1 00:00:00,880 --> 00:00:05,600 Speaker 1: On a warm September day in Berlin, Hungarian biochemist Catalan 2 00:00:05,720 --> 00:00:10,440 Speaker 1: Cattico walks into the nearly century old auditorium at Berlin's 3 00:00:10,480 --> 00:00:14,080 Speaker 1: chetty Tay Hospital. She takes her seat in the front 4 00:00:14,200 --> 00:00:18,400 Speaker 1: row of a World Health Organization event. She surrounded by 5 00:00:18,440 --> 00:00:24,080 Speaker 1: politicians and public health leaders. Just a year earlier, there 6 00:00:24,079 --> 00:00:26,800 Speaker 1: would have been little reason for Catalan to be there 7 00:00:26,800 --> 00:00:29,520 Speaker 1: at all. A few of the other guests would have 8 00:00:29,560 --> 00:00:33,440 Speaker 1: even known her name, But now she's a guest of honor. 9 00:00:34,600 --> 00:00:40,040 Speaker 1: Catalan spent her life researching messenger RNA, the tiny postal 10 00:00:40,120 --> 00:00:45,800 Speaker 1: workers that carry genetic instructions inside cells. For decades, few 11 00:00:45,880 --> 00:00:52,040 Speaker 1: paid attention to what she found that has now changed. Yes. 12 00:00:54,800 --> 00:00:59,920 Speaker 1: Even at the event in September, German Chancellor Angelo Meeric 13 00:01:00,040 --> 00:01:05,679 Speaker 1: called praises Catalan for not giving up. The German Chancellor 14 00:01:05,840 --> 00:01:09,800 Speaker 1: tells the audience that Catalan's thirty years of effort laid 15 00:01:09,840 --> 00:01:14,880 Speaker 1: the groundwork for our current fight against COVID nineteen. In 16 00:01:14,959 --> 00:01:20,119 Speaker 1: her own remarks, Catalan says her collaborators deserve credit too. 17 00:01:21,040 --> 00:01:23,840 Speaker 1: First of all, I would like to correct you because 18 00:01:23,880 --> 00:01:27,040 Speaker 1: there are many, many people contributed to it, and I 19 00:01:27,160 --> 00:01:29,040 Speaker 1: was just one of them. And I am glad that 20 00:01:29,080 --> 00:01:31,800 Speaker 1: I go also goold have I am just representing all 21 00:01:31,840 --> 00:01:35,680 Speaker 1: of those fellow scientists. Then she makes a plea, but 22 00:01:35,760 --> 00:01:38,920 Speaker 1: the dignitaries in the audience to give people with ideas 23 00:01:38,959 --> 00:01:43,240 Speaker 1: that seem crazy a chance. Those who who might have 24 00:01:43,280 --> 00:01:47,240 Speaker 1: an idea which is too weird to support, maybe they 25 00:01:47,280 --> 00:01:51,440 Speaker 1: get more support and sort of problems we were facing 26 00:01:51,480 --> 00:01:57,560 Speaker 1: the future. Catalan doesn't stick around long for drinks. Afterward, 27 00:01:58,280 --> 00:02:00,320 Speaker 1: She's at the co check In less than an hour, 28 00:02:01,040 --> 00:02:04,480 Speaker 1: She's going to Budapest. They're painting her picture onto the 29 00:02:04,520 --> 00:02:08,920 Speaker 1: side of a building there. She's only awards circuit, but 30 00:02:09,040 --> 00:02:11,600 Speaker 1: she says she'd rather be back in her lab as 31 00:02:11,680 --> 00:02:15,680 Speaker 1: soon as possible. Catalan helped lay the groundwork for a 32 00:02:15,760 --> 00:02:18,600 Speaker 1: most important weapon against the deadly virus that has so 33 00:02:18,720 --> 00:02:21,680 Speaker 1: far killed more than five million people around the globe. 34 00:02:22,520 --> 00:02:26,160 Speaker 1: She never expected that, but she also showed the world 35 00:02:26,200 --> 00:02:31,120 Speaker 1: the potential for a new technology, messenger RNA, And this 36 00:02:31,360 --> 00:02:36,959 Speaker 1: is what Catalan had hoped for all along. This is 37 00:02:37,000 --> 00:02:40,200 Speaker 1: a story about what most people would agree is the 38 00:02:40,240 --> 00:02:44,959 Speaker 1: biggest success of the pandemic. Messenger RNA vaccines could never 39 00:02:45,040 --> 00:02:48,600 Speaker 1: have proven themselves so quickly outside the crucible of that 40 00:02:48,680 --> 00:02:54,279 Speaker 1: first pandemic year. The technology may well win some researchers 41 00:02:54,320 --> 00:02:58,760 Speaker 1: and Nobel Prize. It will almost certainly have big implications 42 00:02:58,800 --> 00:03:02,400 Speaker 1: for the future of medicine. Odds are you've taken one 43 00:03:02,440 --> 00:03:06,280 Speaker 1: of these mr Anda vaccines yourself, and you might think 44 00:03:06,320 --> 00:03:08,800 Speaker 1: you know the story of how they swooped onto the 45 00:03:08,800 --> 00:03:12,960 Speaker 1: world stage so quickly, But odds are you don't know 46 00:03:13,000 --> 00:03:16,400 Speaker 1: the half of it. This is also the story of 47 00:03:16,440 --> 00:03:19,680 Speaker 1: as unlikely a bunch of world saving heroes as you'll 48 00:03:19,720 --> 00:03:24,440 Speaker 1: ever encounter. My name is Naomi Kraski, and I'm a 49 00:03:24,480 --> 00:03:27,840 Speaker 1: health journalist for Bloomberg News. In the first half of 50 00:03:27,880 --> 00:03:31,480 Speaker 1: the season, you've heard about the lingering consequences of COVID 51 00:03:31,760 --> 00:03:36,200 Speaker 1: for many patients and hospitals. Now we'll tell you about 52 00:03:36,240 --> 00:03:40,960 Speaker 1: the consequences for science. They're a lot more hopeful. From 53 00:03:40,960 --> 00:03:59,080 Speaker 1: the Prognosis podcast, this is Breakthrough. I first heard of 54 00:03:59,200 --> 00:04:04,080 Speaker 1: messenger are A vaccines more than a year before the 55 00:04:04,200 --> 00:04:09,160 Speaker 1: mysterious new virus emerged in Wuhan. The biggest buzz in 56 00:04:09,200 --> 00:04:12,120 Speaker 1: the drug industry at that time was the idea of 57 00:04:12,240 --> 00:04:15,480 Speaker 1: using the immune system to attack tumors and help cancer 58 00:04:15,480 --> 00:04:19,920 Speaker 1: patients live longer. Scientists were trying a bunch of different 59 00:04:19,920 --> 00:04:22,599 Speaker 1: ways to do this, and a source told me I 60 00:04:22,600 --> 00:04:26,080 Speaker 1: should talk to the people at a German startup called BioNTech. 61 00:04:27,240 --> 00:04:32,560 Speaker 1: Inside European biotech, everybody knew them, but outside that insular 62 00:04:32,600 --> 00:04:36,520 Speaker 1: world a few people had ever heard of them. They 63 00:04:36,520 --> 00:04:40,000 Speaker 1: were trying to make something that had failed many times before, 64 00:04:40,440 --> 00:04:43,440 Speaker 1: a cancer vaccine, but they were trying to do it 65 00:04:43,640 --> 00:04:50,240 Speaker 1: using messenger RNA. Here's BioNTech CEO Uger Shahin speaking at 66 00:04:50,240 --> 00:04:54,719 Speaker 1: a tech conference called codex in. I think you have 67 00:04:54,800 --> 00:04:59,120 Speaker 1: to dare to start without having all solutions in the hand, 68 00:04:59,279 --> 00:05:03,839 Speaker 1: hoping that something would come up. It was fascinating stuff, 69 00:05:04,000 --> 00:05:07,520 Speaker 1: full of hope and cutting edge science, but as is 70 00:05:07,600 --> 00:05:11,040 Speaker 1: often the case in the risky business of biotech, it 71 00:05:11,120 --> 00:05:13,680 Speaker 1: wasn't at all clear that the MR and A vaccines 72 00:05:13,720 --> 00:05:16,839 Speaker 1: would actually work. I wrote a feature story for the 73 00:05:16,880 --> 00:05:19,800 Speaker 1: news wire, then moved on to other topics and went 74 00:05:19,880 --> 00:05:25,040 Speaker 1: on maternity leave. Then in January my boss called. He 75 00:05:25,120 --> 00:05:28,039 Speaker 1: said a new story was keeping him busy, strange new 76 00:05:28,040 --> 00:05:31,599 Speaker 1: coronavirus that had emerged in China and was spreading around 77 00:05:31,600 --> 00:05:34,799 Speaker 1: the world. It's too bad you're not here now, he said. 78 00:05:35,680 --> 00:05:37,880 Speaker 1: It'll probably be all over by the time you're back. 79 00:05:38,960 --> 00:05:41,640 Speaker 1: Of course, when I came back to the office the 80 00:05:41,680 --> 00:05:46,120 Speaker 1: next month, it wasn't over. It was spreading. I live 81 00:05:46,160 --> 00:05:49,600 Speaker 1: in Germany. A few weeks later we went into our 82 00:05:49,640 --> 00:05:54,400 Speaker 1: first lockdown, and it's still not over. The world has changed. 83 00:05:54,960 --> 00:06:06,000 Speaker 1: We're still finding out just how much our story starts. 84 00:06:06,040 --> 00:06:10,840 Speaker 1: In nine the modern study of human genetics was just 85 00:06:11,040 --> 00:06:15,680 Speaker 1: getting going. Only eight years prior, scientists had discovered the 86 00:06:15,680 --> 00:06:19,919 Speaker 1: double helix structure of DNA. Now they were trying to 87 00:06:19,920 --> 00:06:23,720 Speaker 1: figure out how cells act on the instructions encoded in 88 00:06:23,760 --> 00:06:27,800 Speaker 1: the genes. A team from the Pastor Institute in Paris 89 00:06:27,839 --> 00:06:32,040 Speaker 1: identified an elusive molecule that copies pieces of genetic code 90 00:06:32,520 --> 00:06:35,839 Speaker 1: and delivers its instructions into the machinery of the cell. 91 00:06:36,920 --> 00:06:43,000 Speaker 1: They named it messenger RNA. RNA stands for ribonucleic acid, 92 00:06:44,600 --> 00:06:49,040 Speaker 1: where DNA is a double strand RNA is a single strand. 93 00:06:50,000 --> 00:06:53,120 Speaker 1: There are a few types of RNA that play important 94 00:06:53,240 --> 00:06:58,599 Speaker 1: roles in sending DNA's instructions to cells, and messenger RNA 95 00:06:59,480 --> 00:07:03,159 Speaker 1: is essentially leave the errand boy. One of the best 96 00:07:03,200 --> 00:07:07,960 Speaker 1: explanations I've heard of how the biology works comes from 97 00:07:08,000 --> 00:07:13,440 Speaker 1: Derek Rossy Harvard University stem cell biologists. M RNA is 98 00:07:13,480 --> 00:07:19,920 Speaker 1: actually a necessary and obligate intermediate between genes, which are 99 00:07:20,480 --> 00:07:24,320 Speaker 1: encoded in DNA and live in the nucleus, and proteins, 100 00:07:24,680 --> 00:07:27,480 Speaker 1: which do all the busy work of the cell. But 101 00:07:27,560 --> 00:07:31,880 Speaker 1: they're made in another part of the cell called the cytoplasm, 102 00:07:31,920 --> 00:07:35,480 Speaker 1: so the two don't meet the nason the nucleus, protein 103 00:07:35,520 --> 00:07:38,080 Speaker 1: synthesis is and the cytoplasm. So there had to be 104 00:07:38,120 --> 00:07:41,400 Speaker 1: an intermediate molecule, which was discovered to be messenger RNA 105 00:07:41,360 --> 00:07:46,120 Speaker 1: an appropriate an appropriate name. It carries the message encoded 106 00:07:46,120 --> 00:07:50,440 Speaker 1: by the gene out to allow that sort of code 107 00:07:50,480 --> 00:07:54,280 Speaker 1: to be turned into something that has utility, something that 108 00:07:54,360 --> 00:08:00,400 Speaker 1: has function proteins, So DNA makes mRNA makes protein, makes 109 00:08:00,600 --> 00:08:05,320 Speaker 1: all of life. Three French scientists shared a Nobel Prize 110 00:08:05,320 --> 00:08:08,400 Speaker 1: in nineteen sixty five for the discovery of m r 111 00:08:08,480 --> 00:08:13,000 Speaker 1: and A, but for decades after that, m RNA wasn't 112 00:08:13,000 --> 00:08:16,560 Speaker 1: a very high profile area for research. Part of the 113 00:08:16,680 --> 00:08:20,160 Speaker 1: reason was that the molecules fragile and hard to work with. 114 00:08:20,840 --> 00:08:23,360 Speaker 1: Scientists didn't figure out how to synthesize it in the 115 00:08:23,480 --> 00:08:29,520 Speaker 1: lab until in the nineteen nineties. The field Superstars focused 116 00:08:29,520 --> 00:08:33,840 Speaker 1: on DNA instead. DNA was easier than RNA to work 117 00:08:33,880 --> 00:08:37,080 Speaker 1: with and more stable. It was also in the limelight 118 00:08:37,400 --> 00:08:39,440 Speaker 1: thanks to the mapping of the genome and the Human 119 00:08:39,480 --> 00:08:46,240 Speaker 1: Genome Project that lasted from two thousand three. Scientists focused 120 00:08:46,240 --> 00:08:49,720 Speaker 1: on the idea of curing illnesses by fixing errors in 121 00:08:49,760 --> 00:08:54,120 Speaker 1: the genome, but there were a few exceptions researchers who 122 00:08:54,120 --> 00:08:58,680 Speaker 1: stuck with m RNA despite the challenges both scientific and personal. 123 00:08:59,480 --> 00:09:02,240 Speaker 1: One of them came from Hungary. By the time the 124 00:09:02,280 --> 00:09:05,200 Speaker 1: pandemic was a year old, her name would be known 125 00:09:05,320 --> 00:09:08,199 Speaker 1: around the world as the woman who pioneered mr and 126 00:09:08,280 --> 00:09:12,720 Speaker 1: A vaccines. She would be covered endlessly in newspaper articles 127 00:09:12,760 --> 00:09:16,360 Speaker 1: and TV shows. But each time I think I've learned 128 00:09:16,400 --> 00:09:20,000 Speaker 1: everything about her story, something new turns up to surprise me. 129 00:09:29,760 --> 00:09:35,480 Speaker 1: Catalan Cadko was born in Hungary. In she's in grade 130 00:09:35,520 --> 00:09:39,040 Speaker 1: school when the first MR and A discoveries are being lauded. 131 00:09:40,120 --> 00:09:43,920 Speaker 1: The Poster Institute and Nobel Prizes would have seemed very 132 00:09:43,960 --> 00:09:47,800 Speaker 1: far away to her. She grows up under communism in 133 00:09:47,880 --> 00:09:52,440 Speaker 1: gishuis Sarash, a small town in the countryside of eastern Hungary. 134 00:09:53,400 --> 00:09:57,640 Speaker 1: Her father is a butcher, but she knows even as 135 00:09:57,640 --> 00:10:01,640 Speaker 1: a teenager that she wants to be assigned best. Catalan 136 00:10:01,800 --> 00:10:05,199 Speaker 1: declined to be interviewed for this podcast. I and others 137 00:10:05,280 --> 00:10:08,960 Speaker 1: that Bloomberg had already spoken to her many times, and 138 00:10:09,040 --> 00:10:11,480 Speaker 1: she said she wants to focus again on her work 139 00:10:11,600 --> 00:10:14,600 Speaker 1: after being on the interview circuit. I get where she's 140 00:10:14,640 --> 00:10:17,560 Speaker 1: coming from, so we decided to draw from what I 141 00:10:17,600 --> 00:10:21,400 Speaker 1: think is her most unusual interview. She's spoken May with 142 00:10:21,480 --> 00:10:26,079 Speaker 1: Clube Radio, an independent broadcaster based in Budapest. We've dubbed 143 00:10:26,120 --> 00:10:32,280 Speaker 1: her voice from her native Hungarian Madam, I'm not a 144 00:10:32,320 --> 00:10:35,760 Speaker 1: special person at all. I saw that my parents were called, 145 00:10:35,840 --> 00:10:38,240 Speaker 1: and I also tried to help them. Along with my siblings. 146 00:10:39,200 --> 00:10:46,440 Speaker 1: We studied odd that was our job as Catalan earned 147 00:10:46,480 --> 00:10:50,680 Speaker 1: her PhD in Hungary at the University of second just 148 00:10:50,840 --> 00:10:54,240 Speaker 1: a two hour drive from where she grew up. She 149 00:10:54,320 --> 00:10:57,800 Speaker 1: started her postdoctoral research in the same city at the 150 00:10:57,840 --> 00:11:03,400 Speaker 1: Biological Research Center of the Hungarian Academy of Sciences. In 151 00:11:04,400 --> 00:11:08,000 Speaker 1: eight As a PhD student in Hungary, she worked with 152 00:11:08,160 --> 00:11:11,720 Speaker 1: RNA for the first time. It was the start of 153 00:11:11,760 --> 00:11:17,480 Speaker 1: a lifelong obsession. In she got the opportunity to move 154 00:11:17,520 --> 00:11:21,720 Speaker 1: to the US for a job at Temple University in Philadelphia. 155 00:11:22,440 --> 00:11:25,800 Speaker 1: She took it, moving with her husband and toddler daughter. 156 00:11:26,840 --> 00:11:29,920 Speaker 1: The Hungarian government only allowed them to bring a hundred 157 00:11:30,000 --> 00:11:34,160 Speaker 1: dollars with them. Legally, they sold another nine hundred pounds 158 00:11:34,200 --> 00:11:38,160 Speaker 1: about one thousand, two hundred dollars into her daughter's teddy 159 00:11:38,160 --> 00:11:45,560 Speaker 1: bear alment. We flew off. We didn't have any foreign renatives. 160 00:11:46,080 --> 00:11:51,240 Speaker 1: We couldn't count on anyone to send us money. What 161 00:11:51,400 --> 00:11:55,280 Speaker 1: she found wasn't what she had expected either. She says 162 00:11:55,320 --> 00:11:57,959 Speaker 1: the lab wasn't as well equipped as the one back home, 163 00:11:58,559 --> 00:12:02,480 Speaker 1: and one of the co workers doors and yelled. After 164 00:12:02,520 --> 00:12:06,040 Speaker 1: a week she wanted to leave. She stayed out of 165 00:12:06,120 --> 00:12:13,720 Speaker 1: necessity and out of hope. Were in survival mode, and 166 00:12:13,800 --> 00:12:16,439 Speaker 1: I thought I would learn something interesting and we would survive. 167 00:12:17,120 --> 00:12:19,960 Speaker 1: And this is what changes people, that they become so 168 00:12:20,080 --> 00:12:23,120 Speaker 1: defenseless and they must rely on their talent and make 169 00:12:23,200 --> 00:12:28,559 Speaker 1: do with the best they can. By nine, things were 170 00:12:28,559 --> 00:12:33,040 Speaker 1: looking slightly better. Catalan got a research assistant professor position 171 00:12:33,160 --> 00:12:36,439 Speaker 1: at the University of Pennsylvania. This was a chance to 172 00:12:36,480 --> 00:12:40,560 Speaker 1: make a name for herself maybe eventually get tenure. Because 173 00:12:40,559 --> 00:12:42,960 Speaker 1: of the way these jobs work, she was expected to 174 00:12:43,000 --> 00:12:46,520 Speaker 1: win her own grant funding to support her research, but 175 00:12:46,640 --> 00:12:49,880 Speaker 1: she ran into a big roadblock. She was still obsessed 176 00:12:49,880 --> 00:12:52,920 Speaker 1: with m R and A. Well she had seen so 177 00:12:53,000 --> 00:12:55,800 Speaker 1: far in her experiments convinced her that it would make 178 00:12:55,840 --> 00:12:59,839 Speaker 1: a better medicine than d n A. But no, but 179 00:13:00,040 --> 00:13:05,839 Speaker 1: the else agreed. She wrote a lot of grand proposals, 180 00:13:06,120 --> 00:13:10,040 Speaker 1: at one point one every month, she told us, but 181 00:13:10,120 --> 00:13:13,960 Speaker 1: nobody wanted to fund the experiments she wanted to do. 182 00:13:14,679 --> 00:13:17,400 Speaker 1: Nobody wanted to fund work on m R and A. 183 00:13:18,480 --> 00:13:22,360 Speaker 1: She didn't exactly make it easier on herself. One thing 184 00:13:22,400 --> 00:13:25,920 Speaker 1: I learned from her Hungarian interview was that she wasn't 185 00:13:26,040 --> 00:13:30,120 Speaker 1: a networker. Instead, she wanted to spend her time at 186 00:13:30,160 --> 00:13:38,679 Speaker 1: the lab bench doing science. Should I always keep those 187 00:13:38,720 --> 00:13:42,000 Speaker 1: meetings filled with small talk, which could have held my career? 188 00:13:42,559 --> 00:13:46,080 Speaker 1: Those really drove me crazy. Even in stores if they 189 00:13:46,120 --> 00:13:50,280 Speaker 1: were long lines, I thought, you're stealing my time, would you? 190 00:13:53,520 --> 00:13:56,800 Speaker 1: Elliott barn Nathan was her boss at the time. He's 191 00:13:56,840 --> 00:14:00,640 Speaker 1: a cardiologist who was then an associate professor of medicine 192 00:14:00,800 --> 00:14:04,480 Speaker 1: at Penn. Elliott was later to leave academia for a 193 00:14:04,559 --> 00:14:08,200 Speaker 1: drug industry career. He's an executive at Johnson and Johnson now, 194 00:14:09,080 --> 00:14:13,600 Speaker 1: but he remembers Catalan. Well, so the first thing is 195 00:14:14,040 --> 00:14:18,000 Speaker 1: she's incredibly hard working and and brilliant, I mean, really 196 00:14:18,080 --> 00:14:22,880 Speaker 1: truly brilliant. And the thing that's interesting is that she's 197 00:14:22,920 --> 00:14:27,720 Speaker 1: a voracious reader, and and so she would always read 198 00:14:27,840 --> 00:14:30,920 Speaker 1: science and Nature and come into the lab this morning 199 00:14:30,960 --> 00:14:34,520 Speaker 1: with the latest, you know, issue of science that you 200 00:14:34,760 --> 00:14:37,440 Speaker 1: hadn't even come across my desk yet, and she had 201 00:14:37,480 --> 00:14:42,880 Speaker 1: already read it and figured out somebody researching something completely 202 00:14:43,000 --> 00:14:47,520 Speaker 1: different in a different content and a different disease entity. 203 00:14:47,560 --> 00:14:51,800 Speaker 1: But there was a kernel here that was going to 204 00:14:51,960 --> 00:14:54,200 Speaker 1: help us do the next step of what we needed 205 00:14:54,200 --> 00:14:58,760 Speaker 1: to do. And she was always connecting the dots. Messenger 206 00:14:58,960 --> 00:15:02,280 Speaker 1: r n a degree quickly in the body. There are 207 00:15:02,400 --> 00:15:06,440 Speaker 1: enzymes that break loose mrn a down outside of cells, 208 00:15:07,240 --> 00:15:10,080 Speaker 1: but it also goes away quite quickly once it's delivered 209 00:15:10,080 --> 00:15:14,400 Speaker 1: its message inside the cell. Catalan thought that would actually 210 00:15:14,400 --> 00:15:17,760 Speaker 1: be a good thing. She reasoned that you could use 211 00:15:17,960 --> 00:15:20,120 Speaker 1: m RNA to flip a switch in the self for 212 00:15:20,200 --> 00:15:23,960 Speaker 1: a limited period of time. Elliott told me the idea 213 00:15:24,040 --> 00:15:27,200 Speaker 1: would be for it to have the desired effect then 214 00:15:27,280 --> 00:15:32,360 Speaker 1: go away. But convincing the scientific establishment to give her 215 00:15:32,400 --> 00:15:37,480 Speaker 1: experiments a chance proved very, very difficult. They could only 216 00:15:37,520 --> 00:15:42,400 Speaker 1: see the challenge, not the potential benefit for them. mRNA 217 00:15:42,600 --> 00:15:47,000 Speaker 1: was too fragile, too fleeting a dead end, so it 218 00:15:47,080 --> 00:15:49,400 Speaker 1: was very It was very heretical back in those days. 219 00:15:49,720 --> 00:15:53,000 Speaker 1: People said, oh, you're crazy, you know, m RNA will 220 00:15:53,040 --> 00:15:58,800 Speaker 1: never work, it's too unstable. But she really firmly belowd 221 00:15:59,120 --> 00:16:02,360 Speaker 1: She had a vision that it was doable. It was 222 00:16:02,480 --> 00:16:04,880 Speaker 1: just we needed to figure out how to do it. 223 00:16:05,240 --> 00:16:09,520 Speaker 1: Elliott used his own research funding to subsidize Catalan's experiments. 224 00:16:10,120 --> 00:16:13,800 Speaker 1: They had some successes, but time and time again she 225 00:16:13,920 --> 00:16:18,880 Speaker 1: failed to get grant funding. In she was stripped of 226 00:16:18,960 --> 00:16:23,080 Speaker 1: her assistant professor title and demoted to essentially a glorified 227 00:16:23,160 --> 00:16:27,800 Speaker 1: lab researcher. It seemed unlikely that she would ever get 228 00:16:27,840 --> 00:16:32,040 Speaker 1: her own lab. People just couldn't see the see the 229 00:16:32,080 --> 00:16:37,600 Speaker 1: truth of it. Unfortunately, it was a bitter blow. After 230 00:16:37,720 --> 00:16:41,280 Speaker 1: making it all the way from Ural Hungary depend one 231 00:16:41,280 --> 00:16:45,040 Speaker 1: of the top research institutions in the world, Catalan faced 232 00:16:45,080 --> 00:16:48,160 Speaker 1: the very real possibility that she might have to stop 233 00:16:48,240 --> 00:16:52,120 Speaker 1: doing the work that she loved. At the same time, 234 00:16:52,440 --> 00:16:56,080 Speaker 1: she was dealing with a cancer scare and her husband 235 00:16:56,080 --> 00:16:58,600 Speaker 1: was stuck back in Hungary for more than four months 236 00:16:58,760 --> 00:17:01,080 Speaker 1: due to a processing to life for his green card. 237 00:17:02,120 --> 00:17:06,359 Speaker 1: In the Hungarian radio show, she's interviewed alongside a singer 238 00:17:06,560 --> 00:17:11,160 Speaker 1: named Zoran Stephan of It. The show has a unique format. 239 00:17:11,640 --> 00:17:14,840 Speaker 1: It tries to put two people from totally different walks 240 00:17:14,880 --> 00:17:18,800 Speaker 1: of life on the air together. In this case, Oron 241 00:17:18,960 --> 00:17:23,040 Speaker 1: wrote Catalan's favorite song, a ballot called Diamond and Gold. 242 00:17:23,720 --> 00:17:26,320 Speaker 1: She chokes up when she talks about listening to him 243 00:17:26,320 --> 00:17:34,320 Speaker 1: sing when things got tough, that did That's offen. Oran 244 00:17:34,440 --> 00:17:38,480 Speaker 1: released the song in the same year. Catalan moved to 245 00:17:38,520 --> 00:17:42,280 Speaker 1: the US and was the frontman for two Hungarian rock 246 00:17:42,320 --> 00:17:46,159 Speaker 1: bands in the nineteen sixties and nineteen seventies. Under a 247 00:17:46,160 --> 00:17:49,600 Speaker 1: communist regime that opposed rock music, he knew a singer 248 00:17:49,640 --> 00:17:54,480 Speaker 1: or two about persevering during tough times. Zorn's song is 249 00:17:54,520 --> 00:17:56,760 Speaker 1: about how you need to work hard and stay the 250 00:17:56,800 --> 00:18:00,119 Speaker 1: course to achieve your goals. Diamond and Gold of a 251 00:18:00,240 --> 00:18:03,400 Speaker 1: nice shine, he says, but you need to dig deep 252 00:18:03,440 --> 00:18:07,639 Speaker 1: to get it. This resonates with Catalan. She digs a 253 00:18:07,680 --> 00:18:10,960 Speaker 1: long time before she hits pe dirt, and even when 254 00:18:10,960 --> 00:18:13,960 Speaker 1: she does, she's one of only a few who recognizes 255 00:18:14,320 --> 00:18:26,800 Speaker 1: what she's found. The first time I interviewed Catalan was 256 00:18:26,840 --> 00:18:31,959 Speaker 1: in summer. Each time she speaks, I'm struck by how 257 00:18:32,040 --> 00:18:35,080 Speaker 1: little bitterness she expresses about getting shut out by the 258 00:18:35,160 --> 00:18:40,800 Speaker 1: academic establishment for so long. She sounds disappointed, yes, and 259 00:18:40,880 --> 00:18:45,160 Speaker 1: sometimes frustrated. She also talks about how she really got 260 00:18:45,160 --> 00:18:48,600 Speaker 1: a raise. She was hired at forty dollars, and two 261 00:18:48,600 --> 00:18:52,200 Speaker 1: decades later she was making sixty dollars, which she says 262 00:18:52,320 --> 00:18:55,000 Speaker 1: is less than what a laptech would make. But I 263 00:18:55,040 --> 00:18:57,800 Speaker 1: got the impression that because she managed to keep on 264 00:18:57,880 --> 00:19:01,200 Speaker 1: doing science, the lack of our cognition and low pay, 265 00:19:02,000 --> 00:19:06,080 Speaker 1: these were secondary concerns. Here. She is earlier this year, 266 00:19:06,359 --> 00:19:09,520 Speaker 1: speaking for a Bloomberg project about the one year anniversary 267 00:19:09,560 --> 00:19:13,800 Speaker 1: of the pandemic. This is previously unaired audio. As long 268 00:19:13,880 --> 00:19:15,879 Speaker 1: as I was in the lab and focus what I 269 00:19:15,920 --> 00:19:19,760 Speaker 1: can do, I was very happy. I mean, at the weekends, 270 00:19:20,040 --> 00:19:23,000 Speaker 1: long days. And my husband once said that, you know, 271 00:19:24,040 --> 00:19:27,919 Speaker 1: probably my earning is first than in a McDonald's, because 272 00:19:27,960 --> 00:19:33,720 Speaker 1: he collplated probably won all right, So as a biochemist 273 00:19:33,760 --> 00:19:37,280 Speaker 1: in the nineteen nineties, she was probably making less per 274 00:19:37,320 --> 00:19:40,400 Speaker 1: hour than I did at the time. Babysitting the kids 275 00:19:40,480 --> 00:19:46,200 Speaker 1: on my street in Elliott barn Nathan, who was subsidizing 276 00:19:46,200 --> 00:19:49,560 Speaker 1: Catalan's work at Penn, left the university to take a 277 00:19:49,680 --> 00:19:53,679 Speaker 1: job at a biotech. She managed to find a spot 278 00:19:53,800 --> 00:19:58,399 Speaker 1: in another lab, but she needed a close collaborator, someone 279 00:19:58,520 --> 00:20:02,359 Speaker 1: enthusiastic about the science with the cloud, to ensure she 280 00:20:02,400 --> 00:20:06,159 Speaker 1: could fund her projects. How she found this person is 281 00:20:06,240 --> 00:20:09,680 Speaker 1: one of those great water cooler moments that will probably 282 00:20:09,720 --> 00:20:15,359 Speaker 1: go down in science textbooks. In early Catalan started seeing 283 00:20:15,359 --> 00:20:18,639 Speaker 1: a new face at the Xerox machine where she'd copy 284 00:20:18,720 --> 00:20:22,919 Speaker 1: the academic journal articles that she read so eagerly. He 285 00:20:23,080 --> 00:20:27,280 Speaker 1: was an immunologist named Drew Wiseman, fresh off a fellowship 286 00:20:27,320 --> 00:20:30,399 Speaker 1: at Tony Fauci's lab at the National Institute's of Health. 287 00:20:31,359 --> 00:20:35,960 Speaker 1: Drew was also a voracious suiteader of journal articles. Back 288 00:20:36,000 --> 00:20:37,800 Speaker 1: in those days, you'd have to hunt them up in 289 00:20:37,800 --> 00:20:41,720 Speaker 1: the library or somebody else's lab, then copy them and 290 00:20:41,760 --> 00:20:45,960 Speaker 1: take them home. They weren't online. Drew says. They copied 291 00:20:46,080 --> 00:20:51,320 Speaker 1: hundreds of articles before I ran into Katie Carrico over 292 00:20:51,400 --> 00:20:56,359 Speaker 1: a Xerox machine and we would both sort of fight 293 00:20:56,480 --> 00:21:00,000 Speaker 1: over it, but really just wait for each other to finish, 294 00:21:00,280 --> 00:21:05,160 Speaker 1: and we started talking. Drew was interested in dendridic cells, 295 00:21:05,760 --> 00:21:09,480 Speaker 1: which helped the immune system adapt to fight new intruders. 296 00:21:10,280 --> 00:21:16,080 Speaker 1: They migrate throughout the body and collect foreign things, so 297 00:21:16,240 --> 00:21:22,920 Speaker 1: that includes viruses, bacteria, parasites, tumor cells, and they bring 298 00:21:23,040 --> 00:21:28,520 Speaker 1: those two lymph nodes where they start immune reaction. And 299 00:21:28,520 --> 00:21:32,320 Speaker 1: why that's important for a vaccine is that they're the 300 00:21:32,440 --> 00:21:37,440 Speaker 1: critical cell that picks up a vaccine and turns on 301 00:21:37,920 --> 00:21:42,520 Speaker 1: the immune reactions. Drew wanted to work on an HIV vaccine. 302 00:21:43,160 --> 00:21:47,919 Speaker 1: Catalan thought m RNA could help. We started talking and 303 00:21:47,960 --> 00:21:51,439 Speaker 1: I told her about my interest in dendrodic cells and HIV, 304 00:21:52,280 --> 00:21:55,040 Speaker 1: and she told me about her interest in m RNA. 305 00:21:55,880 --> 00:21:59,640 Speaker 1: So we started working together and we started doing experiments 306 00:21:59,680 --> 00:22:04,600 Speaker 1: together other and and that's where our collaboration started. It's 307 00:22:04,600 --> 00:22:08,480 Speaker 1: important to note that up until then, Catalan wasn't really 308 00:22:08,560 --> 00:22:12,200 Speaker 1: thinking about RNA as something you'd used to make a vaccine. 309 00:22:13,000 --> 00:22:17,480 Speaker 1: She wanted to make treatments to use mRNA to spur 310 00:22:17,640 --> 00:22:20,520 Speaker 1: the cells machinery to make a protein that the body 311 00:22:20,600 --> 00:22:25,920 Speaker 1: needs to heal itself. In that sense, Wiseman's involvement broadened 312 00:22:25,920 --> 00:22:30,040 Speaker 1: her perspective. I should also note that some experiments at 313 00:22:30,080 --> 00:22:33,040 Speaker 1: that point had already shown the promise of using genetic 314 00:22:33,119 --> 00:22:39,840 Speaker 1: material to spur the body's cells to produce vaccine. Researchers 315 00:22:39,840 --> 00:22:42,400 Speaker 1: at Merk and Co. Were able to spurn immune response 316 00:22:42,480 --> 00:22:46,680 Speaker 1: in mice by injecting them with DNA that contained instructions 317 00:22:46,720 --> 00:22:52,439 Speaker 1: for influenza proteins. But seeing something working animals and having 318 00:22:52,480 --> 00:22:56,159 Speaker 1: it work in humans, those are two very different things. 319 00:22:57,640 --> 00:23:02,520 Speaker 1: The old line in this mice lie and and macacs exaggerate, 320 00:23:02,840 --> 00:23:07,040 Speaker 1: So if it happens in a mouse, that's never a 321 00:23:07,080 --> 00:23:11,080 Speaker 1: guarantee it'll happen in the human. The body has multiple 322 00:23:11,160 --> 00:23:14,320 Speaker 1: lines of defense against any m RNA that looks like 323 00:23:14,520 --> 00:23:18,800 Speaker 1: it might not belong. These guards are enzymes that will 324 00:23:18,840 --> 00:23:24,080 Speaker 1: break down loose m RNA found outside a cell. If 325 00:23:24,200 --> 00:23:27,440 Speaker 1: m RNA conduct those attacks and try to get inside 326 00:23:27,440 --> 00:23:32,720 Speaker 1: a cell, its troubles aren't over. Derrick Rossi, Harvard stem 327 00:23:32,720 --> 00:23:36,000 Speaker 1: cell scientists we heard from earlier, says the cell's first 328 00:23:36,040 --> 00:23:38,680 Speaker 1: response is to do the exact opposite of what you'd 329 00:23:38,720 --> 00:23:42,359 Speaker 1: want if you're going to use m RNA for a therapy. 330 00:23:42,520 --> 00:23:46,479 Speaker 1: It's to stop making any proteins at all that doesn't 331 00:23:46,520 --> 00:23:49,640 Speaker 1: want viral proteins being made in the cell. And then 332 00:23:49,680 --> 00:23:54,000 Speaker 1: if the response is robust enough, it triggers these ultruastic 333 00:23:54,040 --> 00:23:57,600 Speaker 1: self kill pathways uh and they die because it's better 334 00:23:57,640 --> 00:24:01,399 Speaker 1: for the cell to die than it is to serve 335 00:24:01,440 --> 00:24:06,959 Speaker 1: as a manufacturing facility for a hundred thousand viral particles. Essentially, 336 00:24:07,080 --> 00:24:11,159 Speaker 1: the sell flips a self destruct swich. This makes a 337 00:24:11,160 --> 00:24:15,320 Speaker 1: lot of sense from a biological standpoint. It ensures cells 338 00:24:15,400 --> 00:24:18,840 Speaker 1: stay on track, make the right amount of the right protein, 339 00:24:19,040 --> 00:24:22,800 Speaker 1: and don't get duped into producing a pathogen. But to 340 00:24:22,960 --> 00:24:26,480 Speaker 1: use mr and A as a drug, Catalan and Drew 341 00:24:26,760 --> 00:24:28,959 Speaker 1: had to figure out how to get it into the 342 00:24:29,000 --> 00:24:33,480 Speaker 1: cell without flipping that self destruct switch. That was a 343 00:24:33,560 --> 00:24:36,560 Speaker 1: lot of years of research. It's about seven years of 344 00:24:36,720 --> 00:24:40,600 Speaker 1: work together. And what we did is we first had 345 00:24:40,600 --> 00:24:45,679 Speaker 1: to figure out why it was inflammatory, So what receptors 346 00:24:46,200 --> 00:24:50,200 Speaker 1: was it activating, How was it being recognized. So we 347 00:24:50,640 --> 00:24:56,600 Speaker 1: found some receptors, other people found receptors. In total, there 348 00:24:56,600 --> 00:25:01,840 Speaker 1: are seventeen of them, and we started to look at 349 00:25:01,280 --> 00:25:06,520 Speaker 1: how RNA interacted with those receptors. They did years of 350 00:25:06,600 --> 00:25:11,600 Speaker 1: painstaking experiments trying to disable the self destruct switch. Finally 351 00:25:11,840 --> 00:25:16,680 Speaker 1: the breakthrough came in an unexpected place. You could say 352 00:25:16,720 --> 00:25:20,520 Speaker 1: Look played a role. Look made possible by years of 353 00:25:20,600 --> 00:25:25,320 Speaker 1: hard work. Catalan's old boss, Elliott Barnathan, explained it to me. 354 00:25:26,040 --> 00:25:30,000 Speaker 1: She's a brilliant scientist, and you know, sometimes it's the 355 00:25:30,040 --> 00:25:35,240 Speaker 1: controls that you use that really help you to make 356 00:25:35,600 --> 00:25:38,119 Speaker 1: the advance. Is not necessarily what the experiment is, but 357 00:25:38,400 --> 00:25:41,600 Speaker 1: how well controlled it was. The control group is the 358 00:25:41,640 --> 00:25:45,200 Speaker 1: part of the experiment where you usually don't change anything, 359 00:25:45,800 --> 00:25:48,480 Speaker 1: the part that's supposed to serve as a comparison to 360 00:25:48,560 --> 00:25:54,320 Speaker 1: show whether the hypothesis you're testing is true. Catalan was 361 00:25:54,400 --> 00:25:57,760 Speaker 1: using a special type of RNA called transfer rna as 362 00:25:57,800 --> 00:26:02,080 Speaker 1: a control in one of her experiments. This t RNA 363 00:26:02,600 --> 00:26:07,119 Speaker 1: has an important difference compared to mRNA. There's a different 364 00:26:07,200 --> 00:26:13,000 Speaker 1: arrangement of a structural piece called uridine. So Catalan uses 365 00:26:13,040 --> 00:26:16,240 Speaker 1: this t RNA in the control group and she notices 366 00:26:16,359 --> 00:26:22,840 Speaker 1: something unusual. The immune response inflammation didn't happen in those cells. 367 00:26:23,720 --> 00:26:25,560 Speaker 1: That was sort of the light bulb that went off 368 00:26:25,560 --> 00:26:29,000 Speaker 1: in her head. She decides to make a slight modification 369 00:26:29,080 --> 00:26:32,520 Speaker 1: to the RNA molecule to mimic what naturally occurs in 370 00:26:32,600 --> 00:26:37,800 Speaker 1: transfer rna bingo. The cells don't try to fight off 371 00:26:37,840 --> 00:26:41,600 Speaker 1: the foreign RNA, and even better, they make ten times 372 00:26:41,600 --> 00:26:45,359 Speaker 1: as much protein, and so it was a double whammy. 373 00:26:45,440 --> 00:26:50,240 Speaker 1: And that was really the fundamental patent that both Maderna 374 00:26:50,440 --> 00:26:55,680 Speaker 1: and the fisor bio in tech vaccines use in terms 375 00:26:55,760 --> 00:27:04,320 Speaker 1: of m RNA therapy. In two thousand five, Catalan Currico 376 00:27:04,600 --> 00:27:08,080 Speaker 1: and Drew Wiseman published a paper laying out their method 377 00:27:08,200 --> 00:27:11,000 Speaker 1: for modifying r n A. I asked Drew what he 378 00:27:11,040 --> 00:27:14,239 Speaker 1: thought would happen next. So that was one of my 379 00:27:14,359 --> 00:27:19,119 Speaker 1: more embarrassing moments, because what I said to Katie after 380 00:27:19,200 --> 00:27:22,159 Speaker 1: the paper was published was that our phones are going 381 00:27:22,240 --> 00:27:24,919 Speaker 1: to start ringing off the hook, and people are going 382 00:27:24,960 --> 00:27:27,000 Speaker 1: to call us up and want to work with RNA, 383 00:27:27,720 --> 00:27:31,199 Speaker 1: and drug companies are gonna want to use RNA, and 384 00:27:32,119 --> 00:27:35,880 Speaker 1: our phones never ran. We would sit there looking at 385 00:27:35,880 --> 00:27:39,159 Speaker 1: the phone, and nothing happened. In days and weeks and 386 00:27:39,240 --> 00:27:43,840 Speaker 1: months and years went by and nothing happened. Nobody was interested. 387 00:27:43,960 --> 00:27:47,119 Speaker 1: Even though we published how to make it work well 388 00:27:47,800 --> 00:27:51,160 Speaker 1: and how to use it as a drug, nobody was interested. 389 00:27:52,400 --> 00:27:55,920 Speaker 1: I find that astonishing. I asked him what he thought 390 00:27:56,000 --> 00:27:59,840 Speaker 1: that was. You know, I think that even though we 391 00:28:00,080 --> 00:28:03,680 Speaker 1: published that paper, they still said RNA is too difficult 392 00:28:03,720 --> 00:28:06,359 Speaker 1: to work with and they just didn't want to work 393 00:28:06,359 --> 00:28:11,480 Speaker 1: with RNA. Catalan said she felt like Cassandra, the mythological 394 00:28:11,600 --> 00:28:15,600 Speaker 1: trojan priestess who finds that her gift of prophecy is 395 00:28:15,640 --> 00:28:19,520 Speaker 1: really a curse. I mean, I knew that it can 396 00:28:19,640 --> 00:28:21,800 Speaker 1: be used for everything, and you know, kind of a 397 00:28:21,880 --> 00:28:25,640 Speaker 1: Cassandra feeling that I can see the future and nobody 398 00:28:25,720 --> 00:28:30,480 Speaker 1: believes me. Catalan and Drew filed for a patent to 399 00:28:30,640 --> 00:28:34,119 Speaker 1: keep on doing experiments, but it would take someone with 400 00:28:34,240 --> 00:28:37,840 Speaker 1: more salesman skills to bring the technology to the limelight. 401 00:28:41,480 --> 00:28:44,440 Speaker 1: Derek Rossi, who we heard from earlier, had been a 402 00:28:44,480 --> 00:28:48,760 Speaker 1: postdoctorate fellow at Stanford University when Drew and Catalan published 403 00:28:48,760 --> 00:28:51,680 Speaker 1: their study. He didn't read it at the time, but 404 00:28:51,760 --> 00:28:54,680 Speaker 1: a few years later at Harvard he ran into it 405 00:28:54,760 --> 00:28:57,960 Speaker 1: while trying to solve a problem in stem cell biology. 406 00:28:58,200 --> 00:29:02,400 Speaker 1: He wanted to convert cells back into a state similar 407 00:29:02,480 --> 00:29:06,120 Speaker 1: to that of an embryonic stem cell, a state from 408 00:29:06,160 --> 00:29:08,640 Speaker 1: which a cell can turn into any type of cell 409 00:29:08,680 --> 00:29:13,040 Speaker 1: in the body. A Japanese researcher named Shinya Yamanaka had 410 00:29:13,040 --> 00:29:16,480 Speaker 1: shown this was possible, but he had used a virus 411 00:29:16,560 --> 00:29:20,200 Speaker 1: to deliver the genetic cargo to reprogram the cells, which 412 00:29:20,280 --> 00:29:24,760 Speaker 1: scarred the cell. Derek wanted to use m RNA instead. 413 00:29:25,720 --> 00:29:29,560 Speaker 1: He decided to try a test protein first, something that 414 00:29:29,600 --> 00:29:35,720 Speaker 1: would be easy to recognize if it worked. We encoded 415 00:29:35,840 --> 00:29:39,120 Speaker 1: for the gene for the green fluorescent protein, which is 416 00:29:39,200 --> 00:29:43,880 Speaker 1: a jellyfish gene that fluoresces green under a certain wavelength 417 00:29:43,920 --> 00:29:47,760 Speaker 1: of light uh, and we synthesized that m RNA and 418 00:29:47,760 --> 00:29:49,720 Speaker 1: then we put it onto cells human cells in a 419 00:29:49,800 --> 00:29:55,200 Speaker 1: dish and we got a few green cells, but we 420 00:29:55,600 --> 00:29:59,520 Speaker 1: got a lot of dead cells and bed cells. Of course, 421 00:29:59,560 --> 00:30:01,760 Speaker 1: was not a our goal. We were not trying to 422 00:30:01,800 --> 00:30:06,240 Speaker 1: make a plateful of dead cells. Nope, they wanted green cells, 423 00:30:06,960 --> 00:30:09,520 Speaker 1: or rather, they wanted to get the cells to express 424 00:30:09,720 --> 00:30:15,120 Speaker 1: this green fluorescent protein. So we realized we had another challenge. 425 00:30:15,280 --> 00:30:18,720 Speaker 1: Why what was killing all these cells? When we introduced 426 00:30:18,720 --> 00:30:23,400 Speaker 1: the mRNA they almost gave up. But then Derek turned 427 00:30:23,480 --> 00:30:26,360 Speaker 1: to academic journals to see if anybody else had run 428 00:30:26,400 --> 00:30:31,080 Speaker 1: into this issue, and that is where we came across 429 00:30:31,120 --> 00:30:35,239 Speaker 1: the work of Captaalin Trico and Drew Weissman, whom in 430 00:30:35,600 --> 00:30:39,600 Speaker 1: two thousand and five published a seminal paper which, by 431 00:30:39,640 --> 00:30:47,600 Speaker 1: the way, got largely ignored by the academic press. Derek's 432 00:30:47,640 --> 00:30:51,840 Speaker 1: team followed the instructions in the paper swapping the modified 433 00:30:51,840 --> 00:30:55,080 Speaker 1: building blocks for the RNA. And now when we put 434 00:30:55,120 --> 00:30:59,280 Speaker 1: that jellyfish mRNA onto cells, essentially all the cells in 435 00:30:59,320 --> 00:31:04,400 Speaker 1: the dish where happy and blasting expression of this GFP protein. 436 00:31:04,520 --> 00:31:08,040 Speaker 1: So we were no longer killing cells on mass in 437 00:31:08,080 --> 00:31:12,280 Speaker 1: the dish. And that that discovery that they made, I 438 00:31:12,360 --> 00:31:17,000 Speaker 1: believe is well, it's fundamental to this entire field. Uh 439 00:31:17,040 --> 00:31:19,840 Speaker 1: And I believe it's going to earn them a Nobel 440 00:31:20,040 --> 00:31:23,920 Speaker 1: prize because it really is what allows these mr and 441 00:31:24,000 --> 00:31:28,600 Speaker 1: A vaccines and any mRNA therapeutic down the road. It's 442 00:31:28,680 --> 00:31:33,000 Speaker 1: the enabling sort of peace to the puzzle. Derek's team 443 00:31:33,040 --> 00:31:36,600 Speaker 1: published a paper in showing that they could use mr 444 00:31:36,680 --> 00:31:40,720 Speaker 1: and A to reprogram human skin cells. Now this was 445 00:31:40,800 --> 00:31:45,760 Speaker 1: sexy enough to get people's attention. It made a huge splash. 446 00:31:46,120 --> 00:31:48,840 Speaker 1: You may have seen the headlines. Scientists can now take 447 00:31:48,880 --> 00:31:51,760 Speaker 1: an ordinary cell from the body and transform it into 448 00:31:51,760 --> 00:31:54,680 Speaker 1: a cell that's very similar to an embryonic stem cell. 449 00:31:55,480 --> 00:31:58,280 Speaker 1: Most of the media reports were about the stem cells, 450 00:31:58,520 --> 00:32:02,440 Speaker 1: not the mr and a technolo oology, and that was exciting. Indeed, 451 00:32:02,480 --> 00:32:06,600 Speaker 1: from a basic science perspective, but Derek was already thinking 452 00:32:06,640 --> 00:32:10,960 Speaker 1: about the broader potential, and I was thinking to myself, Okay, 453 00:32:11,560 --> 00:32:15,080 Speaker 1: there's a lot of attention being given to the cell 454 00:32:15,160 --> 00:32:18,240 Speaker 1: based aspect of this, but nobody's really sort of recognizing 455 00:32:18,320 --> 00:32:22,080 Speaker 1: the modified m R and A based aspect. So I 456 00:32:22,080 --> 00:32:25,200 Speaker 1: should go out and try to start a company around this, 457 00:32:25,320 --> 00:32:27,840 Speaker 1: and that's that's the origin of Maderna. And I went 458 00:32:27,840 --> 00:32:32,400 Speaker 1: out and convinced some early investors and people that has 459 00:32:32,440 --> 00:32:36,680 Speaker 1: had potential, and it sort of launched launches the industry. 460 00:32:36,720 --> 00:32:41,880 Speaker 1: I guess Harvard colleague introduced Derek to venture capital company 461 00:32:41,920 --> 00:32:48,560 Speaker 1: Flagship Pioneering, which founded Moderna in operations began the next year. 462 00:32:49,240 --> 00:32:53,920 Speaker 1: Industry veterans signed on, including Stefan pan Cell and experienced 463 00:32:53,960 --> 00:32:58,040 Speaker 1: French executive who took the CEOs job at Moderna. The 464 00:32:58,080 --> 00:33:02,080 Speaker 1: company stayed private for eight years, raising two point five 465 00:33:02,160 --> 00:33:05,720 Speaker 1: billion dollars in venture capital and drug company investment. Along 466 00:33:05,720 --> 00:33:08,800 Speaker 1: the way, then had one of the biggest I p 467 00:33:08,960 --> 00:33:13,600 Speaker 1: O s in biotech history in December. Along the Way, 468 00:33:13,880 --> 00:33:19,000 Speaker 1: Moderna earned a reputation for secrecy until it published a 469 00:33:19,040 --> 00:33:23,120 Speaker 1: few scientific papers, preferring to keep its discoveries under wraps. 470 00:33:24,080 --> 00:33:27,920 Speaker 1: Derek left the company he founded in to focus on 471 00:33:28,000 --> 00:33:32,680 Speaker 1: his research. Catalan still reflects with wonder on how Bancel 472 00:33:32,720 --> 00:33:36,200 Speaker 1: and Moderna were able to collect so much money when 473 00:33:36,280 --> 00:33:40,280 Speaker 1: she wasn't even able to get a research grant. I concluded, 474 00:33:40,400 --> 00:33:44,800 Speaker 1: probably I did not explain well because look, come, come 475 00:33:44,840 --> 00:33:51,120 Speaker 1: a salesman the like Stefanson, poor, and when he goes 476 00:33:51,160 --> 00:33:54,400 Speaker 1: to have a breakfast reader Sioan, then in ten minutes 477 00:33:54,440 --> 00:33:58,560 Speaker 1: already two million dollar. He could convince him that that 478 00:33:58,840 --> 00:34:03,480 Speaker 1: mRNA is good for everything. And I said the same 479 00:34:03,560 --> 00:34:07,960 Speaker 1: to people, and they didn't even give me for the research. 480 00:34:14,160 --> 00:34:18,320 Speaker 1: But in Germany, a very different competitor was also working 481 00:34:18,480 --> 00:34:22,280 Speaker 1: on the m R and A technology. Husband wife team 482 00:34:22,360 --> 00:34:26,200 Speaker 1: Uger Shahina notes them to Achi founded BioNTech in two 483 00:34:26,239 --> 00:34:30,560 Speaker 1: thousand eight. Before Derrick Cross's work brought the idea of 484 00:34:30,640 --> 00:34:34,520 Speaker 1: modified m R and A into the limelight, the pair 485 00:34:34,560 --> 00:34:39,000 Speaker 1: had spent years pursuing immune based treatments for cancer. Starting 486 00:34:39,000 --> 00:34:41,719 Speaker 1: in the nineteen nineties at the University Medical Center of 487 00:34:41,719 --> 00:34:46,840 Speaker 1: the Johanna Schuttenberg University in minz Uger had started exploring 488 00:34:47,000 --> 00:34:50,760 Speaker 1: m R and as delivery method in two thousand something. 489 00:34:50,920 --> 00:34:53,840 Speaker 1: His wife once told me was considered a crazy idea 490 00:34:53,960 --> 00:34:57,879 Speaker 1: at the time, where moderna was polished and corporate from 491 00:34:57,920 --> 00:35:03,400 Speaker 1: the start. BioNTech an academic vibe who uses his university 492 00:35:03,480 --> 00:35:11,239 Speaker 1: email address. They've published hundreds of scientific papers. Who were 493 00:35:11,520 --> 00:35:15,960 Speaker 1: hired Catalan Cardicho away from PENN. They put out a 494 00:35:15,960 --> 00:35:19,920 Speaker 1: press release saying that her work had opened a new 495 00:35:20,040 --> 00:35:24,880 Speaker 1: field of therapy. She finally got her own lab just 496 00:35:25,000 --> 00:35:28,880 Speaker 1: down the hall from the CEO S office. Catalan says 497 00:35:28,960 --> 00:35:33,360 Speaker 1: she joined because she wanted to see her work in action. 498 00:35:34,360 --> 00:35:37,960 Speaker 1: I wanted to see the first patient to be treated, 499 00:35:38,040 --> 00:35:41,400 Speaker 1: some one person at least. I wanted to see that, okay, 500 00:35:41,400 --> 00:35:45,480 Speaker 1: this modified Emma and he helped one one person at least. 501 00:35:46,880 --> 00:35:50,560 Speaker 1: How the story would go from there, well, she never 502 00:35:50,600 --> 00:36:03,200 Speaker 1: expected that. That's next time on Breakthrough. Next week on Breakthrough, 503 00:36:03,360 --> 00:36:06,000 Speaker 1: we'll tell you about the frantic ten months of COVID 504 00:36:06,120 --> 00:36:10,920 Speaker 1: nineteen vaccine development that silenced the doubters in the scientific community. 505 00:36:11,000 --> 00:36:15,000 Speaker 1: At least it was highly likely that this is going 506 00:36:15,120 --> 00:36:19,080 Speaker 1: to be a pandemic, and we started to discuss what 507 00:36:19,120 --> 00:36:24,600 Speaker 1: we can do. This episode of Prognosis Breakthrough was written 508 00:36:24,640 --> 00:36:28,400 Speaker 1: and reported by me Naomi Kresky. So for Foreheads is 509 00:36:28,400 --> 00:36:33,040 Speaker 1: our senior producer. Carl Kevin Robinson Jr. Is our associate producer. 510 00:36:33,680 --> 00:36:36,800 Speaker 1: Our theme music was composed and performed by Hannis Brown. 511 00:36:37,680 --> 00:36:42,120 Speaker 1: Veronica Guyash did voice over, and Emma Cord, Bob Langrath, 512 00:36:42,480 --> 00:36:47,200 Speaker 1: and Sultan Shimon contributed reporting. Rick Shine is our editor. 513 00:36:47,880 --> 00:36:51,480 Speaker 1: Francesca Levy is the head of Bloomberg Podcasts. Be sure 514 00:36:51,480 --> 00:36:54,520 Speaker 1: to subscribe if you haven't already. If you liked this episode, 515 00:36:54,680 --> 00:36:57,319 Speaker 1: please leave us a review. It helps others find out 516 00:36:57,360 --> 00:37:09,680 Speaker 1: about the show. Thanks for listening.