התמלול המלא של We Broke the Rules: The Creative Research of Nanoparticle Expert Dr. Daniel Siegwart

and the scientific enterprise 25 00:02:22,479 --> 00:02:29,520 and that's not commonly talked about. So myself, I just love ideas. I love thinking about ideas, 26 00:02:29,520 --> 00:02:36,160 love working on ideas and their ideas just like an individual wants to paint a painting and they 27 00:02:36,160 --> 00:02:40,960 have an idea of what that could look like or they hear a noise and it gives them an inspiration for 28 00:02:40,960 --> 00:02:46,479 a new song and that's an idea and they work on that song. I think scientists come up with these 29 00:02:46,479 --> 00:02:51,839 ideas and they come from a lot of different places. It's just really, really fun to be a 30 00:02:51,839 --> 00:02:59,520 scientist and work on ideas that are interesting to yourself. I think some scientists are termed 31 00:02:59,520 --> 00:03:05,440 fundamental scientists. It means that they are studying the basic processes by which the earth 32 00:03:05,440 --> 00:03:11,919 and human bodies work. So for example, someone might have a burn on their skin and this burn 33 00:03:11,919 --> 00:03:17,360 does not always heal correctly to look like the skin from before. So fundamental scientists could 34 00:03:17,360 --> 00:03:23,279 look at that and study, okay, what is the composition of skin? What are the factors that make 35 00:03:23,279 --> 00:03:29,679 the skin regrow or not regrow? How do scars form and what are the molecular mechanisms by which 36 00:03:29,679 --> 00:03:36,240 these processes take place? And that understanding is just fundamentally interesting and it can also 37 00:03:36,240 --> 00:03:42,000 lead to that investigator or potentially others, more likely others, coming up with new strategies 38 00:03:42,000 --> 00:03:48,000 that might make that burn heal much more effectively. Other scientists are problem solvers. 39 00:03:48,000 --> 00:03:54,559 These individuals identify a good problem and that's a very purposefully vague term, but a good 40 00:03:54,559 --> 00:04:00,559 problem means that if you solve it, there will immediately be one, two, three, four obvious, 41 00:04:00,559 --> 00:04:06,800 broader, high impacts that result from that problem being solved. And that's how you know that you're 42 00:04:06,800 --> 00:04:11,759 on to something that's a good problem. That falls into this area of applied science, whereby 43 00:04:11,759 --> 00:04:17,359 scientists will apply the basic knowledge to solve a fundamental problem. I think there's probably a 44 00:04:17,359 --> 00:04:24,239 third category, which is creativity or curiosity. As a chemist, that's my background, there is a 45 00:04:24,239 --> 00:04:30,000 phrase I use all the time, which is chemical curiosity. There are molecules that never existed 46 00:04:30,000 --> 00:04:36,640 on earth before and yet we can conceptualize them and make those molecules in the lab. And sometimes 47 00:04:36,640 --> 00:04:41,600 they have very unique properties because they never existed before and sometimes those unique 48 00:04:41,600 --> 00:04:47,760 properties can affect a fundamental goal or an applied goal. And so that's what I love about 49 00:04:47,760 --> 00:04:53,679 science is coming up with ideas and being able to realize those ideas in a meaningful way in 50 00:04:53,679 --> 00:04:59,760 the laboratory. Yeah, that's such a great framing, the importance of creativity in 51 00:04:59,760 --> 00:05:05,519 science. And maybe we can jump into some of that applied creativity that you're talking about in 52 00:05:05,519 --> 00:05:12,239 your lab. I'd love to unpack a little bit, especially for a viewer that's a non-scientist, 53 00:05:12,239 --> 00:05:17,519 what is a nanoparticle? What is a genomic medicine? These are the things you work on in your lab, but 54 00:05:18,079 --> 00:05:24,720 how would you explain your work to someone that is non-technical? Yeah, nanoparticles are small 55 00:05:24,720 --> 00:05:29,679 spheres fundamentally. Small meaning they're smaller than the width of the human hair. That 56 00:05:29,679 --> 00:05:35,119 means we cannot really see them. But if they were to be dissolved or dispersed in this cup of water, 57 00:05:35,119 --> 00:05:40,720 it would look cloudy and usually blue. And that blue is due to how the light is reflecting off 58 00:05:40,720 --> 00:05:45,839 of those very small nanoparticles. And we have techniques to characterize exactly what those 59 00:05:45,839 --> 00:05:52,399 nanoparticles are made. But for LA audience, they are small balls that can transport drugs 60 00:05:52,399 --> 00:06:00,160 inside of cells in our bodies in a very meaningful and important way. The nanoparticles that we focus 61 00:06:00,160 --> 00:06:05,600 on are called lipid nanoparticles, meaning they're made of lipids. Lipids are in all of our cell 62 00:06:05,600 --> 00:06:12,000 membranes and it's a fundamental compound in life. My friend Katie Whitehead describes them as little 63 00:06:12,000 --> 00:06:16,799 balls of fat, which I really like as a phrase because ultimately that's what they're made of. 64 00:06:18,079 --> 00:06:25,440 Awesome. And maybe you could explain a little bit of the why now for nanoparticles and lipid 65 00:06:25,440 --> 00:06:31,119 nanoparticles specifically. A lot of viewers might not know, but lipid nanoparticles are very 66 00:06:31,119 --> 00:06:37,279 tied to, I guess it's a little bit of past history now, but COVID vaccines. But it's 67 00:06:37,519 --> 00:06:43,519 now you're evolving that technology in a big way. So we'd love to cover the why now. 68 00:06:43,519 --> 00:06:51,359 Sure. Most of the drugs that we take, that aspirin, Tylenol, ibuprofen, even anti-cancer 69 00:06:51,359 --> 00:06:56,480 drugs and others are what are called small molecules. So small molecule means they're small. 70 00:06:56,480 --> 00:07:02,720 Obviously it's in the name. Molecule means it's a chemical molecule. Because that chemical 71 00:07:02,720 --> 00:07:08,320 molecule is small, we can eat it. It goes into

our stomach, gets into our blood, and basically 72 00:07:08,320 --> 00:07:13,920 goes all over our bodies. So 30 minutes after eating this medicine, our headache can go away. 73 00:07:14,720 --> 00:07:20,239 And that it remains about two-thirds of all of the FDA approved drugs are small molecules today. 74 00:07:21,040 --> 00:07:26,959 What those small molecules do is they will find something called a protein and that protein may be 75 00:07:26,959 --> 00:07:31,679 causing or be otherwise related to the ailment like a headache or a genetic disease. 76 00:07:32,399 --> 00:07:37,600 And that protein, if it causes that effect or is somehow related to that effect, the small molecule 77 00:07:37,600 --> 00:07:43,440 can bind that protein and inhibit its function. It's like throwing a wrench into a spinning wheel. 78 00:07:43,440 --> 00:07:48,480 It just, it breaks the function, even though that wheel still exists. And consequently, 79 00:07:48,480 --> 00:07:53,359 your headache goes down or the symptoms of your disease go down, or maybe the cancer cell dies. 80 00:07:54,320 --> 00:08:00,640 Now this remains a great way to develop medicines. And in the spectrum of what we understand as the 81 00:08:00,640 --> 00:08:07,359 central dogma of biology, every one of our cells contains DNA. DNA is the blueprint of life. 82 00:08:08,320 --> 00:08:14,559 Periodically, at different times, for different reasons, cells will transcribe the DNA into a 83 00:08:14,559 --> 00:08:20,239 messenger molecule called messenger RNA. And that messenger RNA is now in the cytoplasm, 84 00:08:20,239 --> 00:08:26,399 the soup of the cell, where it's translated into protein based on the genetic sequence. 85 00:08:26,480 --> 00:08:31,679 These proteins are doing everything in our bodies from forming the structures of our cells and our 86 00:08:31,679 --> 00:08:38,799 existence to communicating and orchestrating these complex signaling pathways that make humans, 87 00:08:38,799 --> 00:08:45,599 humans. So genetic medicine is essentially saying, yes, we can take those small molecules that can 88 00:08:45,599 --> 00:08:51,359 bind and inhibit a protein, but potentially we could go upstream in the central dogma of biology. 89 00:08:52,000 --> 00:08:58,080 We could, for example, design a molecule like a short interfering RNA that will bind to the 90 00:08:58,080 --> 00:09:04,159 messenger RNA and lead to destruction of that molecule. So now if there's no messenger RNA, 91 00:09:04,159 --> 00:09:08,960 there's no protein. And if that protein was causing somebody's disease, it's gone now. 92 00:09:09,599 --> 00:09:13,679 But we could also go upstream from there and use something called genome editing, 93 00:09:13,679 --> 00:09:19,679 which could fix the mutation that's causing the mutated mRNA and the mutated protein that causes 94 00:09:19,679 --> 00:09:25,039 somebody's disease or other ailment. Even cancer could be targeted this way. You could 95 00:09:25,039 --> 00:09:31,280 go into the DNA of the cancer and inhibit that molecule in a way that the cancer cell could no 96 00:09:31,280 --> 00:09:37,520 longer survive. So genetic medicine is coming alive because the tools and knowledge and 97 00:09:37,520 --> 00:09:44,719 technologies exist to design specific, safe, and effective strategies to work upstream of those 98 00:09:44,719 --> 00:09:50,559 proteins for the first time. And it's becoming a larger class of medicines, including eight 99 00:09:50,559 --> 00:09:56,719 siRNA drugs that are approved and hopefully many more that inhibit the mRNA step or the DNA step to 100 00:09:57,280 --> 00:10:00,400 really provide long-lasting, durable therapies for people. 101 00:10:01,039 --> 00:10:06,159 Yeah. I think what would be super helpful is maybe a compare-contrast. I think a lot of people 102 00:10:06,159 --> 00:10:13,119 have heard of CRISPR and the discoveries by Jennifer Doudna. How does that compare to the 103 00:10:13,119 --> 00:10:18,960 work you do, which your work is more…it's on the delivery side, but it's also on the mRNA and the 104 00:10:18,960 --> 00:10:25,039 RNA level, so the transcription layer, that middle layer of the central dogma. I think that would be 105 00:10:25,039 --> 00:10:28,559 really helpful for the audience. Right. Well, the complexity of this 106 00:10:28,559 --> 00:10:34,799 small-molecule protein-drug interaction, it's hard to find those perfectly-sized molecules 107 00:10:34,799 --> 00:10:41,440 that can fit the protein. From a drug design point of view, the intervention at this earlier stage 108 00:10:41,520 --> 00:10:46,960 is a little bit easier because we know the sequences of the mRNA or the DNA. But those 109 00:10:46,960 --> 00:10:51,760 medicines, unlike the small molecule, as I described, small molecule can go everywhere 110 00:10:51,760 --> 00:10:59,280 if it's designed properly. These RNA drugs or DNA drugs, they cannot cross cell membranes on their 111 00:10:59,280 --> 00:11:05,520 own. They are negatively-charged, large molecules that just cannot fit through the membranes of 112 00:11:05,520 --> 00:11:11,200 our cells. So, that necessitates a delivery solution, which is where we come in. So, we 113 00:11:11,200 --> 00:11:19,200 use these lipid-based spheres to encapsulate those RNA or DNA drugs. Those molecules are then 114 00:11:19,200 --> 00:11:25,440 protected from degradation and do the properties of the nanoparticle. It can mediate entry into 115 00:11:25,440 --> 00:11:31,919 our cells and release the nucleic acid, so it's functional in the cytoplasm or in the nucleus to 116 00:11:32,559 --> 00:11:38,640 tie up the earlier description of that process. So, delivery is an essential or required part 117 00:11:38,640 --> 00:11:44,080 of the development of genetic medicines. But if you get that right and you have a strategy that's 118 00:11:44,080 --> 00:11:49,840 safe—and I think today we have a safe strategy for the liver of human beings, we have a safe 119 00:11:49,840 -->

00:11:55,520 strategy for vaccines, we potentially have safe strategies for other tissues that's definitely 120 00:11:55,520 --> 00:12:01,760 emerging—and with that backdrop, you can now design the messenger RNA to make virtually any 121 00:12:01,760 --> 00:12:07,760 protein. You could design something like CRISPR that Doudna and Charpentier discovered that can 122 00:12:07,760 --> 00:12:14,320 go site-specifically into the blueprint and say, fix something that went wrong after birth. Yeah, 123 00:12:14,320 --> 00:12:18,479 it's a really exciting time, I would say. Definitely. I think you and me could probably 124 00:12:18,479 --> 00:12:23,520 go on and on about the specifics on the science, and we definitely will dive into that more later 125 00:12:23,520 --> 00:12:27,760 on in the conversation. But I'd love to maybe take a step back and circle back a little bit 126 00:12:27,760 --> 00:12:33,119 to your background. Since this podcast is the Learning from Leaders podcast, you've studied 127 00:12:33,119 --> 00:12:39,679 under some amazing leaders in the labs you've worked under at places like Eli Lilly and 128 00:12:40,640 --> 00:12:48,000 Carnegie Mellon, University of Tokyo, and MIT, and maybe most notably at MIT under Dr. Bob Langer, 129 00:12:48,000 --> 00:12:53,599 who's a very luminary in the field. Can you speak to the various mentors you've had, 130 00:12:53,599 --> 00:12:58,799 the leaders you've had in your life? Of course. I've been fortunate to be able to study with and 131 00:12:58,799 --> 00:13:04,400 learn from some remarkable leaders in my time. And I'm laughing about it right now because the 132 00:13:04,400 --> 00:13:10,000 whole thing is remarkable. I was born son of a construction worker outside of Pittsburgh and 133 00:13:10,640 --> 00:13:13,919 didn't really know much outside of my neighborhood. There were very few role 134 00:13:13,919 --> 00:13:19,840 models growing up that I could learn from and lean on. So every time I get to middle school 135 00:13:19,840 --> 00:13:24,960 or high school, it's like, hmm, that's interesting. Or, oh, I didn't know that a job existed. 136 00:13:24,960 --> 00:13:29,440 And it definitely was not until college that I realized that the job I have now as a professor 137 00:13:29,440 --> 00:13:35,919 leading a research team existed. And in the era of today with social media and communication 138 00:13:35,919 --> 00:13:41,359 improved so much, I recognize that's a pretty ridiculous comment. But going back 30 years, 139 00:13:41,359 --> 00:13:47,760 I really had no idea that these jobs really existed. So each part of it was a stepping stone 140 00:13:47,760 --> 00:13:54,799 and maybe thinking about it alive in the context of this podcast, perhaps my ignorance played a 141 00:13:54,799 --> 00:13:59,599 role in the reception that I took to learning from these different leaders that because I did not 142 00:13:59,599 --> 00:14:05,119 know, I was even more eager to understand how do they command leadership in their different 143 00:14:05,119 --> 00:14:10,320 environments and how do they manage teams. So tracing it through, I first studied with 144 00:14:10,960 --> 00:14:15,200 a Polish-American scientist named Krzysztof Matejewski, a professor at Carnegie Mellon 145 00:14:15,200 --> 00:14:20,799 University. And Krzysztof was just one of the most brilliant people I had ever met. He had such a 146 00:14:20,799 --> 00:14:26,880 command for the knowledge and the literature. And he was able to, on a piece of paper with us in the 147 00:14:26,880 --> 00:14:31,919 office, map out the beginning and the end of a project. And I would take those instructions, 148 00:14:31,919 --> 00:14:36,799 go into the laboratory and do the chemical synthesis in the hoods. And more or less, 149 00:14:36,799 --> 00:14:43,679 it all worked out. He was a very detail-oriented type of leader that functioned well being in the 150 00:14:43,679 --> 00:14:49,840 office and orchestrating the research of the lab. I spent about four months in Tokyo, where I trained 151 00:14:49,840 --> 00:14:57,599 with Kazunori Kataoka, a professor at the University of Tokyo. And Kazunori was more of a supportive 152 00:14:57,599 --> 00:15:04,479 mentor. So in Japan, there is a group-oriented thinking process that I did all of my experiments 153 00:15:04,479 --> 00:15:09,679 in Pittsburgh at Carnegie Mellon by myself. And every experiment in Japan, I did with two, three, 154 00:15:09,679 --> 00:15:15,760 four other people. It was much more of a team, group-oriented way to conduct scientific research. 155 00:15:16,400 --> 00:15:22,559 So in that backdrop, Professor Kataoka provided a role to be a supportive member of the team. So he 156 00:15:22,559 --> 00:15:28,400 would uplift and encourage the collaboration of that team and was generally complimentary. 157 00:15:29,440 --> 00:15:37,440 Bob Langer at MIT is, I think, one of history's greatest inventors, for sure. And Bob was always 158 00:15:37,440 --> 00:15:43,919 focused on solving problems. So he has a unique ability to get his lab members excited about the 159 00:15:43,919 --> 00:15:49,359 challenge of solving a problem at any cost by any method that might be necessary to solve that 160 00:15:49,359 --> 00:15:55,520 problem. And Bob led, I think, a bit more by example and motivation and setting the tone 161 00:15:55,520 --> 00:16:00,960 for the team to also collaborate together and was somehow between these different mentors. 162 00:16:02,000 --> 00:16:10,000 Awesome. And I know Bob Langer is also very focused on commercialization opportunities 163 00:16:10,000 --> 00:16:16,640 that sprout from his lab's work. And so I'd love to maybe kind of transition towards that because 164 00:16:17,599 --> 00:16:23,119 you're actually now a commercialization expert in your own right and perhaps learning from Dr. 165 00:16:23,119 --> 00:16:29,440 Langer. You've started Recode Therapeutics and also Signify Bio. So maybe if you could kind of 166

00:16:29,440 --> 00:16:34,799 connect the through line from your work in the Siegwert Lab to those commercialization opportunities, 167 00:16:34,799 --> 00:16:41,440 that would be really interesting. Yeah. Bob is one of the true out-of-the-box thinkers that I've ever 168 00:16:41,440 --> 00:16:48,080 met before. And with that type of thinking, it just feels like anything is possible. So if we 169 00:16:48,080 --> 00:16:54,719 contemplate the mission of University of Texas Southwestern Medical Center, ultimately, I have a 170 00:16:54,719 --> 00:17:00,159 job as a scientist, but this job is in the environment of hospitals and patient care. 171 00:17:00,159 --> 00:17:07,119 So some of our colleagues are doctors, nurses, administrators, running the testing, doing all 172 00:17:07,199 --> 00:17:11,199 kind of amazing things that are directly relating to the health care of that individual. 173 00:17:12,000 --> 00:17:17,839 For me, I don't see patients, but I feel a responsibility to play a role in contributing 174 00:17:17,839 --> 00:17:24,000 basic knowledge or applied science that could improve the lives of others of humanity. I know 175 00:17:24,000 --> 00:17:29,280 that's a mission, too, of the Southwestern Medical Foundation to support and enrich those activities 176 00:17:29,359 --> 00:17:36,560 to be maximized in our region. So with that backdrop, whenever we have this opportunity 177 00:17:36,560 --> 00:17:43,680 to discover something that could potentially be a new therapy, it creates the next steps 178 00:17:43,680 --> 00:17:47,439 of identifying where does that capital come from, where do the teams come from. 179 00:17:48,319 --> 00:17:54,400 And Bob Langer has always inspired us to solve big problems, not to be afraid of those big problems, 180 00:17:54,400 --> 00:17:59,439 and to feel comfortable that if man can do it, I can do it type of an attitude. 181 00:18:00,000 --> 00:18:07,119 So with Recode, the origin of this company arose because these lipid-based nanoparticles that can 182 00:18:07,119 --> 00:18:13,359 transport these genetic drugs into cells, it does look and behave like one of our natural 183 00:18:13,359 --> 00:18:18,560 nanoparticles that's in our blood right now, something called VLDL. It's a part of when we 184 00:18:18,560 --> 00:18:23,199 eat greasy hamburgers and those fat molecules get out of our stomach and are repackaged with 185 00:18:23,199 --> 00:18:28,400 cholesterols, they get into the liver for recycling and redeposition throughout the 186 00:18:28,400 --> 00:18:34,239 body where 30 to 40 percent of our cell membranes are cholesterol. So because of that similarity, 187 00:18:34,239 --> 00:18:39,280 lipid nanoparticles today are excellent at delivering genetic medicines to the liver. 188 00:18:40,000 --> 00:18:44,479 That's great news for liver diseases, and some of these are in phase 3 clinical trials now, 189 00:18:45,119 --> 00:18:49,599 but it's not so great news for diseases that exist in all different parts of the body. 190 00:18:50,560 --> 00:18:55,839 So coming back to this fundamental and applied science, when I joined UT Southwestern, 191 00:18:55,839 --> 00:19:00,719 we wrote top five challenges on the board, and number one was all nanoparticles go to the liver. 192 00:19:01,359 --> 00:19:08,400 If we could unlock that, one could make a medicine for a lung disease or disease in the pancreas or 193 00:19:08,400 --> 00:19:14,239 disease in the brain, for example. So we just thought about it, and again, going back to the 194 00:19:14,239 --> 00:19:19,439 beginning of the podcast, we did not have any ideas because nothing we had ever tested, and 195 00:19:19,439 --> 00:19:24,079 we were testing thousands of nanoparticles per year, never went anywhere else except for the 196 00:19:24,079 --> 00:19:30,719 liver. So you go back to the fundamentals and the first principles, why do nanoparticles go to the 197 00:19:30,719 --> 00:19:36,640 liver to begin with? And we spent the next five years elucidating those rules, the design rules 198 00:19:36,640 --> 00:19:42,640 of what are the factors that drive those things from a chemical perspective, engineering perspective, 199 00:19:42,640 --> 00:19:48,560 and physiology perspective. Long story short, we elucidated those rules, and now you have the 200 00:19:48,560 --> 00:19:54,239 blueprint how to break the rules. So once you know what the rules are, you can identify or come up 201 00:19:54,239 --> 00:20:00,560 with ideas of how we could break the rules. So we broke the rules by adding additional molecules 202 00:20:00,560 --> 00:20:06,160 into the nanoparticle that now help the system avoid the liver and go to other places in the body, 203 00:20:06,719 --> 00:20:12,880 including to the lungs. So now we had, for the first time, a way to deliver genetic medicines 204 00:20:12,880 --> 00:20:19,119 into the lungs and data and images that we could bring to OrbiMed in New York and show them 205 00:20:19,760 --> 00:20:24,560 that this has not been seen before, there's something unique about the attributes of this 206 00:20:24,560 --> 00:20:29,680 delivery nanoparticle that it's now in the lungs. And those are the feelings you have when you think 207 00:20:29,680 --> 00:20:34,719 this could be a new therapy for somebody, this could be a new medicine. That led to the 208 00:20:34,719 --> 00:20:41,680 formation of Recode Therapeutics, which is now using those lung-targeting lipid nanoparticles 209 00:20:41,680 --> 00:20:47,119 in two phase one clinical trials for genetic respiratory diseases. Very exciting. 210 00:20:47,119 --> 00:20:52,319 Learning from leaders, along with Southwestern Medical Foundation, is powered by generosity. 211 00:20:52,959 --> 00:20:58,640 Your support can help fuel breakthroughs in medical research, provide critical patient care, 212 00:20:58,640 --> 00:21:02,160 and support medical education. Additionally, it helps support the 213 00:21:02,160 --> 00:21:06,959 learning from leaders

podcast and content like this. Every gift matters. Consider making a 214 00:21:06,959 --> 00:21:14,560 contribution today at swmedical.org slash give. That's swmedical.org slash give. 215 00:21:15,119 --> 00:21:19,760 Could you maybe talk about just the timeline of starting that and then where Recode is now? 216 00:21:19,760 --> 00:21:23,760 It has been a decently long journey, but also incredible progress made. 217 00:21:24,319 --> 00:21:30,319 Yeah, we started originally as a transfer RNA company. And for the sake of time, I'll just 218 00:21:30,319 --> 00:21:38,000 describe that at the time of conception of Recode in 2015 was a very new idea. And today, 219 00:21:38,000 --> 00:21:43,439 there's five tRNA companies, and I just read the first clinical trial will start with a tRNA by 220 00:21:43,439 --> 00:21:49,680 a company called Alterna. But in 2015, this was a very new idea. It had never been pursued as a 221 00:21:49,680 --> 00:21:56,000 drug before. There were questions from potential investors around manufacturing and delivery of 222 00:21:56,000 --> 00:22:03,199 these relatively complex biomolecules. So that ultimately was difficult to raise capital for 223 00:22:03,760 --> 00:22:10,239 as a new drug product due to the inherent risk in doing something totally new. I think time goes on, 224 00:22:10,239 --> 00:22:15,599 technology's mature, and now it's quite possible to do that. But 10 years ago, it was quite 225 00:22:15,599 --> 00:22:21,199 challenging. And with this discovery of the organ targeted, we call it selective organ targeting 226 00:22:21,199 --> 00:22:26,479 or SORT. With the discovery of that process, it now opened up new opportunity to change the 227 00:22:26,479 --> 00:22:32,000 payload to a messenger RNA, simplify the journey of making that protein that the body's missing, 228 00:22:32,560 --> 00:22:38,479 and using the particle that can go into the lung cells safely. That set up a new opportunity, 229 00:22:38,479 --> 00:22:44,319 and investors were much more receptive to that opportunity because it was differentiated and a 230 00:22:44,319 --> 00:22:49,119 little closer to clinical trials than perhaps the first idea that we started on. 231 00:22:49,839 --> 00:22:56,000 Fantastic. Well, I think what would now be helpful is you definitely have a lot of lessons learned 232 00:22:56,000 --> 00:23:01,680 from the recode experience. And now you've recently been working on this new venture, 233 00:23:01,680 --> 00:23:08,000 SignifyBio. And I'd love to learn about how you're applying the various lessons learned, 234 00:23:08,000 --> 00:23:14,319 especially on the leadership front, and maybe touch on different technology aspects as well. 235 00:23:14,319 --> 00:23:16,319 But that would be super interesting to hear about. 236 00:23:16,959 --> 00:23:23,920 Sure. SignifyBio is attempting to create medicines for the masses, meaning genetic 237 00:23:23,920 --> 00:23:29,680 medicines were traditionally designed for rare diseases, which remains incredibly important and 238 00:23:29,680 --> 00:23:36,160 requires funding and potentially some private funding and philanthropy funding to ensure that 239 00:23:36,160 --> 00:23:41,680 everyone in America who has a disease and an applicable drug can receive it. In other words, 240 00:23:41,680 --> 00:23:45,199 some of these medicines are quite expensive due to the small patient numbers. 241 00:23:46,400 --> 00:23:51,839 But it occurred to us that we may be able to take a similar platform and create medicines that could 242 00:23:51,839 --> 00:23:59,040 tackle chronic kidney disease or inflammatory diseases or cancers in new ways that are top 243 00:23:59,040 --> 00:24:06,239 killers of Americans. And the general idea was during COVID-19, we went to the pharmacy and 244 00:24:06,239 --> 00:24:11,920 received an intramuscular injection. And this strategy then produces a tiny amount of a spike 245 00:24:11,920 --> 00:24:17,839 protein that's benign, but it does train our bodies to look for something that resembles that 246 00:24:17,839 --> 00:24:24,560 in the future, like a real viral infection. But with this pathway, and similarly, there are now 247 00:24:24,560 --> 00:24:30,800 weight loss drugs and obesity drugs called GLP-1, and those are administered subcutaneously. 248 00:24:30,800 --> 00:24:35,359 So we were thinking, you know, for complex biologics, a third of all new drugs, 249 00:24:36,239 --> 00:24:41,359 what if we just create those medicines directly in the human body? This could save a tremendous 250 00:24:41,359 --> 00:24:47,520 cost because instead of making that protein drug in expensive bioreactors, you can make it directly 251 00:24:47,520 --> 00:24:53,439 a customized version in your own body and would not have to ship it around either would be 252 00:24:53,439 --> 00:24:58,719 made directly in the body. So what's missing there, though, is in the context of these other 253 00:24:58,719 --> 00:25:04,800 examples where one is delivering genetic medicines to the liver. Those medicines stay in the liver, 254 00:25:04,800 --> 00:25:11,280 and that's a natural process by which this biology works out. But here, with the concept of Signify, 255 00:25:11,280 --> 00:25:16,239 we needed a medicine that would leave the subcutaneous fat cells or leave the muscle 256 00:25:16,239 --> 00:25:21,920 or leave the liver and be able to find and treat that disease wherever it might be, like in the 257 00:25:21,920 --> 00:25:29,599 kidneys. So that comes back to basic biology, and nature has already solved this problem. 258 00:25:30,479 --> 00:25:37,280 Nature uses something called a signal peptide, which is like a zip code of instructions incorporated 259 00:25:37,280 --> 00:25:42,880 into those genes, and those instructions will tell the cell to act on the information that it's 260 00:25:42,880 --> 00:25:49,520 received and send that foreign protein out into the bloodstream or to the cell membrane or to the 261 00:25:49,520 -->

00:25:56,719 nucleus in the middle or to the mitochondria or paroxysm. And this also is just coming back, 262 00:25:56,719 --> 00:26:03,040 again, I just love ideas so much. When I worked with Bob Langer at MIT, we were doing these 263 00:26:03,040 --> 00:26:08,479 siRNA gene silencing experiments, which I know is complicated, but basically it's a 264 00:26:08,479 --> 00:26:15,920 lipid fat ball with a RNA molecule that can go into the liver of animals, break the messenger RNA, 265 00:26:15,920 --> 00:26:22,239 and now the protein is no longer made. So I could take a little bit of blood from the 266 00:26:22,239 --> 00:26:27,599 cheek of the animal and measure that protein concentration, which as a chemist was quite 267 00:26:27,599 --> 00:26:32,719 weird because all the action had taken place in the liver, but here I was able to take a little 268 00:26:32,719 --> 00:26:38,000 bit of blood from the cheek and measure the concentration of that protein. So moving to 269 00:26:38,000 --> 00:26:42,479 Dallas, we studied how is that possible, and that's because of these signal peptides. 270 00:26:43,359 --> 00:26:48,400 And we started to compare and contrast and cut and paste all of the different signal peptides 271 00:26:48,400 --> 00:26:56,719 to develop a modular platform by which we can now use a safe strategy to administer subcutaneously 272 00:26:56,719 --> 00:27:03,839 just like a GLP-1 drug, and the body will produce and make that protein for months with a single 273 00:27:03,839 --> 00:27:09,280 administration. I think this could be life-changing for folks who have things like chronic kidney 274 00:27:09,280 --> 00:27:14,000 disease or enzyme replacement therapy where they're going to the outpatient clinic a couple 275 00:27:14,000 --> 00:27:20,479 of times a week. We imagine in time, if this is successfully translated by Signify and brought 276 00:27:20,479 --> 00:27:25,599 into the marketplace, this could be a therapy that is much more patient compliant, more effective, 277 00:27:26,319 --> 00:27:31,920 safer, lasting longer, and hopefully cheaper. And I think it's really important to highlight 278 00:27:31,920 --> 00:27:37,839 this technology could be applied to a huge array of diseases, which is another incredibly 279 00:27:38,479 --> 00:27:45,599 promising aspect of it. And obviously, you have to pick a certain subset of that to focus on first, 280 00:27:45,599 --> 00:27:49,439 but if all goes well, there could be a lot of applications of this. 281 00:27:51,359 --> 00:27:57,599 Yet one other area I would love to explore next is kind of furthering this conversation 282 00:27:57,599 --> 00:28:04,479 on Recode and Signify, collaborations with different investors, as you've alluded to 283 00:28:04,479 --> 00:28:10,719 with OrbaMed, but also industry and biopharma collaborators is really important. The Siegwert 284 00:28:10,719 --> 00:28:16,719 Lab directly has a collaboration on the research side with Pfizer, and then your companies have 285

00:28:16,719 --> 00:28:23,280 various relationships with Pfizer and Eli Lilly and Danaher and others. Could you touch on 286 00:28:23,280 --> 00:28:27,599 these relationships and how important this spectrum of collaboration is? 287 00:28:27,599 --> 00:28:33,199 Yeah. For the listeners, a faculty position at a medical school like the one that I have 288 00:28:34,160 --> 00:28:40,640 is a job akin to running a small business. I'm responsible to raise all the money, 289 00:28:40,640 --> 00:28:46,000 hire the staff, direct the staff, write the findings up so we can share it with the public, 290 00:28:47,359 --> 00:28:53,439 potentially work on patents and teach classes, grade exams, and the list goes on and on. 291 00:28:53,439 --> 00:28:58,319 But the fundraising component of it is really critical. That's where these scientists are hired 292 00:28:59,119 --> 00:29:06,160 to work on these projects to begin with. I find that it's just valuable to work with as many 293 00:29:06,160 --> 00:29:11,839 partners as possible on this journey. There may be partners like the American Cancer Society that 294 00:29:11,839 --> 00:29:17,520 has a core interest in cancer research. As a laboratory, we can partner with them through 295 00:29:17,520 --> 00:29:23,359 granting and volunteer opportunities to advance the mission of the American Cancer Society. 296 00:29:24,000 --> 00:29:29,199 I think other projects are more translational in nature. There, we could partner with a company 297 00:29:29,199 --> 00:29:35,680 like Pfizer to both educate our scientists, facilitate their degree and growth process, 298 00:29:35,680 --> 00:29:42,160 while at the same time enabling them to work on something that they feel confident is a real-world 299 00:29:42,160 --> 00:29:49,280 valuable problem to be solved. That's really helped us over the years and has introduced us 300 00:29:49,280 --> 00:29:53,839 to a number of great leaders like Mr. Juno Kwon, who you mentioned at Actium Group. 301 00:29:54,640 --> 00:30:00,880 My business partner for the last 10 years is Ari Session, born in South Dallas. One of the things 302 00:30:00,880 --> 00:30:08,319 I like talking about with Ari as a leader is he always has a plan A and a plan A prime and a plan 303 00:30:08,319 --> 00:30:14,800 A double prime. In other words, he can orient the team to focus on mission A as the most important 304 00:30:14,800 --> 00:30:20,319 thing that we've ever seen and ever worked on in our life. It's just so imperative that we solve 305 00:30:20,319 --> 00:30:26,400 this particular mission and this goal of the company. Most of the time, you get there, but 306 00:30:26,400 --> 00:30:32,160 sometimes something happens, and that thing that happens could be out of your control. It could be 307 00:30:32,160 --> 00:30:37,199 economic pressures. It could be something just didn't work the way that it worked in the earlier 308 00:30:37,199 --> 00:30:43,680 studies scientifically. In that case, there is a point where you

have to say that this is not going 309 00:30:43,680 --> 00:30:50,959 to work. Ari has a beautiful ability to immediately pivot to plan A prime with the same passion that 310 00:30:50,959 --> 00:30:58,160 he had about plan A, meaning as somebody who works for him or with him, you have this feeling like 311 00:30:58,160 --> 00:31:05,199 we're always guiding towards a common goal. Most of the time, you make it, but in the rare or 312 00:31:05,199 --> 00:31:10,800 sometimes frequent instances where it's just not possible, Ari is equally excited, if not more 313 00:31:10,800 --> 00:31:15,760 excited, about plan A prime and describes it in a way that that was the plan all along. 314 00:31:16,400 --> 00:31:22,880 So I think I definitely have integrated those sort of things into my own management style of 315 00:31:22,880 --> 00:31:28,479 my team, which is composed of graduate students, postdoctoral fellows, research technicians, and 316 00:31:28,479 --> 00:31:34,800 faculty staff now, where every person should be clear what their goal and mission is, what their 317 00:31:34,800 --> 00:31:40,000 project is, what the paper is going to look like that they hope to write, and we're going 100 miles 318 00:31:40,000 --> 00:31:46,160 per hour towards that goal. But sometimes nature introduces curveballs. Sometimes there's new ideas 319 00:31:46,160 --> 00:31:51,680 that arise that allow us to just drive the car a little bit into a different direction, and that 320 00:31:51,680 --> 00:31:58,239 new direction might be more exciting than the first direction. I have a lot of joy of being on 321 00:31:58,239 --> 00:32:02,959 that journey with everybody that I get to work with, and keep in mind these lessons that we've 322 00:32:02,959 --> 00:32:08,000 learned about having individuals feel excited and empowered by the work that they're doing, 323 00:32:08,640 --> 00:32:12,800 goal-oriented for sure, but also open to change when necessary. 324 00:32:12,800 --> 00:32:18,800 Right. I think that combination of extremely mission and goal-driven, but with really strong 325 00:32:18,800 --> 00:32:24,959 adaptability is something that we all can take away, even if we're not scientists in all aspects 326 00:32:24,959 --> 00:32:31,040 of our lives. So thanks for sharing that. I'd like to maybe double down a bit on something 327 00:32:31,040 --> 00:32:38,640 you mentioned. Your CEO of Signify and business partner RA Session is from Dallas, and you might 328 00:32:38,640 --> 00:32:45,920 be a Dallas import, but we now call you a proud Dallasite, and Signify is headquartered here. 329 00:32:46,959 --> 00:32:54,000 Can you touch on the importance of trying to be a leader in the biotech field in our great city? 330 00:32:54,640 --> 00:32:59,119 Yeah, we were chatting a little bit earlier that I'm a huge Pittsburgh Steelers fan. I grew up in 331 00:32:59,199 --> 00:33:07,760 the Pittsburgh area. I was born in McGee Women's Hospital, which is right next to the University of 332 00:33:07,760 --> 00:33:12,000 Pittsburgh, and that's the hospital where they're putting the terrible towels on the babies as they 333 00:33:12,000 --> 00:33:18,800 come out of the womb, and just because you're anointed at the first day of your birth, you're 334 00:33:18,800 --> 00:33:24,640 stuck with that team, or you get the joy of participating on that team in a sense as a viewer. 335 00:33:25,599 --> 00:33:31,280 So I've really enjoyed learning from Mike Tomlin as a football coach. I think a tremendous leader 336 00:33:31,280 --> 00:33:36,560 who is able to simultaneously be a player's coach, wherein he can directly relate to the 337 00:33:36,560 --> 00:33:41,599 individuals who are on his team. At the same time, it's clear where the lines are. It's clear that 338 00:33:41,599 --> 00:33:47,760 he's the boss, and there are certain expectations that he preaches to his team that are the minimum 339 00:33:47,760 --> 00:33:53,760 and expected things that people should be doing, including like being better next year than you 340 00:33:53,760 --> 00:33:58,719 were this current year, and using the off-season to work on something that you know could be 341 00:33:58,719 --> 00:34:03,199 improved. So each year, you're better and better at your particular craft, and the same thing is 342 00:34:03,199 --> 00:34:09,439 true in science, where I challenge our team to identify areas for improvement and consciously 343 00:34:09,439 --> 00:34:14,399 work on that. So each year, we are a better scientist and better equipped to be successful 344 00:34:14,399 --> 00:34:20,879 in life. So Pittsburgh now has homegrown coach Mike McCarthy from Greenfield. We're also really 345 00:34:20,879 --> 00:34:28,239 excited about that. But anyway, back to Dallas. You know, I do really love Dallas. I love living 346 00:34:28,239 --> 00:34:34,639 here. I'm staying here, and it's really valuable to contribute things that could benefit our 347 00:34:34,639 --> 00:34:40,239 community and beyond. So I'm proud that Recode started here. I'm proud that Signify is here, 348 00:34:40,800 --> 00:34:48,159 is using lab space facilitated by the biotech ecosystem that's burgeoning in Dallas, and 349 00:34:48,159 --> 00:34:52,800 definitely feel a commitment and responsibility, just as those coaches do that I've described, 350 00:34:53,439 --> 00:34:58,879 to make this place and our community better and better. Yeah, and could you maybe touch on maybe 351 00:34:58,879 --> 00:35:05,199 the strengths and areas for improvement in the Dallas biotech ecosystem? I think it's probably 352 00:35:05,199 --> 00:35:11,840 not known for that, but there are some really promising momentum going on, so I'd love to 353 00:35:11,840 --> 00:35:17,520 hear about that. Yeah, I think I feel our community has solved some of the difficult-to-solve 354 00:35:17,520 --> 00:35:22,560 challenges, meaning where does the science come from? There are great universities and great 355 00:35:22,560 --> 00:35:28,080 scientists

here, great entrepreneurs here. Where does the infrastructure come from that takes 356 00:35:28,080 --> 00:35:34,320 capital? There's now infrastructure, lab space to rent. Where does the community come from that's 357 00:35:34,320 --> 00:35:41,600 also coming along, where there are commonly held events periodically to interface with other 358 00:35:42,560 --> 00:35:47,919 mid-business leaders in the community? Really, an area for improvement that I hope will happen 359 00:35:47,919 --> 00:35:53,040 over time is there can be a series of success stories or a continuation of success stories here, 360 00:35:53,679 --> 00:36:00,639 and then those individuals can become the C-suite leaders that catalyze and build and grow the next 361 00:36:00,639 --> 00:36:06,159 sets of companies. So we can further the momentum of what we've already been able to build in the 362 00:36:06,159 --> 00:36:11,760 Dallas-Fort Worth metropolitan area by facilitating those leaders to continue this 363 00:36:11,760 --> 00:36:16,800 pipeline and bring along the next sets of leaders in their series of companies. 364 00:36:16,800 --> 00:36:20,159 And very likely, a lot of those leaders might come from your lab. 365 00:36:21,760 --> 00:36:22,879 I hope so. 366 00:36:22,879 --> 00:36:28,399 I thought it might also be helpful to highlight R.A. Session. He formerly worked for Riata 367 00:36:28,399 --> 00:36:34,399 Pharmaceuticals in Dallas, and he's had a variety of really esteemed roles at other companies as 368 00:36:34,399 --> 00:36:40,399 well. But R.A. is a shining example of that continuation of success, and I'm sure he's going 369 00:36:40,399 --> 00:36:50,320 to create even more from the people he trains and leads. And speaking on that leadership front, 370 00:36:50,320 --> 00:36:56,159 maybe let's circle back to some of your leadership techniques, leadership 371 00:36:57,679 --> 00:37:03,679 skills that you try to utilize in your own lab, and how do you try to mentor those under you? 372 00:37:04,639 --> 00:37:11,360 Yeah, I think to put in perspective for the audience, scientists do like, 373 00:37:12,239 --> 00:37:16,080 you know, they're characterized as being introverted. We're not always introverted. I'm 374 00:37:16,080 --> 00:37:20,959 sort of a secret extrovert, meaning I'm not super talkative, but I'm internally a very 375 00:37:21,520 --> 00:37:28,080 confident extrovert personality type. And so the stereotypes are not always correct, 376 00:37:28,080 --> 00:37:33,600 but nevertheless, we do have to put in a lot of time to solve scientific problems. This is a 377 00:37:34,159 --> 00:37:41,840 area of work, so to speak, that just takes time. So long hours and work is a necessary aspect of 378 00:37:41,840 --> 00:37:48,719 the kind of things that we do. So consequently, most of our training is in designing and executing 379 00:37:48,719 --> 00:37:56,159 experiments, not really in leading others or writing grants or leading teams. And consequently, 380 00:37:56,159

--> 00:38:01,280 that typically happens on the job, unless there's a mentor like Bob Langer, who talked about 381 00:38:01,280 --> 00:38:06,399 leadership and strategy all the time. And I certainly do, hopefully with my own team, 382 00:38:06,399 --> 00:38:11,840 talk early and often about facilitating those skills that'll be useful after you've left the 383 00:38:11,840 --> 00:38:18,080 research lab or gone beyond the research lab environment. So from that backdrop, what was 384 00:38:18,080 --> 00:38:25,360 really fun but daunting was moving from MIT in Boston to UT Southwestern, getting the keys to 385 00:38:25,360 --> 00:38:32,639 a new lab, and that lab is completely empty. So it's very exciting, very daunting, and it was 386 00:38:32,639 --> 00:38:35,919 some of the best years of my life, for sure, because you start thinking, 387 00:38:36,639 --> 00:38:41,520 okay, what are we going to work on? What are the skill sets necessary to be successful in that 388 00:38:41,520 --> 00:38:48,479 area? How will I behave as a leader? And personally, which could be helpful to viewers, 389 00:38:48,479 --> 00:38:54,959 I went back through my own history from growing up in that small region just outside of Pittsburgh 390 00:38:55,120 --> 00:39:02,479 to working with Chris Matyszewski and Kazunori Kataoka and Bob Langer and other people to decide 391 00:39:02,479 --> 00:39:09,360 what from those individuals was effective. They're all effective, but very different. What are aspects 392 00:39:09,360 --> 00:39:14,639 of their personalities and their leadership skills that I could integrate into my own leadership 393 00:39:14,639 --> 00:39:21,120 skill? Meaning I may or may not have the same personality attributes as those leaders, and I 394 00:39:21,120 --> 00:39:25,919 need to determine and develop a leadership style that takes advantage of my own natural 395 00:39:25,919 --> 00:39:31,199 inclinations and abilities while still leveraging specific strategies from those individuals. 396 00:39:32,000 --> 00:39:37,840 So I ultimately decided that I wanted to be detail-oriented to a degree like Professor 397 00:39:37,840 --> 00:39:42,399 Matyszewski, meaning I want everybody in our lab to know what they're working on, 398 00:39:42,399 --> 00:39:48,159 know what the near-term goal is and the long-term goal is so no one feels lost and has a mission 399 00:39:48,159 --> 00:39:55,679 critical roadmap in front of them. At the same time, what I really enjoyed in the Langer Lab 400 00:39:55,679 --> 00:40:01,360 was this element of freedom and creativity that I felt free to not only be successful, 401 00:40:01,360 --> 00:40:07,520 but also to make mistakes and learn from mistakes. This was an environment where if you have the idea 402 00:40:07,520 --> 00:40:13,439 and you are outgoing enough and adventurous enough to solve your own problems, get expertise where 403 00:40:13,439 --> 00:40:18,639 you need it and put the whole thing together, wow, it was a playground and dream come true for 404 00:40:18,639 --> 00:40:25,439 me to work there. So for me, I'm trying to somehow be in between these different experiences to 405 00:40:25,439 --> 00:40:31,280 leverage my own natural abilities as a communicator where there's enough detail that lab members 406 00:40:31,280 --> 00:40:36,560 hopefully feel that they always know where their project is going, but still enough support and 407 00:40:36,560 --> 00:40:41,919 freedom so they don't feel the pressure of, I cannot make a mistake. Because once in a while, 408 00:40:41,919 --> 00:40:47,840 those mistakes are good things and those accidents become discoveries and those discoveries become 409 00:40:47,840 --> 00:40:51,520 something that could be really valuable in drug development someday later on. 410 00:40:52,320 --> 00:40:56,959 Fantastic. And maybe a specific example on this front might be interesting. 411 00:40:58,639 --> 00:41:04,879 There's actually a third co-founder of Signify Bio, Dr. Lucas Farbiak, who's one of your mentees. 412 00:41:04,879 --> 00:41:10,479 And could you maybe touch on that relationship and maybe also touch on kind of how you think 413 00:41:10,479 --> 00:41:14,959 about identifying talent like Lucas and mentoring and nurturing them? 414 00:41:15,760 --> 00:41:21,840 Well, first on the talent front, nowadays I use my gut. I've made a couple of mistakes 415 00:41:21,840 --> 00:41:27,280 in hiring over the years. And those were cases where I did rely on reference letters and phone 416 00:41:27,280 --> 00:41:34,159 calls with former advisors to make decisions that were technically accurate, but did not feel right 417 00:41:34,159 --> 00:41:39,840 in the gut. So not everybody may have that gut sense in the world, but for me, 418 00:41:39,840 --> 00:41:45,919 the gut is the most powerful way to make hires. So I'm generally looking for people, whether it's 419 00:41:45,919 --> 00:41:51,520 in the research laboratory, new students, or even in the business community or philanthropy community, 420 00:41:51,520 --> 00:41:56,719 just individuals who have passion that's palpable. So in their eyes, in their body language, 421 00:41:57,439 --> 00:42:02,000 in the excitement that they convey in describing what it is they've done and what it is they want 422 00:42:02,000 --> 00:42:07,360 to do in the future, you just feel it. And those are the individuals that I enjoy working with. 423 00:42:08,560 --> 00:42:12,719 As Mike Tomlin says, the volunteers, not the hostages, that the people who 424 00:42:12,719 --> 00:42:17,280 are excited about living life and being here on earth and doing good things in the world. 425 00:42:17,280 --> 00:42:22,159 If you can put together a team of those personality types, as Bob Langer does really 426 00:42:22,159 --> 00:42:28,000 successfully, the rest of it starts to fill in the gaps, I think, to a large degree. 427 00:42:28,959 --> 00:42:34,479 Yeah, so that's a bit on the hiring side. And then in Bob Langer Lab, there were so

many life 428 00:42:34,479 --> 00:42:39,840 lessons that I did not even appreciate at the time, that as I start reflecting on the four 429 00:42:39,840 --> 00:42:47,360 years that I spent in Langer Lab, these things start to become more coherent. And one of these 430 00:42:47,360 --> 00:42:53,679 is that Bob Langer is not necessarily appointing people in different positions. Bob Langer is 431 00:42:53,679 --> 00:42:59,360 creating the opportunities for people to themselves jump into. And that was not apparent 432 00:42:59,360 --> 00:43:06,399 when I was working there. It seemed like people were going off and doing this or having this job 433 00:43:06,399 --> 00:43:12,719 or this opportunity or this project by direction or fate or other reasons. But now I think what 434 00:43:12,719 --> 00:43:18,000 potentially is happening there is Bob is creating the open doors, and then the individuals themselves 435 00:43:18,080 --> 00:43:24,879 are going into those open doors. So what I mean to say is that I hope that I'm in the future 436 00:43:24,879 --> 00:43:30,800 and in the present able to create open doors for people to get excited about and potentially jump 437 00:43:30,800 --> 00:43:36,479 into. So those doors look different for different people. Some faculty members themselves now and 438 00:43:36,479 --> 00:43:42,000 publishing high impact papers, and I'm so proud and happy for them. Others are working in industry, 439 00:43:42,000 --> 00:43:47,280 they're industry leaders, I'm so proud and happy for them too. And others were able to retain 440 00:43:47,280 --> 00:43:54,000 locally, whereby Dr. Farbiak Lucas, for example, is somebody who I just work well together with, 441 00:43:54,000 --> 00:44:01,040 we get along inside and outside of the lab, we see eye to eye on problem solving. We rarely if 442 00:44:01,040 --> 00:44:06,159 ever have disagreements about things. And that means that, you know, he was able to jump into 443 00:44:06,159 --> 00:44:12,239 an open door with his ambition and skills to become an assistant professor at UT Southwestern 444 00:44:12,239 --> 00:44:18,399 too. And so we work together with Samantha Chatterjee and Pratima Basak and Jing Liu to 445 00:44:18,399 --> 00:44:26,800 collectively manage now a lab of about 40 people. Yeah, such the scale you've grown it to is amazing, 446 00:44:26,800 --> 00:44:34,479 and both on your individual lab and then the companies outside. And I think what some listeners 447 00:44:34,479 --> 00:44:40,719 might not grasp as well as a lot of the foundation, especially on the commercialization side, 448 00:44:40,719 --> 00:44:46,800 comes from IP and your ability to protect the discoveries that you make in order 449 00:44:46,800 --> 00:44:52,399 for them to get into the hands of patients eventually. Could you maybe touch on how do 450 00:44:52,399 --> 00:44:59,840 you know when to create IP and how that factors into your decision making for your lab? 451 00:45:00,560 --> 00:45:05,760 Yeah, intellectual property is important across all industries from, you know, any product that's 452 00:45:05,760 --> 00:45:12,879 sold ideally has some intellectual property behind it to enable that company to take in the 453 00:45:12,879 --> 00:45:18,479 capital required to build the business to begin with and then justify the capital that comes back, 454 00:45:18,479 --> 00:45:23,760 the revenue that comes back into the company. So same is true in the biotechnology space. 455 00:45:24,639 --> 00:45:31,280 I'm an employee of the University of Texas, proud to be that, and we have a fantastic technology 456 00:45:31,280 --> 00:45:36,479 development office that does have enough resources to facilitate patents that are owned 457 00:45:36,479 --> 00:45:42,479 by the University of Texas. And those patents can then be licensed out into potential startup 458 00:45:42,479 --> 00:45:48,560 companies or even existing companies for further development. So securing those patents is really 459 00:45:48,560 --> 00:45:55,360 critical for attracting the massive investment that's needed in drug development. It may cost 460 00:45:55,439 --> 00:46:01,199 $500 million to $2 billion just to develop a drug, and that's partly where the eventual cost 461 00:46:01,199 --> 00:46:07,360 of those medicines comes from. Not to mention they usually fail, and so you really have to 462 00:46:07,360 --> 00:46:13,840 invest in multiple drugs to find one that will be successful for that business. But definitely 463 00:46:13,840 --> 00:46:18,800 intellectual property is a key part of that and something that we work hand-in-hand on with the 464 00:46:18,800 --> 00:46:25,120 university. Bob Langer was always looking for four things in making the decision to start a 465 00:46:25,120 --> 00:46:30,560 new company, and one of those four things was intellectual property, that ideally the university 466 00:46:30,560 --> 00:46:35,679 or company has what can be called a blocking patent, something that's broad and protective 467 00:46:35,679 --> 00:46:41,520 enough in the United States and across the world to prevent larger companies from simply copying 468 00:46:41,520 --> 00:46:46,560 that idea and bringing it faster into the marketplace through their higher resources, 469 00:46:46,560 --> 00:46:52,639 greater resources. Second was a big paper. Scientists do all this work in the basement, 470 00:46:52,639 --> 00:46:57,600 and eventually we want to share it with the world. So we write this down to the best of our abilities 471 00:46:57,600 --> 00:47:04,800 and we publish this in scientific journals, which are like newspapers, and our peers review those 472 00:47:04,800 --> 00:47:10,560 articles to ensure the accuracy. And having a big paper, which means something that is in a 473 00:47:10,560 --> 00:47:15,199 glamour journal like Science or Nature, you know, the equivalent of Dallas Morning News or New York 474 00:47:15,199 --> 00:47:21,120 Times, something that everybody knows, helps to create

validation of that achievement as being 475 00:47:21,120 --> 00:47:27,760 reviewed and vetted and published in the best publications in the world. The third is typically 476 00:47:27,760 --> 00:47:32,719 a platform technology, something that would not be a single drug that the university could license, 477 00:47:32,719 --> 00:47:39,679 but something that could be a family of medicines or a general platform strategy to create tools or 478 00:47:39,679 --> 00:47:46,239 technologies or assays or medicines in a broad set of indications. And the third is a business 479 00:47:46,239 --> 00:47:51,679 expertise that there are things that I certainly have learned and I'm capable of achieving in the 480 00:47:51,679 --> 00:47:56,639 scientific realm driven by ideas and curiosity, but there are plenty of things in the business 481 00:47:56,639 --> 00:48:03,040 world that are outside of my training and my expertise. And having a talented business partner 482 00:48:03,040 --> 00:48:09,520 with social intelligence and execution ability is that fourth piece of the puzzle that allowed Bob to 483 00:48:09,520 --> 00:48:16,239 start dozens of companies over the years. Awesome. And once you have that, those four components and 484 00:48:16,239 --> 00:48:24,159 the key IP and business leaders and publications, a lot can happen. And we've been touching on 485 00:48:24,159 --> 00:48:30,800 your companies a lot already and some of the technical aspects and the technologies you're 486 00:48:30,800 --> 00:48:36,719 bringing forward. Maybe let's look into the future. If all goes great, what could be the 487 00:48:37,679 --> 00:48:43,439 the end impact of all the work that your lab has done maybe in five to 10 years in the future? 488 00:48:44,159 --> 00:48:50,399 You know, as a personality type, to bring in the sports analogies, you know, these athletes are 489 00:48:50,399 --> 00:48:55,520 training to win something like the Super Bowl or the World Series. And what's remarkable as a 490 00:48:55,520 --> 00:49:03,040 spectator is you see these individuals achieve that goal. Once a year, one team achieves that goal. 491 00:49:03,040 --> 00:49:07,600 And yet, the next year, they're equally passionate about doing it again. And 492 00:49:07,600 --> 00:49:12,320 psychologically, you're thinking why and how you've already won. Like, why and how are you 493 00:49:12,959 --> 00:49:20,399 equally determined and irrationally passionate about doing it again? So, as an analogy, I think 494 00:49:20,399 --> 00:49:27,280 many scientists, myself included, just love working. We just love ideas. It's a part of our identity 495 00:49:28,080 --> 00:49:34,080 as a human. And once we publish a paper, I've already moved on to the next project and the next 496 00:49:34,639 --> 00:49:41,679 idea. And sharing that type of, you know, thinking process with my own students as a leader is 497 00:49:41,679 --> 00:49:47,280 something that I enjoy doing because it helps us to be excellent at the tasks that we're doing right 498 00:49:47,280 --> 00:49:53,919 now, but also equally excited about the next task that could come down the line. So, Signify is an 499 00:49:53,919 --> 00:49:58,719 example of something that, you know, the company is doing all the work. I'm just there as an 500 00:49:58,719 --> 00:50:03,600 advisor and cheerleader. But I'm really excited about that from the perspective that that could 501 00:50:03,600 --> 00:50:08,719 be a way to deploy a lot of medicines that would be applicable to treat tens of millions of 502 00:50:08,719 --> 00:50:16,560 Americans on a significant basis. And that is very gratifying and exciting. I think looking forward, 503 00:50:17,120 --> 00:50:21,439 there is an emergence of these tools and technologies called genome editors, 504 00:50:21,439 --> 00:50:25,360 which, again, for the lay audience is simultaneously exciting and scary. That's 505 00:50:25,360 --> 00:50:31,199 the reality of it, that genome editing has the potential to take a child like baby KJ, 506 00:50:31,199 --> 00:50:36,959 who was born in Philadelphia with a rare genetic disease and would have died. And because of genome 507 00:50:36,959 --> 00:50:44,560 editing, that baby is alive and healthy today. The DNA that he was born with leads to a genetic 508 00:50:44,560 --> 00:50:50,080 disease that would be terminal, but yet you can use these molecular scissors to fix the DNA at an 509 00:50:50,080 --> 00:50:56,879 early age. Now, probably for the lifetime of that boy, he will be normal. So, that's an incredible, 510 00:50:56,879 --> 00:51:01,840 you know, power and privilege and responsibility to bear for scientists and doctors who are part 511 00:51:01,840 --> 00:51:08,399 of those journeys. You know, at the same time, one can use genome editing to eradicate mosquitoes, 512 00:51:08,399 --> 00:51:15,360 and that might be a good idea that might lead to a real destruction of some aspects of the ecosystem 513 00:51:15,360 --> 00:51:20,719 that would be difficult to repair if that happens. And so, I enjoy talking about it because 514 00:51:20,719 --> 00:51:26,080 I can convey to the audience that scientists are thinking both scientifically and ethically about 515 00:51:26,080 --> 00:51:31,840 very complex problems like this and trying to decide, do the potential positives outweigh the 516 00:51:31,840 --> 00:51:37,439 potential negatives of something like genome editing? And where we often come to is that in 517 00:51:37,439 --> 00:51:43,760 some cases, just like all doctors have to do with their patients, the benefits outweigh the 518 00:51:43,760 --> 00:51:50,639 potential concerns or challenges of a potential procedure or drug. I think similarly, there are 519 00:51:50,639 --> 00:51:56,080 settings like baby KJ where this could be a life-saving medicine, and there are settings like, 520 00:51:56,080 --> 00:52:01,840 for example, what's called germline editing, where I think we're just not comfortable with pursuing 521 00:52:01,840 -->

00:52:06,479 something like that at this time. We don't know enough to confirm that's safe, and there's plenty 522 00:52:06,479 --> 00:52:12,479 of ethical reasons why that's not a good idea. So, you know, just to convey that's not something 523 00:52:12,479 --> 00:52:20,800 that I'm working on or interested in, but at the same time, for adults who can make decisions or 524 00:52:20,800 --> 00:52:27,439 these rare conditions like baby KJ, which was in the newspaper, I think you can stack up enough of 525 00:52:27,439 --> 00:52:30,639 the positives to feel comfortable with such an approach. 526 00:52:30,639 --> 00:52:38,959 Right. That makes a lot of sense. It's a very complex area of science and innovation, but there are strategies to 527 00:52:38,959 --> 00:52:44,479 mitigate the downsides and really take advantage of the revolutionary aspects of these medicines. 528 00:52:46,719 --> 00:52:52,399 One other question, a little bit of a change of subject, but it circles back to a lot of the 529 00:52:53,199 --> 00:53:03,199 technological techniques that you use in your lab and at your companies. It's overused topic, but 530 00:53:03,199 --> 00:53:11,600 it is a very hot topic. AI is on everyone's mind right now. Actually, you are a practitioner of AI 531 00:53:11,600 --> 00:53:15,919 in a lot of ways, and you have been for a long time. So, could you touch on that 532 00:53:15,919 --> 00:53:20,959 and how that's really unlocked some of the technological advancements in your lab? 533 00:53:22,000 --> 00:53:27,919 One way to frame it is that each day we have a certain number of hours that we're awake and not 534 00:53:27,919 --> 00:53:34,239 sleeping, and I'm trying to maximize as much into those hours as possible, both inside and 535 00:53:34,239 --> 00:53:39,439 outside of the lab, meaning I really do love being physically in the lab and doing that work. I also 536 00:53:39,439 --> 00:53:45,280 love being at home with my family, and I'm aiming to maximize every second of every day to the best 537 00:53:45,280 --> 00:53:52,000 of my abilities. So, in science research, if you could do one experiment per day, that's 538 00:53:52,000 --> 00:53:57,600 pretty good. My PhD advisor asked me to make one polymer per day, and the next day you characterize, 539 00:53:57,840 --> 00:54:02,879 study everything about that polymer, and you make another one. And in one year, if you didn't take 540 00:54:02,879 --> 00:54:09,120 any days off, you would have 365 polymers. Now, if you could accelerate that by making 541 00:54:09,120 --> 00:54:15,840 300 polymers per day, you would have a lot more potential for making a discovery and learning 542 00:54:15,840 --> 00:54:21,600 about the world or learning about problem solving. So, already my lab has integrated 543 00:54:21,600 --> 00:54:27,439 what are called fluid handling robotics, so automated robots that can do experiments with 544 00:54:27,439 --> 00:54:33,199 us in our direction at a scale of hundreds per day, potentially, or thousands per week. And that 545 00:54:33,199 --> 00:54:39,199 definitely accelerates the timeline from having an idea to making a discovery that's useful for 546 00:54:39,199 --> 00:54:46,320 humanity. Similarly, I think AI has the potential and is already starting to help in that capacity 547 00:54:46,320 --> 00:54:52,639 by doing things like troubleshooting ideas and helping us to find better paths to solving that 548 00:54:52,639 --> 00:54:59,199 problem, or doing things in silico that we would normally do thousands of in the laboratory. For 549 00:54:59,199 --> 00:55:05,520 example, if we can narrow down in silico the investigation paths that the project might take, 550 00:55:05,520 --> 00:55:09,439 that definitely is going to save time and maybe open up new doors for discovery. 551 00:55:10,159 --> 00:55:16,560 So, a big effort over the last couple of years has been integrating wet lab data into systems 552 00:55:16,560 --> 00:55:22,879 that could be then prosecuted by these AI models, such that we could take our wet lab data and use 553 00:55:22,879 --> 00:55:28,560 AI to potentially look for things that the human brain is just not able to see because of the 554 00:55:28,560 --> 00:55:33,520 training that we've had in textbooks and life experiences. There may be things that AI can 555 00:55:34,159 --> 00:55:41,360 see, new chemical compounds that would be great, drug discovery, new ways to approach problems 556 00:55:41,360 --> 00:55:46,959 using different materials made for another field that could be applied to a biomedicine field. 557 00:55:46,959 --> 00:55:50,560 Those are some of the ways that it's already happening and will continue to happen. 558 00:55:51,679 --> 00:55:56,479 Well, yeah, I think this is such a good example of there is a lot of doom and gloom out there 559 00:55:56,479 --> 00:56:02,000 about AI and perhaps negative repercussions, but this is such a key example of how it can be used 560 00:56:02,080 --> 00:56:08,639 for the betterment of creation of new technologies and hopefully to impact tens of millions of 561 00:56:08,639 --> 00:56:15,360 patients' lives down the road. So, yeah, I think we're going to start kind of wrapping up the 562 00:56:15,360 --> 00:56:19,840 conversation, but just wanted to highlight how amazing it is. You mentioned being the son of a 563 00:56:19,840 --> 00:56:26,639 construction worker and now you're doing some amazing and exciting things in the biotech 564 00:56:26,639 --> 00:56:34,320 innovation world and in your lab and you touched on Mike Tomlin. My hope is that maybe someday 565 00:56:34,320 --> 00:56:39,840 you'll be a household name like Mike Tomlin. But anyways, Dan, thank you so much for the 566 00:56:39,840 --> 00:56:45,280 conversation today. We did want to finish with one last segment, do a rapid fire segment just 567 00:56:45,280 --> 00:56:49,919 for the audience to get to know you a little better on a personal level. So are you ready 568 00:56:49,919 -->

00:56:57,439 for that? Of course, yeah. What's your go-to work soundtrack or driving soundtrack? 569 00:56:58,239 --> 00:57:03,199 I do love driving. There's a lot of driving in Dallas, so most of the time I listen to the car 570 00:57:03,199 --> 00:57:09,040 engine and try to dissociate my day and analyze my day. But the band that I'm really into right 571 00:57:09,040 --> 00:57:17,120 now is called Goose, G-O-O-S-E. It's a fantastic, modern jam band that I love listening to and we'll 572 00:57:17,120 --> 00:57:23,840 be going to see them play in about a month from now in Dallas. What is a topic or hobby totally 573 00:57:23,840 --> 00:57:29,520 unrelated to science that you're really into outside of work? It might have been clear from 574 00:57:29,520 --> 00:57:35,840 the way I've described our scientific enterprises. I just love ideas and creativity, so music, 575 00:57:35,840 --> 00:57:43,120 movies, writing, reading. Those are the go-to creative arts that I go to every day and really 576 00:57:43,120 --> 00:57:50,560 enjoy. What's your comfort food after a long day? I'm the son of Sicilian immigrants. I love 577 00:57:50,560 --> 00:57:56,080 pizza and pastas, but pizza is my hobby too. Once I've had to leave the laboratory and sit in the 578 00:57:56,080 --> 00:58:02,719 office writing grants at home, I have baking steels in my oven and really love making homemade pizzas. 579 00:58:03,360 --> 00:58:08,800 Awesome. What TV show are you currently watching, if you are a TV watcher? 580 00:58:09,760 --> 00:58:16,879 I don't binge watch too much. I generally watch YouTube videos about cooking and race cars, 581 00:58:16,879 --> 00:58:22,479 and then I like all the classic movies by the world's great directors. So I like re-watching 582 00:58:22,479 --> 00:58:29,840 or watching the great directors at nighttime. If you could instantly master one skill outside 583 00:58:29,840 --> 00:58:35,199 of science, what would it be? I think foreign languages. It's always something that I'm trying 584 00:58:35,199 --> 00:58:40,399 to learn, I want to learn, but just do not have the natural ability to quickly learn languages, 585 00:58:40,399 --> 00:58:45,520 unlike other fields. So if I could have a cheat code for foreign language, that's what I would pick. 586 00:58:46,959 --> 00:58:50,800 And what's a fun fact about you that would surprise your fellow co-workers? 587 00:58:51,760 --> 00:58:56,080 I've been blessed to have opportunities to travel over the years. First, when I was young, 588 00:58:56,080 --> 00:59:01,760 I did it on the cheap by literally calling friends who lived in Belgium or South America 589 00:59:01,760 --> 00:59:07,600 and telling them that I bought a ticket and I'm going to be at their door next week. And that, 590 00:59:07,600 --> 00:59:12,239 coupled to what I do now, which is travel for work, whether that's giving seminars or going 591 00:59:12,239 --> 00:59:17,360 to conferences, has just created an opportunity to have that privilege of seeing some of the 592 00:59:17,360 --> 00:59:24,000 world's great places. So in Japan, for example, my friends and I met at the base of Mount Fuji 593 00:59:24,560 --> 00:59:29,040 in the evening, and we climbed all night long without sleeping. And by sunrise, we were at 594 00:59:29,040 --> 00:59:35,600 the top of Mount Fuji and got to see the sunrise over the Japan landscape, which was pretty awesome. 595 00:59:36,239 --> 00:59:41,199 I've climbed along the Great Wall, I've had the chance to go to Machu Picchu and see the ancient 596 00:59:41,199 --> 00:59:46,800 ruins. And I just love doing that stuff because the world is such an amazing and incredible place. 597 00:59:46,800 --> 00:59:53,439 And being in those positions, whether that's a research lab or in the center of the former 598 00:59:53,439 --> 00:59:59,360 civilization of Machu Picchu, it gives you this sense of awe, and it inspires all of us to work 599 00:59:59,360 --> 01:00:04,239 hard and be our best selves and do the best things that we can for humanity in the short time that we 600 01:00:04,239 --> 01:00:06,080 have on this earth. 601 01:00:06,080 --> 01:00:12,080 Well, what a fantastic way to close. Dr. Siegwert, thank you so much for joining the podcast and this 602 01:00:12,080 --> 01:00:18,879 amazing conversation. And to our listeners, thank you for joining Learning from Leaders. We'll have 603 01:00:18,879 --> 01:00:24,719 some more information about Dr. Siegwert in the link in the bio, and we'll see you next time on the 604 01:00:24,719 --> 01:00:26,080 Learning from Leaders podcast.