1 00:00:15,356 --> 00:00:24,276 Speaker 1: Pushkin. Think about the basic idea of gene therapy. You 2 00:00:24,396 --> 00:00:27,276 Speaker 1: string together a gene, put the gene inside a virus, 3 00:00:27,436 --> 00:00:30,276 Speaker 1: put the virus inside a patient, and then the virus 4 00:00:30,276 --> 00:00:33,276 Speaker 1: delivers the gene to the patient, sells, and then that 5 00:00:33,396 --> 00:00:37,076 Speaker 1: new gene, if everything goes according to plan, makes the 6 00:00:37,156 --> 00:00:41,436 Speaker 1: patient get better. It sounds hard, it is hard, but 7 00:00:41,596 --> 00:00:50,116 Speaker 1: after decades of research, gene therapy is starting to work. 8 00:00:50,996 --> 00:00:53,196 Speaker 1: I'm Jacob Goldstein and this is What's Your Problem, the 9 00:00:53,196 --> 00:00:55,196 Speaker 1: show where I talk to people who are trying to 10 00:00:55,236 --> 00:00:59,756 Speaker 1: make technological progress. My guest today is Shannon boy She's 11 00:00:59,796 --> 00:01:02,556 Speaker 1: a professor of genetics at the University of Florida and 12 00:01:02,596 --> 00:01:06,996 Speaker 1: the co founder and chief scientific officer of Atsina Therapeutics. 13 00:01:07,396 --> 00:01:11,036 Speaker 1: Shannon's problem is this, how do you use gene therapy 14 00:01:11,316 --> 00:01:15,596 Speaker 1: to cure blindness, or at least certain forms of blindness. 15 00:01:15,996 --> 00:01:19,636 Speaker 1: Shannon has been working on gene therapy for twenty years, 16 00:01:19,716 --> 00:01:21,876 Speaker 1: and I wanted to talk with her about the long 17 00:01:22,076 --> 00:01:24,636 Speaker 1: arc of the field, from the wild optimism of the 18 00:01:24,676 --> 00:01:28,516 Speaker 1: early two thousands to the realization that developing gene therapy 19 00:01:28,516 --> 00:01:32,036 Speaker 1: would be a long, hard slog, to the recent promising 20 00:01:32,116 --> 00:01:35,636 Speaker 1: results from an experimental drug that her company has developed 21 00:01:36,396 --> 00:01:39,796 Speaker 1: that drug treats a rare disease that Sharon started studying 22 00:01:40,076 --> 00:01:42,436 Speaker 1: as a grad student back in two thousand and four. 23 00:01:42,996 --> 00:01:45,236 Speaker 1: The disease is called LCA one. 24 00:01:46,036 --> 00:01:49,276 Speaker 2: So babies are born with the disease and usually within 25 00:01:49,316 --> 00:01:51,796 Speaker 2: the first few months of life, their moms or dads 26 00:01:51,876 --> 00:01:54,956 Speaker 2: notice that they're not looking at them directly, they're not 27 00:01:55,036 --> 00:01:59,156 Speaker 2: fixating on objects. Oftentimes the babies will have a roving 28 00:01:59,236 --> 00:02:03,716 Speaker 2: eye movement called nystagmus, and so they're diagnosed usually pretty 29 00:02:03,796 --> 00:02:09,036 Speaker 2: quickly with this condition, and unfortunately, it's profound visual impairment, 30 00:02:09,196 --> 00:02:12,276 Speaker 2: if not total blindness, and that remains with the patient 31 00:02:12,316 --> 00:02:15,236 Speaker 2: throughout the course of their life. So that's really the 32 00:02:15,276 --> 00:02:18,596 Speaker 2: reason that this lab was really interested in studying that 33 00:02:18,636 --> 00:02:20,036 Speaker 2: gene AHU. 34 00:02:20,116 --> 00:02:23,996 Speaker 1: So it's one of the somewhat rare instances where there 35 00:02:24,076 --> 00:02:27,956 Speaker 1: is like a single gene that maps to a single 36 00:02:28,356 --> 00:02:33,836 Speaker 1: in this case, profound problem basically blindness or severe problems 37 00:02:33,836 --> 00:02:37,076 Speaker 1: with vision, which is kind of you would think would 38 00:02:37,116 --> 00:02:41,156 Speaker 1: be the first wave of gene therapy, right exactly, This 39 00:02:41,276 --> 00:02:44,756 Speaker 1: is what the early two thousands, when you're and so 40 00:02:45,436 --> 00:02:47,716 Speaker 1: the human genome has just been mapped, there's like a 41 00:02:47,796 --> 00:02:50,836 Speaker 1: sense of oh, now we know all the genes, right, 42 00:02:50,956 --> 00:02:53,516 Speaker 1: let's figure out how to help people with this new knowledge. 43 00:02:53,596 --> 00:02:56,316 Speaker 2: That's exactly right. This was sort of the step one 44 00:02:56,316 --> 00:02:58,756 Speaker 2: in gene therapy was the simplest form, which is just 45 00:02:58,996 --> 00:03:02,156 Speaker 2: gene replacement. Can we take a healthy copy of a 46 00:03:02,196 --> 00:03:04,796 Speaker 2: gene and put it back into the patient's cells and 47 00:03:04,836 --> 00:03:06,836 Speaker 2: then have that gene go on to make the protein 48 00:03:06,836 --> 00:03:09,356 Speaker 2: it was supposed to make and then hopefully rett or 49 00:03:09,356 --> 00:03:10,596 Speaker 2: the function to those cells. 50 00:03:11,276 --> 00:03:15,156 Speaker 1: And so what as a grad student are you trying 51 00:03:15,156 --> 00:03:15,836 Speaker 1: to figure out? 52 00:03:16,916 --> 00:03:20,436 Speaker 2: The lab was studying the biochemical underpinnings of this disease, 53 00:03:20,476 --> 00:03:23,076 Speaker 2: and they were using a chicken model to do this. 54 00:03:23,676 --> 00:03:25,836 Speaker 2: That's kind of a unique thing in a lab. Usually 55 00:03:25,916 --> 00:03:28,796 Speaker 2: research labs are using mice or rats, but there was 56 00:03:28,836 --> 00:03:31,876 Speaker 2: a naturally occurring chicken model of this disease that had 57 00:03:31,956 --> 00:03:33,796 Speaker 2: profound visual impairment and blindness. 58 00:03:33,836 --> 00:03:37,716 Speaker 1: So naturally occurring chicken model basically means this happens to 59 00:03:37,796 --> 00:03:40,836 Speaker 1: chickens too, Yes, exactly, it is. That's right. 60 00:03:41,116 --> 00:03:44,876 Speaker 2: Yeah. So, at the same time that the lab was studying, 61 00:03:45,076 --> 00:03:47,396 Speaker 2: you know, what was going wrong in this chicken and 62 00:03:47,436 --> 00:03:50,116 Speaker 2: why it was happening, they wanted to ask the question 63 00:03:50,796 --> 00:03:55,556 Speaker 2: would gene therapy be a reasonable approach for treating these chickens. 64 00:03:55,556 --> 00:03:58,396 Speaker 2: Can we restore visions to these chickens with gene therapy. 65 00:03:58,956 --> 00:04:02,836 Speaker 2: So this was a collaborative effort with my other grad 66 00:04:02,876 --> 00:04:06,956 Speaker 2: students and I where we took a vector called Lenti 67 00:04:07,076 --> 00:04:12,236 Speaker 2: virus and we to the chicken embryos. I felt very 68 00:04:12,316 --> 00:04:14,396 Speaker 2: much in grad school like I was a poultry farmer, 69 00:04:15,076 --> 00:04:18,596 Speaker 2: because I would on my way into lab every day, 70 00:04:18,756 --> 00:04:22,036 Speaker 2: stop at the farm, pick up the eggs, burning them 71 00:04:22,036 --> 00:04:25,196 Speaker 2: into lab, and then my fellow grad students and I 72 00:04:25,276 --> 00:04:27,756 Speaker 2: we would make these tiny little holes in the chicken 73 00:04:27,756 --> 00:04:30,996 Speaker 2: egg and we would pull these glass micro needles and 74 00:04:31,196 --> 00:04:35,596 Speaker 2: use them to inject into the chicken embryo. And remember 75 00:04:35,636 --> 00:04:37,756 Speaker 2: this is a disease that you have from birth, so 76 00:04:37,796 --> 00:04:40,836 Speaker 2: we needed to treat these chickens very early. But it 77 00:04:40,876 --> 00:04:44,236 Speaker 2: was a difficult process to get that micro needle injection 78 00:04:44,316 --> 00:04:47,316 Speaker 2: into the head of the chicken embryo and then for 79 00:04:47,396 --> 00:04:49,876 Speaker 2: that chicken to make it all the way to hatch, right, 80 00:04:50,196 --> 00:04:52,556 Speaker 2: And that was I think one of the hardest parts 81 00:04:52,556 --> 00:04:54,796 Speaker 2: of that project. Actually, It's why I said I felt 82 00:04:54,796 --> 00:04:59,036 Speaker 2: like a poultry farmer. Is all of the machinations you know, 83 00:04:59,116 --> 00:05:02,476 Speaker 2: getting the humidity right, the temperature right, the position right, 84 00:05:02,596 --> 00:05:04,796 Speaker 2: making sure you close that egg right, just getting that 85 00:05:05,236 --> 00:05:06,396 Speaker 2: chicken to survive. 86 00:05:07,436 --> 00:05:13,596 Speaker 1: And so you injecting the non mutated form of the gene, 87 00:05:13,596 --> 00:05:18,116 Speaker 1: the good form of the gene, if you will, encased 88 00:05:18,156 --> 00:05:21,756 Speaker 1: in this virus, into the head of the chicken embryo. Yes, 89 00:05:24,076 --> 00:05:24,716 Speaker 1: did it work? 90 00:05:25,756 --> 00:05:28,116 Speaker 2: For a while it did not, because again, it was 91 00:05:28,196 --> 00:05:32,156 Speaker 2: difficult to manipulate a chicken embryo like that and actually 92 00:05:32,156 --> 00:05:34,756 Speaker 2: have it to survive to hatch. But my fellow grad 93 00:05:34,796 --> 00:05:37,556 Speaker 2: students and I did a lot of work to optimize 94 00:05:37,556 --> 00:05:41,076 Speaker 2: that process, and eventually it actually did work. We had 95 00:05:41,396 --> 00:05:44,516 Speaker 2: little chicks that were born, and I can distinctly remember 96 00:05:44,596 --> 00:05:47,756 Speaker 2: them walking around on the lab bench and pecking at 97 00:05:48,116 --> 00:05:50,756 Speaker 2: our jewelry or at Eminem's that we had laid out 98 00:05:50,796 --> 00:05:53,676 Speaker 2: on the surface of the bench. Very different from the 99 00:05:54,076 --> 00:05:57,076 Speaker 2: blind chickens. It was very clear. You know, chickens are 100 00:05:57,516 --> 00:06:02,076 Speaker 2: very visually guided creatures. It's very obvious when a chicken 101 00:06:02,076 --> 00:06:03,396 Speaker 2: can see versus Nazi. 102 00:06:03,836 --> 00:06:07,076 Speaker 3: It was so fun. We were all so so happy. 103 00:06:07,796 --> 00:06:12,236 Speaker 1: Okay, so that's two thousand and four as twenty years ago. Yeah, yeah, 104 00:06:13,636 --> 00:06:16,116 Speaker 1: at that time, is it like, well, we did it 105 00:06:16,156 --> 00:06:18,876 Speaker 1: in chickens, let's do it in people or what. 106 00:06:19,556 --> 00:06:22,716 Speaker 2: So that's actually where my thesis project comes in. So 107 00:06:22,796 --> 00:06:25,076 Speaker 2: all of that chicken work was a really collaborative effort 108 00:06:25,116 --> 00:06:28,276 Speaker 2: and it was exciting, but it had its drawbacks and 109 00:06:28,316 --> 00:06:31,996 Speaker 2: it wasn't clinically translatable for a number of reasons. First 110 00:06:32,036 --> 00:06:34,476 Speaker 2: and foremost, we were never going to do an embryonic 111 00:06:34,556 --> 00:06:38,316 Speaker 2: injection and a patient. Maybe that'll happen one day, honestly, 112 00:06:38,436 --> 00:06:41,596 Speaker 2: but it certainly wasn't close to happening in two thousand 113 00:06:41,636 --> 00:06:44,916 Speaker 2: and four. So we needed a gene therapy that could 114 00:06:44,916 --> 00:06:50,076 Speaker 2: be injected in a patient after birth, right, And unfortunately, 115 00:06:50,116 --> 00:06:52,596 Speaker 2: the virus that we were using in the chicken experiments, 116 00:06:52,636 --> 00:06:57,276 Speaker 2: the Lenti virus, is really poor at a gene delivery 117 00:06:57,316 --> 00:06:59,716 Speaker 2: to a developed retina. So we needed to find a 118 00:06:59,716 --> 00:07:02,436 Speaker 2: more clinically relevant vector to do the gene therapy. 119 00:07:02,956 --> 00:07:05,516 Speaker 1: Let's just pause for a moment and talk about this 120 00:07:05,676 --> 00:07:11,236 Speaker 1: idea of a vector in gene therapy. So the basic idea, right, 121 00:07:11,356 --> 00:07:13,636 Speaker 1: is like, you know what the good gene is, you 122 00:07:13,676 --> 00:07:16,236 Speaker 1: know the gene you want to get into, in this case, 123 00:07:16,356 --> 00:07:19,556 Speaker 1: the person's eye. But there's this weird question of how 124 00:07:19,596 --> 00:07:21,716 Speaker 1: do you get it there, right, Like, you can't just 125 00:07:21,836 --> 00:07:27,476 Speaker 1: put a string of genetic material randomly in someone's body, right, 126 00:07:27,516 --> 00:07:30,796 Speaker 1: It'll just get destroyed. And so there's this basic idea 127 00:07:30,916 --> 00:07:34,436 Speaker 1: that you put it in a virus, right, because a 128 00:07:34,516 --> 00:07:38,836 Speaker 1: virus is like, it's billions of years of evolution to 129 00:07:38,876 --> 00:07:41,116 Speaker 1: be a genetic material delivery mechanism. 130 00:07:41,196 --> 00:07:42,196 Speaker 3: That's exactly right. 131 00:07:42,276 --> 00:07:44,156 Speaker 1: But that's hard for a number of reasons. So like, 132 00:07:44,236 --> 00:07:47,036 Speaker 1: tell me sort of the state of vectors at this 133 00:07:47,196 --> 00:07:49,556 Speaker 1: time twenty years ago when you're figuring this out. 134 00:07:49,996 --> 00:07:52,756 Speaker 2: So there were a number of viral vectors that were 135 00:07:52,756 --> 00:07:55,476 Speaker 2: being used to deliver genes, and each of them had 136 00:07:55,516 --> 00:08:00,316 Speaker 2: their pros and their cons One was named adnovirus. This 137 00:08:00,476 --> 00:08:02,716 Speaker 2: was a good virus because it's big, you can fit 138 00:08:02,796 --> 00:08:05,556 Speaker 2: a lot of genetic material into it, and it was 139 00:08:06,076 --> 00:08:08,996 Speaker 2: used in the early days of gene therapy and on. 140 00:08:09,236 --> 00:08:12,356 Speaker 2: Fortunately it was discovered you know later on that it 141 00:08:12,396 --> 00:08:15,716 Speaker 2: came with you know, some downsides. It's more immunogenic than 142 00:08:15,756 --> 00:08:17,556 Speaker 2: the other viral vectors that are out there. 143 00:08:17,476 --> 00:08:20,316 Speaker 1: Meaning it generates an immune response. So the body's like, oh, 144 00:08:20,396 --> 00:08:23,436 Speaker 1: that's a virus. I'm going to destroy it, and you're like, no, no, no, 145 00:08:23,476 --> 00:08:25,756 Speaker 1: this is a virus that's going to help you exactly. 146 00:08:25,796 --> 00:08:27,356 Speaker 1: It's like, I don't care. I've gotten rid of it. 147 00:08:27,596 --> 00:08:28,836 Speaker 3: Yes, yeah. 148 00:08:28,876 --> 00:08:30,916 Speaker 2: And then of course there was lenty virus, which is 149 00:08:30,916 --> 00:08:33,476 Speaker 2: what we were using in the chickens. It is not 150 00:08:33,636 --> 00:08:36,356 Speaker 2: the vector of choice, for instance, in the eye where 151 00:08:36,396 --> 00:08:39,316 Speaker 2: I work, because it's not very good at delivering genes 152 00:08:39,356 --> 00:08:43,756 Speaker 2: to developed cells in the retina. And then in the 153 00:08:43,876 --> 00:08:48,196 Speaker 2: nineties some exciting work was going on evaluating a newer 154 00:08:48,316 --> 00:08:53,956 Speaker 2: vector called Adno associated virus or AAV. Since the nineties 155 00:08:54,116 --> 00:08:57,996 Speaker 2: early two thousands, AAV has become the gold standard gene 156 00:08:57,996 --> 00:09:01,236 Speaker 2: delivery vector for essentially all of gene therapy. 157 00:09:01,836 --> 00:09:04,996 Speaker 1: And so at this time in two thousand and four, like, 158 00:09:05,076 --> 00:09:07,156 Speaker 1: what was the state of gene therapy? 159 00:09:08,116 --> 00:09:10,516 Speaker 2: Oh, my gosh, it was the heyday. It was such 160 00:09:10,516 --> 00:09:13,716 Speaker 2: a fun time and it was so it was so exciting. 161 00:09:13,916 --> 00:09:17,716 Speaker 2: The my grad school days, my postdoc days, it was 162 00:09:17,796 --> 00:09:20,596 Speaker 2: an extremely exciting time in the field that I would 163 00:09:20,636 --> 00:09:23,396 Speaker 2: say it was filled with hope because there was so 164 00:09:23,636 --> 00:09:26,916 Speaker 2: much proof of concept work going on in animal models 165 00:09:26,916 --> 00:09:31,196 Speaker 2: of disease showing that gene therapy could restore vision, could 166 00:09:31,236 --> 00:09:34,316 Speaker 2: restore muscular function, could restore clotting. 167 00:09:34,756 --> 00:09:35,516 Speaker 3: You know, you name it. 168 00:09:35,516 --> 00:09:39,116 Speaker 2: It was these successes were being seen across neuromuscular disease, 169 00:09:39,236 --> 00:09:44,836 Speaker 2: CNS disease, ocular disease, but everybody was just really excited 170 00:09:44,876 --> 00:09:48,156 Speaker 2: about it, and that extended beyond the scientists in the lab. 171 00:09:48,996 --> 00:09:51,956 Speaker 2: That was true of the macro environment too, So you know, 172 00:09:51,996 --> 00:09:52,356 Speaker 2: I mean. 173 00:09:52,316 --> 00:09:55,756 Speaker 1: Like in the media or like the industry, like the 174 00:09:55,756 --> 00:09:56,916 Speaker 1: pharmaceutical industry. 175 00:09:57,156 --> 00:10:00,116 Speaker 2: Yeah, I'm talking more about like investors and big pharma. 176 00:10:00,236 --> 00:10:03,116 Speaker 2: So I mean investors were keen to throw their money 177 00:10:03,476 --> 00:10:05,636 Speaker 2: at gene therapy at the time because of how much 178 00:10:05,676 --> 00:10:08,676 Speaker 2: promise it was showing in these pre clinical studies, and 179 00:10:08,756 --> 00:10:12,356 Speaker 2: big Arma was keen to acquire, you know, startup companies 180 00:10:12,356 --> 00:10:14,436 Speaker 2: that were in this space because of that promise it 181 00:10:14,516 --> 00:10:17,836 Speaker 2: was showing. And I think their their reason at the 182 00:10:17,876 --> 00:10:20,196 Speaker 2: time was a sound one. They wanted to use. Even 183 00:10:20,236 --> 00:10:23,436 Speaker 2: if those companies were focused on rare disease, it was 184 00:10:23,516 --> 00:10:25,996 Speaker 2: like this platform for them to say, well, if I 185 00:10:26,036 --> 00:10:28,876 Speaker 2: can get gene therapy to work in this small, rare disease, 186 00:10:29,356 --> 00:10:31,996 Speaker 2: that proves that as a company, I'm capable of doing 187 00:10:31,996 --> 00:10:33,636 Speaker 2: this and that eventually I can do it in a 188 00:10:33,676 --> 00:10:36,796 Speaker 2: disease that affects millions of people. So it was a 189 00:10:37,196 --> 00:10:41,236 Speaker 2: really really exciting time, both scientifically and from kind of 190 00:10:41,276 --> 00:10:43,036 Speaker 2: a financial standpoint. 191 00:10:43,636 --> 00:10:46,596 Speaker 1: And then at some point, right there are these these 192 00:10:46,636 --> 00:10:50,396 Speaker 1: strong results using gene therapy to treat a disease called 193 00:10:50,596 --> 00:10:54,036 Speaker 1: LCA two, which is similar to LCA one, the disease 194 00:10:54,076 --> 00:10:56,916 Speaker 1: you work on, and that's like a big moment in 195 00:10:56,956 --> 00:10:57,636 Speaker 1: the field, right. 196 00:10:58,196 --> 00:11:02,276 Speaker 2: Yeah, the RP sixty five LCA two gene therapy trials 197 00:11:02,356 --> 00:11:04,876 Speaker 2: were a huge success, and then they went on to 198 00:11:05,476 --> 00:11:08,196 Speaker 2: form the basis of Luxterno, which is the first approved 199 00:11:08,236 --> 00:11:12,156 Speaker 2: ocular gene therapy, and so everybody was super excited that, Okay, 200 00:11:12,196 --> 00:11:15,436 Speaker 2: they got this approved. We're going to see this, you know, 201 00:11:15,636 --> 00:11:18,436 Speaker 2: flood of other gene therapies getting approved on the heels 202 00:11:18,436 --> 00:11:20,996 Speaker 2: of luxtern And I think that's I think that's when 203 00:11:21,036 --> 00:11:23,396 Speaker 2: it got hard, and you know, there was a little 204 00:11:23,396 --> 00:11:25,196 Speaker 2: bit of a reality check for the field. 205 00:11:25,596 --> 00:11:27,636 Speaker 1: What was that like for you? So you're working on 206 00:11:28,276 --> 00:11:32,436 Speaker 1: LCA one, a very similar disease. Everybody is very excited 207 00:11:32,436 --> 00:11:34,916 Speaker 1: about l c A two, are you like, yes, we 208 00:11:34,996 --> 00:11:37,236 Speaker 1: got l c A two, I'm about to get LCA one. 209 00:11:37,436 --> 00:11:40,076 Speaker 1: Like what what was your where were you at that? Oh? 210 00:11:40,196 --> 00:11:41,756 Speaker 3: I was I was super excited. 211 00:11:41,796 --> 00:11:42,076 Speaker 1: I was. 212 00:11:42,156 --> 00:11:45,076 Speaker 2: I was further behind obviously in my pursuit of l 213 00:11:45,156 --> 00:11:47,276 Speaker 2: c A one, but I had, you know, high hopes 214 00:11:47,316 --> 00:11:49,156 Speaker 2: that it would it would go, that it would work. 215 00:11:49,596 --> 00:11:52,076 Speaker 1: Why didn't it happen as fast as you thought? 216 00:11:52,236 --> 00:11:56,676 Speaker 2: So in twenty fourteen, I hit a stage where you know, 217 00:11:56,716 --> 00:11:59,436 Speaker 2: the technology had been developed, I had done everything that 218 00:11:59,476 --> 00:12:02,036 Speaker 2: I really could, you know, from the academic standpoint, to 219 00:12:02,116 --> 00:12:06,076 Speaker 2: get this ready to move forward. But at that stage 220 00:12:06,116 --> 00:12:08,636 Speaker 2: you hit what the NIH calls the valley of death, 221 00:12:08,676 --> 00:12:11,716 Speaker 2: which is, you know, a period of time where you 222 00:12:11,756 --> 00:12:13,956 Speaker 2: need a lot of capital and a lot of infrastructure 223 00:12:13,996 --> 00:12:16,436 Speaker 2: to move a gene therapy from bench to bedside, and 224 00:12:16,556 --> 00:12:18,596 Speaker 2: you can't do that in an academic lab. 225 00:12:18,676 --> 00:12:21,356 Speaker 1: So to put it to test it in people, basically 226 00:12:21,476 --> 00:12:25,356 Speaker 1: testing it in people is obviously complicated and expensive and 227 00:12:25,596 --> 00:12:28,476 Speaker 1: very at least some extent rightly so, right you're ingesting 228 00:12:28,956 --> 00:12:30,196 Speaker 1: things into people's eyes. 229 00:12:30,716 --> 00:12:31,076 Speaker 3: Yes. 230 00:12:31,316 --> 00:12:34,716 Speaker 2: In twenty fourteen, my husband and I who I work with, 231 00:12:35,116 --> 00:12:39,036 Speaker 2: we were very much in a pattern of developing technologies 232 00:12:39,156 --> 00:12:43,516 Speaker 2: and then out licensing them to different companies. So in 233 00:12:43,596 --> 00:12:47,036 Speaker 2: around twenty fourteen, we partnered with Genzyme, which was a 234 00:12:47,036 --> 00:12:51,076 Speaker 2: company focused on developing gene therapies for rare disease, and 235 00:12:51,116 --> 00:12:54,716 Speaker 2: they took that technology. Shortly thereafter they were acquired by 236 00:12:54,796 --> 00:12:59,596 Speaker 2: Santafie and together with Genzyme Slash Santafee, we conducted all 237 00:12:59,636 --> 00:13:02,556 Speaker 2: of the studies that were what we call I and 238 00:13:02,636 --> 00:13:06,276 Speaker 2: D enabling studies, sort of like the really well documented 239 00:13:06,356 --> 00:13:10,676 Speaker 2: careful safety studies and efficacy studies, dose ranging studies that 240 00:13:10,716 --> 00:13:13,476 Speaker 2: are required to show to the FDA before. 241 00:13:13,276 --> 00:13:14,476 Speaker 3: They let you go into people. 242 00:13:14,716 --> 00:13:18,036 Speaker 1: I INDA is an investigational new drug's exactly. So you're 243 00:13:18,116 --> 00:13:20,076 Speaker 1: like doing all the work to say, like, look, this 244 00:13:20,116 --> 00:13:22,556 Speaker 1: should be an investigational new drug that we can test 245 00:13:22,596 --> 00:13:24,676 Speaker 1: in a very small number of people just to see 246 00:13:24,716 --> 00:13:26,676 Speaker 1: if it's safe to start out with exactly. 247 00:13:27,156 --> 00:13:27,476 Speaker 3: Yeah. 248 00:13:27,516 --> 00:13:30,556 Speaker 2: So, and I'll be honest, that moved a little bit 249 00:13:30,796 --> 00:13:33,596 Speaker 2: slower than I would have liked. But I mean, when 250 00:13:33,596 --> 00:13:36,156 Speaker 2: you work with Big Pharma, and I will say they 251 00:13:36,196 --> 00:13:39,236 Speaker 2: were an amazing, amazing team, excellent group of people, but 252 00:13:39,636 --> 00:13:42,156 Speaker 2: Big Pharma is very siloed, and you know, it can 253 00:13:42,196 --> 00:13:45,636 Speaker 2: take a long time for things to move. And then unfortunately, 254 00:13:46,276 --> 00:13:50,756 Speaker 2: in around twenty eighteen, Cianna FI decided to pivot away 255 00:13:50,756 --> 00:13:55,476 Speaker 2: from ocular gene therapy altogether, so they wanted to give 256 00:13:55,476 --> 00:13:59,116 Speaker 2: the program away, and I was heartbroken. I remember the 257 00:13:59,196 --> 00:14:01,796 Speaker 2: night that someone told me it was happening. I couldn't 258 00:14:01,836 --> 00:14:04,476 Speaker 2: believe it because you know, we were just about to 259 00:14:04,516 --> 00:14:07,396 Speaker 2: treat the first patient and everything was ready to go, 260 00:14:07,476 --> 00:14:10,636 Speaker 2: and I just couldn't believe it. But you know, companies 261 00:14:10,676 --> 00:14:13,916 Speaker 2: make decisions like this all the time. So when that happened, 262 00:14:13,996 --> 00:14:16,516 Speaker 2: that was really what motivated my husband and I to 263 00:14:17,276 --> 00:14:20,396 Speaker 2: co found our own company because we wanted hopefully to 264 00:14:20,436 --> 00:14:23,796 Speaker 2: get that program back so that we could make sure 265 00:14:23,796 --> 00:14:26,796 Speaker 2: that it went forward. And that was just that was 266 00:14:26,876 --> 00:14:30,076 Speaker 2: one reason that we founded at Seena Therapeutics. I would 267 00:14:30,076 --> 00:14:32,596 Speaker 2: say that the broader reason we founded the company was 268 00:14:33,076 --> 00:14:35,876 Speaker 2: out of a sense of frustration because we had developed 269 00:14:35,916 --> 00:14:38,716 Speaker 2: a lot of technologies and we had outlcensed them to 270 00:14:38,796 --> 00:14:42,836 Speaker 2: a variety of companies, and there was a theme emerging 271 00:14:42,956 --> 00:14:46,436 Speaker 2: that the technologies weren't getting to patients. And whether that 272 00:14:46,556 --> 00:14:50,756 Speaker 2: was because of you know, business decisions overriding science decisions, 273 00:14:50,876 --> 00:14:53,836 Speaker 2: or just you know, companies being too big and siloed, 274 00:14:54,516 --> 00:14:57,356 Speaker 2: there were a variety of reasons, but it ultimately we 275 00:14:57,436 --> 00:14:59,716 Speaker 2: formed the company because we were frustrated. We wanted to 276 00:14:59,756 --> 00:15:02,916 Speaker 2: have some more control over the direction that the science 277 00:15:03,036 --> 00:15:03,516 Speaker 2: was taking. 278 00:15:03,836 --> 00:15:06,636 Speaker 1: It's like you want it more than anybody else can 279 00:15:06,716 --> 00:15:07,236 Speaker 1: want it. 280 00:15:07,356 --> 00:15:08,636 Speaker 3: Yes, it was. 281 00:15:08,676 --> 00:15:11,596 Speaker 2: I mean one is my baby, right, and so are 282 00:15:11,636 --> 00:15:13,876 Speaker 2: some of the other indications that I'm working on now. 283 00:15:13,876 --> 00:15:16,356 Speaker 2: But yeah, I wanted to be the one to help 284 00:15:16,436 --> 00:15:19,396 Speaker 2: usher them towards patients and keep it moving in the 285 00:15:19,436 --> 00:15:19,956 Speaker 2: right direction. 286 00:15:21,076 --> 00:15:24,196 Speaker 1: And you mentioned your husband, So is he in the 287 00:15:24,196 --> 00:15:25,796 Speaker 1: same field as you, like, is. 288 00:15:25,916 --> 00:15:26,556 Speaker 3: What's he doing? 289 00:15:26,676 --> 00:15:30,916 Speaker 2: Yeah, he's an AV vectorologist. When I was a grad 290 00:15:30,956 --> 00:15:34,396 Speaker 2: student and I was tasked with my thesis project, which 291 00:15:34,516 --> 00:15:37,636 Speaker 2: was okay, come up with a clinically relevant approach for 292 00:15:37,716 --> 00:15:40,636 Speaker 2: treating this disease. So to do that, I needed to 293 00:15:40,716 --> 00:15:43,756 Speaker 2: shift away from Lenti virus and start using ad no 294 00:15:43,876 --> 00:15:47,756 Speaker 2: associated virus AAV. I needed to shift into a mouse model, 295 00:15:47,796 --> 00:15:50,876 Speaker 2: a mammalian model of the disease, and so to get 296 00:15:50,916 --> 00:15:53,516 Speaker 2: help on the AAV aspect of the project, I went 297 00:15:53,596 --> 00:15:56,516 Speaker 2: to Bill Houseworth, who became my post docmentor, and he 298 00:15:56,596 --> 00:15:59,396 Speaker 2: pointed me in the direction of my now husband. He 299 00:15:59,436 --> 00:16:01,716 Speaker 2: was a scientific research manager at the time. He said, 300 00:16:01,756 --> 00:16:03,556 Speaker 2: you know, he can field any questions you have about 301 00:16:03,636 --> 00:16:05,956 Speaker 2: vector design, and so I went to him and then 302 00:16:05,996 --> 00:16:09,556 Speaker 2: that you know was an excellent collaboration obviously that lossoened 303 00:16:09,556 --> 00:16:12,316 Speaker 2: into a nerd romance and then eventually a marriage in 304 00:16:12,316 --> 00:16:12,676 Speaker 2: two k. 305 00:16:12,876 --> 00:16:16,276 Speaker 1: Yes, it is a very nerdy meet cute, yes. 306 00:16:17,396 --> 00:16:19,956 Speaker 2: But yeah, we're very much in the same field. But 307 00:16:20,476 --> 00:16:22,676 Speaker 2: we have very different skill sets, I would say, so 308 00:16:23,076 --> 00:16:24,196 Speaker 2: we compliment each other. 309 00:16:24,116 --> 00:16:24,996 Speaker 3: Which is nice. 310 00:16:26,396 --> 00:16:30,276 Speaker 1: Obviously to get from, you know, doing this in chickens 311 00:16:30,316 --> 00:16:32,476 Speaker 1: twenty years ago to doing it in people now. There 312 00:16:32,476 --> 00:16:34,476 Speaker 1: were many, many, many things I'm sure that you had 313 00:16:34,516 --> 00:16:38,316 Speaker 1: to figure out. There is there anything in particular that 314 00:16:38,436 --> 00:16:41,076 Speaker 1: was a thing you figured out? Maybe that was a 315 00:16:41,076 --> 00:16:43,636 Speaker 1: thing that people doing gene therapy more broadly were trying 316 00:16:43,636 --> 00:16:47,356 Speaker 1: to figure out, just in the sense of, yeah, something 317 00:16:47,436 --> 00:16:48,596 Speaker 1: you solved along the way. 318 00:16:49,196 --> 00:16:51,636 Speaker 2: In the early days, when I had first transitioned into 319 00:16:51,676 --> 00:16:55,676 Speaker 2: testing the AV vector in the mouse, I did it 320 00:16:55,716 --> 00:16:57,516 Speaker 2: over and over and over again, and it didn't work. 321 00:16:57,796 --> 00:17:00,956 Speaker 2: And I think one big mistake that I made was 322 00:17:00,996 --> 00:17:03,916 Speaker 2: that I was using the same gene, the same coding 323 00:17:03,996 --> 00:17:07,836 Speaker 2: sequence that we had used in the chicken experiments, and interestingly, 324 00:17:07,956 --> 00:17:12,156 Speaker 2: that gene was a bovine gene. In the early two thousands, 325 00:17:12,436 --> 00:17:15,196 Speaker 2: it was a lot easier to generate bovine sequences for 326 00:17:15,236 --> 00:17:18,556 Speaker 2: reasons I don't even actually remember. But what it took 327 00:17:18,756 --> 00:17:21,076 Speaker 2: was figuring out that we needed to deliver the species 328 00:17:21,116 --> 00:17:25,916 Speaker 2: specific gene. So the mouse gene worked, the human gene worked, 329 00:17:25,916 --> 00:17:29,116 Speaker 2: which was great because that was very translatable, So the 330 00:17:29,956 --> 00:17:33,836 Speaker 2: species of the gene was important. Another really important thing 331 00:17:33,956 --> 00:17:34,356 Speaker 2: was the foot. 332 00:17:34,556 --> 00:17:37,436 Speaker 1: So basically the versions of the gene exist in these 333 00:17:37,476 --> 00:17:39,316 Speaker 1: different animals, but they're slightly different. 334 00:17:39,516 --> 00:17:40,036 Speaker 3: Exactly. 335 00:17:40,316 --> 00:17:44,116 Speaker 1: Yes, I have to say retrospectively out of my armchair ignorance. 336 00:17:45,156 --> 00:17:49,236 Speaker 1: I feel like that one seems obvious in retrospect to me. 337 00:17:50,476 --> 00:17:53,596 Speaker 2: But it was strange to me because this bovine sequence 338 00:17:53,636 --> 00:17:54,796 Speaker 2: worked in a chicken, so. 339 00:17:54,956 --> 00:17:56,916 Speaker 1: I think, and a mouse and a person who's more 340 00:17:56,996 --> 00:18:02,436 Speaker 1: like a cow. Yes, okay, what's another one? What's another one? 341 00:18:02,476 --> 00:18:03,156 Speaker 1: You had to figure out? 342 00:18:03,316 --> 00:18:06,036 Speaker 2: Another one was the flavor of AAV that we needed 343 00:18:06,076 --> 00:18:06,396 Speaker 2: to use. 344 00:18:06,476 --> 00:18:09,316 Speaker 1: So the particular nature of the vector. 345 00:18:09,276 --> 00:18:10,076 Speaker 3: Yes, exactly. 346 00:18:10,156 --> 00:18:12,876 Speaker 2: So AV comes in a variety of flavors, and one 347 00:18:12,916 --> 00:18:15,716 Speaker 2: flavor of AV might be good at infecting neurons, and 348 00:18:15,756 --> 00:18:18,916 Speaker 2: another flavor of AV might be good at infecting skin cells, 349 00:18:18,956 --> 00:18:19,436 Speaker 2: for instance. 350 00:18:19,556 --> 00:18:23,396 Speaker 1: Yes, and interestingly, in this point, you want it to infect, right, 351 00:18:23,556 --> 00:18:26,996 Speaker 1: Infecting is delivering the gene exactly. Huh. 352 00:18:27,476 --> 00:18:30,636 Speaker 2: So we tested for the first time in non human 353 00:18:30,676 --> 00:18:34,956 Speaker 2: primates a certain flavor of AV called AAV five, and 354 00:18:35,236 --> 00:18:38,236 Speaker 2: we really for the first time showed that that flavor 355 00:18:38,236 --> 00:18:40,796 Speaker 2: of AV was really useful in the rod and the 356 00:18:40,836 --> 00:18:44,196 Speaker 2: cone foto receptors of the human of a primate retina rather, 357 00:18:44,676 --> 00:18:46,676 Speaker 2: so that was the flavor of AV that we needed 358 00:18:46,676 --> 00:18:48,276 Speaker 2: to figure out. And then I would say the third 359 00:18:48,316 --> 00:18:52,076 Speaker 2: thing that we figured out was a specific regulatory sequence 360 00:18:52,116 --> 00:18:55,716 Speaker 2: that we used to drive expression of the gene. So 361 00:18:55,916 --> 00:19:00,356 Speaker 2: it's called a promoter, and specifically it's the adoptin kinase promoter, 362 00:19:00,436 --> 00:19:03,276 Speaker 2: which drives expression exclusively in photo receptors. 363 00:19:03,476 --> 00:19:06,396 Speaker 1: And so just to be clear, just to unpack that 364 00:19:06,596 --> 00:19:10,036 Speaker 1: a little bit, so the idea is, you don't just 365 00:19:10,116 --> 00:19:15,356 Speaker 1: need to have the gene itself in this vector. You 366 00:19:15,436 --> 00:19:18,556 Speaker 1: need to have the genetic information that tells it in 367 00:19:18,636 --> 00:19:22,196 Speaker 1: what kinds of cells should this gene be expressed exactly, 368 00:19:22,236 --> 00:19:23,916 Speaker 1: and in what kinds of cells should it not be 369 00:19:23,956 --> 00:19:24,956 Speaker 1: expressed exactly. 370 00:19:24,996 --> 00:19:27,836 Speaker 2: And that's important from a safety standpoint, because ideally you 371 00:19:27,876 --> 00:19:31,196 Speaker 2: don't want this gene expressing a protein in cells where 372 00:19:31,196 --> 00:19:32,196 Speaker 2: it's not supposed to be. 373 00:19:32,276 --> 00:19:38,916 Speaker 1: In potent harm in your heart right exactly in a minute. 374 00:19:38,996 --> 00:19:41,996 Speaker 1: What happened when Shannon's drug finally made its way out 375 00:19:41,996 --> 00:19:54,796 Speaker 1: of the lab and into the eyes of patience. I 376 00:19:54,836 --> 00:19:57,676 Speaker 1: asked Shannon how she got from figuring everything out in 377 00:19:57,716 --> 00:20:01,516 Speaker 1: the lab and in animals to actually doing a clinical 378 00:20:01,556 --> 00:20:04,716 Speaker 1: trial to actually testing her drug in patience. 379 00:20:05,396 --> 00:20:09,156 Speaker 2: So I will say that, fortunately, before Santafie let the 380 00:20:09,196 --> 00:20:11,836 Speaker 2: program go, they did dose a couple of patients, so 381 00:20:11,876 --> 00:20:14,836 Speaker 2: we did get them to start the trial thankfully, and 382 00:20:14,916 --> 00:20:17,796 Speaker 2: they were absolutely critical and getting that off the ground. 383 00:20:18,436 --> 00:20:21,516 Speaker 2: But when they handed it back to at Sina, obviously 384 00:20:21,556 --> 00:20:24,876 Speaker 2: we had to build a clinical team and we worked 385 00:20:24,876 --> 00:20:27,476 Speaker 2: closely with Santa fe during that transition period to make 386 00:20:27,516 --> 00:20:30,276 Speaker 2: sure there were no bumps in the road, and then 387 00:20:30,796 --> 00:20:33,676 Speaker 2: we just continued with the trial. We had some amazing 388 00:20:33,676 --> 00:20:38,316 Speaker 2: clinical investigators at the University of Pennsylvania and OHSU, which 389 00:20:38,356 --> 00:20:42,116 Speaker 2: is Oregon Health Sciences University at the CACI Institute, and 390 00:20:42,156 --> 00:20:45,556 Speaker 2: so the surgeons there did the injections. We also had 391 00:20:45,596 --> 00:20:49,356 Speaker 2: a surgeon at Will's I Institute, and just excellent teams 392 00:20:49,396 --> 00:20:53,036 Speaker 2: of physicians focused on inherited retinal disease that we worked 393 00:20:53,036 --> 00:20:55,156 Speaker 2: closely with to monitor these patients over time. 394 00:20:55,956 --> 00:21:00,476 Speaker 1: So you have this virus that you have engineered to 395 00:21:00,596 --> 00:21:05,196 Speaker 1: have this gene and this promoter, you inject it into 396 00:21:05,716 --> 00:21:08,116 Speaker 1: the back of somebody's eye. 397 00:21:08,756 --> 00:21:13,876 Speaker 2: Then what happens, So the virus infects the photo receptors, 398 00:21:14,596 --> 00:21:18,716 Speaker 2: it unloads the DNA inside, and then that DNA remains 399 00:21:18,996 --> 00:21:22,636 Speaker 2: inside that cell over the lifetime of that living cell. 400 00:21:23,036 --> 00:21:26,876 Speaker 2: So the gene will persistently remain inside that cell and 401 00:21:26,956 --> 00:21:30,356 Speaker 2: express that protein that it needs to express. It does 402 00:21:30,396 --> 00:21:33,796 Speaker 2: not integrate into the genome. It remains outside the genome. 403 00:21:33,876 --> 00:21:37,356 Speaker 2: We call that episomal, but it leads to persistent expression 404 00:21:37,396 --> 00:21:40,476 Speaker 2: of that gene and continuous production of that therapeutic protein. 405 00:21:40,636 --> 00:21:44,476 Speaker 2: And when the cell dies that when the cell dies, 406 00:21:44,556 --> 00:21:47,516 Speaker 2: the gene dies with it. So in order for gene 407 00:21:47,516 --> 00:21:51,036 Speaker 2: therapy to be successful, those cells need to be retained. 408 00:21:51,196 --> 00:21:54,156 Speaker 2: If the cells degenerate, then that therapeutic effect can be lost. 409 00:21:54,596 --> 00:21:57,116 Speaker 1: And do cells Do those cells last forever? 410 00:21:57,716 --> 00:22:01,556 Speaker 2: It depends on the indication. So that's why LCA one 411 00:22:01,676 --> 00:22:04,556 Speaker 2: with such an attractive target is because those patients retain 412 00:22:04,596 --> 00:22:08,996 Speaker 2: their photoreceptor structure over their lifetime, So theoretically we could 413 00:22:09,156 --> 00:22:11,196 Speaker 2: at persistent rescue over their lifetime. 414 00:22:11,396 --> 00:22:14,756 Speaker 1: So photoreceptor celves just stay there, they develop, and then 415 00:22:14,796 --> 00:22:17,996 Speaker 1: they just hang out receiving photons forever. 416 00:22:18,276 --> 00:22:19,796 Speaker 3: Yes, in the syndication yep. 417 00:22:20,076 --> 00:22:22,716 Speaker 1: So like how many people are in this in this trial? 418 00:22:23,116 --> 00:22:23,636 Speaker 3: So we had. 419 00:22:23,556 --> 00:22:26,596 Speaker 2: Fifteen people total enrolled in this trial. 420 00:22:26,676 --> 00:22:30,716 Speaker 1: Okay, And how long does it take to find out 421 00:22:30,996 --> 00:22:31,636 Speaker 1: if it works? 422 00:22:32,316 --> 00:22:36,796 Speaker 2: So with this condition, typically we saw responses by about 423 00:22:37,076 --> 00:22:41,196 Speaker 2: four weeks post injection, and those responses get a little 424 00:22:41,236 --> 00:22:44,276 Speaker 2: bit better up until about two or three months post injection, 425 00:22:44,316 --> 00:22:46,596 Speaker 2: at which time the response is plateau. So it's a 426 00:22:46,716 --> 00:22:48,636 Speaker 2: very quick, very quick readout. 427 00:22:48,916 --> 00:22:52,796 Speaker 1: And the patients are they completely blind? Like what is 428 00:22:52,836 --> 00:22:55,716 Speaker 1: there before when they're coming to you? What is the 429 00:22:55,836 --> 00:22:56,876 Speaker 1: state of their vision? 430 00:22:57,196 --> 00:23:00,756 Speaker 2: That's a good question. So there's a range, but we 431 00:23:00,796 --> 00:23:04,396 Speaker 2: would consider all LCA one patients to be profoundly visually impaired, 432 00:23:04,476 --> 00:23:09,476 Speaker 2: so ranging from twenty two hundred all the way to 433 00:23:09,876 --> 00:23:14,716 Speaker 2: light perception only, so legally blind to folks that can 434 00:23:14,756 --> 00:23:15,636 Speaker 2: only see light. 435 00:23:16,476 --> 00:23:20,756 Speaker 1: And so when do you first hear about the results, 436 00:23:20,836 --> 00:23:22,556 Speaker 1: Like how do the results come into you? 437 00:23:23,596 --> 00:23:26,836 Speaker 2: Well, you have to be very careful as a you know, 438 00:23:27,316 --> 00:23:30,236 Speaker 2: co founder and CSO of a company, I you know, 439 00:23:30,436 --> 00:23:33,956 Speaker 2: don't have any direct interaction with the patients. That would 440 00:23:33,956 --> 00:23:35,876 Speaker 2: be it's kind of a conflict of interest, right, But 441 00:23:37,276 --> 00:23:40,116 Speaker 2: you know we do the data starts pouring in into 442 00:23:40,236 --> 00:23:42,516 Speaker 2: the you know, the software that we use to collect 443 00:23:42,516 --> 00:23:45,156 Speaker 2: that data as a company, and you start to see 444 00:23:45,276 --> 00:23:49,236 Speaker 2: the numbers, and on occasion, you know, a patient will 445 00:23:49,396 --> 00:23:52,956 Speaker 2: anecdotally tell the physician something and that physician will report 446 00:23:52,996 --> 00:23:56,436 Speaker 2: it back to the company, like wow, this this person 447 00:23:56,596 --> 00:23:58,836 Speaker 2: was able to see the lines and the crosswalk for 448 00:23:58,876 --> 00:24:03,396 Speaker 2: the first time outside last night. Or this woman was 449 00:24:03,476 --> 00:24:06,956 Speaker 2: really excited because this Halloween was the first time that 450 00:24:06,996 --> 00:24:09,636 Speaker 2: she could read the labels on her kids Halloween. So 451 00:24:09,676 --> 00:24:13,396 Speaker 2: you hear, you hear little stories like that, and it's 452 00:24:13,476 --> 00:24:16,396 Speaker 2: like they make you cry, right, Like you just can't 453 00:24:16,396 --> 00:24:19,396 Speaker 2: believe that it's happening. It's one thing to see a 454 00:24:19,436 --> 00:24:22,556 Speaker 2: mouse regain vision and be able to, you know, swim 455 00:24:22,596 --> 00:24:25,476 Speaker 2: through a maze. But to hear that a patient can 456 00:24:25,516 --> 00:24:28,236 Speaker 2: read something for the first time or navigate outside their 457 00:24:28,236 --> 00:24:30,116 Speaker 2: home for the first time, that's something else. 458 00:24:30,956 --> 00:24:33,236 Speaker 1: Yeah, So you're not spending your career trying to cure 459 00:24:33,236 --> 00:24:34,156 Speaker 1: blindness in mice. 460 00:24:34,316 --> 00:24:36,676 Speaker 3: Nope, Nope. 461 00:24:36,916 --> 00:24:39,836 Speaker 1: So what was the outcome of that trial? 462 00:24:40,596 --> 00:24:43,676 Speaker 2: Sure, so it was a very positive outcome. We just 463 00:24:43,716 --> 00:24:46,476 Speaker 2: published the results in the Lancet a few weeks ago, 464 00:24:46,916 --> 00:24:49,796 Speaker 2: looking at all all fifteen of the Phase one two 465 00:24:49,956 --> 00:24:52,916 Speaker 2: patients out to one year post treatment, and we showed 466 00:24:52,916 --> 00:24:56,116 Speaker 2: that the gene therapy had a very very good safety profile. 467 00:24:56,236 --> 00:24:59,636 Speaker 2: There were no you know, serious adverse events related to 468 00:24:59,676 --> 00:25:04,156 Speaker 2: the medicine itself, and we showed a very profound efficacy. 469 00:25:04,276 --> 00:25:08,516 Speaker 2: So we used a test called FST, which is just 470 00:25:08,556 --> 00:25:11,836 Speaker 2: a measure of retinal sensitivity, and we saw, for instance, 471 00:25:11,836 --> 00:25:15,436 Speaker 2: in one patient there was a ten thousandfold improvement in 472 00:25:15,476 --> 00:25:18,676 Speaker 2: retinal sensitivity. And what that means is it's akin to 473 00:25:18,676 --> 00:25:24,476 Speaker 2: someone being able to navigate under bright sunlight versus someone 474 00:25:24,516 --> 00:25:27,676 Speaker 2: being able to navigate in the light of the full moon. 475 00:25:28,116 --> 00:25:30,836 Speaker 2: So a huge improvement in retinal sensitivity. 476 00:25:30,876 --> 00:25:33,236 Speaker 1: And what is there like a median improvement. 477 00:25:33,516 --> 00:25:37,036 Speaker 2: Yeah, so the median improvement was about one hundredfold improvement, 478 00:25:37,476 --> 00:25:40,556 Speaker 2: so really exciting and significant. And then you know, of 479 00:25:40,556 --> 00:25:43,556 Speaker 2: course the anecdotes come in. We have one video of 480 00:25:43,596 --> 00:25:46,076 Speaker 2: a little girl who saw snowflakes for the first time, 481 00:25:46,916 --> 00:25:49,596 Speaker 2: so you know, it's more than the cold hard numbers 482 00:25:49,676 --> 00:25:53,196 Speaker 2: like one hundredfold improvement in retal sensitivity. It's you're seeing 483 00:25:53,556 --> 00:25:56,356 Speaker 2: a genuine improvement in the patient's quality of life, which 484 00:25:56,396 --> 00:25:56,956 Speaker 2: is amazing. 485 00:25:58,556 --> 00:25:59,836 Speaker 1: So what's next? 486 00:26:00,676 --> 00:26:04,476 Speaker 2: So next will be phase three. Before you can get 487 00:26:04,516 --> 00:26:06,956 Speaker 2: anything commercialized for broader use, you have to do a 488 00:26:06,956 --> 00:26:10,516 Speaker 2: phase three trial. So we're fortunate because our LCA one 489 00:26:10,556 --> 00:26:13,756 Speaker 2: program has received what's called an ARMAT designation, and put simply, 490 00:26:13,876 --> 00:26:17,836 Speaker 2: that is a designation given to programs that cause a 491 00:26:17,876 --> 00:26:22,596 Speaker 2: profound illness at birth and for which you have promising 492 00:26:22,636 --> 00:26:24,796 Speaker 2: proof of concept data showing that you might have a cure. 493 00:26:24,916 --> 00:26:27,796 Speaker 2: So we receive that designation and we need to align 494 00:26:27,996 --> 00:26:30,596 Speaker 2: on a path forward with the FDA. So, in other words, 495 00:26:30,636 --> 00:26:32,836 Speaker 2: what does our phase three trial design need to be? 496 00:26:33,276 --> 00:26:35,876 Speaker 2: And once we decide on that, then we will execute 497 00:26:35,876 --> 00:26:38,836 Speaker 2: that Phase three trial and then hopefully after that we'll 498 00:26:38,836 --> 00:26:42,516 Speaker 2: seek approval from the FDA to commercialize it for broader 499 00:26:42,556 --> 00:26:43,476 Speaker 2: patient access. 500 00:26:44,516 --> 00:26:48,796 Speaker 1: How many people more or less have LCA one. 501 00:26:49,516 --> 00:26:52,316 Speaker 2: So there's about three thousand patients I would say in 502 00:26:52,356 --> 00:26:55,636 Speaker 2: the US and the EU that have they indication. 503 00:26:56,156 --> 00:26:58,956 Speaker 1: So I mean a lot on a human level, but 504 00:26:59,036 --> 00:27:01,796 Speaker 1: on a kind of population level, not a lot. It's 505 00:27:01,916 --> 00:27:07,476 Speaker 1: very rare, that's correct. And so what does that mean? Well, 506 00:27:07,476 --> 00:27:09,076 Speaker 1: what does that mean. I guess on the on the 507 00:27:09,236 --> 00:27:11,036 Speaker 1: business side. Right on the science side, it sort of 508 00:27:11,076 --> 00:27:13,756 Speaker 1: doesn't matter. It's the same science whether a million people 509 00:27:13,796 --> 00:27:15,996 Speaker 1: have it or people have it. But what does it 510 00:27:16,036 --> 00:27:17,836 Speaker 1: mean on the business side. 511 00:27:17,556 --> 00:27:20,916 Speaker 2: It's you know, the pendulum has swung back since the 512 00:27:21,476 --> 00:27:24,916 Speaker 2: early two thousands where investors in big pharma were all 513 00:27:25,036 --> 00:27:29,196 Speaker 2: very eager to throw money into this space, and they're 514 00:27:29,276 --> 00:27:33,676 Speaker 2: less excited about rare disease obviously, But you know, as 515 00:27:33,676 --> 00:27:37,316 Speaker 2: a scientist who sees the obvious impact it's having on 516 00:27:37,356 --> 00:27:41,436 Speaker 2: these patients, I'm going to push it forward with full force. 517 00:27:41,796 --> 00:27:45,236 Speaker 2: We've successfully raised money at SENA to keep this program going. 518 00:27:45,756 --> 00:27:48,396 Speaker 2: We have plans for it moving forward, and I think 519 00:27:48,516 --> 00:27:52,596 Speaker 2: our ability to continue to raise money is increased or 520 00:27:52,796 --> 00:27:55,516 Speaker 2: strengthened by the fact that we have other ongoing clinical 521 00:27:55,556 --> 00:27:58,356 Speaker 2: programs that are also showing success. So you know, if 522 00:27:58,396 --> 00:28:00,556 Speaker 2: you have one rare disease that you have in clinic, 523 00:28:00,636 --> 00:28:03,716 Speaker 2: you might be only quasi interesting to investors are big pharma. 524 00:28:03,716 --> 00:28:05,396 Speaker 2: But we have a bunch of things going on at 525 00:28:05,476 --> 00:28:07,636 Speaker 2: SENA that I think will improve the chances that this 526 00:28:07,676 --> 00:28:08,636 Speaker 2: program is forward. 527 00:28:09,116 --> 00:28:10,036 Speaker 1: What else are you working on? 528 00:28:10,716 --> 00:28:14,436 Speaker 2: So we are working actively on another inherited retinal disease 529 00:28:14,516 --> 00:28:18,276 Speaker 2: called X linked retinoskeesis or XLRS. We're also in a 530 00:28:18,316 --> 00:28:22,436 Speaker 2: phase one two clinical trial and already showing structural and 531 00:28:22,436 --> 00:28:27,116 Speaker 2: functional improvements in those patients using a novel flavor of AV, 532 00:28:27,316 --> 00:28:30,636 Speaker 2: which has been interesting. So really excited. 533 00:28:31,196 --> 00:28:35,796 Speaker 1: So you have a separate indication where you're in clinical trials, yep, 534 00:28:37,476 --> 00:28:40,676 Speaker 1: and anything else. I feel like remember seeing a couple 535 00:28:40,756 --> 00:28:42,356 Speaker 1: more on the website. 536 00:28:41,876 --> 00:28:43,076 Speaker 3: Now, yeah, else. 537 00:28:43,236 --> 00:28:43,396 Speaker 1: Yeah. 538 00:28:43,436 --> 00:28:46,756 Speaker 2: So we're also working on a dual vector technology. So 539 00:28:47,076 --> 00:28:50,476 Speaker 2: there are some indications caused by mutations in large genes 540 00:28:50,476 --> 00:28:53,956 Speaker 2: that don't fit inside a standard AAV vector. So we've 541 00:28:53,996 --> 00:28:57,516 Speaker 2: developed a technology wherein we split that large gene in half. 542 00:28:57,796 --> 00:29:00,436 Speaker 2: We deliver the front half via one AAV in the 543 00:29:00,476 --> 00:29:02,156 Speaker 2: back half via a second AV. 544 00:29:02,716 --> 00:29:04,676 Speaker 3: Those two. Yeah, it's really cool. 545 00:29:05,636 --> 00:29:08,236 Speaker 1: Say one gene, it's one gene and you're putting it 546 00:29:08,276 --> 00:29:11,076 Speaker 1: into two different yes suitcases. 547 00:29:10,516 --> 00:29:12,316 Speaker 3: That's very sically Yeah, and. 548 00:29:12,276 --> 00:29:14,796 Speaker 1: Then dumb question, how does it get put back together? 549 00:29:15,676 --> 00:29:19,876 Speaker 2: So there's a complimentary sequence shared between the front and 550 00:29:19,916 --> 00:29:24,156 Speaker 2: the back half. So when the two suitcases unpack their 551 00:29:24,316 --> 00:29:27,316 Speaker 2: their respective front and back half genes, they find each 552 00:29:27,356 --> 00:29:30,556 Speaker 2: other via that complementary sequence and then they recombine to 553 00:29:30,636 --> 00:29:31,996 Speaker 2: form a full length gene. 554 00:29:32,036 --> 00:29:35,596 Speaker 1: That is wild. Have people done that technique in other 555 00:29:36,956 --> 00:29:40,516 Speaker 1: other you know, indications of gene therapy and other domains 556 00:29:40,556 --> 00:29:40,956 Speaker 1: they have. 557 00:29:41,116 --> 00:29:44,636 Speaker 2: Yes, there's a company recently that it is in the 558 00:29:44,636 --> 00:29:47,916 Speaker 2: hearing space actually, but they use dual vectors to deliver 559 00:29:48,516 --> 00:29:51,796 Speaker 2: a certain gene to patients that had hearing loss and 560 00:29:52,116 --> 00:29:53,676 Speaker 2: restored hearing to these children. 561 00:29:53,916 --> 00:29:57,156 Speaker 1: So, and is the issue the gene is just too long, 562 00:29:57,236 --> 00:30:00,436 Speaker 1: like it physically just doesn't fit inside. That's correct. 563 00:30:00,956 --> 00:30:03,876 Speaker 2: Yeah, So standard AV can only fit about five thousand 564 00:30:03,876 --> 00:30:06,676 Speaker 2: base pairs of DNA, and some of these genes are 565 00:30:06,756 --> 00:30:08,156 Speaker 2: are just too big to fit. 566 00:30:09,236 --> 00:30:11,836 Speaker 1: That is so clever. I love it when people are 567 00:30:11,876 --> 00:30:17,236 Speaker 1: so clever. So let's let's zoom out. And you know, 568 00:30:17,396 --> 00:30:22,956 Speaker 1: you've been working on gene therapy for twenty years ish, 569 00:30:23,396 --> 00:30:26,716 Speaker 1: which is close to the life of gene therapy. Right 570 00:30:26,756 --> 00:30:28,436 Speaker 1: of the field you got in or early, you've been 571 00:30:28,436 --> 00:30:31,196 Speaker 1: there a long time. A lot has happened. Like when 572 00:30:31,236 --> 00:30:35,436 Speaker 1: you zoom out, what do you see? Like where is 573 00:30:35,476 --> 00:30:38,116 Speaker 1: the field now? You know? Yeah, where is it now? 574 00:30:38,196 --> 00:30:40,356 Speaker 1: What's the big picture for gene therapy right now? 575 00:30:40,756 --> 00:30:43,516 Speaker 2: I think the big picture for gene therapy right now 576 00:30:43,676 --> 00:30:46,516 Speaker 2: is we're a little bit bruised, right. You know, we 577 00:30:46,596 --> 00:30:50,516 Speaker 2: have the success of Luxterna getting approved. Then you know 578 00:30:50,556 --> 00:30:54,236 Speaker 2: you've got zulgensimo, which is a huge success story. And 579 00:30:54,276 --> 00:30:58,316 Speaker 2: those were the successes. But we entered a period around 580 00:30:58,396 --> 00:31:01,556 Speaker 2: that same time where I think, unfortunately, folks were taking 581 00:31:01,556 --> 00:31:03,956 Speaker 2: a one size fits all approach to gene therapy. In 582 00:31:03,996 --> 00:31:06,556 Speaker 2: other words, like, Okay, if this flavor of av or 583 00:31:06,596 --> 00:31:10,436 Speaker 2: this regulatory region or this dose worked for lucerna, then 584 00:31:10,436 --> 00:31:13,276 Speaker 2: it's going to work for this other indication, right, And 585 00:31:13,836 --> 00:31:17,236 Speaker 2: I think that hasn't That hasn't played out right. It's 586 00:31:17,276 --> 00:31:20,356 Speaker 2: not a one size fits all approach. Every indication needs 587 00:31:20,756 --> 00:31:24,156 Speaker 2: a treatment tailored to that indication. What cell type is impacted, 588 00:31:25,236 --> 00:31:28,076 Speaker 2: you know, does the gene expression need to be restricted, 589 00:31:28,436 --> 00:31:31,076 Speaker 2: what dose needs to be used, what's the underlying immune 590 00:31:31,116 --> 00:31:34,196 Speaker 2: status of that patient's retina for instance. So it's not 591 00:31:34,276 --> 00:31:36,356 Speaker 2: a one size fits all approach, and I think I 592 00:31:36,356 --> 00:31:38,116 Speaker 2: think people have realized that. 593 00:31:38,636 --> 00:31:40,396 Speaker 1: So like, so does that mean it's going to be 594 00:31:40,516 --> 00:31:43,956 Speaker 1: hard every time? I mean it's going to be hard forever, 595 00:31:44,076 --> 00:31:46,316 Speaker 1: And it's not like great, we figured it out and 596 00:31:46,356 --> 00:31:49,116 Speaker 1: we can just put any gene into this vector and 597 00:31:49,596 --> 00:31:50,636 Speaker 1: will cure everything. 598 00:31:51,156 --> 00:31:53,596 Speaker 2: Yeah, I mean I think it's somewhere in the middle, right, 599 00:31:53,636 --> 00:31:56,436 Speaker 2: It's it's it's never going to be like just plug 600 00:31:56,476 --> 00:31:59,436 Speaker 2: and play, right, But there are certainly tools that are 601 00:31:59,436 --> 00:32:02,716 Speaker 2: being developed along the way that can be you know, 602 00:32:02,836 --> 00:32:05,116 Speaker 2: used in one trial and used in another trial. But 603 00:32:05,836 --> 00:32:07,236 Speaker 2: I think you always have to put a lot of 604 00:32:07,316 --> 00:32:09,796 Speaker 2: thought into it. It can't just simply be Okay, if 605 00:32:09,796 --> 00:32:12,236 Speaker 2: this worked for l c A two, then it's going 606 00:32:12,316 --> 00:32:15,596 Speaker 2: to work for disease X, right. There always has to 607 00:32:15,636 --> 00:32:16,756 Speaker 2: be a thoughtful process. 608 00:32:17,916 --> 00:32:19,796 Speaker 1: I mean, is it harder than you thought it was 609 00:32:19,836 --> 00:32:20,196 Speaker 1: going to be? 610 00:32:23,276 --> 00:32:23,516 Speaker 3: Yes? 611 00:32:24,836 --> 00:32:27,916 Speaker 2: You know, in my grad school days it was hope, hope, hope, 612 00:32:27,996 --> 00:32:32,396 Speaker 2: you know, excitement, excitement, excitement, and then forming my own 613 00:32:32,436 --> 00:32:35,196 Speaker 2: company and being in charge of kind of the fundraising 614 00:32:35,276 --> 00:32:38,916 Speaker 2: behind keeping these programs going. It's been it's been a 615 00:32:38,956 --> 00:32:42,316 Speaker 2: lot of work, but I believe strongly in what I 616 00:32:42,356 --> 00:32:45,036 Speaker 2: do and that it's having a positive impact on patient lives, 617 00:32:45,076 --> 00:32:46,756 Speaker 2: and so it's it's worth that effort. 618 00:32:50,516 --> 00:32:52,596 Speaker 1: We'll be back in a minute with the lightning round. 619 00:32:54,356 --> 00:33:06,156 Speaker 1: M hm hm. Let's finish with the lightning round. Okay, 620 00:33:08,276 --> 00:33:13,836 Speaker 1: what's the best thing about working with your husband. 621 00:33:13,396 --> 00:33:16,796 Speaker 2: Oh, let's see. I think that at the end of 622 00:33:16,796 --> 00:33:20,556 Speaker 2: the day, we can understand each other's stresses. You know, 623 00:33:20,636 --> 00:33:23,676 Speaker 2: It's not like coming home and you know he has 624 00:33:23,676 --> 00:33:25,556 Speaker 2: no idea what I'm talking about. It's like, if I 625 00:33:25,596 --> 00:33:28,676 Speaker 2: have a problem, he can think through it very clearly 626 00:33:28,756 --> 00:33:32,076 Speaker 2: because he understands it at its core and give me 627 00:33:32,116 --> 00:33:35,796 Speaker 2: advice on how to navigate the situation and vice versa. 628 00:33:36,196 --> 00:33:38,156 Speaker 1: What's the worst thing about working with your husband? 629 00:33:41,516 --> 00:33:44,676 Speaker 2: Sometimes there's you know, evenings where I'm done talking about 630 00:33:44,676 --> 00:33:46,636 Speaker 2: a vy. You know, I've done it all day long, 631 00:33:47,196 --> 00:33:49,756 Speaker 2: and we're sitting over the dinner table with our kids 632 00:33:49,756 --> 00:33:52,476 Speaker 2: and he's still talking about, you know, designing a vector 633 00:33:52,476 --> 00:33:54,276 Speaker 2: to do whatever, and I'm like, Okay, we're done here, 634 00:33:54,356 --> 00:33:57,316 Speaker 2: We're done for the night. But I mean it's with 635 00:33:57,396 --> 00:33:59,076 Speaker 2: us all the time, and I think that's what makes 636 00:33:59,116 --> 00:34:00,636 Speaker 2: us better scientists for it. 637 00:34:02,436 --> 00:34:06,196 Speaker 1: What's one interesting or surprising thing you've learned about the 638 00:34:06,276 --> 00:34:07,076 Speaker 1: human eye? 639 00:34:08,436 --> 00:34:12,276 Speaker 2: The human eye, I would say most of all that 640 00:34:13,356 --> 00:34:17,356 Speaker 2: you can be seventy years old and have had a 641 00:34:17,516 --> 00:34:20,836 Speaker 2: congenital form of blindness since you were a baby and 642 00:34:20,916 --> 00:34:24,476 Speaker 2: still benefit from gene therapy. And that's wild to me, Like, 643 00:34:24,796 --> 00:34:26,676 Speaker 2: you know, I got my PhD and neuroscience, so I'm 644 00:34:26,676 --> 00:34:29,756 Speaker 2: always thinking about you know, so what if we restore 645 00:34:29,796 --> 00:34:31,596 Speaker 2: function to the retina, what's that going to mean in 646 00:34:31,636 --> 00:34:33,676 Speaker 2: the brain? Is the brain going to be able to 647 00:34:33,796 --> 00:34:36,476 Speaker 2: be receptive to that message if it's been turned off 648 00:34:36,516 --> 00:34:39,436 Speaker 2: from that message input its entire life? 649 00:34:39,516 --> 00:34:39,716 Speaker 3: Right? 650 00:34:40,436 --> 00:34:42,476 Speaker 2: But we had a seventy year old patient in our 651 00:34:42,676 --> 00:34:46,396 Speaker 2: LCA one clinical trial that showed some benefit following gene therapy, 652 00:34:46,396 --> 00:34:48,996 Speaker 2: and that tells me that the brain is, you know, 653 00:34:49,156 --> 00:34:53,076 Speaker 2: extremely plastic, more plastic than I gave it credit for before. 654 00:34:53,676 --> 00:34:56,236 Speaker 1: Huh, So it's not the eye but the brain. Like 655 00:34:56,236 --> 00:34:59,036 Speaker 1: we're not really seeing with our eye. Our eye is 656 00:34:59,076 --> 00:35:01,396 Speaker 1: just like the window and the brain is really where 657 00:35:01,436 --> 00:35:02,396 Speaker 1: the seeing is happening. 658 00:35:02,436 --> 00:35:02,836 Speaker 3: That's right. 659 00:35:04,836 --> 00:35:06,996 Speaker 1: I read that you have a boat called wet Lab 660 00:35:07,636 --> 00:35:13,076 Speaker 1: we do. H What was the runner up name? Oh? 661 00:35:13,116 --> 00:35:16,036 Speaker 2: I don't think we had a runner up. We've planned 662 00:35:16,076 --> 00:35:17,076 Speaker 2: that one for years. 663 00:35:20,556 --> 00:35:23,116 Speaker 1: What's the biggest fish you ever caught? Oh? 664 00:35:23,156 --> 00:35:26,996 Speaker 2: My goodness. We go all the time and we catch 665 00:35:27,036 --> 00:35:29,436 Speaker 2: big fish so often. I don't remember the biggest one 666 00:35:29,636 --> 00:35:30,236 Speaker 2: that you catch. 667 00:35:30,316 --> 00:35:36,636 Speaker 1: So many big things to tell a first story. Thank 668 00:35:36,676 --> 00:35:39,316 Speaker 1: you so much, for your time. It was very interesting 669 00:35:39,356 --> 00:35:40,156 Speaker 1: to talk to you. I learned. 670 00:35:40,396 --> 00:35:42,636 Speaker 2: Thank you, Thank you. You're a great interviewer. This was 671 00:35:42,676 --> 00:35:43,156 Speaker 2: a pleasure. 672 00:35:46,476 --> 00:35:49,396 Speaker 1: Jennon boy is a professor of genetics at the University 673 00:35:49,396 --> 00:35:52,436 Speaker 1: of Florida and the co founder and chief scientific officer 674 00:35:52,516 --> 00:35:56,116 Speaker 1: of Atsina Therapeutics. Just a quick note, the show is 675 00:35:56,156 --> 00:35:58,876 Speaker 1: going to take a break. We'll be back with new 676 00:35:58,916 --> 00:36:01,676 Speaker 1: episodes in a couple of weeks. In the meantime, please 677 00:36:01,756 --> 00:36:03,956 Speaker 1: let us know who you'd like to hear on the show, 678 00:36:04,356 --> 00:36:06,556 Speaker 1: who I should interview, were, just how we can make 679 00:36:06,556 --> 00:36:10,156 Speaker 1: the show better. You can email us at at pushkin 680 00:36:10,276 --> 00:36:14,836 Speaker 1: dot fm. Today's show was produced by Gabriel Hunter Chang. 681 00:36:15,116 --> 00:36:18,476 Speaker 1: It was edited by Lyddy Jean Kott and engineered by 682 00:36:18,516 --> 00:36:22,076 Speaker 1: Sarah Buguer. You can email us at Problem at pushkin 683 00:36:22,156 --> 00:36:25,356 Speaker 1: dot fm. I'm Jacob Goldstein and we'll be back next 684 00:36:25,356 --> 00:36:35,556 Speaker 1: week with another episode of What's Your Problem.