1 00:00:00,360 --> 00:00:02,400 Speaker 1: Hey, Odd Loots listeners, We're coming to DC. 2 00:00:02,840 --> 00:00:04,840 Speaker 2: We're finally doing it, Joe. It's going to be our 3 00:00:04,840 --> 00:00:09,200 Speaker 2: first live show in Washington, DC, our nation's capital. It's 4 00:00:09,240 --> 00:00:12,440 Speaker 2: also finally going to be the time where we actually 5 00:00:12,520 --> 00:00:13,640 Speaker 2: talk about the Jones Act. 6 00:00:13,880 --> 00:00:17,160 Speaker 1: Listen talk about doing the Jones Act episode of Odd 7 00:00:17,200 --> 00:00:19,880 Speaker 1: Lots for a long time, and it's become this recurring 8 00:00:19,920 --> 00:00:21,880 Speaker 1: joke that we've never done on But we're going to 9 00:00:21,920 --> 00:00:24,599 Speaker 1: do it in grand style because we're going to be 10 00:00:24,600 --> 00:00:27,200 Speaker 1: doing it live in DC and it's actually going to 11 00:00:27,240 --> 00:00:27,840 Speaker 1: be a debate. 12 00:00:28,240 --> 00:00:28,520 Speaker 3: Yeah. 13 00:00:28,640 --> 00:00:33,120 Speaker 2: So we have Sarah Fuentes from the Transportation Institute. She's 14 00:00:33,159 --> 00:00:35,080 Speaker 2: going to be taking the pro side, and we also 15 00:00:35,159 --> 00:00:38,600 Speaker 2: have Colin graybou of the Cato Institute. He'll be taking 16 00:00:38,880 --> 00:00:42,000 Speaker 2: the against side. It's going to be really interesting to 17 00:00:42,040 --> 00:00:43,880 Speaker 2: see how all of that shakes out. 18 00:00:43,960 --> 00:00:46,519 Speaker 1: In addition to that, we're going to be speaking with 19 00:00:46,720 --> 00:00:50,080 Speaker 1: Blair Levin, who was around during the telecom bubble, and 20 00:00:50,400 --> 00:00:53,440 Speaker 1: we have Andrew Ferguson, the new head of the FTC, 21 00:00:53,560 --> 00:00:55,320 Speaker 1: the one who's replaced Lina Kong. We're going to be 22 00:00:55,320 --> 00:00:57,920 Speaker 1: talking about mergers and acquisitions and all that stuff. So 23 00:00:58,240 --> 00:00:59,320 Speaker 1: it should be a really fun night. 24 00:00:59,520 --> 00:01:02,120 Speaker 2: If you want to come and join us for that evening, 25 00:01:02,240 --> 00:01:05,160 Speaker 2: it's going to be on March twelfth at the Miracle Theater. 26 00:01:05,440 --> 00:01:08,399 Speaker 2: Go to Bloomberg dot com forward slash odd Lots and 27 00:01:08,440 --> 00:01:10,839 Speaker 2: you can find the link to purchase tickets. We hope 28 00:01:10,840 --> 00:01:11,440 Speaker 2: to see you there. 29 00:01:14,120 --> 00:01:29,319 Speaker 4: Bloomberg Audio Studios, Podcasts, Radio News. 30 00:01:29,600 --> 00:01:33,480 Speaker 1: Hello and welcome to another episode of the Odd Lots podcast. 31 00:01:33,640 --> 00:01:36,120 Speaker 2: I'm Jio Wisenthal and I'm Tracy Alloway. 32 00:01:36,560 --> 00:01:39,280 Speaker 1: Tracy, you know, we talk a lot about China, talk 33 00:01:39,400 --> 00:01:44,640 Speaker 1: about cars and batteries. It's time for a Chinese biotech. 34 00:01:44,840 --> 00:01:46,640 Speaker 1: It's time for the Chinese pharma episode. 35 00:01:47,000 --> 00:01:47,160 Speaker 4: Joe. 36 00:01:47,240 --> 00:01:50,480 Speaker 2: It's just what I always wanted. Thank you so much. No, 37 00:01:50,640 --> 00:01:54,200 Speaker 2: I am genuinely excited to talk about this. One reason 38 00:01:54,440 --> 00:01:56,880 Speaker 2: is because this is a sector that I don't really 39 00:01:56,960 --> 00:02:00,160 Speaker 2: know that much about. Another reason is it has it's 40 00:02:00,160 --> 00:02:02,560 Speaker 2: actually been in the news quite a bit recently with 41 00:02:02,640 --> 00:02:06,640 Speaker 2: the cuts to NIH funding, which we've discussed and things 42 00:02:06,680 --> 00:02:09,720 Speaker 2: like that, and then more generally, it sort of sits 43 00:02:09,760 --> 00:02:15,000 Speaker 2: in that nexus of policy aimed at boosting specific sectors 44 00:02:15,560 --> 00:02:18,840 Speaker 2: and also competition between the US and China. 45 00:02:19,560 --> 00:02:21,600 Speaker 1: That's exactly right, and it's like you know, we've just 46 00:02:21,639 --> 00:02:24,520 Speaker 1: gotten used to the fact that in many areas of 47 00:02:24,600 --> 00:02:28,960 Speaker 1: sort of physical manufacturing, there are very there are many 48 00:02:29,080 --> 00:02:33,360 Speaker 1: industries in which China can compete and produce things either 49 00:02:34,400 --> 00:02:37,280 Speaker 1: cheaper and higher quality. It seems like many areas relate 50 00:02:37,360 --> 00:02:40,720 Speaker 1: to batteries and automobiles and all kinds of stuff like that. 51 00:02:40,760 --> 00:02:42,640 Speaker 1: We know that. And then of course we had like 52 00:02:42,680 --> 00:02:44,560 Speaker 1: the deep seek moment and a bunch of people like, oh, 53 00:02:44,600 --> 00:02:48,000 Speaker 1: it's not just physical things, it's not just gigantic plants. 54 00:02:48,480 --> 00:02:51,960 Speaker 1: Also a lot of competition in areas like software, particularly 55 00:02:52,040 --> 00:02:56,400 Speaker 1: artificial intelligence. That raised all sorts of questions. And then 56 00:02:56,520 --> 00:02:59,760 Speaker 1: lately the drum is beating that we have to take 57 00:02:59,880 --> 00:03:03,720 Speaker 1: very very seriously pharma and biotech, And this is one 58 00:03:03,760 --> 00:03:07,000 Speaker 1: of those areas that I think most people, certainly me 59 00:03:07,120 --> 00:03:09,880 Speaker 1: would say like in the year twenty twenty five. Still 60 00:03:10,680 --> 00:03:13,680 Speaker 1: my conception my head is that the cutting edge is 61 00:03:13,720 --> 00:03:16,760 Speaker 1: in the US and Europe still, which I can't say 62 00:03:16,760 --> 00:03:18,079 Speaker 1: that for a lot of industries. 63 00:03:18,080 --> 00:03:20,639 Speaker 2: At this point, I want to know how medicines are 64 00:03:20,680 --> 00:03:24,760 Speaker 2: actually made manufactured. I've read a long time ago. I 65 00:03:24,800 --> 00:03:29,120 Speaker 2: read a book about the Twinkie, and it broke down 66 00:03:29,360 --> 00:03:32,440 Speaker 2: every ingredient that went into a twinkie and where it 67 00:03:32,520 --> 00:03:35,400 Speaker 2: came from. And it was really interesting because it turns 68 00:03:35,400 --> 00:03:37,760 Speaker 2: out a lot of those ingredients came from China. I 69 00:03:37,760 --> 00:03:38,280 Speaker 2: didn't know that. 70 00:03:38,360 --> 00:03:40,440 Speaker 1: I didn't know that either, but you know what I did, 71 00:03:40,640 --> 00:03:44,040 Speaker 1: were didn't know because we briefly touched on it in 72 00:03:44,080 --> 00:03:47,880 Speaker 1: our recent episode with the two fellows from Goldman Sax 73 00:03:48,160 --> 00:03:53,000 Speaker 1: about China's role in the pharmaceutical supply chain providing key 74 00:03:53,160 --> 00:03:55,920 Speaker 1: ingredients to India, which then plays a key role. Anyway, 75 00:03:55,960 --> 00:03:58,080 Speaker 1: there's a lot I want to know. I don't think 76 00:03:58,120 --> 00:04:00,560 Speaker 1: I know anything, and I just want to jump into 77 00:04:00,600 --> 00:04:03,720 Speaker 1: this episode because I want to learn more. In that spirit, 78 00:04:03,800 --> 00:04:06,400 Speaker 1: we really do have the perfect guest. He's someone who 79 00:04:06,440 --> 00:04:08,560 Speaker 1: recently put together a slide deck and I kind of 80 00:04:08,560 --> 00:04:11,960 Speaker 1: think that slide deck catalyzed some articles. The deep sea 81 00:04:12,040 --> 00:04:15,200 Speaker 1: moment in biotech got a lot of attention on social media. 82 00:04:15,440 --> 00:04:18,280 Speaker 1: Drowing straight to the source, we're speaking with Tim Oppler. 83 00:04:18,320 --> 00:04:21,679 Speaker 1: He's a managing director in the healthcare investment banking group 84 00:04:21,720 --> 00:04:24,680 Speaker 1: at Stefel. Tim, thank you so much for coming on 85 00:04:24,720 --> 00:04:25,279 Speaker 1: odd Locks. 86 00:04:25,720 --> 00:04:28,719 Speaker 3: Thank you and Joe and Tracy. I really appreciate you 87 00:04:28,800 --> 00:04:30,680 Speaker 3: having me. I'm very excited to be here today. 88 00:04:31,080 --> 00:04:34,000 Speaker 1: What is a managing director in the healthcare investment banking 89 00:04:34,040 --> 00:04:34,960 Speaker 1: group at Stevefoild. 90 00:04:35,040 --> 00:04:37,320 Speaker 3: I don't actually manage a lot of people, so managing 91 00:04:37,360 --> 00:04:40,400 Speaker 3: directors just the title. But basically, I'm a senior banker, 92 00:04:40,440 --> 00:04:44,000 Speaker 3: and investment bankers are in the business of putting people together, 93 00:04:44,040 --> 00:04:46,280 Speaker 3: people that need money with people that have money, people 94 00:04:46,360 --> 00:04:48,520 Speaker 3: that want to license something out, with people that want 95 00:04:48,520 --> 00:04:51,479 Speaker 3: to license something in. So I'm a middleman basically and 96 00:04:51,520 --> 00:04:53,039 Speaker 3: get paid commissions for doing it. 97 00:04:53,120 --> 00:04:57,960 Speaker 2: With your middleman position, could you maybe describe the ecosystem 98 00:04:58,240 --> 00:05:01,000 Speaker 2: of getting new drugs to market, like where does it 99 00:05:01,120 --> 00:05:04,760 Speaker 2: tend to start, what corporate entities does it go through, 100 00:05:05,080 --> 00:05:09,200 Speaker 2: and then what's the process from there to getting into 101 00:05:09,240 --> 00:05:11,000 Speaker 2: an actual physical medicine. 102 00:05:11,480 --> 00:05:14,280 Speaker 3: So great question. So you know, back in the old days, 103 00:05:14,440 --> 00:05:17,680 Speaker 3: if you rolled back the clock fifty years ago, large 104 00:05:17,680 --> 00:05:23,279 Speaker 3: pharmaceutical companies Merck, Pfizer, Eli Lilly had these research and 105 00:05:23,279 --> 00:05:26,080 Speaker 3: development groups and they would sit around and read up 106 00:05:26,200 --> 00:05:29,080 Speaker 3: articles and do their own basic science and say, you know, 107 00:05:29,680 --> 00:05:31,880 Speaker 3: I think we should do something to go after such 108 00:05:31,880 --> 00:05:33,919 Speaker 3: and such type of virus. They would work on it 109 00:05:33,920 --> 00:05:35,400 Speaker 3: for five or six years. They would come up with 110 00:05:35,440 --> 00:05:38,159 Speaker 3: a drug candidate, they would go test it in people, 111 00:05:38,600 --> 00:05:40,920 Speaker 3: it would work hopefully, and then they would get it 112 00:05:40,960 --> 00:05:43,960 Speaker 3: approved and then they go out and market it. Things 113 00:05:43,960 --> 00:05:47,039 Speaker 3: started to change, you may remember, you know, as back 114 00:05:47,080 --> 00:05:49,919 Speaker 3: as the late nineteen seventies, companies like genen Tech and 115 00:05:49,920 --> 00:05:53,359 Speaker 3: Biogen came on the scene. And so today we have 116 00:05:53,440 --> 00:05:56,400 Speaker 3: a huge biotech industry. These are kind of like the 117 00:05:56,920 --> 00:05:59,799 Speaker 3: you might call them the farm league, a big pharma 118 00:06:00,080 --> 00:06:02,560 Speaker 3: are the ones that come up with the new drugs. 119 00:06:03,120 --> 00:06:05,200 Speaker 3: Of course, the farmers are still doing their own work too, 120 00:06:05,920 --> 00:06:08,840 Speaker 3: and so biotech's become a huge part of our ecosystem. 121 00:06:08,880 --> 00:06:11,000 Speaker 3: It's also become a big part of the capital market. 122 00:06:11,080 --> 00:06:14,160 Speaker 3: So there's whole groups of people that you know, god 123 00:06:14,279 --> 00:06:17,360 Speaker 3: mds and PhDs that went to work for funds and 124 00:06:17,400 --> 00:06:20,320 Speaker 3: they sit there, you know, does this drug candidate look 125 00:06:20,400 --> 00:06:21,960 Speaker 3: like it's gonna make it? I'm going to bet for 126 00:06:22,040 --> 00:06:23,440 Speaker 3: it or I'm going to bet against it. 127 00:06:23,680 --> 00:06:24,800 Speaker 1: What does biotech mean? 128 00:06:25,720 --> 00:06:25,840 Speaker 2: Hey? 129 00:06:25,880 --> 00:06:28,279 Speaker 1: Sometimes ask Tracy what fintech means, and I still don't 130 00:06:28,279 --> 00:06:29,000 Speaker 1: know the answer to that. 131 00:06:29,279 --> 00:06:31,200 Speaker 2: But what is biotech touch banking? 132 00:06:32,080 --> 00:06:35,680 Speaker 3: So I'd like to give three different answers. First of all, 133 00:06:35,680 --> 00:06:38,039 Speaker 3: when people say biotech in general, what they mean is 134 00:06:38,120 --> 00:06:40,679 Speaker 3: kind of the more high tech part of the pharmaceutical industry, 135 00:06:40,680 --> 00:06:41,360 Speaker 3: the cool part. 136 00:06:41,560 --> 00:06:43,680 Speaker 1: Yeah, that's always I just figure it's a cool. 137 00:06:44,320 --> 00:06:47,800 Speaker 3: What I mean by biotech is the when you have 138 00:06:47,880 --> 00:06:51,080 Speaker 3: a company whose sole asset is a drug candidate that 139 00:06:51,160 --> 00:06:54,040 Speaker 3: has not yet been approved by the FD so pre commercial. 140 00:06:54,480 --> 00:06:57,560 Speaker 3: When I say biotech, that's what I mean. Other people 141 00:06:58,520 --> 00:07:01,479 Speaker 3: think it refers to biologic, and it's true. The original 142 00:07:01,480 --> 00:07:05,200 Speaker 3: biotechs like Genetech were focused on biologic so it's understandable 143 00:07:05,279 --> 00:07:06,920 Speaker 3: that some people would associate biotech. 144 00:07:07,680 --> 00:07:10,920 Speaker 1: It's a distinct type of therapy from the traditional type 145 00:07:10,960 --> 00:07:13,320 Speaker 1: of medicine that would have been developed at America. 146 00:07:13,720 --> 00:07:17,120 Speaker 3: That's correct. So traditionally there are two types of medicines. 147 00:07:17,160 --> 00:07:20,040 Speaker 3: There's small molecules, those little white pills some people take 148 00:07:20,120 --> 00:07:24,320 Speaker 3: every day, and then there are injectable biologics. Those are 149 00:07:24,480 --> 00:07:28,360 Speaker 3: products that are much more complex, much larger molecules, and 150 00:07:28,400 --> 00:07:29,640 Speaker 3: are made in very different ways. 151 00:07:30,280 --> 00:07:32,559 Speaker 2: Can you talk about that, going right to my question 152 00:07:32,640 --> 00:07:36,720 Speaker 2: about how medicine is actually made, how Yeah. 153 00:07:36,520 --> 00:07:39,480 Speaker 3: So for a small molecule, it's actually a chemical. So 154 00:07:39,960 --> 00:07:43,760 Speaker 3: the pharmaceutical industry actually came out of the chemical industry. 155 00:07:43,760 --> 00:07:46,440 Speaker 3: So if you go back to the history of pharmaceuticals, say, 156 00:07:46,480 --> 00:07:50,480 Speaker 3: like what was going on in sixteen fifty, Well, people 157 00:07:50,480 --> 00:07:53,880 Speaker 3: were literally chemists. In fact, still in England today you 158 00:07:53,920 --> 00:07:56,000 Speaker 3: can walk into what we would call it pharmacy, they 159 00:07:56,040 --> 00:07:58,560 Speaker 3: call it a chemist and they would literally, you know, 160 00:07:58,600 --> 00:08:02,000 Speaker 3: put together your antimony or whatever it was and serve 161 00:08:02,040 --> 00:08:04,840 Speaker 3: it up to you. So that still goes on. But 162 00:08:04,880 --> 00:08:07,880 Speaker 3: of course those small molecule pills are made in giant 163 00:08:07,960 --> 00:08:10,920 Speaker 3: factories of what's called API, which is really just fine chemical. 164 00:08:11,720 --> 00:08:14,640 Speaker 3: The other side of the industry, though, these biologics are 165 00:08:14,720 --> 00:08:18,880 Speaker 3: made typically in bugs. So you would take, for example, yeasts, 166 00:08:19,360 --> 00:08:22,320 Speaker 3: or you might take E. Coli, or you might take 167 00:08:22,360 --> 00:08:25,320 Speaker 3: what are called Chose cells. Those are Chinese hamster ovary cells. 168 00:08:25,440 --> 00:08:27,400 Speaker 3: Why do they use them because they're really good at 169 00:08:27,440 --> 00:08:32,200 Speaker 3: growing biologics. And you insert a piece of DNA into 170 00:08:32,240 --> 00:08:35,920 Speaker 3: the DNA of that species and then that causes that 171 00:08:36,000 --> 00:08:40,440 Speaker 3: species to manufacture the protein of interest, and that's a 172 00:08:40,440 --> 00:08:43,560 Speaker 3: whole other industry. And then those say Chose cells or E. 173 00:08:43,640 --> 00:08:46,200 Speaker 3: Coli cells or whatever they are, they're grown in these 174 00:08:46,240 --> 00:08:49,160 Speaker 3: giant tanks, and so you might have like a forty 175 00:08:49,200 --> 00:08:52,720 Speaker 3: thousand liter tank full of growth medium and those cells 176 00:08:52,760 --> 00:08:55,160 Speaker 3: are just swimming around and making their proteins. Then they're 177 00:08:55,200 --> 00:08:57,520 Speaker 3: harvested and you pull out the protein of interest. 178 00:08:57,960 --> 00:09:01,320 Speaker 2: I'm sorry, did you say Chinese have overy cells? 179 00:09:01,400 --> 00:09:05,720 Speaker 3: I know, and we're talking about China. See, China is everywhere, Tracey. 180 00:09:05,760 --> 00:09:07,720 Speaker 1: I already feel like we're gonna have to have Tim 181 00:09:07,840 --> 00:09:10,360 Speaker 1: back already, right, because like this is already one of 182 00:09:10,360 --> 00:09:13,400 Speaker 1: those topics where like we probably could just talk about 183 00:09:13,440 --> 00:09:16,920 Speaker 1: one niche aspect of the supply chain for some ingredient 184 00:09:17,160 --> 00:09:19,240 Speaker 1: because we're not even actually close to getting to it. 185 00:09:19,320 --> 00:09:21,280 Speaker 1: But we need to build up to we need to 186 00:09:21,320 --> 00:09:22,320 Speaker 1: build up to Chinese. 187 00:09:22,360 --> 00:09:24,120 Speaker 3: Well, let's let's just jump right into it. I want 188 00:09:24,120 --> 00:09:25,320 Speaker 3: to make sure, I want I want to make a 189 00:09:25,360 --> 00:09:30,400 Speaker 3: comment here. Okay, So China is all of a sudden 190 00:09:30,559 --> 00:09:34,559 Speaker 3: starting to be very competitive with the US biotech ecosystem. 191 00:09:34,840 --> 00:09:37,559 Speaker 3: I personally don't think that's a surprise. I don't think 192 00:09:37,600 --> 00:09:39,600 Speaker 3: that's the bad thing. And here here's what's going on. 193 00:09:40,160 --> 00:09:42,320 Speaker 3: We developed the first biologics in the United States in 194 00:09:42,320 --> 00:09:44,760 Speaker 3: the nineteen seventies. Well it's twenty twenty five, right, that 195 00:09:44,840 --> 00:09:47,840 Speaker 3: was fifty years ago. Yeah, I mean you would think 196 00:09:48,679 --> 00:09:50,840 Speaker 3: that the know how of how to make those things 197 00:09:50,960 --> 00:09:54,679 Speaker 3: is spread around, and it has. And so what happened was, 198 00:09:54,920 --> 00:09:59,959 Speaker 3: you know, in the nineteen nineties, two thousands armies of China, 199 00:10:00,040 --> 00:10:02,679 Speaker 3: these people came to the United States for jobs inside 200 00:10:02,679 --> 00:10:06,480 Speaker 3: all those companies, and they learned, not surprisingly, how to 201 00:10:06,520 --> 00:10:09,480 Speaker 3: make what was being made then, which was biologics. You know, 202 00:10:10,960 --> 00:10:12,960 Speaker 3: I don't want to call it racism. I think that's 203 00:10:12,960 --> 00:10:15,960 Speaker 3: probably unfair. But for whatever reason, a lot of these 204 00:10:16,080 --> 00:10:20,120 Speaker 3: Chinese personnel weren't promoted. They didn't become the SVP at 205 00:10:20,160 --> 00:10:24,040 Speaker 3: some big shot US biotech company. You know, they were 206 00:10:24,080 --> 00:10:26,880 Speaker 3: stuck in a director job, and they got frustrated and 207 00:10:26,960 --> 00:10:30,120 Speaker 3: left and went home to China. Now here we are 208 00:10:30,160 --> 00:10:33,360 Speaker 3: twenty twenty five, and they're crawling all over us like 209 00:10:33,679 --> 00:10:35,360 Speaker 3: they know how to do exactly what we know how 210 00:10:35,400 --> 00:10:38,320 Speaker 3: to do, and guess what, just like in batteries, just 211 00:10:38,360 --> 00:10:41,120 Speaker 3: like in telephones and these other sectors, they're pretty good 212 00:10:41,160 --> 00:10:44,080 Speaker 3: at it. And so all of a sudden, US biotech 213 00:10:44,120 --> 00:10:45,960 Speaker 3: I think has really woken up just in the last 214 00:10:46,040 --> 00:10:49,480 Speaker 3: year or two and said, WHOA, we've got competition. Like 215 00:10:49,559 --> 00:10:51,840 Speaker 3: these guys are as good as US. I'd say they're 216 00:10:51,840 --> 00:10:53,880 Speaker 3: probably better in a lot of ways. 217 00:10:54,120 --> 00:10:57,000 Speaker 1: So I definitely want to get to where they are 218 00:10:57,000 --> 00:10:58,880 Speaker 1: and why they might be better and what are the 219 00:10:58,920 --> 00:11:01,760 Speaker 1: conditions that perhaps allow them to be better. One less 220 00:11:01,840 --> 00:11:06,600 Speaker 1: sort of like precursor. Oh yes, und question is for 221 00:11:06,679 --> 00:11:09,280 Speaker 1: these chemicals, I imagine that. Okay, we're going to talk 222 00:11:09,320 --> 00:11:13,000 Speaker 1: about breakthroughs that are happening that are in China, that 223 00:11:13,040 --> 00:11:15,960 Speaker 1: are you know, in terms of therapies or biologics, et cetera. 224 00:11:16,320 --> 00:11:19,240 Speaker 1: But if we wind back a few years to where 225 00:11:19,280 --> 00:11:22,440 Speaker 1: people's brains were stuck at in terms of what is 226 00:11:22,480 --> 00:11:26,640 Speaker 1: the sort of global supply chain of I mean the 227 00:11:26,840 --> 00:11:29,600 Speaker 1: Chinese hamster over is, what is the sort of sort 228 00:11:29,640 --> 00:11:35,760 Speaker 1: of incumbent global supply chain of key materials, ingredients, equipment 229 00:11:35,880 --> 00:11:38,560 Speaker 1: for biotech and what is China's role in that. 230 00:11:39,480 --> 00:11:43,400 Speaker 3: So if you're making a small molecule which comes down 231 00:11:43,480 --> 00:11:46,760 Speaker 3: to that basic fine chemical, let's say it's libatare, Okay, 232 00:11:47,000 --> 00:11:48,880 Speaker 3: you could probably make it for less than a place 233 00:11:48,960 --> 00:11:52,240 Speaker 3: like India or Indonesia or China. So that's called API, 234 00:11:52,320 --> 00:11:55,720 Speaker 3: active pharmaceutical ingredient. And in fact, China has become a 235 00:11:55,960 --> 00:11:59,560 Speaker 3: huge source of API because in many ways, you know, 236 00:11:59,640 --> 00:12:02,360 Speaker 3: China's really good in the chemical industry. So why wouldn't 237 00:12:02,360 --> 00:12:08,880 Speaker 3: they be good in the API industry? 238 00:12:20,920 --> 00:12:25,560 Speaker 2: Can you contextualize some of China's I guess growth in 239 00:12:25,600 --> 00:12:28,480 Speaker 2: this area with some specific numbers, because we see all 240 00:12:28,520 --> 00:12:32,320 Speaker 2: these headlines coming out, like thirty percent of major pharma 241 00:12:32,480 --> 00:12:36,760 Speaker 2: licensing deals now involve Chinese companies. I think that's up 242 00:12:36,800 --> 00:12:40,120 Speaker 2: from like almost zero five years ago. There are some 243 00:12:40,320 --> 00:12:42,120 Speaker 2: interesting data to look at. 244 00:12:42,640 --> 00:12:45,200 Speaker 3: Yeah, So just to give you a couple stats the 245 00:12:45,440 --> 00:12:48,400 Speaker 3: API that source into the US, I don't have the 246 00:12:48,440 --> 00:12:51,640 Speaker 3: exact numbers on my fingertips, but I would say at 247 00:12:51,679 --> 00:12:55,040 Speaker 3: least twenty five to fifty percent of API that's being 248 00:12:55,200 --> 00:12:58,640 Speaker 3: used in the US generic pharmaceutical industry today is sourced 249 00:12:58,679 --> 00:13:01,080 Speaker 3: from China. India is another big piece of that. So 250 00:13:01,120 --> 00:13:05,720 Speaker 3: Indian China are both really big. What's interesting is India 251 00:13:05,760 --> 00:13:10,120 Speaker 3: has not kind of had this phenomenon of their nationals 252 00:13:10,160 --> 00:13:14,680 Speaker 3: coming home and opening up local biotech companies. So China 253 00:13:14,760 --> 00:13:20,199 Speaker 3: created this policy, you know, very intentionally five ten years ago, 254 00:13:20,280 --> 00:13:22,120 Speaker 3: saying hey, we want to be really good in biotech. 255 00:13:22,160 --> 00:13:24,480 Speaker 3: It is strategic for US as a country. It's not 256 00:13:24,520 --> 00:13:26,640 Speaker 3: that they're trying to beat the United States so that 257 00:13:26,679 --> 00:13:29,960 Speaker 3: they need access to these medicines domestically. You know, why 258 00:13:30,679 --> 00:13:33,960 Speaker 3: pay the giant global price that some US pharma company 259 00:13:34,000 --> 00:13:35,679 Speaker 3: wants a charge? Like, why don't you just learn how 260 00:13:35,679 --> 00:13:39,600 Speaker 3: to make it at home? So they very deliberately attracted 261 00:13:39,640 --> 00:13:42,640 Speaker 3: back what are called sea turtles. These are people that 262 00:13:42,760 --> 00:13:45,520 Speaker 3: crossed the sea from the US or Europe back home 263 00:13:45,559 --> 00:13:48,360 Speaker 3: to China. They were then encouraged to start their own 264 00:13:48,360 --> 00:13:51,400 Speaker 3: biotech companies and apply whatever they had learned, you know, 265 00:13:51,400 --> 00:13:53,800 Speaker 3: in their jobs in Bristol Meyers squib or An Artists 266 00:13:53,880 --> 00:13:56,439 Speaker 3: or what have you, and Boyd learned they had and 267 00:13:56,840 --> 00:13:59,960 Speaker 3: support they got, and all of a sudden they're churning 268 00:14:00,080 --> 00:14:04,640 Speaker 3: out really interesting molecules. And so Tracy, just like you said, 269 00:14:04,800 --> 00:14:08,439 Speaker 3: last year, thirty percent of all molecules that were licensed 270 00:14:08,480 --> 00:14:11,719 Speaker 3: in by big Pharma came from China, not from the 271 00:14:11,840 --> 00:14:15,560 Speaker 3: United States, not from Europe, not from Japan. They came 272 00:14:15,559 --> 00:14:18,720 Speaker 3: from China. And I do think that statistic, which was 273 00:14:18,840 --> 00:14:22,040 Speaker 3: generated by our good friends at deal Forma, was really 274 00:14:22,120 --> 00:14:23,680 Speaker 3: kind of a wake up call for a lot of 275 00:14:23,720 --> 00:14:24,680 Speaker 3: folks in our industry. 276 00:14:24,840 --> 00:14:27,480 Speaker 1: And what five years ago that would have been basically. 277 00:14:27,120 --> 00:14:29,720 Speaker 3: Zero, yeah, five percent, zero to five percent? 278 00:14:29,840 --> 00:14:30,560 Speaker 1: How much is it? 279 00:14:30,640 --> 00:14:30,720 Speaker 4: Is? 280 00:14:30,720 --> 00:14:35,400 Speaker 1: It genuinely novel therapies. How much is it? There's sort 281 00:14:35,400 --> 00:14:37,920 Speaker 1: of an existing therapy, but they can make it a 282 00:14:37,920 --> 00:14:41,520 Speaker 1: better version of it, a cheaper version of it. I understand, 283 00:14:41,560 --> 00:14:44,600 Speaker 1: like cheaper it must be sort of a weird concept 284 00:14:44,680 --> 00:14:46,320 Speaker 1: in an area where there's I know, a lot of 285 00:14:46,360 --> 00:14:50,560 Speaker 1: intellectual property. But talk about what is driving that competitiveness 286 00:14:50,600 --> 00:14:51,520 Speaker 1: and market share game. 287 00:14:52,240 --> 00:14:56,000 Speaker 3: There's two or three different things going on. So the 288 00:14:56,040 --> 00:14:59,400 Speaker 3: first thing is we're seeing what are called fast follower molecules. 289 00:14:59,480 --> 00:15:02,760 Speaker 3: Let's just say for the sake of argument, that Daichi 290 00:15:02,800 --> 00:15:05,400 Speaker 3: Senkio comes up with something called a B seven H 291 00:15:05,480 --> 00:15:09,400 Speaker 3: three anti body drug conjugate B seven H three ADC. Well, 292 00:15:10,440 --> 00:15:13,440 Speaker 3: the Chinese guys see that pop up, they see the 293 00:15:13,480 --> 00:15:15,600 Speaker 3: patent filing, they look at it, and they're like, Okay, 294 00:15:16,160 --> 00:15:18,040 Speaker 3: we're gonna make a B seven H three, but instead 295 00:15:18,040 --> 00:15:20,720 Speaker 3: of using this toxin on the ADC, we're gonna use 296 00:15:20,760 --> 00:15:22,400 Speaker 3: that toxin are instead of using this link or we're 297 00:15:22,400 --> 00:15:25,000 Speaker 3: gonna use that one. So those are kind of doing 298 00:15:25,080 --> 00:15:28,960 Speaker 3: small twists around existing constructs. So we call those fast followers. 299 00:15:29,440 --> 00:15:32,840 Speaker 3: China's really good at past followers. The second thing that 300 00:15:32,880 --> 00:15:38,040 Speaker 3: you're seeing are first in class molecules and So the 301 00:15:38,080 --> 00:15:41,400 Speaker 3: hottest biotech in the United States right now is a 302 00:15:41,440 --> 00:15:44,760 Speaker 3: company called Summit Therapeutics. They have a seventeen billion dollar 303 00:15:44,800 --> 00:15:47,920 Speaker 3: market cap as we speak. Remember I define a biotech 304 00:15:48,000 --> 00:15:50,120 Speaker 3: a company that doesn't yet have an approved drug. So 305 00:15:50,680 --> 00:15:53,160 Speaker 3: that's the highest valuation in the world of any company 306 00:15:53,240 --> 00:15:56,120 Speaker 3: in the world today that doesn't have an approved drug. 307 00:15:57,040 --> 00:15:59,880 Speaker 3: That molecule, which is a combination of a PD one 308 00:16:00,040 --> 00:16:03,800 Speaker 3: antibody with the VeVe jef modi PD one by Vegef 309 00:16:03,800 --> 00:16:08,880 Speaker 3: it's called is an excellent molecule. It's working really well 310 00:16:08,880 --> 00:16:12,320 Speaker 3: in mun cancer. And guess what it was invented in China. 311 00:16:12,560 --> 00:16:14,800 Speaker 3: Mert didn't come up with it, Advisor didn't come up 312 00:16:14,800 --> 00:16:16,920 Speaker 3: with it. Habit didn't come up with it. It was 313 00:16:16,960 --> 00:16:19,040 Speaker 3: come up with in China. And it's the most interesting 314 00:16:19,040 --> 00:16:21,000 Speaker 3: biotech molecule in the world today. 315 00:16:21,040 --> 00:16:23,400 Speaker 1: But this is an American company, Summer, right, They went 316 00:16:23,440 --> 00:16:26,200 Speaker 1: and licensed China got it, just like we're seeing the 317 00:16:26,200 --> 00:16:29,680 Speaker 1: big pharmaus US biotechs licensing stuff from China all day long. 318 00:16:30,440 --> 00:16:34,440 Speaker 2: How much does the difference in regulatory regimes play into here, 319 00:16:34,480 --> 00:16:36,560 Speaker 2: because one thing we often hear when it comes to 320 00:16:36,640 --> 00:16:41,280 Speaker 2: outsourcing manufacturing to China whether it's something basic like I 321 00:16:41,280 --> 00:16:47,200 Speaker 2: don't know, clothing or something more advanced like medicines. Is 322 00:16:47,240 --> 00:16:51,320 Speaker 2: that it's cheaper to make stuff in China because you 323 00:16:51,360 --> 00:16:54,680 Speaker 2: don't have as many rules and regulations to either slow 324 00:16:54,720 --> 00:16:57,320 Speaker 2: you down or add on to costs. Is that a 325 00:16:57,320 --> 00:16:58,400 Speaker 2: factor here as well. 326 00:16:59,240 --> 00:17:03,680 Speaker 3: That's a comp located question and a complicated answer. So yeah. 327 00:17:03,760 --> 00:17:10,720 Speaker 3: So for most biologics, the Chinese will allow you to 328 00:17:10,760 --> 00:17:14,240 Speaker 3: get those into patients more quickly. They have what we 329 00:17:14,359 --> 00:17:16,800 Speaker 3: call phase zero studies where you can just go to 330 00:17:16,920 --> 00:17:19,960 Speaker 3: a doctor and say, okay, hey, doc, you've got people 331 00:17:20,000 --> 00:17:24,080 Speaker 3: coming in that are dying of ovarian cancer, use this drug. 332 00:17:24,440 --> 00:17:27,040 Speaker 3: The FDA will not let you do that, right, So, 333 00:17:27,160 --> 00:17:30,120 Speaker 3: the FDA won't let that ovarian cancer drug go into 334 00:17:30,160 --> 00:17:35,000 Speaker 3: a patient until it's gone through typically a Phase one study. Interestingly, 335 00:17:35,080 --> 00:17:37,280 Speaker 3: China's not the only country that does that. Australia does 336 00:17:37,280 --> 00:17:39,720 Speaker 3: that too, and you know, we have kind of new 337 00:17:39,760 --> 00:17:41,359 Speaker 3: sheriff in town at the FDA. It might be an 338 00:17:41,359 --> 00:17:47,840 Speaker 3: interesting thing to explore kind of accelerating that time to 339 00:17:47,880 --> 00:17:50,720 Speaker 3: get to the first patient. So that that's one place 340 00:17:50,720 --> 00:17:54,800 Speaker 3: where China is a head. But in general, their rules 341 00:17:54,840 --> 00:17:56,520 Speaker 3: are just as tough as our rules. It's not like 342 00:17:56,680 --> 00:17:58,639 Speaker 3: they have a you know, a low hurdle and we 343 00:17:58,680 --> 00:18:00,720 Speaker 3: have a high hurdle to jump over to get a 344 00:18:00,800 --> 00:18:05,159 Speaker 3: drug approved. Their vantage is more speed to invent, speed 345 00:18:05,200 --> 00:18:08,520 Speaker 3: to get into the clinic. They're just performing really well 346 00:18:08,520 --> 00:18:11,080 Speaker 3: on a lot of those key performance indicators. 347 00:18:11,359 --> 00:18:16,000 Speaker 1: What about there's the cost of conducting a phase one trial. 348 00:18:16,040 --> 00:18:20,080 Speaker 1: I mean, these are really expensive endeavors in the United States, 349 00:18:20,080 --> 00:18:22,960 Speaker 1: and you can spend millions and it goes nowhere past 350 00:18:23,160 --> 00:18:26,600 Speaker 1: phase one. How does the cost compared to run the 351 00:18:26,640 --> 00:18:27,840 Speaker 1: equivalent in China? 352 00:18:28,160 --> 00:18:30,320 Speaker 3: I mean we should pause for moments. So the US 353 00:18:30,440 --> 00:18:35,120 Speaker 3: has capitalistic medicine system, right, So doctors are for profits. 354 00:18:35,119 --> 00:18:36,960 Speaker 3: So if you're a physician, of course you're trying to 355 00:18:37,000 --> 00:18:39,800 Speaker 3: care for your patients. But let's be honest, a lot 356 00:18:39,840 --> 00:18:43,280 Speaker 3: of those dermatologists and endochronologists that you see, they're running 357 00:18:43,320 --> 00:18:46,199 Speaker 3: a business. The other day I was talking to a cardiologist. 358 00:18:46,280 --> 00:18:49,880 Speaker 3: I said, like, how many patients do you see the year? 359 00:18:49,880 --> 00:18:52,679 Speaker 3: He's like eight thousand, and I was out like asking 360 00:18:52,720 --> 00:18:54,760 Speaker 3: the lady at the front, like, how much like does 361 00:18:54,800 --> 00:18:58,280 Speaker 3: the average patient visit bill? She's like a four or 362 00:18:58,320 --> 00:18:59,800 Speaker 3: five hundred bucks. So you can do the math that 363 00:18:59,800 --> 00:19:02,760 Speaker 3: guy pulling down like five to ten million dollars, right, 364 00:19:02,840 --> 00:19:05,880 Speaker 3: So running a physician practice in the United States can 365 00:19:05,920 --> 00:19:08,280 Speaker 3: be very lucrative. I'm not saying every doc's doing it, 366 00:19:08,600 --> 00:19:11,520 Speaker 3: just to be clear. But now you're a cancer doc 367 00:19:11,560 --> 00:19:14,080 Speaker 3: and you're an MD Anderson or Dane Farber someplace, and 368 00:19:14,160 --> 00:19:18,199 Speaker 3: some company shows up GSK and they want you to 369 00:19:18,240 --> 00:19:21,800 Speaker 3: test this drug. How much are you going to charge 370 00:19:22,040 --> 00:19:25,920 Speaker 3: GSK for each patient? It turns out that the average 371 00:19:26,000 --> 00:19:28,040 Speaker 3: price to enroll a patient and a cancer drial in 372 00:19:28,080 --> 00:19:30,680 Speaker 3: the United States is between two hundred thousand and four 373 00:19:30,760 --> 00:19:33,159 Speaker 3: hundred thousand dollars per pat per patient. 374 00:19:33,280 --> 00:19:36,200 Speaker 1: How much of that goes to the doctor. 375 00:19:36,560 --> 00:19:38,560 Speaker 3: A lot and a lot to the hospital. I mean, 376 00:19:38,560 --> 00:19:40,240 Speaker 3: this is a big issue that's just possed up. 377 00:19:40,200 --> 00:19:43,680 Speaker 1: A big source of hospital and doctor profits that they're 378 00:19:43,840 --> 00:19:47,040 Speaker 1: essentially selling access to their patients. 379 00:19:46,480 --> 00:19:52,160 Speaker 3: You bet, especially at the big places. So developing drugs, 380 00:19:52,320 --> 00:19:55,679 Speaker 3: especially in cancer in the United States is very expensive. 381 00:19:56,320 --> 00:19:58,639 Speaker 3: The other thing I'd note is there's a lot of 382 00:19:58,640 --> 00:20:01,880 Speaker 3: competition for talent in our country. Again, it's a capitalist 383 00:20:02,160 --> 00:20:05,879 Speaker 3: talent market. So you know, let's say you're a doctor 384 00:20:05,960 --> 00:20:08,200 Speaker 3: working in m D Anderson and then GSK comes along 385 00:20:08,200 --> 00:20:10,160 Speaker 3: and says, hey, we'd like you to be our chief 386 00:20:10,240 --> 00:20:14,760 Speaker 3: medical officer. We'd like you to run this cancer program. Like, 387 00:20:14,920 --> 00:20:17,520 Speaker 3: is that a one hundred and fifty thousand dollars job. 388 00:20:17,760 --> 00:20:20,200 Speaker 3: I don't think so. The average chief medical officer in 389 00:20:20,240 --> 00:20:22,240 Speaker 3: the United States city is pulling down between a half 390 00:20:22,280 --> 00:20:25,080 Speaker 3: million and one point five million dollars a year, depending 391 00:20:25,119 --> 00:20:27,000 Speaker 3: on your level of experience and how good you are. 392 00:20:27,520 --> 00:20:30,240 Speaker 3: So all of a sudden, you see biotech companies that 393 00:20:30,280 --> 00:20:32,400 Speaker 3: are going out to raise one hundred million dollars. Well, 394 00:20:32,480 --> 00:20:35,280 Speaker 3: that's how much you need to raise to enroll the 395 00:20:35,280 --> 00:20:37,720 Speaker 3: trial and pay all those people. And you know they 396 00:20:37,720 --> 00:20:41,400 Speaker 3: have lots of posh offices as well, and expensive places, 397 00:20:41,440 --> 00:20:44,800 Speaker 3: and so US biotech is not so efficient. In contrast, 398 00:20:44,840 --> 00:20:47,800 Speaker 3: in China, there are no seven hundred thousand dollars chief 399 00:20:47,880 --> 00:20:50,480 Speaker 3: medical officers. There are no two hundred thousand dollar patients. 400 00:20:50,520 --> 00:20:55,760 Speaker 3: It's a communist country, right. Doctors, you know, make thirty 401 00:20:55,800 --> 00:20:58,880 Speaker 3: thousand dollars a year. You don't get to go make 402 00:20:59,520 --> 00:21:02,800 Speaker 3: hundreds of thousands dollars a year for being a doctor, 403 00:21:03,200 --> 00:21:05,719 Speaker 3: and you definitely don't rent out your patience. 404 00:21:06,920 --> 00:21:10,120 Speaker 1: Tracy, I have to say this is something I sort 405 00:21:10,119 --> 00:21:13,199 Speaker 1: of became aware of in this phenomenon of doctors and 406 00:21:13,240 --> 00:21:18,320 Speaker 1: hospitals renting out their patients, and for this episode, I 407 00:21:18,359 --> 00:21:20,320 Speaker 1: had no idea that that's how it worked, and I 408 00:21:21,200 --> 00:21:24,399 Speaker 1: had no idea of the scale of these numbers. Like, 409 00:21:24,440 --> 00:21:27,359 Speaker 1: if there's one fact that's sort of like expanding my mind, 410 00:21:27,400 --> 00:21:28,199 Speaker 1: this is the one. 411 00:21:28,560 --> 00:21:30,719 Speaker 2: I didn't know it either. We should probably do an 412 00:21:30,760 --> 00:21:34,840 Speaker 2: episode just on the market for renting out cancer patients. 413 00:21:35,240 --> 00:21:36,199 Speaker 2: That sounds very It. 414 00:21:36,240 --> 00:21:39,800 Speaker 1: Sounds bad when you put it that way, like they're gating, right, 415 00:21:39,800 --> 00:21:43,200 Speaker 1: they're profiting from the fact that they You're the sort 416 00:21:43,240 --> 00:21:47,240 Speaker 1: of the channel, right, They're they're the channel through which 417 00:21:47,760 --> 00:21:50,240 Speaker 1: the drug company must find patients. 418 00:21:50,560 --> 00:21:53,000 Speaker 3: I mean, let's just talk reality of a medicine in 419 00:21:53,040 --> 00:21:58,320 Speaker 3: America right now. There are certain specialties that make money. 420 00:21:58,359 --> 00:22:05,760 Speaker 3: Cancer treatment is one, cardiology, surgeries is another. In contrast 421 00:22:06,080 --> 00:22:10,159 Speaker 3: seeing people in the emergency room, seeing people in a 422 00:22:10,160 --> 00:22:14,320 Speaker 3: primary care sense, you lose money doing those activities. Payments 423 00:22:14,320 --> 00:22:18,280 Speaker 3: from insurance companies are poor, and so hospital systems, by 424 00:22:18,400 --> 00:22:21,520 Speaker 3: necessity have become for profit. They have no choice. 425 00:22:21,840 --> 00:22:24,080 Speaker 2: So one of the reasons we wanted to speak to 426 00:22:24,119 --> 00:22:27,320 Speaker 2: you is because, in the course of your work, You've 427 00:22:27,320 --> 00:22:31,080 Speaker 2: talked to a lot of CEOs and executives on I 428 00:22:31,080 --> 00:22:34,320 Speaker 2: guess both sides of the ocean here in China and 429 00:22:34,359 --> 00:22:37,400 Speaker 2: in the US give us a sort of temperature check 430 00:22:37,520 --> 00:22:40,119 Speaker 2: of what people are saying right now when it comes 431 00:22:40,160 --> 00:22:44,560 Speaker 2: to the US VERSUS China pharmaceutical biotech industries. 432 00:22:45,080 --> 00:22:48,000 Speaker 3: So, first of all, the pharma companies, the big pharma companies, 433 00:22:48,040 --> 00:22:51,160 Speaker 3: they're thrilled at China's there. It gives them more options, right, 434 00:22:52,000 --> 00:22:54,800 Speaker 3: you know, there's new molecules, they might be innovative molecules. 435 00:22:54,800 --> 00:22:58,159 Speaker 3: The Chinese companies generally don't globalize on their own. One 436 00:22:58,200 --> 00:23:00,880 Speaker 3: of the interesting things is there's you know, Chinese big 437 00:23:00,880 --> 00:23:03,919 Speaker 3: pharma companies. Name the largest pharma company from China you've 438 00:23:03,920 --> 00:23:07,400 Speaker 3: ever heard of. You can't do it. There isn't one, right, 439 00:23:07,480 --> 00:23:10,920 Speaker 3: It might be an obscure company like King Ray or CSPC. 440 00:23:11,040 --> 00:23:15,640 Speaker 3: They're relatively small compared to our pharmaceutical companies. So it's 441 00:23:15,680 --> 00:23:19,760 Speaker 3: great hunting for those guys. For the Chinese companies, access 442 00:23:19,800 --> 00:23:22,520 Speaker 3: to the US pharmaceutical market is a god send capitalist 443 00:23:22,600 --> 00:23:26,920 Speaker 3: tight prices are low for the US biotech company China 444 00:23:26,960 --> 00:23:30,679 Speaker 3: can be worrisome, But honestly, when I speak to my 445 00:23:30,920 --> 00:23:34,639 Speaker 3: friends in the US biotech ecosystem. There are some concern 446 00:23:34,720 --> 00:23:37,439 Speaker 3: but most of them aren't in direct line of fire 447 00:23:37,680 --> 00:23:42,280 Speaker 3: with Chinese competition. It's the US investor, the US biotech 448 00:23:42,280 --> 00:23:44,320 Speaker 3: investor this kind of worried. So all those stories that 449 00:23:44,359 --> 00:23:46,520 Speaker 3: you were referring to, a lot of them are sort 450 00:23:46,520 --> 00:23:49,480 Speaker 3: of freaked out, saying, hey, like thirty percent of molecules 451 00:23:50,040 --> 00:23:52,560 Speaker 3: are coming from China, what about our biotech companies? 452 00:23:53,040 --> 00:23:55,639 Speaker 1: Wait, so why wouldn't your friends in the industry be 453 00:23:55,760 --> 00:23:59,080 Speaker 1: ang I mean, presumably their leverage their own stocks. If 454 00:23:59,119 --> 00:24:02,399 Speaker 1: the investors weren't, they aren't your friends in the industry more. 455 00:24:02,240 --> 00:24:06,080 Speaker 3: Anxious because the Chinese, by and large are taking older 456 00:24:06,119 --> 00:24:09,960 Speaker 3: technologies and biologics and putting twists and turns on those technologies. 457 00:24:10,359 --> 00:24:13,840 Speaker 3: Most US biotech companies are not in that business right now, 458 00:24:13,920 --> 00:24:17,679 Speaker 3: so by and large they have understood long ago that 459 00:24:17,720 --> 00:24:19,760 Speaker 3: they need to differentiate. But that's not all of them. 460 00:24:19,800 --> 00:24:22,320 Speaker 3: I mean, there are certainly some companies out there that 461 00:24:22,400 --> 00:24:25,720 Speaker 3: are in direct competition. And by the way, you know, 462 00:24:25,760 --> 00:24:28,440 Speaker 3: the other day I was looking at these antibody drug conjugates, 463 00:24:28,440 --> 00:24:31,200 Speaker 3: So China's gotten really good and antibody drug conjugates are 464 00:24:31,280 --> 00:24:36,000 Speaker 3: very popular. I think there's four or five major public 465 00:24:36,240 --> 00:24:39,320 Speaker 3: antibody drug conjugate development companies in the US. 466 00:24:41,160 --> 00:24:44,560 Speaker 1: They all have type of cancer treatment that combines the 467 00:24:44,640 --> 00:24:49,480 Speaker 1: monoclonal antibody with a cytotoxic cancer killing drug. Okay, keep going, 468 00:24:49,680 --> 00:24:50,160 Speaker 1: that's right. 469 00:24:50,240 --> 00:24:55,600 Speaker 3: So an ady C is basically chemotherapy that's directed specifically 470 00:24:55,640 --> 00:24:57,280 Speaker 3: to this cell. So you don't have to worry about 471 00:24:57,600 --> 00:24:59,760 Speaker 3: losing all your hair or whatever if you take an ADC. 472 00:25:00,480 --> 00:25:07,160 Speaker 3: So the average enterprise value that's your market cap, ple's 473 00:25:07,200 --> 00:25:11,880 Speaker 3: your cash of a US ADC biotech today has gone negative. 474 00:25:12,040 --> 00:25:15,000 Speaker 3: Two years ago was quite positive, and I do think 475 00:25:15,080 --> 00:25:33,480 Speaker 3: that those folks have you taken some heat from Chinese college. 476 00:25:34,920 --> 00:25:38,159 Speaker 2: I just want to go back to the anxiety or 477 00:25:38,760 --> 00:25:41,480 Speaker 2: lack of it in the US and just focus on 478 00:25:41,520 --> 00:25:44,800 Speaker 2: the investors for a moment. So the worry is that 479 00:25:45,160 --> 00:25:47,960 Speaker 2: the people who actually fund some of these things, I 480 00:25:47,960 --> 00:25:51,919 Speaker 2: guess venture capital, maybe private equity, things like that, that 481 00:25:52,000 --> 00:25:56,280 Speaker 2: they are going to be intermediated by pharma that's going 482 00:25:56,400 --> 00:25:58,680 Speaker 2: directly to the Chinese companies. 483 00:25:59,440 --> 00:26:02,280 Speaker 3: That's right. So let's imagine you're a venture capitalists out 484 00:26:02,320 --> 00:26:05,480 Speaker 3: in San Francisco. You've got this nice life, you know, 485 00:26:05,600 --> 00:26:08,200 Speaker 3: on sand Hill Road, you come up with some ideas 486 00:26:08,760 --> 00:26:10,959 Speaker 3: for some new biotech companies, you found them, and then 487 00:26:11,000 --> 00:26:15,480 Speaker 3: you're waiting, you know, for merk to come along or 488 00:26:16,000 --> 00:26:18,560 Speaker 3: genetech to come along, almost almost like you know, setting 489 00:26:18,640 --> 00:26:21,480 Speaker 3: a trap for the groundhog in your backyard or something 490 00:26:21,520 --> 00:26:24,960 Speaker 3: like that, and the groundhog never shows up because they 491 00:26:25,000 --> 00:26:27,080 Speaker 3: don't go to your backyard anymore. They've found some other 492 00:26:27,119 --> 00:26:31,440 Speaker 3: place to go. And so what's happening is that pharma 493 00:26:31,560 --> 00:26:34,920 Speaker 3: have learned that they can find really interesting molecules in China, 494 00:26:35,040 --> 00:26:35,560 Speaker 3: you know, one of. 495 00:26:35,560 --> 00:26:37,760 Speaker 1: The themes that comes up in a lot of our 496 00:26:37,800 --> 00:26:41,200 Speaker 1: conversations about Chinese industry in general. So you see these 497 00:26:41,240 --> 00:26:46,919 Speaker 1: stories about sort of incredible growth and manufacturing of whatever 498 00:26:47,400 --> 00:26:51,720 Speaker 1: with pretty slim profits, and famously, like the Chinese stock market, 499 00:26:51,800 --> 00:26:54,000 Speaker 1: it's actually, i think the last few weeks, it's uh, 500 00:26:54,200 --> 00:26:56,480 Speaker 1: this year is kind of doing okay. But like famously, 501 00:26:56,560 --> 00:26:59,040 Speaker 1: the Chinese stock market, for all the growth that they've had, 502 00:26:59,119 --> 00:27:01,639 Speaker 1: for all the success various industries, it's kind of been 503 00:27:01,680 --> 00:27:04,800 Speaker 1: a dog for a long time. And part of the 504 00:27:04,840 --> 00:27:07,680 Speaker 1: story is like, well, there's just so much capital intensity 505 00:27:07,840 --> 00:27:09,840 Speaker 1: and you actually only stay at the cutting edge of 506 00:27:09,840 --> 00:27:12,879 Speaker 1: all these capital intensive businesses. If you're spending all of 507 00:27:12,920 --> 00:27:15,240 Speaker 1: your money that you take in on more research and 508 00:27:15,280 --> 00:27:18,160 Speaker 1: so there isn't a lot left over for the end 509 00:27:18,280 --> 00:27:22,760 Speaker 1: equity investor. It kind of sounds like something similar here 510 00:27:22,800 --> 00:27:26,440 Speaker 1: where it's like, it's not great if you're a US 511 00:27:26,520 --> 00:27:30,119 Speaker 1: equity investor in certain areas that are directly in the 512 00:27:30,160 --> 00:27:34,360 Speaker 1: line of fire. But it doesn't sound like Chinese companies 513 00:27:34,400 --> 00:27:35,879 Speaker 1: themselves are swimming in profits. 514 00:27:36,119 --> 00:27:38,639 Speaker 3: Right. There's no fat cats in China, even though there 515 00:27:38,720 --> 00:27:41,480 Speaker 3: might be nice to think that could be true. So 516 00:27:42,240 --> 00:27:44,199 Speaker 3: I was on a trip recently to China. I was 517 00:27:44,200 --> 00:27:46,240 Speaker 3: in this one building, like you know, just one of 518 00:27:46,320 --> 00:27:50,720 Speaker 3: many buildings that had biotechs in Beijing, and like every floor, 519 00:27:50,920 --> 00:27:53,719 Speaker 3: like it was like an apartment building, every floor had 520 00:27:53,720 --> 00:27:55,600 Speaker 3: another biotech. And I asked one of the guys, I 521 00:27:55,600 --> 00:27:58,400 Speaker 3: SAIDs like, how many biotechs are in this building? Said 522 00:27:58,440 --> 00:28:03,040 Speaker 3: as sixty seventy? How many biotechs are in Beijing? He said, 523 00:28:03,080 --> 00:28:05,879 Speaker 3: nobody knows exactly. So, you know, the US places like 524 00:28:05,920 --> 00:28:08,719 Speaker 3: Bloomberg have phenomenal databases and stuff that they don't have 525 00:28:08,800 --> 00:28:11,200 Speaker 3: that over there. Maybe that's a business for Bloomberg. I 526 00:28:11,240 --> 00:28:11,560 Speaker 3: don't know. 527 00:28:12,280 --> 00:28:14,080 Speaker 2: Well, thank you for the suggestion. 528 00:28:14,160 --> 00:28:19,320 Speaker 3: So yes, he said, I think there's three thousand biotechs 529 00:28:19,320 --> 00:28:21,840 Speaker 3: in Beijing. In other words, there's fifty buildings like that one. 530 00:28:22,320 --> 00:28:24,040 Speaker 3: And I said, well, what about the country? He said, 531 00:28:24,080 --> 00:28:27,800 Speaker 3: nobody knows, but like five to ten thousand biotech companies. 532 00:28:28,240 --> 00:28:31,760 Speaker 3: So they've got a lot of people making molecules that 533 00:28:31,800 --> 00:28:34,679 Speaker 3: are competing for the attention and of a relatively few 534 00:28:35,080 --> 00:28:36,120 Speaker 3: large pharma companies. 535 00:28:36,320 --> 00:28:38,920 Speaker 2: So one of the things you've been emphasizing is this 536 00:28:39,040 --> 00:28:43,120 Speaker 2: idea of China just moving faster than the US on 537 00:28:43,240 --> 00:28:46,080 Speaker 2: this and it does seem like they've come out of 538 00:28:46,200 --> 00:28:52,880 Speaker 2: almost nowhere in recent years. How sustainable is that particular pace, 539 00:28:53,040 --> 00:28:55,320 Speaker 2: Because if China got a leg up because it had 540 00:28:55,360 --> 00:28:59,520 Speaker 2: a generation of researchers who came to US universities and 541 00:28:59,600 --> 00:29:01,960 Speaker 2: maybe worked in the US and then took that knowledge 542 00:29:01,960 --> 00:29:05,680 Speaker 2: back home. Eventually, does that mean that, you know, that 543 00:29:05,800 --> 00:29:09,120 Speaker 2: sort of wave of talent ebbs away and it becomes 544 00:29:09,160 --> 00:29:12,640 Speaker 2: harder or is it a permanent shift that they're going 545 00:29:12,720 --> 00:29:14,200 Speaker 2: to hold on to for a long time. 546 00:29:15,080 --> 00:29:17,840 Speaker 3: I would say that you have to look at where 547 00:29:17,880 --> 00:29:21,560 Speaker 3: their advantage is coming from. So they have a really 548 00:29:21,720 --> 00:29:25,200 Speaker 3: good ecosystem for going from an idea for a new 549 00:29:25,200 --> 00:29:28,960 Speaker 3: biologic to an actual drug that can be tested in patience. 550 00:29:29,840 --> 00:29:31,760 Speaker 3: I don't know, Tracy, if you saw this news last 551 00:29:31,840 --> 00:29:35,280 Speaker 3: year about the Biosecure Act. The US Congress for some 552 00:29:35,320 --> 00:29:38,960 Speaker 3: reason decided that they wanted to Like ban Wushie I 553 00:29:39,040 --> 00:29:41,840 Speaker 3: spoke to the CEO of Wooshi, I said, like they're 554 00:29:41,880 --> 00:29:45,800 Speaker 3: saying that you're communists. He said, yeah, we have members 555 00:29:45,800 --> 00:29:49,560 Speaker 3: of the Communist Party and our company we're actually required to. 556 00:29:49,560 --> 00:29:51,920 Speaker 2: By law, as do many Chinese companies. 557 00:29:51,960 --> 00:29:54,880 Speaker 3: And he said, by the way, have you noticed how 558 00:29:54,880 --> 00:29:57,440 Speaker 3: many Teslas are in China? Has anyone called up Elon 559 00:29:57,560 --> 00:30:00,880 Speaker 3: Musk asked him, does he have anyone the Communist Party 560 00:30:00,880 --> 00:30:05,360 Speaker 3: in this company? How did Tesla get to have one 561 00:30:05,400 --> 00:30:08,480 Speaker 3: third market share of the electric vehicles in China? He said, 562 00:30:08,520 --> 00:30:11,800 Speaker 3: of course, every company China's allied with the Communist Party. 563 00:30:12,480 --> 00:30:17,080 Speaker 3: He said, We're no different than anybody else. So Wouh 564 00:30:18,160 --> 00:30:23,240 Speaker 3: interestingly came up with this concept called idea to I 565 00:30:23,400 --> 00:30:25,800 Speaker 3: and D in six months, That is, you give me 566 00:30:25,960 --> 00:30:28,320 Speaker 3: an idea for a new biologic, and I will give 567 00:30:28,360 --> 00:30:32,240 Speaker 3: you a drug in six months. That seems insane right 568 00:30:32,280 --> 00:30:35,560 Speaker 3: in the US, it's like two three years. So if 569 00:30:35,600 --> 00:30:38,160 Speaker 3: you ask the folks of wooh She, how did you 570 00:30:38,400 --> 00:30:44,800 Speaker 3: get your molecule to go through the system so fast, 571 00:30:44,960 --> 00:30:47,840 Speaker 3: He'll say it's all volume. He said, you need to 572 00:30:47,920 --> 00:30:50,280 Speaker 3: have the people that know what they're doing at each step. 573 00:30:50,280 --> 00:30:53,080 Speaker 3: He said, when you're slow, it's because you're fumbling around. 574 00:30:53,120 --> 00:30:54,920 Speaker 3: You don't have volumes. So like, oh, yeah, we don't 575 00:30:54,920 --> 00:30:57,280 Speaker 3: have the right cell line, let's go make that to 576 00:30:57,360 --> 00:30:59,720 Speaker 3: the customer. They think, well, just take a year to 577 00:30:59,720 --> 00:31:01,600 Speaker 3: get the selling going, But in fact, he said, you know, 578 00:31:01,600 --> 00:31:04,240 Speaker 3: if you already have five good choices of a sell line, 579 00:31:04,280 --> 00:31:05,600 Speaker 3: well you know you ought to be able to get 580 00:31:05,600 --> 00:31:09,200 Speaker 3: that done in two weeks. So wou Shi is behind 581 00:31:09,240 --> 00:31:11,520 Speaker 3: many of those Chinese molecules, and so they're able to 582 00:31:11,560 --> 00:31:16,120 Speaker 3: access a really good industrial partner. And I'm just still 583 00:31:16,120 --> 00:31:19,000 Speaker 3: befuddled by, like, why does US Congress want to deprive 584 00:31:20,000 --> 00:31:23,200 Speaker 3: US biotech of access to woo she. It's kind of 585 00:31:23,320 --> 00:31:24,560 Speaker 3: crazy when you think about it. 586 00:31:24,680 --> 00:31:28,320 Speaker 1: The talent pool in the US and the incredible salaries 587 00:31:28,320 --> 00:31:30,760 Speaker 1: that you could make in normal traditional tech, and I 588 00:31:30,800 --> 00:31:35,720 Speaker 1: have to imagine smart, quantitatively minded people that probably have 589 00:31:35,880 --> 00:31:38,360 Speaker 1: multiple options. They could go to work in a high 590 00:31:38,360 --> 00:31:41,160 Speaker 1: speed trading firm. They could go to work at Google, 591 00:31:41,200 --> 00:31:43,120 Speaker 1: they could go to work at open Ai, they could 592 00:31:43,160 --> 00:31:46,880 Speaker 1: probably apply a lot of their skills in pharma. Have 593 00:31:47,000 --> 00:31:51,600 Speaker 1: what's happened in the US to the supply of talent 594 00:31:51,720 --> 00:31:55,280 Speaker 1: and has the huge salaries that have emerged over the 595 00:31:55,360 --> 00:32:00,480 Speaker 1: last fifteen years in traditional tech, has that been drain 596 00:32:01,280 --> 00:32:04,160 Speaker 1: on the sort of has that pulled people away who 597 00:32:04,200 --> 00:32:06,520 Speaker 1: might have otherwise gone into pharma or biotech? 598 00:32:06,840 --> 00:32:09,320 Speaker 3: I don't think so so much. I mean, you know, 599 00:32:09,360 --> 00:32:11,640 Speaker 3: there are always the folks in a culture that have 600 00:32:11,920 --> 00:32:16,719 Speaker 3: let's say that immigrant mentality, maybe Indian heritage, something like that, 601 00:32:16,760 --> 00:32:18,720 Speaker 3: where you know, you're really motivated to be a lawyer 602 00:32:18,760 --> 00:32:21,360 Speaker 3: or doctor whatever. I do think a lot of those 603 00:32:21,360 --> 00:32:23,960 Speaker 3: folks have gone into the medical profession, and that's certainly 604 00:32:24,040 --> 00:32:26,800 Speaker 3: more and more they're attracted, I think, to the tech profession. 605 00:32:27,600 --> 00:32:31,120 Speaker 3: But your classic sort of biotech scientists is someone who 606 00:32:31,160 --> 00:32:34,760 Speaker 3: got a PhD. You know, they they got interested in 607 00:32:34,800 --> 00:32:38,000 Speaker 3: biology and college, they went off and got a PhD 608 00:32:38,080 --> 00:32:41,760 Speaker 3: from some you know, nice place, and then they got 609 00:32:41,800 --> 00:32:46,080 Speaker 3: a job at industry. Those people would in general not 610 00:32:46,200 --> 00:32:48,960 Speaker 3: be you know, thinking about a programming jobs. 611 00:32:49,080 --> 00:32:49,840 Speaker 1: Got it, Okay? 612 00:32:50,560 --> 00:32:53,600 Speaker 2: I know we're talking mainly about US and China. But 613 00:32:53,760 --> 00:32:56,280 Speaker 2: I have to ask, is Europe in the picture at 614 00:32:56,280 --> 00:32:59,680 Speaker 2: all here? I mean the only this is partly because 615 00:32:59,720 --> 00:33:02,600 Speaker 2: I don't follow pharma that intensely, but it feels like 616 00:33:02,640 --> 00:33:07,440 Speaker 2: the only European name I hear nowadays is Novo Nordisk 617 00:33:07,520 --> 00:33:09,040 Speaker 2: and it's golp Ones. 618 00:33:10,200 --> 00:33:14,320 Speaker 3: I mean, Europe historically was the dominant place in the 619 00:33:14,320 --> 00:33:16,959 Speaker 3: world for the pharmaceutical industry, so it's only I would say, 620 00:33:17,000 --> 00:33:20,320 Speaker 3: in the last thirty years that the US has taken over. Unfortunately, 621 00:33:20,400 --> 00:33:24,200 Speaker 3: Europe started putting in very draconian price controls and so 622 00:33:24,320 --> 00:33:28,560 Speaker 3: that really hurt their domestic pharma industry. But nonetheless, Europe's 623 00:33:28,600 --> 00:33:35,920 Speaker 3: got great universities, you know, whether talking about Gottingen, Erlangen, Lighten, Cambridge, Oxford. 624 00:33:35,960 --> 00:33:38,400 Speaker 3: I mean, these are really good places, and so you 625 00:33:38,440 --> 00:33:41,520 Speaker 3: can imagine the talent and ideas that are flowing out 626 00:33:41,520 --> 00:33:45,000 Speaker 3: of those have really created a very vital and successful 627 00:33:45,040 --> 00:33:47,040 Speaker 3: biotech ecosystem in Europe. 628 00:33:47,240 --> 00:33:50,320 Speaker 1: So right now, as you mentioned, the really big US 629 00:33:50,360 --> 00:33:55,760 Speaker 1: pharma companies are thrilled because they have new options from 630 00:33:55,800 --> 00:33:59,560 Speaker 1: which they can source or license biologics. And you mentioned 631 00:33:59,600 --> 00:34:04,760 Speaker 1: there's really even know at all big Chinese pharmaceutical companies. 632 00:34:05,040 --> 00:34:07,320 Speaker 1: Do you think that could change like right now, like 633 00:34:07,440 --> 00:34:10,399 Speaker 1: still like the J and js and the Pfizers and 634 00:34:10,600 --> 00:34:13,120 Speaker 1: the other big one, Like they're pretty Eli Lillie like 635 00:34:13,160 --> 00:34:16,400 Speaker 1: these are like pretty big chunks of the US market, 636 00:34:16,800 --> 00:34:18,960 Speaker 1: and it seems like they you know, for an investor, 637 00:34:19,120 --> 00:34:21,839 Speaker 1: an in diverse fyed investor, it's a decent chunk of 638 00:34:21,880 --> 00:34:26,120 Speaker 1: their holdings. You know, we've seen, for example, China is 639 00:34:26,200 --> 00:34:30,000 Speaker 1: going taking a shot to break into the aviation duopoly 640 00:34:30,120 --> 00:34:32,319 Speaker 1: of Boeing and Airbus, go up the next level and 641 00:34:32,400 --> 00:34:35,200 Speaker 1: actually compete at the highest level. Would you anticipate that 642 00:34:35,239 --> 00:34:38,200 Speaker 1: at some point in the next few years some company 643 00:34:38,320 --> 00:34:40,440 Speaker 1: or some initiative is like, let's take it to the 644 00:34:40,480 --> 00:34:43,759 Speaker 1: next level, or we're not just licensing, but we want 645 00:34:43,760 --> 00:34:47,080 Speaker 1: to be a behemoth. We want to sell into markets 646 00:34:47,480 --> 00:34:51,120 Speaker 1: around the world that US multinationals are also selling into. 647 00:34:51,800 --> 00:34:54,719 Speaker 3: You know, kind of comes back to like the core 648 00:34:54,960 --> 00:34:59,839 Speaker 3: ideological conversation that we're having about China. Not only are 649 00:34:59,840 --> 00:35:03,920 Speaker 3: there not Chinese global pharma companies in general, there are 650 00:35:04,000 --> 00:35:08,440 Speaker 3: very few global Chinese competitors. Right There's not a Chinese 651 00:35:08,560 --> 00:35:12,600 Speaker 3: version of Coca Cola, not a Chinese version of Procter gamble. 652 00:35:12,680 --> 00:35:15,400 Speaker 3: So you know, the question quickly becomes why, and the 653 00:35:15,400 --> 00:35:19,400 Speaker 3: answer is simple. The country is controlled by the Communist Party. 654 00:35:19,400 --> 00:35:24,360 Speaker 3: Of course, the Communist Party has one goal survive and thrive. Well, 655 00:35:24,960 --> 00:35:28,839 Speaker 3: you don't survive by going and conquering the US soft 656 00:35:28,920 --> 00:35:32,000 Speaker 3: drink market. You survive by keeping the people in your 657 00:35:32,000 --> 00:35:36,000 Speaker 3: country happy and supportive. Right they have had their political instability, 658 00:35:36,320 --> 00:35:39,120 Speaker 3: and so that is the over writing goal. And it's 659 00:35:39,120 --> 00:35:44,319 Speaker 3: for that reason that you don't see global commercial ambitions 660 00:35:44,360 --> 00:35:49,960 Speaker 3: from China. Car. Yeah, on cars, but you know that's 661 00:35:50,000 --> 00:35:53,160 Speaker 3: only because they had to like competing against Tesla, because 662 00:35:53,200 --> 00:35:57,200 Speaker 3: Tesla has taken over their car market. I would say this, 663 00:35:58,360 --> 00:36:02,480 Speaker 3: I do think it could change. I do think that 664 00:36:02,600 --> 00:36:07,680 Speaker 3: China could easily have a large, globally successful pharmaceutical company. 665 00:36:07,680 --> 00:36:11,600 Speaker 3: They have the people, they have the innovation, they have 666 00:36:12,440 --> 00:36:16,279 Speaker 3: the domestic market. All of the pieces are there, but 667 00:36:16,400 --> 00:36:18,600 Speaker 3: for whatever reason, it has not been prioritized. 668 00:36:19,480 --> 00:36:21,680 Speaker 2: I want to go back to the deep seek idea 669 00:36:21,920 --> 00:36:24,320 Speaker 2: and ask if you could talk maybe about the connection 670 00:36:24,480 --> 00:36:28,640 Speaker 2: between AI and biotech here, because we hear people say 671 00:36:28,960 --> 00:36:32,280 Speaker 2: like AI can do these amazing things. It can generate 672 00:36:32,760 --> 00:36:35,520 Speaker 2: formulas for potential new medicines, it can tell you how 673 00:36:35,560 --> 00:36:41,640 Speaker 2: to manufacture them easier, streamline the manufacturing. How is that 674 00:36:41,719 --> 00:36:43,000 Speaker 2: playing out in China? 675 00:36:43,440 --> 00:36:46,400 Speaker 3: I mean, Tracy, that is such a great question. So 676 00:36:46,719 --> 00:36:48,720 Speaker 3: I'll tell you. So last November, I was in China, 677 00:36:48,719 --> 00:36:50,880 Speaker 3: and you know, I'm a banker, like I said, you know, 678 00:36:51,160 --> 00:36:54,040 Speaker 3: just brokering these deals and stuff. So you go around 679 00:36:54,040 --> 00:36:56,080 Speaker 3: and you meet all the Chinese vcs. So we're seeing 680 00:36:56,080 --> 00:36:58,920 Speaker 3: this one VC, but unlike all the other ones, Like 681 00:36:59,000 --> 00:37:00,759 Speaker 3: the guy I was talking to you, like twenty eight 682 00:37:00,800 --> 00:37:02,520 Speaker 3: years old and he's like the head of this VC. 683 00:37:02,719 --> 00:37:05,360 Speaker 3: So he's attracted capital at a very young age. And 684 00:37:05,400 --> 00:37:07,440 Speaker 3: I asked him, just point blank, I'm like, so, why 685 00:37:07,480 --> 00:37:11,920 Speaker 3: are all your companies just making sort of like, you know, 686 00:37:13,120 --> 00:37:16,480 Speaker 3: slightly better molecules than the Western molecules. You're essentially doing 687 00:37:16,480 --> 00:37:19,719 Speaker 3: the fast follower model. He said, Tim, you haven't been 688 00:37:19,760 --> 00:37:23,840 Speaker 3: to going down province. He said, down there, guys my age, 689 00:37:24,239 --> 00:37:28,400 Speaker 3: they've never worked in the United States before. It Eli Lilly, 690 00:37:28,680 --> 00:37:31,880 Speaker 3: he said, the folks there they learned AI, like they 691 00:37:31,960 --> 00:37:34,880 Speaker 3: grew up with AI. They know all about AI. And 692 00:37:34,920 --> 00:37:38,040 Speaker 3: he said, you're gonna see a whole generation of biotech 693 00:37:38,080 --> 00:37:40,760 Speaker 3: coming out of China. It's gonna be first in class 694 00:37:41,040 --> 00:37:44,320 Speaker 3: AI driven innovation. You quickly get into the next conversation, 695 00:37:44,360 --> 00:37:47,719 Speaker 3: which is AI any good at developing drugs? And you know, 696 00:37:47,840 --> 00:37:51,120 Speaker 3: I would say, like a lot of things, maybe the 697 00:37:51,160 --> 00:37:54,720 Speaker 3: first couple generations aren't so good, but AI is getting 698 00:37:54,800 --> 00:37:56,920 Speaker 3: really good at developing drugs. 699 00:37:56,640 --> 00:37:58,239 Speaker 1: Because I can never tell. I was like, if you 700 00:37:58,239 --> 00:37:59,880 Speaker 1: always say, oh, hey, it's gonna be so great a 701 00:38:00,040 --> 00:38:02,440 Speaker 1: little big drugs, I can't tell if that's just one 702 00:38:02,440 --> 00:38:04,480 Speaker 1: of those things people say, but you think it's real. 703 00:38:04,680 --> 00:38:08,200 Speaker 3: I mean, here's my theory of AI. If you go 704 00:38:08,320 --> 00:38:12,120 Speaker 3: to London twenty years ago, you get in a taxi 705 00:38:12,160 --> 00:38:14,640 Speaker 3: and you'd say, take me to Paddington Station. No matter 706 00:38:14,640 --> 00:38:16,480 Speaker 3: where you were in London, the guy would know exactly 707 00:38:16,520 --> 00:38:20,799 Speaker 3: where to go because he'd memorize the street system knowledge. Well, 708 00:38:20,840 --> 00:38:23,200 Speaker 3: then one day came along this thing called a sat NAF, 709 00:38:23,560 --> 00:38:25,680 Speaker 3: and all of a sudden, you didn't need that guy anymore. 710 00:38:25,800 --> 00:38:29,120 Speaker 3: He was obsolete. Overnight Uber moved in, like they're like, 711 00:38:29,200 --> 00:38:30,800 Speaker 3: you know, saying, hey, Uber's going to crash you in 712 00:38:30,800 --> 00:38:33,280 Speaker 3: the Thames River. Of course that was false, and pretty 713 00:38:33,320 --> 00:38:37,040 Speaker 3: soon the market changed fundamentally. That's a medium dimensional problem. 714 00:38:37,160 --> 00:38:39,640 Speaker 3: In other words, a human being can figure out how 715 00:38:39,640 --> 00:38:42,560 Speaker 3: to navigate London with you know, four years of training, 716 00:38:43,120 --> 00:38:46,400 Speaker 3: but a computer can do it in four microseconds. Well, 717 00:38:47,480 --> 00:38:52,200 Speaker 3: coming up with all the drug possibilities against a potential target, 718 00:38:52,600 --> 00:38:56,080 Speaker 3: that's a high dimensional problem. That's too hard. The computer 719 00:38:56,239 --> 00:38:59,920 Speaker 3: actually can't do it. At least today. You can have 720 00:38:59,920 --> 00:39:01,879 Speaker 3: all the unbidio chips in the world. You can't do it. 721 00:39:02,360 --> 00:39:05,160 Speaker 3: But in contrast, these biologics that we're talking about, even 722 00:39:05,160 --> 00:39:09,400 Speaker 3: though they're more complex molecules, it's their complexity that lowers 723 00:39:09,400 --> 00:39:12,800 Speaker 3: the dimensionality of the problem because biologics have to fold 724 00:39:13,120 --> 00:39:15,840 Speaker 3: and fit in a very specific way, so all of 725 00:39:15,880 --> 00:39:18,560 Speaker 3: a sudden it starts to look like the London street map. 726 00:39:19,040 --> 00:39:22,520 Speaker 3: And so what we're seeing are these new companies coming 727 00:39:22,520 --> 00:39:25,800 Speaker 3: out of places like Google that are focused on making 728 00:39:25,840 --> 00:39:28,759 Speaker 3: biologics with AI, and they're really good. So we're going 729 00:39:28,840 --> 00:39:31,560 Speaker 3: to see some excellent AI based molecules. 730 00:39:31,719 --> 00:39:33,560 Speaker 1: Tim, when you get out of here, we're going to 731 00:39:33,680 --> 00:39:35,719 Speaker 1: just like rebook you for the next time we have 732 00:39:35,840 --> 00:39:37,759 Speaker 1: you on, because there's so much stuff here I want 733 00:39:37,760 --> 00:39:39,360 Speaker 1: to ask you about. But you're so great to have 734 00:39:39,440 --> 00:39:43,399 Speaker 1: you on. Tim Oppler, fantastic discussion, Truly the perfect guest 735 00:39:43,600 --> 00:39:44,680 Speaker 1: thank you so much for coming on. 736 00:39:44,680 --> 00:39:47,840 Speaker 3: Odlin, Joe, thank you so much, and Faci, thank. 737 00:39:47,640 --> 00:40:04,160 Speaker 1: You, Tracy. That was obviously a great episode. There's so 738 00:40:04,200 --> 00:40:07,080 Speaker 1: many different interesting things there. We're definitely gonna have to 739 00:40:07,120 --> 00:40:10,400 Speaker 1: have Tim back. I like, actually, like I'd love to 740 00:40:10,520 --> 00:40:13,760 Speaker 1: just talk about that last point he made about complexity. 741 00:40:14,000 --> 00:40:19,400 Speaker 1: And but the point about a major profit center for 742 00:40:19,480 --> 00:40:24,800 Speaker 1: the entire US healthcare system is the cost is borne 743 00:40:24,840 --> 00:40:28,000 Speaker 1: by the pharmaceutical companies to get access to the patients 744 00:40:28,600 --> 00:40:31,440 Speaker 1: is just like to me, that reveals so much, Like 745 00:40:31,480 --> 00:40:34,640 Speaker 1: that says so much right there about the sort of 746 00:40:35,040 --> 00:40:39,520 Speaker 1: tension between the profit motive and frankly speed of innovation. 747 00:40:39,960 --> 00:40:43,600 Speaker 2: Absolutely. The other thing I was thinking about is this 748 00:40:43,680 --> 00:40:47,239 Speaker 2: sort of gets to the idea that US protectionism of 749 00:40:47,360 --> 00:40:51,560 Speaker 2: strategic industries can sometimes backfire. This is like the line 750 00:40:51,600 --> 00:40:54,440 Speaker 2: that a lot of China has been taking this idea 751 00:40:54,560 --> 00:40:57,760 Speaker 2: that well, if you cut China off from key technologies, 752 00:40:57,840 --> 00:41:01,880 Speaker 2: key developments, it's just going to accelerate its own progress. 753 00:41:01,920 --> 00:41:05,640 Speaker 2: It's gonna, I guess, kick its research and development into 754 00:41:05,719 --> 00:41:09,839 Speaker 2: high gear. And I mean it kind of kind of 755 00:41:09,840 --> 00:41:12,720 Speaker 2: seems to be the case. I guess. I'm wondering also 756 00:41:12,880 --> 00:41:16,319 Speaker 2: what happens with the Biosecurity Act with the Trump administration, 757 00:41:16,400 --> 00:41:18,720 Speaker 2: because it's still in a legal limbo. 758 00:41:19,239 --> 00:41:21,520 Speaker 1: It would be interesting. There's so many more angles, you know, 759 00:41:21,520 --> 00:41:23,960 Speaker 1: it would be interesting to learn more about the sort 760 00:41:24,000 --> 00:41:27,840 Speaker 1: of generation of Chinese research scientists in the US that 761 00:41:27,960 --> 00:41:31,000 Speaker 1: felt they had hit a ceiling on how far they 762 00:41:31,000 --> 00:41:33,960 Speaker 1: were allowed to progress within the US companies, and then 763 00:41:34,000 --> 00:41:39,280 Speaker 1: they formed the basis of this booming industry. There's interesting 764 00:41:39,480 --> 00:41:43,200 Speaker 1: parallels in just this idea of like sheer scale, right, 765 00:41:43,440 --> 00:41:47,080 Speaker 1: and sheer scale of the number. You know, China is 766 00:41:47,080 --> 00:41:51,160 Speaker 1: a gigantic country with thousands and thousands of companies and 767 00:41:51,200 --> 00:41:54,120 Speaker 1: the advantage that affords you both in terms of cutting 768 00:41:54,200 --> 00:41:58,560 Speaker 1: edge research but also doing lagging edge production of various 769 00:41:58,600 --> 00:42:01,759 Speaker 1: things at size and at low cost. There's a lot 770 00:42:01,760 --> 00:42:02,839 Speaker 1: of interesting angles there. 771 00:42:02,920 --> 00:42:05,520 Speaker 2: There is a lot, and I expect we're gonna record 772 00:42:05,680 --> 00:42:07,960 Speaker 2: a few more episodes at least on this. We're going 773 00:42:08,040 --> 00:42:11,239 Speaker 2: to fast follow yeah, all of this. Shall we leave 774 00:42:11,280 --> 00:42:11,480 Speaker 2: it there? 775 00:42:11,560 --> 00:42:12,200 Speaker 1: Let's leave it there. 776 00:42:12,600 --> 00:42:15,719 Speaker 2: This has been another episode of the Authoughts podcast. I'm 777 00:42:15,760 --> 00:42:18,960 Speaker 2: Tracy Alloway. You can follow me at Tracy Alloway. 778 00:42:18,719 --> 00:42:21,400 Speaker 1: And I'm Joe Wisenthal. You can follow me at the Stalwart. 779 00:42:21,640 --> 00:42:25,320 Speaker 1: Follow Tim Oppler at Tim Oppler. Follow our producers Carman 780 00:42:25,400 --> 00:42:28,440 Speaker 1: Rodriguez at Carman armand dash Ol Bennett at Dashbot and 781 00:42:28,560 --> 00:42:31,719 Speaker 1: kel Brooks at Kelbrooks. More odd Lots content, go to 782 00:42:31,760 --> 00:42:34,160 Speaker 1: Bloomberg dot com slash odd Lots. We have all of 783 00:42:34,200 --> 00:42:36,960 Speaker 1: our episodes in a daily newsletter, and you can chet 784 00:42:36,960 --> 00:42:38,840 Speaker 1: about all of these topics twenty four to seven in 785 00:42:38,960 --> 00:42:42,000 Speaker 1: our discord Discord dot gg slash off lots. 786 00:42:42,320 --> 00:42:44,520 Speaker 2: And if you enjoy odd Lots, if you like it 787 00:42:44,560 --> 00:42:48,160 Speaker 2: when we talk about Chinese hamster ovarian cells, then please 788 00:42:48,239 --> 00:42:51,640 Speaker 2: leave us a positive review on your favorite podcast platform. 789 00:42:51,960 --> 00:42:55,040 Speaker 2: And remember, if you are a Bloomberg subscriber, you can 790 00:42:55,080 --> 00:42:58,759 Speaker 2: listen to all of our episodes absolutely ad free. All 791 00:42:58,800 --> 00:43:01,200 Speaker 2: you need to do is find the Bloomberg channel on 792 00:43:01,320 --> 00:43:05,320 Speaker 2: Apple Podcast and follow the instructions there. 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