1 00:00:05,625 --> 00:00:06,375 Welcome to the 2 00:00:06,375 --> 00:00:09,500 Proteomics in Proximity podcast, where your co-hosts 3 00:00:09,625 --> 00:00:12,916 Cindy Lawley and Sarantis Chlamydas from Olink Proteomics, 4 00:00:13,250 --> 00:00:16,875 Talk about the intersection of proteomics with genomics for drug target 5 00:00:16,875 --> 00:00:20,875 discovery, the application of proteomics to reveal disease biomarkers, 6 00:00:21,250 --> 00:00:25,875 and current trends in using proteomics to unlock biological mechanisms. 7 00:00:26,250 --> 00:00:29,250 Here we have your hosts, Cindy and Sarantis. 8 00:00:29,416 --> 00:00:32,125 Hey everyone, welcome to Proteomics in Proximity. 9 00:00:32,125 --> 00:00:37,625 Today I have, Evan Mills together with me to talk about a pretty exciting event 10 00:00:37,625 --> 00:00:40,625 that we attended last week where keen to, 11 00:00:40,875 --> 00:00:43,875 to share some of the learnings with you. 12 00:00:44,166 --> 00:00:46,250 So. So, Evan, thanks so much for joining. 13 00:00:46,250 --> 00:00:49,250 Would you mind just giving us a quick introduction? 14 00:00:49,250 --> 00:00:52,000 Yeah. Thanks. Great to be on again. 15 00:00:52,000 --> 00:00:56,500 Been with Olink for about eight years in the next gen proteomics field 16 00:00:56,500 --> 00:01:01,500 for a dozen, former scientists converted into life science tools. 17 00:01:01,625 --> 00:01:05,250 Business person, currently vice president of business development at Olink, 18 00:01:05,500 --> 00:01:09,625 where Cindy and I and some other folks are trying to pave the way 19 00:01:09,625 --> 00:01:12,916 for the future of protein based diagnostics biomarkers. 20 00:01:13,375 --> 00:01:16,666 And, had a really interesting meeting that we're excited 21 00:01:16,666 --> 00:01:18,666 to share the details with you. 22 00:01:18,666 --> 00:01:21,416 Yeah, this is so this event was the Reagan-Udall 23 00:01:21,416 --> 00:01:25,416 Foundation's FDA, event on geroscience. 24 00:01:25,416 --> 00:01:28,416 So gero meaning elderly or aging 25 00:01:28,666 --> 00:01:33,791 and, science and therapeutics, therapies to help us with aging, 26 00:01:33,791 --> 00:01:37,000 of this brave new world that, that, 27 00:01:37,541 --> 00:01:39,875 that seems like such a hot topic right now. 28 00:01:39,875 --> 00:01:42,666 And this is actually putting it in front of the FDA. 29 00:01:42,666 --> 00:01:46,125 So I want to definitely start our conversation. 30 00:01:46,125 --> 00:01:49,125 Even with this image, I can't get out of my head from, 31 00:01:49,500 --> 00:01:52,500 the discussions at Reagen-Udall Foundation's event. 32 00:01:52,916 --> 00:01:54,875 Are you a chicken? 33 00:01:54,875 --> 00:01:56,375 Yeah, exactly. 34 00:01:56,375 --> 00:01:58,375 Exactly that chicken. 35 00:01:58,375 --> 00:02:01,416 So, Sandra Kweder, former regulator at the FDA. 36 00:02:01,416 --> 00:02:03,000 She was on the stage, and she was asked, 37 00:02:03,000 --> 00:02:06,125 what would it feel like if a gerotherapeutic application, 38 00:02:06,125 --> 00:02:10,625 if an application for something that is meant as an indication, for aging, 39 00:02:11,166 --> 00:02:14,000 landed on an FDA desk today, 40 00:02:14,000 --> 00:02:18,041 and she said it might feel like being on a packed 737 41 00:02:18,375 --> 00:02:23,041 doors closed, taking off and someone lets a chicken loose. 42 00:02:23,875 --> 00:02:26,166 It was really funny in the moment, but also like 43 00:02:26,166 --> 00:02:29,166 a perfect metaphor, for the field today. 44 00:02:29,250 --> 00:02:30,000 Yeah, exactly. 45 00:02:30,000 --> 00:02:32,791 Because it's like it is exciting science in the room. 46 00:02:32,791 --> 00:02:36,875 People get really excited about drugs, biologics devices, 47 00:02:36,875 --> 00:02:40,125 mitochondrial peptides, rapamycin combinations 48 00:02:40,500 --> 00:02:44,000 or even ultrasound, immune rejuvenation 49 00:02:44,000 --> 00:02:48,375 proteomics, of course, aging clocks and then intrinsic capacity. 50 00:02:48,375 --> 00:02:50,916 All these things that we'll talk about later. Yeah. 51 00:02:50,916 --> 00:02:54,625 No, there's a lot going on and a really interesting mix of folks in the room. 52 00:02:54,625 --> 00:02:57,250 And I think that's what made this meeting particularly valuable. Right. 53 00:02:57,250 --> 00:03:02,250 Is there were people from the FDA, former regulators, current regulators, 54 00:03:02,541 --> 00:03:05,500 the ARPA-H, XPRIZE, 55 00:03:05,500 --> 00:03:09,666 academics, company representatives from pharma such as Novo Nordisk, 56 00:03:09,666 --> 00:03:13,958 GSK, Regeneron, Bio Age, Cambrian, Stealth Biotherapeutics, 57 00:03:13,958 --> 00:03:17,000 Altos Labs and of course Thermo Fisher, Olink. 58 00:03:17,000 --> 00:03:17,833 All in the audience. 59 00:03:17,833 --> 00:03:20,250 So good mix. Little ol' Olink. 60 00:03:20,250 --> 00:03:21,958 Yeah, yeah. 61 00:03:21,958 --> 00:03:25,333 What I thought, there weren't that many people in the room, 62 00:03:25,333 --> 00:03:26,375 but it was an online event. 63 00:03:26,375 --> 00:03:28,625 It was open and all of the assets are available 64 00:03:28,625 --> 00:03:31,625 online will definitely provide a link in the show notes. 65 00:03:31,708 --> 00:03:34,875 But it didn't feel like your niche longevity meeting. 66 00:03:34,875 --> 00:03:37,083 And I've been to a few of those. 67 00:03:37,083 --> 00:03:39,750 It was very much a field 68 00:03:39,750 --> 00:03:42,833 that's beginning to organize itself around a serious question. 69 00:03:42,833 --> 00:03:42,958 Yeah. 70 00:03:42,958 --> 00:03:46,083 Like what evidence would make the FDA believe 71 00:03:46,083 --> 00:03:49,208 that a therapy is improving how we age? 72 00:03:49,833 --> 00:03:53,375 Yeah, to your point, Cindy, it wasn't, in Silicon Valley, it wasn't 73 00:03:53,375 --> 00:03:57,250 full of ideas without merit or 74 00:03:57,250 --> 00:04:02,083 Brian Johnson's vision for reversing aging. 75 00:04:02,083 --> 00:04:02,833 Yeah, exactly. 76 00:04:02,833 --> 00:04:06,458 I mean,it was very serious in some ways, 77 00:04:06,458 --> 00:04:10,625 which I think the field needs and Susan Winckler was just a tremendous moderator. 78 00:04:10,958 --> 00:04:13,958 So good because she kept pulling that back to 79 00:04:14,000 --> 00:04:16,333 All right, well, let's talk about the exciting biology. 80 00:04:16,333 --> 00:04:18,958 But then the practical regulatory questions. 81 00:04:18,958 --> 00:04:20,958 Right. What is the claim. 82 00:04:20,958 --> 00:04:25,333 Where's the evidence and what's the FDA actually going to be able to do with it. 83 00:04:25,583 --> 00:04:26,583 Yeah, exactly. 84 00:04:26,583 --> 00:04:28,333 So today we're going to talk about what we learned 85 00:04:28,333 --> 00:04:31,458 from that Reagan-Udall Foundation meeting on gerotherapeutics. 86 00:04:32,333 --> 00:04:35,333 What FDA seems ready to discuss 87 00:04:35,333 --> 00:04:40,208 why this concept of intrinsic capacity became such a central idea, 88 00:04:40,208 --> 00:04:43,208 I think of this is sort of the central character, 89 00:04:43,500 --> 00:04:47,083 and where biomarkers and proteomics, because of course, we're at Olink, 90 00:04:47,083 --> 00:04:51,875 Thermo Fisher Scientific, and what what should scientists expect from this? 91 00:04:51,875 --> 00:04:55,458 You know, we want to give a sort of a practical guide for what we heard 92 00:04:55,833 --> 00:05:00,250 and where, where we might where scientists might put their attention. 93 00:05:00,708 --> 00:05:01,583 Yeah, exactly. 94 00:05:01,583 --> 00:05:04,833 And the short version and obviously, we won't get into a lot more detail, 95 00:05:05,333 --> 00:05:08,583 but the future probably doesn't begin with the phrase. 96 00:05:08,583 --> 00:05:10,958 Hey, we reversed aging, right? 97 00:05:10,958 --> 00:05:13,083 It's again, it's going to begin with. 98 00:05:13,083 --> 00:05:17,375 We've helped people function better, longer and more safely. 99 00:05:17,625 --> 00:05:19,083 That's what my mom cares about. 100 00:05:19,083 --> 00:05:21,458 That's what my 88 year old mom cares about, right. 101 00:05:22,458 --> 00:05:24,208 So let's talk about the regulatory challenge. 102 00:05:24,208 --> 00:05:27,708 So a lot of geroscience begins with the idea that aging biology 103 00:05:28,208 --> 00:05:32,000 drives chronic diseases and 104 00:05:32,083 --> 00:05:34,125 Nir Barzilai made that point 105 00:05:34,125 --> 00:05:38,250 that aging is upstream actually, of these diseases, these cardiovascular 106 00:05:38,250 --> 00:05:42,125 diseases, dementia, cancer, diabetes, kidney disease, frailty. 107 00:05:42,583 --> 00:05:43,500 Yeah. 108 00:05:43,500 --> 00:05:46,708 I mean, if you look at the data, right, the number one 109 00:05:46,958 --> 00:05:49,708 risk factor for all these diseases is age. 110 00:05:49,708 --> 00:05:51,750 So this is not a surprise. 111 00:05:51,750 --> 00:05:55,208 And scientifically this is what makes the field so attractive. 112 00:05:55,208 --> 00:05:59,833 If we as a community can figure out an upstream mechanism to target, 113 00:06:00,208 --> 00:06:02,958 you can improve many, many conditions. 114 00:06:02,958 --> 00:06:04,833 You can change the vulnerability 115 00:06:04,833 --> 00:06:07,833 of diseases across multiple organ systems, 116 00:06:07,833 --> 00:06:11,333 which of course just sounds like a panacea 117 00:06:11,333 --> 00:06:14,708 of opportunity for, better health outcomes. 118 00:06:15,083 --> 00:06:16,875 Well, for what I want for my future. 119 00:06:16,875 --> 00:06:19,875 That's it's maybe it'll be just in time for me, but, 120 00:06:20,625 --> 00:06:22,958 But the FDA's not approving enthusiasm. 121 00:06:22,958 --> 00:06:24,083 Certainly there wasenthusiasm 122 00:06:24,083 --> 00:06:27,875 as you said, Susan Winckler was good at keeping the enthusiasm high, 123 00:06:27,875 --> 00:06:31,958 but keeping it, recognizing that that we have a job to do. 124 00:06:32,125 --> 00:06:35,958 Yep. And Sandra Kweder made the, core point multiple times, right. 125 00:06:36,000 --> 00:06:38,500 What's the indication? Because at the end of the day, 126 00:06:39,458 --> 00:06:40,833 aging is 127 00:06:40,833 --> 00:06:46,333 a bit broad and specific indications are how this world has to work. 128 00:06:46,833 --> 00:06:48,333 Yeah. Who's the patient? Right. 129 00:06:48,333 --> 00:06:53,333 Who are you treating and what claim can you put on the label and what's the dose? 130 00:06:53,708 --> 00:06:56,833 What's the risk and how long does someone take it? 131 00:06:56,833 --> 00:06:59,458 Are these things that people are popping every day of their lives? 132 00:06:59,458 --> 00:07:02,458 Right. Right. What benefit are you taking? 133 00:07:02,625 --> 00:07:05,833 What what benefit are you asking the FDA to recognize? 134 00:07:06,250 --> 00:07:06,875 Yeah, yeah. 135 00:07:06,875 --> 00:07:10,208 And Susan and others were kind of translating that language 136 00:07:10,208 --> 00:07:14,083 of the scientific aspiration in the room back to. 137 00:07:14,083 --> 00:07:16,250 All right, let's think about what's the regulatory 138 00:07:16,250 --> 00:07:17,708 checklist one has to go through. 139 00:07:17,708 --> 00:07:20,208 So very pragmatic. 140 00:07:20,208 --> 00:07:21,333 Which is which is good. 141 00:07:21,500 --> 00:07:25,958 It grounds some of the discussion that can be perceived 142 00:07:25,958 --> 00:07:29,333 as pseudoscience if, you know, without that grounding. 143 00:07:29,333 --> 00:07:32,333 So it was a very productive, counterbalance, I'd say. 144 00:07:32,750 --> 00:07:35,250 Yeah, and aging is not a simple thing. Right. 145 00:07:35,250 --> 00:07:40,500 Are we treating aging, age related disease, frailty, sarcopenia. 146 00:07:40,500 --> 00:07:42,625 These are words we have, 147 00:07:43,958 --> 00:07:47,500 batting around quality of life. 148 00:07:47,500 --> 00:07:48,333 Right. 149 00:07:48,333 --> 00:07:49,583 Immune decline, of course. 150 00:07:49,583 --> 00:07:51,458 Resilience that comes up. 151 00:07:51,458 --> 00:07:53,833 Early functional decline, etc.. 152 00:07:54,208 --> 00:07:56,833 And depending on what we look for it kind of has it. 153 00:07:56,833 --> 00:07:59,625 Well, it's not kind of it absolutely has a very big impact 154 00:07:59,625 --> 00:08:01,583 on the developmental path. Yeah. Yeah. 155 00:08:01,583 --> 00:08:04,708 And Steven Kozlowski who I, I believe was actually 156 00:08:05,375 --> 00:08:08,083 that Jamie Justice who we'll talk about later, 157 00:08:08,083 --> 00:08:12,333 she actually came out of his, his group, but he spent years 158 00:08:12,333 --> 00:08:15,958 thinking about how to bring geroscience into randomized clinical trials. 159 00:08:16,375 --> 00:08:19,500 And so he opened up the meeting and reminded us that the current 160 00:08:19,500 --> 00:08:24,583 biomedical paradigm is disease by disease, risk factor 161 00:08:24,583 --> 00:08:29,083 by risk factor, and that geroscience is trying to do something broader. 162 00:08:29,375 --> 00:08:32,833 And unfortunately for geroscience, broader is harder to prove. 163 00:08:33,583 --> 00:08:36,083 And he was saying, it's not just 164 00:08:36,083 --> 00:08:37,333 aging biology is promising, 165 00:08:37,333 --> 00:08:41,333 it's what endpoint would convince us that a therapy really affected 166 00:08:41,333 --> 00:08:45,583 the biology of aging in a way that improves human health and healthspan. 167 00:08:45,750 --> 00:08:49,208 Yeah, and that's really where the practical tension starts, right? 168 00:08:49,208 --> 00:08:49,958 I mean, the healthier 169 00:08:49,958 --> 00:08:53,125 the population, the longer it's going to take to see events, right? 170 00:08:53,125 --> 00:08:56,125 So inherently, if you think about a clinical trial, 171 00:08:56,125 --> 00:09:00,083 if aging is your in endpoint, it's not very pragmatic. 172 00:09:00,958 --> 00:09:01,833 If you want to prevent 173 00:09:01,833 --> 00:09:05,958 disease, disability or death, you're going to need super long trials. 174 00:09:05,958 --> 00:09:10,000 And they're very expensive and they're just not very practical. 175 00:09:10,750 --> 00:09:13,458 And that's why people were talking about stepping stone indications. 176 00:09:13,458 --> 00:09:14,625 Right, right, right. 177 00:09:14,625 --> 00:09:16,958 So yeah in the field will decide. 178 00:09:16,958 --> 00:09:19,583 Maybe we start with sarcopenia. We start with frailty. 179 00:09:19,583 --> 00:09:20,833 Immune decline right. 180 00:09:20,833 --> 00:09:25,333 Cardiovascular risk, obesity, diabetic macular edema, other age 181 00:09:25,333 --> 00:09:28,958 related complications where there's some common biological drivers. 182 00:09:29,333 --> 00:09:31,750 And the end point's a little more concrete. 183 00:09:31,750 --> 00:09:35,833 So can we reduce risk of multiple age related diseases 184 00:09:36,208 --> 00:09:40,083 or preserve the function across domains? 185 00:09:40,375 --> 00:09:41,125 Right. 186 00:09:41,125 --> 00:09:46,208 And I think the scientists in the room are very optimistic that we can. 187 00:09:46,708 --> 00:09:50,333 And I mean, knowing the little I know about proteomics and pathway 188 00:09:50,333 --> 00:09:51,625 level biology, 189 00:09:51,625 --> 00:09:54,708 I'm confident that there are going to be some common nodes that we can nudge 190 00:09:54,708 --> 00:09:55,583 in the right way, 191 00:09:56,708 --> 00:09:59,125 but we have to prove something specific first. 192 00:09:59,125 --> 00:09:59,458 Yeah. 193 00:09:59,458 --> 00:10:03,083 And the FDA's answer. Their shift. 194 00:10:03,083 --> 00:10:07,083 I think I sort of assumed that they were they were like, don't come to us. 195 00:10:07,083 --> 00:10:09,458 But it wasn't that they didn't say, don't come. 196 00:10:09,458 --> 00:10:15,083 They said, come prepared and know what we're saying in this meeting, 197 00:10:15,333 --> 00:10:18,083 because this is how we need to think about it. 198 00:10:18,083 --> 00:10:19,875 Yeah, I think that's a really important distinction, right? 199 00:10:19,875 --> 00:10:24,083 I mean, the door is very much open, but there are some rules, right? 200 00:10:24,083 --> 00:10:27,583 There's just some ground rules that despite the excitement 201 00:10:27,583 --> 00:10:31,083 about the field and everyone's enthusiasm about some new ways 202 00:10:31,083 --> 00:10:33,958 to approach aging, we still got to play by the rules 203 00:10:33,958 --> 00:10:35,583 if we're going to move things forward meaningfully. 204 00:10:35,583 --> 00:10:39,083 And that means we need to have preparation around. 205 00:10:39,583 --> 00:10:42,708 There's a product, there's a population, there's an endpoint, there's a claim. 206 00:10:42,958 --> 00:10:45,458 We're thinking about safety, we're thinking about dose. 207 00:10:45,458 --> 00:10:49,125 And of course, there's should be a very credible biological rationale. 208 00:10:49,125 --> 00:10:50,833 What's the mechanism? Why does it work. 209 00:10:50,833 --> 00:10:51,458 Yeah. Yeah. 210 00:10:51,458 --> 00:10:54,500 And if we're tracking toward that long arc of mortality. 211 00:10:54,583 --> 00:10:54,958 Yeah. 212 00:10:54,958 --> 00:10:57,958 Like you say, that's a long path. 213 00:10:58,625 --> 00:11:02,083 This brings us now to this concept of intrinsic capacity. 214 00:11:02,083 --> 00:11:06,500 That was such a big topic and as a sort of a main character, 215 00:11:06,833 --> 00:11:09,750 what can we do with intrinsic capacity? 216 00:11:09,750 --> 00:11:12,750 And it's probably worth spending some time unpacking that. 217 00:11:12,958 --> 00:11:13,833 I think so. 218 00:11:13,833 --> 00:11:18,375 So, there are several people that touched on, intrinsic capacity. 219 00:11:18,375 --> 00:11:21,333 John Beard gave this conceptual foundation. 220 00:11:21,333 --> 00:11:22,750 Health is a continuum. 221 00:11:22,750 --> 00:11:25,125 Most of medicine's threshold based. Right. 222 00:11:25,125 --> 00:11:29,625 You he crossed a line and suddenly you have diabetes or hypertension or dementia. 223 00:11:29,625 --> 00:11:32,625 You start on a drug regimen or you have an ICD-10 code. 224 00:11:33,083 --> 00:11:35,625 But aging doesn't work that way. Right? 225 00:11:35,625 --> 00:11:37,750 As we all know, aging is gradual. 226 00:11:37,750 --> 00:11:40,083 Functional decline is gradual. 227 00:11:40,083 --> 00:11:42,750 And if we wait right until there is a very clear 228 00:11:42,750 --> 00:11:46,333 manifestation or disability, it might be too late, right? 229 00:11:46,333 --> 00:11:48,333 And we might miss an opportunity right? 230 00:11:48,333 --> 00:11:52,333 So intrinsic capacity, this idea of capturing an individual's underlying 231 00:11:52,333 --> 00:11:57,458 capacity across multiple domains and those domains or locomotor, 232 00:11:57,458 --> 00:12:01,875 movement, cognitive, sensory, psychological. 233 00:12:01,875 --> 00:12:04,083 And then this concept of vitality. 234 00:12:04,083 --> 00:12:04,333 Yeah. 235 00:12:04,333 --> 00:12:07,083 And I'll try to translate that to very simple language. 236 00:12:07,083 --> 00:12:08,333 Can you move. 237 00:12:08,333 --> 00:12:11,000 Can you think can you see in here. 238 00:12:11,000 --> 00:12:15,458 Can you maintain your psychological well-being and can you withstand stress. 239 00:12:15,833 --> 00:12:16,208 Yeah. 240 00:12:16,208 --> 00:12:19,833 And what makes it powerful is that older adults like certainly my mother 241 00:12:19,833 --> 00:12:22,333 she cares more about function 242 00:12:22,333 --> 00:12:25,083 than wrinkles or diagnostic labels. 243 00:12:25,083 --> 00:12:25,833 Exactly right. 244 00:12:25,833 --> 00:12:29,875 I mean, that's at the end of the day, the only outcomes that matter to human 245 00:12:29,875 --> 00:12:35,458 beings, labels might be convenient constructs for, the medical world. 246 00:12:35,458 --> 00:12:39,208 But when we think of that aging, it's, you know, those moments that matter, right? 247 00:12:39,208 --> 00:12:40,208 Can you hear your family? 248 00:12:40,208 --> 00:12:42,250 Are you able to move to watch your grandkids 249 00:12:42,250 --> 00:12:45,250 play soccer or whatever the case may be? 250 00:12:46,083 --> 00:12:47,708 Can you maintain your independence? 251 00:12:47,708 --> 00:12:52,000 That's obviously something I dealt with, with my grandparents 252 00:12:52,000 --> 00:12:55,958 a decade or so ago it's challenging, right, to see the people, 253 00:12:56,958 --> 00:12:59,250 just with far fewer capacities. 254 00:12:59,250 --> 00:13:00,708 So. Yeah. 255 00:13:00,708 --> 00:13:04,833 And this, I think, was important for us at Olink to be thinking about because certainly 256 00:13:04,833 --> 00:13:08,583 people have been building these, these aging clocks and we're excited about that. 257 00:13:08,583 --> 00:13:10,000 What what do you do with them? 258 00:13:10,000 --> 00:13:14,333 So intrinsic capacity is giving us this vocabulary for health span 259 00:13:14,708 --> 00:13:18,208 that's more clinically meaningful than your aging clock improved. 260 00:13:18,708 --> 00:13:19,458 Yeah. Yeah. 261 00:13:19,458 --> 00:13:21,000 And the meeting had a lot of caution right. 262 00:13:21,000 --> 00:13:24,958 To be clear, I mean, I think as innovative proteomics 263 00:13:24,958 --> 00:13:29,500 supporters as you know, Cindy and I are, I came out of meeting very positive. 264 00:13:29,500 --> 00:13:31,833 But there was a lot of caution, right? 265 00:13:31,833 --> 00:13:34,583 Kelly Anderson from ARPA-H, for example, 266 00:13:34,583 --> 00:13:37,125 she had a consensus presentation that made it really clear 267 00:13:37,125 --> 00:13:40,625 that intrinsic capacity is not yet a validated surrogate endpoint. 268 00:13:40,958 --> 00:13:42,083 Right. We're not there. 269 00:13:42,333 --> 00:13:46,083 Surrogate endpoint, just meaning it's not yet proven to stand in for later 270 00:13:46,083 --> 00:13:50,625 outcomes like disability, disease survival, hospitalization. 271 00:13:50,958 --> 00:13:51,333 Right. 272 00:13:51,333 --> 00:13:53,458 And there was a general caution around surrogates 273 00:13:53,458 --> 00:13:56,458 and a fairly high burden of proof for them to be implemented. 274 00:13:56,625 --> 00:13:59,833 So the message was really intrinsic capacity is not quite ready 275 00:14:00,125 --> 00:14:03,208 as a universal global primary endpoint for every aging trial. 276 00:14:03,458 --> 00:14:05,833 Yeah, but it might still be useful. Yes. 277 00:14:05,833 --> 00:14:09,083 And as a structured, yeah, multi-domain clinical 278 00:14:09,083 --> 00:14:13,000 outcome framework I loved how that was positioned. 279 00:14:13,000 --> 00:14:15,250 It really felt eye opening to me. 280 00:14:16,333 --> 00:14:19,583 And that caution, that restraint as you say, mattered. 281 00:14:19,583 --> 00:14:21,958 It gave credibility, I think, to the room. 282 00:14:21,958 --> 00:14:25,458 And and that's where this a PROSPR data. 283 00:14:25,708 --> 00:14:28,375 Andrew Brack talked quite a bit about PROSPR. Right. 284 00:14:28,375 --> 00:14:31,375 So there's there's ARPA-H, PROSPR and there's 285 00:14:32,375 --> 00:14:35,583 XPRIZE and these things are coming and what we'll get to those. 286 00:14:35,583 --> 00:14:38,958 But, he basically was saying we need to build the data 287 00:14:38,958 --> 00:14:42,333 before we can expect regulation to move. 288 00:14:42,833 --> 00:14:43,333 Exactly. 289 00:14:43,333 --> 00:14:46,083 Innovation often precedes regulation. Right. 290 00:14:46,083 --> 00:14:48,083 And that's very clear here. 291 00:14:48,083 --> 00:14:51,833 And the onus is on the innovators to to build the proof. 292 00:14:52,708 --> 00:14:54,583 So PROSPR is actually really interesting. 293 00:14:54,583 --> 00:14:56,708 It's not just another aging study. 294 00:14:56,708 --> 00:14:59,833 It's an effort, very focused effort to build the infrastructure, 295 00:15:00,125 --> 00:15:03,250 intrinsic capacity scoring at home measurements, blood 296 00:15:03,250 --> 00:15:06,708 based biomarkers, intervention studies and validation pathways. 297 00:15:07,333 --> 00:15:11,375 And Andrew made the case that that intrinsic capacity can be a North star. 298 00:15:12,000 --> 00:15:15,125 But not necessarily a single label tomorrow. 299 00:15:15,708 --> 00:15:17,208 Exactly. Like we said before. Right. 300 00:15:17,208 --> 00:15:19,208 It can be a one in a series 301 00:15:19,208 --> 00:15:22,208 of important steps to build the evidence required in the field. 302 00:15:22,458 --> 00:15:22,708 Yeah. 303 00:15:22,708 --> 00:15:26,083 So so how can intrinsic capacity be used now what did you hear. 304 00:15:26,083 --> 00:15:29,833 Not not someday, but in the next wave of trials. 305 00:15:30,125 --> 00:15:33,125 I mean, I think there were a few examples, right, that come to mind. 306 00:15:33,833 --> 00:15:36,125 First enrichment. Right. 307 00:15:36,125 --> 00:15:39,750 Let's identify people who are having early decline. 308 00:15:40,000 --> 00:15:42,375 And might be more likely to benefit from an intervention. 309 00:15:42,375 --> 00:15:45,583 So so pharma tries to do this across lots of disease areas. 310 00:15:45,875 --> 00:15:49,958 You want to enrich your population for the patients most best suited to benefit. 311 00:15:50,208 --> 00:15:55,458 So that could be a pretty low bar I would say to use the score. 312 00:15:55,708 --> 00:15:58,458 So pick your pick the right folks for your team. 313 00:15:58,458 --> 00:15:59,583 Second. 314 00:15:59,583 --> 00:16:01,083 Stratification. Right. 315 00:16:01,083 --> 00:16:04,458 So that would be making sure that the groups are, balanced at baseline. 316 00:16:04,458 --> 00:16:07,458 Obviously you're going to have some sort of control arm in any trial, 317 00:16:07,958 --> 00:16:08,583 for an RCT. 318 00:16:09,708 --> 00:16:12,583 You want starting from the same 319 00:16:12,583 --> 00:16:14,000 basic area, right? 320 00:16:14,000 --> 00:16:16,708 The same starting block, if you will, in the race. 321 00:16:16,708 --> 00:16:18,333 That that could be another use. 322 00:16:18,333 --> 00:16:21,208 And third intrinsic capacity could also be used 323 00:16:21,208 --> 00:16:23,208 as a secondary endpoint. Right. 324 00:16:23,208 --> 00:16:26,333 There's a lower bar if it's a secondary endpoint. 325 00:16:26,333 --> 00:16:30,125 So if the trial's constructed around mortality and disease 326 00:16:30,583 --> 00:16:34,083 you could still have, this IC measure 327 00:16:34,333 --> 00:16:38,333 to track comparable data on function across different domains. 328 00:16:38,708 --> 00:16:39,500 And in some cases, 329 00:16:39,500 --> 00:16:43,583 the intrinsic capacity could act as a domain specific primary endpoint, 330 00:16:43,958 --> 00:16:47,083 especially if the mechanism clearly maps to that domain. 331 00:16:47,583 --> 00:16:48,083 Got it. 332 00:16:48,083 --> 00:16:50,750 So if you're doing locomotor or muscle function 333 00:16:50,750 --> 00:16:52,625 endpoints, a therapy targeting immune 334 00:16:52,625 --> 00:16:56,458 aging might focus on immune response or an infection related outcome. 335 00:16:56,458 --> 00:17:00,833 So you can stick to the domain that seems relevant to that indication. 336 00:17:01,083 --> 00:17:01,833 Yeah. Exactly. Right. 337 00:17:01,833 --> 00:17:05,833 And domain level reporting can avoid the hiding of trade offs. 338 00:17:05,833 --> 00:17:06,083 Right. 339 00:17:06,083 --> 00:17:09,083 If we can narrow in on one of those five domains, 340 00:17:09,083 --> 00:17:11,958 we can have a more real picture of what's happening. 341 00:17:11,958 --> 00:17:14,958 If a composite score improves, 342 00:17:14,958 --> 00:17:17,208 let's say locomotor function is better, 343 00:17:17,208 --> 00:17:21,000 but cognitive function is declining or there's some sort of adverse effect, 344 00:17:21,375 --> 00:17:24,375 you would uncover that in a, in a more focused. 345 00:17:24,625 --> 00:17:26,708 Yeah. Scoring. Got it. 346 00:17:26,708 --> 00:17:31,208 So so we've got intrinsic capacity just to kind of close us on this. 347 00:17:31,583 --> 00:17:34,625 Intrinsic capacity is not a destination yet, but it is. 348 00:17:34,625 --> 00:17:37,625 It does appear like it can be a very useful bridge. 349 00:17:38,208 --> 00:17:38,583 Yeah. 350 00:17:38,583 --> 00:17:42,125 And I think there is a lot of enthusiasm in the audience, for 351 00:17:42,833 --> 00:17:46,000 something to bridge the aging biology 352 00:17:46,000 --> 00:17:49,750 that we're all aware of and better able to measure to some clinical benefit. 353 00:17:50,375 --> 00:17:53,708 So I do believe that there will be a lot more work done on this 354 00:17:53,708 --> 00:17:56,708 intrinsic capacity concept, and it holds a lot of promise. 355 00:17:56,833 --> 00:17:57,583 Yeah. That's awesome. 356 00:17:57,583 --> 00:18:00,708 Okay, so now let's switch over to our, you know, 357 00:18:00,708 --> 00:18:04,625 love biomarkers proteomics and and and evidence. 358 00:18:04,625 --> 00:18:05,500 Right. So 359 00:18:06,833 --> 00:18:11,583 You got on the stage and you were able to, to speak to how we see things. 360 00:18:11,583 --> 00:18:14,583 Yeah, we certainly have, I think a pretty humble 361 00:18:14,875 --> 00:18:17,875 approach to, to where it fits into all of this. 362 00:18:18,208 --> 00:18:18,583 Yeah. 363 00:18:18,583 --> 00:18:21,583 No, I think we're trying to be, 364 00:18:21,875 --> 00:18:22,958 realistic. Right? 365 00:18:22,958 --> 00:18:25,958 And knowing that there's a big burden of proof for biomarkers. 366 00:18:26,458 --> 00:18:28,083 And the meeting was helpful in that regard. Right. 367 00:18:28,083 --> 00:18:33,083 It was a bit of a reality check so we've done some incredible studies. 368 00:18:33,083 --> 00:18:36,458 We've supported studies, I should say from large populations 369 00:18:36,458 --> 00:18:40,958 where there's some really powerful signals for aging biology. 370 00:18:40,958 --> 00:18:43,083 Right. There's Austin Argentieri. 371 00:18:43,083 --> 00:18:46,833 I always think of his publication where a 200 protein 372 00:18:47,083 --> 00:18:51,250 signature is incredibly powerful at predicting biological age 373 00:18:51,458 --> 00:18:54,458 and possible, disease risks. 374 00:18:54,583 --> 00:18:58,083 And you could even get down to 20 proteins and get some really powerful data. 375 00:18:59,125 --> 00:19:03,333 But the practical question is, okay, now what what are we going to do with that? 376 00:19:03,333 --> 00:19:06,750 So there's recognition that biomarkers have tremendous promise. 377 00:19:07,083 --> 00:19:08,583 But they're not magic. 378 00:19:08,583 --> 00:19:11,500 And there's a lot of work that needs to be done. 379 00:19:11,500 --> 00:19:14,458 So a biomarker can do many jobs right. 380 00:19:14,458 --> 00:19:16,708 It can show targeted engagement. 381 00:19:16,708 --> 00:19:18,333 It's certainly we're seeing that in pharma. 382 00:19:18,333 --> 00:19:20,583 It can help select patients. 383 00:19:20,583 --> 00:19:22,458 It can help identify responders. 384 00:19:22,458 --> 00:19:26,083 Where we see a lot of this evidence and publications that come out on Olink, 385 00:19:26,458 --> 00:19:28,833 it can track biology over time. 386 00:19:28,833 --> 00:19:30,875 It can certainly support mechanisms. 387 00:19:30,875 --> 00:19:33,125 So I see these as layers. Right. 388 00:19:33,125 --> 00:19:35,583 But it's not automatically a surrogate endpoint. 389 00:19:35,583 --> 00:19:38,583 I think that was really, really crystal clear. 390 00:19:38,833 --> 00:19:41,083 Yeah. And it is it's crucial right. 391 00:19:41,083 --> 00:19:43,500 It's a crucial distinction a surrogate endpoint 392 00:19:43,500 --> 00:19:45,250 has to predict clinical benefit right. 393 00:19:45,250 --> 00:19:48,250 Not just correlate that big difference. 394 00:19:48,333 --> 00:19:51,333 Not just look different in older people or as we age, 395 00:19:51,708 --> 00:19:53,083 not just change after a therapy. 396 00:19:53,083 --> 00:19:54,333 It has to be tied to something 397 00:19:54,333 --> 00:19:57,708 meaningful function, disease risk, disability survival. 398 00:19:58,125 --> 00:20:00,125 Yeah. So the aging clock is interesting. 399 00:20:00,125 --> 00:20:03,208 And there are several organ aging clocks now that have come out. 400 00:20:03,208 --> 00:20:06,125 We'll put the links in the show notes for those publications. 401 00:20:06,125 --> 00:20:12,208 But the regulatory question is what does that change mean for the person exactly. 402 00:20:12,583 --> 00:20:14,083 Does it mean that they walk better? 403 00:20:14,083 --> 00:20:15,083 Do they think better? 404 00:20:15,083 --> 00:20:16,583 Do they get fewer infections? 405 00:20:16,583 --> 00:20:18,125 Do they avoid hospitalization? 406 00:20:18,125 --> 00:20:20,083 Do they live longer? Right. 407 00:20:20,083 --> 00:20:21,500 These are the questions. Yeah. 408 00:20:21,500 --> 00:20:24,333 And that's where proteomics I think can be very powerful. 409 00:20:24,333 --> 00:20:27,333 But only only if it's connected to outcomes. 410 00:20:27,708 --> 00:20:28,083 Yeah. 411 00:20:28,083 --> 00:20:29,333 And being there 412 00:20:29,333 --> 00:20:32,958 and having some sidebar conversations I mean this is how I see it, right? 413 00:20:33,375 --> 00:20:36,833 Proteomics is a biological information layer, right? 414 00:20:37,083 --> 00:20:39,625 Aging is very dynamic and multisystem. 415 00:20:39,625 --> 00:20:44,125 It's literally the perfect application for proteomics versus other omics. 416 00:20:44,375 --> 00:20:48,583 Because of the dynamic nature of the human proteome, proteins are reflective 417 00:20:48,833 --> 00:20:51,625 of immune function, inflammation, tissue remodeling, 418 00:20:51,625 --> 00:20:55,208 metabolism, organ stress and responses to interventions. 419 00:20:55,208 --> 00:20:55,625 Right. 420 00:20:55,625 --> 00:20:59,333 So it's just so intuitive that proteomics are going 421 00:20:59,333 --> 00:21:02,333 to be a very powerful tool. 422 00:21:02,458 --> 00:21:04,208 We just need to build the evidence. 423 00:21:04,583 --> 00:21:07,000 Like Kári Stefánsson says they are the 424 00:21:07,000 --> 00:21:08,916 business molecules of the body. 425 00:21:08,958 --> 00:21:10,791 But but I loved your phrase. 426 00:21:10,791 --> 00:21:13,791 They're not biological entertainment. 427 00:21:14,291 --> 00:21:14,708 Yeah. 428 00:21:14,708 --> 00:21:17,791 I mean I, I wasn't sure that was the right way to go, 429 00:21:17,791 --> 00:21:23,416 but I think that there's some fear of pseudoscience, in this field. 430 00:21:23,416 --> 00:21:23,583 Right. 431 00:21:23,583 --> 00:21:24,583 There's a lot of people 432 00:21:24,583 --> 00:21:28,291 that have made some claims about aging reversal and chasing numbers. 433 00:21:28,291 --> 00:21:31,541 Yeah, yeah, things that just feel a little icky. 434 00:21:31,541 --> 00:21:34,541 If you're a scientist and you really are, 435 00:21:34,666 --> 00:21:37,541 focused on hard data and hard outcomes, 436 00:21:38,791 --> 00:21:41,666 so even these protein signatures that, 437 00:21:41,666 --> 00:21:44,666 I've gotten excited about for the last decade of my career, 438 00:21:45,041 --> 00:21:47,791 it doesn't prove that we have enough information, right? 439 00:21:47,791 --> 00:21:48,458 It's not. 440 00:21:48,458 --> 00:21:52,416 The goal is in producing cool dashboards and cool signatures. 441 00:21:52,416 --> 00:21:55,166 How do we help drug development and patient care? 442 00:21:55,166 --> 00:21:55,958 Yeah, yeah. 443 00:21:55,958 --> 00:21:58,833 And that's where the company representation was interesting. 444 00:21:58,833 --> 00:22:01,458 So there are these these therapeutic developers, right. 445 00:22:01,458 --> 00:22:04,666 You mentioned some of them BioAge,Cambrian, 446 00:22:05,791 --> 00:22:09,416 Stealth Biotherapeutics, Regeneron. 447 00:22:09,458 --> 00:22:11,416 They were all thinking about targets and trials. 448 00:22:11,416 --> 00:22:12,416 Right. 449 00:22:12,416 --> 00:22:16,041 We also had Novo Nordisk and GSK in the in the panel session 450 00:22:16,041 --> 00:22:17,333 that you participated in. 451 00:22:17,333 --> 00:22:19,041 They're talking and thinking about of course, 452 00:22:19,041 --> 00:22:22,666 chronic disease prevention and trial strategy. 453 00:22:23,083 --> 00:22:26,833 And then of course, you were representing Olink and 454 00:22:26,833 --> 00:22:30,666 that is that technology layer, right for the data layer. 455 00:22:30,958 --> 00:22:31,791 Yeah, exactly. 456 00:22:31,791 --> 00:22:35,541 And for an enabling platform, which is what we would consider ourselves. 457 00:22:35,791 --> 00:22:38,916 The question is not what claim do we want from our drug? 458 00:22:38,916 --> 00:22:43,541 It's how can this measurement help the whole field make better claims? 459 00:22:44,041 --> 00:22:46,416 Is there some sort of common biology that can be uncovered 460 00:22:46,416 --> 00:22:49,416 in this information layer that can help the whole field? 461 00:22:49,916 --> 00:22:54,291 And what do you think is going to make proteomics actionable in this space? 462 00:22:54,791 --> 00:22:58,083 I think honestly, Cindy I think about this a lot because, 463 00:22:58,958 --> 00:22:59,708 and you and I, 464 00:22:59,708 --> 00:23:03,208 have been very fortunate to be a part of the generation of a lot of data, 465 00:23:03,666 --> 00:23:05,416 and other companies have generated a lot of data. 466 00:23:06,916 --> 00:23:10,416 And I think everyone's appetite is really, 467 00:23:11,166 --> 00:23:14,583 is rising for what's going to happen next. 468 00:23:15,041 --> 00:23:17,416 And I think it's going to come from two key things, right? 469 00:23:17,416 --> 00:23:19,041 We need longitudinal data. 470 00:23:19,041 --> 00:23:22,958 We need to see how proteins are changing over time in relation to the endpoints 471 00:23:22,958 --> 00:23:24,041 that we're trying to measure. 472 00:23:24,041 --> 00:23:26,208 And we need interventional data. 473 00:23:26,208 --> 00:23:28,291 We have to show sensitivity to change. 474 00:23:28,291 --> 00:23:32,291 Otherwise a signature of the biomarkers are going to have very limited utility. 475 00:23:32,458 --> 00:23:35,541 We could even talk about functional data, clinical outcomes. 476 00:23:35,541 --> 00:23:36,541 Obviously. 477 00:23:36,541 --> 00:23:40,666 I mean, if a protein signature that's an easy blood draw, 478 00:23:41,708 --> 00:23:44,708 can predict a decline in intrinsic capacity 479 00:23:45,083 --> 00:23:48,916 or changes when a therapy works or it tracks with improvement. 480 00:23:48,916 --> 00:23:51,833 In one of those five conditions that we mentioned, 481 00:23:51,833 --> 00:23:54,083 I think it becomes super valued. 482 00:23:54,083 --> 00:23:59,541 So it's I think it's a combination of the industry partners and perhaps 483 00:23:59,541 --> 00:24:03,833 governments coming together to realize we need to make some investments. 484 00:24:03,833 --> 00:24:06,416 We and we need to generate this data now. 485 00:24:06,416 --> 00:24:08,083 And so the future evidence, I think, 486 00:24:09,291 --> 00:24:11,458 that package is going to have layers. 487 00:24:11,458 --> 00:24:14,291 It's a it's a stack with mechanistic foundation. 488 00:24:14,291 --> 00:24:14,458 Right. 489 00:24:14,458 --> 00:24:18,791 That mechanistic foundation I think Olink is showing to be incredibly valuable. 490 00:24:19,000 --> 00:24:20,041 I think that's a good analogy. 491 00:24:20,041 --> 00:24:21,750 I mean, I think a stack works, right. 492 00:24:21,750 --> 00:24:23,625 Like at the bottom you're going to have mechanisms. 493 00:24:23,625 --> 00:24:26,375 So I go so what's the mechanisms that we're trying to treat and measure 494 00:24:26,375 --> 00:24:29,375 and understand whether it's mTOR, mitochondrial activation, 495 00:24:29,625 --> 00:24:32,875 inflammation, senescence of cells, metabolism etc. . 496 00:24:33,416 --> 00:24:35,625 Then there's the biomarkers and the omics. 497 00:24:35,625 --> 00:24:37,291 I kind of layer on top of that 498 00:24:37,291 --> 00:24:40,291 to tell us what's going on when we're modifying these things. 499 00:24:40,291 --> 00:24:41,500 Right. 500 00:24:41,500 --> 00:24:44,500 And then ultimately it's functional measures. 501 00:24:44,875 --> 00:24:48,125 So impact the mechanism measure that impact 502 00:24:48,416 --> 00:24:51,791 and then see the actual impact in human function. 503 00:24:52,375 --> 00:24:54,750 That's where I think it's all going. 504 00:24:54,750 --> 00:24:59,000 Yeah, and the stronger the connection among those layers, the stronger the evidence. 505 00:24:59,000 --> 00:25:02,250 I think that's where we need to track to 100% . 506 00:25:02,500 --> 00:25:06,166 And if your mechanism and biomarker response and the functional improvement 507 00:25:06,500 --> 00:25:10,166 all kind of align and it makes biological sense, 508 00:25:10,750 --> 00:25:12,875 then we have a very convincing story. 509 00:25:12,875 --> 00:25:15,750 And that's how I think the field really moves forward. 510 00:25:15,750 --> 00:25:17,000 That's exciting okay. 511 00:25:17,000 --> 00:25:20,416 So so the field is not waiting for this right there. 512 00:25:20,416 --> 00:25:23,416 There are trials going now. 513 00:25:23,416 --> 00:25:24,625 There are already underway. 514 00:25:24,625 --> 00:25:27,041 So let's talk about XPRIZE and PROSPR. 515 00:25:27,041 --> 00:25:29,416 PROSPR being the ARPA-H. 516 00:25:29,416 --> 00:25:33,750 That made it feel urgent that human human studies are already happening. 517 00:25:34,041 --> 00:25:35,375 Yeah, it's. Which is amazing, right? 518 00:25:35,375 --> 00:25:38,625 I mean, I remember reading about some crazy parabiosis 519 00:25:38,916 --> 00:25:41,875 experiments with mice and all these things. 520 00:25:41,875 --> 00:25:43,291 But, yes, this is not theoretical. 521 00:25:43,291 --> 00:25:45,375 There's there's real things happening in humans. 522 00:25:45,375 --> 00:25:50,250 And we just had a podcast, recording that'll be up soon, with Tony Wyss-Coray, 523 00:25:50,250 --> 00:25:52,250 talking a little bit about parabiosis. 524 00:25:52,250 --> 00:25:55,291 So that'll that'll be coming soon, but, but yeah. 525 00:25:55,291 --> 00:25:56,166 So Jamie Justice. 526 00:25:56,166 --> 00:25:58,291 So we promised we'd talk a bit about Jamie. 527 00:25:58,291 --> 00:26:02,125 So her XPRIZE, this Healthspan XPRIZE, 528 00:26:02,666 --> 00:26:05,625 competition, made it really clear. 529 00:26:05,625 --> 00:26:08,625 And she has this role. It's so phenomenal. 530 00:26:08,625 --> 00:26:11,791 She's the perfect person for this, where she's willing to 531 00:26:11,875 --> 00:26:15,375 to step out onto this ledge and say, hey, look, we need to start 532 00:26:15,375 --> 00:26:18,625 defining what this looks like, and we won't get it perfect. 533 00:26:19,041 --> 00:26:21,625 But let's not let perfect be the enemy of the good. 534 00:26:21,625 --> 00:26:22,666 Let's move this forward. 535 00:26:22,666 --> 00:26:26,375 And we need to have a framework within which we are moving. 536 00:26:26,375 --> 00:26:26,875 And her. 537 00:26:27,875 --> 00:26:30,875 She's just so easy to iterate with, right? 538 00:26:30,875 --> 00:26:34,541 She's so great at bringing community together and helping them, 539 00:26:35,041 --> 00:26:36,916 feel not left out. 540 00:26:36,916 --> 00:26:39,125 Help them feel part of the discussion. 541 00:26:39,125 --> 00:26:41,666 She kept bringing discussion back to action. 542 00:26:42,000 --> 00:26:43,875 Teams are already testing interventions. 543 00:26:43,875 --> 00:26:46,000 They're already collecting human data. 544 00:26:46,000 --> 00:26:47,125 They need a framework. 545 00:26:47,125 --> 00:26:50,125 Now, what can we do to help them? 546 00:26:50,208 --> 00:26:53,625 Yeah, I mean, she was, very powerful speaker. 547 00:26:54,166 --> 00:26:59,291 Her point was really powerful because Prize isn't asking the field 548 00:26:59,291 --> 00:27:01,666 to debate on and on, because you could imagine 549 00:27:01,666 --> 00:27:05,041 getting into a loop of academic debate about what's the best. 550 00:27:05,375 --> 00:27:06,416 You know, they're moving, right? 551 00:27:06,416 --> 00:27:09,416 They're they're funding these studies, which is amazing. 552 00:27:09,875 --> 00:27:13,250 But they're asking these teams to demonstrate restoration 553 00:27:13,250 --> 00:27:16,250 of muscle cognitive function and immune function. 554 00:27:16,375 --> 00:27:16,750 Yeah. 555 00:27:16,750 --> 00:27:21,291 And that is aligning with that broader conversation or around intrinsic capacity. 556 00:27:21,666 --> 00:27:25,875 Multi-domain function based not just a biomarker contest. 557 00:27:26,250 --> 00:27:27,250 Right? Right. Yeah. 558 00:27:27,250 --> 00:27:31,666 And so so this competition is really trying to motivate and, 559 00:27:32,000 --> 00:27:36,916 help fund some of the most innovative, gerotherapeutic companies. 560 00:27:37,375 --> 00:27:38,916 And the diversity in the approaches 561 00:27:38,916 --> 00:27:41,916 that were presented at the meeting were really, really interesting. 562 00:27:41,916 --> 00:27:44,375 Stealth Biotherapeutics is doing 563 00:27:44,375 --> 00:27:47,375 a mitochondrial activation peptide. 564 00:27:48,000 --> 00:27:52,166 Mount Sinai, has a team with exercise rapamycin, 565 00:27:52,166 --> 00:27:55,750 spermidine, macrophages and inflammatory biology as a focus. 566 00:27:56,125 --> 00:27:57,750 and then UT Health SanAntonio 567 00:27:57,750 --> 00:27:59,625 Yeah. And so it's a mouthful. 568 00:27:59,625 --> 00:28:03,625 UT Health San Antonio using low frequency ultrasound right. 569 00:28:03,750 --> 00:28:06,000 Now the device. Yeah. Cool. 570 00:28:06,000 --> 00:28:10,166 As I was at ASCO this past weekend, American Society of Clinical Oncology 571 00:28:10,583 --> 00:28:12,958 there were numerous devices that are... 572 00:28:12,958 --> 00:28:17,208 I forget what it is, but it's different wavelengths of 573 00:28:17,375 --> 00:28:22,125 sound and light to help with the cancer cell division. 574 00:28:22,125 --> 00:28:23,916 So anyway, amazing. 575 00:28:23,916 --> 00:28:26,500 I think the diversity was just fascinating to me. 576 00:28:26,500 --> 00:28:30,666 So the gerotherapeutics may not be one modality, right? 577 00:28:30,666 --> 00:28:33,875 It may be drugs, it may be biologics, peptides 578 00:28:34,125 --> 00:28:37,625 may be devices, it may be lifestyle combinations. 579 00:28:37,625 --> 00:28:38,000 Right. 580 00:28:38,000 --> 00:28:40,875 That exercise is a very powerful intervention. 581 00:28:40,875 --> 00:28:42,750 If we can structure and understand 582 00:28:42,750 --> 00:28:45,750 the nature of what's most effective, I think that's really important. 583 00:28:45,875 --> 00:28:46,541 And then of course, 584 00:28:46,541 --> 00:28:49,875 cell therapies and other approaches that might be a little more complex. 585 00:28:50,375 --> 00:28:50,750 Yeah. 586 00:28:50,750 --> 00:28:52,875 And then PROSPR. 587 00:28:52,875 --> 00:28:56,500 This ARPA-H initiative, PROSPR, is focus on the other side 588 00:28:56,500 --> 00:28:59,500 of the equation, which is the measurement in the regulatory infrastructure. 589 00:28:59,541 --> 00:29:03,166 And both these innovative companies are working together 590 00:29:03,166 --> 00:29:08,375 hand in hand so that as we've been kind of circling around, we make real progress. 591 00:29:09,041 --> 00:29:09,416 Right. 592 00:29:09,416 --> 00:29:12,625 And then Andrew Brack, his ARPA-H framing 593 00:29:12,625 --> 00:29:16,000 was that the field needs data to support regulatory decisions. 594 00:29:16,666 --> 00:29:18,291 You have to regulate evidence. 595 00:29:18,291 --> 00:29:20,416 You can't regulate hope. 596 00:29:20,416 --> 00:29:24,000 Yes. And PROSPR is trying to generate that evidence. 597 00:29:24,000 --> 00:29:24,625 Right. 598 00:29:24,625 --> 00:29:25,541 Clinical trial ready 599 00:29:25,541 --> 00:29:29,666 intrinsic capacity at home measurements blood biomarkers and intervention studies. 600 00:29:30,041 --> 00:29:30,375 Yeah. 601 00:29:30,375 --> 00:29:34,875 And then Brianna Stubbs her THRIVE presentation, she's she's out of Buck. 602 00:29:34,875 --> 00:29:37,791 I believe I think of their collaborators and also a Stanford right. 603 00:29:37,791 --> 00:29:39,250 Yeah that's a great example right. 604 00:29:39,250 --> 00:29:43,125 The teams are developing clinical at home intrinsic capacity scores 605 00:29:43,125 --> 00:29:48,250 with wearables app based assessments, patient reported outcomes voice and video. 606 00:29:48,625 --> 00:29:52,250 And then there's these blood micro sampling for the omics. 607 00:29:52,250 --> 00:29:53,875 So excited about that. 608 00:29:53,875 --> 00:29:54,125 Yeah. 609 00:29:54,125 --> 00:29:56,000 And I think I mean we might need 610 00:29:56,000 --> 00:29:59,916 those kinds of creative infrastructures to actually measure healthspan at scale. 611 00:29:59,916 --> 00:30:00,125 Right. 612 00:30:00,125 --> 00:30:03,375 It's not going to be as simple as some of the existing, 613 00:30:04,125 --> 00:30:05,875 method methodologies, I think. Yeah. 614 00:30:05,875 --> 00:30:09,291 And they're not just saying, hey, does intrinsic capacity correlate with age. 615 00:30:09,750 --> 00:30:14,500 They're asking whether it predicts meaningful outcomes and whether it changes 616 00:30:14,500 --> 00:30:18,875 with interventions just to be super crystal clear about the the structure. 617 00:30:19,250 --> 00:30:20,000 Yep, yep. 618 00:30:20,000 --> 00:30:21,916 As we were saying earlier, right. 619 00:30:21,916 --> 00:30:24,875 The response to intervention is critical. 620 00:30:24,875 --> 00:30:27,416 If it predicts risk, if a measurement. 621 00:30:27,416 --> 00:30:32,375 It predicts risk, but it does not respond when that risk profile is changed. 622 00:30:32,791 --> 00:30:34,416 It could be useful for prognosis. Right. 623 00:30:34,416 --> 00:30:37,625 But it's not going to have nearly the utility for a trial endpoint. 624 00:30:37,625 --> 00:30:39,041 In fact it will it won't work. 625 00:30:39,041 --> 00:30:41,041 Yeah. Yeah. And we need to know that right. 626 00:30:41,041 --> 00:30:44,375 So XPRIZE is pushing intervention teams to show functional efforts. 627 00:30:44,875 --> 00:30:48,750 And PROSPR is building the tools to measure those efforts more consistently. 628 00:30:48,750 --> 00:30:50,666 That's that's kind of how I saw it. 629 00:30:50,666 --> 00:30:53,125 Yeah. And it's a really nice partnership. Right. 630 00:30:53,125 --> 00:30:56,500 Because both are creating data that the field just needs so badly. 631 00:30:57,041 --> 00:30:58,041 Awesome. So okay. 632 00:30:58,041 --> 00:31:02,916 So let's transition to what scientists and maybe sponsors, people who are in this 633 00:31:02,916 --> 00:31:07,125 area of a scientist or a biotech leader or a translational team is listening. 634 00:31:07,500 --> 00:31:09,666 What should they take away from this meeting? 635 00:31:09,666 --> 00:31:10,000 Yeah. 636 00:31:10,000 --> 00:31:13,625 So I think a piece of very practical advice 637 00:31:13,625 --> 00:31:16,916 and strong advice would be let's not lead with we reverse aging. 638 00:31:17,375 --> 00:31:21,875 I think that immediately and immediately discredits anything that comes after. 639 00:31:22,750 --> 00:31:23,625 So let's start there. 640 00:31:25,125 --> 00:31:26,125 What function improves? 641 00:31:26,125 --> 00:31:27,750 Right. What risk is reduced? 642 00:31:27,750 --> 00:31:31,416 What population level benefits are we experiencing with this. 643 00:31:31,416 --> 00:31:33,541 Right. What's the mechanism. What's the endpoint? 644 00:31:33,541 --> 00:31:37,375 I mean, it's almost like we have to think of an exotic, super cool 645 00:31:37,375 --> 00:31:41,625 concept and dumb it down a bit to be more believable. 646 00:31:41,625 --> 00:31:42,875 If that makes sense. 647 00:31:42,875 --> 00:31:46,041 I think I think manage it, manage expectations on it. 648 00:31:46,041 --> 00:31:46,625 Right? 649 00:31:46,625 --> 00:31:49,500 Dumb it down is a tricky but yeah, I yeah, I know what you mean. 650 00:31:49,500 --> 00:31:50,625 Yeah, it's bad and it's. 651 00:31:50,625 --> 00:31:53,625 Yeah. But being it we need to be specific. 652 00:31:53,916 --> 00:31:55,416 Right, right, right. Exactly. 653 00:31:55,416 --> 00:31:57,375 I mean I think that's what I'm trying to get at is 654 00:31:57,375 --> 00:32:00,541 the FDA can respond to specific questions. 655 00:32:01,000 --> 00:32:03,750 It's much, much harder to support broad 656 00:32:03,750 --> 00:32:07,375 philosophical claims about aging and reversal. 657 00:32:07,916 --> 00:32:08,500 Yeah. Yeah. 658 00:32:08,500 --> 00:32:12,125 And then I think the third thing is to think domain by domain, right. 659 00:32:12,125 --> 00:32:15,375 We talked about these domains like that make up intrinsic capacity. 660 00:32:15,666 --> 00:32:16,125 Right. 661 00:32:16,125 --> 00:32:19,125 The mitochondrial presentation was super interesting. 662 00:32:19,125 --> 00:32:22,625 So if there is a functional outcome that is linked to 663 00:32:22,625 --> 00:32:26,250 what one would expect if you improve mitochondrial function, focus there. 664 00:32:26,250 --> 00:32:27,500 Right. 665 00:32:27,500 --> 00:32:29,916 If you're focused on muscle biology, let's talk 666 00:32:29,916 --> 00:32:33,125 about a locomotor story. 667 00:32:33,125 --> 00:32:35,750 I think that that helps build the credibility. 668 00:32:35,791 --> 00:32:36,291 Awesome. 669 00:32:36,291 --> 00:32:39,041 And then collect intrinsic capacity measures. 670 00:32:39,041 --> 00:32:42,791 Now even as exploratory endpoints right 671 00:32:42,791 --> 00:32:46,666 track to, to get to where we want to be. 672 00:32:46,833 --> 00:32:47,083 Yeah. 673 00:32:47,083 --> 00:32:49,791 And we'll know from the evidence if we can get there. 674 00:32:49,791 --> 00:32:50,041 Yeah. 675 00:32:50,041 --> 00:32:54,791 And I think I mean, it doesn't it feels like a low risk move to start doing that. 676 00:32:54,791 --> 00:32:58,041 It feels like there's a real inertia behind the concept. 677 00:32:59,041 --> 00:33:00,958 You know, Andrew Brack had had that ask. 678 00:33:00,958 --> 00:33:01,166 Right. 679 00:33:01,166 --> 00:33:04,166 The field needs data across these trials. 680 00:33:04,291 --> 00:33:05,916 And if everyone's using these 681 00:33:05,916 --> 00:33:08,541 different measurements, it's going to be super hard to build consensus. 682 00:33:08,541 --> 00:33:13,166 So I think it would be a great proactive move for scientists in the field 683 00:33:13,208 --> 00:33:17,416 to kind of come together around this intrinsic capacity concept. 684 00:33:17,916 --> 00:33:18,416 Loveit. 685 00:33:18,416 --> 00:33:22,208 And then biomarkers need a job right. 686 00:33:22,916 --> 00:33:26,666 Don't measure everything just because you can, decide 687 00:33:26,666 --> 00:33:30,083 what each biomarker or signature contributes, right. 688 00:33:30,583 --> 00:33:33,416 Does it contribute to your claim for targeted engagement for patients, 689 00:33:33,416 --> 00:33:37,416 selection for safety response mechanism or surrogate development. 690 00:33:38,166 --> 00:33:39,416 Yeah. Yeah. And do not. 691 00:33:39,416 --> 00:33:41,666 This is so crystal clear. 692 00:33:41,666 --> 00:33:44,666 So it's sort of really put a damper on things. 693 00:33:44,666 --> 00:33:49,166 But it's like do not neglect dose duration safety and manufacturing. 694 00:33:49,166 --> 00:33:52,416 These are critical pieces the FDA cares a lot about. 695 00:33:52,708 --> 00:33:56,583 Yeah, I mean, in the defense of the folks who presented their visions 696 00:33:56,583 --> 00:33:59,041 for their therapeutic concepts, they had ten minutes. 697 00:33:59,041 --> 00:34:02,041 But there was a comment at the end from the regulators, 698 00:34:02,541 --> 00:34:04,708 not one presentation addressed those things. 699 00:34:04,708 --> 00:34:06,083 And you just mentioned. Yeah. 700 00:34:06,083 --> 00:34:07,916 And they're super important. 701 00:34:07,916 --> 00:34:10,166 So yeah, again brings. 702 00:34:10,166 --> 00:34:12,041 Us down to earth. Right. 703 00:34:12,041 --> 00:34:13,208 Well said I'll say. 704 00:34:13,208 --> 00:34:16,208 And collaborate where collaboration makes sense. 705 00:34:16,208 --> 00:34:17,666 Yeah. Aligning on endpoints. 706 00:34:17,666 --> 00:34:20,541 The IC concept I think is really powerful. 707 00:34:21,583 --> 00:34:23,916 One of the things, Cindy, that you and I have lived 708 00:34:23,916 --> 00:34:25,916 is the collaboration of the pharma 709 00:34:25,916 --> 00:34:29,541 partners to run 600,000 human samples in the UK Biobank, right. 710 00:34:29,708 --> 00:34:32,583 Those kinds of collaborative industry, government efforts. 711 00:34:32,583 --> 00:34:36,708 I mean, UK government has co-funded that work. 712 00:34:36,708 --> 00:34:38,750 I think in the US, we should really take a look in the mirror 713 00:34:38,750 --> 00:34:41,583 and think about how can we co-fund some work 714 00:34:41,583 --> 00:34:44,333 to build data at scale, because that data is part 715 00:34:44,333 --> 00:34:47,208 of that biological foundation layer that we talked about. 716 00:34:47,708 --> 00:34:52,958 From that, one can then build these signatures, build things that, 717 00:34:52,958 --> 00:34:56,583 once tested properly, could become extremely valuable in the field. 718 00:34:57,208 --> 00:35:01,000 And it's by leveraging that, measuring millions to understand one 719 00:35:01,000 --> 00:35:04,000 that we're laying the foundation and building harmonization structure 720 00:35:04,333 --> 00:35:08,083 to know what these biomarkers, what we can expect from them. 721 00:35:08,333 --> 00:35:11,750 Give them that job and then individual sponsors. 722 00:35:11,750 --> 00:35:12,833 The message was individual 723 00:35:12,833 --> 00:35:15,833 sponsors still need to go to the FDA with their own products 724 00:35:16,208 --> 00:35:18,750 and their own development plans. 725 00:35:18,750 --> 00:35:19,500 True, right? 726 00:35:19,500 --> 00:35:21,625 I mean, even if we have this shared infrastructure 727 00:35:21,625 --> 00:35:25,083 and shared data generation and shared endpoint harmonization, 728 00:35:25,458 --> 00:35:29,125 you still have to get your product specific evidence right. 729 00:35:29,125 --> 00:35:32,000 You still have to get through the typical process. 730 00:35:32,000 --> 00:35:32,333 Yeah. 731 00:35:32,333 --> 00:35:34,958 And I think Susan Winckler, she was so great. 732 00:35:34,958 --> 00:35:36,833 I just loved her moderation the whole day. 733 00:35:36,833 --> 00:35:39,208 She so much, so much energy. 734 00:35:39,208 --> 00:35:42,083 And she kept bringing the room back to practical next steps. 735 00:35:42,083 --> 00:35:44,458 Right? Yeah. She didn't dampen the enthusiasm. 736 00:35:44,458 --> 00:35:46,083 But what can be done collectively? 737 00:35:46,083 --> 00:35:49,208 What must be done, you know, sponsored by sponsor? 738 00:35:49,208 --> 00:35:50,750 What needs FDA input? 739 00:35:50,750 --> 00:35:53,333 What needs more evidence? Exactly. 740 00:35:53,333 --> 00:35:55,083 And I think that is the right balance. 741 00:35:55,083 --> 00:35:58,333 I think this theme has come up in this podcast multiple times, but 742 00:35:58,958 --> 00:36:04,000 I think it was one of the best parts of the meeting is that there was 743 00:36:04,000 --> 00:36:07,583 a pragmatic realization on both sides that we have to meet in the middle, 744 00:36:08,708 --> 00:36:12,333 to make things actually happen and move things forward. 745 00:36:12,333 --> 00:36:13,208 Yeah, yeah. 746 00:36:13,208 --> 00:36:16,583 And that is how healthspan therapeutics will advance. 747 00:36:16,583 --> 00:36:20,958 Not one giant leap, but a series of credible 748 00:36:20,958 --> 00:36:25,000 stepping stone steps in the right direction. So. 749 00:36:25,333 --> 00:36:28,083 Okay, so so after this meeting, then 750 00:36:28,083 --> 00:36:32,708 are you more optimistic or are you more cautious? Boy. 751 00:36:33,333 --> 00:36:34,833 I'm going to take the easy way out. 752 00:36:34,833 --> 00:36:36,458 You're going to say both. Yeah. 753 00:36:36,458 --> 00:36:38,958 So you cop out, right? 754 00:36:38,958 --> 00:36:41,250 I'll make a stand and say optimistic. 755 00:36:41,250 --> 00:36:45,625 Because the fact that the meeting happened means that regulators 756 00:36:45,625 --> 00:36:47,125 want there to be progress. 757 00:36:47,125 --> 00:36:50,500 The fact that they said come to us means that they want, 758 00:36:52,125 --> 00:36:55,500 gerotherapeutics to be brought to market. 759 00:36:56,333 --> 00:36:59,333 So on balance, I'm very optimistic now 760 00:36:59,458 --> 00:37:03,625 that the doses of realism are hard to ignore. 761 00:37:03,958 --> 00:37:07,500 So I'm optimistic, but I think the timeline might be longer 762 00:37:07,500 --> 00:37:11,000 than people want it to be because there's a process that needs to be gone through. 763 00:37:11,000 --> 00:37:13,375 So I hold optimism. 764 00:37:13,375 --> 00:37:19,208 And I do hope that with increasing evidence of perhaps some novel approaches, 765 00:37:20,333 --> 00:37:21,083 there will be a true 766 00:37:21,083 --> 00:37:24,083 meeting in the middle of the regulators and the innovative science. 767 00:37:24,333 --> 00:37:26,250 I think that's fair. I think that's fair. 768 00:37:26,250 --> 00:37:28,125 Opt optimism with discipline. 769 00:37:28,125 --> 00:37:29,458 So so my takeaway 770 00:37:29,458 --> 00:37:33,333 is that gerotherapeutics may not arrive first as a broad aging indication. 771 00:37:33,708 --> 00:37:38,708 They may arrive through frailty, sarcopenia, immune function, 772 00:37:38,708 --> 00:37:43,250 metabolic disease, locomotor decline, cognitive domains, 773 00:37:43,250 --> 00:37:47,333 other age related conditions, more and more domain specific. 774 00:37:47,625 --> 00:37:51,875 Yes. And I think that's exactly where the conclusion fell. 775 00:37:52,958 --> 00:37:56,458 And as a stepping stones accumulate as more and more of them are developed, 776 00:37:56,458 --> 00:38:01,208 the field will eventually build sufficient evidence for broader healthspan claims. 777 00:38:01,458 --> 00:38:01,833 Yeah. 778 00:38:01,833 --> 00:38:06,333 And the and the story is not that the FDA is blocking longevity. 779 00:38:06,333 --> 00:38:07,708 That is not the story. 780 00:38:07,708 --> 00:38:09,833 No, I don't think so. Not at all. 781 00:38:09,833 --> 00:38:11,250 I think the story is longevity. 782 00:38:11,250 --> 00:38:12,958 Science is maturing. 783 00:38:12,958 --> 00:38:15,833 And it's growing up. 784 00:38:16,083 --> 00:38:17,375 And that means moving from this 785 00:38:17,375 --> 00:38:21,083 fascinating biology to evidence that people can trust. 786 00:38:21,583 --> 00:38:26,708 Evidence that shows people can feel, function and survive better and longer. 787 00:38:27,583 --> 00:38:28,083 Perfect. 788 00:38:28,083 --> 00:38:31,083 That's the real promise of better aging. 789 00:38:31,208 --> 00:38:33,250 And maybe the best way to get that chicken 790 00:38:33,250 --> 00:38:36,500 safely back in its cage on the 737. 791 00:38:36,500 --> 00:38:38,875 Well done way to bring it back home Evan. 792 00:38:38,875 --> 00:38:41,083 Thanks so much and everybody out there, 793 00:38:41,083 --> 00:38:43,083 thanks for listening. I hope this was useful. 794 00:38:45,208 --> 00:38:49,083 Well, that wraps up this episode of Proteomics in Proximity. 795 00:38:49,583 --> 00:38:53,708 Huge thanks to our guests and authors of such impactful publications. 796 00:38:54,083 --> 00:38:56,708 I also want to thank you for tuning in. 797 00:38:56,708 --> 00:38:58,958 Really appreciate you being here. 798 00:38:58,958 --> 00:39:01,458 If you enjoyed the content of this episode, 799 00:39:01,458 --> 00:39:04,583 please think about sharing it with friends or colleagues 800 00:39:04,583 --> 00:39:06,833 you think might be interested in the content. 801 00:39:06,833 --> 00:39:10,958 In addition, if you'd be willing to head over to Apple or Spotify 802 00:39:10,958 --> 00:39:14,375 or wherever you digest your podcasts and give us a rating and review, 803 00:39:14,375 --> 00:39:16,458 this will help others find the podcast 804 00:39:16,458 --> 00:39:19,750 when they're searching for proteomics or precision medicine podcasts. 805 00:39:19,958 --> 00:39:23,583 And mostly I want to say we would love to hear from you. 806 00:39:23,750 --> 00:39:25,833 So we have a dedicated email address. 807 00:39:25,833 --> 00:39:28,708 pip@olink.com, please reach out. 808 00:39:28,708 --> 00:39:32,708 Let us know what you're interested in hearing about what you care about 809 00:39:32,708 --> 00:39:36,708 and any feedback on the episodes that we have already done so far. 810 00:39:37,000 --> 00:39:40,458 This is all about you, and so we're really keen 811 00:39:40,458 --> 00:39:43,458 to make sure that we're meeting what you like to hear about. 812 00:39:43,708 --> 00:39:51,375 Thank you so much and we'll see you soon.