1 00:00:15,356 --> 00:00:23,876 Speaker 1: Pushkin. One of the most important technological breakthroughs so far 2 00:00:23,996 --> 00:00:30,476 Speaker 1: this century is CRISPER aka Clustered regularly spaced palindromic repeats 3 00:00:30,876 --> 00:00:35,396 Speaker 1: aka the extraordinary gene editing tool that is right now 4 00:00:35,516 --> 00:00:40,556 Speaker 1: making its way to actual human patience. The FDA approved 5 00:00:40,596 --> 00:00:44,996 Speaker 1: the first CRISPER produced drug last December, and now scientists 6 00:00:45,076 --> 00:00:48,276 Speaker 1: are trying to improve on the original Crisper to bring 7 00:00:48,476 --> 00:00:56,876 Speaker 1: more treatments to market. I'm Jacob Goldstein and this is 8 00:00:56,876 --> 00:00:59,076 Speaker 1: What's Your Problem, the show where I talk to people 9 00:00:59,076 --> 00:01:02,676 Speaker 1: who are trying to make technological progress. My guest today 10 00:01:02,756 --> 00:01:07,276 Speaker 1: is Rachel Horowitz, the co founder and CEO of Caribou Biosciences. 11 00:01:07,876 --> 00:01:11,716 Speaker 1: Rachel's problem is this, how can you make CRISPER work better? 12 00:01:12,236 --> 00:01:15,516 Speaker 1: And how can you use it to engineer human immune 13 00:01:15,516 --> 00:01:19,476 Speaker 1: cells to fight cancer. We started off talking about Rachel's 14 00:01:19,516 --> 00:01:23,556 Speaker 1: graduate work at UC Berkeley. She studied with Jennifer DOWDNA, 15 00:01:23,676 --> 00:01:26,036 Speaker 1: who would go on to win the Nobel Prize for 16 00:01:26,076 --> 00:01:29,316 Speaker 1: her work on crisper. At the time, Rachel's work was 17 00:01:29,316 --> 00:01:35,916 Speaker 1: focused on a protein called CAST six. Is it right 18 00:01:35,996 --> 00:01:38,716 Speaker 1: that you spent five years studying one protein? 19 00:01:39,916 --> 00:01:44,556 Speaker 2: I spent five years studying one small protein composed of 20 00:01:44,596 --> 00:01:48,356 Speaker 2: only one hundred and eighty seven amino acids, So I 21 00:01:48,436 --> 00:01:49,636 Speaker 2: was pretty far down the road hole. 22 00:01:50,476 --> 00:01:53,396 Speaker 1: I mean, are you the world expert in that protein? 23 00:01:53,476 --> 00:01:55,556 Speaker 1: Is there? Do you know more about that than anyone 24 00:01:55,556 --> 00:01:56,396 Speaker 1: who has ever lived? 25 00:01:57,476 --> 00:01:59,436 Speaker 2: There are probably three of us who know more than 26 00:01:59,476 --> 00:02:00,956 Speaker 2: we ever wanted two about that protein. 27 00:02:01,356 --> 00:02:03,196 Speaker 1: Just give me a little hit of that protein? Well, like, 28 00:02:03,356 --> 00:02:05,916 Speaker 1: what is it? Why'd you spend five years studying it? 29 00:02:05,916 --> 00:02:10,396 Speaker 2: It was my entry point to Crisper. I joined Jennifer 30 00:02:10,436 --> 00:02:14,516 Speaker 2: dowdna's lab as a brand new baby PhD student in 31 00:02:14,596 --> 00:02:18,676 Speaker 2: two thousand and seven. This was the dark ages of Crisper. 32 00:02:18,716 --> 00:02:22,716 Speaker 2: There were three peer reviewed manuscripts that had been published 33 00:02:22,716 --> 00:02:25,076 Speaker 2: at the time, so it took me about forty five 34 00:02:25,116 --> 00:02:26,756 Speaker 2: minutes to get up to speed on the field. It 35 00:02:26,796 --> 00:02:31,716 Speaker 2: was great, and I was joining a project headed up 36 00:02:31,756 --> 00:02:34,716 Speaker 2: by a post doctoral fellow in the lab, and he 37 00:02:34,756 --> 00:02:40,116 Speaker 2: had identified these Crisper associated or CAST proteins and he 38 00:02:40,156 --> 00:02:42,876 Speaker 2: was trying to study all of them. Now he was 39 00:02:42,916 --> 00:02:46,636 Speaker 2: able to make and study all but one. One was 40 00:02:46,796 --> 00:02:49,756 Speaker 2: proving difficult in the lab, so he gave that one 41 00:02:49,756 --> 00:02:52,196 Speaker 2: to me to see if I could sorted out. We 42 00:02:52,316 --> 00:02:55,116 Speaker 2: did eventually sort it out, and in the end it 43 00:02:55,156 --> 00:02:58,596 Speaker 2: turned out to be a very important little protein. It's 44 00:02:58,676 --> 00:03:03,596 Speaker 2: actually responsible for making these small Crisper RNAs that are 45 00:03:03,636 --> 00:03:06,876 Speaker 2: at the heart of Crisper biology. And so I had 46 00:03:06,916 --> 00:03:10,036 Speaker 2: a lot of fun for many years really understand how 47 00:03:10,076 --> 00:03:14,556 Speaker 2: that particular protein functioned, what it did, how it did 48 00:03:14,596 --> 00:03:18,396 Speaker 2: it on a molecular level, and then ultimately zooming far 49 00:03:18,476 --> 00:03:20,996 Speaker 2: far out how it fits into the broader use of 50 00:03:21,036 --> 00:03:21,876 Speaker 2: Crisper systems. 51 00:03:22,156 --> 00:03:23,996 Speaker 1: Yeah. I mean, if you're going to spend five years 52 00:03:24,036 --> 00:03:27,516 Speaker 1: studying one protein, studying a protein that's essential to crisper, 53 00:03:27,876 --> 00:03:31,916 Speaker 1: and doing it in like twenty ten, is as good 54 00:03:32,356 --> 00:03:33,036 Speaker 1: as good as. 55 00:03:32,876 --> 00:03:35,356 Speaker 2: It gets right right place, right time. 56 00:03:36,156 --> 00:03:39,316 Speaker 1: And just to be clear briefly, just so we have it, 57 00:03:39,596 --> 00:03:40,516 Speaker 1: what is crisper. 58 00:03:41,116 --> 00:03:47,076 Speaker 2: Crisper is a technology for editing the genome. Crisper allows 59 00:03:47,156 --> 00:03:51,476 Speaker 2: us to do a few different things to change genomes. 60 00:03:52,476 --> 00:03:54,916 Speaker 2: We can hit the delete key. We can get rid 61 00:03:54,956 --> 00:03:57,836 Speaker 2: of a gene that we don't want to express anymore. 62 00:03:58,676 --> 00:04:02,076 Speaker 2: We can make a small change, maybe even as simple 63 00:04:02,156 --> 00:04:07,236 Speaker 2: as a single nucleotide of DNA, and we can insert 64 00:04:07,276 --> 00:04:10,356 Speaker 2: one or multiple new genes to actually give a cell 65 00:04:10,436 --> 00:04:12,276 Speaker 2: new capabilities it didn't have. Before. 66 00:04:13,116 --> 00:04:16,716 Speaker 1: So just in the last months, right order of magnitude months, 67 00:04:16,956 --> 00:04:20,556 Speaker 1: there have been I guess the first drug approvals sort 68 00:04:20,596 --> 00:04:22,876 Speaker 1: of based on Crisper, right, tell me about those. 69 00:04:23,676 --> 00:04:26,516 Speaker 2: It's incredibly exciting. At the end of last year, the 70 00:04:26,556 --> 00:04:30,596 Speaker 2: first ever Crisper edited therapy was approved by the FDA. 71 00:04:30,716 --> 00:04:35,036 Speaker 2: It's now but approved by other regulatory agencies outside the 72 00:04:35,116 --> 00:04:38,596 Speaker 2: US too. So this is a cellular therapy for the 73 00:04:38,636 --> 00:04:42,916 Speaker 2: treatment of sickle cell and beta thalacemia. So this is 74 00:04:42,956 --> 00:04:46,556 Speaker 2: the use case where you take cells, you use Crisper 75 00:04:46,676 --> 00:04:49,876 Speaker 2: to change them, and then you deliver the cells as 76 00:04:49,916 --> 00:04:53,196 Speaker 2: the therapy back to these patients and the vision is 77 00:04:54,036 --> 00:04:56,156 Speaker 2: to try to actually cure sickle cell disease. 78 00:04:56,236 --> 00:05:00,756 Speaker 1: It's quite remarkable and really fast from when you were 79 00:05:00,796 --> 00:05:03,756 Speaker 1: in grad school and this kind of wasn't quite the 80 00:05:03,796 --> 00:05:06,996 Speaker 1: original work, but this early work was happening. Right. It's 81 00:05:07,116 --> 00:05:12,116 Speaker 1: twelve years, which for go from a lab and kind 82 00:05:12,156 --> 00:05:14,596 Speaker 1: of just basic proof of concept to a thing in 83 00:05:14,636 --> 00:05:16,796 Speaker 1: the world seems wildly fast. 84 00:05:17,756 --> 00:05:21,516 Speaker 2: It's lightning speed. I'm not aware of any other life 85 00:05:21,556 --> 00:05:27,036 Speaker 2: science technology that went from really important publication in science 86 00:05:27,116 --> 00:05:32,196 Speaker 2: magazine to approved therapy anywhere near that fast. There are 87 00:05:32,196 --> 00:05:35,756 Speaker 2: probably a few things to thank for That. One is 88 00:05:36,396 --> 00:05:41,916 Speaker 2: Crisper's actually not the first genomediting technology. Genomediting has been 89 00:05:41,956 --> 00:05:45,396 Speaker 2: around for a while, but the other approaches are much 90 00:05:45,516 --> 00:05:50,836 Speaker 2: harder to use, and so this really unlocked a much faster, 91 00:05:51,116 --> 00:05:53,876 Speaker 2: broader scale of genomediting. So there was a lot of 92 00:05:54,436 --> 00:05:59,396 Speaker 2: resident expertise and capability that could be turbocharged by the 93 00:05:59,396 --> 00:06:01,076 Speaker 2: introduction of chris per Gino mediting. 94 00:06:01,116 --> 00:06:02,796 Speaker 1: It's like there were people who sort of knew how 95 00:06:02,796 --> 00:06:05,236 Speaker 1: to do it already and then this incredible tool kind 96 00:06:05,276 --> 00:06:06,876 Speaker 1: of fell out of the sky and was like, Oh, 97 00:06:06,916 --> 00:06:08,916 Speaker 1: we can just do the thing. We're doing way better 98 00:06:09,476 --> 00:06:13,196 Speaker 1: exactly we're saying there were a couple of reasons. Was 99 00:06:13,196 --> 00:06:15,076 Speaker 1: that one reason was there another reason. 100 00:06:15,276 --> 00:06:18,676 Speaker 2: That's one, and I think another is that there were 101 00:06:18,796 --> 00:06:24,116 Speaker 2: things developed for other fields or biology well understood that 102 00:06:24,236 --> 00:06:27,876 Speaker 2: could quickly be taken advantage of. So, for example, the 103 00:06:27,996 --> 00:06:32,036 Speaker 2: genetic cause of sickle cell disease has been known for decades, 104 00:06:32,596 --> 00:06:36,156 Speaker 2: and yet there hasn't been the right tool to do 105 00:06:36,356 --> 00:06:39,316 Speaker 2: much of anything about it. And so this was sort 106 00:06:39,356 --> 00:06:44,596 Speaker 2: of the perfect marriage of this incredible enabling technology and 107 00:06:44,676 --> 00:06:47,836 Speaker 2: its ability to solve a biology problem that's been well 108 00:06:47,916 --> 00:06:49,276 Speaker 2: understood for a very long time. 109 00:06:50,116 --> 00:06:53,276 Speaker 1: Can you give me a sense of the landscape of 110 00:06:54,756 --> 00:06:59,396 Speaker 1: how crisper is being used in drug therapies Now, broadly. 111 00:07:00,596 --> 00:07:04,196 Speaker 2: Crisper is being used in two very fundamental ways for 112 00:07:04,276 --> 00:07:08,076 Speaker 2: drug development. The first is basic research and the second 113 00:07:08,276 --> 00:07:14,036 Speaker 2: is actually designing and doing new therapies, and that falls 114 00:07:14,276 --> 00:07:17,996 Speaker 2: largely into two categories. One is the kind of work 115 00:07:17,996 --> 00:07:20,596 Speaker 2: that we are doing here at Caribou, where we use 116 00:07:20,756 --> 00:07:26,556 Speaker 2: crisper to actually modify or engineer cells, and the cells 117 00:07:26,916 --> 00:07:30,436 Speaker 2: are the therapy. So by the time we deliver, for example, 118 00:07:30,956 --> 00:07:34,196 Speaker 2: our Carte cell therapy CEB tend to patients, there's no 119 00:07:34,316 --> 00:07:37,636 Speaker 2: Crisper inside of those cells anymore. Crisper is gone. It 120 00:07:37,676 --> 00:07:41,196 Speaker 2: has modified the genome in multiple ways, and the cell 121 00:07:41,796 --> 00:07:46,196 Speaker 2: is the therapeutic. The other strategy that some companies are 122 00:07:46,316 --> 00:07:51,316 Speaker 2: using is to actually deliver Crisper inside the human body, 123 00:07:51,796 --> 00:07:55,116 Speaker 2: and the idea is to try to correct a gene 124 00:07:55,476 --> 00:07:59,116 Speaker 2: that causes a rare genetic disorder, and so in that case, 125 00:07:59,276 --> 00:08:01,316 Speaker 2: crisper itself is the therapy. 126 00:08:02,356 --> 00:08:05,356 Speaker 1: So in that latter case, I mean that is gene 127 00:08:05,436 --> 00:08:11,356 Speaker 1: therapy essentially what people have therapy, and what's what seems 128 00:08:11,396 --> 00:08:14,156 Speaker 1: to be next in line what's farthest along anyways in 129 00:08:14,236 --> 00:08:17,196 Speaker 1: terms of other crisper derived therapies. 130 00:08:18,436 --> 00:08:20,516 Speaker 2: Yeah, there's some very exciting work coming out of a 131 00:08:20,556 --> 00:08:25,716 Speaker 2: company called Intellia Therapeutics where they're actually using crisper as 132 00:08:25,756 --> 00:08:30,116 Speaker 2: the drug. So they are delivering it packaged inside these 133 00:08:30,156 --> 00:08:34,076 Speaker 2: little fat particles to go directly to a patient's liver 134 00:08:34,316 --> 00:08:37,956 Speaker 2: to correct a gene that causes a disease. And they 135 00:08:37,996 --> 00:08:41,996 Speaker 2: are running what's called a phase three trial for one 136 00:08:41,996 --> 00:08:43,196 Speaker 2: of those medicines right now. 137 00:08:44,316 --> 00:08:46,876 Speaker 1: So I feel like this is a dumb question, But 138 00:08:46,956 --> 00:08:49,436 Speaker 1: as I imagine that, like, does that mean that the 139 00:08:49,436 --> 00:08:52,956 Speaker 1: therapy has to get to like every cell in the liver? 140 00:08:53,276 --> 00:08:55,676 Speaker 1: Like is it going to change the genome of every 141 00:08:55,716 --> 00:08:57,796 Speaker 1: cell in your liver? Is that the way that works? 142 00:08:58,756 --> 00:09:01,356 Speaker 2: Thank goodness, No, that's not requite. 143 00:09:01,356 --> 00:09:04,356 Speaker 1: It couldn't be that, right, It couldn't be that most 144 00:09:04,436 --> 00:09:07,356 Speaker 1: of them like what like what? But it's sell by cell. 145 00:09:07,436 --> 00:09:11,076 Speaker 1: It's like that the particle hits one liver cell and 146 00:09:11,156 --> 00:09:13,156 Speaker 1: changes the genome, and then another one hits another one 147 00:09:13,196 --> 00:09:15,636 Speaker 1: and then is there some kipping point? Like how does 148 00:09:15,636 --> 00:09:16,076 Speaker 1: it work? 149 00:09:17,636 --> 00:09:19,516 Speaker 2: It's a wonderful question, and I think there are a 150 00:09:19,556 --> 00:09:22,076 Speaker 2: lot of people who sit in a lot of conference 151 00:09:22,116 --> 00:09:26,236 Speaker 2: rooms staring at whiteboards trying to understand what is that 152 00:09:26,316 --> 00:09:30,996 Speaker 2: tipping point? Because I think it's biologically unrealistic to think 153 00:09:31,036 --> 00:09:33,916 Speaker 2: you can edit one hundred percent of cells in the liver, 154 00:09:34,036 --> 00:09:36,556 Speaker 2: and if that's what's needed for a therapy, you're probably 155 00:09:36,596 --> 00:09:40,996 Speaker 2: out of luck, and instead focusing on diseases where there's 156 00:09:41,036 --> 00:09:46,116 Speaker 2: some model or suggestion that you know, maybe editing ten 157 00:09:46,196 --> 00:09:49,036 Speaker 2: percent of the cells or fifteen or twenty percent of 158 00:09:49,076 --> 00:09:52,476 Speaker 2: the cells would be enough, and there's confidence that the 159 00:09:52,516 --> 00:09:54,196 Speaker 2: technology might be able to accomplish that. 160 00:09:54,396 --> 00:09:57,116 Speaker 1: Well, what you mentioned that there's a therapy and did 161 00:09:57,156 --> 00:10:00,876 Speaker 1: you say phase three in the final stage of clinical trials? 162 00:10:00,876 --> 00:10:02,596 Speaker 1: What disease is that targeting? 163 00:10:03,236 --> 00:10:08,356 Speaker 2: So Intellia is working on a disease called transthyretin amyloidosis 164 00:10:08,636 --> 00:10:13,076 Speaker 2: or a TTR. For sure. It's a disease caused by 165 00:10:13,236 --> 00:10:19,556 Speaker 2: misfolded proteins and it leads to neurodegeneration and cardiomyopathies. 166 00:10:20,116 --> 00:10:21,156 Speaker 1: That's the one in the liver. 167 00:10:21,956 --> 00:10:26,876 Speaker 2: They are editing liver cells because the liver produces the 168 00:10:26,916 --> 00:10:31,156 Speaker 2: misfolded protein that causes problems elsewhere in the body. 169 00:10:31,836 --> 00:10:37,116 Speaker 1: So, okay, clearly Crisper is this wildly useful breakthrough, but 170 00:10:37,316 --> 00:10:41,356 Speaker 1: it's not perfect. And your company was founded in a 171 00:10:41,396 --> 00:10:46,996 Speaker 1: way to address this key weakness of Crisper as originally developed. 172 00:10:47,036 --> 00:10:51,276 Speaker 1: So what is the weakness in particular that your company 173 00:10:51,316 --> 00:10:51,996 Speaker 1: is focusing on? 174 00:10:53,276 --> 00:10:58,356 Speaker 2: Specificity? When I say specificity, I mean editing the one 175 00:10:58,556 --> 00:11:01,556 Speaker 2: site in the genome that we intend to and not 176 00:11:01,956 --> 00:11:06,076 Speaker 2: accidentally making changes anywhere else. Right in Microsoft Word, you 177 00:11:06,116 --> 00:11:08,836 Speaker 2: put the cursor exactly where you want to write new text, 178 00:11:09,396 --> 00:11:11,756 Speaker 2: not a mystery where the new text is going to land. 179 00:11:12,596 --> 00:11:16,916 Speaker 2: Using a biological tool like Crisper, more often than not, 180 00:11:17,156 --> 00:11:20,676 Speaker 2: you edit the site that you intend to. But biology 181 00:11:21,156 --> 00:11:24,916 Speaker 2: is noisy, and sometimes the system lands in places you 182 00:11:24,956 --> 00:11:28,916 Speaker 2: didn't expect and can make changes in places you didn't want. 183 00:11:29,116 --> 00:11:31,436 Speaker 2: That could be a problem for what you're trying to do. 184 00:11:31,796 --> 00:11:34,876 Speaker 2: And so our team for years has been focused on 185 00:11:35,156 --> 00:11:40,316 Speaker 2: the challenge of specificity and ultimately developing new technologies to 186 00:11:40,396 --> 00:11:41,276 Speaker 2: address this head on. 187 00:11:42,276 --> 00:11:44,276 Speaker 1: What percent of the time does Crisper get it wrong? 188 00:11:44,316 --> 00:11:45,756 Speaker 1: It's the question I want to ask, and I'm sure 189 00:11:45,756 --> 00:11:48,516 Speaker 1: that's too broad a question, But how do you think 190 00:11:48,516 --> 00:11:49,796 Speaker 1: about that? How should I think about that? 191 00:11:50,436 --> 00:11:56,036 Speaker 2: It varies dramatically so the way Crisper actually works, it's 192 00:11:56,156 --> 00:11:59,516 Speaker 2: usually a specific protein called CAST nine that cuts the 193 00:11:59,596 --> 00:12:02,596 Speaker 2: genome at the site that you're trying to edit. But 194 00:12:02,676 --> 00:12:05,956 Speaker 2: CAST nine on its own can't do anything. It's inert. 195 00:12:06,036 --> 00:12:09,676 Speaker 2: If you will, it needs an RNA, a piece of 196 00:12:09,796 --> 00:12:14,596 Speaker 2: RNA that's actually specifically designed to match the sequence of 197 00:12:14,636 --> 00:12:17,316 Speaker 2: the genome that you're trying to modify. It partners with 198 00:12:17,396 --> 00:12:19,996 Speaker 2: this RNA and the RNA takes it to the right place. 199 00:12:20,596 --> 00:12:24,636 Speaker 2: So depending on which RNA you've designed, the edits could 200 00:12:24,676 --> 00:12:28,276 Speaker 2: be more or less specific. There are plenty of examples 201 00:12:28,476 --> 00:12:32,396 Speaker 2: of first generation Crisper cast nine where you could get 202 00:12:32,476 --> 00:12:36,476 Speaker 2: really efficient editing at the site you want, and really 203 00:12:36,476 --> 00:12:39,676 Speaker 2: efficient editing at several other sites as well that you 204 00:12:39,716 --> 00:12:42,436 Speaker 2: did not want. And then there are many of us, 205 00:12:42,876 --> 00:12:48,676 Speaker 2: my company Caribou bios Sciences included, who have invented, developed 206 00:12:48,796 --> 00:12:52,596 Speaker 2: access to new technologies that can overcome some of these 207 00:12:52,636 --> 00:12:53,876 Speaker 2: specificity challenges. 208 00:12:54,396 --> 00:12:56,436 Speaker 1: I mean, it seems like in your case that particular 209 00:12:56,556 --> 00:13:00,476 Speaker 1: technology is sort of the core proposition that the company 210 00:13:00,556 --> 00:13:02,676 Speaker 1: is founded on. Right, Can we take crisper and make 211 00:13:02,716 --> 00:13:05,036 Speaker 1: it work more reliably? 212 00:13:05,436 --> 00:13:06,156 Speaker 2: Absolutely? 213 00:13:06,716 --> 00:13:08,396 Speaker 1: So, what do you do to make it work better? 214 00:13:09,716 --> 00:13:12,116 Speaker 2: So? At the heart of our company is what we 215 00:13:12,236 --> 00:13:18,116 Speaker 2: call the Shardona technology. Now, Shardona is an acronym. Cchr 216 00:13:18,316 --> 00:13:23,836 Speaker 2: DNA stands for a mouthful crisper hybrid RNA DNA technology. 217 00:13:23,996 --> 00:13:25,796 Speaker 2: You now see why we use an acronym. 218 00:13:25,876 --> 00:13:27,676 Speaker 1: But each of those words, I mean, it's like a 219 00:13:27,796 --> 00:13:32,796 Speaker 1: relatively sort of you know, comprehensible acronym, right, like crisper 220 00:13:32,876 --> 00:13:36,836 Speaker 1: hybrid RNA DNA. It's like, that's not wildly complicated. 221 00:13:37,596 --> 00:13:41,116 Speaker 2: Fair, I appreciate that, And to be fair, it does 222 00:13:41,156 --> 00:13:44,476 Speaker 2: actually describe what the technology is. So I just told 223 00:13:44,516 --> 00:13:48,676 Speaker 2: you usually CAST nine or other crisper proteins need an 224 00:13:48,916 --> 00:13:51,876 Speaker 2: RNA partner to get to the right side in the genome. 225 00:13:52,396 --> 00:13:56,916 Speaker 2: What some of my colleagues did is actually develop hybrid guides, 226 00:13:57,196 --> 00:14:01,196 Speaker 2: guides that are part RNA and part DNA. And it 227 00:14:01,276 --> 00:14:06,476 Speaker 2: turns out the inclusion of DNA improves the specificity dramatically. 228 00:14:07,116 --> 00:14:10,516 Speaker 2: We can measure this in a very quantitative way and 229 00:14:10,596 --> 00:14:14,076 Speaker 2: see that it improves the specificity of editing by many 230 00:14:14,236 --> 00:14:15,236 Speaker 2: orders of magnitude. 231 00:14:16,196 --> 00:14:18,956 Speaker 1: A huh, So it's not like ten percent better, it's 232 00:14:18,996 --> 00:14:20,996 Speaker 1: like one hundred times better. 233 00:14:21,076 --> 00:14:23,636 Speaker 2: A thousand times better, even more. In some cases. 234 00:14:24,596 --> 00:14:26,796 Speaker 1: Is there a sort of layperson's answer to why. 235 00:14:27,876 --> 00:14:32,276 Speaker 2: Absolutely. It all has to do with what we would 236 00:14:32,356 --> 00:14:39,436 Speaker 2: call it biochemistry affinity, meaning what is the binding tightness 237 00:14:39,996 --> 00:14:44,556 Speaker 2: of the crisper system for the target genome? And it 238 00:14:44,636 --> 00:14:50,316 Speaker 2: might intuitively feel like higher binding, higher affinity is better, 239 00:14:50,836 --> 00:14:54,476 Speaker 2: but it actually turns out the opposite is true, huh, 240 00:14:55,116 --> 00:14:59,596 Speaker 2: And that by including DNA we actually decrease the affinity 241 00:15:00,116 --> 00:15:03,756 Speaker 2: of the complex for the target. And the reason you 242 00:15:03,796 --> 00:15:07,396 Speaker 2: want to decrease the affinity is that really the entire 243 00:15:07,556 --> 00:15:11,996 Speaker 2: human genome resents a laundry list of potential off target 244 00:15:12,036 --> 00:15:15,196 Speaker 2: sites we don't want to edit. So you want low 245 00:15:15,316 --> 00:15:18,076 Speaker 2: enough affinity that you're not accidentally grabbing all these other 246 00:15:18,116 --> 00:15:21,916 Speaker 2: pieces of the genome and instead grabbing the one site 247 00:15:21,956 --> 00:15:23,396 Speaker 2: that you actually want to modify. 248 00:15:24,236 --> 00:15:27,036 Speaker 1: So is the challenge then to see how low you 249 00:15:27,076 --> 00:15:29,516 Speaker 1: can get the affinity and have it still work. I mean, 250 00:15:29,836 --> 00:15:32,196 Speaker 1: I get that you don't want it to not bind 251 00:15:32,236 --> 00:15:34,076 Speaker 1: things that it's not supposed to bind to, or not 252 00:15:34,156 --> 00:15:35,996 Speaker 1: cut things that it's not supposed to cut, but you 253 00:15:36,036 --> 00:15:37,876 Speaker 1: do want it to bind to or cut the thing 254 00:15:37,916 --> 00:15:40,076 Speaker 1: that it is supposed to cut. So presumably there's some 255 00:15:41,276 --> 00:15:44,076 Speaker 1: optimal spot or maybe what's optimal depends on the use case. 256 00:15:44,116 --> 00:15:46,476 Speaker 1: But how do you strike that balance. 257 00:15:47,116 --> 00:15:50,316 Speaker 2: It's a very careful balancing act. You're absolutely right. Our 258 00:15:50,356 --> 00:15:53,676 Speaker 2: research team has spent a huge amount of time working 259 00:15:53,756 --> 00:15:57,836 Speaker 2: on this and has found ways to really develop the 260 00:15:57,956 --> 00:16:02,996 Speaker 2: appropriate way to balance these two needs for each time. 261 00:16:03,076 --> 00:16:03,916 Speaker 2: We need to make an. 262 00:16:03,876 --> 00:16:10,116 Speaker 1: Edit still to come on the show. How Rachel and 263 00:16:10,196 --> 00:16:13,276 Speaker 1: her colleagues are using this new kind of Crisper technology 264 00:16:13,676 --> 00:16:27,316 Speaker 1: to create new treatments for cancer. There's this promising new 265 00:16:27,396 --> 00:16:30,916 Speaker 1: kind of cancer treatment called car T cell therapy. T 266 00:16:31,116 --> 00:16:33,556 Speaker 1: cells are a key part of the immune system, and 267 00:16:33,596 --> 00:16:36,876 Speaker 1: the basic idea here is to engineer T cells to 268 00:16:36,956 --> 00:16:40,996 Speaker 1: attack cancer cells. As you'll hear, a few car T 269 00:16:41,156 --> 00:16:45,276 Speaker 1: cell therapies have been approved, but they're complicated and expensive. 270 00:16:45,956 --> 00:16:48,916 Speaker 1: So Rachel and her colleagues are using Crisper to try 271 00:16:48,916 --> 00:16:50,756 Speaker 1: to come up with a new kind of car T 272 00:16:50,916 --> 00:16:56,516 Speaker 1: cell therapy that is both simpler and cheaper. So let's 273 00:16:56,516 --> 00:16:59,316 Speaker 1: talk about some of the some of the projects you're 274 00:16:59,316 --> 00:17:02,076 Speaker 1: working on with the technology. We'll start with what's farthest 275 00:17:02,076 --> 00:17:03,316 Speaker 1: along clinically. 276 00:17:03,036 --> 00:17:07,676 Speaker 2: Furthest along is a cell therapy that we call CB ten, 277 00:17:08,236 --> 00:17:12,396 Speaker 2: and we are developing this to treat relapsed or refractory 278 00:17:12,556 --> 00:17:15,756 Speaker 2: B cell non Hodgkin lymphoma. So that's a kind of 279 00:17:15,756 --> 00:17:19,436 Speaker 2: blood cancer, and it's when B cells, part of the 280 00:17:19,436 --> 00:17:24,196 Speaker 2: immune system, become diseased. And so we are using our 281 00:17:24,316 --> 00:17:29,556 Speaker 2: Crisper genomeediting, our Chardonnay technology to actually take healthy T 282 00:17:29,756 --> 00:17:34,596 Speaker 2: cells from healthy donors and then modify them through Crisper 283 00:17:35,196 --> 00:17:39,156 Speaker 2: to teach them how to find and kill these kinds 284 00:17:39,196 --> 00:17:44,316 Speaker 2: of diseased B cell cancers. These therapies are called car 285 00:17:44,396 --> 00:17:47,276 Speaker 2: T cell therapies. We're not the first to work on them. 286 00:17:47,276 --> 00:17:50,916 Speaker 2: There are many who are advancing these kinds of therapies. 287 00:17:51,676 --> 00:17:55,676 Speaker 2: And CAR again is an acronym. It stands for chimeric 288 00:17:55,956 --> 00:18:00,556 Speaker 2: antigen receptor, and it describes a special protein that we 289 00:18:00,716 --> 00:18:05,036 Speaker 2: can encourage the T cells to express that gives them 290 00:18:05,076 --> 00:18:09,916 Speaker 2: the ability to specifically recognize and kill these B cells. 291 00:18:10,556 --> 00:18:15,036 Speaker 1: As I understand it, other companies have developed car T 292 00:18:15,236 --> 00:18:20,956 Speaker 1: cell therapies that take an individual patient's own immune cells 293 00:18:21,556 --> 00:18:25,276 Speaker 1: and develop them in the lab essentially, and then put 294 00:18:25,276 --> 00:18:28,316 Speaker 1: them back into the patient to target cancer. Right. That 295 00:18:28,836 --> 00:18:32,956 Speaker 1: is unsurprisingly, very very very expensive, right, because it's sort 296 00:18:32,956 --> 00:18:36,236 Speaker 1: of like you're developing a custom drug for each patient, 297 00:18:36,396 --> 00:18:40,076 Speaker 1: which is great in a way, but also it's like 298 00:18:40,116 --> 00:18:45,036 Speaker 1: this bespoke, sort of individually tailored drug that it just 299 00:18:45,116 --> 00:18:47,316 Speaker 1: costs a lot to make and it costs a lot 300 00:18:47,356 --> 00:18:50,116 Speaker 1: to buy. And so my understanding is that you're trying 301 00:18:50,156 --> 00:18:52,876 Speaker 1: to develop a version that is more like a traditional 302 00:18:52,916 --> 00:18:56,596 Speaker 1: drug that doesn't have to be customized to every patient. 303 00:18:56,716 --> 00:19:00,516 Speaker 2: Is that right, That's exactly correct. So there are today 304 00:19:00,556 --> 00:19:04,956 Speaker 2: in the United States six approved car te cell therapies, 305 00:19:05,516 --> 00:19:09,956 Speaker 2: and each one of them is what's called anatologous cell therapy, 306 00:19:10,356 --> 00:19:14,396 Speaker 2: which is scientific fancy word for patient specific. 307 00:19:14,716 --> 00:19:17,236 Speaker 1: And so those drugs, just to be clear, those are approved, 308 00:19:17,276 --> 00:19:20,236 Speaker 1: they're in use, they exist in the world. 309 00:19:19,756 --> 00:19:22,076 Speaker 2: Those are approved commercial products today. 310 00:19:21,876 --> 00:19:24,516 Speaker 1: And they cost like hundreds of thousands of dollars per patient. 311 00:19:24,996 --> 00:19:29,596 Speaker 2: Correct. Yeah, So they are proof positive that the immune system, 312 00:19:29,796 --> 00:19:35,076 Speaker 2: specifically T cells, can be incredibly powerful anti cancer agents. 313 00:19:35,876 --> 00:19:40,356 Speaker 2: They also demonstrate how challenging it is to develop one 314 00:19:40,556 --> 00:19:44,836 Speaker 2: batch of therapy for each and every patient. That is 315 00:19:44,916 --> 00:19:48,036 Speaker 2: not scalable. That is not going to be something that 316 00:19:48,156 --> 00:19:52,596 Speaker 2: delivers this kind of therapy to broader and broader patient populations, 317 00:19:53,236 --> 00:19:57,516 Speaker 2: and it's also restricted to cancer patients who have sufficiently 318 00:19:57,596 --> 00:20:00,636 Speaker 2: good T cells to make the product in the first place. 319 00:20:00,796 --> 00:20:04,116 Speaker 1: Oh, that's interesting, Like if you're a patient in your 320 00:20:04,116 --> 00:20:07,396 Speaker 1: immune system is just totally beat down by having cancer 321 00:20:07,476 --> 00:20:09,596 Speaker 1: or being treated for cancer, then you don't have the 322 00:20:09,636 --> 00:20:11,916 Speaker 1: T cells to generate this therapy. 323 00:20:12,356 --> 00:20:18,076 Speaker 2: That's exactly correct. There's also quite a lot of complexity 324 00:20:18,396 --> 00:20:22,876 Speaker 2: and almost handholding, if you will, necessary to make these 325 00:20:23,316 --> 00:20:29,396 Speaker 2: patient specific therapies, and so cancer centers like MD Anderson 326 00:20:29,916 --> 00:20:34,676 Speaker 2: or the University of Pennsylvania, they have tremendous expertise and 327 00:20:34,716 --> 00:20:37,916 Speaker 2: they have the staff to really work with patients to 328 00:20:38,156 --> 00:20:41,756 Speaker 2: shepherd them through this process to ensure that they can 329 00:20:41,796 --> 00:20:45,276 Speaker 2: actually support them provide any additional therapies they need while 330 00:20:45,276 --> 00:20:48,676 Speaker 2: they're waiting for their therapy to be manufactured, to give 331 00:20:48,716 --> 00:20:51,756 Speaker 2: them access to this kind of therapy. But that's not 332 00:20:51,796 --> 00:20:54,596 Speaker 2: where the majority of patients are treated. The majority of 333 00:20:54,636 --> 00:20:59,196 Speaker 2: patients are treated in community hospitals and community clinics that 334 00:20:59,316 --> 00:21:03,076 Speaker 2: don't have the resources to shepherd patients through this kind 335 00:21:03,156 --> 00:21:05,916 Speaker 2: of very complex stuff. 336 00:21:07,036 --> 00:21:11,436 Speaker 1: So, so what do you have to do to make 337 00:21:11,716 --> 00:21:13,956 Speaker 1: a one size fits most version of this, right you 338 00:21:13,996 --> 00:21:15,956 Speaker 1: want to? It would be good for the world if 339 00:21:15,956 --> 00:21:19,916 Speaker 1: we could move away from having to design this drug 340 00:21:20,436 --> 00:21:23,236 Speaker 1: literally for each patient and have a drug that'll work 341 00:21:23,276 --> 00:21:25,396 Speaker 1: for almost everybody. That's what you're trying to do. How 342 00:21:25,436 --> 00:21:25,996 Speaker 1: do you do it? 343 00:21:26,636 --> 00:21:29,676 Speaker 2: Yeah, the vision is to develop what the field would 344 00:21:29,716 --> 00:21:34,396 Speaker 2: call allogeneic or off the shelf car T cell therapies. 345 00:21:34,236 --> 00:21:37,316 Speaker 1: Which is what most drugs are, right, Like just a 346 00:21:37,396 --> 00:21:40,236 Speaker 1: drug and the doctor gives you the drug and hopefully 347 00:21:40,276 --> 00:21:41,036 Speaker 1: it makes you better. 348 00:21:41,116 --> 00:21:44,516 Speaker 2: Right right, So step one is we need to use 349 00:21:44,796 --> 00:21:48,316 Speaker 2: healthy T cells from healthy donors instead of T cells 350 00:21:48,356 --> 00:21:49,396 Speaker 2: from cancer patients. 351 00:21:49,436 --> 00:21:49,756 Speaker 1: Okay. 352 00:21:50,556 --> 00:21:54,356 Speaker 2: Step two is you have to make this safe. Right, Typically, 353 00:21:54,396 --> 00:21:56,836 Speaker 2: if you take a T cell from one person and 354 00:21:56,876 --> 00:22:00,036 Speaker 2: put it in another person, you are probably going to 355 00:22:00,116 --> 00:22:03,556 Speaker 2: cause what is called graft versus host disease, which is 356 00:22:03,596 --> 00:22:07,236 Speaker 2: where the other person's T cells attack parts of your. 357 00:22:07,156 --> 00:22:11,316 Speaker 1: Body, analogous to what is that transplant patients have. Basically, 358 00:22:11,676 --> 00:22:13,596 Speaker 1: it's like yeah. 359 00:22:13,236 --> 00:22:16,396 Speaker 2: Correct, correct, So right off the bat, we have to 360 00:22:16,436 --> 00:22:19,236 Speaker 2: prevent that, and we do that through genome editing by 361 00:22:19,236 --> 00:22:22,356 Speaker 2: getting rid of something called the T cell receptor. It's 362 00:22:22,396 --> 00:22:24,396 Speaker 2: the thing on the surface of the T cell that 363 00:22:24,396 --> 00:22:27,596 Speaker 2: would usually empower it to cause graph versus host disease. 364 00:22:27,676 --> 00:22:30,356 Speaker 2: So that's hitting the delete key once to get rid 365 00:22:30,396 --> 00:22:30,556 Speaker 2: of that. 366 00:22:30,836 --> 00:22:31,196 Speaker 1: Okay. 367 00:22:32,516 --> 00:22:34,756 Speaker 2: The second step is you have to give the T 368 00:22:34,956 --> 00:22:38,516 Speaker 2: cells the CAR so it knows what it's looking for 369 00:22:38,916 --> 00:22:42,036 Speaker 2: on the surface of cancer cells to appropriately identify and 370 00:22:42,156 --> 00:22:46,356 Speaker 2: kill them. And many of our peers stop there. Those 371 00:22:46,396 --> 00:22:49,396 Speaker 2: two combined would be what they envision as a product, 372 00:22:50,316 --> 00:22:53,196 Speaker 2: but our team looked at that and said, that will 373 00:22:53,196 --> 00:22:53,996 Speaker 2: never be enough. 374 00:22:54,396 --> 00:22:56,436 Speaker 1: And so just to be clear, when you see people stop, 375 00:22:56,436 --> 00:22:59,356 Speaker 1: there are people trying that, like, is that version of 376 00:23:00,196 --> 00:23:02,116 Speaker 1: this drug in trials? Now? 377 00:23:02,396 --> 00:23:05,436 Speaker 2: Yes, multiple human clinical trials are being run with something 378 00:23:05,436 --> 00:23:06,076 Speaker 2: that looks like. 379 00:23:06,036 --> 00:23:08,636 Speaker 1: That, and so if that works, that would be a 380 00:23:09,236 --> 00:23:13,076 Speaker 1: off the shelf, one sized, fistmost normal drug kind of drug. 381 00:23:13,196 --> 00:23:14,916 Speaker 1: You're skeptical that it's going to work. 382 00:23:15,436 --> 00:23:17,316 Speaker 2: Correct, We think you have to take it a step 383 00:23:17,356 --> 00:23:21,636 Speaker 2: farther and I'll tell you why. Okay, Yeah, these off 384 00:23:21,676 --> 00:23:25,116 Speaker 2: the shelf T cells, they are foreign to the patient's 385 00:23:25,116 --> 00:23:28,156 Speaker 2: immune system, and the patient's immune system is going to 386 00:23:28,196 --> 00:23:32,196 Speaker 2: figure that out fairly quickly and actually kill off the 387 00:23:32,236 --> 00:23:35,676 Speaker 2: CAR T cells. And that's very different from when you've 388 00:23:35,676 --> 00:23:38,036 Speaker 2: had a product made from your very own T cells. 389 00:23:38,116 --> 00:23:41,036 Speaker 2: They can last for a very long time, and so 390 00:23:41,076 --> 00:23:44,356 Speaker 2: we look at that differential in time and say that's 391 00:23:44,356 --> 00:23:45,636 Speaker 2: a problem we have to solve. 392 00:23:45,916 --> 00:23:48,516 Speaker 1: Why doesn't everybody agree with you? 393 00:23:49,916 --> 00:23:52,196 Speaker 2: I would say more and more people agree with us 394 00:23:52,196 --> 00:23:54,916 Speaker 2: if you look at what's happening in the field. In fact, 395 00:23:55,036 --> 00:23:58,116 Speaker 2: many of the first off the shelf car T cells 396 00:23:58,156 --> 00:24:00,996 Speaker 2: that have been tried in human clinical trials have been 397 00:24:00,996 --> 00:24:03,996 Speaker 2: retired because they didn't work as well as people had hoped. 398 00:24:04,356 --> 00:24:06,196 Speaker 2: And I think many are now going back to the 399 00:24:06,276 --> 00:24:09,636 Speaker 2: drawing board to think about what are other things we 400 00:24:09,716 --> 00:24:13,396 Speaker 2: can do to empower or enhance these T cells to 401 00:24:13,436 --> 00:24:14,556 Speaker 2: overcome these challenges. 402 00:24:14,956 --> 00:24:17,596 Speaker 1: So what do you do, you were getting to the 403 00:24:17,596 --> 00:24:19,236 Speaker 1: sort of next steps, what do you do to make 404 00:24:19,276 --> 00:24:21,356 Speaker 1: it better tolerated by the patient's immune system. 405 00:24:21,876 --> 00:24:24,436 Speaker 2: Yeah, we think about how to bridge that gap, both 406 00:24:25,036 --> 00:24:29,796 Speaker 2: very literally and in ways that are more about boosting 407 00:24:29,876 --> 00:24:35,556 Speaker 2: the biology than necessarily entangling with the patient's immune system. So, 408 00:24:35,716 --> 00:24:38,756 Speaker 2: for example, in some of our other cell therapies that 409 00:24:38,796 --> 00:24:43,236 Speaker 2: we're developing for other blood cancers like multiple myeloma and AML, 410 00:24:43,996 --> 00:24:47,516 Speaker 2: we actually deploy what we call immune cloaking, and so 411 00:24:47,596 --> 00:24:51,036 Speaker 2: this is where we use our genomeediting to change what 412 00:24:51,236 --> 00:24:53,956 Speaker 2: is or is not decorating the surface of the car 413 00:24:54,076 --> 00:24:57,396 Speaker 2: T cells to try to slow down how the patient's 414 00:24:57,396 --> 00:25:00,516 Speaker 2: immune system could recognize and clear the therapy. So that's 415 00:25:00,516 --> 00:25:03,316 Speaker 2: a very literal way of addressing this challenge. 416 00:25:03,436 --> 00:25:05,956 Speaker 1: Cloaking is a cool name for it, to basically make 417 00:25:05,996 --> 00:25:08,276 Speaker 1: the cell better at hiding from the immune. 418 00:25:07,956 --> 00:25:10,236 Speaker 2: System exactly exactly Is. 419 00:25:10,236 --> 00:25:14,356 Speaker 1: There an example of a particular change you make to 420 00:25:14,436 --> 00:25:14,876 Speaker 1: that end. 421 00:25:16,316 --> 00:25:19,756 Speaker 2: Yes, So what we do is actually get rid of 422 00:25:20,436 --> 00:25:23,116 Speaker 2: some of the proteins that would usually sit on the 423 00:25:23,156 --> 00:25:26,596 Speaker 2: surface of a Carte cell. These are called HILA class 424 00:25:26,596 --> 00:25:31,836 Speaker 2: one molecules, and it helps to prevent the patient's immune 425 00:25:31,916 --> 00:25:36,276 Speaker 2: system from readily recognizing and clearing the therapy. It's a 426 00:25:36,316 --> 00:25:39,236 Speaker 2: little more complex than that. There's actually a special kind 427 00:25:39,356 --> 00:25:42,756 Speaker 2: that we then decorate the surface with to help ensure 428 00:25:42,756 --> 00:25:46,396 Speaker 2: that all parts of the immune system cannot rapidly recognize it. 429 00:25:46,996 --> 00:25:49,036 Speaker 2: I also want to be clear, we don't think this 430 00:25:49,196 --> 00:25:53,236 Speaker 2: creates a perfect stealth cell that lasts forever. There are 431 00:25:53,276 --> 00:25:55,716 Speaker 2: lots of things about these cells that we expect the 432 00:25:55,756 --> 00:26:00,036 Speaker 2: patient's immune system to ultimately recognize and cause it to reject. 433 00:26:00,436 --> 00:26:03,476 Speaker 2: This is about buying additional time with a hope that 434 00:26:03,476 --> 00:26:05,836 Speaker 2: that allows additional anti tumor activity. 435 00:26:06,756 --> 00:26:11,036 Speaker 1: And is there is there a balance you have to 436 00:26:11,076 --> 00:26:15,236 Speaker 1: strike there where, well, the cell still has to work, right, 437 00:26:15,276 --> 00:26:17,076 Speaker 1: I mean, is there a universe where you do so 438 00:26:17,236 --> 00:26:20,236 Speaker 1: much to try and cloak the cell that it can't 439 00:26:20,556 --> 00:26:23,916 Speaker 1: whatever do its cellular business and persist as a cell 440 00:26:23,996 --> 00:26:25,476 Speaker 1: until it finds the tumor? 441 00:26:26,796 --> 00:26:30,156 Speaker 2: Right? I think there's some extreme world where you try 442 00:26:30,196 --> 00:26:33,476 Speaker 2: to put so many different genometics into the T cell 443 00:26:34,076 --> 00:26:39,276 Speaker 2: that you break it, maybe both on a cell specific level, 444 00:26:39,716 --> 00:26:42,836 Speaker 2: but also a population level. So if you think about it, 445 00:26:43,076 --> 00:26:46,996 Speaker 2: we're trying to take this population of millions and millions 446 00:26:47,036 --> 00:26:53,236 Speaker 2: of T cells and provide three, four five different genometics. Now, 447 00:26:53,276 --> 00:26:57,476 Speaker 2: genomeediting is very efficient, but it's not one hundred percent efficient. 448 00:26:57,636 --> 00:27:01,236 Speaker 2: You know, some medits might be eighty percent, otherre's ninety percent, 449 00:27:01,356 --> 00:27:04,836 Speaker 2: maybe ninety five percent. So that means, as you now 450 00:27:04,876 --> 00:27:08,836 Speaker 2: look at this whole population of T cells, every time 451 00:27:08,916 --> 00:27:12,196 Speaker 2: you add a new edit, it means a fraction of 452 00:27:12,236 --> 00:27:15,396 Speaker 2: a fraction of a fraction of the cells actually have 453 00:27:15,556 --> 00:27:18,716 Speaker 2: all the edits that you desire. So we set a 454 00:27:18,756 --> 00:27:22,516 Speaker 2: pretty high bar for ourselves. We only bring a program 455 00:27:22,636 --> 00:27:26,076 Speaker 2: forward into the clinic if we can manufacture it in 456 00:27:26,116 --> 00:27:28,876 Speaker 2: such a way that at least half of all of 457 00:27:28,916 --> 00:27:32,116 Speaker 2: the T cells have all the edits that we're going for, 458 00:27:32,596 --> 00:27:34,436 Speaker 2: and we've been able to do that with three different 459 00:27:34,476 --> 00:27:35,276 Speaker 2: therapies so far. 460 00:27:35,516 --> 00:27:42,716 Speaker 1: So you're saying that even with your improved version of Crisper, 461 00:27:43,636 --> 00:27:48,476 Speaker 1: it's still sufficiently error prone. Not that it's highly error prone, 462 00:27:48,476 --> 00:27:52,676 Speaker 1: but it still makes enough mistakes that something like half 463 00:27:52,716 --> 00:27:55,036 Speaker 1: of the cells you're creating won't be exactly the way 464 00:27:55,076 --> 00:27:55,756 Speaker 1: you want them to be. 465 00:27:56,676 --> 00:27:59,316 Speaker 2: I would say, it's not that it's making mistakes, meaning 466 00:27:59,316 --> 00:28:02,836 Speaker 2: it's not making off target changes elsewhere that we didn't want. 467 00:28:03,596 --> 00:28:06,476 Speaker 2: It's instead that in some fraction of the cells, they're 468 00:28:06,516 --> 00:28:07,436 Speaker 2: just not getting. 469 00:28:07,116 --> 00:28:12,196 Speaker 1: The edit right of omission rather than a sinecommission. Yes, exactly. 470 00:28:12,276 --> 00:28:17,756 Speaker 1: So it's the affinity, like you nailed the low affinity. 471 00:28:17,516 --> 00:28:20,516 Speaker 1: So does that suggest just to sort of zoom out 472 00:28:20,516 --> 00:28:22,116 Speaker 1: for a sec I mean it suggests that there is 473 00:28:23,196 --> 00:28:26,316 Speaker 1: room for improvement on the kind of platform level. Presumably. 474 00:28:27,036 --> 00:28:29,756 Speaker 2: I think so. And I'll give an example of even 475 00:28:29,796 --> 00:28:32,276 Speaker 2: the work we've done over the past few years. So 476 00:28:32,516 --> 00:28:37,396 Speaker 2: our first program, which is for lymphoma, benefits from three edits. 477 00:28:37,956 --> 00:28:41,476 Speaker 2: Fast forward now to our third program for AML. It 478 00:28:41,516 --> 00:28:45,556 Speaker 2: has five different genomeedics in it. We're able to hit 479 00:28:45,596 --> 00:28:49,916 Speaker 2: the delete key on three different genes in two different places. 480 00:28:49,916 --> 00:28:53,196 Speaker 2: We can insert new genes to give new functionality to 481 00:28:53,236 --> 00:28:57,076 Speaker 2: the T cells. And I think this already represents really 482 00:28:57,116 --> 00:29:00,236 Speaker 2: pushing the envelope in terms of what you can accomplish. 483 00:29:00,716 --> 00:29:02,836 Speaker 2: And I think there's further room to run with that 484 00:29:02,916 --> 00:29:03,316 Speaker 2: as well. 485 00:29:04,516 --> 00:29:09,516 Speaker 1: If things go well, When when might you be, you know, 486 00:29:09,916 --> 00:29:11,276 Speaker 1: submitting a drug for approval? 487 00:29:12,396 --> 00:29:16,996 Speaker 2: Fantastic question. We hope to start a phase three trial 488 00:29:17,076 --> 00:29:21,836 Speaker 2: with CB ten next year. If you look at the 489 00:29:22,316 --> 00:29:24,516 Speaker 2: kinds of phase three trials that have been run for 490 00:29:24,596 --> 00:29:28,956 Speaker 2: these cell therapies before, they usually take two years or 491 00:29:29,036 --> 00:29:31,636 Speaker 2: more to run, and then there's some time after that 492 00:29:31,956 --> 00:29:35,036 Speaker 2: put all the documents together for the regulatory agencies. So 493 00:29:35,716 --> 00:29:37,916 Speaker 2: a lot of work yet to be done. Very excited 494 00:29:37,956 --> 00:29:38,916 Speaker 2: about what's coming next. 495 00:29:42,436 --> 00:29:44,676 Speaker 1: We'll be back in a minute with the lighting round. 496 00:29:55,996 --> 00:29:59,036 Speaker 1: Let's have a lightning round. Let's finish with the lighting round. 497 00:30:01,436 --> 00:30:04,396 Speaker 1: What's more frustrating pipetting or knitting? 498 00:30:07,276 --> 00:30:07,716 Speaker 2: Knitting? 499 00:30:11,196 --> 00:30:12,436 Speaker 1: What's the hardest thing you ever knit? 500 00:30:15,116 --> 00:30:15,636 Speaker 2: Mittens? 501 00:30:16,276 --> 00:30:18,636 Speaker 1: Tell me about pipe petting. I feel like you and 502 00:30:18,636 --> 00:30:19,676 Speaker 1: pipetting have history. 503 00:30:20,876 --> 00:30:24,636 Speaker 2: You know. Pipeetting is about moving clear liquids from one 504 00:30:24,716 --> 00:30:29,236 Speaker 2: to another. I spent many, many years where that is 505 00:30:29,276 --> 00:30:32,356 Speaker 2: what I did every day, and obviously it gave me 506 00:30:32,396 --> 00:30:36,636 Speaker 2: the ability to do hands on wet lab research. And 507 00:30:36,716 --> 00:30:39,956 Speaker 2: there's a piece of it that I desperately miss, which 508 00:30:39,996 --> 00:30:42,836 Speaker 2: is being the first to know the answer to an 509 00:30:42,836 --> 00:30:47,476 Speaker 2: interesting biological question. Right, there's a magical aha moment when 510 00:30:47,556 --> 00:30:52,036 Speaker 2: you see the results first. Now these days, I'm not 511 00:30:52,196 --> 00:30:54,156 Speaker 2: that far away from the people who get to do 512 00:30:54,236 --> 00:30:57,116 Speaker 2: the really cool work in our lab, So I'm at 513 00:30:57,156 --> 00:31:00,156 Speaker 2: peace with the balancing act of not having to pipette 514 00:31:00,716 --> 00:31:04,116 Speaker 2: and you know, being the third, fourth, tenth person who 515 00:31:04,156 --> 00:31:05,236 Speaker 2: learns the cool news. 516 00:31:05,236 --> 00:31:08,716 Speaker 1: Worthwhile trade off at the end. Indeed, did you go 517 00:31:08,756 --> 00:31:11,356 Speaker 1: to grad school assuming you would work in industry? Is 518 00:31:11,396 --> 00:31:15,956 Speaker 1: there some moment when you are making a leap off 519 00:31:15,996 --> 00:31:18,276 Speaker 1: of this path? You know you're getting a PhD? You 520 00:31:18,276 --> 00:31:20,436 Speaker 1: clearly have been very good at school. Lots of people 521 00:31:21,316 --> 00:31:24,076 Speaker 1: just stay in school all their lives and become professors 522 00:31:24,116 --> 00:31:27,276 Speaker 1: and have wonderful careers. Was there some moment when you 523 00:31:27,556 --> 00:31:30,836 Speaker 1: decided to step off of that path? Leap off of 524 00:31:30,836 --> 00:31:31,316 Speaker 1: that path? 525 00:31:32,276 --> 00:31:34,556 Speaker 2: I was probably one of the few people in my 526 00:31:34,636 --> 00:31:38,196 Speaker 2: PhD program who came to school knowing I didn't want 527 00:31:38,236 --> 00:31:41,676 Speaker 2: to be a professor when I grew up. I actually 528 00:31:41,676 --> 00:31:44,036 Speaker 2: thought I wanted to be a patent attorney when I 529 00:31:44,076 --> 00:31:49,916 Speaker 2: grew up. However, we started working with patent attorneys because 530 00:31:49,956 --> 00:31:51,956 Speaker 2: of all the cool technology that was coming out of 531 00:31:51,996 --> 00:31:55,716 Speaker 2: the lab, and I pretty quickly realized that's not the 532 00:31:55,796 --> 00:31:57,116 Speaker 2: job that I want to do. 533 00:31:57,276 --> 00:31:58,996 Speaker 1: Good figure figured that out. 534 00:31:59,436 --> 00:32:02,316 Speaker 2: Indeed before I went to three years of law school. 535 00:32:02,716 --> 00:32:06,116 Speaker 2: So it created sort of this moment of well, I 536 00:32:06,156 --> 00:32:08,796 Speaker 2: don't know what I'm going to do when I grew up. 537 00:32:09,156 --> 00:32:11,596 Speaker 2: Now know a few things I don't want to do, 538 00:32:12,436 --> 00:32:15,436 Speaker 2: and it meant I started thinking a lot about the 539 00:32:15,476 --> 00:32:18,636 Speaker 2: industry side of science. I took a lot of business 540 00:32:18,636 --> 00:32:21,196 Speaker 2: school classes at that point in time to try to 541 00:32:21,436 --> 00:32:24,636 Speaker 2: learn and learn a new vocabulary. But I think that 542 00:32:24,676 --> 00:32:27,916 Speaker 2: made it easier to take the entrepreneur or a leap, 543 00:32:28,276 --> 00:32:30,516 Speaker 2: because I wasn't on a different path than I had 544 00:32:30,516 --> 00:32:31,316 Speaker 2: to jump off to. 545 00:32:31,276 --> 00:32:34,676 Speaker 1: Go on basically your way out of going to law school. Indeed, 546 00:32:37,436 --> 00:32:40,956 Speaker 1: so you were on the Forbes thirty under thirty, the 547 00:32:41,116 --> 00:32:44,436 Speaker 1: Fortune forty under forty. As far as I know, there's 548 00:32:44,556 --> 00:32:47,316 Speaker 1: no fifty under fifty. So do you have like a 549 00:32:47,436 --> 00:32:49,996 Speaker 1: next move, Well. 550 00:32:50,076 --> 00:32:52,276 Speaker 2: There is a fifty over fifty, but I've got to 551 00:32:52,276 --> 00:32:53,396 Speaker 2: wait a few more years for that. 552 00:32:56,596 --> 00:32:58,556 Speaker 1: I'm glad that you've got it lined up, though it's 553 00:32:58,556 --> 00:33:03,676 Speaker 1: important to have a goal and you've got time. What's 554 00:33:03,836 --> 00:33:08,276 Speaker 1: one thing that you wish people understood better about genes? 555 00:33:10,196 --> 00:33:14,476 Speaker 2: I think many people expect there's a very clear one 556 00:33:14,516 --> 00:33:19,396 Speaker 2: to one connection that a gene means X. There are 557 00:33:19,716 --> 00:33:23,916 Speaker 2: very few genes in our genome that result in one 558 00:33:24,356 --> 00:33:29,756 Speaker 2: specific outcome. We as human beings are the product of 559 00:33:29,876 --> 00:33:37,156 Speaker 2: this incredibly complicated cross signaling across every gene in our genome, 560 00:33:37,476 --> 00:33:41,036 Speaker 2: and any one trait, even as simple as how tall 561 00:33:41,076 --> 00:33:45,316 Speaker 2: we are, is the output of many, many, many different genes. 562 00:33:46,196 --> 00:33:49,036 Speaker 2: And so I do wish there was a better understanding 563 00:33:49,116 --> 00:33:53,236 Speaker 2: of just the rich complexity of our own biology, because 564 00:33:53,236 --> 00:33:55,876 Speaker 2: then I think it feeds directly into how do you 565 00:33:56,076 --> 00:34:02,156 Speaker 2: use a technology like crisper to change disease biology? And 566 00:34:02,276 --> 00:34:05,516 Speaker 2: there are some examples, but not a ton of examples 567 00:34:05,556 --> 00:34:07,076 Speaker 2: where one edit is enough. 568 00:34:07,836 --> 00:34:11,436 Speaker 1: It's like the the Mendelian pea plants maybe do more 569 00:34:11,436 --> 00:34:13,956 Speaker 1: harm than good as a teaching to a like No, no, 570 00:34:13,996 --> 00:34:15,476 Speaker 1: it's not usually like that. 571 00:34:16,356 --> 00:34:16,636 Speaker 2: Fair. 572 00:34:16,796 --> 00:34:23,196 Speaker 1: Yes. Rachel Horwitz is the co founder and CEO of 573 00:34:23,356 --> 00:34:28,556 Speaker 1: Caribou Biosciences. Today's show was produced by Gabriel Hunter Chang. 574 00:34:28,876 --> 00:34:32,196 Speaker 1: It was edited by Lyddy jeene Kott and engineered by 575 00:34:32,236 --> 00:34:35,836 Speaker 1: Sarah Bruguier. You can email us at problem at Pushkin 576 00:34:35,916 --> 00:34:39,156 Speaker 1: dot FM. I'm Jacob Goldstein and we'll be back next 577 00:34:39,156 --> 00:34:50,076 Speaker 1: week with another episode of What's Your Problem.