1 00:00:05,625 --> 00:00:08,625 Welcome to the Proteomics in Proximity podcast, 2 00:00:08,625 --> 00:00:11,750 where your co-host Cindy Lawley and Sarantis Chlamydas 3 00:00:11,750 --> 00:00:13,041 from Olink Proteomics, 4 00:00:13,041 --> 00:00:17,791 talk about the intersection of proteomics with genomics for drug target discovery, 5 00:00:17,791 --> 00:00:20,916 the application of proteomics to reveal disease biomarkers, 6 00:00:21,250 --> 00:00:25,916 and current trends in using proteomics to unlock biological mechanisms. 7 00:00:26,250 --> 00:00:29,250 Here we have your host, Cindy and Sarantis. 8 00:00:29,666 --> 00:00:33,458 Hey everyone, welcome to Proteomics in Proximity. 9 00:00:33,458 --> 00:00:35,750 Today we have some exciting guests. 10 00:00:35,750 --> 00:00:41,500 We've got Rory Collins from the UK Biobank and Chris Whelan from J&J. 11 00:00:41,791 --> 00:00:46,000 We also have my colleague from Olink, Evan Mills. 12 00:00:46,500 --> 00:00:49,333 I would really like each of you to introduce yourself. 13 00:00:49,333 --> 00:00:53,041 Talk a little bit about your why and maybe a little bit 14 00:00:53,041 --> 00:00:57,208 about why you're here today and what we plan to discuss. 15 00:00:57,958 --> 00:00:59,625 Rory, let's start with you. 16 00:00:59,625 --> 00:01:04,208 Well, thanks very much, Cindy, for inviting me to talk about, UK Biobank 17 00:01:04,208 --> 00:01:07,750 and this fantastic step forward, 18 00:01:08,500 --> 00:01:11,583 in analyzing proteomics in UK Biobank. 19 00:01:12,416 --> 00:01:16,708 So, fundamentally, I'm a cardiovascular epidemiologist in clinical trials. 20 00:01:16,708 --> 00:01:20,958 So I've been at the University of Oxford for the last 40 odd years. 21 00:01:22,041 --> 00:01:23,041 And back in 22 00:01:23,041 --> 00:01:28,125 2005, I was asked by the Wellcome Trust and the medical Research Council 23 00:01:28,416 --> 00:01:32,666 if I would take on the role of establishing UK Biobank. 24 00:01:32,958 --> 00:01:37,583 So this cohort of half a million, British men and women, 25 00:01:38,333 --> 00:01:42,125 who provided lots of information from questionnaires, 26 00:01:42,125 --> 00:01:46,166 allowed us to measure them in all sorts of ways and to provide, 27 00:01:46,916 --> 00:01:49,916 biological samples, in particular blood samples 28 00:01:50,083 --> 00:01:53,083 that we stored, 20 years ago. 29 00:01:54,291 --> 00:01:57,291 We then been following them up with their consent, 30 00:01:57,291 --> 00:02:02,333 through linkage to all of their medical and other health related records, 31 00:02:03,000 --> 00:02:06,041 and importantly, making all of these data available 32 00:02:06,291 --> 00:02:11,041 to scientists around the world, whether academic or commercial scientists, 33 00:02:11,333 --> 00:02:15,666 to try to understand the determinants of different diseases 34 00:02:15,666 --> 00:02:18,666 and better ways to prevent and treat those diseases. 35 00:02:19,541 --> 00:02:23,500 The samples have had biochemistry done, hematology 36 00:02:23,500 --> 00:02:27,208 done, genetics done on them, including sequencing. 37 00:02:27,958 --> 00:02:31,250 But what's happening now, I think, is a massive step forward, 38 00:02:31,750 --> 00:02:35,125 the ability to measure thousands of proteins 39 00:02:35,500 --> 00:02:39,125 on these very large numbers of individuals 40 00:02:39,541 --> 00:02:42,916 is going to be, a huge, improvement 41 00:02:42,916 --> 00:02:46,916 in our ability to understand how to better prevent and treat disease. 42 00:02:47,791 --> 00:02:48,625 I love that. 43 00:02:48,625 --> 00:02:51,416 So, Chris, how about you background. 44 00:02:51,416 --> 00:02:54,958 And then I'd love it if you could talk about, you know, J&J's perspective. 45 00:02:55,250 --> 00:02:57,250 Great to be back, Cindy. Thanks for having me again. 46 00:02:57,250 --> 00:03:00,333 It's terrific to see Evan on the podcastthis time as well. 47 00:03:00,333 --> 00:03:04,500 So yes, I'm Chris Whelan and I lead the Pharma Proteomics project. 48 00:03:04,500 --> 00:03:08,083 I co-founded that consortium about, five years ago now. 49 00:03:08,708 --> 00:03:11,750 Formally, my PhD was in neuroscience and in genetics. 50 00:03:11,750 --> 00:03:15,625 But over the last few years, I've really transitioned and deep dived 51 00:03:15,750 --> 00:03:17,041 into proteome mix. 52 00:03:17,041 --> 00:03:19,833 I think that deep dive was driven by a desire 53 00:03:19,833 --> 00:03:23,666 to understand, human health and disease at a much finer grained level. 54 00:03:23,708 --> 00:03:28,583 I ultimately, I want to live in a world where we can directly monitor and detect 55 00:03:28,583 --> 00:03:32,041 and treat illnesses in a more powerful way than currently possible. 56 00:03:32,041 --> 00:03:35,041 And, UK Biobank is enabling that. 57 00:03:35,125 --> 00:03:37,041 And why do you see proteomics? 58 00:03:37,041 --> 00:03:41,041 You know, this proteomics project that that, Rory referenced, 59 00:03:41,333 --> 00:03:43,375 where do you see this as beneficial to pharma? 60 00:03:43,375 --> 00:03:45,000 Just at a very high level, because I think 61 00:03:45,000 --> 00:03:48,750 we're going to dig into this more as we along our discussion today. 62 00:03:48,750 --> 00:03:49,416 But I'd love just 63 00:03:50,375 --> 00:03:51,708 your why 64 00:03:51,708 --> 00:03:55,583 because you it's taken a lot of work to put this together. 65 00:03:55,583 --> 00:03:59,250 There are, you know, 13 pharma partners in the first project. 66 00:03:59,250 --> 00:04:02,166 The pilot of over 54,000 samples. 67 00:04:02,166 --> 00:04:06,041 And now there's 14 pharma partners in this latest iteration. 68 00:04:06,291 --> 00:04:07,750 Sure thing. Yeah, absolutely. 69 00:04:07,750 --> 00:04:11,000 I mean, I'll sound like a broken record soon, but I'm all about precision 70 00:04:11,000 --> 00:04:15,083 medicine, finding the right drug for the right patient at the right time. 71 00:04:15,083 --> 00:04:19,041 And I think proteomics will help us get there quicker than on the other tools 72 00:04:19,041 --> 00:04:20,458 that are currently available. 73 00:04:20,458 --> 00:04:22,708 I think it's the key that unlocks precision medicine. 74 00:04:22,708 --> 00:04:26,375 So you need a lot of, statistical power to do proteomics in a, 75 00:04:27,625 --> 00:04:29,666 sort of solid manner. 76 00:04:29,666 --> 00:04:31,208 And I can't think of a better cohort 77 00:04:31,208 --> 00:04:34,416 in which to do a really well power study than UK Biobank. 78 00:04:34,416 --> 00:04:38,000 So, you know, as we all know, we've just announced 79 00:04:38,000 --> 00:04:41,750 the latest iteration of the UK b PGP project. 80 00:04:41,750 --> 00:04:45,833 So it was 13 partners last time around, its 14 partners this time around. 81 00:04:45,833 --> 00:04:48,958 And the last time it was about 55,000 samples. 82 00:04:48,958 --> 00:04:53,625 This time we're starting with 300,000, and we hope to expand that to 600,000 83 00:04:53,625 --> 00:04:56,625 pending additional, sources of funding. 84 00:04:56,625 --> 00:04:58,250 Amazing. Super exciting. 85 00:04:58,250 --> 00:05:02,791 All right, Evan, you're on your why, your background, whatever you'd like to share. 86 00:05:03,291 --> 00:05:04,041 Sure. 87 00:05:04,041 --> 00:05:06,583 Thanks again, Cindy it's nice to be back on the podcast. 88 00:05:06,583 --> 00:05:09,750 I had a previous experience, 89 00:05:10,208 --> 00:05:13,500 so, I've been with proteomics companies, 90 00:05:13,500 --> 00:05:17,333 and I would consider next generation proteomics companies for about 11 years. 91 00:05:17,875 --> 00:05:21,958 I started my career as a research scientist in neuroscience and oncology. 92 00:05:23,250 --> 00:05:23,958 But my goal was 93 00:05:23,958 --> 00:05:26,958 always to do something that could actually impact patients. 94 00:05:27,041 --> 00:05:31,791 So I moved into a pharmaceutical, sales role, which was not very satisfying, 95 00:05:31,791 --> 00:05:32,333 to be frank. 96 00:05:32,333 --> 00:05:36,000 But I've been in the life science tools business for about 16 years. 97 00:05:36,416 --> 00:05:41,083 And, my goal is to put the best tools in the hands of the best scientists 98 00:05:41,083 --> 00:05:44,875 on the planet to make meaningful change towards improving human health. 99 00:05:45,583 --> 00:05:48,583 And so, fortunately, Chris and I had lunch, 100 00:05:48,916 --> 00:05:52,916 one fateful day in Boston, I think it was probably, gosh, five years ago now. 101 00:05:52,916 --> 00:05:55,000 And Chris just asked the question. 102 00:05:55,000 --> 00:05:57,833 He said, hey, I'm on the UKB steering committee. 103 00:05:57,833 --> 00:06:00,416 And, you know, we were thinking of a phenotypic 104 00:06:00,416 --> 00:06:03,208 data arm, like, what could we do after sequencing? 105 00:06:03,208 --> 00:06:06,208 Right? Where we were doing whole exome, we're going to do whole genome. 106 00:06:06,208 --> 00:06:07,666 What do we do next? 107 00:06:07,666 --> 00:06:11,458 And he said, do you think all and could possibly measure 50,000 samples. 108 00:06:11,458 --> 00:06:15,875 And at the time it was a bit of a pipe dream, but I kind of knew what was coming. 109 00:06:15,875 --> 00:06:17,666 And I said, I think we can. 110 00:06:17,666 --> 00:06:21,166 And so that began this beautiful process that brought us to where we are 111 00:06:21,166 --> 00:06:25,875 now, where, I've been in the fortunate position of, representing a technology 112 00:06:25,875 --> 00:06:29,750 that's really, enabling quite a bit for the research community and, 113 00:06:30,625 --> 00:06:34,541 working with Rory and team, you know, the combination of really, 114 00:06:35,833 --> 00:06:37,708 game changing tools 115 00:06:37,708 --> 00:06:43,375 with unique to the world resources with, people like Chris that have 116 00:06:43,375 --> 00:06:47,166 the passion and motivation to make things happen, has brought us to where we are. 117 00:06:47,166 --> 00:06:50,375 So, I'm very fortunate and excited to see what comes after this. 118 00:06:50,916 --> 00:06:51,750 Amazing. 119 00:06:51,750 --> 00:06:54,583 As an aside, I will say the episodes 120 00:06:54,583 --> 00:06:58,250 that Evan and Chris were on, respectively, 121 00:06:58,583 --> 00:07:01,333 are two of the episodes that I get the most inquiries 122 00:07:01,333 --> 00:07:02,958 on that we get the most hits on. 123 00:07:02,958 --> 00:07:04,791 There are very popular episodes. 124 00:07:04,791 --> 00:07:08,000 In fact, somebody sent me an email saying that they wanted to work 125 00:07:08,000 --> 00:07:11,416 for Evan after his podcast episode, so you should go back. 126 00:07:11,458 --> 00:07:13,166 Plus, 127 00:07:13,166 --> 00:07:15,833 we'll put a link to that episode and Chris's 128 00:07:15,833 --> 00:07:19,000 episode in the show notes, because those were very good episodes. 129 00:07:19,708 --> 00:07:22,708 The work that you all have 130 00:07:23,458 --> 00:07:26,250 really spearheaded 131 00:07:26,250 --> 00:07:30,125 and, consolidated resources to do 132 00:07:31,500 --> 00:07:34,416 required money. 133 00:07:34,416 --> 00:07:39,416 And that money in part has come from pharmaceutical companies. 134 00:07:39,750 --> 00:07:42,166 In part, it's come from an investment in. Olink. 135 00:07:42,166 --> 00:07:45,625 I think Evan, you and I both know that's been, you know, a lot of 136 00:07:45,625 --> 00:07:49,291 of a subject of internal conversations where we're really about 137 00:07:50,083 --> 00:07:54,000 advancing precision medicine here at Olink as well, and understanding diversity. 138 00:07:54,000 --> 00:07:57,666 And I think this next step will have a lot of diverse samples in it. 139 00:07:59,208 --> 00:08:03,416 You know, ten times the ones that they had in the first project. 140 00:08:03,875 --> 00:08:07,375 But but when thinking about funding, certainly 141 00:08:07,375 --> 00:08:12,291 the UK government has been a big supporter of the UK Biobank. 142 00:08:12,791 --> 00:08:17,541 And so Rory in particular you if you're in an elevator 143 00:08:17,541 --> 00:08:22,375 and you need to talk to someone from the NHS or from from Wellcome Trust 144 00:08:22,375 --> 00:08:25,416 or from one of the funding agencies that 145 00:08:25,791 --> 00:08:29,083 where you're you're familiar with their goals. 146 00:08:29,458 --> 00:08:33,041 What is your pitch about why proteomics should be done 147 00:08:33,625 --> 00:08:37,791 on a large cohort with outcome data 148 00:08:38,000 --> 00:08:41,000 like the health records in the UK Biobank? 149 00:08:41,166 --> 00:08:43,833 What do you say in that elevator? 150 00:08:45,708 --> 00:08:46,416 Well, the first thing I'd 151 00:08:46,416 --> 00:08:49,500 say is, why should they engage with the UK Biobank? 152 00:08:50,250 --> 00:08:53,000 So. So what's so important about UK Biobank? 153 00:08:53,000 --> 00:08:57,000 It's not a research project or, it's not a national resource. 154 00:08:57,000 --> 00:08:58,250 It's an international resource. 155 00:08:58,250 --> 00:09:02,958 So it's something that's used by thousands of scientists around the world. 156 00:09:03,000 --> 00:09:06,208 It is the Hubble telescope 157 00:09:06,500 --> 00:09:10,708 or the CERN accelerator or biological Science. 158 00:09:10,708 --> 00:09:14,958 Now, industry in academia go to the UK Biobank data, 159 00:09:15,666 --> 00:09:18,291 because it allows them to do things 160 00:09:18,291 --> 00:09:21,291 that they couldn't otherwise do. 161 00:09:21,375 --> 00:09:23,083 And I think the UK government 162 00:09:23,083 --> 00:09:26,083 are proud to have been part of creating that, 163 00:09:26,541 --> 00:09:28,833 through the Medical Research Council and obviously 164 00:09:28,833 --> 00:09:31,833 the Wellcome Trust charity. 165 00:09:33,541 --> 00:09:36,541 I think there are two points that one can make to them 166 00:09:37,000 --> 00:09:40,875 by putting resource into UK Biobank, creating UK Biobank. 167 00:09:40,875 --> 00:09:45,708 They've leveraged enormous investment, from external sources. 168 00:09:47,083 --> 00:09:50,000 For the sequencing for 169 00:09:50,000 --> 00:09:54,958 the proteomics now for imaging of participants, 170 00:09:55,916 --> 00:09:59,875 so from a financial perspective, their investment is leveraging additional 171 00:10:00,250 --> 00:10:05,041 investment in a resource that is of value to UK scientists and global scientists. 172 00:10:06,208 --> 00:10:08,125 More importantly, I think 173 00:10:08,125 --> 00:10:10,875 from all of our perspective 174 00:10:10,875 --> 00:10:13,875 is they're leveraging better health. 175 00:10:14,208 --> 00:10:18,750 They're providing data that is allow that is allowing scientists 176 00:10:18,750 --> 00:10:22,625 around the world to work out better how to prevent 177 00:10:23,250 --> 00:10:25,666 and treat disease. 178 00:10:25,666 --> 00:10:28,833 And I think what's really important about the proteomics, 179 00:10:29,666 --> 00:10:31,833 as Chris has alluded to, 180 00:10:31,833 --> 00:10:34,833 is that in a way, it's the common pathway. 181 00:10:35,583 --> 00:10:38,958 There's been lots of excitement over the last ten, 182 00:10:39,000 --> 00:10:42,000 20 years around genetics. 183 00:10:42,750 --> 00:10:45,291 But the genetics 184 00:10:45,291 --> 00:10:48,291 lead to disease through a pathway, 185 00:10:48,416 --> 00:10:53,291 and that's a common pathway for lifestyle, environment, genetics and other factors. 186 00:10:53,583 --> 00:10:56,125 And the proteins will be that common pathway. 187 00:10:56,125 --> 00:11:00,625 And I think that's why the analysis of proteomics, thousands 188 00:11:00,625 --> 00:11:05,916 of proteins, thousands of pathways to tell us how lifestyle environment 189 00:11:06,208 --> 00:11:11,333 genetics leads a particular individual to determine a particular disease. 190 00:11:11,500 --> 00:11:15,083 And that's where I think we're going to see massive 191 00:11:17,875 --> 00:11:20,291 knowledge generated, 192 00:11:20,291 --> 00:11:24,875 which will help us to work out how to better prevent and treat disease. 193 00:11:25,291 --> 00:11:29,208 And that would be my rather long elevator pitch. 194 00:11:29,208 --> 00:11:32,208 But, I was in a tall building. 195 00:11:33,208 --> 00:11:34,916 That's a long elevator ride. 196 00:11:34,916 --> 00:11:39,958 I will say you've created an environment where the sharing is controlled 197 00:11:39,958 --> 00:11:45,083 and managed and safe that welcomes international participants 198 00:11:45,083 --> 00:11:47,833 to feel comfortable interacting with those data. 199 00:11:47,833 --> 00:11:51,000 And that's, I think, a fundamental piece that I've seen 200 00:11:51,333 --> 00:11:54,125 that people really appreciate there. 201 00:11:54,125 --> 00:11:55,541 Chris, I'd love your thoughts on 202 00:11:55,541 --> 00:11:59,458 how you convince leadership within not just your company. 203 00:11:59,500 --> 00:12:05,166 How do you support those scientists that are representing other companies 204 00:12:05,166 --> 00:12:08,875 in speaking to their leadership about being in a part of 205 00:12:10,041 --> 00:12:13,041 this team effort, this consortium? 206 00:12:13,708 --> 00:12:16,500 Somewhat ironically, for a proteomics consortium, 207 00:12:16,500 --> 00:12:20,000 we're actually predominantly populated by human geneticists. 208 00:12:20,333 --> 00:12:21,666 That's actually been a huge, 209 00:12:23,125 --> 00:12:24,541 driver in, 210 00:12:24,541 --> 00:12:28,916 convincing our leadership teams that and the value of proteomics. 211 00:12:29,458 --> 00:12:31,750 We've been 212 00:12:31,750 --> 00:12:34,041 advocating for the last 5 213 00:12:34,041 --> 00:12:38,166 to 6 years on the value of human genetics for drug discovery. 214 00:12:38,541 --> 00:12:42,708 I think we've all seen the papers and the presentations that I've suggested that, 215 00:12:42,833 --> 00:12:45,083 if your drug target has supporting evidence 216 00:12:45,083 --> 00:12:48,333 from human genetics, it's at least twice as likely to ultimately 217 00:12:48,333 --> 00:12:51,333 make it to the market or be approved by regulatory bodies. 218 00:12:51,875 --> 00:12:55,541 But there's still a lot of missing pieces between, you know, the genetic variant 219 00:12:55,541 --> 00:12:57,041 and the actual disease phenotype. 220 00:12:57,041 --> 00:13:01,041 And I think the proteomics is increasingly being recognized as a tool that we can 221 00:13:01,041 --> 00:13:05,708 use to bridge that gap and help understand that and much more finer grained, 222 00:13:06,208 --> 00:13:07,250 molecular level, 223 00:13:07,250 --> 00:13:11,458 what's happening between that pathway between gene and disease, phenotypes. 224 00:13:11,458 --> 00:13:15,291 So there's growing traction is growing appreciation from our heads of R&D, 225 00:13:15,625 --> 00:13:19,875 that this is a very important, potentially transformative new research tool, even. 226 00:13:19,875 --> 00:13:22,875 Any thoughts you want to share on any of that? 227 00:13:23,000 --> 00:13:26,333 I mean, you had to convince our internal 228 00:13:26,875 --> 00:13:29,541 leadership of the importance of this project. 229 00:13:29,541 --> 00:13:33,875 I don't think it you know, I think they they came on board pretty quickly. 230 00:13:33,875 --> 00:13:38,958 But no, but it's a fair questions in the I mean, to enable really 231 00:13:38,958 --> 00:13:43,375 transformative projects sometimes requires a commercial entity to take some risk. 232 00:13:43,916 --> 00:13:46,541 And I think that's exactly what happened here. 233 00:13:47,833 --> 00:13:51,125 The promise of proteomics is, is fairly clear, 234 00:13:51,125 --> 00:13:54,208 just based on the central dogma and what you've heard from Rory and Chris. 235 00:13:54,208 --> 00:13:57,416 I mean, there's clear utility in looking at the proteins, but, 236 00:13:58,375 --> 00:14:03,250 technological limitations and frankly, cost have been significant barriers. 237 00:14:03,750 --> 00:14:06,750 So I have to give all credit to John Heimer. 238 00:14:07,083 --> 00:14:10,375 You know, CEO of Olink, who really had the foresight 239 00:14:10,375 --> 00:14:13,250 to go to the board and push for something 240 00:14:13,250 --> 00:14:16,541 that was simply unheard of to enable the project. 241 00:14:17,208 --> 00:14:20,416 And I think we can't underscore the importance of, 242 00:14:20,666 --> 00:14:24,541 you know, companies that have a really powerful technology. 243 00:14:24,541 --> 00:14:28,166 Sometimes you have to put profits aside and just think about impact. 244 00:14:29,000 --> 00:14:31,791 And I think this is a really good example of that. 245 00:14:31,791 --> 00:14:33,000 I love that. 246 00:14:33,000 --> 00:14:36,958 I think it's a technology that I've described as a rising tide 247 00:14:36,958 --> 00:14:41,083 lifts all boats and there are many of those technologies 248 00:14:41,083 --> 00:14:45,375 around, for human health in the context of genetics, 249 00:14:45,750 --> 00:14:51,000 I've seen few that are dull, I'd say punching above their weight 250 00:14:51,000 --> 00:14:54,583 and, you know, over delivering what I expected anyway. 251 00:14:55,000 --> 00:14:57,958 So I am very excited about things to, 252 00:14:57,958 --> 00:15:02,750 I'd like to touch back on diversity in running the entire UK Biobank, 253 00:15:03,000 --> 00:15:05,833 for example, with the whole exome data, whole 254 00:15:05,833 --> 00:15:08,833 sequencing data, whole genome sequencing data. 255 00:15:08,916 --> 00:15:12,708 Yeah, there's a large representation of African diaspora 256 00:15:13,041 --> 00:15:18,625 and South Asian, ancestry that I just like people to realize. 257 00:15:18,625 --> 00:15:19,750 I just want to point it out, 258 00:15:19,750 --> 00:15:23,000 because that's one of the things that I'm particularly excited about. 259 00:15:23,375 --> 00:15:28,375 And the plans for running this larger next step in proteomics. 260 00:15:28,375 --> 00:15:31,500 So I wonder if, if any of you would like to make comments on that? 261 00:15:31,500 --> 00:15:34,708 Chris, I'll ask you first if that's all right or we can 262 00:15:35,458 --> 00:15:36,625 we can go to you, Rory. 263 00:15:36,625 --> 00:15:39,583 I'm certainly happy to comment on this. 264 00:15:39,583 --> 00:15:40,916 I mean, as an epidemiologist. 265 00:15:40,916 --> 00:15:44,541 So I think people being much more similar than dissimilar. 266 00:15:46,166 --> 00:15:50,041 So I find the focus on diversity 267 00:15:51,500 --> 00:15:53,750 a little bit odd in a way. 268 00:15:53,750 --> 00:15:56,750 You know, we are all human. 269 00:15:57,166 --> 00:15:58,833 Blood pressure is strongly 270 00:15:58,833 --> 00:16:02,125 predictive of the risk of stroke in all ethnic groups. 271 00:16:03,000 --> 00:16:06,000 But cholesterol is strongly predictive 272 00:16:06,125 --> 00:16:09,875 of the risk of cardiovascular disease in all ethnic groups. 273 00:16:10,583 --> 00:16:14,166 The reason why, as an epidemiologist, one would want to measure 274 00:16:14,166 --> 00:16:18,083 cholesterol, for example, in different populations 275 00:16:18,583 --> 00:16:22,583 is that the levels are different in different populations. 276 00:16:22,583 --> 00:16:26,000 So it was really our work in China 277 00:16:26,541 --> 00:16:30,583 showing that very much lower levels of cholesterol than we see in the West 278 00:16:30,833 --> 00:16:34,041 were associated with very much lower rates of coronary artery disease. 279 00:16:34,541 --> 00:16:37,541 That drove our studies in the UK. 280 00:16:38,750 --> 00:16:42,750 To look at lowering cholesterol in people with so-called normal 281 00:16:42,958 --> 00:16:47,000 cholesterol levels, and demonstrated that we could lower their risk. 282 00:16:47,666 --> 00:16:52,916 So the reason for thinking about studies in different settings, 283 00:16:54,291 --> 00:16:55,416 is to be able 284 00:16:55,416 --> 00:17:01,166 to study a wider range of risk factor levels or to study populations 285 00:17:01,166 --> 00:17:06,291 that have different levels of disease, higher rates, or lower rates of disease. 286 00:17:06,291 --> 00:17:11,541 If you want to study cerebral hemorrhage, do your studies in China, not in the UK 287 00:17:11,791 --> 00:17:14,791 or in Western populations, because it's much more common there. 288 00:17:15,833 --> 00:17:21,208 And so what we need is not, diversity so much in terms of ethnicity, 289 00:17:21,208 --> 00:17:26,666 but diversity in terms of risk exposure is the reason why I think that that's 290 00:17:26,666 --> 00:17:31,375 valuable from a genetic perspective, is that there have been particular 291 00:17:32,083 --> 00:17:35,000 genetic, variants, 292 00:17:35,000 --> 00:17:38,583 if you like, that, have been in particular populations. 293 00:17:38,750 --> 00:17:41,750 And that makes it very valuable to be able to study, 294 00:17:42,500 --> 00:17:45,166 genetics in, in different populations. 295 00:17:45,166 --> 00:17:49,375 But but equally, as I say, for studying environmental or lifestyle, 296 00:17:50,416 --> 00:17:53,416 in different populations as. 297 00:17:54,291 --> 00:17:56,375 You're right that there will be quite 298 00:17:56,375 --> 00:17:59,666 a lot of diversity within UK Biobank, 299 00:18:00,500 --> 00:18:02,916 but not enough to really 300 00:18:02,916 --> 00:18:06,208 look at the full range of exposure levels 301 00:18:06,833 --> 00:18:09,625 and to look at the full range of disease levels. 302 00:18:09,625 --> 00:18:14,250 But in the same way that the first 50,000 participants in UK 303 00:18:14,250 --> 00:18:17,250 Biobank having proteomics is a pilot 304 00:18:17,875 --> 00:18:20,750 for doing it in the whole of UK Biobank, 305 00:18:20,750 --> 00:18:25,125 I see UK Biobank as being a pilot for doing proteomics. 306 00:18:25,125 --> 00:18:28,375 In the other large scale studies that have been established 307 00:18:29,250 --> 00:18:34,416 in other parts of the world, in Mexico, in China, in North America, 308 00:18:34,416 --> 00:18:39,541 particularly in Hispanic populations, and say the all of us study. 309 00:18:40,083 --> 00:18:42,916 So no one study answers all questions. 310 00:18:42,916 --> 00:18:46,916 I think what we're doing is building on the knowledge we have 311 00:18:46,916 --> 00:18:48,583 and then building on that knowledge. 312 00:18:48,583 --> 00:18:52,708 And that's why I think this is a very, a very important next step 313 00:18:52,875 --> 00:18:55,875 in understanding the diversity of human disease. 314 00:18:56,083 --> 00:18:59,458 I like how you flip that from thinking about diversity, which is a, 315 00:19:00,625 --> 00:19:01,375 a, you 316 00:19:01,375 --> 00:19:04,375 know, foundationally sort of genetic construct 317 00:19:04,458 --> 00:19:07,666 to looking at representation of disease state, 318 00:19:08,000 --> 00:19:10,708 because that's where we're going to be able to understand 319 00:19:10,708 --> 00:19:14,750 more about proteins showing up in those disease states. 320 00:19:14,750 --> 00:19:18,541 And so representing, you know, as much of an understanding 321 00:19:18,958 --> 00:19:21,583 in epidemiology as we can. 322 00:19:21,583 --> 00:19:25,500 Yeah, the kind of minority of people from, 323 00:19:26,125 --> 00:19:27,125 African backgrounds 324 00:19:27,125 --> 00:19:31,375 or Asian backgrounds in UK Biobank will not be the ones that tell us 325 00:19:31,375 --> 00:19:35,541 predominantly about the relevance of proteins to disease in Africa, Eurasia. 326 00:19:35,875 --> 00:19:38,875 It will be the totality of UK Biobank that will do that. 327 00:19:39,166 --> 00:19:42,166 That's really helpful. Perspective. 328 00:19:42,250 --> 00:19:44,333 And so with this expansion project, I mean, as Rory said, 329 00:19:44,333 --> 00:19:47,833 we'll not only capture more samples from underrepresented populations, 330 00:19:47,833 --> 00:19:50,833 which is absolutely crucial, but will also capture, in my opinion, 331 00:19:51,375 --> 00:19:53,541 samples from underrepresented illnesses. 332 00:19:53,541 --> 00:19:58,625 So we're going to start by asking 300,000 samples, 250,000. 333 00:19:58,625 --> 00:20:01,875 Approximately of those samples will be from the baseline visit. 334 00:20:01,916 --> 00:20:06,958 And then an additional approximate 50,000 will be from various repeat assessments. 335 00:20:07,500 --> 00:20:11,000 As somebody who, primarily works in neuroscience and in rare diseases, 336 00:20:11,458 --> 00:20:14,250 we were maybe somewhat underpowered to study 337 00:20:14,250 --> 00:20:17,541 certain diseases of interest in that pilot proteomics data set. 338 00:20:17,666 --> 00:20:20,125 Let's just take an almost like moisty gravis. 339 00:20:20,125 --> 00:20:20,791 Right? 340 00:20:20,791 --> 00:20:23,708 That's an illness that I'm quite interested in. 341 00:20:23,708 --> 00:20:26,875 But we only had a couple of dozen cases in that pilot project. 342 00:20:27,000 --> 00:20:29,875 We'll go from a couple dozen to hundreds of cases. 343 00:20:29,875 --> 00:20:31,541 Schizophrenia is another good example. 344 00:20:31,541 --> 00:20:33,333 I have a lot of interest in that. 345 00:20:33,333 --> 00:20:35,666 We have maybe 150 cases in the pilot. 346 00:20:35,666 --> 00:20:38,916 We'll go to maybe more than 2000 cases in the full scale project. 347 00:20:38,916 --> 00:20:41,916 So that will be a game changer for biomarker discovery. 348 00:20:42,083 --> 00:20:45,958 But it's also incredibly exciting because of those folks 349 00:20:45,958 --> 00:20:50,250 with repeat samples, there will be approximately up to 80,000, 350 00:20:51,000 --> 00:20:54,000 maybe up to 40,000 in the first 300 K cohort, 351 00:20:54,000 --> 00:20:58,916 but ultimately up to 80,000 that will have plasma proteomics on samples 352 00:20:58,916 --> 00:21:02,416 that are collected contemporaneously with whole body MRI scans. 353 00:21:02,416 --> 00:21:06,291 So that will give us next level biological granularity. 354 00:21:06,291 --> 00:21:08,666 We can go from microscopic to microscopic. 355 00:21:08,666 --> 00:21:10,791 And that didn't really exist in the pilot study. 356 00:21:10,791 --> 00:21:14,416 So you can imagine not just saying if this blood protein 357 00:21:14,666 --> 00:21:18,333 is changed in people with bipolar disorder, you could say this 358 00:21:18,333 --> 00:21:22,083 blood protein associates with white matter microstructure 359 00:21:22,166 --> 00:21:26,166 alterations in the corpus callosum of people with bipolar disorder. 360 00:21:26,166 --> 00:21:28,916 It just gives a level of granularity that could really be game changing. 361 00:21:29,875 --> 00:21:30,416 Yeah, it 362 00:21:30,416 --> 00:21:33,416 feels like functional genomics, you know, like 363 00:21:33,541 --> 00:21:37,375 it just feels like we're getting it doesn't answer all questions. 364 00:21:37,375 --> 00:21:40,625 It's corroborative, perhaps with true methods of functional genomics. 365 00:21:40,708 --> 00:21:43,541 I just think there's so much potential. 366 00:21:43,541 --> 00:21:46,041 Chris, would you be willing to talk to us a little bit 367 00:21:46,041 --> 00:21:50,083 about how bringing you mentioned genetics helps us? 368 00:21:50,083 --> 00:21:51,750 I think there's a Matt Nelson paper on this. 369 00:21:51,750 --> 00:21:53,125 There's a couple of other publications around 370 00:21:53,125 --> 00:21:57,583 how genetics helps build confidence in clinical trial success. 371 00:21:57,958 --> 00:22:01,166 How does, bringing proteins, 372 00:22:01,416 --> 00:22:05,000 genetics and clinical outcome data like we have in the UK Biobank? 373 00:22:05,000 --> 00:22:08,208 How does that help your company 374 00:22:08,208 --> 00:22:11,583 or pharma company in general, have more confidence 375 00:22:11,583 --> 00:22:16,500 in the therapeutic targets that they're building molecules for? 376 00:22:17,750 --> 00:22:20,916 So there's a multitude of ways that we're using these kinds of data. 377 00:22:20,916 --> 00:22:24,791 I think the lowest hanging fruit, as you pointed out, Cindy, is specifically 378 00:22:24,875 --> 00:22:27,416 for target discovery. 379 00:22:27,416 --> 00:22:29,500 We mentioned earlier the, 380 00:22:30,541 --> 00:22:31,041 increased 381 00:22:31,041 --> 00:22:34,583 confidence in drug targets that have supporting evidence from human genetics, 382 00:22:34,916 --> 00:22:37,625 what the protein data allow us to do when they're combined with 383 00:22:37,625 --> 00:22:40,708 genomics is actually pinpoint the proteins that we should be targeting. 384 00:22:40,916 --> 00:22:44,041 Obviously, most of the drugs that we develop are targeting proteins. 385 00:22:44,041 --> 00:22:45,083 They're not targeting genes. 386 00:22:45,083 --> 00:22:48,750 So just finding the gene that's linked to your disease and having high confidence 387 00:22:48,833 --> 00:22:51,750 in the gene linked to your disease doesn't get you all the way. 388 00:22:51,750 --> 00:22:55,250 Ultimately, you need to figure out which protein has a causal link to disease. 389 00:22:55,375 --> 00:22:58,333 So we employ techniques like Mendelian randomization 390 00:22:58,333 --> 00:23:02,583 that help identify or establish that causal association with disease. 391 00:23:02,708 --> 00:23:06,833 And we've done this across the board for, countless disease areas. 392 00:23:06,833 --> 00:23:09,833 The example that I often point to, because it's my team 393 00:23:09,833 --> 00:23:13,041 at JNJ who did a lot of the work, is, Parkinson's disease. 394 00:23:13,041 --> 00:23:15,000 We did some proteome genomic modeling. 395 00:23:15,000 --> 00:23:18,000 We identified dozens of new targets for Parkinson's 396 00:23:18,000 --> 00:23:22,083 disease that weren't previously identified using traditional Gwas. 397 00:23:22,083 --> 00:23:24,500 So galectin three is a good example there. 398 00:23:24,500 --> 00:23:26,750 We published in that recently in nature columns. 399 00:23:26,750 --> 00:23:29,875 But we've also identified inflammatory targets for schizophrenia 400 00:23:29,875 --> 00:23:32,500 and Alzheimer's disease and a variety of other conditions. 401 00:23:32,500 --> 00:23:35,375 I would say that one of the things I'm most excited about 402 00:23:35,375 --> 00:23:39,041 in terms of the applications of proteomics in the context of pharma, 403 00:23:39,291 --> 00:23:44,166 is how we're applying eye on the protein data themselves in a sort of an unbiased 404 00:23:44,166 --> 00:23:48,625 manner to find insights, new insights into different kinds of complex illnesses. 405 00:23:48,625 --> 00:23:53,333 So, the example I often point towards is major depressive disorder, depression. 406 00:23:53,750 --> 00:23:56,750 We are currently writing of a paper where we've identified 407 00:23:57,208 --> 00:24:01,041 three different, kinds of, depression based on the proteomics, 408 00:24:01,041 --> 00:24:02,208 one that has a strong 409 00:24:02,208 --> 00:24:05,208 inflammatory component and one that has a metabolic component, 410 00:24:05,208 --> 00:24:08,833 and one that seems to involve disruptions to synapses and neurons, 411 00:24:09,333 --> 00:24:12,833 that could potentially lead to new and tailored treatments for depression. 412 00:24:13,250 --> 00:24:14,875 Pending some further analysis. 413 00:24:14,875 --> 00:24:18,791 You can imagine a world where, you recruit into your clinical trial based on, 414 00:24:19,666 --> 00:24:23,375 an underlying proteomics signature, not just a clinical, signature. 415 00:24:23,666 --> 00:24:24,500 So in principle, 416 00:24:24,500 --> 00:24:28,750 I can absolutely see the trajectory of improving clinical trial success. 417 00:24:28,750 --> 00:24:32,541 And I'm excited to see, once we've had these data around a while, 418 00:24:32,791 --> 00:24:34,958 what the actual impact is. 419 00:24:34,958 --> 00:24:36,583 Yeah. Thank you. 420 00:24:36,583 --> 00:24:36,875 Yeah. 421 00:24:36,875 --> 00:24:41,958 I mean, that brings to mind a question, I think, for both, you, Chris and Rory. 422 00:24:42,416 --> 00:24:44,750 You know, Rory as a cardiologist, right. 423 00:24:44,750 --> 00:24:45,333 So some 424 00:24:47,000 --> 00:24:50,541 cardiovascular epidemiologists and someone who has spent time, 425 00:24:50,541 --> 00:24:53,625 you know, in the world of caring for patients and individuals. 426 00:24:53,625 --> 00:24:56,791 And Chris does a very entrepreneurial thinker in this 427 00:24:56,791 --> 00:24:59,958 space who's had firsthand experience with these data. 428 00:25:01,041 --> 00:25:03,750 You know, what do you think of the most exciting 429 00:25:03,750 --> 00:25:07,750 near future possibilities for clinical impact? 430 00:25:08,833 --> 00:25:11,833 Well, I think it comes to the right person 431 00:25:12,125 --> 00:25:15,125 point that Chris has made. 432 00:25:15,375 --> 00:25:19,166 And he gave a beautiful example there of the depression. 433 00:25:19,875 --> 00:25:22,375 So you, 434 00:25:22,375 --> 00:25:25,041 there's the right treatment for the right person. 435 00:25:25,041 --> 00:25:27,583 And if there are 436 00:25:27,583 --> 00:25:29,625 more than one type of depression 437 00:25:29,625 --> 00:25:34,791 with more than one kind of pathway, then the idea that you would use 438 00:25:34,791 --> 00:25:38,083 a specific treatment for a specific subtype, 439 00:25:38,583 --> 00:25:41,583 I think is exciting, 440 00:25:41,666 --> 00:25:44,250 that probably will take some time, 441 00:25:44,250 --> 00:25:49,333 before you get treatments that are specific for particular subtypes 442 00:25:50,291 --> 00:25:53,291 where I can see very rapid, 443 00:25:53,708 --> 00:25:56,708 emergence of value from the proteomic 444 00:25:56,708 --> 00:25:59,708 data is is coming back to this right person. 445 00:25:59,916 --> 00:26:04,708 Can we identify the people who are at risk of developing a disease 446 00:26:05,125 --> 00:26:09,208 much more precisely than we do at the present time? 447 00:26:09,500 --> 00:26:12,500 Can we use the proteomic data, 448 00:26:12,708 --> 00:26:17,625 combined with other data to identify the people who will develop a disease, 449 00:26:18,208 --> 00:26:20,666 and therefore be able to intervene with treatments? 450 00:26:20,666 --> 00:26:25,458 We already have, in a focused way, but early in the condition, 451 00:26:26,458 --> 00:26:30,000 and I think that may well be something that comes out of these data 452 00:26:30,000 --> 00:26:33,708 very rapidly and could be implemented very rapidly. 453 00:26:34,166 --> 00:26:38,666 Who should we be giving cholesterol lowering drugs to at the moment? 454 00:26:38,666 --> 00:26:41,666 We wait until they get to a certain age, pretty much. 455 00:26:42,166 --> 00:26:45,166 Or we wait until they have a cardiovascular event. 456 00:26:45,583 --> 00:26:48,416 But could we use the genetic data 457 00:26:48,416 --> 00:26:53,333 and the proteomic data to identify the people who we should intervene 458 00:26:53,333 --> 00:26:58,083 in before their arteries flare up in order to avoid them? 459 00:26:58,750 --> 00:27:01,875 Ever getting to that point where they have an event. 460 00:27:02,375 --> 00:27:04,916 It makes sense that the, the genetics and the polygenic 461 00:27:04,916 --> 00:27:07,916 risk scores are going to going to tell some of the story. 462 00:27:08,125 --> 00:27:11,875 I think proteins, as we've talked about, are catching 463 00:27:12,125 --> 00:27:16,125 additional information that are telling us about the person today. 464 00:27:16,250 --> 00:27:20,250 Well, they are they combine the genetics, the lifestyle, the environment 465 00:27:20,250 --> 00:27:23,375 that pretty much, you know, to a large extent the common pathways. 466 00:27:24,000 --> 00:27:24,291 Yeah. 467 00:27:24,291 --> 00:27:28,291 And we've seen lots of publications coming out recently with the first pilot data 468 00:27:28,583 --> 00:27:32,166 with polygenic risk scores, protein risk scores and show, 469 00:27:32,458 --> 00:27:34,166 that they, that they complement each other, 470 00:27:34,166 --> 00:27:36,208 that they're really, supportive of each other. 471 00:27:37,375 --> 00:27:37,583 Yeah. 472 00:27:37,583 --> 00:27:40,791 No, Chris, I mean, I'm curious to get your perspectives and thoughts. 473 00:27:40,791 --> 00:27:43,916 I mean, I know that we've certainly had some conversations on the topic 474 00:27:43,916 --> 00:27:47,458 and it's super exciting, you know, seeing all the publications. 475 00:27:47,458 --> 00:27:49,250 But, you know, what? 476 00:27:49,250 --> 00:27:52,708 What are your thoughts on near-term possibilities and what could be tractable? 477 00:27:53,000 --> 00:27:54,958 Yeah, I was going to say I mean, you and I have talked 478 00:27:54,958 --> 00:27:58,250 for hours and hours over the phone and over, over a few beers. 479 00:27:58,250 --> 00:28:01,208 On the topic of disease prediction is something we both are 480 00:28:01,208 --> 00:28:02,666 incredibly passionate about. 481 00:28:02,666 --> 00:28:03,958 And I do think, as Rory says, 482 00:28:03,958 --> 00:28:06,916 that we'll see the implications of those prediction tools. 483 00:28:06,916 --> 00:28:10,250 I would say by the end of the decade, I think even shorter term, 484 00:28:10,750 --> 00:28:13,250 we'll probably see the most clinical update 485 00:28:13,250 --> 00:28:16,916 uptake in the very short term in pharmaceutical trials. 486 00:28:16,916 --> 00:28:18,708 And I'll say that I'll put my money where my mouth is. 487 00:28:18,708 --> 00:28:19,916 I think we're already doing this. 488 00:28:19,916 --> 00:28:23,083 We're already employing proteomics on trials to help 489 00:28:23,083 --> 00:28:26,333 better understand the impact of the drugs that we are. 490 00:28:27,208 --> 00:28:30,208 You know, that we're putting through phase one, phase two, phase three. 491 00:28:30,208 --> 00:28:33,416 I'm applying it in our neuroscience trials, a change showing how 492 00:28:33,416 --> 00:28:35,916 different drugs impact the blood proteome, 493 00:28:35,916 --> 00:28:39,708 with potential implications for repurposing and for drug filings. 494 00:28:39,833 --> 00:28:42,791 I think I just saw a paper published in Nature Medicine yesterday, which did 495 00:28:42,791 --> 00:28:47,291 this for, semaglutide showed the proteomic impact of semaglutide. 496 00:28:48,000 --> 00:28:50,375 So you'll see more and more of that over the coming years. 497 00:28:50,375 --> 00:28:51,541 I'm sure. 498 00:28:51,541 --> 00:28:51,750 Yeah. 499 00:28:51,750 --> 00:28:55,500 And if I could just share from, you know, my viewpoint, which is one of supporting 500 00:28:55,750 --> 00:28:59,291 a lot of scientists, both in the pharmaceutical space and then 501 00:28:59,333 --> 00:29:04,458 in the, you know, more traditionally academic research grant driven space, 502 00:29:05,208 --> 00:29:08,750 there's a real, and a coming together, 503 00:29:09,708 --> 00:29:13,250 merging is probably the better word of these worlds, right? 504 00:29:13,500 --> 00:29:17,250 Where there's folks that have these beautifully characterized cohorts 505 00:29:17,666 --> 00:29:22,125 where if they have the access to population data from UK Biobank, 506 00:29:22,458 --> 00:29:26,166 they can then kind of hone in on a disease area of interest that they've spent 507 00:29:26,166 --> 00:29:31,166 perhaps a good chunk of their careers understanding, leverage, proteomic. 508 00:29:31,208 --> 00:29:34,375 Yes. Look at it in the context of a large population. 509 00:29:34,666 --> 00:29:37,666 And then there's often a, you know, triad of, of, 510 00:29:37,791 --> 00:29:40,625 collaboration with drug development companies. 511 00:29:40,625 --> 00:29:43,791 And I think that's a really powerful combination because, 512 00:29:44,166 --> 00:29:47,083 you know, you're lending someone's disease expertise 513 00:29:47,083 --> 00:29:50,750 that's bolstered with the weight of a population cohort. 514 00:29:51,333 --> 00:29:53,833 And then that can really inform far 515 00:29:53,833 --> 00:29:56,833 more efficient drug development decisions. 516 00:29:56,916 --> 00:29:59,541 For folks, you know, that that see the value in this. So, 517 00:30:00,625 --> 00:30:01,708 I can just share that. 518 00:30:01,708 --> 00:30:04,708 I think that's incredibly exciting is happening today. 519 00:30:05,250 --> 00:30:09,625 And the next steps, I believe, are some version of, of risk scores 520 00:30:09,625 --> 00:30:12,625 and how they can be. 521 00:30:14,125 --> 00:30:16,166 Implemented in some way 522 00:30:16,166 --> 00:30:19,708 that's cost effective, convenient and, 523 00:30:20,041 --> 00:30:23,500 accessible to, to a as much of the population as possible. 524 00:30:23,500 --> 00:30:25,041 I mean, that's certainly some time away, 525 00:30:25,041 --> 00:30:28,375 but I think it may come more quickly than people think. 526 00:30:29,208 --> 00:30:32,000 We now have an amazing team 527 00:30:32,000 --> 00:30:35,666 that represents, you know, many aspects of Thermo Fisher Scientific, 528 00:30:35,666 --> 00:30:38,666 but what comes to mind is the complementarity of Olink, 529 00:30:39,708 --> 00:30:42,541 you know what Evan calls the next generation proteomics. 530 00:30:42,541 --> 00:30:44,708 Where does mass spec fit in? 531 00:30:44,708 --> 00:30:48,833 If you can share within that drug discovery pipeline 532 00:30:49,041 --> 00:30:52,708 for corroborating anything you're seeing in the UK Biobank data, 533 00:30:52,708 --> 00:30:54,916 is there anything that you can share about that? 534 00:30:54,916 --> 00:30:55,625 Yeah, certainly. 535 00:30:55,625 --> 00:31:00,583 I think Mass Spec is still viewed in many ways as the, the, the gold standard. 536 00:31:00,583 --> 00:31:01,416 Within pharma. 537 00:31:01,416 --> 00:31:05,083 We have a, growing mass spec team at our change 538 00:31:05,083 --> 00:31:08,458 a site in Cambridge, Massachusetts, led by Harris Bell team and 539 00:31:09,791 --> 00:31:11,625 in many ways, the mass spec 540 00:31:11,625 --> 00:31:15,416 sits alongside the affinity based proteomics for discovery. 541 00:31:15,750 --> 00:31:20,458 We have an ongoing project for, movement disorders, where we are 542 00:31:20,916 --> 00:31:25,500 employing both affinity based proteomics, Olink as well as mass 543 00:31:25,500 --> 00:31:29,666 spectrometry, to identify potential subtypes of movement disorders. 544 00:31:29,666 --> 00:31:32,541 And the data do very much complement each other. 545 00:31:32,541 --> 00:31:35,541 We see similar subtypes using both methods, but with the mass 546 00:31:35,541 --> 00:31:39,458 spec, you know, you can often take it just that little bit step further. 547 00:31:40,166 --> 00:31:41,666 Especially when you're using tissue 548 00:31:41,666 --> 00:31:43,166 like brain tissue, you can take a little bit step 549 00:31:43,166 --> 00:31:46,541 further and maybe go a little bit further looking at proteome forms, etc.. 550 00:31:46,958 --> 00:31:49,708 Well, I mean, I think along the lines 551 00:31:49,708 --> 00:31:52,708 of, you know, where this is all going. 552 00:31:52,791 --> 00:31:55,791 I think another important piece of that puzzle is, 553 00:31:55,958 --> 00:31:58,541 you know, to get the attention 554 00:31:58,541 --> 00:32:01,916 and capture the imagination of the general public outside of this, 555 00:32:02,250 --> 00:32:06,625 you know, population research community, drug development community. 556 00:32:07,250 --> 00:32:10,208 I think there may have to be some sort of killer application 557 00:32:10,208 --> 00:32:14,833 or some sort of moment that raises people's awareness of the power 558 00:32:14,833 --> 00:32:18,291 and the potential impact of proteomics and how it could perhaps 559 00:32:18,291 --> 00:32:19,833 impact their own lives. 560 00:32:19,833 --> 00:32:22,875 I mean, Rory, as someone who's, you know, spent 561 00:32:23,666 --> 00:32:27,750 as much time as anybody thinking of population epidemiology 562 00:32:27,750 --> 00:32:31,708 and the impact of the resource you and others have built in the UK, 563 00:32:32,458 --> 00:32:34,750 what do you think a killer app could be? 564 00:32:35,750 --> 00:32:38,416 I always laugh about, 565 00:32:38,416 --> 00:32:43,208 people's perception of health and the way in which medicine has gone. 566 00:32:43,208 --> 00:32:46,458 So here's, someone who is training 567 00:32:46,458 --> 00:32:49,458 cardiology and has been doing 568 00:32:49,500 --> 00:32:52,500 working in that area for a long time. 569 00:32:52,583 --> 00:32:54,583 I think the general public thinks, well, you know, 570 00:32:54,583 --> 00:32:57,583 nothing much has happened. 571 00:32:57,791 --> 00:33:01,250 Except if you actually think back 40 years, we had nothing, really 572 00:33:01,250 --> 00:33:05,166 that was useful for controlling blood pressure, for controlling cholesterol. 573 00:33:05,500 --> 00:33:08,500 You had a heart attack, you got into a coronary care unit, 574 00:33:09,166 --> 00:33:11,958 you were monitored and given some pain relief. 575 00:33:11,958 --> 00:33:16,166 The progress in the last 40 years has been phenomenal. 576 00:33:17,041 --> 00:33:20,250 And I think the general public doesn't really know that. 577 00:33:20,958 --> 00:33:22,750 And maybe that's the right way. 578 00:33:22,750 --> 00:33:26,958 Maybe the thing will be that what we need to do, as with genetics 579 00:33:27,375 --> 00:33:32,666 and with proteomics, is they just get incorporated into, the system. 580 00:33:33,958 --> 00:33:36,958 We shouldn't be trying to train the public 581 00:33:37,416 --> 00:33:42,166 or indeed most doctors in genetics or proteomics or whatever 582 00:33:42,166 --> 00:33:46,041 we need to be, or build systems where it's like turning on the light. 583 00:33:46,916 --> 00:33:50,250 It just part of the standard things that happen. 584 00:33:51,416 --> 00:33:54,958 So I think the more invisible it is, 585 00:33:56,166 --> 00:33:58,166 the more likely it is 586 00:33:58,166 --> 00:34:01,166 to really change the way in which, 587 00:34:01,708 --> 00:34:04,708 people are cared for, in which the NHS works. 588 00:34:05,541 --> 00:34:08,958 We will we will provide better care. 589 00:34:10,375 --> 00:34:12,083 More precisely. 590 00:34:12,083 --> 00:34:15,083 Yeah, it will be precise population health. 591 00:34:15,208 --> 00:34:18,125 We will be ensuring that we've identified 592 00:34:18,125 --> 00:34:22,291 the people who are at risk well before they develop the disease. 593 00:34:22,625 --> 00:34:25,875 We will have the kinds of treatments that Chris is talking about 594 00:34:26,541 --> 00:34:30,833 that are specific for the condition they are going to develop. 595 00:34:31,125 --> 00:34:34,125 And we will be able to implement those treatments 596 00:34:34,500 --> 00:34:38,583 in a more precise way for the individuals who will benefit from them. 597 00:34:38,583 --> 00:34:42,791 And the more that is kind of like turning on the tap 598 00:34:43,000 --> 00:34:46,041 by turning on the electricity, by going to the television, 599 00:34:47,291 --> 00:34:50,458 the better the more it is success. 600 00:34:51,333 --> 00:34:55,958 So it sounds like integrated, woven throughout. 601 00:34:56,625 --> 00:35:00,500 What health care will be in the future is the killer app. 602 00:35:00,500 --> 00:35:05,125 So woven through, you know, the ability to understand what proteins are doing well 603 00:35:05,250 --> 00:35:09,875 through our, predictive capabilities and woven through improved 604 00:35:09,875 --> 00:35:14,208 clinical trials is the way to really make the biggest impact. 605 00:35:14,208 --> 00:35:15,833 Is that fair to say, Rory? 606 00:35:15,833 --> 00:35:17,958 Yeah. I have no idea how the internet works. 607 00:35:17,958 --> 00:35:18,750 It just works. 608 00:35:18,750 --> 00:35:21,250 People use it, and that's what you want. 609 00:35:21,250 --> 00:35:22,583 You want this stuff. 610 00:35:22,583 --> 00:35:27,750 You want genetics and proteomics to not be cutting edge, but just 611 00:35:29,041 --> 00:35:31,833 the things that happen. 612 00:35:31,833 --> 00:35:36,041 And if we can make it like that, then I think health services 613 00:35:36,375 --> 00:35:41,500 will function so much better and our governments will get better. 614 00:35:41,875 --> 00:35:43,958 Bang for their bucks or patients. 615 00:35:43,958 --> 00:35:46,250 The public will get better health. 616 00:35:47,333 --> 00:35:49,791 I think Rory's answer was excellent. 617 00:35:49,791 --> 00:35:53,375 I will say, you know, for folks like us, sort of nerdy folks, super passionate 618 00:35:53,375 --> 00:35:56,625 about proteomics, maybe the proteomics equivalent of the folks that work 619 00:35:57,291 --> 00:35:59,708 in chat rooms on the internet in 1993. 620 00:35:59,708 --> 00:36:00,583 Right. 621 00:36:00,583 --> 00:36:03,583 We'll probably be looking for, 622 00:36:03,666 --> 00:36:07,708 more subtle signs, or, a more subtle moment. 623 00:36:07,708 --> 00:36:11,000 I think there might be two or all of those two different scenarios. 624 00:36:11,000 --> 00:36:15,083 I think the first scenario will be one in which we can unequivocally show 625 00:36:15,083 --> 00:36:19,291 the proteomics saves millions of dollars in health care and drug development costs. 626 00:36:19,541 --> 00:36:24,625 Longer term, it's still I shouldn't really post this to Olink, but it is still 627 00:36:24,625 --> 00:36:27,958 a relatively expensive technology to implement a higher throughput. 628 00:36:27,958 --> 00:36:30,125 So we need to show that that expense pays off. 629 00:36:30,125 --> 00:36:33,625 And whether it's through reducing the time it gets to phase three, 630 00:36:33,625 --> 00:36:35,875 reducing the number of patients we need for a trial, 631 00:36:35,875 --> 00:36:36,958 or increasing the likelihood 632 00:36:36,958 --> 00:36:39,958 that a drug candidate actually will turn into a successful treatment. 633 00:36:40,125 --> 00:36:42,291 We just need to show that proteomics saves money. 634 00:36:42,291 --> 00:36:45,916 Or the second, maybe more powerful example is if we can show 635 00:36:45,916 --> 00:36:47,625 that proteomics saves lives. 636 00:36:47,625 --> 00:36:51,416 So maybe somebody discovers stage one cancer using 637 00:36:51,416 --> 00:36:54,833 a proteomic test and gets treated early enough to go into complete remission. 638 00:36:54,958 --> 00:36:59,000 And that detection of stage one cancer wasn't possible through any other means 639 00:36:59,000 --> 00:37:00,375 but a proteomic test. 640 00:37:00,375 --> 00:37:05,875 Or maybe, probably a genomic modeling that identifies a drug target 641 00:37:05,875 --> 00:37:09,625 that turns into a cure for a disease like multiple sclerosis. 642 00:37:09,875 --> 00:37:11,791 You know, perhaps maybe some of these, like, 643 00:37:11,791 --> 00:37:14,166 misdiagnosed with the disease, like Parkinson's disease. 644 00:37:14,166 --> 00:37:16,250 Maybe they have Lewy body dementia. 645 00:37:16,250 --> 00:37:20,083 And the proteomic tests can show that actually they have a wrong diagnosis. 646 00:37:20,125 --> 00:37:21,291 It's Lewy body dementia, 647 00:37:21,291 --> 00:37:23,458 and they should be on this treatment instead of this treatment. 648 00:37:23,458 --> 00:37:25,083 So I think we will get there. 649 00:37:25,083 --> 00:37:27,250 I think proteomics can and will save lives. 650 00:37:27,250 --> 00:37:29,958 And when that happens, it'll finally be mainstream. 651 00:37:30,958 --> 00:37:31,666 I love it. 652 00:37:31,666 --> 00:37:33,083 So Chris 653 00:37:33,083 --> 00:37:37,416 I love how you just have really been thinking about these things so clearly. 654 00:37:37,416 --> 00:37:41,125 You're so succinct in how you summarize the impact you expect in the future. 655 00:37:41,125 --> 00:37:45,041 So I'd like to kind of wind up can start with you, Chris's. 656 00:37:45,291 --> 00:37:50,125 If there were no resource limitations and the UK Biobank farmer proteomics 657 00:37:50,500 --> 00:37:53,291 project has been run on the full UK 658 00:37:53,291 --> 00:37:56,291 Biobank with a longitudinal representation in there. 659 00:37:57,125 --> 00:38:00,125 Imagine a time in the future and it's, 660 00:38:00,208 --> 00:38:03,208 you know, exceeded all your expectations. 661 00:38:03,875 --> 00:38:05,458 No resource limitation. 662 00:38:05,458 --> 00:38:11,500 What do you imagine where you're sitting today that you would want to enable next? 663 00:38:12,041 --> 00:38:17,875 I promise I will answer the question, but I'm going to take 30s to just give Rory 664 00:38:17,916 --> 00:38:22,291 an American and I and the whole UK but Biobank team some credit. 665 00:38:22,291 --> 00:38:26,166 I think it's already a world class cohort, and I don't think proteomics 666 00:38:26,166 --> 00:38:29,791 at this unprecedented scale could happen in any other population. 667 00:38:29,791 --> 00:38:31,041 Biobank. 668 00:38:31,041 --> 00:38:33,875 And they've enabled that kind of an innovation by encouraging open 669 00:38:33,875 --> 00:38:38,333 access, by embracing firm collaborations, and by really just incorporating 670 00:38:38,333 --> 00:38:42,208 this multi modal framework that I still believe is unparalleled. 671 00:38:42,208 --> 00:38:44,750 I don't know of any other studies that have 80,000 MRI scans. 672 00:38:44,750 --> 00:38:45,666 It's phenomenal. 673 00:38:45,666 --> 00:38:50,125 As somebody who I did my postdoc with, with MRI scans or the Enigma consortium, 674 00:38:50,125 --> 00:38:52,166 and at that time we were stitching together 675 00:38:52,166 --> 00:38:54,083 scans from different labs around the world. 676 00:38:54,083 --> 00:38:56,375 And now there's this one study from, 677 00:38:56,375 --> 00:38:59,375 you know, three different sites across the UK, all with the same scanner. 678 00:38:59,375 --> 00:39:00,500 It's mind boggling. 679 00:39:00,500 --> 00:39:03,041 So it's a really hard act to follow. 680 00:39:03,041 --> 00:39:05,125 I think that the Beatles have already left the stage right. 681 00:39:05,125 --> 00:39:07,666 So you're going to need the Stones and Queen and Led Zeppelin and 682 00:39:07,666 --> 00:39:08,750 some Frankenstein. 683 00:39:10,041 --> 00:39:10,500 Put them 684 00:39:10,500 --> 00:39:13,541 on the stage and you might stand a chance of following up successfully. 685 00:39:13,541 --> 00:39:15,791 So I guess in Biobank terms. Right. 686 00:39:15,791 --> 00:39:18,500 I think that that Frankenstein, 687 00:39:18,500 --> 00:39:20,500 that that would probably be a cohort that already 688 00:39:20,500 --> 00:39:22,875 has the open access model of UK Biobank. 689 00:39:22,875 --> 00:39:26,250 It already has the longitudinal design, the large collection 690 00:39:26,250 --> 00:39:29,333 of multimodal data that I mentioned, including those MRI scans. 691 00:39:29,333 --> 00:39:30,458 But maybe, 692 00:39:30,458 --> 00:39:34,708 maybe in addition, you could add maybe recruitment of more nonwhite participants. 693 00:39:34,708 --> 00:39:36,625 I think at the time of recruitment for UCP, 694 00:39:36,625 --> 00:39:40,541 it was very representative of the, UK population. 695 00:39:40,541 --> 00:39:43,708 But maybe increasing the nonwhite participants could be useful. 696 00:39:43,916 --> 00:39:45,708 The ability to recall participants 697 00:39:45,708 --> 00:39:48,833 for clinical trials could be useful, and maybe the integration. 698 00:39:48,833 --> 00:39:50,958 This is more of a sort of, 699 00:39:50,958 --> 00:39:52,958 a pipe dream because it's very specific. 700 00:39:52,958 --> 00:39:54,041 But the integration 701 00:39:54,041 --> 00:39:57,083 of more specialized clinical skills for someone who works in neuroscience, 702 00:39:57,083 --> 00:40:01,250 I'd love to see the unified Parkinson's Disease Rating Scale updates, 703 00:40:01,250 --> 00:40:04,000 or maybe the hospital scale for depression, things like that. 704 00:40:04,000 --> 00:40:06,625 So fantastic. 705 00:40:06,625 --> 00:40:10,708 And Rory, no resource limitation exceeded all your expectations. 706 00:40:10,708 --> 00:40:14,125 What's next for the UK Biobank or health care? 707 00:40:14,125 --> 00:40:17,125 As a, epidemiologist? 708 00:40:17,125 --> 00:40:20,000 Well, the great thing about being involved in UK Biobank 709 00:40:20,000 --> 00:40:24,000 is that my expectations have always been exceeded by the way 710 00:40:24,000 --> 00:40:27,000 in which the scientists around the world have used the data. 711 00:40:27,791 --> 00:40:31,166 And, I mean, that was what the Wellcome Trust and the MRC wanted. 712 00:40:31,166 --> 00:40:35,625 They wanted the data to be used by as many different imaginations as possible. 713 00:40:35,958 --> 00:40:39,208 And I think that has been really exciting to watch. 714 00:40:39,208 --> 00:40:43,375 Just how different people have approached the same data in different ways 715 00:40:43,375 --> 00:40:48,166 and discovered really interestingly different things. But 716 00:40:49,041 --> 00:40:52,541 we focused a lot on the baseline 717 00:40:52,541 --> 00:40:56,500 samples, the samples stored from 20 years ago. 718 00:40:57,833 --> 00:41:00,125 I think that, as Chris said, 719 00:41:00,125 --> 00:41:04,000 the repeat samples being combined with imaging is very interesting. 720 00:41:04,666 --> 00:41:10,041 But I think also what will be interesting is the change from baseline 721 00:41:10,041 --> 00:41:14,916 to that repeat sample and changes in proteomic data 722 00:41:15,208 --> 00:41:19,500 and how that predicts disease subsequently, in the longer term, 723 00:41:20,666 --> 00:41:24,833 we will have that repeat data on 100,000 724 00:41:24,833 --> 00:41:28,083 or so people who've come to our imaging assessments. 725 00:41:28,791 --> 00:41:30,833 But I think what we should be trying to do 726 00:41:30,833 --> 00:41:34,208 is getting repeat samples on the whole of the cohort. 727 00:41:35,125 --> 00:41:37,791 Because my view is that 728 00:41:37,791 --> 00:41:40,791 where a proteomic measures 729 00:41:41,000 --> 00:41:44,708 from 20 years ago are likely to be very strongly predictive of disease, 730 00:41:45,291 --> 00:41:48,291 changes in proteomic measures 731 00:41:48,625 --> 00:41:51,500 are likely to be even more strongly predictive. 732 00:41:51,500 --> 00:41:54,750 And more specifically predictive of a particular disease. 733 00:41:54,750 --> 00:41:58,000 Is and the cohort is now maturing. 734 00:41:58,000 --> 00:42:01,916 So what I would like to see is getting all of the cohort back, 735 00:42:02,541 --> 00:42:07,375 getting biological samples from all of them, assessing all of them 736 00:42:07,375 --> 00:42:11,625 in terms of their frailty and their aging, so that one could look to see 737 00:42:12,625 --> 00:42:13,625 how do the baseline 738 00:42:13,625 --> 00:42:18,833 samples relate to aging processes in all of the participants, 739 00:42:19,166 --> 00:42:23,208 but then look to see how the changes in the proteomic data 740 00:42:23,500 --> 00:42:27,541 between baseline and, say, now are associated 741 00:42:27,541 --> 00:42:32,416 with development of disease in the next five, ten, 15, 20 years. 742 00:42:33,250 --> 00:42:37,666 And I think change in proteomics, unlike genomics, 743 00:42:37,791 --> 00:42:41,375 is going to be a massively powerful source of information. 744 00:42:42,583 --> 00:42:45,625 I'll also add to, you know, what, I think the UK 745 00:42:45,625 --> 00:42:48,750 Biobank done has done exceptionally well 746 00:42:49,458 --> 00:42:54,916 is, created an environment of trust with the participants. 747 00:42:55,375 --> 00:42:59,333 The altruism of a half million UK Biobank participants is unbelievable. 748 00:42:59,833 --> 00:43:02,833 I mean, that trust is really critical. 749 00:43:03,125 --> 00:43:06,583 And something that we take, very, very seriously. 750 00:43:06,833 --> 00:43:08,541 But their altruism is extraordinary. 751 00:43:08,541 --> 00:43:13,583 The fact to Chris's point that 100,000 of them have been willing 752 00:43:13,625 --> 00:43:18,958 to travel up to 100 miles, spent five hours going through an imaging visit, 753 00:43:19,375 --> 00:43:22,250 and then 60 to 70% of them 754 00:43:22,250 --> 00:43:25,250 are willing to come and do it again is unbelievable. 755 00:43:25,500 --> 00:43:26,541 Yeah. It's amazing. 756 00:43:26,541 --> 00:43:31,166 I think, the UK Biobank participants are the ones who really deserve 757 00:43:31,500 --> 00:43:34,333 all of our respect and all of our gratitude for what 758 00:43:34,333 --> 00:43:37,583 they're doing for the health, around the world. 759 00:43:38,250 --> 00:43:41,875 Rory put it very well that, you know, we should all be grateful 760 00:43:41,875 --> 00:43:45,916 for the resource and the altruism that's enabled it with UK B. 761 00:43:45,916 --> 00:43:50,666 And I think in the course of this discussion, I'm struck by perhaps two core 762 00:43:51,416 --> 00:43:55,416 points of, of impact on the worlds that, that this work can have. 763 00:43:55,583 --> 00:43:56,666 One of them is, 764 00:43:56,666 --> 00:44:00,541 you know, as Chris mentioned, precision medicine, right drug, right patient. 765 00:44:00,666 --> 00:44:04,000 And that's along the lines of a disease, endo type exercise where 766 00:44:04,500 --> 00:44:07,541 if you can get enough data on enough people, 767 00:44:07,541 --> 00:44:11,541 there's probably more than just 1 or 2 kinds of Alzheimer's, right? 768 00:44:11,875 --> 00:44:15,458 There's probably lots of subtypes for all of these common diseases 769 00:44:15,458 --> 00:44:20,083 that are creating real societal challenge and dissecting those differences, 770 00:44:20,083 --> 00:44:23,541 and eventually coming up with treatments to address those differences. 771 00:44:24,083 --> 00:44:26,333 It will have incredible impact. 772 00:44:26,333 --> 00:44:29,333 So, so, so that's one vector that I'm very excited about. 773 00:44:29,458 --> 00:44:34,250 And I think, you know, to really advance that, we have to keep doing more. 774 00:44:34,416 --> 00:44:36,750 We need more cohorts. You need volume. 775 00:44:36,750 --> 00:44:37,916 You need n right. 776 00:44:37,916 --> 00:44:40,916 We need a lot of patients to be analyzed. 777 00:44:41,375 --> 00:44:44,583 But the real power of proteomics is in its dynamic nature. 778 00:44:45,083 --> 00:44:48,041 And the longitudinal data that we're going to get a really nice 779 00:44:48,041 --> 00:44:52,125 taste of from the full project here, I think will point 780 00:44:52,375 --> 00:44:56,500 very clearly that perturbation cohorts, cohorts 781 00:44:56,500 --> 00:45:01,375 that do have multiple time points over as long a period as is feasible, 782 00:45:01,791 --> 00:45:05,416 will really start to help us understand the dynamic. 783 00:45:05,708 --> 00:45:09,916 The proteins whose dynamics are really important for diagnostic purposes. 784 00:45:10,375 --> 00:45:13,375 So I think we need to do a lot more of everything. 785 00:45:13,416 --> 00:45:17,041 And I'm not just saying that because I work for a company that supports that. 786 00:45:17,041 --> 00:45:22,250 I just think we really, as a community will benefit across multiple vectors. 787 00:45:23,125 --> 00:45:25,583 But by continuing this work and, 788 00:45:25,583 --> 00:45:29,208 you know, on a personal level, I'm committed to, supporting, 789 00:45:29,208 --> 00:45:32,208 you know, the kind of innovation that John Heimer did 790 00:45:32,416 --> 00:45:36,416 to try to make things happen, irrespective of commercial gain. 791 00:45:36,791 --> 00:45:39,416 And I hope that we can continue, partnerships 792 00:45:39,416 --> 00:45:42,541 like the one we've built and the friendships and relationships, 793 00:45:42,958 --> 00:45:45,041 they've have built with Chris and and Rory. 794 00:45:45,041 --> 00:45:48,333 I mean, that's the kind of stuff that matters far more than everything else. 795 00:45:48,333 --> 00:45:51,416 So, yeah, an opportunity of a lifetime. 796 00:45:51,416 --> 00:45:53,125 It really is such a privilege. 797 00:45:53,125 --> 00:45:54,958 We're very fortunate. 798 00:45:54,958 --> 00:45:57,000 John definitely, you know, deserves his juice. 799 00:45:57,000 --> 00:45:59,333 John Reimer, he really made this happen. But so did you. 800 00:45:59,333 --> 00:46:01,250 Evan, you've been instrumental. 801 00:46:01,250 --> 00:46:04,375 You know, you mentioned when we had lunch and I'd had that vision 802 00:46:04,375 --> 00:46:08,541 to conduct proteomics since maybe 2016, in a cohort like UK Biobank. 803 00:46:08,541 --> 00:46:10,083 But it wasn't until I met Evan the 804 00:46:10,083 --> 00:46:13,083 like the following year that it became a reality. 805 00:46:13,500 --> 00:46:16,041 I think I came to him with that idea, and others had maybe 806 00:46:16,041 --> 00:46:19,041 dismissed it slightly or derided it, but he listened and he believed in it. 807 00:46:19,041 --> 00:46:22,333 He shared it and the, you know, moved mountains at Olink to make it happen. So, 808 00:46:23,541 --> 00:46:25,500 thanks, Evan. 809 00:46:25,500 --> 00:46:26,708 Thank you. Chris. 810 00:46:26,708 --> 00:46:29,708 Very kind. Awesome. 811 00:46:29,708 --> 00:46:34,250 So, you know, speaking of sort of what's next in terms of cohorts, 812 00:46:34,250 --> 00:46:37,958 I'll just make a call out to those who are listening to this podcast 813 00:46:38,458 --> 00:46:42,791 that Olink has an absolute passion, commitment, excitement 814 00:46:42,791 --> 00:46:48,208 around the matchmaking function of being able to bring, cohorts 815 00:46:48,208 --> 00:46:51,583 to our pharma partners, bringing those to our non-farm farm of, 816 00:46:52,916 --> 00:46:56,708 nonprofit partners, to biotech partners, those folks 817 00:46:56,708 --> 00:47:00,791 who are in search of the right samples to demonstrate, 818 00:47:03,000 --> 00:47:05,541 an understanding of various diseases. 819 00:47:05,541 --> 00:47:08,166 And so I think we need more cohorts. 820 00:47:08,166 --> 00:47:10,958 We need an understanding of the value and the 821 00:47:10,958 --> 00:47:13,958 uniqueness of all of the cohorts that we can, 822 00:47:14,416 --> 00:47:15,166 connect you. 823 00:47:15,166 --> 00:47:19,416 We can build those connections because that's, I think, really, 824 00:47:20,541 --> 00:47:22,875 an opportunity to bring people together. 825 00:47:23,875 --> 00:47:24,416 With that, 826 00:47:24,416 --> 00:47:25,250 I will say thank you 827 00:47:25,250 --> 00:47:29,541 all for being here to talk about this phenomenal international resource 828 00:47:30,000 --> 00:47:33,500 that many folks are querying over and over again 829 00:47:33,500 --> 00:47:36,500 to build an understanding of the insights over time. 830 00:47:36,708 --> 00:47:38,875 Sarantis will be back with us next time. 831 00:47:38,875 --> 00:47:42,000 And with that, I will bring this episode 832 00:47:42,208 --> 00:47:45,208 of Proteomics in Proximity to a close. 833 00:47:45,458 --> 00:47:48,291 Thank you. 834 00:47:48,291 --> 00:47:49,500 Well, that wraps 835 00:47:49,500 --> 00:47:52,500 up this episode of Proteomics in Proximity. 836 00:47:52,708 --> 00:47:56,666 Huge thanks to our guests and authors of such impactful publications. 837 00:47:57,250 --> 00:47:59,791 I also want to thank you for tuning in. 838 00:47:59,791 --> 00:48:02,125 Really appreciate you being here. 839 00:48:02,125 --> 00:48:03,875 If you enjoyed the content of this 840 00:48:03,875 --> 00:48:07,708 episode, please think about sharing it with friends or colleagues 841 00:48:07,708 --> 00:48:10,000 you think might be interested in the content. 842 00:48:10,000 --> 00:48:14,041 In addition, if you'd be willing to head over to Apple or Spotify 843 00:48:14,041 --> 00:48:17,541 or wherever you digest your podcasts and give us a rating and review, 844 00:48:17,541 --> 00:48:19,583 this will help others find the podcast 845 00:48:19,583 --> 00:48:22,958 when they're searching for proteomics or precision medicine podcasts. 846 00:48:23,125 --> 00:48:26,791 And mostly I want to say we would love to hear from you. 847 00:48:26,875 --> 00:48:30,750 So we have a dedicated email address pip@olink.com. 848 00:48:30,750 --> 00:48:31,833 Please reach out. 849 00:48:31,833 --> 00:48:35,833 Let us know what you're interested in hearing about what you care about, 850 00:48:35,833 --> 00:48:39,916 and any feedback on the episodes that we have already done so far. 851 00:48:40,166 --> 00:48:43,583 This is all about you, and so we're really keen 852 00:48:43,583 --> 00:48:46,583 to make sure that we're meeting what you'd like to hear about. 853 00:48:46,833 --> 00:48:48,958 Thank you so much, and we'll see you soon.