1 00:00:05,625 --> 00:00:06,416 Welcome to the 2 00:00:06,416 --> 00:00:09,500 Proteomics in Proximity podcast, where your co-host 3 00:00:09,625 --> 00:00:12,958 Cindy Lawley and Sarantis Chlamydas from Olink Proteomics 4 00:00:13,291 --> 00:00:16,916 talk about the intersection of proteomics with genomics for drug target discovery, 5 00:00:17,916 --> 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 00:00:29,458 --> 00:00:30,416 – Hello there. 9 00:00:30,416 --> 00:00:33,416 Welcome back to Proteomics in Proximity. 10 00:00:33,791 --> 00:00:37,916 I'm your host, Cindy Lawley, with my co-host Sarantis Chlamydas. 11 00:00:38,333 --> 00:00:41,125 We have a very special episode for you today. 12 00:00:41,125 --> 00:00:45,625 We have a few guests coming to us from our new home, 13 00:00:45,625 --> 00:00:49,625 our new family, our new neighborhood, Thermo Fisher Scientific. 14 00:00:49,833 --> 00:00:53,250 Some of you might recall that last summer we completed 15 00:00:53,250 --> 00:00:57,333 the joining of Olink and Thermo Fisher Scientific. 16 00:00:57,583 --> 00:01:03,416 I'm excited to report that we've got Gianluca and, Karen to join us 17 00:01:03,416 --> 00:01:09,375 to talk about why and what that means for all of us and all of you. 18 00:01:09,500 --> 00:01:12,208 Sarantis, do you want to tell us a little bit more about our guests? 19 00:01:12,208 --> 00:01:12,541 – Thank you. 20 00:01:12,541 --> 00:01:15,000 I’m really pleased to welcome our guests today. 21 00:01:15,000 --> 00:01:16,958 Truly honored to have Gianluca. 22 00:01:16,958 --> 00:01:20,291 And he's the Executive Vice President of Thermo Fisher Scientific. 23 00:01:20,291 --> 00:01:23,291 Karen Nelson is Chief Scientific Officer. 24 00:01:23,333 --> 00:01:26,333 And, we are looking forward to discuss about integration 25 00:01:26,666 --> 00:01:29,916 of all in proteomics to the Thermo Fisher Scientific family 26 00:01:30,416 --> 00:01:33,416 and not only as a strategic alliance and, 27 00:01:33,458 --> 00:01:36,916 movement, but also the next step opening 28 00:01:37,041 --> 00:01:41,333 and promoting actually a world that is healthier, that is safer 29 00:01:41,583 --> 00:01:45,750 and actually, makes our life easier for everybody. 30 00:01:46,625 --> 00:01:50,833 I would like to pass the words to Gianluca if you don't mind. 31 00:01:51,083 --> 00:01:53,958 I would like to know you a little bit more better. 32 00:01:53,958 --> 00:01:58,666 And to I get to know your journey and your career path that you had 33 00:01:58,666 --> 00:02:02,708 in Thermo Fisher and I am looking forward to hearing from both of you. 34 00:02:02,791 --> 00:02:04,208 Thank you very much. 35 00:02:04,208 --> 00:02:05,750 – Sarantis, thank you very much. 36 00:02:05,750 --> 00:02:09,708 And, Cindy, thanks for having both myself, and Karen, 37 00:02:09,708 --> 00:02:14,291 we were incredibly excited to have the opportunity to join you. 38 00:02:15,000 --> 00:02:18,083 As you might remember, many months back, 39 00:02:18,083 --> 00:02:21,083 when we first talk, I knew about you. 40 00:02:21,416 --> 00:02:25,416 Not because of Olink but because of this podcast that really helped me 41 00:02:25,625 --> 00:02:29,458 as a non-scientist over the years to get acquainted 42 00:02:29,875 --> 00:02:34,041 with the magic world of proteomics. 43 00:02:34,375 --> 00:02:38,500 And having been 20 years in the industry, I always thought 44 00:02:38,500 --> 00:02:42,041 that proteomics that the certain point would be very transformative. 45 00:02:42,041 --> 00:02:44,666 And, indeed it is the case. 46 00:02:44,666 --> 00:02:50,333 And that's why ultimately we decided, about a year ago to acquire Olink 47 00:02:51,041 --> 00:02:55,333 and now Olink being part of the Thermo Fisher Scientific family. 48 00:02:56,041 --> 00:03:00,125 It is, truly being transformative to the world of science. 49 00:03:00,125 --> 00:03:01,916 So I'm incredibly excited to be here. 50 00:03:01,916 --> 00:03:04,250 A little bit about me... 51 00:03:04,250 --> 00:03:09,125 – Can I just pop in and say that Gianluca and I met at the castle in Uppsala 52 00:03:09,708 --> 00:03:11,833 at a dinner, and, 53 00:03:11,833 --> 00:03:16,750 And he knew about the podcast, which was just so flattering to us. 54 00:03:16,791 --> 00:03:18,458 We were very excited to hear that. Sorry. 55 00:03:18,458 --> 00:03:20,125 Go ahead, Gianluca. 56 00:03:20,125 --> 00:03:21,833 – I knew about the pod. 57 00:03:21,833 --> 00:03:25,166 I was a fan. And, I'm continuing to be. 58 00:03:25,250 --> 00:03:26,666 – You even had it on your phone. 59 00:03:26,666 --> 00:03:28,750 You even showed me. I said, oh, please. 60 00:03:30,416 --> 00:03:31,500 He had it up. 61 00:03:31,500 --> 00:03:33,125 It was right at the end of the episode. 62 00:03:33,125 --> 00:03:35,666 I was gobsmacked. 63 00:03:35,666 --> 00:03:36,875 – I had the proof. 64 00:03:36,875 --> 00:03:38,041 The proof is in the pudding. 65 00:03:38,041 --> 00:03:39,666 You see, it wasn’t only marketing. 66 00:03:39,666 --> 00:03:42,208 – Frist step towards trust between us, Gianluca. 67 00:03:42,208 --> 00:03:45,166 – Absolutely. 68 00:03:45,166 --> 00:03:47,000 Well, so. And this was a little bit about me. 69 00:03:47,000 --> 00:03:51,625 I've been at Thermo Fisher Scientific for the best part of the last 20 years. 70 00:03:51,833 --> 00:03:53,333 I was in Europe at first. 71 00:03:53,333 --> 00:03:56,625 Then I spent a few years in South America. 72 00:03:56,625 --> 00:04:01,875 In Brazil, I moved, then in 2012, to China. 73 00:04:03,083 --> 00:04:06,416 Actually one of the biggest achievement while I was in South America 74 00:04:06,625 --> 00:04:10,541 when I was running, what at that time was Life Technologies in South 75 00:04:10,541 --> 00:04:15,875 America is meeting my wife, and we together moved in China in 2012. 76 00:04:16,583 --> 00:04:18,083 When we moved in China, 77 00:04:18,083 --> 00:04:20,875 we announced that Thermo Fisher was acquiring Life Technologies. 78 00:04:20,875 --> 00:04:25,250 We ended up spending five years running, Thermo Fisher 79 00:04:25,250 --> 00:04:29,541 in China had two kids that were born in Shanghai. 80 00:04:29,541 --> 00:04:34,458 So I think technically made in China and then imported in the US 81 00:04:35,208 --> 00:04:39,625 when tariffs were not a thing back in 2018, 82 00:04:40,166 --> 00:04:43,458 we moved to California and then here in Boston, 83 00:04:43,458 --> 00:04:47,583 I now run our product and technology businesses at the company 84 00:04:48,000 --> 00:04:51,291 and as said, I'm incredibly excited about proteomics. 85 00:04:51,291 --> 00:04:56,625 We have ample time to talk about that, over the course of this podcast. 86 00:04:57,000 --> 00:04:58,291 And so, without further ado, 87 00:04:58,291 --> 00:05:02,750 maybe I'll hand it over to Karen to tell us a little bit about herself. 88 00:05:02,750 --> 00:05:04,583 – Absolutely. Looking forward. 89 00:05:04,583 --> 00:05:05,250 Looking forward. 90 00:05:05,250 --> 00:05:07,500 Actually, I would like to know, I think as well 91 00:05:07,500 --> 00:05:10,541 that Karen has a tremendous career and academic career. 92 00:05:10,541 --> 00:05:14,333 And it was she was leading Craig Venter Institute, almost a decade. 93 00:05:14,708 --> 00:05:17,583 How was the transition from I mean, it's a common question right, 94 00:05:17,583 --> 00:05:21,833 the transition from academy to a big a technology industry institution? 95 00:05:22,041 --> 00:05:23,083 How did you find this? 96 00:05:23,083 --> 00:05:25,041 How was your vision? When do you change? 97 00:05:25,041 --> 00:05:27,541 – Well, Sarantis and Cindy, thanks for having us today. 98 00:05:27,541 --> 00:05:29,666 It's such a pleasure to be with you guys, 99 00:05:29,666 --> 00:05:31,833 and Cindy, I'm just getting caught up on your podcast, 100 00:05:31,833 --> 00:05:32,833 but they really are great. 101 00:05:32,833 --> 00:05:34,291 So thanks for doing that. 102 00:05:34,291 --> 00:05:34,625 – Aw thanks. 103 00:05:34,625 --> 00:05:39,416 – You know, so I joined four years ago and, Sarantis, to your question, 104 00:05:39,416 --> 00:05:42,708 probably one of the best career moves of my probably is well, 105 00:05:42,708 --> 00:05:46,083 I'm by now, I'm sure it has been the best career decision of my life. 106 00:05:47,375 --> 00:05:50,375 I spent close to 23 years 107 00:05:50,375 --> 00:05:53,041 in a non-for-profit research world 108 00:05:53,458 --> 00:05:57,333 with the Venter team, and they're, you know, they were part of the team 109 00:05:57,333 --> 00:05:59,083 that did the human genome, 110 00:05:59,083 --> 00:06:02,250 my biggest claim to fame was doing the first human microbiome. 111 00:06:02,250 --> 00:06:06,833 So a lot of time spent in genomics technologies and then my coming over 112 00:06:06,833 --> 00:06:10,708 to Thermo Fisher and seeing the breadth and possibilities of what we have 113 00:06:10,708 --> 00:06:14,875 all the way from instruments through consumables in the life sciences 114 00:06:15,083 --> 00:06:19,041 through clinical trials that has been such an amazing, 115 00:06:19,875 --> 00:06:22,250 experience for me, both in terms of people 116 00:06:22,250 --> 00:06:25,625 and learning and the willingness of everybody wanting to partner. 117 00:06:25,625 --> 00:06:27,000 And then, 118 00:06:27,000 --> 00:06:30,916 you know, when I heard that you guys were joining the family, it was like, 119 00:06:31,041 --> 00:06:33,958 you know, Gainluca, and I were like, yes, this is perfect 120 00:06:33,958 --> 00:06:38,416 because it was such a nice complement to what we had already in-house 121 00:06:38,416 --> 00:06:43,416 and had the potential to really accelerate what we're doing to make the world 122 00:06:43,416 --> 00:06:44,916 healthier and safer. 123 00:06:44,916 --> 00:06:47,833 So thanks for having us here today. 124 00:06:47,833 --> 00:06:50,750 – Yeah, thanks, Karen. Because we do. We feel very welcome. 125 00:06:50,750 --> 00:06:51,500 – Wonderful. 126 00:06:51,500 --> 00:06:54,708 – I just want to highlight and put on records that you said 127 00:06:54,708 --> 00:06:58,208 that was the best move of your life and career. 128 00:06:58,416 --> 00:07:01,416 – She moved from Craig Venter to me. 129 00:07:02,250 --> 00:07:06,041 And so I don't know if I have the same scientific pedigree, 130 00:07:06,041 --> 00:07:09,041 I have to say, but I think that there's a compliment coming. 131 00:07:09,208 --> 00:07:12,000 – Well, it's been such a pleasure to be here. 132 00:07:12,000 --> 00:07:13,791 – He’s throwing down the gantlet, Karen, to Craig. 133 00:07:13,791 --> 00:07:14,083 – I know, I know. 134 00:07:14,083 --> 00:07:17,083 – Now I'm thinking maybe we'll hear from Craig. 135 00:07:17,208 --> 00:07:17,625 – But, you know. 136 00:07:17,625 --> 00:07:20,541 But just the magnitude and the impact of Thermo Fisher. 137 00:07:20,541 --> 00:07:22,375 You know, I am one of those, 138 00:07:22,375 --> 00:07:25,875 who grew up on Thermo Fisher reagents and instruments and supplies. 139 00:07:25,875 --> 00:07:30,000 You know, it was Gibco in every aspect of your lab, 140 00:07:30,000 --> 00:07:33,666 for example, and different pieces of equipment and thermal cyclers. 141 00:07:33,666 --> 00:07:37,291 So it's actually, you know, my opportunity to be back, 142 00:07:38,083 --> 00:07:40,375 at home, just like you guys are here now. 143 00:07:40,375 --> 00:07:46,833 – And I'll say it's not an easy thing to make a transition that that layers 144 00:07:46,833 --> 00:07:50,916 on what Craig Venter accomplished and what you accomplished with him. 145 00:07:50,916 --> 00:07:54,958 I mean, just the whole history is just phenomenal. 146 00:07:55,166 --> 00:07:56,625 – Well Cindy, you're part of that too. 147 00:07:56,625 --> 00:07:59,416 You made the jump. So you know what it's like. 148 00:07:59,416 --> 00:07:59,791 – Yeah. 149 00:08:01,083 --> 00:08:02,000 So that's why I'm 150 00:08:02,000 --> 00:08:05,000 complimenting us both for having such a good... 151 00:08:06,541 --> 00:08:08,208 – Good mentors and great guidance. 152 00:08:08,208 --> 00:08:09,291 – That's right. 153 00:08:09,291 --> 00:08:14,125 It's all about having a cool technology, having wonderful people around 154 00:08:14,125 --> 00:08:17,708 you and those wonderful people then create a good culture. 155 00:08:18,000 --> 00:08:22,083 And I think with that in place, changing the world, feeling 156 00:08:22,083 --> 00:08:25,666 like we've got a purpose, I mean, that's just icing on the cake, right? 157 00:08:26,958 --> 00:08:29,958 So, Gianluca, we've got mass spectrometry, 158 00:08:30,458 --> 00:08:33,583 an amazing capability here at Thermo Fisher. 159 00:08:33,750 --> 00:08:36,875 And I will say, I as Karen says, I come from the genetics world. 160 00:08:37,125 --> 00:08:40,416 So, so proteins have always been really scary to me. 161 00:08:40,416 --> 00:08:43,541 I felt like Olink gives me a little bit of a, 162 00:08:44,166 --> 00:08:46,541 an easing into proteomics. 163 00:08:46,541 --> 00:08:49,708 But why Olink in the context 164 00:08:49,708 --> 00:08:53,083 of the enormous capabilities at Thermo Fisher 165 00:08:53,250 --> 00:08:57,250 using mass spectrometry and the advances that we're seeing in that space? 166 00:08:57,500 --> 00:08:57,958 – Yes. 167 00:08:59,166 --> 00:09:00,791 Obviously starts from the 168 00:09:00,791 --> 00:09:04,500 understanding of the importance of proteins 169 00:09:04,958 --> 00:09:09,125 and the importance of proteins, to obviously our, 170 00:09:09,666 --> 00:09:13,750 incredibly complex and fascinating biological systems. 171 00:09:14,916 --> 00:09:20,333 And as a leader in proteomics, through our mass spectrometry franchise, 172 00:09:20,333 --> 00:09:23,458 we had the opportunity to have great insights 173 00:09:23,708 --> 00:09:29,208 on what was happening in the market and also the understanding that, 174 00:09:29,458 --> 00:09:35,041 not only having solutions that are helping you to go incredibly deep 175 00:09:35,375 --> 00:09:38,916 and, in a way, in an untargeted fashion 176 00:09:39,041 --> 00:09:42,791 to study proteins was not enough to be able 177 00:09:42,791 --> 00:09:47,291 to provide to our customers the full breadth of what they truly need, 178 00:09:47,791 --> 00:09:51,541 which to us is also a high affinity method. 179 00:09:52,041 --> 00:09:55,333 A throughput like, Olink provides 180 00:09:55,833 --> 00:09:58,833 that truly is being transformational. 181 00:09:58,833 --> 00:10:03,125 When you look at the result of the UK Biobank 182 00:10:03,125 --> 00:10:07,375 in terms of both the amount of information that, 183 00:10:08,375 --> 00:10:09,000 the UK 184 00:10:09,000 --> 00:10:13,125 Biobank cohort has been able to create and the one that is ahead of us, 185 00:10:13,500 --> 00:10:17,291 but more importantly, the number of publications 186 00:10:17,291 --> 00:10:21,708 and the understanding of correlation between the presence of protein, 187 00:10:21,750 --> 00:10:26,541 their level disease, this has been truly transformational. 188 00:10:26,541 --> 00:10:29,541 And the pivotal moment thinking science. 189 00:10:29,791 --> 00:10:31,500 And we'll discover more and more. 190 00:10:31,500 --> 00:10:35,583 And so, we felt that was very natural 191 00:10:35,583 --> 00:10:41,041 to actually combine, the all the technology 192 00:10:41,166 --> 00:10:45,208 with the incredible leadership that we already had in mass spectrometry. 193 00:10:45,208 --> 00:10:50,333 And indeed, we are now seeing how engaging with customers, 194 00:10:50,541 --> 00:10:53,541 the two technology can be incredibly complementary 195 00:10:53,875 --> 00:10:58,000 to have a more comprehensive understanding of the human proteome. 196 00:10:58,416 --> 00:11:01,791 And I can't wait to see what's going to happen 197 00:11:01,791 --> 00:11:04,416 in the next few years, because I do believe 198 00:11:04,416 --> 00:11:07,333 there's going to be transformational in the way that ultimately 199 00:11:07,333 --> 00:11:12,000 we actually prevent then detect and treat diseases 200 00:11:12,000 --> 00:11:17,166 that today are not, treated as well as they should. 201 00:11:17,166 --> 00:11:19,208 – To monitor, right? Who's really at risk? 202 00:11:19,208 --> 00:11:23,041 And I, I joke about the Goldilocks, 203 00:11:24,625 --> 00:11:27,958 too hot to cold and just right. 204 00:11:27,958 --> 00:11:31,083 And I don't want to dismiss your microbiome 205 00:11:31,083 --> 00:11:34,208 metabolites, but they are so hot, they move so quickly. 206 00:11:34,208 --> 00:11:37,541 Right? DNA moves at the pace of generations. 207 00:11:37,541 --> 00:11:38,791 That's really slow. 208 00:11:38,791 --> 00:11:41,791 It's hard to nudge, but proteins, 209 00:11:42,208 --> 00:11:44,583 it's just, it's the baby bear bowl. 210 00:11:44,583 --> 00:11:46,208 It's just fabulous. 211 00:11:46,208 --> 00:11:46,875 – So, Cindy, 212 00:11:46,875 --> 00:11:50,958 I wanted to pick up on something you said about proteomics being scary. 213 00:11:50,958 --> 00:11:54,583 I don't know if it was scary as much as we didn't have the right tools. 214 00:11:54,583 --> 00:11:55,416 Right. 215 00:11:55,416 --> 00:11:58,625 And now, finally, we do have to. And, 216 00:11:59,791 --> 00:12:04,833 if you think about Astral and Astral Zoom and how deep you can go on a sample now, 217 00:12:04,833 --> 00:12:08,541 I mean, it's unbelievable what's changed over 218 00:12:08,541 --> 00:12:11,833 the past 15, 20 years in terms of technology development. 219 00:12:11,833 --> 00:12:14,958 So we're now in a better comfort, space. 220 00:12:14,958 --> 00:12:20,791 And to Gianluca’s point about integrating the Olink data with, proteomics data. 221 00:12:20,791 --> 00:12:23,791 I mean, it's such a tremendous advance for science. 222 00:12:23,833 --> 00:12:26,458 And from where I sit, yeah, for sure. 223 00:12:26,458 --> 00:12:30,583 – And I guess I think of it more as scary from a geneticist point of view, 224 00:12:30,625 --> 00:12:34,625 where we had this, you know, certainly in 2007 225 00:12:34,625 --> 00:12:38,375 when we advanced to the to the massively parallel sequencing 226 00:12:38,750 --> 00:12:42,416 and being able to analyze those data, right, that we just shifted the burden 227 00:12:42,958 --> 00:12:46,500 from the collection of the data to the reconstituting the data 228 00:12:46,708 --> 00:12:49,125 and comparing it to reference genomes 229 00:12:50,375 --> 00:12:53,416 and handling those data or even transcriptomic data 230 00:12:53,666 --> 00:12:57,458 just feels a little more, you know, it's A C T and G 231 00:12:58,041 --> 00:13:02,208 right there isn't the conformational or the, conformational complexity 232 00:13:02,208 --> 00:13:06,041 or the protein protein interaction complexity, but I think, 233 00:13:06,750 --> 00:13:11,250 geneticists are really getting comfortable with full length proteins. 234 00:13:11,250 --> 00:13:15,916 And then mass Spec is going to be the only way, today to dig 235 00:13:15,916 --> 00:13:19,250 into proteome forms, to dig into what are the post-translational 236 00:13:19,250 --> 00:13:22,250 modified aspects of those proteins that are actually, 237 00:13:22,708 --> 00:13:24,291 providing the bulk of those signals? 238 00:13:24,291 --> 00:13:26,875 And, of course, we're seeing from the UK Biobank data 239 00:13:26,875 --> 00:13:31,208 amazing signatures of 5 to 20 proteins that are predicting disease 240 00:13:31,458 --> 00:13:33,583 better than anything a doctor has available today. 241 00:13:33,583 --> 00:13:36,250 Like this is, you know, just echoing on what you're saying. 242 00:13:36,250 --> 00:13:38,666 Gian, it's a very exciting time now. 243 00:13:38,666 --> 00:13:43,291 – I mean, I want to follow up in on Gianluca’s comment about UK Biobank. 244 00:13:43,291 --> 00:13:46,583 We see recently also that pharma investing from these data sets, 245 00:13:46,583 --> 00:13:49,833 right, of generating real world evidence, real world data. 246 00:13:50,333 --> 00:13:53,916 And we see that they invest in cultural on platforms like Olink. 247 00:13:54,416 --> 00:13:58,291 How will you see both of you the role that can play in this platform 248 00:13:58,291 --> 00:14:03,375 kind of way of working pharma together with governmental institutions, 249 00:14:03,625 --> 00:14:06,875 with academic institutions to enable precision medicine? 250 00:14:07,166 --> 00:14:11,125 How do you see Thermo Fisher plays a role that are and having all of this 251 00:14:11,583 --> 00:14:14,875 weight and breadth of multi-omics approache? 252 00:14:15,458 --> 00:14:16,625 – It’s a great question. 253 00:14:16,625 --> 00:14:20,541 And, you know, maybe because, the probably 254 00:14:20,541 --> 00:14:24,208 only non-scientist of the crew here in the pod, 255 00:14:24,208 --> 00:14:28,166 – I disagree. – You disagree? 256 00:14:28,166 --> 00:14:29,458 – Honorary member. – No, you’re scientist. 257 00:14:29,458 --> 00:14:30,875 – Honorary member. 258 00:14:30,875 --> 00:14:33,666 – You are absolutely a scientist. 259 00:14:33,666 --> 00:14:37,916 – I learned in street fighting, spending a lot of time with customers. 260 00:14:37,916 --> 00:14:43,416 But, you know, I've been thinking a lot and being, at heart, 261 00:14:44,250 --> 00:14:47,250 a lover of technology and innovation. 262 00:14:48,000 --> 00:14:50,250 I've been, obviously 263 00:14:50,250 --> 00:14:53,250 very impressed what is happening in the world of, 264 00:14:53,625 --> 00:14:56,791 automotive technologies and, transportation? 265 00:14:57,541 --> 00:14:59,625 We now have self-driving cars. 266 00:14:59,625 --> 00:15:03,750 I wake up in the morning and my Tesla 267 00:15:04,208 --> 00:15:07,833 doesn't even need me to tell where I'm going, 268 00:15:07,833 --> 00:15:12,125 because the Tesla knows that more probably I’m going to the office. 269 00:15:12,208 --> 00:15:15,208 So I sit on my Tesla and there's already, 270 00:15:15,333 --> 00:15:18,333 a predefined destination in the morning. 271 00:15:19,291 --> 00:15:21,541 And then I put self driving 272 00:15:21,541 --> 00:15:24,541 and the car drives itself to the office. 273 00:15:24,708 --> 00:15:26,458 Intervention is very limited. 274 00:15:27,500 --> 00:15:29,375 And my hope is that 275 00:15:29,375 --> 00:15:33,208 at a certain point, health becomes the same. 276 00:15:34,291 --> 00:15:37,291 We are on autopilot. 277 00:15:37,500 --> 00:15:40,500 And to be able to do that you actually need, 278 00:15:41,500 --> 00:15:44,916 devices and detectors and signals. 279 00:15:45,291 --> 00:15:49,083 And I think proteins are providing just that. 280 00:15:50,000 --> 00:15:54,416 We actually don't understand everything to the same extent 281 00:15:54,416 --> 00:15:58,833 that, digital technologies are able to understand, 282 00:15:59,458 --> 00:16:04,291 visual signs and help a car driving itself yet. 283 00:16:04,875 --> 00:16:09,291 But I think that's going to be obviously, a big transformation. 284 00:16:09,833 --> 00:16:13,125 And that is why I think to your question, pharma 285 00:16:13,125 --> 00:16:18,666 companies are so interested now in getting a head start in proteomics. 286 00:16:19,250 --> 00:16:21,750 Because, we've seen again, 287 00:16:21,750 --> 00:16:26,791 through some of the result of the UK Biobank protein 288 00:16:27,208 --> 00:16:32,916 signatures and this, poly protein, you can call it race course. 289 00:16:32,916 --> 00:16:37,250 My opinion, but I'll leave it to the scientists on the part to confirm 290 00:16:37,500 --> 00:16:41,666 is more than rescore, I think very different to look at poly 291 00:16:41,666 --> 00:16:46,291 protein signatures and polygenic risk scores as an example. 292 00:16:46,625 --> 00:16:50,708 That's why I think pharma so interested in this poly protein signature 293 00:16:51,041 --> 00:16:55,083 as an indicator of either an early onset of disease 294 00:16:55,500 --> 00:16:59,583 or perhaps an indication of, response to therapy. 295 00:17:00,458 --> 00:17:03,500 So again, over time, I think the, 296 00:17:04,000 --> 00:17:06,833 utility of protein 297 00:17:06,833 --> 00:17:10,666 signature is going to be quite broad in nature, 298 00:17:10,666 --> 00:17:16,125 whether he's prevention, early detection, whether he's response to treatment. 299 00:17:17,333 --> 00:17:18,458 And I 300 00:17:18,458 --> 00:17:22,416 think more things that can be done through, those signatures. 301 00:17:22,416 --> 00:17:24,666 So it's quite exciting. 302 00:17:24,666 --> 00:17:27,666 Does the interest in the space, 303 00:17:28,041 --> 00:17:30,333 the key to me is still 304 00:17:30,333 --> 00:17:34,416 generating millions and millions of data points so that ultimately 305 00:17:34,416 --> 00:17:38,958 we can apply artificial intelligence and truly get to it to cells. 306 00:17:38,958 --> 00:17:43,208 Do I can I call self-driving human or is a bit to, provocative? 307 00:17:43,208 --> 00:17:44,208 I don't know. 308 00:17:44,208 --> 00:17:46,625 – I think self, 309 00:17:46,625 --> 00:17:48,000 so why do we want to call that? 310 00:17:48,000 --> 00:17:49,166 I mean, it's a really good question. 311 00:17:49,166 --> 00:17:50,708 We should come up with a phrase, Gian, 312 00:17:50,708 --> 00:17:54,833 I don't know that self-driving human, but it’s self, I mean, we're certainly 313 00:17:54,833 --> 00:17:57,916 putting power in the hands of the individual, right? Yes. 314 00:17:57,916 --> 00:18:02,083 We the community, the scientific community by building such tools. 315 00:18:02,333 --> 00:18:05,833 And I think this point about self-driving, I love the comparison. 316 00:18:05,833 --> 00:18:09,791 You know, I love a good analogy, but, you know, I was talking to one of the 317 00:18:09,791 --> 00:18:13,666 I live in the Bay area, this, you know, you can't swing a dead cat without hitting 318 00:18:13,666 --> 00:18:18,708 somebody who's been involved in some aspect of collecting self-driving. 319 00:18:18,708 --> 00:18:20,000 I would never swing a cat, by the way. 320 00:18:20,000 --> 00:18:25,458 Is involved in some aspect of this technological advance. 321 00:18:25,458 --> 00:18:29,833 And even where I used to live in the agricultural belt of California, 322 00:18:30,250 --> 00:18:33,750 they used an old air force base to do some of the practicing out of that. 323 00:18:33,750 --> 00:18:38,041 But but what turned out to be the most important thing is that the right 324 00:18:38,041 --> 00:18:42,375 data were used the minute you have incorrect data. 325 00:18:42,666 --> 00:18:45,125 And here I'm calling back to specificity. 326 00:18:45,125 --> 00:18:45,416 Right. 327 00:18:45,416 --> 00:18:48,416 And the importance of knowing exactly what protein you're measuring. 328 00:18:48,583 --> 00:18:51,166 If the minute you have wrong data, the algorithm 329 00:18:51,166 --> 00:18:52,833 can't discern the difference. 330 00:18:52,833 --> 00:18:56,916 And so I think that was the turning point based upon the conversations 331 00:18:56,916 --> 00:19:03,000 I've had with a few of these folks of, of being able to, to really make that work 332 00:19:03,000 --> 00:19:06,333 because it's phenomenal to see those cars drive themselves. 333 00:19:06,333 --> 00:19:10,541 They can come pick you up at the curb with not a driver in the seat. 334 00:19:10,583 --> 00:19:12,125 It's impressive. 335 00:19:12,125 --> 00:19:12,500 – Actually. 336 00:19:12,500 --> 00:19:16,000 You're not far away from the concept of digital twins, right in pharma convention 337 00:19:16,333 --> 00:19:21,500 that they have the digital twins where actually can project things in AI. 338 00:19:21,500 --> 00:19:21,958 Right. 339 00:19:21,958 --> 00:19:25,916 And predict things in AI, I think that's we are really close on this, 340 00:19:25,916 --> 00:19:27,833 really close on this on this era. 341 00:19:27,833 --> 00:19:29,625 – And the virtual cell. Right. 342 00:19:29,625 --> 00:19:30,416 We could navigate through it. 343 00:19:30,416 --> 00:19:33,958 – So Cindy, just pick up on what one of the points Gianluca was making 344 00:19:33,958 --> 00:19:34,541 is that, 345 00:19:34,541 --> 00:19:37,083 you know, some of the preliminary data coming out of UK 346 00:19:37,083 --> 00:19:40,416 Biobank in terms of stratifying patients is really amazing. 347 00:19:40,458 --> 00:19:40,916 Right. 348 00:19:40,916 --> 00:19:46,583 And so to Gianluca's, point be, you know, the early diagnosis, 349 00:19:46,625 --> 00:19:51,541 like the protein signature that's coming really early on, we're going to be 350 00:19:51,541 --> 00:19:56,791 at a point where we can use that to change the trajectory of health or treatment. 351 00:19:56,791 --> 00:19:59,916 And I think we're just at the beginning here. 352 00:19:59,916 --> 00:20:04,875 And, you know, you know, I just think it's going to change how we think 353 00:20:04,875 --> 00:20:08,583 about human health could go all the way to animal health, environmental health. 354 00:20:08,583 --> 00:20:11,916 But it's just such an amazing opportunity for the field. 355 00:20:11,916 --> 00:20:13,166 When you start to look at 356 00:20:13,166 --> 00:20:16,041 the initial implications of the data that's coming out. 357 00:20:16,041 --> 00:20:20,166 And I, I think, Gianluca, we have multiple collaborations now, right? 358 00:20:20,166 --> 00:20:21,583 It's not just the UK Biobank. 359 00:20:21,583 --> 00:20:24,000 I think it's we're processing... 360 00:20:24,000 --> 00:20:27,000 – Topmed, FinnGen, yeah. – Yeah. 361 00:20:27,208 --> 00:20:29,958 – We're involved in our future health where, yeah, there's 362 00:20:29,958 --> 00:20:33,750 there's so many places where there's opportunities. 363 00:20:33,750 --> 00:20:37,458 And I think it just for context on UK Biobank, what a sweet data 364 00:20:37,500 --> 00:20:41,250 set that is just to remind listeners that, you know, 365 00:20:41,250 --> 00:20:45,125 those collections were made for individuals between 40 and 67 years 366 00:20:45,125 --> 00:20:49,375 of age, and it's been almost 20 years for them to develop diseases. 367 00:20:49,375 --> 00:20:52,375 And so to Karen's point to Gian, to your point, 368 00:20:52,375 --> 00:20:56,666 this is a beautiful set to be able to look at those baseline blood samples 369 00:20:56,916 --> 00:21:01,291 and see the signatures for the same diseases in different individuals. 370 00:21:01,625 --> 00:21:05,250 And that has, you know, certainly coming out of, Ruth Travis's group, 371 00:21:06,125 --> 00:21:09,583 they've been able to do that in cancer that hit the UK news last year around 372 00:21:09,583 --> 00:21:14,250 how they could see a median of 12 years before diagnosis of cancer. 373 00:21:14,250 --> 00:21:16,291 12 years. That's phenomenal. Right? 374 00:21:16,291 --> 00:21:19,083 A minimum of seven years. That was what hit the news. 375 00:21:19,083 --> 00:21:21,958 And then, and then of course, the Carrasco Zanini paper 376 00:21:21,958 --> 00:21:25,958 coming out of Claudia Langenberg's lab, where 67 out of the 200 diseases 377 00:21:26,208 --> 00:21:29,958 in just the pilot of the UK Biobank Pharma Proteomics Project. 378 00:21:30,791 --> 00:21:33,166 They were able to see these 5 to 20 379 00:21:33,166 --> 00:21:36,416 protein signatures that outperform anything a doctor has available today. 380 00:21:36,416 --> 00:21:40,250 So just for some context, that some of that exciting stuff coming out of 381 00:21:41,791 --> 00:21:44,583 that study, that pilot and those pilot 382 00:21:44,583 --> 00:21:48,458 data are available for application to use them, 383 00:21:48,458 --> 00:21:51,958 and they're free for many, many, many academic institutions 384 00:21:51,958 --> 00:21:55,083 So it's, it's an exciting time. 385 00:21:55,666 --> 00:21:58,666 – One of the thing I want to add, one thing that is, 386 00:21:58,791 --> 00:22:01,708 maybe the elephant in the room or the human in the room, 387 00:22:01,708 --> 00:22:03,541 call it, the way we want. 388 00:22:03,541 --> 00:22:04,791 – The self-driving human. 389 00:22:05,000 --> 00:22:06,625 – The self-driving human. 390 00:22:06,625 --> 00:22:09,583 But, in serious terms. 391 00:22:09,583 --> 00:22:12,375 One of the things that I’ve been, 392 00:22:12,375 --> 00:22:16,291 fascinated with is ultimately human psychology. 393 00:22:16,291 --> 00:22:21,041 And I think I'm not misquoting this, but, 394 00:22:21,333 --> 00:22:24,333 in a study that was run a few years back, 395 00:22:24,958 --> 00:22:28,625 they highlighted how 50% of the individuals 396 00:22:28,625 --> 00:22:33,041 that should take medicines that are critical due 397 00:22:33,041 --> 00:22:36,916 to the chronic condition and in many cases, a life 398 00:22:36,916 --> 00:22:40,833 saving, medicines, they're in noncompliance. 399 00:22:40,833 --> 00:22:45,166 50% of them die in noncompliance, which tells you, 400 00:22:45,791 --> 00:22:50,625 the fact that, for some reason, humans are not very, 401 00:22:51,125 --> 00:22:53,791 self caring, if you wish. 402 00:22:53,791 --> 00:22:56,000 And so one thing that 403 00:22:56,000 --> 00:23:00,958 I think is going to be very important to your point on the amount of available 404 00:23:00,958 --> 00:23:05,541 information, is how do we disseminate this information? 405 00:23:05,833 --> 00:23:09,000 How do we make sure there's enough, learning, 406 00:23:09,666 --> 00:23:13,666 opportunities both for doctors but also for individuals 407 00:23:14,041 --> 00:23:16,791 to become more self-aware 408 00:23:16,791 --> 00:23:20,416 on how they actually can stop the car before the car crashes. 409 00:23:20,708 --> 00:23:23,458 With the analogy of self-driving cars, a 410 00:23:23,458 --> 00:23:26,458 self-driving car normally doesn't crash. 411 00:23:27,000 --> 00:23:30,833 And I think from a health standpoint, we should make sure 412 00:23:30,833 --> 00:23:35,500 that we prevent disease more effectively going forward. 413 00:23:35,875 --> 00:23:39,125 With all of this insight and information that we have available. 414 00:23:39,416 --> 00:23:42,458 Then one of the concerns I have, I don't know your perspective, is 415 00:23:42,625 --> 00:23:47,500 how do we influence culture and who is going to be, actually 416 00:23:47,500 --> 00:23:51,791 helping consumers to do a better job 417 00:23:51,791 --> 00:23:54,791 in taking good care of themselves? 418 00:23:54,791 --> 00:23:57,458 That, to me, is still an unresolved issue. 419 00:23:57,458 --> 00:24:00,625 If we don't resolve it as a society, then 420 00:24:00,625 --> 00:24:04,750 it would be very difficult to take full advantage of, these tools. 421 00:24:04,750 --> 00:24:07,750 But maybe these alternative solutions, 422 00:24:07,916 --> 00:24:12,291 that will, be implemented over time as a result of, 423 00:24:12,833 --> 00:24:16,916 now much better tools to help, consumers and patients. 424 00:24:17,750 --> 00:24:21,000 – Karen, do you have ideas about that from the genetics space? 425 00:24:21,000 --> 00:24:21,250 Right. 426 00:24:21,250 --> 00:24:24,250 This feels like a parallel, 427 00:24:24,333 --> 00:24:26,875 of education needed, right. 428 00:24:26,875 --> 00:24:31,958 That what what was what was what helped us there from your perspective? 429 00:24:33,375 --> 00:24:33,583 – Well there’s 430 00:24:33,583 --> 00:24:37,416 I mean, to Gianluca's point it probably it has to happen in parallel, 431 00:24:37,416 --> 00:24:42,125 right, to get absorption into the general population. 432 00:24:42,125 --> 00:24:45,875 I think back then we had set aside funds for like ethical, 433 00:24:45,875 --> 00:24:47,833 legal and social implications. 434 00:24:47,833 --> 00:24:50,125 Remember all of that good stuff, Cindy? 435 00:24:50,125 --> 00:24:50,458 – Yeah. Oh Yeah. 436 00:24:50,458 --> 00:24:54,750 – And, it was a percentage of all funding going into genomic research was, 437 00:24:55,583 --> 00:24:58,958 used for like education and legal work and getting, 438 00:25:00,291 --> 00:25:02,541 because there were a lot of questions. 439 00:25:02,541 --> 00:25:03,625 A lot of concerns. 440 00:25:03,625 --> 00:25:06,000 And not everybody is trained in science. 441 00:25:06,000 --> 00:25:07,416 And so they don't understand. 442 00:25:07,416 --> 00:25:11,666 And, you know, we tend to be positive and can only, 443 00:25:12,833 --> 00:25:15,208 just try to discuss the positive implications. 444 00:25:15,208 --> 00:25:16,083 But there are concerns 445 00:25:16,083 --> 00:25:19,583 about some of these, research areas that people don't quite understand. 446 00:25:19,958 --> 00:25:25,458 And I think, the education and the value and highlighting, you know, like the car 447 00:25:26,708 --> 00:25:27,750 analogy. 448 00:25:27,750 --> 00:25:30,541 Gianluca, you're saying that when the light comes on, on the car, 449 00:25:30,541 --> 00:25:34,166 take it in and don't wait till like, the engine shuts down right. 450 00:25:34,166 --> 00:25:36,416 And we're getting to the point where we can 451 00:25:36,416 --> 00:25:39,416 have that light come on really early when there's a problem in the car. 452 00:25:40,291 --> 00:25:41,875 Not on an Italian car though, right? 453 00:25:41,875 --> 00:25:44,875 I'm talking about in other parts of the world. So. 454 00:25:44,875 --> 00:25:48,875 But I think, as a society, Cindy, we probably need to start 455 00:25:48,875 --> 00:25:51,333 having those conversations about getting the information out, 456 00:25:51,333 --> 00:25:52,833 getting the positive findings out. 457 00:25:52,833 --> 00:25:56,500 You know, it's right now, it's so positive, like, how do we get that out 458 00:25:57,000 --> 00:26:00,958 in words that the general community can understand? 459 00:26:00,958 --> 00:26:03,708 And I think it's up to us to try and accelerate that. 460 00:26:03,708 --> 00:26:06,708 – And isn't it easier because proteins aren't 461 00:26:06,875 --> 00:26:09,625 tied to our personal genetic information, right. 462 00:26:09,625 --> 00:26:10,541 – Right, it should be. 463 00:26:10,541 --> 00:26:15,250 – That might be because, yeah, it's not an identifier of us as individuals. 464 00:26:15,250 --> 00:26:17,208 Our protein signature. 465 00:26:17,208 --> 00:26:18,958 – Right. But it's hard to understand, right. 466 00:26:18,958 --> 00:26:22,958 Because you talk about proteins and proteoforms and splicing 467 00:26:22,958 --> 00:26:24,083 and all this good stuff. 468 00:26:24,083 --> 00:26:29,875 And so it's about how to translate that in words that, the average person 469 00:26:30,250 --> 00:26:34,500 can understand the benefit of the research that's ongoing right now. 470 00:26:35,333 --> 00:26:37,541 – I think to your point, I mean, it's also really important 471 00:26:37,541 --> 00:26:38,916 not only to generate the data, right, 472 00:26:38,916 --> 00:26:43,125 but having the tools to interpret the data and make sense out of this data. 473 00:26:43,125 --> 00:26:44,458 And I think that's also really important. 474 00:26:44,458 --> 00:26:47,500 We'll come to the future and it'll come really important to the future as well. 475 00:26:47,666 --> 00:26:51,291 Interpret the data, make them easy for the people to understand 476 00:26:51,291 --> 00:26:53,750 and make the right use on that. Yeah, that's an excellent point, Karen. 477 00:26:53,750 --> 00:26:57,291 I actually would like to follow up on this, Karen, if you have this NGS 478 00:26:57,333 --> 00:27:02,250 background and experience, what excites you more in this NGS proteomics nowadays? 479 00:27:02,250 --> 00:27:05,666 And do you see this like a game changer for clinical trials for example? 480 00:27:05,666 --> 00:27:09,208 Do you see that will make clinical trials smarter having NGS 481 00:27:09,208 --> 00:27:12,458 proteomics integrated to the omics pipelines, for example. 482 00:27:12,625 --> 00:27:13,958 – Quick answer is yes. 483 00:27:13,958 --> 00:27:15,750 You know, you think about NGS. 484 00:27:15,750 --> 00:27:17,375 It's like a static point 485 00:27:17,375 --> 00:27:20,416 in time that you're understanding what's going on in the cell. 486 00:27:20,416 --> 00:27:21,416 Now, we can actually 487 00:27:22,458 --> 00:27:24,333 understand over a time period 488 00:27:24,333 --> 00:27:27,833 how a cell is behaving, how it's reacting, what it what it's doing. 489 00:27:28,083 --> 00:27:30,416 You can do it with a human. You can do it with a bacterial cell. 490 00:27:30,416 --> 00:27:31,458 You can do it with a plant. 491 00:27:31,458 --> 00:27:33,958 I mean, you can understand 492 00:27:33,958 --> 00:27:38,666 what the DNA has resulted in terms of activity at the cell level. 493 00:27:38,666 --> 00:27:40,250 And I think it's so exciting. 494 00:27:40,250 --> 00:27:41,666 I mean, genomics was exciting. 495 00:27:41,666 --> 00:27:47,250 We built massive informatics resources, software resources, massive tools, AI. 496 00:27:47,250 --> 00:27:50,250 We were using all these tools back then, 497 00:27:50,291 --> 00:27:53,291 but now we have a chance to actually understand 498 00:27:54,000 --> 00:27:57,500 what are the messages coming out and what are they turning into. 499 00:27:57,500 --> 00:28:01,708 And how can not that we want to stop it, but how can we use that information 500 00:28:01,833 --> 00:28:05,916 to influence the outcomes and our health in the long term? 501 00:28:05,916 --> 00:28:10,458 And I think it's gonna, over time, become a critical part of, 502 00:28:11,583 --> 00:28:13,916 you know, a companion, part of clinical trials? 503 00:28:13,916 --> 00:28:14,583 I would think so. 504 00:28:14,583 --> 00:28:18,125 I don't do that on a daily basis, but I would absolutely believe that 505 00:28:18,625 --> 00:28:21,875 they're going to you can take a plasma sample on a daily basis 506 00:28:21,875 --> 00:28:25,916 and look at how certain protein markers are fluctuating. 507 00:28:25,916 --> 00:28:27,833 Imagine a time like that. Right. 508 00:28:27,833 --> 00:28:31,500 – Well, Chris Whelan talked about this in depression where they had 509 00:28:31,500 --> 00:28:34,375 three endotypes based on proteins after the clinical trial. 510 00:28:34,375 --> 00:28:36,375 Right. So this is an in that trial. 511 00:28:36,375 --> 00:28:39,208 But they responded differently to the therapy. 512 00:28:39,208 --> 00:28:40,125 Each of those three. 513 00:28:40,125 --> 00:28:40,625 Yeah sorry. 514 00:28:40,625 --> 00:28:41,291 Go ahead Karen. 515 00:28:41,291 --> 00:28:44,625 – No I was just saying that I think you guys had a partnership where 516 00:28:44,625 --> 00:28:50,166 initial outputs from Olink data turned actually into, diagnostic in the clinic. 517 00:28:50,166 --> 00:28:50,625 Right. 518 00:28:50,625 --> 00:28:54,875 And it took a couple of years, but I believe that that's going 519 00:28:54,875 --> 00:28:59,125 to become at scale that we can have these early biomarkers 520 00:28:59,583 --> 00:29:02,791 being detected as a result of the technologies that we're, 521 00:29:03,958 --> 00:29:05,750 fostering in-house. 522 00:29:05,750 --> 00:29:06,541 I really believe that. 523 00:29:06,541 --> 00:29:09,625 – Yeah, yeah, those diseases and there's application 524 00:29:09,625 --> 00:29:13,416 and disease specific signatures that our customers are building. 525 00:29:13,416 --> 00:29:18,250 I think that is one of the most exciting things I was excited about in joining 526 00:29:18,250 --> 00:29:22,291 the Thermo Fisher family is that we can we can accelerate those. 527 00:29:22,958 --> 00:29:23,958 Right. 528 00:29:23,958 --> 00:29:26,958 – And Sarantis, my last happy, happy point. 529 00:29:27,000 --> 00:29:30,000 Imagine being able to live through the genomics revolution 530 00:29:30,416 --> 00:29:32,750 and landing where I can live through the proteomics revolution. 531 00:29:32,750 --> 00:29:35,208 – That’s amazing. – I mean, 532 00:29:35,208 --> 00:29:38,208 not a lot of people get that opportunity, right, Cindy? 533 00:29:38,375 --> 00:29:39,583 – That's right, that's right. 534 00:29:39,583 --> 00:29:43,500 It's pretty special, it really is. 535 00:29:43,666 --> 00:29:46,916 – What do you think also this will open the door to let's say FDA 536 00:29:46,916 --> 00:29:50,416 and to other and let's say institutions that can be biomarkers 537 00:29:50,416 --> 00:29:54,166 that are approved will make probably all of these procedures much easier. 538 00:29:54,208 --> 00:29:55,916 Having all of these nice data sets. 539 00:29:55,916 --> 00:29:58,708 What is your opinion there, Gianluca, on that? 540 00:29:58,708 --> 00:30:01,958 – I think regulators, have their hands full, 541 00:30:02,375 --> 00:30:06,125 to stay up to speed with the technology for sure. 542 00:30:06,125 --> 00:30:07,708 And it's actually, 543 00:30:08,750 --> 00:30:11,000 refreshing to see that 544 00:30:11,000 --> 00:30:14,125 I think we had recently one of the first, 545 00:30:15,208 --> 00:30:17,708 AI driven biomarkers 546 00:30:17,708 --> 00:30:21,041 approved, in the pathology space. 547 00:30:21,291 --> 00:30:23,041 If I'm not mistaken. 548 00:30:23,041 --> 00:30:27,625 And so it tells you that, they're starting to move at pace. 549 00:30:28,083 --> 00:30:31,083 And that's a very good news. 550 00:30:31,291 --> 00:30:33,625 By the way, I Chatgpted 551 00:30:33,625 --> 00:30:37,875 the data point that I quoted on Nonadherence, 552 00:30:38,458 --> 00:30:41,750 and, in fact, there was a study from the W.H.O. 553 00:30:41,750 --> 00:30:46,708 that says that, Nonadherence can account for up to 50% of treatment failure. 554 00:30:47,125 --> 00:30:50,250 And just to put this in context, in the US, 555 00:30:50,250 --> 00:30:53,250 around 125,000 deaths 556 00:30:53,291 --> 00:30:56,916 and out to 25% hospitalization each year, 557 00:30:57,750 --> 00:31:01,708 that are driven from nonadherence, with medication. 558 00:31:01,708 --> 00:31:06,208 So, it's indeed highlighting how the need of, 559 00:31:07,291 --> 00:31:10,375 you know, information and education 560 00:31:10,375 --> 00:31:13,375 of the patient population is, super critical. 561 00:31:13,708 --> 00:31:16,166 But also, I think technology plays a big role. 562 00:31:16,166 --> 00:31:17,250 Why we're seeing... 563 00:31:17,250 --> 00:31:19,958 – Sentry Mode, right, Gianluca? I mean. 564 00:31:20,083 --> 00:31:21,666 – Sentry Mode. Yes. 565 00:31:21,666 --> 00:31:23,791 – Get a pop up. 566 00:31:23,791 --> 00:31:28,708 – Get a pop up, but, you know, in that case, you obviously have the car that, 567 00:31:29,666 --> 00:31:34,041 allows you to do that in a patient ward. 568 00:31:34,083 --> 00:31:38,333 Actually, you need technology to be able to detect, as Karen was highlighting. 569 00:31:39,083 --> 00:31:42,416 What about detecting this biomarker on a continuous basis? 570 00:31:42,750 --> 00:31:46,541 And I feel we're still in the era of mainframe, 571 00:31:47,375 --> 00:31:50,125 in the life science space. 572 00:31:50,125 --> 00:31:52,375 You have this big instrument... 573 00:31:52,375 --> 00:31:55,375 – Or it’s the Volkswagen Bub from 1966. 574 00:31:55,708 --> 00:31:57,333 – Yes. 575 00:31:57,333 --> 00:31:59,250 That's a good analogy. 576 00:31:59,250 --> 00:32:02,250 – Beautiful car, but. – We use Fiat. 577 00:32:02,916 --> 00:32:07,208 You know, I know that you now you're in the US. 578 00:32:07,208 --> 00:32:10,208 So you say Fiat means fix it again, Tony. 579 00:32:10,541 --> 00:32:11,916 But I 580 00:32:11,916 --> 00:32:14,875 as an Italian, I could take offense for that. 581 00:32:14,875 --> 00:32:16,625 But actually... 582 00:32:16,625 --> 00:32:17,958 – Yeah, you’ll get in trouble. 583 00:32:17,958 --> 00:32:24,291 – It was my first car and I truly enjoyed my Fiat. 584 00:32:24,291 --> 00:32:27,375 But I think technology will need to play a big role. 585 00:32:27,375 --> 00:32:30,375 And I could envision over time 586 00:32:30,541 --> 00:32:35,458 we already have much smaller and compact sequencers 587 00:32:35,458 --> 00:32:40,166 and the we know the detection technology for Olink case sequencing. 588 00:32:40,166 --> 00:32:44,375 But over time, we're going to have smaller and smaller technologies 589 00:32:44,375 --> 00:32:48,166 that can be ultimately integrated in, 590 00:32:49,125 --> 00:32:52,916 our houses and will probably benefit from, 591 00:32:53,458 --> 00:32:56,791 an availability of biomarkers at the level. 592 00:32:56,791 --> 00:32:59,791 And to certain extent, you know, even with a WHOOP 593 00:32:59,875 --> 00:33:02,708 today, you can have these digital biomarkers 594 00:33:02,708 --> 00:33:06,541 that are augmenting your insights on your health. 595 00:33:06,541 --> 00:33:11,333 And I think this will happen over time for things like protein for sure, 596 00:33:11,375 --> 00:33:15,583 which are challenging to measure as you know, with the current technology, 597 00:33:15,583 --> 00:33:18,916 but they're becoming easier and easier to measure with modern technologies. 598 00:33:19,208 --> 00:33:23,666 – And then you think to that, let's say one of the barriers you have to go with like 599 00:33:23,833 --> 00:33:27,416 finding new ways of sequencing or more easy ways of sequencing, 600 00:33:27,416 --> 00:33:31,250 or more handy ways of sequencing, right, that there will be the future, 601 00:33:31,375 --> 00:33:33,625 how you envision it. – Totally, totally. 602 00:33:33,625 --> 00:33:36,500 That's why, we're investing, 603 00:33:36,500 --> 00:33:40,625 a billion three, a billion four every year in R&D. 604 00:33:40,625 --> 00:33:44,041 That's Karen’s 605 00:33:44,041 --> 00:33:47,416 ward, she’s very tightly managing, 606 00:33:48,000 --> 00:33:51,958 the priority around, everything that we spend in innovation. 607 00:33:51,958 --> 00:33:55,750 But, certainly that's the direction we're driving towards. 608 00:33:55,750 --> 00:33:58,750 And I'm sure Karen has some perspective on that. 609 00:33:59,291 --> 00:34:02,916 – No, I agree, I was laughing to myself because I remember 610 00:34:02,916 --> 00:34:07,083 in the early days of the microbiome, they wanted to put a digital detector 611 00:34:07,083 --> 00:34:10,666 in the toilet and so people could know if they were sick. 612 00:34:10,666 --> 00:34:12,375 But but think about it, right, Cindy? 613 00:34:12,375 --> 00:34:16,625 I mean, imagine being able to look at inflammation in a urine sample. 614 00:34:16,708 --> 00:34:19,250 It could become our future that, you know, 615 00:34:19,250 --> 00:34:24,041 you have some way to check at home and this becomes a part of your lifestyle. 616 00:34:24,041 --> 00:34:26,250 So I, I do agree with that 100%. 617 00:34:26,250 --> 00:34:29,916 But in terms of our investment in R&D and pushing the envelope 618 00:34:29,916 --> 00:34:33,625 and integrating cost company, these are these opportunities. 619 00:34:33,625 --> 00:34:35,000 Again, you know, it's 620 00:34:35,000 --> 00:34:38,500 the things that wake you up in the morning excited to go to work. 621 00:34:38,500 --> 00:34:40,208 We think about these opportunities. 622 00:34:40,208 --> 00:34:44,208 And I will tell you honestly that Gianluca 623 00:34:44,208 --> 00:34:49,666 and I think the proteomics space is one of the especially bringing Olink in, 624 00:34:50,500 --> 00:34:54,083 is one of the biggest, most exciting areas for us. 625 00:34:54,083 --> 00:34:56,208 So we don't advertise it all the time. 626 00:34:56,208 --> 00:34:57,166 But we are very, 627 00:34:58,291 --> 00:35:01,458 proud of the acquisition and what it means for the company. 628 00:35:01,458 --> 00:35:04,166 – I love to hear it. – You are very welcome. 629 00:35:04,166 --> 00:35:04,833 Welcome on board. 630 00:35:04,833 --> 00:35:06,875 Very happy to have you on board. – Thank you. 631 00:35:06,875 --> 00:35:10,416 – It's been a year now, Sarantis, and it's like, I've known you forever. 632 00:35:10,416 --> 00:35:12,333 – Yeah. 633 00:35:13,083 --> 00:35:15,333 – Yeah, we get under your skin. We get there. 634 00:35:15,333 --> 00:35:16,958 – Yeah. We are, we are looking forward to. 635 00:35:16,958 --> 00:35:18,750 – It's very exciting. – It's very exciting. 636 00:35:18,750 --> 00:35:22,416 It's very exciting to see that the matching right with the omics pipeline 637 00:35:22,416 --> 00:35:23,375 that you’re having. 638 00:35:23,375 --> 00:35:26,875 And it's really clear, nice fit on that because now it's a complete 639 00:35:27,000 --> 00:35:30,125 now it's a complete toolbox right end to end for the customer 640 00:35:30,333 --> 00:35:32,625 from data generation also to data interpretation. 641 00:35:32,625 --> 00:35:34,708 That's the beauty. That's the beauty. 642 00:35:34,708 --> 00:35:35,625 – Yes. Correct. 643 00:35:36,083 --> 00:35:37,583 – Can I add one thing? 644 00:35:38,541 --> 00:35:39,583 I want to add one thing 645 00:35:39,583 --> 00:35:42,583 which is super important because obviously, 646 00:35:43,083 --> 00:35:47,041 the Olink technology as well as largely Mass Spec 647 00:35:47,041 --> 00:35:52,333 for untargeted protein, detection and quantification, 648 00:35:53,375 --> 00:35:55,375 has been used in 649 00:35:55,375 --> 00:35:59,875 the translational and research space, but one of the big unlock 650 00:35:59,875 --> 00:36:04,291 will be when those technologies are getting to the clinics. And 651 00:36:05,041 --> 00:36:08,583 I think we have an interesting parallel with what we did with Ion Torrent. 652 00:36:09,125 --> 00:36:13,166 We acquired the company back in 2011, I believe, 653 00:36:13,875 --> 00:36:18,541 and now we are basically the enabler of, 654 00:36:19,375 --> 00:36:22,625 vast majority of clinical testing, 655 00:36:23,208 --> 00:36:26,166 that are next generation sequencing based, 656 00:36:26,166 --> 00:36:30,125 thanks to the fact that the team has worked to streamline that technology. 657 00:36:30,125 --> 00:36:33,708 Now we have a fully integrated and automated NGS, 658 00:36:34,541 --> 00:36:39,791 that is used for therapy selection, with, you know, great products. 659 00:36:39,791 --> 00:36:42,916 We just announced that we got FDA approval 660 00:36:42,916 --> 00:36:46,250 for, our Genexus system. And, 661 00:36:47,458 --> 00:36:48,625 you know, 662 00:36:48,625 --> 00:36:52,166 a set of, panel and markers used for therapy selection. 663 00:36:52,166 --> 00:36:54,125 So it's incredibly exciting. 664 00:36:54,125 --> 00:36:56,000 It's been a long journey. 665 00:36:56,000 --> 00:36:58,333 But it's a journey that, we learned 666 00:36:58,333 --> 00:37:01,583 how to, go through and execute. 667 00:37:01,583 --> 00:37:05,375 And I do envision the same journey on a shorter timeline 668 00:37:05,375 --> 00:37:09,125 because we learned a lot over the last decade, 15 years. 669 00:37:09,625 --> 00:37:13,416 And I'm super excited thinking at when we're going 670 00:37:13,416 --> 00:37:17,541 to see, proteomics, into the clinics at scale. 671 00:37:18,041 --> 00:37:21,333 It will require obviously content, technology, etc. 672 00:37:21,333 --> 00:37:25,708 but, it's, exciting to think about when is going to happen. 673 00:37:26,041 --> 00:37:29,791 – And I think the what's critical and we do this today with our focus panels 674 00:37:30,083 --> 00:37:33,041 is being able to turn over those custom subsets 675 00:37:33,041 --> 00:37:36,750 of proteins that we already understand exactly how they cross react together. 676 00:37:36,750 --> 00:37:39,250 We already understand how they play. 677 00:37:39,250 --> 00:37:43,958 And those, you know, authentication steps of, you know, two antibodies and 678 00:37:44,208 --> 00:37:47,375 and one hybridization, those that three factor authentication 679 00:37:47,375 --> 00:37:50,416 is really enabling this to happen quickly. 680 00:37:50,750 --> 00:37:52,541 And so I would encourage 681 00:37:52,541 --> 00:37:56,166 folks to keep an eye on this space because we're already seeing, you know, 682 00:37:56,458 --> 00:38:00,000 there's over 700 references to the UK Biobank. 683 00:38:00,250 --> 00:38:03,416 You know, foundation paper that came from the pharma partners. 684 00:38:03,416 --> 00:38:05,958 That was in October of 2023. 685 00:38:05,958 --> 00:38:08,958 So lots of references by using those data, 686 00:38:09,125 --> 00:38:12,125 some of them just mining the data for new insights. 687 00:38:12,875 --> 00:38:14,500 It's a rich source. 688 00:38:14,500 --> 00:38:17,833 – And if you don't trust a one factor authentication 689 00:38:17,833 --> 00:38:21,666 for your digital needs, why should you trust it for your health? 690 00:38:21,666 --> 00:38:22,125 – That’s true. 691 00:38:22,125 --> 00:38:24,541 – Exactly. – So use Olink. 692 00:38:24,541 --> 00:38:28,875 That's the only two factor authentication at scale out there. 693 00:38:29,375 --> 00:38:32,125 – And antibodies are what we use in therapies. 694 00:38:32,125 --> 00:38:32,375 Right. 695 00:38:32,375 --> 00:38:36,208 So I'm also excited although much of it's behind closed doors. 696 00:38:36,500 --> 00:38:41,041 But when pharma are able to publish on mechanistic insights 697 00:38:41,041 --> 00:38:45,708 from the, the therapeutic targets that they're identifying, right. 698 00:38:45,708 --> 00:38:49,708 This is a big push for why 14 pharma partners are paying 699 00:38:49,708 --> 00:38:53,666 for 600,000 samples 700 00:38:53,666 --> 00:38:57,750 to be run, the entire cohort in the UK Biobank. 701 00:38:57,750 --> 00:39:03,416 And I think that the mapping of those causal pathways across different 702 00:39:03,416 --> 00:39:07,500 diseases is going to be a reference that we will come back to for many years. 703 00:39:07,500 --> 00:39:08,875 It feels very good, Karen. 704 00:39:08,875 --> 00:39:11,791 It f feels like the GWAS revolution over again. 705 00:39:11,791 --> 00:39:12,208 Right. 706 00:39:12,625 --> 00:39:18,333 It's like you say, it's just such a privilege to have a front row seat and a tiny, 707 00:39:19,458 --> 00:39:20,291 impact 708 00:39:20,291 --> 00:39:23,125 on where this goes and where it will go next. 709 00:39:23,125 --> 00:39:27,250 – Well to be part of the team, Cindy, we're all part of the team, right? 710 00:39:27,250 --> 00:39:29,375 – It's awesome. It's awesome. 711 00:39:29,375 --> 00:39:31,291 So I'm having the time of my life. 712 00:39:31,291 --> 00:39:32,958 I am having the time of my life. 713 00:39:32,958 --> 00:39:34,166 How about you, Sarantis? 714 00:39:34,166 --> 00:39:37,083 – Oh it's so great I mean, I'm coming from the omics perspective, right. 715 00:39:37,083 --> 00:39:40,583 And being in a big family, with the omics family coming together, 716 00:39:40,583 --> 00:39:42,125 which for me is the most exciting thing ever. 717 00:39:42,125 --> 00:39:48,333 You know, looking forward to break into clinics, looking for biomarkers and push and beat 718 00:39:48,375 --> 00:39:52,125 and convince FDA to have biomarkers for clinical diagnostics. 719 00:39:52,166 --> 00:39:54,875 That's I think is the next step I have to go through. 720 00:39:54,875 --> 00:39:57,000 – And, Cindy, one more plug. 721 00:39:57,000 --> 00:40:02,375 One more little comment to Miller who is on our team sent me a list of about 722 00:40:02,375 --> 00:40:06,125 ten microbiome papers where they're using Olink to look at inflammation. 723 00:40:06,375 --> 00:40:07,583 And doing that correlation. 724 00:40:07,583 --> 00:40:09,375 – Thanks for the reminder. 725 00:40:09,375 --> 00:40:14,625 – But it's just amazing how it's spanning multiple different, 726 00:40:14,875 --> 00:40:16,291 areas of science, right. 727 00:40:16,291 --> 00:40:19,750 That you probably wouldn't have thought about the connection originally. 728 00:40:19,916 --> 00:40:21,875 – That's right. And we can put those in the show notes. 729 00:40:21,875 --> 00:40:23,125 So if folks are interested in that. 730 00:40:23,125 --> 00:40:27,208 I had just had a customer, ask me specifically about that as well. 731 00:40:27,208 --> 00:40:30,541 Another person who came from that microbiome. 732 00:40:30,541 --> 00:40:33,541 It’s still happening, this microbiome – Still trying. 733 00:40:33,541 --> 00:40:35,625 – revolution where we're learning so much. Yeah. 734 00:40:35,625 --> 00:40:36,875 It's like they. 735 00:40:36,875 --> 00:40:38,541 I always think of them paying 736 00:40:38,541 --> 00:40:43,083 the biome is paying currency like it's paying rent to live in our body. 737 00:40:43,083 --> 00:40:43,333 Right? 738 00:40:43,333 --> 00:40:46,208 By giving us these metabolites like serotonin and things like this. 739 00:40:46,208 --> 00:40:50,791 Again, Gianluca and I, I think both really love a good analogy. 740 00:40:51,625 --> 00:40:54,208 So, so final thoughts, 741 00:40:54,208 --> 00:40:57,416 we're just going to kind of, wind down here. 742 00:40:57,416 --> 00:40:59,250 It's a pretty great place to end on. 743 00:40:59,250 --> 00:41:02,791 Gianluca, do you have some final thoughts you'd like to leave our listeners with? 744 00:41:03,041 --> 00:41:04,541 – Absolutely. 745 00:41:04,541 --> 00:41:09,291 The future is bright and is bright, thanks to the 746 00:41:09,875 --> 00:41:14,875 for us, 120 plus thousand colleagues at the company showing up every day 747 00:41:15,208 --> 00:41:19,625 trying to create those incredible innovation that, 748 00:41:20,541 --> 00:41:25,083 equally talented and incredibly important customers and partners 749 00:41:25,083 --> 00:41:28,666 are using to actually transform the way that, 750 00:41:29,250 --> 00:41:31,666 we identify early 751 00:41:31,666 --> 00:41:36,666 or fully, then ultimately identify treatment and treat diseases that, 752 00:41:37,041 --> 00:41:40,125 today are either not well treated or untreatable. 753 00:41:40,583 --> 00:41:44,083 And that's why it is incredibly exciting. 754 00:41:44,083 --> 00:41:48,166 To do that well, you actually need the right technology. 755 00:41:48,166 --> 00:41:50,458 Back to the analogy of the self-driving car. 756 00:41:50,458 --> 00:41:51,875 You need the right sensors. 757 00:41:51,875 --> 00:41:55,041 You need the right orchestration, information, 758 00:41:55,250 --> 00:41:59,583 and you want to rely on something for your life. 759 00:41:59,583 --> 00:42:05,125 When you drive on a self-driving car or when you actually drive through your life 760 00:42:05,125 --> 00:42:08,625 and you make medical decision, you actually want to rely, 761 00:42:09,250 --> 00:42:12,958 on the best of the technologies and our two factor 762 00:42:12,958 --> 00:42:13,916 authentication at Olink, is something very special. 763 00:42:16,750 --> 00:42:19,750 And I think it has been, 764 00:42:19,916 --> 00:42:22,333 proven, testified by the adoption 765 00:42:22,333 --> 00:42:27,541 that we've seen from the UK Biobank study as an example and the many partners 766 00:42:27,541 --> 00:42:30,916 in the pharma space that now are working with us to create 767 00:42:30,916 --> 00:42:34,166 the next generation of medicine and diagnostic. 768 00:42:34,500 --> 00:42:36,125 So it is incredibly exciting. 769 00:42:36,125 --> 00:42:41,500 It has been a great journey thus far, and I can't wait for the next decade 770 00:42:41,500 --> 00:42:44,875 to play out so that, together as an industry, 771 00:42:45,125 --> 00:42:48,833 we actually can truly transform the way that, medicine works. 772 00:42:49,625 --> 00:42:50,333 – Beautiful. 773 00:42:50,333 --> 00:42:51,583 Karen? 774 00:42:51,583 --> 00:42:53,541 – Cindy, I can't add anything more to that. 775 00:42:53,541 --> 00:42:55,000 I'm good. 776 00:42:55,000 --> 00:42:55,833 That was perfect. 777 00:42:55,833 --> 00:42:56,750 That was really good. 778 00:42:56,750 --> 00:43:00,083 So thank you again for having us. 779 00:43:00,083 --> 00:43:02,208 It's been really a lot of fun. 780 00:43:02,208 --> 00:43:04,500 – I’m so looking forward to it. 781 00:43:04,500 --> 00:43:08,125 So in the show notes, I'll add some of the genetic corroboration 782 00:43:08,125 --> 00:43:10,416 of the Olink specificity and also some mass spec 783 00:43:10,416 --> 00:43:13,125 corroboration of the Olink specificity as well as Elisa. 784 00:43:13,125 --> 00:43:15,333 Right. There's lots of ways to corroborate. 785 00:43:15,333 --> 00:43:18,500 You just have to be able to see the protein in whatever matrix you're 786 00:43:18,500 --> 00:43:19,458 looking at. 787 00:43:19,458 --> 00:43:22,666 Any final words from you, Sarantis – Oh, I mean, it's exciting. 788 00:43:22,666 --> 00:43:23,583 It's exciting times. 789 00:43:23,583 --> 00:43:26,708 And I mean looking forward to break the barrier. 790 00:43:26,708 --> 00:43:27,666 Right. 791 00:43:27,666 --> 00:43:30,958 To make our world healthier, safer. 792 00:43:30,958 --> 00:43:33,125 Right. And, the best place to live. 793 00:43:33,125 --> 00:43:34,541 Thank you very much for your time. 794 00:43:34,541 --> 00:43:36,708 And it was great honor to have you, both of you here. 795 00:43:36,708 --> 00:43:37,625 – Thank you, both. 796 00:43:37,625 --> 00:43:38,458 – Awesome. 797 00:43:38,458 --> 00:43:41,458 Well, just as a reminder, if you want to reach out to us, 798 00:43:41,458 --> 00:43:45,541 we're at pip@olink.com that stands for Proteomics in Proximity. 799 00:43:45,541 --> 00:43:46,875 We love to hear from you. 800 00:43:46,875 --> 00:43:51,125 We love to get feedback and questions that you'd like to hear answered here. 801 00:43:51,708 --> 00:43:53,375 Thank you very much for tuning in. 802 00:43:55,708 --> 00:43:59,583 Well, that wraps up this episode of Proteomics in Proximity. 803 00:44:00,125 --> 00:44:04,208 Huge thanks to our guests and authors of such impactful publications. 804 00:44:04,625 --> 00:44:07,208 I also want to thank you for tuning in. 805 00:44:07,208 --> 00:44:09,458 Really appreciate you being here. 806 00:44:09,458 --> 00:44:11,250 If you enjoyed the content of this 807 00:44:11,250 --> 00:44:15,125 episode, please think about sharing it with friends or colleagues 808 00:44:15,125 --> 00:44:17,375 you think might be interested in the content. 809 00:44:17,375 --> 00:44:21,458 In addition, if you'd be willing to head over to Apple or Spotify 810 00:44:21,458 --> 00:44:24,916 or wherever you digest your podcasts and give us a rating and review, 811 00:44:24,916 --> 00:44:26,958 this will help others find the podcast 812 00:44:26,958 --> 00:44:30,291 when they're searching for proteomics or precision medicine podcasts. 813 00:44:30,541 --> 00:44:34,125 And mostly I want to say we would love to hear from you. 814 00:44:34,291 --> 00:44:37,791 So we have a dedicated email address pip@olink.com. 815 00:44:38,083 --> 00:44:39,250 Please reach out, 816 00:44:39,250 --> 00:44:43,208 let us know what you're interested in hearing about what you care about 817 00:44:43,208 --> 00:44:47,250 and any feedback on the episodes that we have already done so far. 818 00:44:47,541 --> 00:44:50,958 This is all about you, and so we're really keen 819 00:44:50,958 --> 00:44:53,958 to make sure that we're meeting what you like to hear about. 820 00:44:54,208 --> 00:44:56,333 Thank you so much and we'll see you soon.