Jacob Kimmel on Dwarkesh Patel: Why Evolution Ignored Aging

1. 0:00 – 12:48

   Three reasons evolution didn't optimize for longevity

   1. JKJacob Kimmel0:00

      You always have to start by asking yourself, "Did evolution spend a lot of time optimizing this?" If yes, my job is going to be insanely hard.

   2. DPDwarkesh Patel0:06

      (laughs)

   3. JKJacob Kimmel0:06

      If no, potentially there are some low-hanging fruit. And so I think that puts human aging and longevity really in this category of problem in which it should be, relatively speaking, easy to try and intervene and provide health. We have a gene called TRIM5alpha.

   4. DPDwarkesh Patel0:19

      Mm.

   5. JKJacob Kimmel0:20

      TRIM5alpha once protected against an HIV-like pathogen. It's currently protecting against a virus which no longer exists, and you can edit it back to actually restrict HIV dramatically. You can reprogram a cell's type and a cell's age simultaneously just by turning on four genes. Out of the 20,000 genes in the genome, the tens of millions of biomolecular interactions, just four genes is enough. That's a shocking fact.

   6. DPDwarkesh Patel0:40

      Today, I have the pleasure of chatting with Jacob Kimmel, who is president and co-founder of NewLimit, where they epigenetically reprogram cells to their younger states. Jacob, thanks so much for coming on the podcast.

   7. JKJacob Kimmel0:50

      Thanks so much for having me. Looking forward to the conversation.

   8. DPDwarkesh Patel0:51

      All right, first question, what's the first principles argument for why evolution just, like, uh, discards us so easily? Look, I know evolution cares about our kids, but if we have longer, healthier lifespans, we can have more kids, right? Or we can care for them longer, we can care for our grandkids. So is there some pleiotropic effect that anti-aging medicine would have which actually selects against you staying young for longer?

   9. JKJacob Kimmel1:17

      Mm-hmm. Yeah, so I think there, there are a couple different ways one can tackle this. One is you have to think about, what's the selective pressure that would make one live longer?

   10. DPDwarkesh Patel1:24

       Right.

   11. JKJacob Kimmel1:24

       And encode for higher health over longer durations. Do you have that selective pressure present? There's another which is, are there any antiselective pressures that are actually pushing against that? And there's a third piece of this, which is something like the constraints of your optimizer. If we think about the genome as a set of parameters and the optimizer is natural selection-

   12. DPDwarkesh Patel1:41

       Yeah.

   13. JKJacob Kimmel1:41

       ... then you've got some constraints on how that actually works. You can only do so many mutations at a time. You have to kind of spend your steps that update your genome-

   14. DPDwarkesh Patel1:48

       Right.

   15. JKJacob Kimmel1:48

       ... in certain ways. So tackling those from a few different directions, like what would the positive possible selection be? As you highlighted, it might be something like, well, if I'm able to extend the lifespan of an individual, they can have more children, they can care for those children more effectively. That genome should propagate more, more readily into the population. And so one of the challenges then if you're trying to think back in sort of a thought experiment style of evolution, of, of, uh, evolutionary simulation here would be, what were the conditions under which a person would actually live long enough for that phenotype to be selected for, and how often would that occur? And so this brings us back to some very hypothetical questions, things like, what was the baseline hazard rate during the majority of-

   16. DPDwarkesh Patel2:26

       Mm.

   17. JKJacob Kimmel2:26

       ... human and primate evolution? The hazard rate is simply what is the likelihood you're going to die on any given day. And that integrates everything. That's, like, diseases from aging. That's getting eaten by a tiger. That's falling off a cliff. That's, like, scraping your foot on a rock and getting an infection and dying from that. And so from the best evidence we have, the baseline hazard rate was very, very high.

   18. DPDwarkesh Patel2:45

       (laughs)

   19. JKJacob Kimmel2:45

       And so even absent aging, you're unlikely to actually reach those outer limits of possible health, where aging is one of the main limitations. And so the number of individuals in the population that are gonna make it later in that lifespan, where using some of your evolutionary updates to try and actually push your lifespan upward, is relatively limited. So the amount of gradient signal flowing back to the genome then is not as high as one might intuitively think.

   20. DPDwarkesh Patel3:09

       Right. By the way, uh, just on that, often people who are trying to forecast AI will discuss basically how hard did evolution try to optimize for intelligence, and what were the, the things which optimizing for intelligence would have prevented evolution for selecting for at the same time, which would make it so that even if intelligence were a relatively easy thing to build in this universe-

   21. JKJacob Kimmel3:30

       Mm-hmm.

   22. DPDwarkesh Patel3:31

       ... it would've taken evolution so long to get at human-level intelligence. And potentially, if it was, if intelligence would be really easy, then it might imply that, you know, we're gonna get superintelligence and, you know, Jupiter-level intelligence, et cetera, et cetera. The sky's the limit. So one argument, you know, like, birth canal sizes, et cetera, or the fact that, you know, we had to spend most of our resources on the immune system. But what you just hint at that is actually an independent argument that if you have this high hazard rate, that would imply that you can't be a kid for too long, because you gotta, you know... The kids die all the time, and you gotta become an adult so that you can have your kids.

   23. JKJacob Kimmel4:08

       Yeah, you gotta contribute resources back-

   24. DPDwarkesh Patel4:09

       Yeah.

   25. JKJacob Kimmel4:09

       ... to the group.

   26. DPDwarkesh Patel4:10

       Right.

   27. JKJacob Kimmel4:10

       You can't just be a freeloader.

   28. DPDwarkesh Patel4:11

       Exactly.

   29. JKJacob Kimmel4:11

       You need to get calories, go out in the jungle, get some berries.

   30. DPDwarkesh Patel4:13

       Right. Like if you're just, um, if you're just hanging out, learning stuff for 50 years, you're just gonna die before you get to have kids yourself.
2. 12:48 – 26:08

   Why didn't humans evolve their own antibiotics?

   1. JKJacob Kimmel12:48

      evidence.

   2. DPDwarkesh Patel12:48

      Antibiotics are an even more clear case of that because-... here is something that evolution actually cares a lot about.

   3. JKJacob Kimmel12:53

      Mm-hmm.

   4. DPDwarkesh Patel12:53

      Um, right? So it feels like antibiotics should have-

   5. JKJacob Kimmel12:55

      Why, why didn't humans evolve their own antibiotics?

   6. DPDwarkesh Patel12:57

      Yeah, yeah.

   7. JKJacob Kimmel12:57

      Yeah. Uh, that's actually an excellent question that I haven't heard posed before. Um, so we think about, where do antibiotics come from? To your point, we could synthesize them. They're just metabolites largely of other, other bacteria-

   8. DPDwarkesh Patel13:06

      Right.

   9. JKJacob Kimmel13:06

      ... and fungi. You think about the story of penicillin, what happens. Alexander Fleming finds some fungi growing on a dish-

   10. DPDwarkesh Patel13:11

       Yeah.

   11. JKJacob Kimmel13:11

       ... and the fungi secrete this, uh, penicillin antibiotic compound, and so there's no bacteria growing near the fungi. And he says he has this li- lightbulb moment of, "Oh my gosh, they're probably making something that kills bacteria." There's no prima facie reason that you couldn't imagine encoding an antibiotic cassette into a mammalian genome. I think part of the challenge that you run into is that you're always in evolutionary competition. There's this notion of what's called the Red Queen Hypothesis. It's an allusion to the story in Lewis Carroll's Through the Looking Glass, where the Red Queen is running really fast just to stay in place. So when you look at sort of pathogen-host interactions or competition between bacteria and, and fungi that are all trying to compete for the same niche, what you find is they're evolving very rapidly in competition with one another. It's an arms race. Every time a bacteria evolves a new evasion mechanism, the fungus that occupies the niche will evolve some new-

   12. DPDwarkesh Patel13:55

       Right.

   13. JKJacob Kimmel13:55

       ... antibiotic. And so part of why that there is this competitiveness between the two is they both have very large population sizes in terms of number of genomes per unit resource they're consuming. There are trillions of bacteria in a drop of water that you might pick up, so there's trillions of copies of the genome, massive analog parallel computation.

   14. DPDwarkesh Patel14:12

       Yeah.

   15. JKJacob Kimmel14:12

       And then at the same time, they can tolerate really high mutation rates because they're prokaryotic. They don't have multiple cells. So if one cell manages to mutate too much and it d- isn't viable or it grows too fast, it doesn't really compromise the population in the whole genome, whereas for metazoans like you and I, if even one of our cells has too many mutations, it might turn into a cancer-

   16. DPDwarkesh Patel14:31

       Yeah.

   17. JKJacob Kimmel14:31

       ... and eventually kill off the organism. So basically, what I'm getting at, and this is a long-winded way of, of getting there, is that bacteria and other types of microorganisms are very well-adapted to building these complex metabolic cascades that are necessary to make something like antibiotics, and they are necessar- it's necessary to maintain that same mutation rate and population size in order to maintain the competition. Even if our human genomes stumbled into making an antibiotic, most pathogens probably would have mutated around it pretty quickly.

   18. DPDwarkesh Patel14:59

       Wait, actually that, that should imply that there's, m- through evolutionary history, millions of, quote-unquote, "naive antibiotics"-

   19. JKJacob Kimmel15:09

       Mm-hmm.

   20. DPDwarkesh Patel15:09

       ... which could have acted as antibiotics, but now basically all the bacteria have evolved around it. Do we see evidence of these, like, historical antibiotics that some fungi came up with and bacteria revolved around, and all, there's evidence for remnant in their DNA?

   21. JKJacob Kimmel15:23

       I'm, I'm going a bit beyond my own knowledge here. So I wanna say my strong hypothesis would be yes. I can't point to direct-

   22. DPDwarkesh Patel15:29

       Right.

   23. JKJacob Kimmel15:29

       ... evidence today. There are some examples of this where, for instance, bacteria that, uh, fight off viruses that infect them, bacteriophages-

   24. DPDwarkesh Patel15:36

       Yeah.

   25. JKJacob Kimmel15:36

       ... have things like CRISPR systems.

   26. DPDwarkesh Patel15:38

       Right.

   27. JKJacob Kimmel15:38

       So you can actually go and look at the spacers, the individual guide sequences that tell the CRISPR system-

   28. DPDwarkesh Patel15:42

       Yeah.

   29. JKJacob Kimmel15:43

       ... which genome do you go, where do you cut, and you find some of these guides that are very ancient. It seems like this bacterial genome might not have encountered that particular pathogen for quite a while, and so you can actually get sort of an evolutionary history of, what was the warfare like? What were the various conflicts throughout this genomic history? Just by looking at those sequences. In mammals, where I do know a bit better, we do have examples of this, where there is this co-evolution of pathogen and host. Imagine you have some anti-pathogen gene A fighting off some virus X. Well, you then actually update, so now you have virus X prime and anti-pathogen gene A prime. Now, virus X prime goes away, but actually virus X still exists, and we've lost our ability to fight it. Those examples really do happen, and so there's a prominent one in the human genomes. We have a gene called TRIM5alpha.

   30. DPDwarkesh Patel16:26

       Mm.
3. 26:08 – 45:24

   De-aging cells via epigenetic reprogramming

   1. DPDwarkesh Patel26:08

      um, uh, evolution didn't select for aging. What are you doing? What's your approach at New Limit that you think is, um, is likely to find the true cause of aging?

   2. JKJacob Kimmel26:19

      Yeah, so we're working on something called epigenetic reprogramming, which very broadly is using genes called transcription factors. I like to think about these as sort of the orchestra conductors of the genome. They don't perform many functions directly themselves, but they bind specific s- pieces of DNA, and then they tell which genes to turn on and which genes to turn off. They eventually put chemical marks on top of DNA, on some proteins that DNA surrounds, and this is one of the answers, this particular layer of regulation called the epigenome. It's the answer to this fundamental biological question of how do all my cells have the same genome, but ultimately do very different things? Your eyeball and your kidney have the same code, and yet they're performing different functions, and that may sound a little bit simplistic, but ultimately, (laughs) I think it's kind of a profound realization. And so that epigenetic code is really what's important for cells to define their functions. That's what's telling them which genes to evoke from your genome. What is, has now become relatively apparent is that the epigenome can degrade with age. It changes. The particular marks that tell your cells which genes to use can shift as you get older. This means that cells aren't able to use the right genetic programs at the right times to respond to their environment. You're then more susceptible to disease. You have a less, uh, a less resilience to many insults that you might experience. And our hope is that by remodeling the epigenome back toward the state it was in when you were young, right after development, that you'll be able to actually address myriad different diseases whose... one of strong contributing factors is that cells are less functional than when you were at an earlier point in your life. So we're going after this by trying to find combinations of these transcription factors that are able to actually remodel the epigenome so that they can bind to just the right places in the DNA and then shift the chemical marks back toward that state when you were a young individual.

   3. DPDwarkesh Patel27:53

      Mm. If you were just making these broad, uh, changes to a cell state, uh, through these transcription factors, which have many effects, are there other aspects of a cell state that are likely to get modified at the same time in a way that would be deleterious, or would it be, um, a sort of straightforward effect on cell state?

   4. JKJacob Kimmel28:13

      Oh, how I wish it were straightforward.

   5. DPDwarkesh Patel28:15

      (laughs)

   6. JKJacob Kimmel28:15

      Um, no, i- it's very likely... The, each of these transcription factors binds hundreds to thousands of places in the genome.

   7. DPDwarkesh Patel28:21

      Yeah.

   8. JKJacob Kimmel28:21

      And one way of thinking about it is if you imagine the genome as sort of the base components of cell function, then these transcription factors are kind of like the basis set in linear algebra. It's different combinations and different weights of each of the genes. And so most of them are targeting pretty broad programs, and there are no guarantees that aging actually involves moving perfectly along any of the vectors in this particular basis set.

   9. DPDwarkesh Patel28:42

      Mm.

   10. JKJacob Kimmel28:42

       And so it's probably going to be a little tricky to figure out a combination that actually takes you backward. There's, again, no guarantees from evolution that it's just a simple reset. And so it's actually a critical part of the process that we run through as we try and discover these medicinal combinations of transcription factors we can turn on, is to ensure that they not only are making an aged cell revert to a younger state. We measure that couple different ways. One is simply measuring which genes those cells are using. They use different genes as they get older. You can measure that just by sequencing all of the mRNAs, which are really the expressed form of the genes being utilized in the genome at a given time. You see that aged cells use different genes, can I revert them back to a younger state? Colloquially, we call this, you know, a looks-like assay. Can I make an old cell look like a young one-

   11. DPDwarkesh Patel29:21

       Mm-hmm.

   12. JKJacob Kimmel29:21

       ... based on the genes it's using? And maybe more importantly, we go down and drill to the functional level, and we measure, can I actually make an aged cell perform its functions, its object-

   13. DPDwarkesh Patel29:28

       Right.

   14. JKJacob Kimmel29:29

       ... roles within the body the same way a young cell would? And these are the really critical things you care about for treating diseases. Can I make a hepatocyte, a liver cell in Greek, um, function better in your liver so it's able to process metabolites like the foods you eat, how it's able to process toxins like alcohol and caffeine? Um, can I make a T cell respond to pathogens and other antigens that are presented within your body? So these are the ways in which we measure age, and so we need to ensure that not only does the combination of TFs that we find actually have positive effects along those axes, but we then wanna also measure any potential detrimental effects that ab- of that age.

   15. DPDwarkesh Patel29:59

       Right.

   16. JKJacob Kimmel30:00

       So there are canonical examples where you can seemingly reverse the age of a cell, for instance, at the level of a transcriptome, but simultaneously, you might be changing that cell's type or identity. So Shinya Yamanaka, a scientist who won the Nobel in 2012 for some work he did in about 2007, discovered that you could just take four transcription factors, and actually, just by turning on these four genes, turn an adult cell all the way back into a young embryonic stem cell. This is a pretty emergent, amazing existence proof that shows that you can reprogram a cell's type and a cell's age simultaneously just by turning on four genes. Out of the 20,000 genes in the genome, the tens of millions of biomolecular interactions, just four genes is enough. That's a shocking fact.And so we actually have known for many years now that you can reprogram the age of a cell. The challenge is that simultaneously you're doing a bunch of other stuff-

   17. DPDwarkesh Patel30:44

       Right.

   18. JKJacob Kimmel30:44

       ... as you alluded to. You're changing its type, and that might be pathological. If you did that in the body, it would probably cause a type of tumor called a teratoma. So we measure not only at the level of the genes the cell is using. Do you still look like the right type of cell? Are you still a hepatocyte? Are you still a T cell? If not, that's probably pathological. But you can also use that same information to check for a number of other pathologies that might develop. Did I make this T cell hyperinflammatory in a way that would be bad? Did I make this liver cell, uh, potentially neoplastic, proliferate too much, even when the organism's healthy and undamaged? And you can check for each of those at the level of gene expression programs, and then likewise functionally. Before you put these molecules in a human, you actually just functionally check in an animal. You make an itemized list of the possible risks you might run into, "Here are the ways it might be toxic. Here are the ways it might cause cancer." Are we able to measure determi- uh, deterministically and empirically that that doesn't actually occur?

   19. DPDwarkesh Patel31:32

       Okay, this is a dumb question, but it will help me understand why an AI model is necessary to do any of this work. So you mentioned the Yamanaka factors. From my understanding, the way he identified these four transcription factors was that he found the 24, um, transcription factors that associated, uh, that have high expression in embryonic cells, and then he just turned them all on in a somatic cell. Basically, he systematically removed from this set until he found, like, the minimal set that still induces a cell to become a stem cell and that just, like, uh, doesn't require any fancy AI models, et cetera. Why can't we do the same things for the transcription factors that are associated with younger cells, or expressed more in younger cells as opposed to older cells, and then keep eliminating from them until we find the ones that are necessary to just make a cell young?

   20. JKJacob Kimmel32:23

       I wish it were so easy.

   21. DPDwarkesh Patel32:24

       (laughs)

   22. JKJacob Kimmel32:24

       Um, you're entirely right. You know, Shinya Yamanaka was able to do this with a relatively small team, with relatively few resources, and achieve this remarkable feat.

   23. DPDwarkesh Patel32:31

       Yeah.

   24. JKJacob Kimmel32:32

       So it's entirely worth asking. Why can't a similar procedure work for arbitrary problems in reprogramming cell state?

   25. DPDwarkesh Patel32:38

       Yeah.

   26. JKJacob Kimmel32:38

       Whether it be trying to make an aged cell act like a young one, diseased cell act like a healthy one, why can't you just take 24-

   27. DPDwarkesh Patel32:43

       Right.

   28. JKJacob Kimmel32:43

       ... transcription factors and randomly sort through them? So there were two features of Shinya's problem that I think make it amenable to that sort of interrogation that aren't present for many other types of problems, and this is why he's such a remarkable scientist. Most of science is problem selection. You don't actually get better at pipetting or running experiments after a certain age, but you do get better at picking what to do, and, and he's amazing at this. So the first feature is that measuring your success criterion is trivial in the particular case he was investigating. He's starting with somatic cells that, in this case, were a type of fibroblast, which literally is defined as cells that stick to glass and grow in a dish when you grind up a tissue.

   29. DPDwarkesh Patel33:15

       Mm-hmm.

   30. JKJacob Kimmel33:15

       So it's like sounds fancy, but it's a very, very simplistic thing. So he's starting with fibroblasts. You can look at them under a microscope, and you can see they're fibroblasts just based on how they look. And then the cells he's reprogramming toward are embryonic stem cells. So these are tiny cells. They're mostly nucleus. They grow really, really fast. They look different. They detach from a dish. They grow up into a 3D structure, and they express some genes that will just never be turned on in a fibroblast by definition. So actually, how he ran the experiment was he just set up a simple reporter system. So he took a gene that should never be on in a fibroblast, should only be on in the embryo, and he put a little reporter behind it so that these cells would actually turn blue when you dumped a chemical on them. And then he ran this experiment in many, many dishes with, you know, millions upon millions of cells. The second really key feature of the problem is this notion that those cells he's converting into amplify. They divide and grow really quickly. So in order for you to find a successful combination, you don't actually need it to be efficient almost at all. The original efficiency Yamanaka published, the number of cells in the dish that convert from somatic to an induced pluripotent state back into a stem cell is something like a basis point or a 10th of a basis point.
4. 45:24 – 1:07:03

   Viral vectors and other delivery mechanisms

   1. DPDwarkesh Patel45:24

      If you're right that transcription factors are the modality evolution has, uh, used to have complex phenotypic effects, optimize for different things, two-part question. One, why haven't pathogens which have a strong interest in having complex phenotypic effects on your body also utilized the, um, transcription factors as the way to fuck you over and steal your resources? And two, we've been trying to design drugs for centuries. Why aren't all the big drugs, the top-selling drugs ones that just, um, modulate transcription factors?

   2. JKJacob Kimmel46:04

      Yeah, yeah, why don't, why don't we have a million of these pills?

   3. DPDwarkesh Patel46:05

      Yeah.

   4. JKJacob Kimmel46:05

      Okay, I'll try and take those in stride, and they're pretty different answers. First answer is, there actually are pathogens that, that utilize transcription factors as part of their life cycle. So, like, a famous example of this is HIV. HIV encodes a protein called Tat, and Tat actually activates NF-kappaB. And so HIV, sorry to back up a little bit, is a retrovirus, starts out as RNA, turns itself into DNA, shoves itself into the genome of your CD4+ T cells. And so then it needs this ornate machinery to actually control when does it make more HIV and when does it go latent so it can hide and your immune system can't clear it out? And this is why HIV is so pernicious, is you can kill every single cell in the body that's actively making HIV with, like, a really good drug, but then a few of them that have, like, lingered and hunkered down just turn back on. And so people call this the latent reservoir.

   5. DPDwarkesh Patel46:48

      Same with hep B, right?

   6. JKJacob Kimmel46:49

      Well, hep B, hep C can both do this sort of, like, latent sort of behavior.

   7. DPDwarkesh Patel46:54

      Yeah.

   8. JKJacob Kimmel46:54

      Um, and so HIV is probably the most pernicious of these, and one way it does it is this gene called Tat actually interacts with NF-kappaB. NF-kappaB is a master transcription factor within immune cells. Typically, if I'm going to, like, horribly reduce what it does, and some immunologists can, can crucify me later, it, like, increases the inflammatory response of most cells. They become more likely to attack given pathogens around them on the margin. Um, and so it'll turn on NF-kappaB activity and then uses that to drive its, its own transcription and its own life cycle. And so it... I can't remember quite all the details now exactly of how it works, but part of this circuitry is what allows it to, in some subset of cells where some of that upstream transcription factor machinery in the host might be deactivated, it goes latent. And so as long as the population of cells it's infecting always has a few that are, like, turning off the-

   9. DPDwarkesh Patel47:40

      Yeah.

   10. JKJacob Kimmel47:40

       ... transcription factors upstream that drive its own transcription, then HIV is able to persist in this-

   11. DPDwarkesh Patel47:44

       Interesting.

   12. JKJacob Kimmel47:44

       ... latent reservoir within human cells. So it's just one example offhand. Then there are a number of other pathogens, and, and unfortunately, I don't have quite as much molecular detail on some of these, but they will interface with other parts of the cell that eventually result in transcription factor translocation to the nucleus and then transcription factors being active. This actually segues a little bit to your second question on why aren't there more medicines targeting TFs? In a way, I think many of our medicines ultimately downstream are leading to changes in TF activity, but we haven't been able to directly target them due to their physical location within cells, and so we go several layers upstream. If you think about how a cell works in sensing its environment, it has many receptors on the surface. It has the ability to sense mechanical tension and things like this. And ultimately, most of what these signaling pathways lead to is to tell the cell, "Use some different genes than you're using right now." That's often what's occurring. And so that ultimately leads to transcription factors being some of the final effectors in these signaling cascades. So a lot of the drugs we have that, for instance, inhibit a particular cytokine that might bind to a receptor, or they block that receptor directly, or maybe they hit a certain signaling pathway, ultimately, the way that they're-

   13. DPDwarkesh Patel48:50

       Interesting.

   14. JKJacob Kimmel48:50

       ... exerting their effect is then downstream of that signaling pathway. Some transcription factor is either being turned on or not turned on, and you're using different genes in the cell. And so we're kinda taking these, like, crazy bank shots because we can't hit the TFs directly. So that sort of begs the question, like, why can't you just go after the TF directly?Traditionally, we'd use what are called small molecule drugs, where they're defined just by their size. The reason they have to be small is they need to be small enough to wiggle through the membrane of a cell and get inside. And then you run into a challenge, which is if you want to actually stick a small molecule between two proteins that have a pretty big interface, meaning, like, they've got big swaths on the side of them that all, you know, sort of line up and, and, uh, form a synapse with one another, then you would need a big molecule in order to inhibit that. And it turns out that TFs binding DNA is a pretty darn big surface. And so small molecules aren't great at disrupting that, and certainly even worse at activating it. So small molecules can get all the way into the nucleus, but they can't do much once they're there. They're just too small. And then the other classic modalities we have are recombinant proteins. We make a protein, like a hormone, in a big vat. We grow it in some Chinese hamster ovary cells. We extract it. We inject it into you. This is how, for instance, like, human insulin works that we make today. Or you make antibodies. Antibodies produced by the immune system, these run around and find proteins that have a particular sequence, they bind to it, and often, they just, like, stop it from working by glomming a big thing onto the side. So those are too big to get through the cell membrane, so then they can't actually get to a TF or do anything directly. So we take these bank shots. So what changes that today and why I think it's pretty exciting is we now have new nucleic acid and genetic medicines where you can, for instance, deliver RNAs to a cell that can get through using tricks like lipid nanoparticles. You wrap them in a fat bubble, looks kind of like a cell membrane. It can fuse with the cell, put the mRNAs in the cytosol. You can make a copy of a transcription factor there, and then it translocates the nucleus the same way a natural one would and exerts its effect. And likewise, there are other ways to do this using things like viral vectors. But I think that we've only very recently actually gotten the tools we need to start addressing transcription factors as first-class targets, rather than treating them as, like, uh, maybe some ancillary third order thing that's gonna happen.

   15. DPDwarkesh Patel50:48

       Interesting. So the drugs we have can't target them, but your claim is that a lot of drugs actually do work by binding to the things we actually can target and those having some effect on transcription factors. So this brings us to questions about delivery, which is the next thing I wanna ask you. You mentioned lipid nanoparticles, this is what the COVID vaccines were made of. The ultimate question if we're gonna work on de-aging is how do we make every single cell in the body, even if you identify what is the right transcription factor to de-age a cell, and even if they are shared across cell types or you figure out the right one for every single cell type, how do you get it to every single cell in the body? Um, yeah. (laughs) How do we, how do we do this?

   16. JKJacob Kimmel51:30

       How do you deliver stuff? How do you get them in there? So I think there are many ways one could imagine solving it. I'll sort of, like, narrow the scope of the problem to saying I think delivering nucleic acid is a pretty good first order primitive.

   17. DPDwarkesh Patel51:40

       Mm.

   18. JKJacob Kimmel51:41

       Ultimately, the genome is nucleic acids. The RNAs that come out of it are nucleic acids. So if you can get nucleic acid into a cell, you can drug pretty much anything in the genome effectively. So you can reduce this problem to asking how do I get nucleic acids wherever I want them to any cell type very specifically? So today, there are two main modalities that people use, both of which have some downsides. The first one that we've touched on already is lipid nanoparticles. These are basically fat bubbles, and by default, they get taken up by tissues which take up fat, like the liver, um, and they can be used sort of like Trojan horses. So they can release some arbitrary nucleic acid, usually RNA, maybe encoding your favorite genes, in our case transcription factors, into the cell types of interest. You can play with the fats, and you can also tie stuff onto the outside of the fat. Like, you can attach part of an antibody, for example, to make it go to different cell types in the body. And I think the field is making a lot of progress on being able to target various different cell types with lipid nanoparticles. So even if nothing else worked for the next several decades, I think companies like ours would have more than enough problems to solve and with the cells that we can actually target. Another prominent way people go after this is using viral vectors, the basic idea being viruses had a lot of evolutionary history and very large population sizes. They've evolved to get into our cells. Maybe we can learn something from them. Even better Trojan horses. So one type of virus people use a lot is called an AAV. Those AAVs, um, carry DNA genomes, and so you can get genes, whole genes into cells that they've got some packaging sizes. You can think of it kind of like a very small delivery truck, so you can't put everything you want into it. They can go to certain cell types as well. And then on top of just where do you actually get the nucleic acid to begin with, you can engineer the sequences a bit, and that basically allows you to add, like, a, a not gate on it. You can make it turn off the nucleic acid in certain cell types, but you're never gonna use the sequence engineering to get nucleic acid into cells where it didn't get delivered in the first place. So you can sort of start broad with your delivery vector and then use sequence to narrow down, to make it more specific-

   19. DPDwarkesh Patel53:28

       Mm-hmm.

   20. JKJacob Kimmel53:28

       ... but not the other way around. So I think both of those methods are super promising. Again, if nothing else emerged for decades, we'd still have tons and tons of problems as a therapeutic development community to solve even using just those. I do think I have one sort of very controversial opinion which, you know, people can roast me for later, but, uh-

   21. DPDwarkesh Patel53:45

       You have just one? (laughs) You're trying to solve aging-

   22. JKJacob Kimmel53:47

       I know. (laughs)

   23. DPDwarkesh Patel53:47

       ... and you have only one? (laughs)

   24. JKJacob Kimmel53:48

       I have many controversial opinions. One of them is that I think both of these probably in the limit will not be the way that we're delivering medicines in the year 2100. Um, if you think about viral vectors, no matter what, they're always gonna be some amount, some amount of mutagenic. Um, you're always going to have your immune system trying to fight them off. You can play tricks, you can try and cloak them, et cetera, et cetera, but they're always gonna have some toxicity risk. They also don't go everywhere. It's not that we have examples of, like, a single viral species that infects every cell type in the body and we just need to engineer it to make it safe. It's, we would have to also engineer the virus to go to new cell types. So there's some limitations there. LNPs likewise have some problems. They can go to tons of cell types. That's what largely we're working on. We're super excited about it. But there are some physical constraints. They just have a certain size and they have to get from your bloodstream, out of your bloodstream toward a given target cell, and they have to not fuse into any of the other cells along the way. So there's a whole gamut they have to run.Ultimately, I think we're probably gonna have to solve delivery the way that our own genome solved delivery. So we have the same problem that arose during evolution, which is how do I patrol the body, find arbitrary signals in the environment, and then deliver some important cargo there when some set of events happens? How do I, you know, find a specific place and only near those cell types release my cargo? And really, the- the problem was solved by the immune system. So we have cell types in our body, T cells and B cells, which are effectively engineered by evolution to run around, invaginate whatever tissues they need to. They can climb almost anywhere in the body, so there's nowhere they can't get access almost. And then once they sense a particular set of signals, and they've got a very ornate circuitry to do this, they run basically an AND gate logic, they can release a specified payload. And right now, the way our genome sets them up, the payload they release is largely either, uh, enzymes that will kill some cell that they're targeting or kill some pathogen or some signal flares that call in other parts of the immune system to do the same thing. So that's super cool, but you can think about it as a modular system that evolution has already gifted us. We've got some signal and environmental recognition systems, so we can find particular areas of the body that we want to find and then some sort of payload delivery system. I can deliver some arbitrary set of things. And I imagine if we were to, like, Rip Van Winkle ourselves into 2100 and wake up, the way we will be delivering these nucleic acid payloads is actually by engineering cells to do it, to perform this very ornate function. Those cells might actually live with you. You probably will get engrafted with them, and they might persist with you for many years. They deliver the medicine only when the environment within your body actually dictates that you need it, and so you'll actually won't be seeing a physician every time this medicine is active. Rather, you'll have a more ornate, responsive circuit. The other exciting thing about cells is that they're big and they have big genomes, and so you actually have a large palette to encode complex infrastructure and complex circuitry. So you don't need to limit yourself to, like, the very small RNAs you can get in that might encode a gene or two or, in our case, a few transcription factors. You don't have to limit yourself to this tiny AAV genome that's only a few kilobases. You've got billions of base pairs to play with in terms of encoding all your logic. So I think that's ultimately how delivery will get solved. We've got many, many stepping stones along the way, but if I could, like, clone myself and work on an even riskier endeavor, that's probably what I would do.

   25. DPDwarkesh Patel56:53

       This is actually in- in- in- in... I mean, in a way, we treat cancer this way with CAR T therapy, right? We take the T cells out, and then we tell them, "Go find a cancer with this receptor and kill it." But does- the reason that works is that the cancer cells we're trying to target are also free-floating in the blood, and is that what it targets? Basically, um, could this deliver to literally every single cell in the body?

   26. JKJacob Kimmel57:15

       N- not literally every single cell. I'll, like, asterisk it there. So example, T cells don't go into your brain.

   27. DPDwarkesh Patel57:20

       Right.

   28. JKJacob Kimmel57:20

       You don't have... Th- they can, but it's generally a- a pathology when they get in there. So it's not, like, literally every cell, but almost every cell in your body is surveilled by the immune system.

   29. DPDwarkesh Patel57:29

       Mm-hmm.

   30. JKJacob Kimmel57:29

       So there are very, very few what we call immune privileged compartments in your body. It's things like the joints of your knees and your shoulders, your eyeball, and your brain, basically. Um, there might be a couple of these. I think the ear probably falls into that category. A funny way of thinking about this is all the gene therapy people using viruses, they want to deliver to the immune privileged compartments because their- their, uh, drugs are immunogenic, and they're limited to a very, very small set of diseases.

Episode duration: 1:45:20