1 00:00:00,120 --> 00:00:01,080 Speaker 1: How's San Francisco. 2 00:00:01,920 --> 00:00:03,080 Speaker 2: It's uh, it's great. 3 00:00:03,120 --> 00:00:04,960 Speaker 3: You know, like I think that a lot of people 4 00:00:04,960 --> 00:00:07,520 Speaker 3: told me it was going to be a dystopian healscape 5 00:00:07,600 --> 00:00:09,080 Speaker 3: and it's not. 6 00:00:09,920 --> 00:00:12,040 Speaker 1: Wait, it's the first time you've actually gone right to 7 00:00:12,080 --> 00:00:14,200 Speaker 1: the JP Morgan healthcare conference. 8 00:00:14,720 --> 00:00:18,360 Speaker 2: To this one. Yeah, the specific conference, And what's. 9 00:00:18,160 --> 00:00:20,520 Speaker 1: Your first impression of it? I guess is there anything 10 00:00:20,520 --> 00:00:21,280 Speaker 1: that surprised you. 11 00:00:21,400 --> 00:00:24,040 Speaker 2: I guess what really surprised me was the security. 12 00:00:24,280 --> 00:00:26,599 Speaker 3: Like I've been to conferences before where it's kind of 13 00:00:26,640 --> 00:00:29,200 Speaker 3: like you can't even go into the hotel without like 14 00:00:29,280 --> 00:00:31,360 Speaker 3: your past and they're really strict about it. So I 15 00:00:31,360 --> 00:00:34,519 Speaker 3: guess that was the most surprising thing. But other than that, 16 00:00:34,600 --> 00:00:38,000 Speaker 3: it's you know, the meetings are cool. Conference, It's still 17 00:00:38,040 --> 00:00:39,720 Speaker 3: a conference, right, Like, it's not I'm not going to 18 00:00:39,800 --> 00:00:42,159 Speaker 3: be like, oh god, this is you know, the coolest 19 00:00:42,159 --> 00:00:44,599 Speaker 3: thing I've ever seen in my life, although that did 20 00:00:44,680 --> 00:00:47,040 Speaker 3: kind of happen here with there was a company that 21 00:00:47,120 --> 00:00:49,879 Speaker 3: was presenting that has I guess like you can call 22 00:00:49,880 --> 00:00:53,080 Speaker 3: it like drug GPT, right, it's like artificial intelligence but 23 00:00:53,200 --> 00:00:56,680 Speaker 3: used to like find new drugs, and that was really cool. 24 00:00:59,400 --> 00:01:00,760 Speaker 4: I did a left one. 25 00:01:02,080 --> 00:01:07,959 Speaker 1: Jimmy Okay, Simony up barges, this isn't After School Special, 26 00:01:08,000 --> 00:01:10,720 Speaker 1: except I've decided I'm going to base my entire personality 27 00:01:10,720 --> 00:01:13,720 Speaker 1: going forward on campaigning for a strategic pork reserve in 28 00:01:13,760 --> 00:01:14,200 Speaker 1: the US. 29 00:01:14,280 --> 00:01:16,000 Speaker 4: Where's the best with imposta? 30 00:01:16,200 --> 00:01:18,759 Speaker 1: These are the important question? Is that robots taking over 31 00:01:18,800 --> 00:01:19,399 Speaker 1: the world. No. 32 00:01:19,480 --> 00:01:22,360 Speaker 4: I think that like in a couple of years, the 33 00:01:22,440 --> 00:01:24,640 Speaker 4: AI will do a really good job of making the 34 00:01:24,640 --> 00:01:27,880 Speaker 4: odd Lots podcast and people to say, I don't really 35 00:01:27,920 --> 00:01:29,800 Speaker 4: need to listen to Joe and Tracy anymore. 36 00:01:30,000 --> 00:01:30,600 Speaker 2: We do have. 37 00:01:32,720 --> 00:01:36,680 Speaker 1: Perfect You're listening to lots More where we catch up 38 00:01:36,680 --> 00:01:39,560 Speaker 1: with friends about what's going on right now, because. 39 00:01:39,280 --> 00:01:42,319 Speaker 4: Even when odd Lots is over, there's always lots more. 40 00:01:42,560 --> 00:01:46,240 Speaker 1: And we really do have the perfect guest. We're talking 41 00:01:46,280 --> 00:01:50,200 Speaker 1: to James Van Gelan of saitriniresearch dot com, also known 42 00:01:50,200 --> 00:01:52,880 Speaker 1: as Satrini on Twitter. You might remember him. We had 43 00:01:52,920 --> 00:01:56,080 Speaker 1: him on odd Thloughts back in August to talk about 44 00:01:56,280 --> 00:01:59,680 Speaker 1: weight loss drugs, including ozembic and how they were going 45 00:01:59,720 --> 00:02:03,440 Speaker 1: to chat everything. That was kind of a prescient conversation, 46 00:02:03,800 --> 00:02:07,360 Speaker 1: I think because since then we've seen even more excitement 47 00:02:07,400 --> 00:02:09,280 Speaker 1: for a lot of these drugs and the stocks have 48 00:02:09,320 --> 00:02:13,240 Speaker 1: taken off even more, James, is that what everyone's talking 49 00:02:13,320 --> 00:02:14,440 Speaker 1: about at that conference? 50 00:02:15,000 --> 00:02:17,360 Speaker 3: I think it's it's split kind of fifty to fifty 51 00:02:17,440 --> 00:02:21,920 Speaker 3: between the GLP one drugs and then the ADC cancer 52 00:02:21,960 --> 00:02:25,239 Speaker 3: stuff because of the side of Kinetics acquisition that was announced. 53 00:02:25,919 --> 00:02:29,760 Speaker 4: I don't like going to conferences. Typically, you know, everyone 54 00:02:29,800 --> 00:02:31,880 Speaker 4: licet in a while, they're okay, But what is like, 55 00:02:32,120 --> 00:02:34,840 Speaker 4: I know, like the JP Morgan Healthcare Conference. I know 56 00:02:34,880 --> 00:02:36,480 Speaker 4: it's a big deal. Why is it such a big deal? 57 00:02:36,520 --> 00:02:39,239 Speaker 4: What is it exactly? And like why this one in particular, 58 00:02:39,280 --> 00:02:41,640 Speaker 4: how did it become like such an important event for 59 00:02:41,720 --> 00:02:42,600 Speaker 4: healthcare investors. 60 00:02:42,960 --> 00:02:45,120 Speaker 2: I don't know the history of the conference, but when 61 00:02:45,160 --> 00:02:48,760 Speaker 2: you look at the agenda speaking here, it's you know, 62 00:02:48,840 --> 00:02:51,359 Speaker 2: everyone that's a big deal. Everyone that you would want 63 00:02:51,400 --> 00:02:53,720 Speaker 2: to know what they're doing, they're kind of here. I 64 00:02:53,760 --> 00:02:56,480 Speaker 2: think that there's even more value in just sitting in 65 00:02:56,560 --> 00:02:59,040 Speaker 2: some of the hotel bars, like over hearing how. 66 00:02:58,960 --> 00:03:02,359 Speaker 3: People are talking, you know, like people seem pretty optimistic 67 00:03:02,440 --> 00:03:04,960 Speaker 3: on like med tech in the funding environment, and that's 68 00:03:05,120 --> 00:03:07,920 Speaker 3: almost more valuable to me than the conference itself. 69 00:03:08,320 --> 00:03:11,560 Speaker 4: So, by the way, we're recording this January tenth, Novo 70 00:03:11,680 --> 00:03:15,799 Speaker 4: Nordisk hitting yet another all time high right now. It's 71 00:03:15,919 --> 00:03:19,040 Speaker 4: kind of crazy, Tracy, Like, for all of the hype 72 00:03:19,480 --> 00:03:23,520 Speaker 4: about the GLP one drugs, A, the stocks keep going 73 00:03:23,600 --> 00:03:26,920 Speaker 4: higher and B. I feel like every day there's some 74 00:03:27,000 --> 00:03:29,919 Speaker 4: other article or thing about here's another thing that they 75 00:03:29,960 --> 00:03:33,720 Speaker 4: do well, or another area that we're researching that shows 76 00:03:33,760 --> 00:03:36,120 Speaker 4: promise where it's not just about weight less, you know, 77 00:03:36,160 --> 00:03:39,360 Speaker 4: like compulsion or other things. Like it's pretty wild how 78 00:03:39,400 --> 00:03:40,520 Speaker 4: this is not slowing down. 79 00:03:40,760 --> 00:03:44,080 Speaker 1: This is my opportunity to reject the idea of the 80 00:03:44,160 --> 00:03:47,040 Speaker 1: all thoughts curse. Oh and that we top tech a 81 00:03:47,080 --> 00:03:50,040 Speaker 1: lot of these things. We did not top tick Nova 82 00:03:50,080 --> 00:03:51,920 Speaker 1: Nordisk stock. Okay, we were a. 83 00:03:51,880 --> 00:03:53,320 Speaker 4: Launch that we haven't top ticked. 84 00:03:53,480 --> 00:03:54,360 Speaker 2: Yeah. 85 00:03:54,440 --> 00:03:57,280 Speaker 3: Actually, I actually I actually when I got the invite 86 00:03:57,280 --> 00:04:00,320 Speaker 3: to come back, I remember everyone that was like, if 87 00:04:00,360 --> 00:04:02,240 Speaker 3: you go on a podcast and you talk about this trend, 88 00:04:02,320 --> 00:04:04,880 Speaker 3: like that's it, it's over, you know. So when I 89 00:04:04,880 --> 00:04:06,360 Speaker 3: got the invite to come back, I went and I 90 00:04:06,440 --> 00:04:09,640 Speaker 3: looked at like Lily and Novo and yeah, so I 91 00:04:09,680 --> 00:04:12,600 Speaker 3: think it's like eighty one percent of the time they 92 00:04:12,600 --> 00:04:17,040 Speaker 3: have been at all time highs since. Yeah, since that's so, 93 00:04:17,480 --> 00:04:19,920 Speaker 3: I think we can put the odlus course rum or two. 94 00:04:20,160 --> 00:04:22,320 Speaker 4: We're not trying to time the market. We're not here 95 00:04:22,360 --> 00:04:26,280 Speaker 4: to make recommendations, but we are just trying to establish 96 00:04:26,520 --> 00:04:29,560 Speaker 4: that not every time we talk about a trend it's 97 00:04:29,600 --> 00:04:31,440 Speaker 4: at the top. And I'll just say the other one. 98 00:04:31,920 --> 00:04:35,080 Speaker 4: We've done multiple episodes over the years, over the last 99 00:04:35,120 --> 00:04:38,680 Speaker 4: year relating to in Nvidia and semiconductors and Tracy and 100 00:04:38,720 --> 00:04:41,760 Speaker 4: Nvidia at another all time high today. So we're just 101 00:04:41,800 --> 00:04:42,839 Speaker 4: getting these on the record. 102 00:04:42,880 --> 00:04:44,800 Speaker 1: We're just journalists who want to be timely. 103 00:04:44,920 --> 00:04:46,960 Speaker 4: We're just journalists who want to be We just we 104 00:04:47,120 --> 00:04:50,080 Speaker 4: just want to be timely. Okay, So within the GLP 105 00:04:50,240 --> 00:04:54,680 Speaker 4: one world, what's hot? What are people talking about? What's 106 00:04:54,760 --> 00:04:56,839 Speaker 4: interesting within GLP ones? 107 00:04:57,279 --> 00:04:59,240 Speaker 2: Well, there's there's two main things. Right. 108 00:04:59,279 --> 00:05:03,920 Speaker 3: You got the oral drugs, and like Lily's CEO had 109 00:05:03,960 --> 00:05:06,359 Speaker 3: a fireside chat type thing, and. 110 00:05:06,400 --> 00:05:07,560 Speaker 2: I have this big realization. 111 00:05:07,680 --> 00:05:09,720 Speaker 3: I don't know if this was like common knowledge among 112 00:05:09,760 --> 00:05:11,600 Speaker 3: people that are healthcare specialists, but I had a big 113 00:05:11,640 --> 00:05:15,479 Speaker 3: realization that basically, with these oral drugs, it's not so 114 00:05:15,600 --> 00:05:18,760 Speaker 3: much about the fact that like patients don't want to 115 00:05:18,880 --> 00:05:21,560 Speaker 3: use these auto injectors, because as we've seen, people are 116 00:05:21,560 --> 00:05:25,400 Speaker 3: pretty much fine with the auto injectors. The thing is, 117 00:05:25,440 --> 00:05:30,240 Speaker 3: the auto injectors are super expensive and like very fight constrained, 118 00:05:30,760 --> 00:05:34,120 Speaker 3: so it's kind of like the oral drug, right, Like 119 00:05:34,200 --> 00:05:38,320 Speaker 3: it's very easy to make a pill, and the high. 120 00:05:38,240 --> 00:05:41,080 Speaker 2: Chain is like not the same as the auto injector. 121 00:05:41,160 --> 00:05:43,359 Speaker 3: So it turns out the way that he was speaking, 122 00:05:43,360 --> 00:05:48,240 Speaker 3: it really gave me the idea that like these oral drugs, right, 123 00:05:48,279 --> 00:05:51,160 Speaker 3: like Lily has orf and Novo has one too that's 124 00:05:51,200 --> 00:05:53,760 Speaker 3: in the in the works, and they seem to be 125 00:05:54,040 --> 00:05:55,279 Speaker 3: kind of like the rate limitter. 126 00:05:55,400 --> 00:05:55,560 Speaker 2: Right. 127 00:05:55,640 --> 00:05:58,760 Speaker 3: He had something that he said about how he sees 128 00:05:59,040 --> 00:06:01,880 Speaker 3: supply for these drugs being a constraint for you know, 129 00:06:02,000 --> 00:06:05,120 Speaker 3: the next ten years, and then he said, that's not 130 00:06:05,200 --> 00:06:08,360 Speaker 3: a bad thing, right, because we have you know, half 131 00:06:08,440 --> 00:06:10,920 Speaker 3: of we have I don't know, five hundred thousand people 132 00:06:11,240 --> 00:06:13,800 Speaker 3: in the US that are on these drugs and one 133 00:06:13,880 --> 00:06:15,159 Speaker 3: hundred and ten million. 134 00:06:14,880 --> 00:06:17,880 Speaker 2: People that theoretically could or should take them. 135 00:06:18,360 --> 00:06:23,159 Speaker 3: And that doesn't even count in Brazil or China or India. 136 00:06:23,320 --> 00:06:24,839 Speaker 2: Right, that's the main thing. 137 00:06:25,680 --> 00:06:28,360 Speaker 1: So one of the things that lives rent free in 138 00:06:28,400 --> 00:06:30,960 Speaker 1: my head ever since we first spoke to you was 139 00:06:31,320 --> 00:06:35,960 Speaker 1: Titan Stapler. Do you remember that, like this this company 140 00:06:35,960 --> 00:06:39,640 Speaker 1: that makes Titan stomach staplers, and I'm wondering, you know, 141 00:06:39,680 --> 00:06:42,960 Speaker 1: obviously people are talking about the sort of positive impact 142 00:06:43,520 --> 00:06:46,359 Speaker 1: of weight loss drugs GLP one on their bottom lines, 143 00:06:46,360 --> 00:06:48,440 Speaker 1: but is there anyone at the conference that you've heard 144 00:06:48,600 --> 00:06:50,880 Speaker 1: or seen talking about negative impact? 145 00:06:51,720 --> 00:06:55,919 Speaker 3: Honestly, I think that ever since right around the first 146 00:06:55,920 --> 00:06:59,040 Speaker 3: time we spoke, there was a wave of some people 147 00:06:59,080 --> 00:07:01,200 Speaker 3: that fell on the soul, so to speak, kind of 148 00:07:01,240 --> 00:07:04,840 Speaker 3: like how CHEG did with artificial intelligence. I think that 149 00:07:05,279 --> 00:07:07,599 Speaker 3: once they saw the stock reactions, once you come out 150 00:07:07,839 --> 00:07:10,760 Speaker 3: and you say, you know, GLP one drugs are killing us, 151 00:07:10,840 --> 00:07:13,400 Speaker 3: and then your stock goes down ten percent a day, 152 00:07:13,440 --> 00:07:16,040 Speaker 3: I think they're a little more careful about it now, 153 00:07:16,280 --> 00:07:20,200 Speaker 3: they're not readily offering that information. But also at the 154 00:07:20,240 --> 00:07:24,600 Speaker 3: same time, some obviously I had a very like high 155 00:07:24,600 --> 00:07:28,720 Speaker 3: conviction thesis on the idea that you know, sepath machines 156 00:07:29,000 --> 00:07:32,679 Speaker 3: and the Titan stapler and all these things would actively affected. 157 00:07:32,760 --> 00:07:36,680 Speaker 3: But it got so priced in so quickly, and some 158 00:07:36,760 --> 00:07:39,280 Speaker 3: of that was not GLP one stuff, right, Some of 159 00:07:39,280 --> 00:07:42,920 Speaker 3: that was basically just rates, Like don't attribute to GLP 160 00:07:43,000 --> 00:07:45,080 Speaker 3: one's what you can attribute to interest rates. I guess 161 00:07:45,160 --> 00:07:48,040 Speaker 3: and so I think with Medtech where it is, it's 162 00:07:48,120 --> 00:07:51,480 Speaker 3: kind of even with the impact there, they can kind 163 00:07:51,480 --> 00:07:53,680 Speaker 3: of say, okay, you know, but it's a little bit 164 00:07:53,720 --> 00:07:54,560 Speaker 3: better than you've heared. 165 00:07:55,240 --> 00:07:59,160 Speaker 4: Res Med a big maker of CPAP machines that's bounced 166 00:07:59,160 --> 00:08:01,200 Speaker 4: a little bit over the last few months. Those still 167 00:08:01,240 --> 00:08:02,640 Speaker 4: down from where it was a year ago. 168 00:08:02,920 --> 00:08:03,080 Speaker 2: You know. 169 00:08:03,160 --> 00:08:05,600 Speaker 4: I was walking back to the office from lunch today 170 00:08:05,640 --> 00:08:08,760 Speaker 4: and I saw a sign for weight Watchers about like 171 00:08:09,040 --> 00:08:12,640 Speaker 4: get on their GLP one program. So obviously they're trying 172 00:08:12,640 --> 00:08:15,280 Speaker 4: to like pivot to being a GLP one player, show 173 00:08:15,320 --> 00:08:17,600 Speaker 4: how they can fit in. But that's stock not doing 174 00:08:17,640 --> 00:08:17,960 Speaker 4: so well. 175 00:08:18,000 --> 00:08:19,920 Speaker 2: It's down. 176 00:08:20,680 --> 00:08:20,880 Speaker 1: Yeah. 177 00:08:22,440 --> 00:08:22,680 Speaker 2: Yeah. 178 00:08:22,840 --> 00:08:24,800 Speaker 3: Brown Trip went up like one hundred and fifty percent 179 00:08:24,840 --> 00:08:27,280 Speaker 3: then went all the way down. And the thing if 180 00:08:27,280 --> 00:08:29,400 Speaker 3: you're in the GLP one space at all and Eli 181 00:08:29,440 --> 00:08:32,280 Speaker 3: Lilly starts doing what you're doing, it's not great. 182 00:08:33,640 --> 00:08:35,200 Speaker 2: Tracy, Tracy. 183 00:08:35,240 --> 00:08:36,920 Speaker 4: I feel like investors, you know, they love to get 184 00:08:36,960 --> 00:08:39,000 Speaker 4: cute with things like I missed the Eli Lily trace 185 00:08:39,360 --> 00:08:41,440 Speaker 4: by this other company, and then you see, like you know, 186 00:08:41,520 --> 00:08:44,520 Speaker 4: in videos, the winners so far been the winner, Novo's 187 00:08:44,520 --> 00:08:47,000 Speaker 4: the winner Lily, and then all these other like secondary 188 00:08:47,080 --> 00:08:49,080 Speaker 4: tertiary plays. It's like kind of dice here. 189 00:08:49,400 --> 00:08:52,760 Speaker 1: Yeah, James, you mentioned something else that I think is 190 00:08:52,840 --> 00:08:54,880 Speaker 1: kind of like a big factor here, which is just 191 00:08:55,080 --> 00:08:57,600 Speaker 1: interest rates and the move we've seen there. And you 192 00:08:57,679 --> 00:09:01,960 Speaker 1: mentioned everyone being sort of enthusiastic about funding availability for 193 00:09:02,120 --> 00:09:05,520 Speaker 1: pharma going into twenty twenty four. How much is some 194 00:09:05,600 --> 00:09:09,480 Speaker 1: of this excitement just a pure rates play on the 195 00:09:09,520 --> 00:09:11,960 Speaker 1: cost of funding for you know, these companies that spend 196 00:09:11,960 --> 00:09:14,800 Speaker 1: a lot of money on research and development actually coming down. 197 00:09:15,200 --> 00:09:17,760 Speaker 3: I think it's like a rate of change thing, right. 198 00:09:18,080 --> 00:09:22,719 Speaker 3: It got so bad that even an incremental improvement is 199 00:09:22,760 --> 00:09:26,040 Speaker 3: going to be met with overwhelming kind of enthusiasm. Enthusiasm 200 00:09:26,200 --> 00:09:29,520 Speaker 3: because the regional bank stuff, you know, Silicon Valley Bank 201 00:09:29,559 --> 00:09:34,560 Speaker 3: collapsing hurt the biopharma and biotech industry significantly. And I 202 00:09:34,600 --> 00:09:36,960 Speaker 3: think that it's kind of been so long where rates 203 00:09:36,960 --> 00:09:39,120 Speaker 3: were just going up and everything's going to stay higher 204 00:09:39,160 --> 00:09:41,800 Speaker 3: for longer, and now you know, there's a glimmer of 205 00:09:41,840 --> 00:09:45,320 Speaker 3: hope on the horizon. And it's very easy if you're 206 00:09:45,360 --> 00:09:47,920 Speaker 3: in this industry and you've just been hearing no for 207 00:09:47,960 --> 00:09:51,360 Speaker 3: the past you know, year and a half, to say, oh, finally, you. 208 00:09:51,320 --> 00:10:06,920 Speaker 4: Know, wait, can we go back? I think you said 209 00:10:06,920 --> 00:10:09,360 Speaker 4: there were like two things in the GLP one space. 210 00:10:09,360 --> 00:10:11,640 Speaker 4: You mentioned auto injectors, and so I was curious what 211 00:10:11,679 --> 00:10:12,360 Speaker 4: the other one was. 212 00:10:12,760 --> 00:10:17,040 Speaker 3: The other thing is that we're kind of finding out, right, 213 00:10:17,080 --> 00:10:20,160 Speaker 3: I guess we already knew. But the thing with the 214 00:10:20,240 --> 00:10:23,640 Speaker 3: GLP one drugs is that they will make you lose weight, right, 215 00:10:23,720 --> 00:10:28,280 Speaker 3: and if you are morbidly obese, that's great. Now, the 216 00:10:28,400 --> 00:10:32,040 Speaker 3: problem is you are going to lose some muscle too. 217 00:10:32,160 --> 00:10:35,199 Speaker 3: Right when you say you lose weight, it's not necessarily 218 00:10:35,200 --> 00:10:37,920 Speaker 3: that all that weight is fat, and that can be 219 00:10:37,920 --> 00:10:40,600 Speaker 3: a problem just because kind of common sense, right, it's 220 00:10:40,600 --> 00:10:43,920 Speaker 3: better to have muscle mass. Muscle mass is great, especially 221 00:10:43,920 --> 00:10:48,160 Speaker 3: if you're diabetic, and also from like a cosmetic aesthetics perspective. 222 00:10:48,440 --> 00:10:51,640 Speaker 3: So now kind of the thing that everyone is focusing 223 00:10:51,679 --> 00:10:56,319 Speaker 3: on is finding a combination drug to be used alongside 224 00:10:56,480 --> 00:10:59,640 Speaker 3: these GLP WANs that makes it so that you either 225 00:10:59,679 --> 00:11:02,280 Speaker 3: do not lose muscle when you are losing fat, or 226 00:11:02,280 --> 00:11:05,440 Speaker 3: that you actually gain muscle. So there was some data 227 00:11:05,480 --> 00:11:09,160 Speaker 3: from Regeneron that was, in my opinion, just like everything 228 00:11:09,160 --> 00:11:13,080 Speaker 3: else that happens, very bullish. For Lily, they had an acquisition. 229 00:11:13,559 --> 00:11:18,040 Speaker 3: They bought Versatus for four billion dollars and Versatus has 230 00:11:18,040 --> 00:11:21,000 Speaker 3: a drug called the migramat and when you combine the 231 00:11:21,080 --> 00:11:22,319 Speaker 3: migramat with you. 232 00:11:22,240 --> 00:11:24,840 Speaker 2: Know, semaglue tide or another GLP one drug. 233 00:11:25,480 --> 00:11:28,240 Speaker 3: The phase one trial was showing that you will lose 234 00:11:28,440 --> 00:11:31,400 Speaker 3: twelve percent of your body weight, but you will also 235 00:11:31,520 --> 00:11:34,560 Speaker 3: increase your lean muscle mass by six percent, which is 236 00:11:34,920 --> 00:11:37,959 Speaker 3: a cheap code, right, Like there is nobody doesn't want 237 00:11:37,960 --> 00:11:39,640 Speaker 3: to lose ten percent of their body weight and then 238 00:11:39,679 --> 00:11:44,320 Speaker 3: also gain lean muscle mass. It's also very important with 239 00:11:44,400 --> 00:11:47,600 Speaker 3: like the elderly population, you know, because they if they 240 00:11:47,640 --> 00:11:51,479 Speaker 3: get weaker, then they're more at risk for falls or injuries, 241 00:11:51,520 --> 00:11:54,000 Speaker 3: and you know, making sure that like bone mineral density 242 00:11:54,040 --> 00:11:56,160 Speaker 3: stays high, so you know, but at the same time, 243 00:11:56,200 --> 00:11:58,120 Speaker 3: it's also if you make the drug that seems like 244 00:11:58,120 --> 00:11:58,959 Speaker 3: a money printer to me. 245 00:11:59,600 --> 00:12:03,560 Speaker 1: Well, so I think muscle building drugs already exist in 246 00:12:03,600 --> 00:12:06,600 Speaker 1: the form of anabolic steroids, although I doubt anyone's recommending 247 00:12:07,160 --> 00:12:09,079 Speaker 1: recommending that you go on a zempic and then take 248 00:12:09,120 --> 00:12:12,480 Speaker 1: steroids as well. But James, you mentioned at the very 249 00:12:12,480 --> 00:12:15,280 Speaker 1: beginning of this conversation that you saw something cool and 250 00:12:15,880 --> 00:12:18,920 Speaker 1: you sent me a little video of this and when 251 00:12:18,920 --> 00:12:20,520 Speaker 1: you send me the video, I had a sort of 252 00:12:20,679 --> 00:12:23,719 Speaker 1: realization that we are truly in the future in some 253 00:12:23,800 --> 00:12:26,439 Speaker 1: respects because you sent me a video of a presentation 254 00:12:27,280 --> 00:12:32,040 Speaker 1: of basically like an AI for drug development, so drug GPT, 255 00:12:32,640 --> 00:12:36,360 Speaker 1: and you recorded the video using your meta sunglasses. 256 00:12:36,640 --> 00:12:39,000 Speaker 3: I'm speaking to you guys right now on them. It 257 00:12:39,080 --> 00:12:43,360 Speaker 3: was definitely future coded for sure. I mean the thing was, 258 00:12:43,600 --> 00:12:47,600 Speaker 3: this was Recursion Pharma's presentation and theirs was at you know, 259 00:12:47,720 --> 00:12:50,280 Speaker 3: seven fifteen in the morning, so it wasn't very crowded, 260 00:12:50,320 --> 00:12:53,880 Speaker 3: and then Nvidio presented later, but Recursion was the one 261 00:12:53,880 --> 00:12:56,240 Speaker 3: that had the demo. They did it right in front 262 00:12:56,240 --> 00:12:58,520 Speaker 3: of you. It's like if you loaded up Chatgypt and 263 00:12:58,559 --> 00:13:01,480 Speaker 3: started asking you questions, but they where they It's called 264 00:13:01,559 --> 00:13:04,640 Speaker 3: low I call it drug GPT just because it's funny. 265 00:13:04,720 --> 00:13:09,480 Speaker 3: But they opened up this program and then started they said, 266 00:13:09,520 --> 00:13:11,880 Speaker 3: you know, give us a list of molecules that are 267 00:13:11,920 --> 00:13:16,000 Speaker 3: gonna target RAF one or and it just gave him 268 00:13:16,120 --> 00:13:19,000 Speaker 3: fifty new drugs and then they can refine it down 269 00:13:19,040 --> 00:13:21,680 Speaker 3: based on how likely it is to have this side 270 00:13:21,679 --> 00:13:24,040 Speaker 3: effect or that side effect, or where the structural activity 271 00:13:24,080 --> 00:13:28,080 Speaker 3: relationship is or you know, the dose response curve just 272 00:13:28,120 --> 00:13:30,679 Speaker 3: based on you know, this predictive. 273 00:13:30,360 --> 00:13:36,520 Speaker 2: AI and how do we know well, I mean, how 274 00:13:36,559 --> 00:13:37,760 Speaker 2: do we know that the problem? 275 00:13:37,840 --> 00:13:39,640 Speaker 4: Yeah, I mean like so like they like they give 276 00:13:39,679 --> 00:13:42,200 Speaker 4: you some molecules and stuff, but like, how does anyone 277 00:13:42,240 --> 00:13:45,040 Speaker 4: know that ultimately this is where does it go from there? 278 00:13:45,160 --> 00:13:47,760 Speaker 4: What's the evidence that this is uh, you know, produce 279 00:13:47,760 --> 00:13:51,200 Speaker 4: as drugs or find new combinations of molecules faster than 280 00:13:51,280 --> 00:13:52,120 Speaker 4: traditional routes. 281 00:13:53,360 --> 00:13:56,000 Speaker 2: Well, when he was doing it, yeah, kind of in 282 00:13:56,080 --> 00:13:57,960 Speaker 2: the audience, you have to assume that there are some 283 00:13:58,000 --> 00:14:01,560 Speaker 2: people that know biochemistry, and they got. 284 00:14:01,400 --> 00:14:04,080 Speaker 3: Into some stuff that you know, I couldn't really follow, 285 00:14:04,120 --> 00:14:06,320 Speaker 3: but he was talking about how, oh you know, if 286 00:14:06,440 --> 00:14:08,560 Speaker 3: there are any chemists in the audience, you'll recognize this 287 00:14:08,640 --> 00:14:11,960 Speaker 3: drug looks a lot like this drug. And basically they 288 00:14:12,559 --> 00:14:15,440 Speaker 3: went through the whole I mean, the thing is I 289 00:14:15,520 --> 00:14:17,320 Speaker 3: dropped out of at school, right, so, like I could 290 00:14:17,360 --> 00:14:21,520 Speaker 3: find very limited amount of time. But I think the 291 00:14:21,560 --> 00:14:24,440 Speaker 3: main point that they were getting across was that in 292 00:14:24,480 --> 00:14:28,800 Speaker 3: biotech and biopharmat they spend so much time and money 293 00:14:29,240 --> 00:14:32,640 Speaker 3: on things that might just not work. And if you 294 00:14:32,720 --> 00:14:36,360 Speaker 3: have a program that can basically make you faster to fail. 295 00:14:36,320 --> 00:14:37,880 Speaker 2: Right, where you're. 296 00:14:38,160 --> 00:14:40,720 Speaker 3: Kind of figuring out what's going to be a waste 297 00:14:40,720 --> 00:14:43,000 Speaker 3: of time and what might not be more effectively. 298 00:14:43,200 --> 00:14:44,800 Speaker 2: That's huge, right. 299 00:14:44,920 --> 00:14:47,280 Speaker 3: Maybe maybe it's not that this is going to find 300 00:14:47,480 --> 00:14:50,360 Speaker 3: the cure for cancer, but the company that you know 301 00:14:50,680 --> 00:14:53,360 Speaker 3: eventually does find the cure for cancer, they're going to 302 00:14:53,400 --> 00:14:55,080 Speaker 3: be a lot more efficient with it because they're not 303 00:14:55,120 --> 00:14:57,160 Speaker 3: going to be wasting their time on stuff that you know, 304 00:14:57,200 --> 00:14:59,480 Speaker 3: maybe this program can tell them obviously won't work. 305 00:15:00,040 --> 00:15:03,080 Speaker 1: Often are people mentioning AI in general at that conference? 306 00:15:03,120 --> 00:15:05,760 Speaker 1: I'm curious if it's sort of like infiltrating to the 307 00:15:05,800 --> 00:15:10,080 Speaker 1: degree that it seems to be in financials conferences for instance. 308 00:15:10,520 --> 00:15:12,440 Speaker 3: Oh yeah, it was a ton I mean, you know, 309 00:15:12,680 --> 00:15:15,560 Speaker 3: Lily didn't mention AI. Nova didn't mention AI. They had 310 00:15:15,640 --> 00:15:18,120 Speaker 3: their own thing. But then you know you had Sonofi 311 00:15:18,440 --> 00:15:21,560 Speaker 3: and Jen both talking about their new AI platforms, and 312 00:15:21,600 --> 00:15:24,560 Speaker 3: then Video was here. Right then Video presented here and 313 00:15:24,960 --> 00:15:28,880 Speaker 3: had a whole thing about their predictive analytics platform and 314 00:15:29,000 --> 00:15:32,520 Speaker 3: Boonemo and it was I mean, you guys should have 315 00:15:32,600 --> 00:15:38,600 Speaker 3: seen when the presenter from Nvidia concluded her speech, it 316 00:15:38,680 --> 00:15:41,640 Speaker 3: was like Night of the Walking Debt. Literally, the entire 317 00:15:41,680 --> 00:15:44,440 Speaker 3: audience just like shambling up, like just trying to get 318 00:15:44,480 --> 00:15:46,600 Speaker 3: a little bit of the in Vidia magic on speak. 319 00:15:48,840 --> 00:15:51,720 Speaker 1: Oh, that's always the most awkward moment at conferences when 320 00:15:51,720 --> 00:15:54,040 Speaker 1: the speakers get off stage and everyone kind of like 321 00:15:54,120 --> 00:15:57,600 Speaker 1: awkwardly stands around waiting for their chance to introduce themselves 322 00:15:57,640 --> 00:15:58,600 Speaker 1: in network. 323 00:15:58,320 --> 00:16:02,600 Speaker 4: Hand them MACR about their company. Yes, totally. 324 00:16:02,880 --> 00:16:04,760 Speaker 2: Yeah, I was worried it was going to get violent, 325 00:16:04,800 --> 00:16:04,880 Speaker 2: you know. 326 00:16:05,040 --> 00:16:08,160 Speaker 3: And like the other thing, the ratio is at these conferences, 327 00:16:08,280 --> 00:16:10,600 Speaker 3: like finance conferences not great and like this is like 328 00:16:10,640 --> 00:16:13,480 Speaker 3: a woman with ninety men and two women just send 329 00:16:13,720 --> 00:16:16,920 Speaker 3: That was like it was like not the coolest scene. 330 00:16:17,760 --> 00:16:20,160 Speaker 4: By the way, Tracy, you know what stock is at 331 00:16:20,360 --> 00:16:23,000 Speaker 4: basically the same level it was ten years ago. 332 00:16:23,480 --> 00:16:25,360 Speaker 1: I have no idea Pfizer. 333 00:16:26,080 --> 00:16:28,800 Speaker 4: Oh that's one company that my understanding is is not 334 00:16:29,040 --> 00:16:31,760 Speaker 4: in the game at all. On GOLP one. Of course, 335 00:16:31,760 --> 00:16:34,160 Speaker 4: it went nuts in twenty twenty one. Yeah, the hot 336 00:16:34,240 --> 00:16:38,080 Speaker 4: thing was COVID injections. But obviously I think many fewer 337 00:16:38,160 --> 00:16:41,200 Speaker 4: people these days are getting boosters than may people may 338 00:16:41,240 --> 00:16:43,720 Speaker 4: have expected two years ago. And that stock is literally 339 00:16:43,720 --> 00:16:45,600 Speaker 4: where it was. No, maybe it's paid dividend and stuff 340 00:16:45,600 --> 00:16:48,160 Speaker 4: since then. That's not literally where it was in twenty fourteen, 341 00:16:48,840 --> 00:16:50,920 Speaker 4: So don't miss the golp one. 342 00:16:51,640 --> 00:16:55,600 Speaker 2: Pfizer had an oral g g golp one and you know, 343 00:16:55,680 --> 00:16:57,440 Speaker 2: ditched it. The side effects were just too bad. 344 00:16:57,480 --> 00:16:59,440 Speaker 3: And that's that's gonna be like a serious risk to 345 00:16:59,440 --> 00:17:02,880 Speaker 3: look out for, right because you know, with the oral 346 00:17:02,920 --> 00:17:05,960 Speaker 3: preparations of these like peptides or like any really like 347 00:17:06,080 --> 00:17:09,280 Speaker 3: endocrine drug, there's always it seems at least, you know, 348 00:17:09,320 --> 00:17:10,639 Speaker 3: I don't have science to back this up, but it 349 00:17:10,640 --> 00:17:12,520 Speaker 3: seems like when you have an endocrine drug and you're 350 00:17:13,040 --> 00:17:15,399 Speaker 3: trying to turn it into an oral instead of you know, 351 00:17:15,400 --> 00:17:19,080 Speaker 3: an injectable, the side effects get really crazy. And that's 352 00:17:19,119 --> 00:17:23,000 Speaker 3: definitely I think the biggest risk to like lily Novo 353 00:17:23,480 --> 00:17:26,560 Speaker 3: would be one of them gets the oral and the 354 00:17:26,640 --> 00:17:29,680 Speaker 3: other one has side effects that are way too bad, 355 00:17:29,920 --> 00:17:32,600 Speaker 3: right because, like I said in the beginning, right that 356 00:17:32,680 --> 00:17:35,160 Speaker 3: the oral drug will make it so that they can 357 00:17:35,200 --> 00:17:38,280 Speaker 3: take advantage of this huge demand right there. They're constrained 358 00:17:38,280 --> 00:17:40,320 Speaker 3: by supply, but the supply. 359 00:17:40,080 --> 00:17:43,280 Speaker 2: Of the oral drug would you know, kind of alleviate that. 360 00:17:43,480 --> 00:17:45,480 Speaker 3: And if one of them has it then the other 361 00:17:45,520 --> 00:17:48,040 Speaker 3: one doesn't, that could really change the landscape. 362 00:17:48,440 --> 00:17:51,919 Speaker 1: So James, in addition to bringing us dispatches from the 363 00:17:51,960 --> 00:17:55,199 Speaker 1: JPM healthcare conference and being our sort of eyes on 364 00:17:55,240 --> 00:17:58,440 Speaker 1: the ground or sunglasses on the ground. Maybe what else 365 00:17:58,440 --> 00:17:59,960 Speaker 1: are you going to do in San Francisco? Like, who 366 00:18:00,000 --> 00:18:01,320 Speaker 1: are you talking to while you're there? 367 00:18:02,119 --> 00:18:02,320 Speaker 2: Oh? 368 00:18:02,359 --> 00:18:05,199 Speaker 3: You know, I I message all the people that I 369 00:18:05,280 --> 00:18:09,320 Speaker 3: know that have a kind of venture capital healthcare vibe. 370 00:18:09,359 --> 00:18:13,159 Speaker 3: And I'm not really super involved, Like I just like 371 00:18:13,240 --> 00:18:16,520 Speaker 3: trade and you know, I'm in my own office and 372 00:18:16,520 --> 00:18:18,960 Speaker 3: trading and I'm not super linked up with all the 373 00:18:18,960 --> 00:18:20,840 Speaker 3: finance people. So I didn't get invited to any of 374 00:18:20,920 --> 00:18:23,400 Speaker 3: the like events or anything. But you know, I can 375 00:18:23,440 --> 00:18:25,800 Speaker 3: still have a good time in San Francisco myself. 376 00:18:26,680 --> 00:18:28,879 Speaker 1: James, I think that's it. Oh yeah, go ahead. 377 00:18:29,480 --> 00:18:32,560 Speaker 2: I just want to say, Joe, well you are a 378 00:18:32,560 --> 00:18:33,440 Speaker 2: really musician. 379 00:18:34,080 --> 00:18:37,200 Speaker 4: Finally, finally a little respector on here. No, I appreciate 380 00:18:37,240 --> 00:18:40,400 Speaker 4: that I met James in person at our concert in December. 381 00:18:41,040 --> 00:18:42,679 Speaker 4: Very kind of you to say, on are thank you 382 00:18:42,680 --> 00:18:43,040 Speaker 4: so much. 383 00:18:43,840 --> 00:18:46,159 Speaker 2: I was surprised. Was I kind of expected it was 384 00:18:46,160 --> 00:18:47,080 Speaker 2: a joke, but you are. 385 00:18:48,640 --> 00:18:51,480 Speaker 4: Very very nice of you to say. Great, let's leave 386 00:18:51,520 --> 00:18:53,000 Speaker 4: it here. This is a great place to end it 387 00:18:53,040 --> 00:18:54,920 Speaker 4: on this note I love let's leave it here. There's 388 00:18:54,920 --> 00:18:56,560 Speaker 4: a great place to end. He kind of broke up 389 00:18:56,560 --> 00:18:57,560 Speaker 4: there at the end, so I don't know if we 390 00:18:57,640 --> 00:18:59,720 Speaker 4: got that on audio. I think I heard it. 391 00:19:00,960 --> 00:19:02,040 Speaker 2: I heard it, I heard it. 392 00:19:06,640 --> 00:19:09,760 Speaker 4: Lots More is produced by Carmen Rodriguez and Dashel Bennett, 393 00:19:09,800 --> 00:19:12,920 Speaker 4: with help from Moses Ondam and kil Brooks. Our sound 394 00:19:13,000 --> 00:19:15,760 Speaker 4: engineer is Blake Maples. Sage Bauman is the head of 395 00:19:15,760 --> 00:19:16,920 Speaker 4: Bloomberg Podcasts. 396 00:19:17,160 --> 00:19:20,320 Speaker 1: Please rate, review, and subscribe to Odd, Lots and Lots 397 00:19:20,320 --> 00:19:24,080 Speaker 1: More on your favorite podcast platforms, and remember that Bloomberg 398 00:19:24,080 --> 00:19:27,160 Speaker 1: subscribers can listen to all our podcasts ad free by 399 00:19:27,240 --> 00:19:34,160 Speaker 1: connecting through Apple Podcasts. Thanks for listening. It's drug GPT 400 00:19:34,440 --> 00:19:35,360 Speaker 1: Joe Oh