1 00:00:00,240 --> 00:00:05,600 Speaker 1: Now here's a highlight from Coast to Coast AM on iHeartRadio. 2 00:00:05,120 --> 00:00:07,960 Speaker 2: And welcome back George Noriri, doctor Tim Harris with us 3 00:00:07,960 --> 00:00:11,760 Speaker 2: as we talk about biotechnology. Tim, we talked a little 4 00:00:11,760 --> 00:00:15,560 Speaker 2: bit about cancer. Is there any other affliction or disease 5 00:00:15,640 --> 00:00:17,959 Speaker 2: that you think biotechnology can tackle. 6 00:00:19,040 --> 00:00:22,640 Speaker 3: Yeah, there's a lot of interest in tackling autoimmune diseases 7 00:00:22,800 --> 00:00:26,760 Speaker 3: like hum got arthritis and must and multiple and uh 8 00:00:28,320 --> 00:00:31,520 Speaker 3: multiples crosis and lupus. 9 00:00:32,040 --> 00:00:33,000 Speaker 4: They're on all. 10 00:00:32,960 --> 00:00:37,040 Speaker 3: The big companies agendas. They're on the bithech companies agendas 11 00:00:37,040 --> 00:00:40,000 Speaker 3: as well, and it's not surprising. They afflict a large 12 00:00:40,040 --> 00:00:42,600 Speaker 3: number of people. And some of the medicines that have 13 00:00:42,640 --> 00:00:45,199 Speaker 3: been made so far and work in some people, but 14 00:00:45,280 --> 00:00:48,040 Speaker 3: they don't work in everybody, and we need to look 15 00:00:48,080 --> 00:00:50,839 Speaker 3: at those patients who do not do well on the 16 00:00:50,920 --> 00:00:54,000 Speaker 3: current drugs and find some new ones, and biotech will 17 00:00:54,040 --> 00:00:55,360 Speaker 3: definitely help that process. 18 00:00:56,960 --> 00:01:00,000 Speaker 2: Is artificial intelligence fueling some of this as well? 19 00:01:01,200 --> 00:01:01,840 Speaker 4: Yeah? I think so. 20 00:01:02,040 --> 00:01:07,720 Speaker 3: I mean, it's an oft used word, and I think 21 00:01:07,760 --> 00:01:11,760 Speaker 3: people kind of overhype artificial intelligence a little bit. It's 22 00:01:11,800 --> 00:01:16,400 Speaker 3: really large scale data analytics, and it's only as good 23 00:01:16,440 --> 00:01:21,840 Speaker 3: as the data you analyze and when you're analyzing data 24 00:01:21,880 --> 00:01:27,520 Speaker 3: on let's say, libraries of chemicals, they are completely clear 25 00:01:27,600 --> 00:01:30,000 Speaker 3: that you know what they are, and you can scan 26 00:01:30,120 --> 00:01:33,360 Speaker 3: the data and ask questions about those molecules and how 27 00:01:33,360 --> 00:01:37,040 Speaker 3: they find to different proteins. But if you're thinking about 28 00:01:37,040 --> 00:01:42,080 Speaker 3: biological systems, like what the effect of different things are 29 00:01:42,160 --> 00:01:46,440 Speaker 3: in different animals, then that's a very different story because 30 00:01:46,760 --> 00:01:51,360 Speaker 3: animals are biologically heterogeneous, and so you get a lot 31 00:01:51,360 --> 00:01:55,160 Speaker 3: of rather noisy data, and being able to use artificial 32 00:01:55,200 --> 00:02:00,520 Speaker 3: intelligence to examine that rather noisy data is not as 33 00:02:00,560 --> 00:02:04,680 Speaker 3: straightforward as some people think. So the idea that artificial 34 00:02:04,680 --> 00:02:07,840 Speaker 3: intelligence will not only discover your drug for you, but 35 00:02:07,880 --> 00:02:10,840 Speaker 3: it will also develop your drug for you is probably 36 00:02:10,880 --> 00:02:11,360 Speaker 3: not true. 37 00:02:11,400 --> 00:02:12,600 Speaker 4: Yet it may never be. 38 00:02:12,560 --> 00:02:17,480 Speaker 3: True because drug discovery and drug development is an experimental science. 39 00:02:17,560 --> 00:02:21,120 Speaker 3: You have to do experiments in cells and experiments in 40 00:02:21,160 --> 00:02:24,160 Speaker 3: animals to get to an answer that makes sense. 41 00:02:25,320 --> 00:02:28,040 Speaker 2: Are there any other ethical questions that need to be 42 00:02:28,120 --> 00:02:29,559 Speaker 2: considered with biotech? 43 00:02:30,840 --> 00:02:32,000 Speaker 4: And I think there's obviously. 44 00:02:32,919 --> 00:02:36,520 Speaker 3: I mean, in the old days, it was regulated. When 45 00:02:36,520 --> 00:02:39,760 Speaker 3: recomminant DNA started, it was regulated by the people doing 46 00:02:39,760 --> 00:02:42,959 Speaker 3: the work. They held a meeting at Asilomar in nineteen 47 00:02:43,000 --> 00:02:47,799 Speaker 3: seventy five where major players in recomminant DNA technology, you 48 00:02:47,840 --> 00:02:50,959 Speaker 3: all got together talked about what they thought the risks 49 00:02:51,000 --> 00:02:53,560 Speaker 3: were and came up with a set of guidelines which 50 00:02:53,919 --> 00:02:59,000 Speaker 3: actually showed were used by both here in America and 51 00:02:59,080 --> 00:03:04,000 Speaker 3: in the UK and elsewhere as guidelines for doing recominant 52 00:03:04,080 --> 00:03:08,560 Speaker 3: DNA research. I think that same kind of view, or 53 00:03:08,600 --> 00:03:13,080 Speaker 3: same kind of way of looking at the risks associated 54 00:03:13,120 --> 00:03:18,079 Speaker 3: with new technologies should be evaluated. There are obviously physical 55 00:03:18,160 --> 00:03:21,800 Speaker 3: risks as well as ethical questions, and those ethical questions 56 00:03:22,200 --> 00:03:26,200 Speaker 3: can be answered by good discussion between people who are 57 00:03:26,200 --> 00:03:28,240 Speaker 3: expert in those areas. 58 00:03:29,000 --> 00:03:31,120 Speaker 2: Tell me about the title of your book, Tim In 59 00:03:31,160 --> 00:03:35,960 Speaker 2: Pursuit of Unicorns. As we mentioned, they are mythical beings, 60 00:03:36,160 --> 00:03:39,320 Speaker 2: but you never know what could be developed in a laboratory. 61 00:03:39,920 --> 00:03:41,960 Speaker 3: Well, there was nothing to do with that. It was 62 00:03:42,000 --> 00:03:47,200 Speaker 3: to do with the fact that biotech companies worth over 63 00:03:47,240 --> 00:03:50,600 Speaker 3: a billion dollars were referred to or other tech companies 64 00:03:50,640 --> 00:03:54,119 Speaker 3: as well as biotech companies were referred to as unicorns. 65 00:03:54,840 --> 00:03:58,800 Speaker 3: And so I thought, after writing a book about the 66 00:03:58,840 --> 00:04:02,240 Speaker 3: history of the development of not only the technology but 67 00:04:02,280 --> 00:04:05,760 Speaker 3: also the companies that were set up to use the 68 00:04:05,840 --> 00:04:07,360 Speaker 3: technology to make. 69 00:04:07,560 --> 00:04:10,560 Speaker 4: A new drugs to help patients with. 70 00:04:12,600 --> 00:04:12,800 Speaker 3: There. 71 00:04:12,880 --> 00:04:13,840 Speaker 4: I thought that there. 72 00:04:13,840 --> 00:04:21,200 Speaker 3: Was an aspiration, unspoken aspiration actually to be a billion 73 00:04:21,240 --> 00:04:24,360 Speaker 3: dollar company. So in Pursuit of Unicorns was a play 74 00:04:24,400 --> 00:04:29,560 Speaker 3: on that. And some of the companies that were started 75 00:04:30,720 --> 00:04:35,520 Speaker 3: and had valuations of multiple billions of dollars disappeared because 76 00:04:35,560 --> 00:04:39,640 Speaker 3: the technology didn't work or the products didn't work. And 77 00:04:39,680 --> 00:04:45,320 Speaker 3: so the mythical nature of the Unicorn company is also 78 00:04:45,520 --> 00:04:49,320 Speaker 3: a fact, like the myth of unicorns themselves. So it 79 00:04:49,440 --> 00:04:53,080 Speaker 3: was really a play on the word unicorn and the 80 00:04:54,200 --> 00:04:55,880 Speaker 3: mythology associated with them. 81 00:04:55,880 --> 00:04:57,720 Speaker 4: And actually it enabled. 82 00:04:57,400 --> 00:05:02,040 Speaker 3: Us to use the Dutch tapestries that hang in the 83 00:05:02,080 --> 00:05:05,080 Speaker 3: cloisters in Northern Manhattan as the cover. 84 00:05:04,960 --> 00:05:05,760 Speaker 5: Art for the book. 85 00:05:06,200 --> 00:05:09,640 Speaker 3: And I've always said, always was told never judge a 86 00:05:09,680 --> 00:05:13,960 Speaker 3: book by its cover. The cover is fabulous, and I 87 00:05:14,000 --> 00:05:16,080 Speaker 3: hope that the book is as fabulous as the cover. 88 00:05:16,800 --> 00:05:21,919 Speaker 2: Good for you. We're growing some organs right now in 89 00:05:22,000 --> 00:05:26,000 Speaker 2: a laboratory. How extensive is that going to get? With biotech? 90 00:05:27,160 --> 00:05:33,400 Speaker 3: It's a important technology for helping to understand how tissues 91 00:05:33,520 --> 00:05:34,800 Speaker 3: work in three dimensions. 92 00:05:34,880 --> 00:05:37,240 Speaker 4: It's all very well having cells. 93 00:05:36,920 --> 00:05:39,680 Speaker 5: In a dish, but as you know, they're. 94 00:05:38,880 --> 00:05:41,920 Speaker 3: In two dimensions they're not really folding up or looking 95 00:05:42,040 --> 00:05:45,240 Speaker 3: like a heart or looking like a kidney, and being 96 00:05:45,240 --> 00:05:51,600 Speaker 3: able to use induce stem cell technology to make organs 97 00:05:51,600 --> 00:05:54,960 Speaker 3: of different kinds with different differentiated. 98 00:05:54,240 --> 00:05:56,120 Speaker 4: Cells in those organs. 99 00:05:56,320 --> 00:05:59,839 Speaker 3: It's going to be a very helpful tool for testing 100 00:06:00,880 --> 00:06:03,920 Speaker 3: the molecules that we want to use in patients before 101 00:06:03,920 --> 00:06:08,960 Speaker 3: we actually get to treating patients. Having human organized if 102 00:06:09,000 --> 00:06:11,880 Speaker 3: you want to call them that in dishes is a 103 00:06:11,960 --> 00:06:15,159 Speaker 3: very useful technology to go along with mice and rats 104 00:06:15,200 --> 00:06:18,480 Speaker 3: and other animals that are used to test these molecules 105 00:06:18,480 --> 00:06:22,040 Speaker 3: that we want to use in patients with these different diseases. 106 00:06:22,839 --> 00:06:26,040 Speaker 2: How far will that kind of technology go, the ability 107 00:06:26,080 --> 00:06:28,159 Speaker 2: to grow organs. 108 00:06:27,920 --> 00:06:31,679 Speaker 3: I think well, I mean there's a regenerative medicine piece 109 00:06:31,720 --> 00:06:34,839 Speaker 3: to that as well, which is it would be very 110 00:06:34,839 --> 00:06:38,960 Speaker 3: helpful to be able to grow different organs so that 111 00:06:39,080 --> 00:06:43,159 Speaker 3: you could from some of patient's own cells we create 112 00:06:43,240 --> 00:06:47,080 Speaker 3: an organ that was misfunctioning like a kidney. And that's 113 00:06:47,160 --> 00:06:50,640 Speaker 3: quite a long way away, but that is something that 114 00:06:50,680 --> 00:06:53,839 Speaker 3: people are clearly interested in doing, as well. 115 00:06:53,720 --> 00:06:55,600 Speaker 5: As having it as a test. 116 00:06:55,400 --> 00:06:58,240 Speaker 4: System for testing drugs of different kinds. 117 00:06:59,120 --> 00:07:01,720 Speaker 2: Tim and your care, what would you say is the 118 00:07:01,720 --> 00:07:04,080 Speaker 2: most exciting thing you've gone through? 119 00:07:05,720 --> 00:07:06,400 Speaker 4: Well, I have been. 120 00:07:06,800 --> 00:07:10,080 Speaker 3: Fortunately, I've been in a situation where I've been on 121 00:07:10,120 --> 00:07:13,520 Speaker 3: this journey for fifty years. What is the most exciting 122 00:07:13,560 --> 00:07:15,520 Speaker 3: thing were? I can tell you one of the most 123 00:07:16,000 --> 00:07:22,200 Speaker 3: exciting and kind of anxiety inducing was being a thirty 124 00:07:22,280 --> 00:07:26,080 Speaker 3: something year old scientist at the one of the biggest 125 00:07:26,160 --> 00:07:33,239 Speaker 3: patent battles trials in London where the Welcome Genetech TPA 126 00:07:33,400 --> 00:07:36,000 Speaker 3: trial went on and I was called up into the 127 00:07:36,080 --> 00:07:41,240 Speaker 3: dock to be an expert witness, and going through the 128 00:07:41,240 --> 00:07:44,000 Speaker 3: whole process of swearing on the Bible to tell the 129 00:07:44,040 --> 00:07:46,000 Speaker 3: truth and the whole truth and nothing but the truth 130 00:07:46,480 --> 00:07:50,400 Speaker 3: was not only pretty exciting but kind of anxiety inducing. 131 00:07:50,800 --> 00:07:53,640 Speaker 4: And when the patent barristers quiz you. 132 00:07:54,160 --> 00:07:57,679 Speaker 3: Over your technology and what you did in order to 133 00:07:58,000 --> 00:08:00,920 Speaker 3: identify whether it was novel or not not, which is 134 00:08:00,960 --> 00:08:04,280 Speaker 3: obviously important as part of a patent case, and they 135 00:08:04,320 --> 00:08:06,920 Speaker 3: seem to come across as knowing more about it than 136 00:08:06,960 --> 00:08:09,560 Speaker 3: you did when you had been spending three years of 137 00:08:09,600 --> 00:08:12,400 Speaker 3: your life doing it and they've been spending about three 138 00:08:12,400 --> 00:08:15,280 Speaker 3: months of their life learning the technology so that they 139 00:08:15,280 --> 00:08:18,760 Speaker 3: could ask you awkward questions WHI was some kind of 140 00:08:18,800 --> 00:08:22,960 Speaker 3: interesting and exciting in the same way because in that room, 141 00:08:23,040 --> 00:08:27,160 Speaker 3: that courtroom, there were four Nobel Prize winners and at 142 00:08:27,280 --> 00:08:30,760 Speaker 3: least two other people who subsequently received Nobel Prizes. So 143 00:08:31,120 --> 00:08:34,560 Speaker 3: it was pretty or inspiring for someone who was in 144 00:08:34,600 --> 00:08:35,440 Speaker 3: their early thirties. 145 00:08:35,520 --> 00:08:39,240 Speaker 2: So I said, have you had any disappointments in the field. 146 00:08:40,559 --> 00:08:40,679 Speaker 4: Oh? 147 00:08:40,760 --> 00:08:44,160 Speaker 3: Yeah, I mean piles of disappointments. It goes with the territory. 148 00:08:44,160 --> 00:08:47,400 Speaker 3: You know, when you're starting new companies with new technology, 149 00:08:48,000 --> 00:08:50,280 Speaker 3: sometimes it doesn't work. You think it's going to work, 150 00:08:50,320 --> 00:08:53,320 Speaker 3: but when you've done some key experiments, you realize that 151 00:08:53,360 --> 00:08:56,600 Speaker 3: actually it's not going to work, and so you have 152 00:08:56,760 --> 00:08:59,080 Speaker 3: to shut the doors on the company. I've done that 153 00:08:59,120 --> 00:09:03,440 Speaker 3: at least twice. And that is a that is they say, 154 00:09:03,480 --> 00:09:06,200 Speaker 3: you know that that's character building when you have to 155 00:09:06,200 --> 00:09:09,520 Speaker 3: close down a company that you started and you give 156 00:09:09,600 --> 00:09:11,960 Speaker 3: the you know, you give the reagents that you brought 157 00:09:12,440 --> 00:09:14,760 Speaker 3: to the company across the road because you've got nowhere 158 00:09:14,800 --> 00:09:16,640 Speaker 3: else to put them and you don't want to throw 159 00:09:16,679 --> 00:09:20,360 Speaker 3: them away. I don't think it was particularly character building. 160 00:09:20,440 --> 00:09:24,520 Speaker 3: I found it rather disappointing actually, And in the end 161 00:09:25,080 --> 00:09:27,960 Speaker 3: you bounce back and you find new technology and you 162 00:09:28,559 --> 00:09:31,440 Speaker 3: start over, you have to have a you have to 163 00:09:31,480 --> 00:09:37,240 Speaker 3: have some impatience to try to get some more useful. 164 00:09:36,840 --> 00:09:38,480 Speaker 4: Things for the patients. 165 00:09:38,480 --> 00:09:40,720 Speaker 3: And it's for most people in the industry, it's all 166 00:09:40,840 --> 00:09:44,320 Speaker 3: driven by wanting to make things that help people who 167 00:09:44,360 --> 00:09:46,880 Speaker 3: are worse off than they are. It's not driven by 168 00:09:47,520 --> 00:09:50,400 Speaker 3: the money that they're going to make. It's driven by 169 00:09:50,960 --> 00:09:53,599 Speaker 3: money that you can collect in order to get investments 170 00:09:53,640 --> 00:09:56,959 Speaker 3: in your company. But it's not driven by money you 171 00:09:57,000 --> 00:09:58,880 Speaker 3: can make. That kind of comes as an add on 172 00:09:58,920 --> 00:10:02,439 Speaker 3: if you're successful. It comes from wanting to do things 173 00:10:02,440 --> 00:10:03,520 Speaker 3: to help other people. 174 00:10:04,080 --> 00:10:06,520 Speaker 2: Are there any ethical considerations here? 175 00:10:08,679 --> 00:10:09,320 Speaker 4: I think there are. 176 00:10:09,360 --> 00:10:11,400 Speaker 3: As I said, you know, some of the technology is 177 00:10:12,800 --> 00:10:16,520 Speaker 3: there have been thoughts about modifying human embryos. 178 00:10:16,040 --> 00:10:18,319 Speaker 4: By using some of the gene editing. 179 00:10:18,080 --> 00:10:21,000 Speaker 3: Technology, but that is banned. You're not allowed to do that, 180 00:10:21,120 --> 00:10:24,160 Speaker 3: and for very good reason. And when you're thinking about 181 00:10:24,800 --> 00:10:28,600 Speaker 3: modifying humans, what would you modify anyway? Would you modify 182 00:10:28,640 --> 00:10:31,600 Speaker 3: their height, or would you modify their intelligence, or would 183 00:10:31,640 --> 00:10:35,199 Speaker 3: you modify what they look like? And beauty is in 184 00:10:35,240 --> 00:10:37,560 Speaker 3: the eye of the beholder. I don't think you know 185 00:10:38,160 --> 00:10:40,679 Speaker 3: what you want to change anyway. You don't know what 186 00:10:40,720 --> 00:10:42,840 Speaker 3: to change in order to change beauty. 187 00:10:42,920 --> 00:10:44,960 Speaker 4: At the moment, it's such. 188 00:10:44,720 --> 00:10:48,200 Speaker 3: A combination of different genes and different gene functions that 189 00:10:48,240 --> 00:10:51,600 Speaker 3: you wouldn't know how to do that anyway. So I 190 00:10:51,640 --> 00:10:54,439 Speaker 3: think it's just as well that that kind of technology 191 00:10:54,480 --> 00:10:56,079 Speaker 3: is not being used. 192 00:10:55,840 --> 00:10:58,960 Speaker 2: For that tim How much more do we know today 193 00:10:59,000 --> 00:11:01,920 Speaker 2: than we did twenty five years ago, and what has 194 00:11:02,200 --> 00:11:04,120 Speaker 2: created that knowledge? 195 00:11:04,880 --> 00:11:09,040 Speaker 3: It's almost exponential, the amount of knowledge that we have 196 00:11:09,160 --> 00:11:12,640 Speaker 3: now compared to what we used to have and the 197 00:11:12,720 --> 00:11:14,679 Speaker 3: number of you only if you look at the number 198 00:11:14,720 --> 00:11:18,560 Speaker 3: of scientific journals that publish different papers, there's been an 199 00:11:18,679 --> 00:11:22,040 Speaker 3: enormous expansion in the number of journals and the number 200 00:11:22,080 --> 00:11:25,200 Speaker 3: of papers that are published in those journals. And it's 201 00:11:25,240 --> 00:11:28,520 Speaker 3: to do again with access to capital. It's different kind 202 00:11:28,559 --> 00:11:31,559 Speaker 3: of capital because it's capital in the form of grants 203 00:11:31,600 --> 00:11:35,760 Speaker 3: that the academics can apply for. They're very competitive. Only 204 00:11:36,280 --> 00:11:38,920 Speaker 3: six to ten percent of the grants that are applied 205 00:11:38,960 --> 00:11:43,920 Speaker 3: for get funded. But that's enough to push the technology forward. 206 00:11:44,400 --> 00:11:50,000 Speaker 3: And that's that's fortunate because we rely on the academic 207 00:11:50,000 --> 00:11:52,679 Speaker 3: community to be the hotbed for innovation. 208 00:11:54,040 --> 00:11:55,520 Speaker 2: It's exciting, though, isn't it. 209 00:11:56,280 --> 00:11:59,800 Speaker 3: Well, you couldn't have a more exciting thing to do. 210 00:12:00,040 --> 00:12:03,280 Speaker 3: So when if there any kids listening to this and 211 00:12:03,280 --> 00:12:06,040 Speaker 3: they're thinking about what they want to do for a career, 212 00:12:06,679 --> 00:12:10,320 Speaker 3: starting your own company and trying to get it to work, 213 00:12:10,360 --> 00:12:12,960 Speaker 3: and moving forward to all the different issues you have 214 00:12:13,040 --> 00:12:15,679 Speaker 3: to deal with us. Nothing more satisfying at the end 215 00:12:15,679 --> 00:12:18,040 Speaker 3: of the day when you go home at ten o'clock 216 00:12:18,040 --> 00:12:21,520 Speaker 3: at night probably than to sit there and think, well, actually, 217 00:12:21,559 --> 00:12:25,920 Speaker 3: we achieve something today, and we will achieve something tomorrow, 218 00:12:26,240 --> 00:12:27,840 Speaker 3: and so I'm going to get up and go to work. 219 00:12:27,840 --> 00:12:30,400 Speaker 3: And on a Sunday night, you want to be thinking, oh, 220 00:12:30,480 --> 00:12:31,440 Speaker 3: it's Monday tomorrow, I. 221 00:12:31,440 --> 00:12:34,920 Speaker 4: Can go to work. Not Monday tomorrow, I have to 222 00:12:34,960 --> 00:12:37,560 Speaker 4: go to work. You don't want to think like that. 223 00:12:37,840 --> 00:12:41,160 Speaker 3: Unfortunately, most of my working life I've been in a 224 00:12:41,200 --> 00:12:44,199 Speaker 3: situation where good, it's Monday, I can go to work. 225 00:12:45,640 --> 00:12:49,920 Speaker 2: It seems like people as they get older develop Allheimer's disease, dementia, 226 00:12:50,080 --> 00:12:53,840 Speaker 2: Parkinson's disease. Are we ever going to have biotechnology tackle 227 00:12:53,920 --> 00:12:54,760 Speaker 2: those things? 228 00:12:55,080 --> 00:12:57,360 Speaker 3: They are tackling them now. There are a number of 229 00:12:57,880 --> 00:13:01,880 Speaker 3: drugs that are actually have been marketed recently by different 230 00:13:01,880 --> 00:13:06,880 Speaker 3: companies which are looking at removing some of the proteins 231 00:13:06,880 --> 00:13:11,880 Speaker 3: that are responsible for Alzheimer's disease, and there are similar 232 00:13:12,760 --> 00:13:17,440 Speaker 3: activities in Parkinson's disease. So they are certainly diseases which 233 00:13:17,480 --> 00:13:21,640 Speaker 3: are high on the agenda of many companies to try 234 00:13:21,679 --> 00:13:25,079 Speaker 3: to find better products to treat those diseases, which are 235 00:13:25,440 --> 00:13:30,280 Speaker 3: highly debilitating for patients and hugely expensive for the community. 236 00:13:31,600 --> 00:13:34,480 Speaker 2: How would you grade the United States and its efforts 237 00:13:34,480 --> 00:13:35,359 Speaker 2: with biotech? 238 00:13:37,040 --> 00:13:41,240 Speaker 3: Number one, for sure, for a variety of reasons. One 239 00:13:41,559 --> 00:13:45,480 Speaker 3: access to capital, two centersive accedence from an academic. 240 00:13:46,760 --> 00:13:49,400 Speaker 5: Point of view, and just scale. 241 00:13:49,200 --> 00:13:49,280 Speaker 4: And. 242 00:13:51,080 --> 00:13:53,960 Speaker 3: I guess experience. There's a lot of biotech experience and 243 00:13:54,520 --> 00:13:57,199 Speaker 3: experience in innovolation, and that's true in the tech sector 244 00:13:57,240 --> 00:13:59,200 Speaker 3: as well as the biotech. 245 00:13:58,760 --> 00:13:59,679 Speaker 4: Sector for that matter. 246 00:14:00,160 --> 00:14:04,440 Speaker 3: But we shouldn't be complacent because other countries are catching up. 247 00:14:04,520 --> 00:14:04,840 Speaker 4: First. 248 00:14:05,400 --> 00:14:08,960 Speaker 3: When I was a student and would read the literature, 249 00:14:09,520 --> 00:14:14,280 Speaker 3: I guess one in probably five hundred papers would come 250 00:14:14,320 --> 00:14:19,520 Speaker 3: from the Far East. Now it's probably one in three 251 00:14:20,280 --> 00:14:21,120 Speaker 3: or one in two. 252 00:14:21,640 --> 00:14:24,520 Speaker 4: Papers that I read come from the Far East. 253 00:14:25,000 --> 00:14:27,600 Speaker 3: That is great in one way because it just increases 254 00:14:27,640 --> 00:14:31,080 Speaker 3: the amount of knowledge, But that's kind of concerning in 255 00:14:31,120 --> 00:14:34,080 Speaker 3: another way, because I think America needs to keep its 256 00:14:34,160 --> 00:14:36,680 Speaker 3: lead in innovation and I. 257 00:14:36,640 --> 00:14:39,200 Speaker 4: Come from the UK, so I want the UK to do. 258 00:14:39,120 --> 00:14:43,120 Speaker 3: The same thing and invest in science and innovation. 259 00:14:43,280 --> 00:14:45,080 Speaker 4: And you have to work to do that. 260 00:14:45,240 --> 00:14:49,200 Speaker 3: You have to explain to the politicians that science is 261 00:14:49,320 --> 00:14:53,840 Speaker 3: driving an awful lot of good for the economy as 262 00:14:53,880 --> 00:14:56,640 Speaker 3: well as for the patients with the diseases that are 263 00:14:56,680 --> 00:14:59,000 Speaker 3: costing sort sort of money to manage. 264 00:15:00,000 --> 00:15:02,400 Speaker 2: Did you ever see the movie Tim Jurassic Park. 265 00:15:03,400 --> 00:15:08,120 Speaker 3: I've seen every Jurassic Park movie. The first one was 266 00:15:08,160 --> 00:15:11,160 Speaker 3: probably the best and the most inter That is, Michael 267 00:15:11,240 --> 00:15:14,400 Speaker 3: Crichton pushed the boundaries. I mean, you couldn't do what 268 00:15:14,560 --> 00:15:19,000 Speaker 3: he suggested, but it was close to what you could do, 269 00:15:19,280 --> 00:15:20,800 Speaker 3: and the fact that it was. 270 00:15:20,720 --> 00:15:23,040 Speaker 4: Close to what you could do made it more believable and. 271 00:15:23,120 --> 00:15:24,800 Speaker 5: Made for a pretty good film. 272 00:15:24,840 --> 00:15:29,320 Speaker 2: Actually, we will we ever get there, and I don't. 273 00:15:29,120 --> 00:15:29,760 Speaker 5: Think we will. 274 00:15:29,880 --> 00:15:32,360 Speaker 3: In fact, there are lots of ideas about how you 275 00:15:32,400 --> 00:15:36,400 Speaker 3: can manipulate what minism so that you can recreate dinosaurs, 276 00:15:36,400 --> 00:15:38,760 Speaker 3: but I don't think that's going to happen, And I mean, 277 00:15:38,800 --> 00:15:41,680 Speaker 3: I'm not sure that people are really that interested in 278 00:15:41,720 --> 00:15:45,280 Speaker 3: doing that. I've got a jeep at home, and I've 279 00:15:45,320 --> 00:15:48,840 Speaker 3: got some and I've got a Jurassic Park sticker on 280 00:15:48,880 --> 00:15:52,680 Speaker 3: the side of the jeep and my mirror has these 281 00:15:52,720 --> 00:15:55,480 Speaker 3: creatures are closer than you think, or whatever it was. 282 00:15:55,760 --> 00:15:59,680 Speaker 3: Whatever the quote was, so I'm I like to think 283 00:15:59,720 --> 00:16:00,880 Speaker 3: about Jurassic Falcon. 284 00:16:02,000 --> 00:16:03,440 Speaker 4: It was a pretty glassy movie. 285 00:16:03,640 --> 00:16:06,920 Speaker 1: Listen to more Coast to Coast AM every weeknight at 286 00:16:06,920 --> 00:16:10,200 Speaker 1: one am Eastern, and go to Coast to coastam dot 287 00:16:10,200 --> 00:16:11,000 Speaker 1: com for more