AI transcript
0:00:00 [MUSIC]
0:00:06 Pushkin.
0:00:06 [MUSIC]
0:00:11 >> Hey everybody, I’m Kai Rizdal, the host of Marketplace,
0:00:13 your daily download on the economy.
0:00:16 Money influences so much of what we do and how we live.
0:00:19 That’s why it’s essential to understand how this economy works.
0:00:23 At Marketplace, we break down everything from inflation and
0:00:26 student loans to the future of AI so that you can understand what it all means for you.
0:00:32 Marketplace is your secret weapon for understanding this economy.
0:00:35 Listen, wherever you get your podcasts.
0:00:37 [MUSIC]
0:00:41 One of the most important technological breakthroughs so
0:00:44 far this century is CRISPR, aka clustered regularly spaced palindromic repeats.
0:00:51 AKA the extraordinary gene editing tool that is right now making its way to actual human patients.
0:01:00 The FDA approved the first CRISPR produced drug last December, and
0:01:05 now scientists are trying to improve on the original CRISPR to bring more treatments to market.
0:01:11 [MUSIC]
0:01:16 I’m Jacob Goldstein, and this is What’s Your Problem,
0:01:18 the show where I talk to people who are trying to make technological progress.
0:01:23 My guest today is Rachel Horwitz, the co-founder and CEO of Caribou Biosciences.
0:01:29 Rachel’s problem is this, how can you make CRISPR work better?
0:01:33 And how can you use it to engineer human immune cells to fight cancer?
0:01:38 We started off talking about Rachel’s graduate work at UC Berkeley.
0:01:42 She studied with Jennifer Doudna, who would go on to win the Nobel Prize for her work on CRISPR.
0:01:49 At the time, Rachel’s work was focused on a protein called Cas6.
0:01:53 [MUSIC]
0:01:56 Is it right that you spent five years studying one protein?
0:02:01 >> I spent five years studying one small protein composed of only 187 amino acids.
0:02:09 So I was pretty far down the rail.
0:02:11 >> I mean, are you the world expert in that protein?
0:02:15 Do you know more about that than anyone who has ever lived?
0:02:18 >> There are probably three of us who know more than we ever wanted to about that protein.
0:02:22 >> Just give me a little hint of that protein.
0:02:24 What is it? Why’d you spend five years studying it?
0:02:27 >> It was my entry point to CRISPR.
0:02:29 So I joined Jennifer Doudna’s lab as a brand new baby PhD student in 2007.
0:02:37 This was the dark ages of CRISPR.
0:02:39 There were three peer-reviewed manuscripts that had been published at the time.
0:02:44 So it took me about 45 minutes to get up to speed on the field.
0:02:47 It was great.
0:02:49 I was joining a project headed up by a post-doctoral fellow in the lab,
0:02:55 and he had identified these CRISPR associated or Cas proteins,
0:03:00 and he was trying to study all of them.
0:03:02 Now, he was able to make and study all but one.
0:03:07 One was proving difficult in the lab.
0:03:09 So he gave that one to me to see if I could sort it out.
0:03:12 We did eventually sort it out.
0:03:15 In the end, it turned out to be a very important little protein.
0:03:19 It’s actually responsible for making
0:03:21 these small CRISPR RNAs that are at the heart of CRISPR biology.
0:03:27 So I had a lot of fun for many years really understanding how
0:03:31 that particular protein functioned, what it did,
0:03:34 how it did it on a molecular level,
0:03:37 and then ultimately zooming far,
0:03:39 far out how it fits into the broader use of CRISPR systems.
0:03:43 Yeah. I mean, if you’re going to spend five years studying one protein,
0:03:46 studying a protein that’s essential to CRISPR,
0:03:48 and doing it in like 2010 is as good as it gets, right?
0:03:55 Right place, right time.
0:03:56 Just to be clear, briefly, just so we have it, what is CRISPR?
0:04:01 CRISPR is a technology for editing the genome.
0:04:06 CRISPR allows us to do a few different things to change genomes.
0:04:13 We can hit the delete key.
0:04:15 We can get rid of a gene that we don’t want to express anymore.
0:04:19 We can make a small change,
0:04:21 maybe even as simple as a single nucleotide of DNA,
0:04:26 and we can insert one or multiple new genes
0:04:30 to actually give a cell new capabilities it didn’t have before.
0:04:33 Just in the last months, order of magnitude months,
0:04:37 there have been just the first drug approvals based on CRISPR, right?
0:04:43 Tell me about those.
0:04:44 It’s incredibly exciting.
0:04:46 At the end of last year,
0:04:47 the first ever CRISPR-edited therapy was approved by the FDA.
0:04:51 It’s now been approved by other regulatory agencies outside the US too.
0:04:57 This is a cellular therapy for the treatment of sickle cell and beta thalassemia.
0:05:03 This is the use case where you take cells and you use CRISPR to change them,
0:05:08 and then you deliver the cells as the therapy back to these patients,
0:05:13 and the vision is to try to actually cure sickle cell disease.
0:05:17 It’s quite remarkable.
0:05:18 Really fast from when you were in grad school,
0:05:22 and this wasn’t quite the original work,
0:05:25 but this early work was happening, right?
0:05:27 It’s 12 years, which for to go from a lab
0:05:32 and just basic proof of concept to a thing in the world seems wildly fast.
0:05:38 It’s lightning speed.
0:05:39 I’m not aware of any other life science technology
0:05:44 that went from really important publication in Science Magazine
0:05:49 to approved therapy anywhere near that fast.
0:05:53 There are probably a few things to thank for that.
0:05:56 One is CRISPR is actually not the first genome editing technology.
0:06:01 Genome editing has been around for a while,
0:06:04 but the other approaches are much harder to use,
0:06:08 and so this really unlocked a much faster, broader scale of genome editing.
0:06:14 So there was a lot of resident expertise and capability
0:06:18 that could be turbocharged by the introduction of CRISPR genome editing.
0:06:22 It’s like there were people who knew how to do it already,
0:06:24 and then this incredible tool kind of fell out of the sky
0:06:27 and was like, “Oh, we can just do the thing we’re doing way better.”
0:06:30 Exactly.
0:06:32 Were you saying there were a couple reasons?
0:06:34 Was that one reason? Was there another reason?
0:06:36 That’s one, and I think another is
0:06:39 that there were things developed for other fields
0:06:42 or biology well understood that could quickly be taken advantage of.
0:06:47 So, for example, the genetic cause of sickle cell disease
0:06:51 has been known for decades,
0:06:53 and yet there hasn’t been the right tool to do much of anything about it.
0:06:59 And so this was sort of the perfect marriage
0:07:02 of this incredible enabling technology
0:07:05 and its ability to solve a biology problem
0:07:08 that’s been well understood for a very long time.
0:07:11 Can you give me a sense of the landscape
0:07:14 of how CRISPR is being used in drug therapies now broadly?
0:07:21 CRISPR is being used in two very fundamental ways for drug development.
0:07:26 The first is basic research,
0:07:28 and the second is actually designing and doing new therapies,
0:07:34 and that falls largely into two categories.
0:07:37 One is the kind of work that we are doing here at Caribou,
0:07:40 where we use CRISPR to actually modify or engineer cells,
0:07:46 and the cells are the therapy.
0:07:49 So, by the time we deliver, for example, our CAR-T cell therapy, CB10 to patients,
0:07:54 there’s no CRISPR inside of those cells anymore.
0:07:57 CRISPR is gone, it has modified the genome in multiple ways,
0:08:01 and the cell is the therapeutic.
0:08:05 The other strategy that some companies are using
0:08:08 is to actually deliver CRISPR inside the human body,
0:08:12 and the idea is to try to correct a gene
0:08:16 that causes a rare genetic disorder.
0:08:19 And so, in that case, CRISPR itself is the therapy.
0:08:22 So, in that latter case, that is gene therapy, essentially,
0:08:27 what people have called gene therapy.
0:08:29 And what seems to be next in line?
0:08:33 What’s farthest along, anyways, in terms of other CRISPR-derived therapies?
0:08:39 Yeah, there’s some very exciting work coming out of a company called IntelliA Therapeutics,
0:08:44 where they’re actually using CRISPR as the drug.
0:08:47 So, they are delivering it, packaged inside these little fat particles,
0:08:53 to go directly to a patient’s liver to correct a gene that causes a disease.
0:08:58 And they are running what’s called a phase three trial
0:09:02 for one of those medicines right now.
0:09:05 So, I feel like this is a dumb question, but as I imagine that,
0:09:09 does that mean that the therapy has to get to every cell in the liver?
0:09:14 Is it going to change the genome of every cell in your liver?
0:09:17 Is that the way that works?
0:09:19 Thank goodness, no, that’s not required.
0:09:22 Yeah, it couldn’t be that, right?
0:09:23 It couldn’t be that.
0:09:24 Most of them, but it’s cell by cell.
0:09:28 It’s like that a particle hits one liver cell and changes the genome,
0:09:33 and then another one hits another one.
0:09:34 And then, is there some tipping point?
0:09:36 Like, how does it work?
0:09:38 It’s a wonderful question.
0:09:39 And I think there are a lot of people who sit in a lot of conference rooms
0:09:43 staring at whiteboards, trying to understand what is that tipping point.
0:09:48 Because I think it’s biologically unrealistic to think you can edit 100% of cells in the liver.
0:09:55 And if that’s what’s needed for a therapy, you’re probably out of luck.
0:09:58 And instead, focusing on diseases where there’s some model or suggestion that,
0:10:05 you know, maybe editing 10% of the cells or 15% or 20% of the cells would be enough.
0:10:11 And there’s confidence that the technology might be able to accomplish that.
0:10:15 Well, you mentioned that there’s a therapy in, did you say,
0:10:18 phase three in the final stage of clinical trials.
0:10:21 What disease is that targeting?
0:10:23 So, Intellia is working on a disease called transthyretin amyloidosis, or ATTR for short.
0:10:32 It’s a disease caused by misfolded proteins, and it leads to neurodegeneration
0:10:38 and cardiomyopathies.
0:10:40 That’s the one in the liver?
0:10:42 They are editing liver cells because the liver produces the misfolded protein
0:10:49 that causes problems elsewhere in the body.
0:10:52 So, OK, clearly CRISPR is this, you know, wildly useful breakthrough.
0:10:58 But it’s not perfect.
0:10:59 And your company was founded in a way to address this key weakness of CRISPR as originally developed.
0:11:07 So, what is the weakness in particular that your company is focusing on?
0:11:13 Specificity.
0:11:15 When I say specificity, I mean editing the one site in the genome that we intend to,
0:11:21 and not accidentally making changes anywhere else, right?
0:11:25 In Microsoft Word, you put the cursor exactly where you want to write new text.
0:11:30 It’s not a mystery where the new text is going to land.
0:11:32 Using a biological tool like CRISPR, more often than not, you edit the site that you intend to,
0:11:39 but biology is noisy.
0:11:42 And sometimes the system lands in places you didn’t expect,
0:11:47 and can make changes in places you didn’t want.
0:11:50 That could be a problem for what you’re trying to do.
0:11:52 And so, our team for years has been focused on the challenge of specificity,
0:11:58 and ultimately developing new technologies to address this head-on.
0:12:02 What percent of the time does CRISPR get it wrong?
0:12:05 It’s the question I want to ask, and I’m sure that’s too broad a question.
0:12:07 But how do you think about that?
0:12:09 How should I think about that?
0:12:10 It varies dramatically.
0:12:13 So, the way CRISPR actually works, it’s usually a specific protein called Cas9
0:12:19 that cuts the genome at the site that you’re trying to edit.
0:12:23 But Cas9 on its own can’t do anything.
0:12:26 It’s inert, if you will.
0:12:28 It needs an RNA, a piece of RNA, that’s actually specifically designed
0:12:34 to match the sequence of the genome that you’re trying to modify.
0:12:37 It partners with this RNA, and the RNA takes it to the right place.
0:12:40 So, depending on which RNA you’ve designed, the edits could be more or less specific.
0:12:47 There are plenty of examples of first-generation CRISPR-Cas9
0:12:52 where you could get really efficient editing at the site you want,
0:12:55 and really efficient editing at several other sites as well that you did not want.
0:13:01 And then there are many of us, my company, Caribou Biosciences Included,
0:13:06 who have invented, developed, accessed new technologies
0:13:12 that can overcome some of these specificity challenges.
0:13:15 I mean, it seems like, in your case, that particular technology
0:13:18 is sort of the core proposition that the company has founded on, right?
0:13:22 Can we take CRISPR and make it work more reliably?
0:13:26 Absolutely.
0:13:27 So, what do you do to make it work better?
0:13:29 So, at the heart of our company is what we call the Chardonnay technology.
0:13:35 Now, Chardonnay is an acronym.
0:13:38 CHR-DNA stands for a Mouthful CRISPR-Hybrid RNA-DNA Technology.
0:13:44 You now see why we use an acronym.
0:13:46 But each of those words, I mean, it’s like a relatively sort of,
0:13:50 you know, comprehensible acronym, right?
0:13:52 Like CRISPR-Hybrid RNA-DNA.
0:13:55 It’s like that’s not wildly complicated.
0:13:57 Fair. I appreciate that.
0:13:59 And to be fair, it does actually describe what the technology is.
0:14:04 So, I just told you, usually, Cas9 or other CRISPR proteins need an RNA partner
0:14:11 to get to the right site in the genome.
0:14:13 What some of my colleagues did is actually develop hybrid guides.
0:14:18 Guides that are part RNA and part DNA.
0:14:21 And it turns out the inclusion of DNA improves the specificity dramatically.
0:14:27 We can measure this in a very quantitative way and see that it improves the specificity
0:14:33 of editing by many orders of magnitude.
0:14:36 Aha. So, it’s not like 10% better.
0:14:39 It’s like 100 times better?
0:14:42 1000 times better, even more in some cases.
0:14:44 Is there a sort of lay person’s answer to why?
0:14:47 Absolutely. It all has to do with what we would call a biochemistry affinity.
0:14:56 Meaning, what is the binding tightness of the CRISPR system for the target genome?
0:15:04 And it might intuitively feel like higher binding, higher affinity is better.
0:15:11 But it actually turns out the opposite is true.
0:15:14 And that by including DNA, we actually decrease the affinity of the complex for the target.
0:15:23 And the reason you want to decrease the affinity is that really the entire human genome
0:15:29 represents a laundry list of potential off-target sites we don’t want to edit.
0:15:35 So, you want low enough affinity that you’re not accidentally grabbing all these other pieces
0:15:39 of the genome and instead grabbing the one site that you actually want to modify.
0:15:44 So, is the challenge then to see how low you can get the affinity and have it still work?
0:15:50 I mean, I get that you don’t want it to not bind things that it’s not supposed to bind to
0:15:54 or not cut things that it’s not supposed to cut.
0:15:56 But you do want it to bind to or cut the thing that it is supposed to cut.
0:15:59 So, presumably, there’s some optimal spot or maybe what’s optimal depends on the use case.
0:16:05 But how do you strike that balance?
0:16:08 It’s a very careful balancing act. You’re absolutely right.
0:16:11 Our research team has spent a huge amount of time working on this and has found ways to really
0:16:17 develop the appropriate way to balance these two needs for each time we need to make an edit.
0:16:25 Still to come on the show, how Rachel and her colleagues are using this new kind of CRISPR
0:16:33 technology to create new treatments for cancer.
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0:17:29 Hey everybody, I’m Kai Rizdal, the host of Marketplace, your daily download on the economy.
0:17:34 Money influences so much of what we do and how we live. That’s why it’s essential to understand
0:17:39 how this economy works. At Marketplace, we break down everything from inflation and student loans
0:17:45 to the future of AI so that you can understand what it all means for you. Marketplace is your
0:17:51 secret weapon for understanding this economy. Listen wherever you get your podcasts.
0:17:58 There’s this promising new kind of cancer treatment called CAR T-cell therapy. T-cells are
0:18:05 a key part of the immune system, and the basic idea here is to engineer T-cells to attack cancer
0:18:11 cells. As you’ll hear, a few CAR T-cell therapies have been approved, but they’re complicated and
0:18:18 expensive. So Rachel and her colleagues are using CRISPR to try to come up with a new kind
0:18:23 of CAR T-cell therapy that is both simpler and cheaper. Let’s talk about some of the
0:18:30 projects you’re working on with the technology. Let’s start with what’s farthest along clinically.
0:18:36 Farthest along is a cell therapy that we call CB10, and we are developing this to treat relapsed or
0:18:44 refractory B-cell non-Hodgkin lymphoma. That’s a kind of blood cancer, and it’s when B-cells,
0:18:52 part of the immune system, become diseased. We are using our CRISPR genome editing, our Chardonnay
0:18:59 technology, to actually take healthy T-cells from healthy donors and then modify them through CRISPR
0:19:08 to teach them how to find and kill these kinds of diseased B-cell cancers. These therapies are
0:19:16 called CAR T-cell therapies. We’re not the first to work on them. There are many who are advancing
0:19:22 these kinds of therapies. CAR, again, is an acronym. It stands for Chimeric Antigen Receptor, and it
0:19:31 describes a special protein that we can encourage the T-cells to express that gives them the ability
0:19:39 to specifically recognize and kill these B-cells. As I understand it, other companies have developed
0:19:48 CAR T-cell therapies that take an individual patient’s own immune cells and develop them in the lab,
0:19:57 essentially, and then put them back into the patient to target cancer. That is, unsurprisingly,
0:20:03 very, very, very expensive because it’s like you’re developing a custom drug for each patient,
0:20:09 which is great in a way, but also it’s like this bespoke, sort of individually tailored drug that
0:20:17 it just costs a lot to make and it costs a lot to buy. My understanding is that you’re trying to
0:20:23 develop a version that is more like a traditional drug that doesn’t have to be customized to every
0:20:29 patient. Is that right? That’s exactly correct. There are today in the United States six approved
0:20:37 CAR T-cell therapies, and each one of them is what’s called anatologous cell therapy,
0:20:43 which is a scientific fancy word for patient-specific.
0:20:48 And so those drugs, just to be clear, those are approved. They’re in use. They exist in the world.
0:20:53 Those are approved commercial products today.
0:20:55 And they cost like hundreds of thousands of dollars per patient.
0:20:58 Correct. Yeah.
0:20:59 So they are proof positive that the immune system, specifically T-cells, can be incredibly
0:21:06 powerful anti-cancer agents. They also demonstrate how challenging it is to develop
0:21:13 one batch of therapy for each and every patient. That is not scalable. That is not going to be
0:21:20 something that delivers this kind of therapy to broader and broader patient populations.
0:21:26 And it’s also restricted to cancer patients who have sufficiently good T-cells
0:21:32 to make the product in the first place.
0:21:34 Oh, that’s interesting. Like if you’re a patient and your immune system is just totally beat down
0:21:39 by having cancer or being treated for cancer, then you don’t have the T-cells to generate this therapy.
0:21:45 That’s exactly correct. There’s also quite a lot of complexity and almost hand-holding,
0:21:53 if you will, necessary to make these patient-specific therapies. And so cancer centers like MD Anderson
0:22:03 or the University of Pennsylvania, they have tremendous expertise and they have the staff
0:22:09 to really work with patients to shepherd them through this process to ensure that they can
0:22:15 actually support them, provide any additional therapies they need while they’re waiting for
0:22:19 their therapy to be manufactured to give them access to this kind of therapy.
0:22:24 But that’s not where the majority of patients are treated. The majority of patients are treated in
0:22:29 community hospitals and community clinics that don’t have the resources to shepherd patients
0:22:35 through this kind of very complex stuff. So what do you have to do to make a one-size-fits-most
0:22:46 version of this, right? It would be good for the world if we could move away from having to
0:22:51 design this drug literally for each patient and have a drug that’ll work for almost everybody.
0:22:57 That’s what you’re trying to do. How do you do it? Yeah, the vision is to develop what the field
0:23:02 would call allogeneic or off-the-shelf CAR T-cell therapies. Which is what most drugs are, right?
0:23:10 Like just a drug and the doctor gives you the drug and hopefully it makes you better, right?
0:23:14 Right. So step one is we need to use healthy T-cells from healthy donors instead of T-cells from
0:23:21 cancer patients. Step two is you have to make this safe, right? Typically, if you take a T-cell
0:23:28 from one person and put it in another person, you are probably going to cause what is called
0:23:34 graft versus host disease. Which is where the other person’s T-cells attack parts of your body.
0:23:41 Analogous to the problems that transplant patients have, basically. It’s like a transplant, yeah.
0:23:46 Correct, correct. So right off the bat, we have to prevent that. And we do that through genome
0:23:51 editing by getting rid of something called the T-cell receptor. It’s the thing on the surface
0:23:56 of the T-cell that would usually empower it to cause graft versus host disease. So that’s
0:24:01 hitting the delete key once to get rid of that. Okay. The second step is you have to give the
0:24:08 T-cells the car. So it knows what it’s looking for on the surface of cancer cells to appropriately
0:24:14 identify and kill them. And many of our peers stop there. Those two combined would be what they
0:24:21 envision as a product. But our team looked at that and said that will never be enough.
0:24:27 And so just to be clear, when you see people stop there, are people trying that? Like is that version
0:24:32 of this drug in trials now? Yes. Multiple human clinical trials are being run with
0:24:38 something that looks like that. And so if that works, that would be off the shelf,
0:24:43 one-size-fits-most, normal drug kind of drug. You’re skeptical that it’s going to work.
0:24:48 Correct. We think you have to take it a step further and I’ll tell you why.
0:24:52 Okay. Yeah. These off the shelf T-cells, they are foreign to the patients immune system.
0:24:59 And the patients immune system is going to figure that out fairly quickly and actually kill off
0:25:05 the car T-cells. And that’s very different from when you’ve had a product made from your very own
0:25:10 T-cells, they can last for a very long time. And so we look at that differential in time and say,
0:25:17 that’s a problem we have to solve. Why doesn’t everybody agree with you?
0:25:22 I would say more and more people agree with us if you look at what’s happening in the field.
0:25:27 In fact, many of the first off the shelf CAR T-cells that have been tried in human clinical trials
0:25:34 have been retired because they didn’t work as well as people had hoped.
0:25:37 And I think many are now going back to the drawing board to think about what are other
0:25:42 things we can do to empower or enhance these T-cells to overcome these challenges.
0:25:48 So what do you do? You were getting to the sort of next steps. What do you do to make it better
0:25:53 tolerated by the patients immune system? Yeah. We think about how to bridge that gap both very
0:25:58 literally and in ways that are more about boosting the biology than necessarily entangling with the
0:26:06 patients immune system. So, for example, in some of our other cell therapies that we’re developing
0:26:12 for other blood cancers like multiple myeloma and AML, we actually deploy what we call immune
0:26:20 cloaking. And so this is where we use our genome editing to change what is or is not decorating
0:26:26 the surface of the CAR T-cells to try to slow down how the patients immune system could recognize
0:26:32 and clear the therapy. So that’s a very literal way of addressing this challenge.
0:26:36 Cloaking is a cool name for it to basically make the cell better at hiding from the immune system.
0:26:42 Exactly. Exactly. Is there an example of a particular change you make to that end?
0:26:48 Yes. So what we do is actually get rid of some of the proteins that would usually sit on the surface
0:26:57 of a CAR T-cell. These are called HLA Class 1 molecules. And it helps to prevent the patients
0:27:04 immune system from readily recognizing and clearing the therapy. It’s a little more complex than that.
0:27:11 There’s actually a special kind that we then decorate the surface with to help ensure that
0:27:16 all parts of the immune system cannot rapidly recognize it. I also want to be clear, we don’t
0:27:22 think this creates a perfect stealth cell that lasts forever. There are lots of things about
0:27:27 these cells that we expect the patients immune system to ultimately recognize and cause it to
0:27:32 reject. This is about buying additional time with the hope that that allows additional anti-tumor
0:27:38 activity. Is there a balance you have to strike there where, well, the cell still has to work,
0:27:48 right? I mean, is there a universe where you do so much to try and cloak the cell that it can’t,
0:27:54 whatever, do its cellular business and persist as a cell until it finds the tumor?
0:27:58 Right. I think there’s some extreme world where you try to put so many different genome edits
0:28:05 into the T-cell that you break it, maybe both on a cell-specific level, but also a population level.
0:28:15 So if you think about it, we’re trying to take this population of millions and millions of T-cells
0:28:21 and provide three, four, five different genome edits. Now, genome editing is very efficient,
0:28:29 but it’s not 100% efficient. Some edits might be 80%, others 90%, maybe 95%.
0:28:35 So that means as you now look at this whole population of T-cells, every time you add a new
0:28:43 edit, it means a fraction of a fraction of a fraction of the cells actually have all the
0:28:49 edits that you desire. So we set a pretty high bar for ourselves. We only bring a program forward
0:28:56 into the clinic. If we can manufacture it in such a way that at least half of all of the T-cells
0:29:03 have all the edits that we’re going for. And we’ve been able to do that with three different
0:29:07 therapies so far. So you’re saying that even with your improved version of CRISPR,
0:29:16 it’s still sufficiently error-prone, not that it’s highly error-prone, but it still makes enough
0:29:22 mistakes that something like half of the cells you’re creating won’t be exactly the way you want
0:29:28 them to be. I would say it’s not that it’s making mistakes, meaning it’s not making off-target
0:29:34 changes elsewhere that we didn’t want. It’s instead that in some fraction of the cells,
0:29:39 they’re just not getting the edit. Right. It’s the sin of omission rather than a
0:29:43 sin of commission. Exactly. Yes. Exactly. So it’s the affinity. You nailed the low affinity.
0:29:48 So does that suggest, just to sort of zoom out for a sec, it suggests that there is room for
0:29:57 improvement on the kind of platform level, presumably. I think so. And I’ll give an example
0:30:02 of even the work we’ve done over the past few years. So our first program, which is for lymphoma,
0:30:08 benefits from three edits. Fast forward now to our third program for AML. It has five different
0:30:16 genome edits in it. We’re able to hit the delete key on three different genes in two different
0:30:22 places. We can insert new genes to give new functionality to the T cells. And I think this
0:30:28 already represents really pushing the envelope in terms of what you can accomplish.
0:30:34 And I think there’s further room to run with that as well.
0:30:36 If things go well, when might you be submitting a drug for approval?
0:30:44 Fantastic question. We hope to start a phase three trial with CB10 next year.
0:30:53 If you look at the kinds of phase three trials that have been run for these cell therapies before,
0:30:59 they usually take two years or more to run. And then there’s some time after that to put
0:31:05 all the documents together for the regulatory agencies. So a lot of work yet to be done. Very
0:31:10 excited about what’s coming next. We’ll be back in a minute with the lightning round.
0:31:24 Hey, everybody. I’m Kai Rizdal, the host of Marketplace, your daily download on the economy.
0:31:34 Money influences so much of what we do and how we live. That’s why it’s essential to understand
0:31:39 how this economy works. At Marketplace, we break down everything from inflation and student loans
0:31:45 to the future of AI so that you can understand what it all means for you. Marketplace is your
0:31:51 secret weapon for understanding this economy. Listen wherever you get your podcasts.
0:31:55 Let’s have a lightning round. Let’s finish with the lightning round.
0:32:02 What’s more frustrating, pipetting or knitting?
0:32:08 Knitting.
0:32:11 What’s the hardest thing you ever knit?
0:32:18 Mittens. Tell me about pipetting. I feel like you and pipetting have history.
0:32:23 Pipetting is about moving clear liquids from one tube to another. I spent many, many years
0:32:32 where that is what I did every day. And obviously, it gave me the ability to do hands-on wet lab
0:32:39 research. And there’s a piece of it that I desperately miss, which is being the first
0:32:44 to know the answer to an interesting biological question, right? There’s a magical aha moment
0:32:50 when you see the results first. Now these days, I’m not that far away from the people who get
0:32:57 to do the really cool work in our lab. So I’m at peace with the balancing act of not having to
0:33:03 pipet and being the third, fourth, tenth person who learns the cool news.
0:33:08 A worthwhile trade-off in the end. Indeed.
0:33:12 Did you go to grad school assuming you would work in industry? Is there some moment when you
0:33:16 are making a leap off of this path? You know, you’re getting a PhD. Clearly, you’ve been very
0:33:22 good at school. Lots of people just stay in school all their lives and become professors and have
0:33:28 wonderful careers. Was there some moment when you decided to step off of that path, leap off of that
0:33:34 path? I was probably one of the few people in my PhD program who came to school knowing I didn’t
0:33:41 want to be a professor when I grew up. I actually thought I wanted to be a patent attorney when I
0:33:47 grew up. However, we started working with patent attorneys because of all the cool technology that
0:33:54 was coming out of the lab. And I pretty quickly realized that’s not the job that I want to do.
0:34:00 Good thing you figured that out. Indeed. Before I went to three years of law school,
0:34:06 so it created sort of this moment of, well, I don’t know what I’m going to do when I grow up.
0:34:12 I now know a few things I don’t want to do. And it meant I started thinking a lot about
0:34:18 the industry side of science. I took a lot of business school classes at that point in time
0:34:24 to try to learn and learn a new vocabulary. But I think that made it easier to take the
0:34:30 entrepreneurial leap because I wasn’t on a different path that I had to jump off to go on.
0:34:35 Basically, you’re way out of going to law school? Indeed.
0:34:38 So you were on the Forbes 30 under 30, the Fortune 40 under 40. As far as I know, there’s no 50
0:34:48 under 50. So do you have like a next move? Well, there is a 50 over 50, but I’ve got to wait a
0:34:56 few more years for that. I’m glad that you’ve got it lined up though. It’s important to have a goal.
0:35:03 And you’ve got time. What’s one thing that you wish people understood better about genes?
0:35:11 I think many people expect there’s a very clear one-to-one connection that a gene means X.
0:35:21 There are very few genes in our genome that result in one specific outcome. We,
0:35:30 as human beings, are the product of this incredibly complicated cross signaling across
0:35:38 every gene in our genome. And any one trait, even as simple as how tall we are,
0:35:45 is the output of many, many, many different genes. And so I do wish there was a better understanding
0:35:52 of just the rich complexity of our own biology. Because then I think it feeds directly into
0:35:59 how do you use a technology like CRISPR to change disease biology? And there are some examples,
0:36:07 but not a ton of examples where one edit is enough. It’s like the Mendelian pea plants maybe do more
0:36:15 harm than good as a teaching tool. Like, no, no, it’s not usually like that. Fair, yes.
0:36:23 Rachel Horowitz is the co-founder and CEO of Caraboo Biosciences. Today’s show was produced
0:36:30 by Gabriel Hunter-Chang. It was edited by Lydia Jean-Cott and engineered by Sarah Bouguere.
0:36:37 You can email us at problem@pushkin.fm. I’m Jacob Goldstein, and we’ll be back next week
0:36:43 with another episode of What’s Your Problem?
0:36:53 Hey, everybody. I’m Kai Rizdal, the host of Marketplace, your daily download on the economy.
0:37:01 Money influences so much of what we do and how we live. That’s why it’s essential to understand
0:37:06 how this economy works. At Marketplace, we break down everything from inflation and student loans
0:37:12 to the future of AI so that you can understand what it all means for you. Marketplace is your
0:37:18 secret weapon for understanding this economy. Listen wherever you get your podcasts.
0:00:06 Pushkin.
0:00:06 [MUSIC]
0:00:11 >> Hey everybody, I’m Kai Rizdal, the host of Marketplace,
0:00:13 your daily download on the economy.
0:00:16 Money influences so much of what we do and how we live.
0:00:19 That’s why it’s essential to understand how this economy works.
0:00:23 At Marketplace, we break down everything from inflation and
0:00:26 student loans to the future of AI so that you can understand what it all means for you.
0:00:32 Marketplace is your secret weapon for understanding this economy.
0:00:35 Listen, wherever you get your podcasts.
0:00:37 [MUSIC]
0:00:41 One of the most important technological breakthroughs so
0:00:44 far this century is CRISPR, aka clustered regularly spaced palindromic repeats.
0:00:51 AKA the extraordinary gene editing tool that is right now making its way to actual human patients.
0:01:00 The FDA approved the first CRISPR produced drug last December, and
0:01:05 now scientists are trying to improve on the original CRISPR to bring more treatments to market.
0:01:11 [MUSIC]
0:01:16 I’m Jacob Goldstein, and this is What’s Your Problem,
0:01:18 the show where I talk to people who are trying to make technological progress.
0:01:23 My guest today is Rachel Horwitz, the co-founder and CEO of Caribou Biosciences.
0:01:29 Rachel’s problem is this, how can you make CRISPR work better?
0:01:33 And how can you use it to engineer human immune cells to fight cancer?
0:01:38 We started off talking about Rachel’s graduate work at UC Berkeley.
0:01:42 She studied with Jennifer Doudna, who would go on to win the Nobel Prize for her work on CRISPR.
0:01:49 At the time, Rachel’s work was focused on a protein called Cas6.
0:01:53 [MUSIC]
0:01:56 Is it right that you spent five years studying one protein?
0:02:01 >> I spent five years studying one small protein composed of only 187 amino acids.
0:02:09 So I was pretty far down the rail.
0:02:11 >> I mean, are you the world expert in that protein?
0:02:15 Do you know more about that than anyone who has ever lived?
0:02:18 >> There are probably three of us who know more than we ever wanted to about that protein.
0:02:22 >> Just give me a little hint of that protein.
0:02:24 What is it? Why’d you spend five years studying it?
0:02:27 >> It was my entry point to CRISPR.
0:02:29 So I joined Jennifer Doudna’s lab as a brand new baby PhD student in 2007.
0:02:37 This was the dark ages of CRISPR.
0:02:39 There were three peer-reviewed manuscripts that had been published at the time.
0:02:44 So it took me about 45 minutes to get up to speed on the field.
0:02:47 It was great.
0:02:49 I was joining a project headed up by a post-doctoral fellow in the lab,
0:02:55 and he had identified these CRISPR associated or Cas proteins,
0:03:00 and he was trying to study all of them.
0:03:02 Now, he was able to make and study all but one.
0:03:07 One was proving difficult in the lab.
0:03:09 So he gave that one to me to see if I could sort it out.
0:03:12 We did eventually sort it out.
0:03:15 In the end, it turned out to be a very important little protein.
0:03:19 It’s actually responsible for making
0:03:21 these small CRISPR RNAs that are at the heart of CRISPR biology.
0:03:27 So I had a lot of fun for many years really understanding how
0:03:31 that particular protein functioned, what it did,
0:03:34 how it did it on a molecular level,
0:03:37 and then ultimately zooming far,
0:03:39 far out how it fits into the broader use of CRISPR systems.
0:03:43 Yeah. I mean, if you’re going to spend five years studying one protein,
0:03:46 studying a protein that’s essential to CRISPR,
0:03:48 and doing it in like 2010 is as good as it gets, right?
0:03:55 Right place, right time.
0:03:56 Just to be clear, briefly, just so we have it, what is CRISPR?
0:04:01 CRISPR is a technology for editing the genome.
0:04:06 CRISPR allows us to do a few different things to change genomes.
0:04:13 We can hit the delete key.
0:04:15 We can get rid of a gene that we don’t want to express anymore.
0:04:19 We can make a small change,
0:04:21 maybe even as simple as a single nucleotide of DNA,
0:04:26 and we can insert one or multiple new genes
0:04:30 to actually give a cell new capabilities it didn’t have before.
0:04:33 Just in the last months, order of magnitude months,
0:04:37 there have been just the first drug approvals based on CRISPR, right?
0:04:43 Tell me about those.
0:04:44 It’s incredibly exciting.
0:04:46 At the end of last year,
0:04:47 the first ever CRISPR-edited therapy was approved by the FDA.
0:04:51 It’s now been approved by other regulatory agencies outside the US too.
0:04:57 This is a cellular therapy for the treatment of sickle cell and beta thalassemia.
0:05:03 This is the use case where you take cells and you use CRISPR to change them,
0:05:08 and then you deliver the cells as the therapy back to these patients,
0:05:13 and the vision is to try to actually cure sickle cell disease.
0:05:17 It’s quite remarkable.
0:05:18 Really fast from when you were in grad school,
0:05:22 and this wasn’t quite the original work,
0:05:25 but this early work was happening, right?
0:05:27 It’s 12 years, which for to go from a lab
0:05:32 and just basic proof of concept to a thing in the world seems wildly fast.
0:05:38 It’s lightning speed.
0:05:39 I’m not aware of any other life science technology
0:05:44 that went from really important publication in Science Magazine
0:05:49 to approved therapy anywhere near that fast.
0:05:53 There are probably a few things to thank for that.
0:05:56 One is CRISPR is actually not the first genome editing technology.
0:06:01 Genome editing has been around for a while,
0:06:04 but the other approaches are much harder to use,
0:06:08 and so this really unlocked a much faster, broader scale of genome editing.
0:06:14 So there was a lot of resident expertise and capability
0:06:18 that could be turbocharged by the introduction of CRISPR genome editing.
0:06:22 It’s like there were people who knew how to do it already,
0:06:24 and then this incredible tool kind of fell out of the sky
0:06:27 and was like, “Oh, we can just do the thing we’re doing way better.”
0:06:30 Exactly.
0:06:32 Were you saying there were a couple reasons?
0:06:34 Was that one reason? Was there another reason?
0:06:36 That’s one, and I think another is
0:06:39 that there were things developed for other fields
0:06:42 or biology well understood that could quickly be taken advantage of.
0:06:47 So, for example, the genetic cause of sickle cell disease
0:06:51 has been known for decades,
0:06:53 and yet there hasn’t been the right tool to do much of anything about it.
0:06:59 And so this was sort of the perfect marriage
0:07:02 of this incredible enabling technology
0:07:05 and its ability to solve a biology problem
0:07:08 that’s been well understood for a very long time.
0:07:11 Can you give me a sense of the landscape
0:07:14 of how CRISPR is being used in drug therapies now broadly?
0:07:21 CRISPR is being used in two very fundamental ways for drug development.
0:07:26 The first is basic research,
0:07:28 and the second is actually designing and doing new therapies,
0:07:34 and that falls largely into two categories.
0:07:37 One is the kind of work that we are doing here at Caribou,
0:07:40 where we use CRISPR to actually modify or engineer cells,
0:07:46 and the cells are the therapy.
0:07:49 So, by the time we deliver, for example, our CAR-T cell therapy, CB10 to patients,
0:07:54 there’s no CRISPR inside of those cells anymore.
0:07:57 CRISPR is gone, it has modified the genome in multiple ways,
0:08:01 and the cell is the therapeutic.
0:08:05 The other strategy that some companies are using
0:08:08 is to actually deliver CRISPR inside the human body,
0:08:12 and the idea is to try to correct a gene
0:08:16 that causes a rare genetic disorder.
0:08:19 And so, in that case, CRISPR itself is the therapy.
0:08:22 So, in that latter case, that is gene therapy, essentially,
0:08:27 what people have called gene therapy.
0:08:29 And what seems to be next in line?
0:08:33 What’s farthest along, anyways, in terms of other CRISPR-derived therapies?
0:08:39 Yeah, there’s some very exciting work coming out of a company called IntelliA Therapeutics,
0:08:44 where they’re actually using CRISPR as the drug.
0:08:47 So, they are delivering it, packaged inside these little fat particles,
0:08:53 to go directly to a patient’s liver to correct a gene that causes a disease.
0:08:58 And they are running what’s called a phase three trial
0:09:02 for one of those medicines right now.
0:09:05 So, I feel like this is a dumb question, but as I imagine that,
0:09:09 does that mean that the therapy has to get to every cell in the liver?
0:09:14 Is it going to change the genome of every cell in your liver?
0:09:17 Is that the way that works?
0:09:19 Thank goodness, no, that’s not required.
0:09:22 Yeah, it couldn’t be that, right?
0:09:23 It couldn’t be that.
0:09:24 Most of them, but it’s cell by cell.
0:09:28 It’s like that a particle hits one liver cell and changes the genome,
0:09:33 and then another one hits another one.
0:09:34 And then, is there some tipping point?
0:09:36 Like, how does it work?
0:09:38 It’s a wonderful question.
0:09:39 And I think there are a lot of people who sit in a lot of conference rooms
0:09:43 staring at whiteboards, trying to understand what is that tipping point.
0:09:48 Because I think it’s biologically unrealistic to think you can edit 100% of cells in the liver.
0:09:55 And if that’s what’s needed for a therapy, you’re probably out of luck.
0:09:58 And instead, focusing on diseases where there’s some model or suggestion that,
0:10:05 you know, maybe editing 10% of the cells or 15% or 20% of the cells would be enough.
0:10:11 And there’s confidence that the technology might be able to accomplish that.
0:10:15 Well, you mentioned that there’s a therapy in, did you say,
0:10:18 phase three in the final stage of clinical trials.
0:10:21 What disease is that targeting?
0:10:23 So, Intellia is working on a disease called transthyretin amyloidosis, or ATTR for short.
0:10:32 It’s a disease caused by misfolded proteins, and it leads to neurodegeneration
0:10:38 and cardiomyopathies.
0:10:40 That’s the one in the liver?
0:10:42 They are editing liver cells because the liver produces the misfolded protein
0:10:49 that causes problems elsewhere in the body.
0:10:52 So, OK, clearly CRISPR is this, you know, wildly useful breakthrough.
0:10:58 But it’s not perfect.
0:10:59 And your company was founded in a way to address this key weakness of CRISPR as originally developed.
0:11:07 So, what is the weakness in particular that your company is focusing on?
0:11:13 Specificity.
0:11:15 When I say specificity, I mean editing the one site in the genome that we intend to,
0:11:21 and not accidentally making changes anywhere else, right?
0:11:25 In Microsoft Word, you put the cursor exactly where you want to write new text.
0:11:30 It’s not a mystery where the new text is going to land.
0:11:32 Using a biological tool like CRISPR, more often than not, you edit the site that you intend to,
0:11:39 but biology is noisy.
0:11:42 And sometimes the system lands in places you didn’t expect,
0:11:47 and can make changes in places you didn’t want.
0:11:50 That could be a problem for what you’re trying to do.
0:11:52 And so, our team for years has been focused on the challenge of specificity,
0:11:58 and ultimately developing new technologies to address this head-on.
0:12:02 What percent of the time does CRISPR get it wrong?
0:12:05 It’s the question I want to ask, and I’m sure that’s too broad a question.
0:12:07 But how do you think about that?
0:12:09 How should I think about that?
0:12:10 It varies dramatically.
0:12:13 So, the way CRISPR actually works, it’s usually a specific protein called Cas9
0:12:19 that cuts the genome at the site that you’re trying to edit.
0:12:23 But Cas9 on its own can’t do anything.
0:12:26 It’s inert, if you will.
0:12:28 It needs an RNA, a piece of RNA, that’s actually specifically designed
0:12:34 to match the sequence of the genome that you’re trying to modify.
0:12:37 It partners with this RNA, and the RNA takes it to the right place.
0:12:40 So, depending on which RNA you’ve designed, the edits could be more or less specific.
0:12:47 There are plenty of examples of first-generation CRISPR-Cas9
0:12:52 where you could get really efficient editing at the site you want,
0:12:55 and really efficient editing at several other sites as well that you did not want.
0:13:01 And then there are many of us, my company, Caribou Biosciences Included,
0:13:06 who have invented, developed, accessed new technologies
0:13:12 that can overcome some of these specificity challenges.
0:13:15 I mean, it seems like, in your case, that particular technology
0:13:18 is sort of the core proposition that the company has founded on, right?
0:13:22 Can we take CRISPR and make it work more reliably?
0:13:26 Absolutely.
0:13:27 So, what do you do to make it work better?
0:13:29 So, at the heart of our company is what we call the Chardonnay technology.
0:13:35 Now, Chardonnay is an acronym.
0:13:38 CHR-DNA stands for a Mouthful CRISPR-Hybrid RNA-DNA Technology.
0:13:44 You now see why we use an acronym.
0:13:46 But each of those words, I mean, it’s like a relatively sort of,
0:13:50 you know, comprehensible acronym, right?
0:13:52 Like CRISPR-Hybrid RNA-DNA.
0:13:55 It’s like that’s not wildly complicated.
0:13:57 Fair. I appreciate that.
0:13:59 And to be fair, it does actually describe what the technology is.
0:14:04 So, I just told you, usually, Cas9 or other CRISPR proteins need an RNA partner
0:14:11 to get to the right site in the genome.
0:14:13 What some of my colleagues did is actually develop hybrid guides.
0:14:18 Guides that are part RNA and part DNA.
0:14:21 And it turns out the inclusion of DNA improves the specificity dramatically.
0:14:27 We can measure this in a very quantitative way and see that it improves the specificity
0:14:33 of editing by many orders of magnitude.
0:14:36 Aha. So, it’s not like 10% better.
0:14:39 It’s like 100 times better?
0:14:42 1000 times better, even more in some cases.
0:14:44 Is there a sort of lay person’s answer to why?
0:14:47 Absolutely. It all has to do with what we would call a biochemistry affinity.
0:14:56 Meaning, what is the binding tightness of the CRISPR system for the target genome?
0:15:04 And it might intuitively feel like higher binding, higher affinity is better.
0:15:11 But it actually turns out the opposite is true.
0:15:14 And that by including DNA, we actually decrease the affinity of the complex for the target.
0:15:23 And the reason you want to decrease the affinity is that really the entire human genome
0:15:29 represents a laundry list of potential off-target sites we don’t want to edit.
0:15:35 So, you want low enough affinity that you’re not accidentally grabbing all these other pieces
0:15:39 of the genome and instead grabbing the one site that you actually want to modify.
0:15:44 So, is the challenge then to see how low you can get the affinity and have it still work?
0:15:50 I mean, I get that you don’t want it to not bind things that it’s not supposed to bind to
0:15:54 or not cut things that it’s not supposed to cut.
0:15:56 But you do want it to bind to or cut the thing that it is supposed to cut.
0:15:59 So, presumably, there’s some optimal spot or maybe what’s optimal depends on the use case.
0:16:05 But how do you strike that balance?
0:16:08 It’s a very careful balancing act. You’re absolutely right.
0:16:11 Our research team has spent a huge amount of time working on this and has found ways to really
0:16:17 develop the appropriate way to balance these two needs for each time we need to make an edit.
0:16:25 Still to come on the show, how Rachel and her colleagues are using this new kind of CRISPR
0:16:33 technology to create new treatments for cancer.
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0:17:29 Hey everybody, I’m Kai Rizdal, the host of Marketplace, your daily download on the economy.
0:17:34 Money influences so much of what we do and how we live. That’s why it’s essential to understand
0:17:39 how this economy works. At Marketplace, we break down everything from inflation and student loans
0:17:45 to the future of AI so that you can understand what it all means for you. Marketplace is your
0:17:51 secret weapon for understanding this economy. Listen wherever you get your podcasts.
0:17:58 There’s this promising new kind of cancer treatment called CAR T-cell therapy. T-cells are
0:18:05 a key part of the immune system, and the basic idea here is to engineer T-cells to attack cancer
0:18:11 cells. As you’ll hear, a few CAR T-cell therapies have been approved, but they’re complicated and
0:18:18 expensive. So Rachel and her colleagues are using CRISPR to try to come up with a new kind
0:18:23 of CAR T-cell therapy that is both simpler and cheaper. Let’s talk about some of the
0:18:30 projects you’re working on with the technology. Let’s start with what’s farthest along clinically.
0:18:36 Farthest along is a cell therapy that we call CB10, and we are developing this to treat relapsed or
0:18:44 refractory B-cell non-Hodgkin lymphoma. That’s a kind of blood cancer, and it’s when B-cells,
0:18:52 part of the immune system, become diseased. We are using our CRISPR genome editing, our Chardonnay
0:18:59 technology, to actually take healthy T-cells from healthy donors and then modify them through CRISPR
0:19:08 to teach them how to find and kill these kinds of diseased B-cell cancers. These therapies are
0:19:16 called CAR T-cell therapies. We’re not the first to work on them. There are many who are advancing
0:19:22 these kinds of therapies. CAR, again, is an acronym. It stands for Chimeric Antigen Receptor, and it
0:19:31 describes a special protein that we can encourage the T-cells to express that gives them the ability
0:19:39 to specifically recognize and kill these B-cells. As I understand it, other companies have developed
0:19:48 CAR T-cell therapies that take an individual patient’s own immune cells and develop them in the lab,
0:19:57 essentially, and then put them back into the patient to target cancer. That is, unsurprisingly,
0:20:03 very, very, very expensive because it’s like you’re developing a custom drug for each patient,
0:20:09 which is great in a way, but also it’s like this bespoke, sort of individually tailored drug that
0:20:17 it just costs a lot to make and it costs a lot to buy. My understanding is that you’re trying to
0:20:23 develop a version that is more like a traditional drug that doesn’t have to be customized to every
0:20:29 patient. Is that right? That’s exactly correct. There are today in the United States six approved
0:20:37 CAR T-cell therapies, and each one of them is what’s called anatologous cell therapy,
0:20:43 which is a scientific fancy word for patient-specific.
0:20:48 And so those drugs, just to be clear, those are approved. They’re in use. They exist in the world.
0:20:53 Those are approved commercial products today.
0:20:55 And they cost like hundreds of thousands of dollars per patient.
0:20:58 Correct. Yeah.
0:20:59 So they are proof positive that the immune system, specifically T-cells, can be incredibly
0:21:06 powerful anti-cancer agents. They also demonstrate how challenging it is to develop
0:21:13 one batch of therapy for each and every patient. That is not scalable. That is not going to be
0:21:20 something that delivers this kind of therapy to broader and broader patient populations.
0:21:26 And it’s also restricted to cancer patients who have sufficiently good T-cells
0:21:32 to make the product in the first place.
0:21:34 Oh, that’s interesting. Like if you’re a patient and your immune system is just totally beat down
0:21:39 by having cancer or being treated for cancer, then you don’t have the T-cells to generate this therapy.
0:21:45 That’s exactly correct. There’s also quite a lot of complexity and almost hand-holding,
0:21:53 if you will, necessary to make these patient-specific therapies. And so cancer centers like MD Anderson
0:22:03 or the University of Pennsylvania, they have tremendous expertise and they have the staff
0:22:09 to really work with patients to shepherd them through this process to ensure that they can
0:22:15 actually support them, provide any additional therapies they need while they’re waiting for
0:22:19 their therapy to be manufactured to give them access to this kind of therapy.
0:22:24 But that’s not where the majority of patients are treated. The majority of patients are treated in
0:22:29 community hospitals and community clinics that don’t have the resources to shepherd patients
0:22:35 through this kind of very complex stuff. So what do you have to do to make a one-size-fits-most
0:22:46 version of this, right? It would be good for the world if we could move away from having to
0:22:51 design this drug literally for each patient and have a drug that’ll work for almost everybody.
0:22:57 That’s what you’re trying to do. How do you do it? Yeah, the vision is to develop what the field
0:23:02 would call allogeneic or off-the-shelf CAR T-cell therapies. Which is what most drugs are, right?
0:23:10 Like just a drug and the doctor gives you the drug and hopefully it makes you better, right?
0:23:14 Right. So step one is we need to use healthy T-cells from healthy donors instead of T-cells from
0:23:21 cancer patients. Step two is you have to make this safe, right? Typically, if you take a T-cell
0:23:28 from one person and put it in another person, you are probably going to cause what is called
0:23:34 graft versus host disease. Which is where the other person’s T-cells attack parts of your body.
0:23:41 Analogous to the problems that transplant patients have, basically. It’s like a transplant, yeah.
0:23:46 Correct, correct. So right off the bat, we have to prevent that. And we do that through genome
0:23:51 editing by getting rid of something called the T-cell receptor. It’s the thing on the surface
0:23:56 of the T-cell that would usually empower it to cause graft versus host disease. So that’s
0:24:01 hitting the delete key once to get rid of that. Okay. The second step is you have to give the
0:24:08 T-cells the car. So it knows what it’s looking for on the surface of cancer cells to appropriately
0:24:14 identify and kill them. And many of our peers stop there. Those two combined would be what they
0:24:21 envision as a product. But our team looked at that and said that will never be enough.
0:24:27 And so just to be clear, when you see people stop there, are people trying that? Like is that version
0:24:32 of this drug in trials now? Yes. Multiple human clinical trials are being run with
0:24:38 something that looks like that. And so if that works, that would be off the shelf,
0:24:43 one-size-fits-most, normal drug kind of drug. You’re skeptical that it’s going to work.
0:24:48 Correct. We think you have to take it a step further and I’ll tell you why.
0:24:52 Okay. Yeah. These off the shelf T-cells, they are foreign to the patients immune system.
0:24:59 And the patients immune system is going to figure that out fairly quickly and actually kill off
0:25:05 the car T-cells. And that’s very different from when you’ve had a product made from your very own
0:25:10 T-cells, they can last for a very long time. And so we look at that differential in time and say,
0:25:17 that’s a problem we have to solve. Why doesn’t everybody agree with you?
0:25:22 I would say more and more people agree with us if you look at what’s happening in the field.
0:25:27 In fact, many of the first off the shelf CAR T-cells that have been tried in human clinical trials
0:25:34 have been retired because they didn’t work as well as people had hoped.
0:25:37 And I think many are now going back to the drawing board to think about what are other
0:25:42 things we can do to empower or enhance these T-cells to overcome these challenges.
0:25:48 So what do you do? You were getting to the sort of next steps. What do you do to make it better
0:25:53 tolerated by the patients immune system? Yeah. We think about how to bridge that gap both very
0:25:58 literally and in ways that are more about boosting the biology than necessarily entangling with the
0:26:06 patients immune system. So, for example, in some of our other cell therapies that we’re developing
0:26:12 for other blood cancers like multiple myeloma and AML, we actually deploy what we call immune
0:26:20 cloaking. And so this is where we use our genome editing to change what is or is not decorating
0:26:26 the surface of the CAR T-cells to try to slow down how the patients immune system could recognize
0:26:32 and clear the therapy. So that’s a very literal way of addressing this challenge.
0:26:36 Cloaking is a cool name for it to basically make the cell better at hiding from the immune system.
0:26:42 Exactly. Exactly. Is there an example of a particular change you make to that end?
0:26:48 Yes. So what we do is actually get rid of some of the proteins that would usually sit on the surface
0:26:57 of a CAR T-cell. These are called HLA Class 1 molecules. And it helps to prevent the patients
0:27:04 immune system from readily recognizing and clearing the therapy. It’s a little more complex than that.
0:27:11 There’s actually a special kind that we then decorate the surface with to help ensure that
0:27:16 all parts of the immune system cannot rapidly recognize it. I also want to be clear, we don’t
0:27:22 think this creates a perfect stealth cell that lasts forever. There are lots of things about
0:27:27 these cells that we expect the patients immune system to ultimately recognize and cause it to
0:27:32 reject. This is about buying additional time with the hope that that allows additional anti-tumor
0:27:38 activity. Is there a balance you have to strike there where, well, the cell still has to work,
0:27:48 right? I mean, is there a universe where you do so much to try and cloak the cell that it can’t,
0:27:54 whatever, do its cellular business and persist as a cell until it finds the tumor?
0:27:58 Right. I think there’s some extreme world where you try to put so many different genome edits
0:28:05 into the T-cell that you break it, maybe both on a cell-specific level, but also a population level.
0:28:15 So if you think about it, we’re trying to take this population of millions and millions of T-cells
0:28:21 and provide three, four, five different genome edits. Now, genome editing is very efficient,
0:28:29 but it’s not 100% efficient. Some edits might be 80%, others 90%, maybe 95%.
0:28:35 So that means as you now look at this whole population of T-cells, every time you add a new
0:28:43 edit, it means a fraction of a fraction of a fraction of the cells actually have all the
0:28:49 edits that you desire. So we set a pretty high bar for ourselves. We only bring a program forward
0:28:56 into the clinic. If we can manufacture it in such a way that at least half of all of the T-cells
0:29:03 have all the edits that we’re going for. And we’ve been able to do that with three different
0:29:07 therapies so far. So you’re saying that even with your improved version of CRISPR,
0:29:16 it’s still sufficiently error-prone, not that it’s highly error-prone, but it still makes enough
0:29:22 mistakes that something like half of the cells you’re creating won’t be exactly the way you want
0:29:28 them to be. I would say it’s not that it’s making mistakes, meaning it’s not making off-target
0:29:34 changes elsewhere that we didn’t want. It’s instead that in some fraction of the cells,
0:29:39 they’re just not getting the edit. Right. It’s the sin of omission rather than a
0:29:43 sin of commission. Exactly. Yes. Exactly. So it’s the affinity. You nailed the low affinity.
0:29:48 So does that suggest, just to sort of zoom out for a sec, it suggests that there is room for
0:29:57 improvement on the kind of platform level, presumably. I think so. And I’ll give an example
0:30:02 of even the work we’ve done over the past few years. So our first program, which is for lymphoma,
0:30:08 benefits from three edits. Fast forward now to our third program for AML. It has five different
0:30:16 genome edits in it. We’re able to hit the delete key on three different genes in two different
0:30:22 places. We can insert new genes to give new functionality to the T cells. And I think this
0:30:28 already represents really pushing the envelope in terms of what you can accomplish.
0:30:34 And I think there’s further room to run with that as well.
0:30:36 If things go well, when might you be submitting a drug for approval?
0:30:44 Fantastic question. We hope to start a phase three trial with CB10 next year.
0:30:53 If you look at the kinds of phase three trials that have been run for these cell therapies before,
0:30:59 they usually take two years or more to run. And then there’s some time after that to put
0:31:05 all the documents together for the regulatory agencies. So a lot of work yet to be done. Very
0:31:10 excited about what’s coming next. We’ll be back in a minute with the lightning round.
0:31:24 Hey, everybody. I’m Kai Rizdal, the host of Marketplace, your daily download on the economy.
0:31:34 Money influences so much of what we do and how we live. That’s why it’s essential to understand
0:31:39 how this economy works. At Marketplace, we break down everything from inflation and student loans
0:31:45 to the future of AI so that you can understand what it all means for you. Marketplace is your
0:31:51 secret weapon for understanding this economy. Listen wherever you get your podcasts.
0:31:55 Let’s have a lightning round. Let’s finish with the lightning round.
0:32:02 What’s more frustrating, pipetting or knitting?
0:32:08 Knitting.
0:32:11 What’s the hardest thing you ever knit?
0:32:18 Mittens. Tell me about pipetting. I feel like you and pipetting have history.
0:32:23 Pipetting is about moving clear liquids from one tube to another. I spent many, many years
0:32:32 where that is what I did every day. And obviously, it gave me the ability to do hands-on wet lab
0:32:39 research. And there’s a piece of it that I desperately miss, which is being the first
0:32:44 to know the answer to an interesting biological question, right? There’s a magical aha moment
0:32:50 when you see the results first. Now these days, I’m not that far away from the people who get
0:32:57 to do the really cool work in our lab. So I’m at peace with the balancing act of not having to
0:33:03 pipet and being the third, fourth, tenth person who learns the cool news.
0:33:08 A worthwhile trade-off in the end. Indeed.
0:33:12 Did you go to grad school assuming you would work in industry? Is there some moment when you
0:33:16 are making a leap off of this path? You know, you’re getting a PhD. Clearly, you’ve been very
0:33:22 good at school. Lots of people just stay in school all their lives and become professors and have
0:33:28 wonderful careers. Was there some moment when you decided to step off of that path, leap off of that
0:33:34 path? I was probably one of the few people in my PhD program who came to school knowing I didn’t
0:33:41 want to be a professor when I grew up. I actually thought I wanted to be a patent attorney when I
0:33:47 grew up. However, we started working with patent attorneys because of all the cool technology that
0:33:54 was coming out of the lab. And I pretty quickly realized that’s not the job that I want to do.
0:34:00 Good thing you figured that out. Indeed. Before I went to three years of law school,
0:34:06 so it created sort of this moment of, well, I don’t know what I’m going to do when I grow up.
0:34:12 I now know a few things I don’t want to do. And it meant I started thinking a lot about
0:34:18 the industry side of science. I took a lot of business school classes at that point in time
0:34:24 to try to learn and learn a new vocabulary. But I think that made it easier to take the
0:34:30 entrepreneurial leap because I wasn’t on a different path that I had to jump off to go on.
0:34:35 Basically, you’re way out of going to law school? Indeed.
0:34:38 So you were on the Forbes 30 under 30, the Fortune 40 under 40. As far as I know, there’s no 50
0:34:48 under 50. So do you have like a next move? Well, there is a 50 over 50, but I’ve got to wait a
0:34:56 few more years for that. I’m glad that you’ve got it lined up though. It’s important to have a goal.
0:35:03 And you’ve got time. What’s one thing that you wish people understood better about genes?
0:35:11 I think many people expect there’s a very clear one-to-one connection that a gene means X.
0:35:21 There are very few genes in our genome that result in one specific outcome. We,
0:35:30 as human beings, are the product of this incredibly complicated cross signaling across
0:35:38 every gene in our genome. And any one trait, even as simple as how tall we are,
0:35:45 is the output of many, many, many different genes. And so I do wish there was a better understanding
0:35:52 of just the rich complexity of our own biology. Because then I think it feeds directly into
0:35:59 how do you use a technology like CRISPR to change disease biology? And there are some examples,
0:36:07 but not a ton of examples where one edit is enough. It’s like the Mendelian pea plants maybe do more
0:36:15 harm than good as a teaching tool. Like, no, no, it’s not usually like that. Fair, yes.
0:36:23 Rachel Horowitz is the co-founder and CEO of Caraboo Biosciences. Today’s show was produced
0:36:30 by Gabriel Hunter-Chang. It was edited by Lydia Jean-Cott and engineered by Sarah Bouguere.
0:36:37 You can email us at problem@pushkin.fm. I’m Jacob Goldstein, and we’ll be back next week
0:36:43 with another episode of What’s Your Problem?
0:36:53 Hey, everybody. I’m Kai Rizdal, the host of Marketplace, your daily download on the economy.
0:37:01 Money influences so much of what we do and how we live. That’s why it’s essential to understand
0:37:06 how this economy works. At Marketplace, we break down everything from inflation and student loans
0:37:12 to the future of AI so that you can understand what it all means for you. Marketplace is your
0:37:18 secret weapon for understanding this economy. Listen wherever you get your podcasts.
Last year, the FDA approved a treatment for sickle cell disease using a revolutionary new gene editing technology called CRISPR. Rachel Haurwitz conducted pioneering research on CRISPR as a graduate student. Now she’s the co-founder and CEO of Caribou Biosciences. Rachel’s problem is this: How can you improve CRISPR and use it to engineer human immune cells to fight cancer?
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