even more recently if we're honing in on the AI part.
1:04:12
SPEAKER_02: It's interesting because aging has gone through.
1:04:15
SPEAKER_02: mega transformations,
1:04:16
SPEAKER_02: longevity field,
1:04:17
SPEAKER_02: depending how far back you want to go,
1:04:19
SPEAKER_02: if you want to go all the way to,
1:04:20
SPEAKER_02: like,
1:04:20
SPEAKER_02: Fountain of Youth.
1:04:21
SPEAKER_00: Yeah.
1:04:23
SPEAKER_00: Longevity field, foundation, 1505, Pulse de Leon.
1:04:25
SPEAKER_02: But,
1:04:27
SPEAKER_02: you know,
1:04:27
SPEAKER_02: we do go through these waves,
1:04:28
SPEAKER_02: right,
1:04:29
SPEAKER_02: of between magic and science,
1:04:32
SPEAKER_02: and this is real,
1:04:34
SPEAKER_02: and now it's a cult,
1:04:35
SPEAKER_02: and now,
1:04:35
SPEAKER_02: you know,
1:04:35
SPEAKER_02: like,
1:04:36
SPEAKER_02: these oscillations.
1:04:37
SPEAKER_02: But you've always been coming at this from this... This is not like...
1:04:42
SPEAKER_02: You've always been coming to this from the science side.
1:04:45
SPEAKER_02: You didn't grow up in the whole sort of longevity cult of the Bay Area.
1:04:52
SPEAKER_01: No, no.
1:04:53
I don't think I'm still part of the longevity cult in the Bay
1:04:56
Area as far as I know.
1:04:58
I come at this very much from the hard science angle of
1:05:02
ultimately what I find
1:05:03
rewarding is creating real value in the world and making
1:05:05
people's lives better.
1:05:07
And the difference between science and magic is that science
1:05:09
actually works.
1:05:10
SPEAKER_01: And if the science isn't solid,
1:05:12
SPEAKER_01: you're just not going to create value for people at the end of the day.
1:05:14
SPEAKER_01: It doesn't matter how exciting it sounds.
1:05:16
And so I would rather be a little bit more conservative in some
1:05:21
of my statements.
1:05:22
I would rather be working on something that is a little bit
1:05:26
nearer term or closer
1:05:27
SPEAKER_01: at hand if I think it's actually going to work.
1:05:30
SPEAKER_01: Yeah.
1:05:31
than to maybe participate in some of the more outlandish sort
1:05:34
of let's live forever
1:05:35
SPEAKER_01: activities in the field.
1:05:37
SPEAKER_01: I don't necessarily disparage folks who are interested in that domain,
1:05:40
but I personally am really interested by delivering real value
1:05:43
for real people.
1:05:44
And I think sticking to the most rigorous aspects of the
1:05:46
science is the way to do that.
1:05:48
SPEAKER_02: And there's four founders of the company?
1:05:50
SPEAKER_01: So there's three co-founders, myself, Brian Armstrong, Blake Byers.
1:05:53
SPEAKER_02: Okay.
1:05:53
SPEAKER_02: And they...
1:05:55
SPEAKER_02: They're co-founders of the company,
1:05:57
SPEAKER_02: but they obviously have lots going on in their lives as well.
1:06:01
SPEAKER_02: And what was the...
1:06:02
SPEAKER_02: You guys were all just sitting around one day and were like,
1:06:07
SPEAKER_02: transcription factors are so exciting.
1:06:09
SPEAKER_02: We should go do something here.
1:06:11
SPEAKER_02: Or what was the genesis...
1:06:12
SPEAKER_01: Not that far off, honestly.
1:06:15
SPEAKER_01: So I was working on epigenetic reprogramming previously.
1:06:17
SPEAKER_01: I was running a lab at Calico.
1:06:19
SPEAKER_01: I had published a couple pieces of science around this.
1:06:22
SPEAKER_01: And I think my co-founders,
1:06:24
SPEAKER_01: Blake and Brian,
1:06:24
SPEAKER_01: independently,
1:06:25
SPEAKER_01: had been spending some time together thinking about,
1:06:27
SPEAKER_01: like,
1:06:27
SPEAKER_01: what are the really important problems we can work on?
1:06:29
SPEAKER_01: They'd both been very successful in their other endeavors and thought,
1:06:32
SPEAKER_01: you know,
1:06:33
what's one of the core quests we can go on that would really
1:06:36
improve humanity?
1:06:37
SPEAKER_01: And I think if you sit down and really think about it,
1:06:38
SPEAKER_01: like,
1:06:39
SPEAKER_01: what are the most important problems in the world?
1:06:40
SPEAKER_01: I can list off a few.
1:06:41
SPEAKER_01: It's like,
1:06:42
SPEAKER_01: creating maybe artificial intelligences,
1:06:44
SPEAKER_01: massive potential to make the world better,
1:06:46
SPEAKER_01: maybe making humans multi-planetary,
1:06:48
SPEAKER_01: you need a backup system for the human race off of Earth,
1:06:51
SPEAKER_01: creating infinite energy,
1:06:52
SPEAKER_01: making fusion,
1:06:54
SPEAKER_01: not dying.
1:06:55
SPEAKER_01: These are kind of all in the same order of importance.
1:06:59
SPEAKER_01: And I think in that latter category,
1:07:01
SPEAKER_01: my co-founders felt,
1:07:02
SPEAKER_01: as we were just talking about,
1:07:04
SPEAKER_01: more attention could be going here.
1:07:05
SPEAKER_01: Yes, there are a few envoys.
1:07:06
SPEAKER_01: It's not to disparage anyone who's already in the field,
1:07:08
SPEAKER_01: but just relative to the potential impact,
1:07:10
SPEAKER_01: There's not a lot of attention.
1:07:11
We ended up getting connected through some mutual friends
1:07:13
talking about the work we
1:07:14
SPEAKER_01: were doing around epigenetic reprogramming.
1:07:16
And I think all of us were excited about the idea to actually
1:07:19
make medicines using
1:07:20
SPEAKER_01: these tools,
1:07:20
SPEAKER_01: not just to do the basic science,
1:07:22
SPEAKER_01: that's great too,
1:07:22
SPEAKER_01: but to try and translate it,
1:07:23
SPEAKER_01: to try and make medicines this decade,
1:07:26
SPEAKER_01: not just this century.
1:07:27
SPEAKER_01: And if you look at the history of the biotechnology ecosystem,
1:07:31
I believe it to be true that an incumbent has never introduced
1:07:34
a new therapeutic modality.
1:07:36
SPEAKER_01: Never once.
1:07:36
SPEAKER_01: It's always a small startup.
1:07:37
SPEAKER_01: It's always a scrappy group of people like a Genentech,
1:07:40
a bunch of guys in the South San Francisco warehouse next to an
1:07:42
old steel mill that
1:07:43
SPEAKER_01: introduced the new therapeutics.
1:07:45
We can dive into some reasons why,
1:07:47
but that's just empirically what happens.
1:07:49
SPEAKER_01: And so we felt,
1:07:50
SPEAKER_01: if we want to see these medicines in the world,
1:07:51
SPEAKER_01: we probably need to build a company.
1:07:52
SPEAKER_01: That's just historically how this has happened.
1:07:55
SPEAKER_01: And so we got to talking about what that company would look like,
1:07:57
SPEAKER_01: and that eventually became New Limit.
1:07:59
SPEAKER_01: Okay.
1:08:00
SPEAKER_02: Okay,
1:08:01
so I know you guys just did this update where you brought
1:08:03
people up to speed on
1:08:05
SPEAKER_02: some of your liver work,
1:08:06
SPEAKER_02: some of your T-cell work.
1:08:08
SPEAKER_02: You talked about how the model...
1:08:10
SPEAKER_02: You're going to keep scaling it up,
1:08:12
SPEAKER_02: getting data better and better,
1:08:14
SPEAKER_02: hopefully,
1:08:14
SPEAKER_02: to find more of these promising combinations,
1:08:17
but just to give people a sense of what you're working on very
1:08:23
focused over the
1:08:23
SPEAKER_02: next year,
1:08:24
SPEAKER_02: two years,
1:08:25
SPEAKER_02: and if it is going into any other types of cells.
1:08:29
SPEAKER_01: Yeah, yeah, absolutely.
1:08:30
SPEAKER_01: So I'll maybe break this down into like three bullet points.
1:08:33
SPEAKER_01: So one of the things we're trying to do is actually advance products.
1:08:35
SPEAKER_01: So we have a few what we call leads now.
1:08:37
SPEAKER_01: So these are like real prototype medicines you can hold in your hand.
1:08:40
SPEAKER_01: There's a tube of them.
1:08:41
SPEAKER_01: It's not science fiction.
1:08:42
SPEAKER_01: And we want to start taking those toward the clinic.
1:08:44
SPEAKER_01: And so ideally,
1:08:45
SPEAKER_01: by the end of this year,
1:08:46
SPEAKER_01: we'll have what we call a preclinical candidate,
1:08:48
SPEAKER_01: a medicine that we think
1:08:49
SPEAKER_01: This really is worth batting on.
1:08:50
SPEAKER_02: For the liver or it could be?
1:08:52
SPEAKER_01: This would be in the liver right now, but almost certainly.
1:08:54
SPEAKER_01: If we found it in T-cells, I'm not going to be upset.
1:08:56
SPEAKER_01: It's probably in the liver.
1:08:59
And then the other sort of piece of this is on the technology
1:09:01
and what we call our
1:09:02
SPEAKER_01: discovery engine side.
1:09:03
SPEAKER_01: So we want to scale up the amount of data we're generating.
1:09:05
SPEAKER_01: We want to build models that are even more performant.
1:09:08
And sort of the high order bit there would be not only can we
1:09:10
test roughly an order
1:09:11
SPEAKER_01: of magnitude more sets of TFs,
1:09:13
SPEAKER_01: but of those sets we test,
1:09:15
can we use those models so that the number of them that
1:09:18
actually yield interesting
1:09:19
SPEAKER_01: results goes up several fold.
1:09:21
SPEAKER_01: Right now,
1:09:21
it's roughly one in a hundred of the things we test is actually interesting,
1:09:25
SPEAKER_01: which is...
1:09:26
SPEAKER_01: Way better than it was when we started.
1:09:27
SPEAKER_01: But still, you could go from 1 in 100 to 2 in 100 to 5 in 100.
1:09:31
SPEAKER_01: And really what that reflects is that for every dollar you're investing,
1:09:34
SPEAKER_01: you're getting back X more discoveries.
1:09:36
SPEAKER_01: So we want that to improve over the next couple of years.
1:09:38
SPEAKER_01: And then finally,
1:09:39
SPEAKER_01: we want to work on more than just the liver and T cells,
1:09:41
SPEAKER_01: to your earlier question.
1:09:42
SPEAKER_01: So we're going to be onboarding our third cell type over the coming year.
1:09:45
SPEAKER_01: This will likely be endothelial cells.
1:09:46
So these are the cells of your vasculature,
1:09:48
your veins and your capillaries,
1:09:49
your arteries.
1:09:50
SPEAKER_01: In some ways, this is the tissue that's inside of every other tissue.
1:09:53
And I think you'd be surprised to learn how many of the
1:09:55
challenges your other
1:09:56
organs experience can really come from challenges in getting
1:10:00
access to sort of the
1:10:01
SPEAKER_01: information superhighway of your body,
1:10:02
SPEAKER_01: which is your bloodstream.
1:10:04
SPEAKER_01: So those are sort of the big three headlines over the coming years.
1:10:07
And I hope if we have a chance to connect again a few years
1:10:09
down the line after that,
1:10:10
we'll be talking about those milestones being things like
1:10:13
actually entering human trials.
1:10:15
SPEAKER_02: And then today,
1:10:16
SPEAKER_02: I don't know if you got the full scope of what we get to see,
1:10:20
SPEAKER_02: but I know we are going to get to probably see
1:10:22
SPEAKER_02: This mouse with a human liver.
1:10:26
SPEAKER_01: He just looks like a normal mouse, but you can see him.
1:10:27
SPEAKER_01: He's got a human liver.
1:10:28
SPEAKER_01: And kind of the remarkable thing is he doesn't look weird.
1:10:30
SPEAKER_02: Yeah.
1:10:31
SPEAKER_02: And then what else might we see in that?
1:10:33
SPEAKER_01: Yeah, so people can show you some of the data from that.
1:10:35
SPEAKER_01: Folks will probably show you isolations of cells from blood.
1:10:39
Folks can show you the automated microscopes we use to measure
1:10:42
how well T cells are
1:10:43
SPEAKER_01: able to perform their job of killing other cells.
1:10:45
Folks can show you big factories of viruses that we cook up
1:10:48
that we use to deliver reproductions.
1:10:50
SPEAKER_01: sorry, that we use to deliver reprogramming factors.
1:10:53
Folks can show you areas where we take transcription factors
1:10:56
from an idea into
1:10:57
SPEAKER_01: actually building molecular constructs we use to start experiments.
1:11:01
People can show you DNA sequencing and how we profile cells,
1:11:04
a couple different types of that.
1:11:05
SPEAKER_01: So there will be all sorts of fun stuff.
1:11:07
You get to look at robots moving liquid around,
1:11:09
always everybody's favorite.
1:11:10
SPEAKER_02: Love a robot, a pipetting robot, always the best.
1:11:14
SPEAKER_02: Well, thank you so much.
1:11:15
SPEAKER_02: Thanks for your time.
1:11:16
SPEAKER_02: We're looking forward to exploring.
1:11:18
SPEAKER_01: Yeah, thank you, Ashley.
1:11:19
SPEAKER_01: Great to have you here.
1:11:19
SPEAKER_01: And thank you, everyone, for your attention.
1:11:21
SPEAKER_02: That was great, man.
1:11:23
SPEAKER_02: Awesome.
1:11:24
SPEAKER_01: Thank you.
1:11:24
SPEAKER_02: Thank you.
1:11:27
SPEAKER_02: I hope everyone enjoyed this episode.
1:11:30
SPEAKER_02: As always, Core Memory is hosted by me, Ashley Vance.
1:11:35
SPEAKER_02: Our guest today was Jacob Kimmel.
1:11:39
SPEAKER_02: The show is produced by me and David Nicholson.
1:11:43
SPEAKER_02: Our theme song is by James Mercer and John Sortland.
1:11:48
SPEAKER_02: And the show is edited by the...
1:11:50
SPEAKER_02: not egregious, John Sortland.
1:11:53
SPEAKER_02: And it was sponsored by E1 Ventures.
1:11:57
SPEAKER_02: Turn to E1 Ventures for all your venture capital needs.
1:12:01
SPEAKER_02: Thank you so much, E1, and we'll see you on the next episode.