1 00:00:05,625 --> 00:00:08,625 Welcome to the Proteomics and Proximity Podcast. 2 00:00:08,625 --> 00:00:13,208 Where your co-hosts, Cindy Lawley and Sarantis Chlamydas from Olink Proteomics, 3 00:00:13,291 --> 00:00:16,916 talk about the intersection of proteomics with genomics for drug target 4 00:00:16,916 --> 00:00:21,125 discovery, the application of proteomics to reveal disease biomarkers, 5 00:00:21,208 --> 00:00:26,166 and current trends in using proteomics to unlock biological mechanisms. 6 00:00:26,250 --> 00:00:30,458 Here we have your hosts, Cindy and Sarantis. 7 00:00:30,500 --> 00:00:32,458 Hey, everyone. 8 00:00:32,458 --> 00:00:36,041 Hello and welcome back to Proteomics in Proximity. 9 00:00:36,041 --> 00:00:41,041 Thanks to our 11 listeners at Sam Ray, Carolina, others. 10 00:00:41,041 --> 00:00:44,583 We are grateful for your attention 11 00:00:44,583 --> 00:00:47,791 and your feedback, and 12 00:00:47,875 --> 00:00:51,416 our listeners have given us some valuable feedback over time 13 00:00:51,416 --> 00:00:55,000 and they've found us through different social media avenues. 14 00:00:55,083 --> 00:00:58,291 But to make that easier, we're announcing that 15 00:00:58,291 --> 00:01:01,791 we've got actually an email address now, so we'll put this into the show notes. 16 00:01:01,791 --> 00:01:07,083 But it's just P-I-P for Proteomics in Proximity at Olink.com. [pip@olink.com] 17 00:01:07,166 --> 00:01:11,000 And and we'd be happy to hear from you around suggestions 18 00:01:11,000 --> 00:01:16,000 you have or any interview recommendations you might have. 19 00:01:16,083 --> 00:01:20,666 And with that, today, we are talking to Evan Mills. 20 00:01:20,666 --> 00:01:23,916 Evan, I'll let him introduce himself, but he is 21 00:01:24,000 --> 00:01:27,750 an illustrious pharma executive, here 22 00:01:27,750 --> 00:01:31,208 actually at Olink, and we're excited to talk to him 23 00:01:31,208 --> 00:01:34,666 about how pharma are finding proteomics 24 00:01:34,750 --> 00:01:37,750 super relevant on many different levels. 25 00:01:37,916 --> 00:01:40,916 So with that, let's get on with it. 26 00:01:40,916 --> 00:01:42,750 Hey, Sarantis, how are you? 27 00:01:42,750 --> 00:01:44,583 Hello. I'm fine. Thank you, Cindy. Welcome, Evan. 28 00:01:44,583 --> 00:01:46,041 I'm looking forward to 29 00:01:46,041 --> 00:01:48,500 hear from you all the great news. 30 00:01:48,500 --> 00:01:50,333 Likewise. Good afternoon, Sarantis. 31 00:01:50,333 --> 00:01:53,500 Good early morning to you, Cindy, on the West coast. 32 00:01:53,708 --> 00:01:56,375 It's a little dark over here. A little dark 33 00:01:56,375 --> 00:01:58,375 No, that's all right. I'm really honored to be here. 34 00:01:58,375 --> 00:02:01,708 And I've been wanting to talk about how proteomics 35 00:02:01,708 --> 00:02:06,208 and the pharmaceutical industry are aligning for really exciting things. 36 00:02:06,208 --> 00:02:07,500 So very happy to be here. 37 00:02:07,500 --> 00:02:11,833 Can you give us just a little background on your history in this area? 38 00:02:11,833 --> 00:02:15,916 You've been in this for a while. 39 00:02:16,000 --> 00:02:16,541 I have. 40 00:02:16,541 --> 00:02:16,791 I have. 41 00:02:16,791 --> 00:02:21,000 So I was a bench scientist really, you know, passionate about oncology 42 00:02:21,000 --> 00:02:22,166 and neuroscience research. 43 00:02:22,166 --> 00:02:24,625 I did some work at Yale University for awhile 44 00:02:24,625 --> 00:02:29,375 and then I got into the sales commercial side of this world, 45 00:02:29,458 --> 00:02:33,083 started actually in the pharmaceutical sales industry, which 46 00:02:33,166 --> 00:02:36,000 was exciting because of the opportunity to help patients, 47 00:02:36,000 --> 00:02:36,708 right? 48 00:02:36,708 --> 00:02:40,791 But my real passion was in the science and about a decade ago, 49 00:02:40,833 --> 00:02:43,875 there was a very innovative proteomics company 50 00:02:43,875 --> 00:02:47,458 that caught my attention and that's where I started this journey, 51 00:02:47,708 --> 00:02:51,375 where I've now been at Olink for over five years. 52 00:02:51,458 --> 00:02:55,250 And yeah, supporting the most innovative, 53 00:02:55,250 --> 00:03:00,750 ambitious researchers in this multi-omics space has just been a phenomenal journey. 54 00:03:00,833 --> 00:03:05,541 So my background is: I love science, I want to help people, 55 00:03:05,625 --> 00:03:12,958 I want to have some sort of translational impact with the work I do. And 56 00:03:13,041 --> 00:03:16,166 right now at this moment, there's never been more momentum in that direction. 57 00:03:16,166 --> 00:03:17,958 It's really, really exciting. Yes, very exciting. 58 00:03:17,958 --> 00:03:21,291 We've just had here at Olink 59 00:03:21,291 --> 00:03:25,000 three pretty exciting nature papers 60 00:03:25,083 --> 00:03:28,083 come out in 61 00:03:28,125 --> 00:03:31,125 I think it's the online [version on] October 4th. 62 00:03:31,208 --> 00:03:35,875 But the print journal [on] October 11 with a beautiful frog on the cover. 63 00:03:35,958 --> 00:03:40,375 It's an exciting time with those three papers. 64 00:03:40,375 --> 00:03:43,375 So those include a lot of applications 65 00:03:43,583 --> 00:03:47,083 around why pharma would invest in proteomics. 66 00:03:47,083 --> 00:03:52,041 So I'd love to get your thoughts on why. 67 00:03:52,041 --> 00:03:57,000 Why did 13 pharma come together, invest in proteomics? 68 00:03:57,125 --> 00:03:58,041 What's the outcome? 69 00:03:58,041 --> 00:04:02,291 What's the result that they see out of that? 70 00:04:02,375 --> 00:04:05,375 Yeah, I mean that's been a real career 71 00:04:05,375 --> 00:04:09,750 highlight, is being able to be involved in that project from its inception. 72 00:04:09,833 --> 00:04:12,500 And you know, Cindy, with your background in genetics, 73 00:04:12,500 --> 00:04:17,625 there was a previously formed consortium around whole exome sequencing 74 00:04:17,625 --> 00:04:21,000 in the UK Biobank and then eventually the whole genome sequencing. 75 00:04:21,083 --> 00:04:25,375 But there was this idea, and I was having lunch 76 00:04:25,458 --> 00:04:29,083 outside of the Harvard symposium with Dr. 77 00:04:29,083 --> 00:04:34,041 Chris Whelan, very smart geneticist who was at Biogen at the time. 78 00:04:34,041 --> 00:04:35,625 He's now at Janssen. 79 00:04:35,625 --> 00:04:36,708 And he just asked the question. 80 00:04:36,708 --> 00:04:40,083 He's like, "Hey, we're thinking about what makes sense to do next. 81 00:04:40,083 --> 00:04:43,416 We have all this richness in the genomic data, 82 00:04:43,500 --> 00:04:46,125 but we want to do something closer to phenotype. 83 00:04:46,125 --> 00:04:49,500 Would it even be conceivable for Olink to run 50,000 samples?" 84 00:04:49,583 --> 00:04:51,291 And this is before, 85 00:04:51,291 --> 00:04:54,416 you know, some of the innovation that would have made that possible. 86 00:04:54,625 --> 00:04:57,166 And we said, "Yeah, I think we can do that. 87 00:04:57,166 --> 00:04:59,250 I think we can get there, I think we can do that." 88 00:04:59,250 --> 00:05:02,708 So it was just born out of curiosity 89 00:05:02,708 --> 00:05:06,625 and the desire to get closer to phenotype. 90 00:05:06,708 --> 00:05:07,208 So the 91 00:05:07,208 --> 00:05:11,166 goal really of this ambitious project was: 92 00:05:11,250 --> 00:05:14,166 can we both better understand 93 00:05:14,166 --> 00:05:17,583 drug targets that have causal links to disease 94 00:05:17,666 --> 00:05:22,500 and can we simultaneously find biomarkers to help the drug development process? 95 00:05:22,583 --> 00:05:26,125 Because obviously with proteomics you can do both, right? 96 00:05:26,125 --> 00:05:29,916 It can act as a bit of a filter to tell you which of these 97 00:05:30,000 --> 00:05:34,541 genomic disease associations have a plausible biological story 98 00:05:34,625 --> 00:05:38,833 and which ones should be pursued, and which ones should perhaps be killed, 99 00:05:38,916 --> 00:05:42,250 but simultaneously you can develop a suite of tools 100 00:05:42,291 --> 00:05:45,291 to determine risk based on proteomics, to determine disease 101 00:05:45,291 --> 00:05:48,416 progression based on proteomics, and to discover biomarkers, 102 00:05:48,416 --> 00:05:51,541 which are obviously always desired 103 00:05:51,541 --> 00:05:54,541 to aid clinical development. So, 104 00:05:54,541 --> 00:05:56,750 I mean, we're just starting to see all the publications. 105 00:05:56,750 --> 00:06:00,625 We're starting to understand all the utility that's going 106 00:06:00,625 --> 00:06:02,750 to come from this data set. 107 00:06:02,833 --> 00:06:05,333 And it was just such an ambitious, 108 00:06:05,333 --> 00:06:10,500 smart idea by Chris and then eventually Melissa and Linden and Brad, 109 00:06:10,583 --> 00:06:14,541 that countless others who contributed to the project. 110 00:06:14,541 --> 00:06:16,916 Evan, going back to this journey, this amazing journey, 111 00:06:16,916 --> 00:06:20,041 how easy or difficult was it to convince the genomics community 112 00:06:20,041 --> 00:06:23,708 because you mentioned it was like this heavy genomics community, right? 113 00:06:23,791 --> 00:06:26,833 Change their mindset in a way to measure proteins, 114 00:06:26,875 --> 00:06:31,000 how easy or difficult was this process? 115 00:06:31,041 --> 00:06:32,875 It's really hard. 116 00:06:32,875 --> 00:06:33,625 I think it is. 117 00:06:33,625 --> 00:06:38,041 I think it was really hard because if you just think about the tools 118 00:06:38,041 --> 00:06:41,041 one would need to develop to measure, right? 119 00:06:41,041 --> 00:06:44,500 Our DNA is very nicely organized into a helical structure. 120 00:06:44,666 --> 00:06:48,875 There's four bases to measure, and Illumina and others 121 00:06:48,875 --> 00:06:52,833 now have developed amazing tools that can measure that at scale. 122 00:06:52,916 --> 00:06:54,333 Think about the proteome, right? 123 00:06:54,333 --> 00:06:56,333 There's 20 amino acids. 124 00:06:56,333 --> 00:06:58,791 They combine in a myriad of different ways. 125 00:06:58,791 --> 00:07:04,666 I mean, it's just such a formidable challenge that geneticists would say, 126 00:07:04,750 --> 00:07:05,958 "I'm not so sure. 127 00:07:05,958 --> 00:07:07,583 I'm not so sure the tools exist. 128 00:07:07,583 --> 00:07:11,833 And oh, by the way, yeah, we can measure everything with genomics. 129 00:07:11,833 --> 00:07:13,500 We can measure everything. 130 00:07:13,500 --> 00:07:17,458 And you're approaching us with something that measures 1500 at the time?" 131 00:07:17,666 --> 00:07:18,208 Right. 132 00:07:18,208 --> 00:07:21,958 And then 3000 of what people assume would be 133 00:07:21,958 --> 00:07:25,041 maybe 20,000 proteins that you could try to capture 134 00:07:25,041 --> 00:07:28,916 in plasma / serum, there's a big debate about that. 135 00:07:29,000 --> 00:07:30,791 So it is challenging, 136 00:07:30,791 --> 00:07:37,000 BUT the obvious central dogma of being closer to disease 137 00:07:37,000 --> 00:07:37,625 and things 138 00:07:37,625 --> 00:07:42,791 that are reflective of real-time biology versus your blueprint for your biology 139 00:07:42,875 --> 00:07:45,833 was compelling enough for them to give it a shot, but it was not easy. 140 00:07:45,833 --> 00:07:49,791 So you must have focused on what is the near-term 141 00:07:49,791 --> 00:07:53,833 return on investment for pharma, for running a proteomics project. 142 00:07:54,083 --> 00:07:59,208 And I would consider this UK Biobank sort of pQTL developing therapeutic targets. 143 00:07:59,208 --> 00:08:00,625 All of the - 144 00:08:00,625 --> 00:08:04,958 all of those things you've already mentioned is more mid- or long-term goals. 145 00:08:05,041 --> 00:08:08,541 How did you - I think it's a great question Sarantis asked - 146 00:08:08,541 --> 00:08:12,416 how do you talk to them about what you believe is the value? 147 00:08:12,416 --> 00:08:12,958 And I will 148 00:08:12,958 --> 00:08:18,708 also say we - Gary and I - looked ... Gary is our illustrious person 149 00:08:18,708 --> 00:08:23,625 who manages our database of over 1400 peer-reviewed publications 150 00:08:23,708 --> 00:08:28,375 and he has seen over 84 of those are pharma relevant publication. 151 00:08:28,375 --> 00:08:33,666 So there's a significant number of publications that have been 152 00:08:33,750 --> 00:08:36,958 that have been put out there that document 153 00:08:36,958 --> 00:08:41,291 some value to pharma, but that's pretty recent. 154 00:08:41,375 --> 00:08:45,541 How did you approach them when you first came to Olink? 155 00:08:45,625 --> 00:08:46,958 Yeah, no, it's a good question. 156 00:08:46,958 --> 00:08:51,416 So we can take a bit of a sidebar from the UK Biobank discussion because 157 00:08:51,500 --> 00:08:54,208 really, fundamentally, drug developers 158 00:08:54,208 --> 00:08:58,958 are trying to bring effective therapeutics to market faster 159 00:08:59,041 --> 00:09:03,583 and they also invest enormous resources into each program. 160 00:09:03,833 --> 00:09:06,958 And it takes what, 10 to 15 years on average, you know, to get something approved. 161 00:09:06,958 --> 00:09:11,750 And how many millions of dollars, right? 162 00:09:11,833 --> 00:09:12,500 And patience. 163 00:09:12,500 --> 00:09:13,916 And then what, 164 00:09:13,916 --> 00:09:17,916 90% of clinical trials fail I think, or somewhere around there. 165 00:09:18,000 --> 00:09:19,250 Yeah. 166 00:09:19,250 --> 00:09:22,666 I recently had a discussion with an executive vice 167 00:09:22,666 --> 00:09:26,500 president of research at a major company who said he would be the world's 168 00:09:26,583 --> 00:09:31,625 best drug developer if he failed 80% of the time. 169 00:09:31,833 --> 00:09:36,375 Isn't that wild? If he could go 80% after failing 90%, he would be the best. 170 00:09:36,375 --> 00:09:38,291 And it's - right? 171 00:09:38,291 --> 00:09:41,166 It's just such a high attrition game. 172 00:09:41,166 --> 00:09:43,958 But those are, Cindy, the way that 173 00:09:44,000 --> 00:09:46,666 a lot of people in the industry are starting to look at 174 00:09:46,666 --> 00:09:52,458 this is: with population-scale proteomics or high-throughput proteomics, 175 00:09:52,541 --> 00:09:56,708 you can learn a lot about things you've already invested in. 176 00:09:56,708 --> 00:10:00,541 So let's say that you have a drug that's approved such as - 177 00:10:00,625 --> 00:10:02,791 I can never say that correctly. 178 00:10:02,791 --> 00:10:05,541 Jardiance, let's go with Jardiance [empaglioflozin]. 179 00:10:05,541 --> 00:10:06,208 Yeah. 180 00:10:06,208 --> 00:10:09,791 You know, a very, very effective SLT2 181 00:10:09,791 --> 00:10:12,791 inhibitor used for the treatment of 182 00:10:12,833 --> 00:10:15,083 diabetic control. 183 00:10:15,083 --> 00:10:19,833 They've also noticed, after having it in enough humans in the wild, 184 00:10:19,916 --> 00:10:22,916 that there's significant benefits to heart failure. 185 00:10:23,166 --> 00:10:28,500 So if you can access - and this is one of the publications that you referenced - 186 00:10:28,583 --> 00:10:32,500 if you can access samples from completed clinical trials 187 00:10:32,541 --> 00:10:36,541 and most companies are sitting on these, they're just in their freezers 188 00:10:36,625 --> 00:10:40,666 waiting to be analyzed if they have the exploratory consents. 189 00:10:40,750 --> 00:10:41,666 If you take a 190 00:10:41,666 --> 00:10:44,666 look at proteomics at scale from 191 00:10:44,708 --> 00:10:49,000 lots of humans treated in the clinical setting, you can learn 192 00:10:49,000 --> 00:10:53,458 a tremendous amount about why certain people respond and certain people don't. 193 00:10:53,541 --> 00:10:53,791 Right. 194 00:10:53,791 --> 00:10:57,458 That's the Holy Grail, essentially is: can you proactively know 195 00:10:57,458 --> 00:10:59,666 which patients could go, should go, which therapy. 196 00:10:59,666 --> 00:11:01,250 We often call that "stratifying 197 00:11:01,250 --> 00:11:06,916 patients," just to use the term that we've used before. 198 00:11:07,000 --> 00:11:08,666 Yep. Yes, absolutely. 199 00:11:08,666 --> 00:11:11,500 And you know, understanding the mechanism of these drugs, right? 200 00:11:11,500 --> 00:11:15,833 Because, you know, you have a target, you have a hypothesis, you tested it in cell- 201 00:11:15,833 --> 00:11:19,541 based models, animal-based models, but you don't really have a chance 202 00:11:19,541 --> 00:11:24,208 to look at scale in a human population to see how it impacts the human body. 203 00:11:24,291 --> 00:11:28,375 So then with that data, you can A) better understand why there's 204 00:11:28,375 --> 00:11:32,250 this benefit in an indication for which the drug was not initially approved. 205 00:11:32,333 --> 00:11:35,583 You can understand - Excuse me, 206 00:11:35,666 --> 00:11:37,375 what other pathways are being 207 00:11:37,375 --> 00:11:40,375 impacted by your therapeutic. Are there repurposing opportunities? 208 00:11:40,625 --> 00:11:43,916 Is there a way to very rapidly 209 00:11:44,000 --> 00:11:46,791 take this thing you've invested in, this asset, 210 00:11:46,791 --> 00:11:50,541 and figure out that there's more places that you could help people, 211 00:11:50,541 --> 00:11:55,000 there's more indications where this drug would actually be a really good fit. 212 00:11:55,041 --> 00:11:57,625 So that's a very short win, short-term win. 213 00:11:57,625 --> 00:12:01,333 And we've noticed multiple clients building this as a strategy 214 00:12:01,416 --> 00:12:05,875 to take Olink Proteomics in this case to better 215 00:12:05,875 --> 00:12:09,291 understand already approved drugs, which, in some ways, is counterintuitive. 216 00:12:09,291 --> 00:12:09,541 Right? 217 00:12:09,541 --> 00:12:12,416 I mean, ideally you think from the beginning 218 00:12:12,416 --> 00:12:15,666 you would want to know everything you can about the drug, 219 00:12:15,750 --> 00:12:18,250 but there's this reverse translation 220 00:12:18,250 --> 00:12:21,500 movement that seems to be bearing quite a bit of fruit for the industry. 221 00:12:21,500 --> 00:12:22,916 That was actually my next- 222 00:12:22,916 --> 00:12:24,041 I'm sorry, 223 00:12:24,041 --> 00:12:26,125 that's certainly my intriguing invite. 224 00:12:26,125 --> 00:12:30,750 This is my next question, Evan, do you see now this trend of a strategy 225 00:12:30,875 --> 00:12:32,291 in the pharma 226 00:12:32,291 --> 00:12:32,916 because you talk with 227 00:12:32,916 --> 00:12:36,208 the executives, right? And you would know the strategy and you discuss 228 00:12:36,250 --> 00:12:37,250 about this. Do you see this coming? 229 00:12:37,250 --> 00:12:42,916 Do you see that using large-scale proteomics, a big number of data to reposition 230 00:12:42,916 --> 00:12:46,833 a drug, for example, to identify mechanisms of action even in the late stage? 231 00:12:46,916 --> 00:12:49,958 How is your feeling? And why would they ever publish this? 232 00:12:49,958 --> 00:12:50,166 Right. 233 00:12:50,166 --> 00:12:53,291 We think of pharma as needing to hold these things tight. 234 00:12:53,291 --> 00:12:56,583 So yeah, great questions, Sarantis. 235 00:12:56,666 --> 00:12:57,416 That's a good question. 236 00:12:57,416 --> 00:12:59,791 So the answer is yes, in pockets. 237 00:12:59,791 --> 00:13:03,458 I think it's just becoming 238 00:13:03,541 --> 00:13:06,750 a strategy for the more innovative companies. 239 00:13:06,958 --> 00:13:08,125 Right. 240 00:13:08,125 --> 00:13:11,541 There's always some concern, right, for ongoing trials. 241 00:13:11,625 --> 00:13:15,666 Do we really want to know that much at a phase three? 242 00:13:15,750 --> 00:13:16,416 Right. 243 00:13:16,416 --> 00:13:20,750 If we have a candidate compound, do we want to do exploratory research? 244 00:13:20,833 --> 00:13:22,625 Maybe we find something we can't explain. 245 00:13:22,625 --> 00:13:24,500 Maybe we find some safety signals. 246 00:13:24,500 --> 00:13:27,416 So what I'm describing is drugs approved. 247 00:13:27,416 --> 00:13:31,291 Let's extract as much value from that asset as we can. 248 00:13:31,375 --> 00:13:35,166 And there's definitely companies that are taking that on as a strategy. 249 00:13:35,250 --> 00:13:38,458 And to your point, I mean, having gotten to know 250 00:13:38,541 --> 00:13:41,916 folks in pharma really well for the last decade, I mean, 251 00:13:42,000 --> 00:13:45,000 they're great scientists. I think there's this - 252 00:13:45,083 --> 00:13:47,458 I think, not to insult 253 00:13:47,458 --> 00:13:50,500 any of my academic colleagues or people 254 00:13:50,500 --> 00:13:54,166 I've worked with or people that, you know, I've supported over the last 20 years. 255 00:13:54,250 --> 00:13:56,750 I think there's incredibly talented 256 00:13:56,750 --> 00:14:00,000 scientists that see the opportunity 257 00:14:00,083 --> 00:14:02,916 to have a fast path to impact. 258 00:14:02,916 --> 00:14:05,083 And they do want to share. They want to publish. 259 00:14:05,083 --> 00:14:06,416 I mean, look at this consortium. 260 00:14:06,416 --> 00:14:09,625 It was 13 companies that are competitors coming together 261 00:14:09,625 --> 00:14:14,000 I was complimenting one of the pharma researchers on a hire, 262 00:14:14,041 --> 00:14:16,833 a new hire from academia. 263 00:14:16,833 --> 00:14:20,750 And she was saying they came to me because they're a physician, 264 00:14:20,750 --> 00:14:23,958 an M.D., Ph.D., and they said, 265 00:14:23,958 --> 00:14:28,458 I can help one patient at a time in my practice. 266 00:14:28,541 --> 00:14:31,750 But if I come here and do more 267 00:14:31,833 --> 00:14:34,916 broad-based research, I can affect millions. 268 00:14:35,125 --> 00:14:38,541 And I was like, Wow, that's that's an interesting perspective. 269 00:14:38,541 --> 00:14:41,125 I like that. And it lines up with what you're saying. 270 00:14:41,125 --> 00:14:46,208 I think a great example of reverse translation that you've talked about. 271 00:14:46,250 --> 00:14:47,875 I think one of the 272 00:14:47,875 --> 00:14:52,000 examples you've talked about in the past of 273 00:14:52,000 --> 00:14:55,791 of this, you know, taking samples that are sitting in the freezer 274 00:14:55,791 --> 00:15:02,000 where a massive investment has been made is the one from Simina Ticau. 275 00:15:02,083 --> 00:15:02,875 And 276 00:15:02,875 --> 00:15:06,666 Paul Nioi. Paul is, of course, 277 00:15:06,750 --> 00:15:09,166 also on the UK Biobank 278 00:15:09,166 --> 00:15:13,250 flagship paper that came out last week / this week, 279 00:15:13,333 --> 00:15:17,250 whichever online footprint you want to reference. 280 00:15:17,333 --> 00:15:21,458 Can you tell us about that example? 281 00:15:21,541 --> 00:15:24,083 Yeah, this is a really, really interesting story 282 00:15:24,083 --> 00:15:29,250 and that this originated about five years ago and was published in 2019. 283 00:15:29,333 --> 00:15:32,708 So it's a bit dated, but I think the point is incredibly powerful. 284 00:15:32,708 --> 00:15:36,458 So, you know, hereditary transthyretin-mediated amyloidosis is 285 00:15:36,458 --> 00:15:38,000 a genetically defined disease 286 00:15:38,000 --> 00:15:40,041 that really has - 287 00:15:40,041 --> 00:15:41,916 And can I just say that you can pronounce that, 288 00:15:41,916 --> 00:15:43,416 but empaglioflozin is pretty 289 00:15:43,416 --> 00:15:45,833 darn easier to say than - 290 00:15:45,833 --> 00:15:46,458 I'm sorry. It just seems funny. 291 00:15:46,458 --> 00:15:51,750 [Empaglioflozin is] Jardiance, but anyway, you know, back to back to hATTR. 292 00:15:51,750 --> 00:15:53,875 No, 293 00:15:53,958 --> 00:15:54,916 you know, 294 00:15:54,916 --> 00:15:58,500 I've probably told that story more times than - 295 00:15:58,583 --> 00:16:00,750 Yeah, 296 00:16:00,750 --> 00:16:02,208 my gosh, it's hard. 297 00:16:02,208 --> 00:16:04,250 298 00:16:04,250 --> 00:16:07,041 So no, but, hATTR is a really, 299 00:16:07,041 --> 00:16:11,041 really debilitating disease with a variable rate of onset. 300 00:16:11,125 --> 00:16:15,375 So if it's the hereditary form, it runs in your family. 301 00:16:15,375 --> 00:16:15,750 Right. 302 00:16:15,750 --> 00:16:17,833 You can be screened to know if you're carrier 303 00:16:17,833 --> 00:16:20,500 and know if you're at risk for developing the disease. 304 00:16:20,500 --> 00:16:23,458 Alnylam developed a drug, patisiran, 305 00:16:23,458 --> 00:16:25,541 that is an siRNA - excuse me, 306 00:16:25,541 --> 00:16:29,125 RNAi-based therapeutic where they are 307 00:16:29,125 --> 00:16:33,583 very effective at slowing the symptoms and helping these patients. 308 00:16:33,583 --> 00:16:37,583 However, even with this genetically defined population, 309 00:16:37,666 --> 00:16:40,791 it was hard to know when the disease was becoming active, 310 00:16:40,791 --> 00:16:43,791 when these patients were a good candidate for treatment. 311 00:16:43,833 --> 00:16:46,708 So they ran a retrospective study. 312 00:16:46,708 --> 00:16:50,750 This is before Olink had an NGS readout, so it only measured 313 00:16:50,750 --> 00:16:56,250 like 1100 proteins and they discovered neurofilament light [NFL], 314 00:16:56,458 --> 00:17:01,833 which is a very ubiquitous biomarker for neuronal damage. 315 00:17:01,916 --> 00:17:05,958 But they found that this biomarker, this neurofilament light, was 316 00:17:05,958 --> 00:17:11,041 A) indicative of disease progression, was also a biomarker of efficacy. 317 00:17:11,041 --> 00:17:15,583 so after patients were treated with patisiran, it dropped significantly 318 00:17:15,666 --> 00:17:18,666 and it was a disease biomarker, it was 4-fold elevated 319 00:17:18,916 --> 00:17:23,250 in the patients versus healthy controls that they measured in the study. 320 00:17:23,333 --> 00:17:28,083 And so now what's really interesting is there's a protein-based assay 321 00:17:28,333 --> 00:17:31,958 that could give treatment decision information, right? 322 00:17:31,958 --> 00:17:33,958 So it's being validated 323 00:17:33,958 --> 00:17:37,458 and it's only a single biomarker and it's a ubiquitous biomarker. 324 00:17:37,458 --> 00:17:42,708 But in this subset, you know, proteomics is giving you some actionable insights 325 00:17:42,708 --> 00:17:45,916 in a genetically defined population where they're now 326 00:17:45,958 --> 00:17:50,291 developing cutoffs to try to see, hey, if you come to your clinician 327 00:17:50,375 --> 00:17:53,916 and NFL is measured, and once you hit a certain cutoff, 328 00:17:54,000 --> 00:17:58,125 that might actually indicate, even though you don't have symptoms, 329 00:17:58,208 --> 00:18:01,208 the disease process has started and you are a candidate for treatment. 330 00:18:01,416 --> 00:18:02,625 So it's great for the patient. 331 00:18:02,625 --> 00:18:04,250 It's obviously great for Alnylam. 332 00:18:04,250 --> 00:18:08,875 So they can, you know, justify patients getting on their therapy. 333 00:18:08,958 --> 00:18:12,541 And it was where a proteomic screen, right? 334 00:18:12,541 --> 00:18:14,208 they didn't know what to look for. 335 00:18:14,208 --> 00:18:15,916 They didn't have this hypothesis. 336 00:18:15,916 --> 00:18:18,916 They just wanted to see what what's changing 337 00:18:18,958 --> 00:18:21,958 in these patients after treatment, what's changing over time. 338 00:18:22,041 --> 00:18:26,958 And I think that's a powerful way that unbiased proteomics 339 00:18:27,208 --> 00:18:30,166 can point us in the direction of actionable 340 00:18:30,166 --> 00:18:34,000 biomarkers to help patients and clinical development. 341 00:18:34,041 --> 00:18:37,333 So yeah, that was that was a really interesting story. 342 00:18:37,583 --> 00:18:38,000 Great. 343 00:18:38,000 --> 00:18:40,166 Actually, I would like to go way back 344 00:18:40,166 --> 00:18:44,333 because you mentioned about pharma and academia and then we know 345 00:18:44,333 --> 00:18:47,333 at the beginning it was really difficult to communicate, right? 346 00:18:47,333 --> 00:18:49,708 The two little worlds, they were like separated: 347 00:18:49,708 --> 00:18:53,458 academic research versus pharma research. Do you see this changing? 348 00:18:53,625 --> 00:18:57,458 And do you see a benefit of this change? 349 00:18:57,541 --> 00:18:58,166 Yeah, absolutely. 350 00:18:58,166 --> 00:18:59,958 So we just got off the phone. 351 00:18:59,958 --> 00:19:04,250 Cindy and I were just on a call with a really, really impressive 352 00:19:04,333 --> 00:19:08,583 academic researcher who mentioned that she's on the board 353 00:19:08,583 --> 00:19:13,708 for two very large important studies that are being run by pharma companies. 354 00:19:13,708 --> 00:19:13,958 Right? 355 00:19:13,958 --> 00:19:18,750 She's an expert in her field and she's advising on how they should 356 00:19:18,750 --> 00:19:21,916 spend, you know, their research dollars to best move, 357 00:19:22,166 --> 00:19:25,166 you know, very important therapies through the clinic. 358 00:19:25,208 --> 00:19:27,833 I see it happening all the time. 359 00:19:27,833 --> 00:19:28,333 I mean, so, 360 00:19:28,333 --> 00:19:33,333 our team focuses on primarily pharma and large population cohorts. 361 00:19:33,416 --> 00:19:35,791 Right. And there's 362 00:19:35,875 --> 00:19:37,750 incredible connections between the two. 363 00:19:37,750 --> 00:19:37,958 Right? 364 00:19:37,958 --> 00:19:41,333 Because if you think about it, if I'm a pharma company 365 00:19:41,333 --> 00:19:46,166 and I'm interested in atopic dermatitis, for example, 366 00:19:46,250 --> 00:19:51,375 it would behoove me to really profile with all these new omics technologies 367 00:19:51,375 --> 00:19:56,791 as many patients from the best cohorts in the world that have atopic dermatitis. 368 00:19:56,875 --> 00:19:59,875 You could do that through a population cohort 369 00:20:00,083 --> 00:20:02,875 and you know there's going to be some subset. 370 00:20:02,875 --> 00:20:06,833 What's probably more efficient is to work with, you know, KOLs in the field. 371 00:20:06,833 --> 00:20:09,541 Yeah. And then they've collected the samples. 372 00:20:09,541 --> 00:20:11,666 Yeah. You provide the resources 373 00:20:11,666 --> 00:20:14,666 and then with that right from the protein side 374 00:20:14,666 --> 00:20:19,000 you could discover, yeah, are there disease progression biomarkers, are there 375 00:20:19,083 --> 00:20:19,791 endo types, 376 00:20:19,791 --> 00:20:23,500 are there sub phenotypes where there's slightly different molecular drivers 377 00:20:23,708 --> 00:20:27,916 that we could then approach with different molecular entities 378 00:20:27,916 --> 00:20:30,125 that we either have or that we could develop 379 00:20:30,125 --> 00:20:33,250 to have a higher rate of success in the clinic. Precision medicine. 380 00:20:33,250 --> 00:20:35,250 So absolutely. 381 00:20:35,250 --> 00:20:36,791 No. Yeah, Yeah. 382 00:20:36,791 --> 00:20:39,791 And that's been a term that's been really kind of reserved for oncology. 383 00:20:39,875 --> 00:20:41,291 Right? Primarily. 384 00:20:41,291 --> 00:20:45,791 And, you know, I think that 385 00:20:45,875 --> 00:20:48,125 that's because the tools have existed 386 00:20:48,125 --> 00:20:52,083 at the genetic level and obviously cancer is a very genetically driven disease. 387 00:20:52,083 --> 00:20:55,875 But if you look at, you know, some of the more 388 00:20:55,958 --> 00:21:00,333 multi-system diseases that, you know, in the cardiometabolic space, 389 00:21:00,416 --> 00:21:02,875 in the autoimmune space, you know, proteins 390 00:21:02,875 --> 00:21:08,041 I think will be the next big thing in terms of finding 391 00:21:08,125 --> 00:21:11,958 signals that can differentiate subtypes of patients 392 00:21:11,958 --> 00:21:16,375 and then give them better, better treatment options in the future. 393 00:21:16,458 --> 00:21:20,416 You talk about cardiometabolic. Would you consider 394 00:21:20,500 --> 00:21:24,500 like a blockbuster kind of disease, do you see 395 00:21:24,750 --> 00:21:28,875 pharma investing more on these or expanding on this research? 396 00:21:28,875 --> 00:21:33,000 Because for me, seeing pharma, they are moving far away 397 00:21:33,000 --> 00:21:36,916 without of course leaving behind the traditional type, if we can say 398 00:21:36,916 --> 00:21:40,208 a disease like cancer, I see that now pharma is going to rare disease, 399 00:21:40,208 --> 00:21:44,500 they're going to cardiometabolic disease, they're going to PCT disease. 400 00:21:44,541 --> 00:21:46,166 What is your feeling? What 401 00:21:46,166 --> 00:21:50,375 do you see in the upcoming years with pharma? 402 00:21:50,458 --> 00:21:51,958 I mean, 403 00:21:51,958 --> 00:21:55,166 without getting too philosophical about why, 404 00:21:55,250 --> 00:21:59,083 you know, the GLP1, GIP1 that 405 00:21:59,166 --> 00:22:03,916 you know, the other Lilly and Novo competition and others, 406 00:22:04,041 --> 00:22:07,041 you know, there's Pfizer and a lot of other companies are getting involved. 407 00:22:07,041 --> 00:22:08,625 Right. 408 00:22:08,625 --> 00:22:11,625 There's just a huge societal issue with obesity 409 00:22:11,750 --> 00:22:15,875 and there's enormous amounts of investment happening in that field. 410 00:22:15,958 --> 00:22:18,416 I do think that there's a bit 411 00:22:18,416 --> 00:22:22,083 of a gold rush right now, but scientifically, 412 00:22:22,083 --> 00:22:26,625 what's really interesting is, you know, it's not just about obesity. 413 00:22:26,708 --> 00:22:30,708 I've been fortunate enough to talk to some of the leadership at these companies 414 00:22:30,708 --> 00:22:34,250 who are really trying to develop the next, 415 00:22:34,416 --> 00:22:37,833 you know, Mounjaro, the next Semaglutide, 416 00:22:37,916 --> 00:22:41,791 and what they're noticing is there's so many knock on benefits 417 00:22:41,791 --> 00:22:45,083 and there's so many benefits to multimorbidity 418 00:22:45,166 --> 00:22:49,250 that they want to both understand at the molecular level 419 00:22:49,333 --> 00:22:52,500 what's driving that, but also understand, you know, are there patients 420 00:22:52,500 --> 00:22:56,750 who have a more aggressive form of obesity for lack of a better term? 421 00:22:56,750 --> 00:22:57,000 Right. 422 00:22:57,000 --> 00:23:00,583 Is there a subtype of patients that really need 423 00:23:00,666 --> 00:23:04,291 30% weight loss or 40% weight loss? 424 00:23:04,375 --> 00:23:08,458 So it's a fascinating effort and I mean, given 425 00:23:08,458 --> 00:23:12,750 the reality that it's a very environmentally driven condition, 426 00:23:12,833 --> 00:23:16,416 proteomics, I think, will will be an indispensable tool. 427 00:23:16,500 --> 00:23:22,500 I mean, again, the other day, talking to, you know, a KOL in this space saying 428 00:23:22,583 --> 00:23:23,875 these companies and 429 00:23:23,875 --> 00:23:27,083 society generally says, well, let's do genomics first, right? 430 00:23:27,166 --> 00:23:29,291 Like, we have all these samples. 431 00:23:29,291 --> 00:23:30,875 We're going to just do whole genome sequencing 432 00:23:30,875 --> 00:23:32,750 and see if there's some sort of signal in the genetics 433 00:23:32,750 --> 00:23:35,041 that's going to help us answer these questions. 434 00:23:35,041 --> 00:23:38,500 And they're starting to say, hey, wait a second, there's these 435 00:23:38,500 --> 00:23:42,916 proteomic tools now that don't you think it makes more sense in obesity 436 00:23:43,000 --> 00:23:46,583 to look at the proteins and they're dynamic and you can look 437 00:23:46,583 --> 00:23:50,250 at multiple timepoints and see what's changing post-treatment, etc., etc. 438 00:23:50,333 --> 00:23:53,250 So it's just an interesting side note that 439 00:23:53,250 --> 00:23:57,833 in this field I think proteomics is going to be particularly valid. 440 00:23:57,833 --> 00:23:59,875 And I just want to define a couple of terms. Okay. 441 00:23:59,875 --> 00:24:01,416 KOL as key opinion leader. 442 00:24:01,416 --> 00:24:03,916 We use that a lot at Olink around here. 443 00:24:03,916 --> 00:24:08,041 People that are driving and influencing 444 00:24:08,125 --> 00:24:11,125 decisions that are happening out in the field particularly or, 445 00:24:11,166 --> 00:24:14,166 you know, we are thinking in terms of genetics and proteomics 446 00:24:14,250 --> 00:24:18,291 and then the the semaglutide and these GLP1 agonists 447 00:24:18,291 --> 00:24:23,083 that Evan mentioned are not only relevant in obesity, 448 00:24:23,291 --> 00:24:28,500 but they're actually being almost prescribed 449 00:24:28,583 --> 00:24:32,416 where people pay out of pocket in some offerings. 450 00:24:32,416 --> 00:24:36,541 So I've met people that are really keen to be on them 451 00:24:36,541 --> 00:24:41,875 or are on them and who have had a lot of success in reducing their, 452 00:24:41,958 --> 00:24:45,958 maybe not in the, you know, obese category, 453 00:24:45,958 --> 00:24:48,958 but an overweight category where again, you can expect 454 00:24:49,125 --> 00:24:52,166 based on what we've seen, health benefits there as well. 455 00:24:52,166 --> 00:24:57,875 So I just wanted to throw that in. Really interesting space, right? 456 00:24:57,875 --> 00:25:02,625 And yeah, maybe also on that mode, a lot of these drugs 457 00:25:02,625 --> 00:25:06,583 and lot of these inhibitors, as you mentioned, Evan, there are influencing 458 00:25:06,583 --> 00:25:08,583 more than one disease, right? There are targeting 459 00:25:08,583 --> 00:25:10,291 more than one. 460 00:25:10,291 --> 00:25:12,458 And I think that's sort of where some of them left off. 461 00:25:12,458 --> 00:25:14,250 You have a drug for more than one disease 462 00:25:14,250 --> 00:25:18,875 is like my feeling or have you seen this happening from your perspective? 463 00:25:18,958 --> 00:25:19,791 Yeah, for sure. 464 00:25:19,791 --> 00:25:21,625 I mean, it's 465 00:25:21,625 --> 00:25:25,250 then that's where the deeper understanding of the mechanism of these drugs. 466 00:25:25,250 --> 00:25:28,500 Right. Which, you know, Yes. 467 00:25:28,500 --> 00:25:32,916 There are great model systems that if you using a cyno [cynomolgus macaques] 468 00:25:32,916 --> 00:25:37,000 model, monkey model, you know, eventually mouse models. 469 00:25:37,000 --> 00:25:39,791 Rat models. There's all kinds of models. 470 00:25:39,791 --> 00:25:42,458 And you can get a good sense of how your drug's behaving. 471 00:25:42,458 --> 00:25:46,875 But you know, often with these phase one or phase two studies, 472 00:25:46,958 --> 00:25:49,916 the amount of patients is fairly small. 473 00:25:49,916 --> 00:25:52,541 You can get an idea, but that's why I do, 474 00:25:52,541 --> 00:25:56,000 I believe that, you know, companies are investing significant 475 00:25:56,000 --> 00:25:59,125 resources to look at the bigger studies. 476 00:25:59,125 --> 00:25:59,416 Right. 477 00:25:59,416 --> 00:26:02,500 Because you can just see, you just get more statistical power. 478 00:26:02,500 --> 00:26:06,708 You have a better chance of really understanding how 479 00:26:06,750 --> 00:26:10,791 my drug's impacting multiple pathways, multiple organ systems. 480 00:26:10,875 --> 00:26:14,416 And then once you have that knowledge, it just makes you so much better 481 00:26:14,416 --> 00:26:18,208 informed for new therapeutic ideas, 482 00:26:18,291 --> 00:26:21,208 even just repurposing the existing therapeutics. 483 00:26:21,208 --> 00:26:23,875 So, yes, Sarantis, 484 00:26:23,958 --> 00:26:26,041 the more indications, the better, 485 00:26:26,041 --> 00:26:30,166 I mean, just from a simple pragmatic business perspective. 486 00:26:30,250 --> 00:26:33,375 But having the molecular, you know, justification 487 00:26:33,500 --> 00:26:36,708 I think is what, as a society, we should all ask for. 488 00:26:36,708 --> 00:26:39,666 I mean, just seems to be what they - 489 00:26:39,666 --> 00:26:41,083 Obviously when we're consenting 490 00:26:41,083 --> 00:26:45,000 and all of us where you know participants in these sorts of trials. 491 00:26:45,000 --> 00:26:50,875 I think another promise here and we're going into ASHG soon [ASHG = American Society of Human Genetics] 492 00:26:50,958 --> 00:26:55,708 and we've got 25 different posters of folks leveraging it, leveraging 493 00:26:55,708 --> 00:27:00,250 some proteomics from Olink, which is really exciting to see. 494 00:27:00,250 --> 00:27:04,708 This is a genetics conference and clearly there's this value of layering 495 00:27:04,750 --> 00:27:08,541 the genetics onto - or, the proteomics onto the genetics. 496 00:27:08,625 --> 00:27:09,833 There's also seven talks 497 00:27:09,833 --> 00:27:13,083 that doesn't include the talks that we're sponsoring. 498 00:27:13,166 --> 00:27:16,750 So I think 499 00:27:16,833 --> 00:27:21,166 in this in this environment, 500 00:27:21,250 --> 00:27:25,000 I guess I'm wondering: 501 00:27:25,083 --> 00:27:28,583 what are you most excited about, Evan? 502 00:27:28,666 --> 00:27:29,708 Sorry. 503 00:27:29,708 --> 00:27:31,500 504 00:27:31,500 --> 00:27:32,625 No, that's fine. 505 00:27:32,625 --> 00:27:36,000 I mean, it's a hard question. 506 00:27:36,083 --> 00:27:38,916 Let's let's just talk about this environment being 507 00:27:38,916 --> 00:27:42,250 the fact that there are three publications in Nature, right? 508 00:27:42,333 --> 00:27:45,208 Three publications that just dropped 509 00:27:45,208 --> 00:27:48,958 about the promise of population proteomics. 510 00:27:49,041 --> 00:27:49,833 Right. 511 00:27:49,833 --> 00:27:52,541 So, I mean, 512 00:27:52,541 --> 00:27:54,083 I just think it's the beginning, right? 513 00:27:54,083 --> 00:27:59,125 So 50,000 samples from a largely northern European cohort has led to a treasure 514 00:27:59,125 --> 00:28:04,958 trove of insights, 14,000 associations, 80%-plus of which were novel. 515 00:28:05,000 --> 00:28:06,500 People can dig into that for a 516 00:28:06,500 --> 00:28:10,750 long time. And reference back to it with their own studies to 517 00:28:10,791 --> 00:28:11,916 - Yeah, that's the way forward - 518 00:28:11,916 --> 00:28:14,041 corroborate the signals that they're seeing. 519 00:28:14,041 --> 00:28:15,833 I think we've talked about that before. 520 00:28:15,833 --> 00:28:18,291 Yeah. Go ahead, Evan. 521 00:28:18,291 --> 00:28:21,958 And that's super exciting right, because that will provide a bit 522 00:28:21,958 --> 00:28:25,458 of a backbone to understand causality 523 00:28:25,541 --> 00:28:28,958 and give us insights into drug targets and biomarkers. 524 00:28:29,000 --> 00:28:31,125 That's great. 525 00:28:31,208 --> 00:28:32,916 You know, but it's just a small 526 00:28:32,916 --> 00:28:36,750 subset of the world's available resources from a cohort perspective. 527 00:28:36,750 --> 00:28:39,750 So there's enormous benefit to going bigger 528 00:28:39,791 --> 00:28:43,208 as the AstraZeneca rare variant paper shows. 529 00:28:43,208 --> 00:28:44,041 Right. 530 00:28:44,041 --> 00:28:45,625 To capture these rare variants. 531 00:28:45,625 --> 00:28:48,708 And this is what the Regeneron Genetics Center has done for years, right? 532 00:28:48,708 --> 00:28:54,500 They're doing genomics on all of these very large populations, these founder 533 00:28:54,500 --> 00:28:59,541 populations, to find these signals that really come out when you go big. 534 00:28:59,625 --> 00:29:02,291 That will happen at the protein level as well. 535 00:29:02,291 --> 00:29:04,500 I think going to different parts of the world, 536 00:29:04,500 --> 00:29:09,416 there's just going to be enormous richness as we go from that. Diversity 537 00:29:09,500 --> 00:29:12,083 Without question, everyone wants to do that. 538 00:29:12,083 --> 00:29:15,375 But if you say the thing I'm most excited about, to be honest, 539 00:29:15,375 --> 00:29:20,208 is proteomic risk scores and the potential 540 00:29:20,208 --> 00:29:26,500 for a whole suite of tools to help 541 00:29:26,541 --> 00:29:31,500 perhaps, you know, consumers one day, certainly drug developers, 542 00:29:31,583 --> 00:29:36,041 perhaps health insurance companies, who knows where this all goes. 543 00:29:36,125 --> 00:29:39,625 But, you know, speaking to Ben Sun and some of the head analysts from the UK 544 00:29:39,625 --> 00:29:45,000 Biobank project, they, with just 50,000 samples and machine learning, 545 00:29:45,000 --> 00:29:48,708 and I'd say algorithms, are able to pick up on these patterns 546 00:29:48,833 --> 00:29:52,333 right out of sometimes a small number of proteins. 547 00:29:52,333 --> 00:29:56,166 I believe Claudia Langenberg and Robert Scott had a paper where 548 00:29:56,250 --> 00:29:58,833 it was between like five and 20 proteins 549 00:29:58,833 --> 00:30:03,875 could distinguish your risk of a large number of common diseases. 550 00:30:03,958 --> 00:30:04,333 I think 551 00:30:04,333 --> 00:30:07,333 once those are validated and those are refined, 552 00:30:07,416 --> 00:30:11,541 that is a game changer because then I'm a drug developer, 553 00:30:11,541 --> 00:30:14,541 I can apply these algorithms to all my clinical trials 554 00:30:14,666 --> 00:30:17,666 and better understand, "Hey, are we on the right track 555 00:30:17,875 --> 00:30:23,625 and what other impacts are we having on a wide range of diseases?" 556 00:30:23,708 --> 00:30:28,041 I mean, to me that's incredibly exciting. 557 00:30:28,041 --> 00:30:30,541 And it's not without its challenges, right? 558 00:30:30,541 --> 00:30:33,750 I mean, you have to validate these things and sufficiently 559 00:30:33,833 --> 00:30:38,041 have statistically powered studies, 560 00:30:38,125 --> 00:30:40,791 but one could imagine that there could be 561 00:30:40,791 --> 00:30:44,250 a suite of tools in the future based on, you know, 562 00:30:44,291 --> 00:30:48,375 a manageable number of measurements that could be used clinically. 563 00:30:48,458 --> 00:30:52,458 And that's where I think the next big evolution will be 564 00:30:52,541 --> 00:30:57,625 is taking this data that's been generated by either 565 00:30:57,625 --> 00:31:01,750 academic funding, pharma funding, government funding to really 566 00:31:01,750 --> 00:31:05,666 look at a lot of diseases at the protein level, at scale, 567 00:31:05,750 --> 00:31:08,333 using these new proteomic technologies 568 00:31:08,333 --> 00:31:11,916 and then whittling it down to things that are clinically actionable 569 00:31:12,000 --> 00:31:15,125 that you would have never found if you didn't take a broader view. 570 00:31:15,375 --> 00:31:17,125 Right. I think that's the difference. 571 00:31:17,125 --> 00:31:20,833 And just to double click on those authors, so there's Ryan Dhindsa 572 00:31:20,875 --> 00:31:22,833 on this rare variant paper. 573 00:31:22,833 --> 00:31:23,666 He's the first author. 574 00:31:23,666 --> 00:31:28,041 He's at Baylor working also with AstraZeneca, where Slavé 575 00:31:28,041 --> 00:31:32,666 Petrovski is the the PI on that paper. 576 00:31:32,750 --> 00:31:37,500 There's Ben Sun who you mentioned and Chris Whelan paper. 577 00:31:37,500 --> 00:31:38,791 That's our [UK Biobank] flagship paper. 578 00:31:38,791 --> 00:31:40,750 We consider it sort of 579 00:31:40,750 --> 00:31:44,708 the broadest group from the UK Biobank Pharma Proteomics Project 580 00:31:44,791 --> 00:31:50,041 and then, of course, the I want to also just touch on Grimur 581 00:31:50,125 --> 00:31:53,208 Eldjarn and Kari Stefansson's 582 00:31:53,208 --> 00:31:56,208 paper. 583 00:31:56,333 --> 00:32:01,500 Well if only to to highlight 584 00:32:01,583 --> 00:32:05,291 something Kari said about proteomics in general 585 00:32:05,375 --> 00:32:09,625 and that was along the lines of what you're describing, that proteomics, 586 00:32:09,625 --> 00:32:13,458 that an algorithm they've been able to develop with proteomics, can predict 587 00:32:13,708 --> 00:32:16,458 all-cause mortality in any individual. 588 00:32:16,458 --> 00:32:19,208 So how many years does one have left to live? Right. 589 00:32:19,208 --> 00:32:21,250 So if I go into a clinical trial 590 00:32:21,250 --> 00:32:25,375 and I've got a prediction of 30 years left to live, 591 00:32:25,458 --> 00:32:30,458 and then I go onto this drug and part way through that trial, or maybe three 592 00:32:30,458 --> 00:32:34,666 quarters of the way through that trial, you look at my proteomics score 593 00:32:34,750 --> 00:32:38,500 on my prediction, on how long do I have to live. 594 00:32:38,583 --> 00:32:43,791 This is a way to have very short clinical trials 595 00:32:43,791 --> 00:32:48,291 that actually are representative of a longer period. 596 00:32:48,291 --> 00:32:50,416 I mean, imagine a depression trial. 597 00:32:50,416 --> 00:32:52,500 I remember there was there was one trial on depression. 598 00:32:52,500 --> 00:32:54,000 It was something like six weeks. Right. 599 00:32:54,000 --> 00:32:57,083 If you're talking about major depressive disorder, a six-week 600 00:32:57,083 --> 00:33:01,416 window is a hard one to draw conclusions from. 601 00:33:01,500 --> 00:33:02,833 And we do the best we can. 602 00:33:02,833 --> 00:33:07,916 But having something like this that is a reflection in the future 603 00:33:07,916 --> 00:33:16,166 of what this is doing to your proteins, I think is very exciting. 604 00:33:16,250 --> 00:33:17,458 I mean, 605 00:33:17,458 --> 00:33:20,541 yeah, I'm thinking about data that's new. 606 00:33:20,541 --> 00:33:24,291 Let's say there's an era of proteins, versus big data generation 607 00:33:24,291 --> 00:33:27,583 for biomarker discovery, then what is coming next? 608 00:33:27,583 --> 00:33:31,208 The in vitro diagnostics era is booming, for example? Then some of these biomarkers 609 00:33:31,208 --> 00:33:35,416 be like customized and used for clinical diagnosis. 610 00:33:35,666 --> 00:33:39,041 So how do you see this road map? I know this is difficult to predict, but 611 00:33:39,208 --> 00:33:43,416 what do you see this coming actually from your perspective? 612 00:33:43,500 --> 00:33:44,583 I think so. I think so. 613 00:33:44,583 --> 00:33:48,166 And Cindy's point, I think is really - so to sort of touch on that real quick 614 00:33:48,166 --> 00:33:51,958 and then and I'll touch on that, Sarantis, because they're certainly connected. 615 00:33:51,958 --> 00:33:54,625 But they're slightly different in my view. 616 00:33:54,625 --> 00:33:59,833 So this idea of having a risk score to help, you know, shorten a trial, right. 617 00:33:59,916 --> 00:34:03,916 Give you some sort of a surrogate end point or some sort of early read. 618 00:34:04,000 --> 00:34:06,541 I mean, I remember, you know, Kari [Stefansson] in a 619 00:34:06,541 --> 00:34:10,541 presentation he gave mentioning that, you know, 620 00:34:10,625 --> 00:34:13,916 if you could apply this, you know, risk score, 621 00:34:14,000 --> 00:34:14,791 you could 622 00:34:14,791 --> 00:34:18,833 cut the time of the cardiovascular outcomes trial, you know, significantly, 623 00:34:18,833 --> 00:34:23,166 I think by more than half and save hundreds of millions of dollars. 624 00:34:23,250 --> 00:34:24,000 Right. 625 00:34:24,000 --> 00:34:27,291 And I think broadly that would help everybody 626 00:34:27,500 --> 00:34:28,291 because the 627 00:34:28,291 --> 00:34:31,333 the companies developing therapeutics would not have to spend so much money, 628 00:34:31,333 --> 00:34:34,541 it would be less expensive and the right patients would get, 629 00:34:34,583 --> 00:34:35,333 you know, the right drug 630 00:34:35,333 --> 00:34:38,333 because they're at higher risk if you use an enrichment strategy. 631 00:34:38,541 --> 00:34:40,875 So I think that's absolutely coming. 632 00:34:40,875 --> 00:34:43,708 There's no doubt about it. But then, 633 00:34:43,791 --> 00:34:44,208 you know the 634 00:34:44,208 --> 00:34:47,208 real end game, I think, Sarantis, was 635 00:34:47,208 --> 00:34:50,208 what you've referred to in terms of this in vitro diagnostics piece. 636 00:34:50,375 --> 00:34:55,333 You know, so I was recently visiting Roche Diagnostics, you know, in Basel. 637 00:34:55,333 --> 00:35:00,083 And they're, you know, world leaders in diagnostic tests 638 00:35:00,083 --> 00:35:04,375 and by and large, today it's a single-plex assay. 639 00:35:04,375 --> 00:35:05,000 Yeah. 640 00:35:05,000 --> 00:35:09,291 You measure one thing. and there's a lot of reasons for that 641 00:35:09,416 --> 00:35:15,083 you know it's challenging to have multiplexed assays validated to the level 642 00:35:15,083 --> 00:35:19,958 that today we're used to being required from the FDA and others. 643 00:35:20,000 --> 00:35:22,833 But biologically and just 644 00:35:22,833 --> 00:35:25,375 you know, if you just think about 645 00:35:25,375 --> 00:35:28,750 the complexity of disease, single market is probably not the best thing to do. 646 00:35:28,833 --> 00:35:33,958 So I do think that's coming and I hope in the rest of my career 647 00:35:34,041 --> 00:35:37,875 I have a role to play in that 648 00:35:37,958 --> 00:35:41,625 because if we can have very predictive 649 00:35:41,833 --> 00:35:46,333 multi marker tests to be used in the diagnostics space, 650 00:35:46,416 --> 00:35:50,375 that to me will be the biggest societal benefit 651 00:35:50,375 --> 00:35:53,375 that can come from all of the amazing work that's happening right now. 652 00:35:53,375 --> 00:35:55,833 I think that that's where this all goes. 653 00:35:55,833 --> 00:36:00,958 And you can just imagine a future where there's much more resolution 654 00:36:01,041 --> 00:36:04,583 to your personal risk for disease, your personal response 655 00:36:04,583 --> 00:36:07,583 to therapies that we just don't see today. 656 00:36:07,708 --> 00:36:10,708 So yeah, I think that's where it goes. 657 00:36:10,875 --> 00:36:11,500 It's a hard road. 658 00:36:11,500 --> 00:36:15,500 Well, but we're already seeing multi-gene testing in cancer 659 00:36:15,541 --> 00:36:20,750 and stratifying and diagnosing to help better serve cancer patients. 660 00:36:20,750 --> 00:36:26,208 So I think there's still a lot to be done there and I think you know that 661 00:36:26,291 --> 00:36:27,500 pan-cancer study that 662 00:36:27,500 --> 00:36:32,125 came out of Mathias Uhlén's team, which we've talked about on the podcast 663 00:36:32,125 --> 00:36:37,916 before, is a great place where proteomics is making inroads. 664 00:36:38,000 --> 00:36:40,041 So, yeah, fantastic. 665 00:36:40,041 --> 00:36:41,250 Also, 666 00:36:41,250 --> 00:36:44,375 to add onto that, as we said, one biomarker is not enough. 667 00:36:44,416 --> 00:36:47,000 We have a lot of examples in papers 668 00:36:47,000 --> 00:36:47,666 where you see the 669 00:36:47,666 --> 00:36:51,750 additive value of having more than one biomarker 670 00:36:51,750 --> 00:36:54,833 that are really great. Erik Michaëlsson, Mathias Uhlén, 671 00:36:54,875 --> 00:36:58,500 and you know, there are plenty of papers and so there is a divide. 672 00:36:58,500 --> 00:37:01,625 And I think that if only the community start realizing that 673 00:37:01,666 --> 00:37:05,333 having more than one biomarker will increase the value of their work 674 00:37:05,333 --> 00:37:06,875 Yeah yeah. 675 00:37:06,875 --> 00:37:08,541 And it really just depends on 676 00:37:08,541 --> 00:37:11,541 who you're talking to in terms of what they think the next big thing is. 677 00:37:11,541 --> 00:37:14,250 Right? You asked for my opinion, I gave you my opinion. 678 00:37:14,250 --> 00:37:18,458 You know, someone else could say, "Hey, I just want to measure 679 00:37:18,541 --> 00:37:23,041 ten, you know, million samples and then we're going to get much 680 00:37:23,125 --> 00:37:26,958 richer insights into the next best drug targets. 681 00:37:27,041 --> 00:37:31,083 And then that's going to create more efficient pipelines and a better, 682 00:37:31,166 --> 00:37:35,875 you know, drug development universe in the next 50 years." 683 00:37:35,958 --> 00:37:38,666 And yes, I think that'll happen, too. 684 00:37:38,666 --> 00:37:43,208 But but yeah, there's just on all ends of the drug development spectrum, 685 00:37:43,291 --> 00:37:46,583 these innovations, you know, that Olink and others have made 686 00:37:46,583 --> 00:37:49,625 I think are really, really going to be transformative. 687 00:37:49,625 --> 00:37:50,541 And they already are. 688 00:37:50,541 --> 00:37:53,083 They already are. But it's so early. 689 00:37:53,083 --> 00:37:57,000 I'm sorry, not to go on a tangent, but it really is early. 690 00:37:57,000 --> 00:37:57,416 Yeah. 691 00:37:57,416 --> 00:38:02,125 You know, and part of with whole genome sequencing, 692 00:38:02,125 --> 00:38:06,000 the cost dropping has really enabled things. 693 00:38:06,083 --> 00:38:08,875 And that's an important point. To be honest, 694 00:38:08,875 --> 00:38:09,500 it really is. 695 00:38:09,500 --> 00:38:13,375 You know, if we're just going to be frank and honest about, 696 00:38:13,375 --> 00:38:18,458 you know, the opportunities to help as many people as possible, 697 00:38:18,541 --> 00:38:19,541 if a tool is 698 00:38:19,541 --> 00:38:22,875 prohibitively expensive, it's never going to have broad adoption. 699 00:38:22,916 --> 00:38:26,291 And when I joined Olink 700 00:38:26,375 --> 00:38:29,250 things cost a certain amount of money and now things cost less. 701 00:38:29,250 --> 00:38:31,500 There's certainly - 702 00:38:31,500 --> 00:38:34,333 We get more from it. Yeah, yeah, yeah. 703 00:38:34,333 --> 00:38:35,125 Yeah, exactly. Yeah. 704 00:38:35,125 --> 00:38:38,625 There's more data coming out for a lower all over cost. 705 00:38:38,666 --> 00:38:39,541 Right. 706 00:38:39,541 --> 00:38:42,750 And that's what the market has expected. 707 00:38:42,750 --> 00:38:47,000 That's what people are demanding and again 708 00:38:47,000 --> 00:38:51,041 that's very hard. Innovation, it takes a lot of innovation. 709 00:38:51,125 --> 00:38:54,291 But, you know, that's I believe I'm excited to be here 710 00:38:54,291 --> 00:38:57,500 because I know that the mission is the democratization of proteomics 711 00:38:57,500 --> 00:39:01,041 to just get it out there, get it in the hands of the best and 712 00:39:01,041 --> 00:39:03,166 brightest analysts out there. Right. 713 00:39:03,166 --> 00:39:05,458 All of the great big data 714 00:39:05,458 --> 00:39:09,375 folks who have developed such great tools in that genetic space. 715 00:39:09,583 --> 00:39:12,708 And I'll also say, you know, when you were talking to Chris Whelan 716 00:39:12,708 --> 00:39:16,708 well before this whole UK-PPP project 717 00:39:16,791 --> 00:39:19,041 came to fruition, there was 718 00:39:19,041 --> 00:39:22,791 no guarantee that Olink was going to be the chosen technology. 719 00:39:22,833 --> 00:39:26,250 It's such an honor that the tools 720 00:39:26,250 --> 00:39:29,250 and the priorities that we 721 00:39:29,375 --> 00:39:32,333 thought were important - specificity, all that - 722 00:39:32,333 --> 00:39:34,416 that those were also important and continue 723 00:39:34,416 --> 00:39:38,791 to be very important to pharma and and then I'm going to also 724 00:39:38,791 --> 00:39:44,500 just point out that we're now at just, as of this year, at about 5400 proteins 725 00:39:44,541 --> 00:39:48,750 and a really increased 726 00:39:48,833 --> 00:39:52,250 streamlined workflow, increased throughput capability, 727 00:39:52,250 --> 00:39:56,583 which is very exciting to see, too. Any 728 00:39:56,666 --> 00:39:58,250 last comments? Yeah. 729 00:39:58,250 --> 00:40:00,833 Please go ahead, Evan. 730 00:40:00,833 --> 00:40:01,708 No, no, I will. 731 00:40:01,708 --> 00:40:04,875 And I hope this I hope I can say this because, you know, I'm an Olink employee 732 00:40:04,875 --> 00:40:08,291 and this is a Proteomics in Proximity podcast, right? 733 00:40:08,291 --> 00:40:12,541 So I do think that there's going to be 734 00:40:12,625 --> 00:40:17,291 multiple tools eventually that are going to answer these questions, right? 735 00:40:17,291 --> 00:40:23,166 I mean, I'm not so myopic as to think that Olink is the only tool out there, 736 00:40:23,250 --> 00:40:23,750 I think we 737 00:40:23,750 --> 00:40:26,916 have some really compelling attributes 738 00:40:26,916 --> 00:40:31,375 for the large scale projects and for these large clinical analyzes. 739 00:40:31,375 --> 00:40:35,041 But I get excited about continued innovation across, 740 00:40:35,166 --> 00:40:39,208 you know, the earlier side of the research spectrum where there could be tools 741 00:40:39,208 --> 00:40:43,166 that can rapidly tell you about all these different proteoforms 742 00:40:43,166 --> 00:40:44,958 and phosphorylation states. 743 00:40:44,958 --> 00:40:48,291 And yeah, it's a community, right, that's coming together. 744 00:40:48,291 --> 00:40:51,291 And I think that, 745 00:40:51,291 --> 00:40:54,625 there's just so much has happened in the last 746 00:40:54,875 --> 00:40:57,833 decade that I've really been focused in the space 747 00:40:57,833 --> 00:41:01,958 and it's going to continue to evolve. And 748 00:41:02,000 --> 00:41:04,791 I'm grateful that we've gotten 749 00:41:04,791 --> 00:41:07,791 13 companies together to do something really big. 750 00:41:07,875 --> 00:41:10,625 We continue to be integrally 751 00:41:10,625 --> 00:41:16,333 involved in the strategy of drug development 752 00:41:16,500 --> 00:41:20,375 from a large number of the world's best companies. 753 00:41:20,458 --> 00:41:25,166 And I just think that it's all leading to a more efficient process. 754 00:41:25,250 --> 00:41:28,500 I mean, I have X number of years on this planet. 755 00:41:28,583 --> 00:41:32,791 I want my time to be spent making a difference 756 00:41:33,000 --> 00:41:35,125 for my kids and their kids. 757 00:41:35,125 --> 00:41:39,500 And I truly believe that this kind of work is going to enable that. 758 00:41:39,500 --> 00:41:42,500 So thank you for having me on. 759 00:41:42,500 --> 00:41:44,125 Yeah, it was great. Sarantis, 760 00:41:44,125 --> 00:41:45,125 any last words from you? 761 00:41:45,125 --> 00:41:47,666 I mean, it was great. It was great to hear 762 00:41:47,666 --> 00:41:51,416 your perspective and I agree with you. 763 00:41:51,416 --> 00:41:55,708 I think that proteomics is the major research 764 00:41:55,833 --> 00:41:57,000 from now on. And you're going to see 765 00:41:57,000 --> 00:42:00,000 a lot of papers. And it's only the beginning. 766 00:42:00,000 --> 00:42:04,208 And we're looking forward to the upcoming projects. Fantastic. 767 00:42:04,250 --> 00:42:06,125 Well that's it for us today. 768 00:42:06,125 --> 00:42:08,833 Again, thank you, Evan, for joining us. 769 00:42:08,833 --> 00:42:10,458 Thank you very much. Thank you. 770 00:42:10,458 --> 00:42:15,083 I think there are a couple of authors that we may not have said clearly, 771 00:42:15,083 --> 00:42:19,333 and that was Faiez Zannad who was integral in this. 772 00:42:19,416 --> 00:42:21,833 And Milton Packer, I don't think we mentioned 773 00:42:21,833 --> 00:42:26,875 Milton, who both were integral in really understanding and repurposing, 774 00:42:27,083 --> 00:42:33,250 identifying, repurposing opportunities and their 775 00:42:33,291 --> 00:42:36,416 empagliflozin [Jardiance]. 776 00:42:36,416 --> 00:42:37,708 And I think there's an "a" in there. 777 00:42:37,708 --> 00:42:41,375 Empagliflozin. 778 00:42:41,458 --> 00:42:45,916 Yeah, so I just wanted to click on this and we'll put those into the show notes 779 00:42:45,958 --> 00:42:46,500 as well. 780 00:42:46,500 --> 00:42:49,500 Thanks as always to my co-host, Sarantis. 781 00:42:49,541 --> 00:42:52,750 Thank you, of course. If you enjoyed listening 782 00:42:52,750 --> 00:42:56,416 to Proteomics in Proximity, please share it with a friend or a colleague 783 00:42:56,416 --> 00:43:01,125 who you think might also enjoy it, maybe we'll get more than 11 listeners, we'll see. 784 00:43:01,291 --> 00:43:05,541 And remember, you can reach out to us at Proteomics in Proximity 785 00:43:05,625 --> 00:43:10,000 at PIP@olink.com and you know, anything, 786 00:43:10,041 --> 00:43:13,041 any feedback, positive, negative, 787 00:43:13,041 --> 00:43:14,458 who should we interview? 788 00:43:14,458 --> 00:43:17,791 We would be grateful for the suggestions and the feedback. 789 00:43:17,875 --> 00:43:20,666 And with that, we'll close. 790 00:43:20,666 --> 00:43:26,958 Thanks, everyone. Thank you. 791 00:43:27,041 --> 00:43:30,250 Thank you for listening to the Proteomics in Proximity podcast 792 00:43:30,333 --> 00:43:33,625 brought to you by Olink Proteomics. To contact the hosts 793 00:43:33,625 --> 00:43:37,833 or for further information, simply email info@olink.com.