WEBVTT 00:00:00.723 --> 00:00:02.899 So let me ask for a show of hands. 00:00:02.899 --> 00:00:07.091 How many people here are over the age of 48? 00:00:07.091 --> 00:00:09.972 Well, there do seem to be a few. NOTE Paragraph 00:00:09.972 --> 00:00:12.147 Well, congratulations, 00:00:12.147 --> 00:00:16.017 because if you look at this particular slide of U.S. life expectancy, 00:00:16.017 --> 00:00:19.115 you are now in excess of the average life span 00:00:19.115 --> 00:00:21.902 of somebody who was born in 1900. NOTE Paragraph 00:00:21.902 --> 00:00:25.436 But look what happened in the course of that century. 00:00:25.436 --> 00:00:27.098 If you follow that curve, 00:00:27.098 --> 00:00:29.712 you'll see that it starts way down there. 00:00:29.712 --> 00:00:32.181 There's that dip there for the 1918 flu. 00:00:32.181 --> 00:00:34.603 And here we are at 2010, 00:00:34.603 --> 00:00:37.659 average life expectancy of a child born today, age 79, 00:00:37.659 --> 00:00:39.555 and we are not done yet. 00:00:39.555 --> 00:00:40.890 Now, that's the good news. 00:00:40.890 --> 00:00:42.731 But there's still a lot of work to do. NOTE Paragraph 00:00:42.731 --> 00:00:44.365 So, for instance, if you ask, 00:00:44.365 --> 00:00:47.091 how many diseases do we now know 00:00:47.091 --> 00:00:49.150 the exact molecular basis? 00:00:49.150 --> 00:00:52.708 Turns out it's about 4,000, which is pretty amazing, 00:00:52.708 --> 00:00:54.944 because most of those molecular discoveries 00:00:54.944 --> 00:00:57.609 have just happened in the last little while. 00:00:57.609 --> 00:01:00.905 It's exciting to see that in terms of what we've learned, 00:01:00.905 --> 00:01:03.012 but how many of those 4,000 diseases 00:01:03.012 --> 00:01:05.360 now have treatments available? 00:01:05.360 --> 00:01:07.248 Only about 250. 00:01:07.248 --> 00:01:10.006 So we have this huge challenge, this huge gap. NOTE Paragraph 00:01:10.006 --> 00:01:12.586 You would think this wouldn't be too hard, 00:01:12.586 --> 00:01:14.112 that we would simply have the ability 00:01:14.112 --> 00:01:17.138 to take this fundamental information that we're learning 00:01:17.138 --> 00:01:20.283 about how it is that basic biology teaches us 00:01:20.283 --> 00:01:22.185 about the causes of disease 00:01:22.185 --> 00:01:25.211 and build a bridge across this yawning gap 00:01:25.211 --> 00:01:27.591 between what we've learned about basic science 00:01:27.591 --> 00:01:29.086 and its application, 00:01:29.086 --> 00:01:32.343 a bridge that would look maybe something like this, 00:01:32.343 --> 00:01:35.955 where you'd have to put together a nice shiny way 00:01:35.955 --> 00:01:38.923 to get from one side to the other. NOTE Paragraph 00:01:38.923 --> 00:01:41.523 Well, wouldn't it be nice if it was that easy? 00:01:41.523 --> 00:01:43.668 Unfortunately, it's not. 00:01:43.668 --> 00:01:46.259 In reality, trying to go from fundamental knowledge 00:01:46.259 --> 00:01:48.923 to its application is more like this. 00:01:48.923 --> 00:01:50.838 There are no shiny bridges. 00:01:50.838 --> 00:01:52.490 You sort of place your bets. 00:01:52.490 --> 00:01:54.451 Maybe you've got a swimmer and a rowboat 00:01:54.451 --> 00:01:55.975 and a sailboat and a tugboat 00:01:55.975 --> 00:01:57.703 and you set them off on their way, 00:01:57.703 --> 00:02:00.367 and the rains come and the lightning flashes, 00:02:00.367 --> 00:02:01.881 and oh my gosh, there are sharks in the water 00:02:01.881 --> 00:02:03.902 and the swimmer gets into trouble, 00:02:03.902 --> 00:02:05.486 and, uh oh, the swimmer drowned 00:02:05.486 --> 00:02:08.698 and the sailboat capsized, 00:02:08.698 --> 00:02:10.399 and that tugboat, well, it hit the rocks, 00:02:10.399 --> 00:02:13.039 and maybe if you're lucky, somebody gets across. NOTE Paragraph 00:02:13.039 --> 00:02:15.028 Well, what does this really look like? 00:02:15.028 --> 00:02:17.082 Well, what is it to make a therapeutic, anyway? 00:02:17.082 --> 00:02:20.083 What's a drug? A drug is made up 00:02:20.083 --> 00:02:22.408 of a small molecule of hydrogen, carbon, 00:02:22.408 --> 00:02:24.659 oxygen, nitrogen, and a few other atoms 00:02:24.659 --> 00:02:26.882 all cobbled together in a shape, 00:02:26.882 --> 00:02:29.259 and it's those shapes that determine whether, in fact, 00:02:29.259 --> 00:02:32.572 that particular drug is going to hit its target. 00:02:32.572 --> 00:02:34.795 Is it going to land where it's supposed to? 00:02:34.795 --> 00:02:37.951 So look at this picture here -- a lot of shapes dancing around for you. 00:02:37.951 --> 00:02:40.338 Now what you need to do, if you're trying to develop 00:02:40.338 --> 00:02:41.795 a new treatment for autism 00:02:41.795 --> 00:02:44.014 or Alzheimer's disease or cancer 00:02:44.014 --> 00:02:45.806 is to find the right shape in that mix 00:02:45.806 --> 00:02:48.723 that will ultimately provide benefit and will be safe. 00:02:48.723 --> 00:02:51.890 And when you look at what happens to that pipeline, 00:02:51.890 --> 00:02:53.391 you start out maybe with thousands, 00:02:53.391 --> 00:02:55.033 tens of thousands of compounds. 00:02:55.033 --> 00:02:57.182 You weed down through various steps 00:02:57.182 --> 00:02:58.565 that cause many of these to fail. 00:02:58.565 --> 00:03:01.905 Ultimately, maybe you can run a clinical trial with four or five of these, 00:03:01.905 --> 00:03:04.947 and if all goes well, 14 years after you started, 00:03:04.947 --> 00:03:06.958 you will get one approval. 00:03:06.958 --> 00:03:08.988 And it will cost you upwards of a billion dollars 00:03:08.988 --> 00:03:11.132 for that one success. NOTE Paragraph 00:03:11.132 --> 00:03:14.436 So we have to look at this pipeline the way an engineer would, 00:03:14.436 --> 00:03:15.644 and say, "How can we do better?" 00:03:15.644 --> 00:03:18.321 And that's the main theme of what I want to say to you this morning. 00:03:18.321 --> 00:03:20.134 How can we make this go faster? 00:03:20.134 --> 00:03:23.199 How can we make it more successful? NOTE Paragraph 00:03:23.199 --> 00:03:24.540 Well, let me tell you about a few examples 00:03:24.540 --> 00:03:26.796 where this has actually worked. 00:03:26.796 --> 00:03:29.747 One that has just happened in the last few months 00:03:29.747 --> 00:03:33.457 is the successful approval of a drug for cystic fibrosis. 00:03:33.457 --> 00:03:35.111 But it's taken a long time to get there. 00:03:35.111 --> 00:03:39.713 Cystic fibrosis had its molecular cause discovered in 1989 00:03:39.713 --> 00:03:42.041 by my group working with another group in Toronto, 00:03:42.041 --> 00:03:44.176 discovering what the mutation was in a particular gene 00:03:44.176 --> 00:03:45.804 on chromosome 7. 00:03:45.804 --> 00:03:47.842 That picture you see there? 00:03:47.842 --> 00:03:49.945 Here it is. That's the same kid. 00:03:49.945 --> 00:03:53.289 That's Danny Bessette, 23 years later, 00:03:53.289 --> 00:03:54.568 because this is the year, 00:03:54.568 --> 00:03:57.006 and it's also the year where Danny got married, 00:03:57.006 --> 00:04:00.063 where we have, for the first time, the approval by the FDA 00:04:00.063 --> 00:04:03.800 of a drug that precisely targets the defect in cystic fibrosis 00:04:03.800 --> 00:04:05.738 based upon all this molecular understanding. 00:04:05.738 --> 00:04:07.162 That's the good news. 00:04:07.162 --> 00:04:10.791 The bad news is, this drug doesn't actually treat all cases of cystic fibrosis, 00:04:10.791 --> 00:04:13.000 and it won't work for Danny, and we're still waiting 00:04:13.000 --> 00:04:15.335 for that next generation to help him. NOTE Paragraph 00:04:15.335 --> 00:04:18.530 But it took 23 years to get this far. That's too long. 00:04:18.530 --> 00:04:20.223 How do we go faster? NOTE Paragraph 00:04:20.223 --> 00:04:22.921 Well, one way to go faster is to take advantage of technology, 00:04:22.921 --> 00:04:25.585 and a very important technology that we depend on 00:04:25.585 --> 00:04:27.881 for all of this is the human genome, 00:04:27.881 --> 00:04:30.469 the ability to be able to look at a chromosome, 00:04:30.469 --> 00:04:33.139 to unzip it, to pull out all the DNA, 00:04:33.139 --> 00:04:36.089 and to be able to then read out the letters in that DNA code, 00:04:36.089 --> 00:04:38.170 the A's, C's, G's and T's 00:04:38.170 --> 00:04:41.441 that are our instruction book and the instruction book for all living things, 00:04:41.441 --> 00:04:42.955 and the cost of doing this, 00:04:42.955 --> 00:04:45.610 which used to be in the hundreds of millions of dollars, 00:04:45.610 --> 00:04:47.523 has in the course of the last 10 years 00:04:47.523 --> 00:04:49.922 fallen faster than Moore's Law, down to the point 00:04:49.922 --> 00:04:53.929 where it is less than 10,000 dollars today to have your genome sequenced, or mine, 00:04:53.929 --> 00:04:57.728 and we're headed for the $1,000 genome fairly soon. 00:04:57.728 --> 00:04:59.054 Well, that's exciting. 00:04:59.054 --> 00:05:02.864 How does that play out in terms of application to a disease? NOTE Paragraph 00:05:02.864 --> 00:05:05.144 I want to tell you about another disorder. 00:05:05.144 --> 00:05:07.456 This one is a disorder which is quite rare. 00:05:07.456 --> 00:05:10.224 It's called Hutchinson-Gilford progeria, 00:05:10.224 --> 00:05:13.529 and it is the most dramatic form of premature aging. 00:05:13.529 --> 00:05:17.312 Only about one in every four million kids has this disease, 00:05:17.312 --> 00:05:20.672 and in a simple way, what happens is, 00:05:20.672 --> 00:05:23.373 because of a mutation in a particular gene, 00:05:23.373 --> 00:05:26.040 a protein is made that's toxic to the cell 00:05:26.040 --> 00:05:28.337 and it causes these individuals to age 00:05:28.337 --> 00:05:30.921 at about seven times the normal rate. NOTE Paragraph 00:05:30.921 --> 00:05:34.064 Let me show you a video of what that does to the cell. 00:05:34.064 --> 00:05:37.199 The normal cell, if you looked at it under the microscope, 00:05:37.199 --> 00:05:40.088 would have a nucleus sitting in the middle of the cell, 00:05:40.088 --> 00:05:43.967 which is nice and round and smooth in its boundaries 00:05:43.967 --> 00:05:45.722 and it looks kind of like that. 00:05:45.722 --> 00:05:47.586 A progeria cell, on the other hand, 00:05:47.586 --> 00:05:50.688 because of this toxic protein called progerin, 00:05:50.688 --> 00:05:52.972 has these lumps and bumps in it. 00:05:52.972 --> 00:05:55.987 So what we would like to do after discovering this 00:05:55.987 --> 00:05:57.839 back in 2003 00:05:57.839 --> 00:06:01.057 is to come up with a way to try to correct that. 00:06:01.057 --> 00:06:04.145 Well again, by knowing something about the molecular pathways, 00:06:04.145 --> 00:06:06.144 it was possible to pick 00:06:06.144 --> 00:06:08.761 one of those many, many compounds that might have been useful 00:06:08.761 --> 00:06:10.222 and try it out. 00:06:10.222 --> 00:06:12.797 In an experiment done in cell culture 00:06:12.797 --> 00:06:14.839 and shown here in a cartoon, 00:06:14.839 --> 00:06:17.533 if you take that particular compound 00:06:17.533 --> 00:06:20.789 and you add it to that cell that has progeria, 00:06:20.789 --> 00:06:23.010 and you watch to see what happened, 00:06:23.010 --> 00:06:25.972 in just 72 hours, that cell becomes, 00:06:25.972 --> 00:06:28.240 for all purposes that we can determine, 00:06:28.240 --> 00:06:30.082 almost like a normal cell. NOTE Paragraph 00:06:30.082 --> 00:06:34.423 Well that was exciting, but would it actually work in a real human being? 00:06:34.423 --> 00:06:37.808 This has led, in the space of only four years 00:06:37.808 --> 00:06:41.309 from the time the gene was discovered to the start of a clinical trial, 00:06:41.309 --> 00:06:43.506 to a test of that very compound. 00:06:43.506 --> 00:06:45.469 And the kids that you see here 00:06:45.469 --> 00:06:48.031 all volunteered to be part of this, 00:06:48.031 --> 00:06:49.492 28 of them, 00:06:49.492 --> 00:06:52.587 and you can see as soon as the picture comes up 00:06:52.587 --> 00:06:55.969 that they are in fact a remarkable group of young people 00:06:55.969 --> 00:06:57.388 all afflicted by this disease, 00:06:57.388 --> 00:06:59.637 all looking quite similar to each other. 00:06:59.637 --> 00:07:01.311 And instead of telling you more about it, 00:07:01.311 --> 00:07:05.297 I'm going to invite one of them, Sam Berns from Boston, 00:07:05.297 --> 00:07:07.730 who's here this morning, to come up on the stage 00:07:07.730 --> 00:07:09.950 and tell us about his experience 00:07:09.950 --> 00:07:11.860 as a child affected with progeria. 00:07:11.860 --> 00:07:15.918 Sam is 15 years old. His parents, Scott Berns and Leslie Gordon, 00:07:15.918 --> 00:07:18.039 both physicians, are here with us this morning as well. 00:07:18.039 --> 00:07:20.577 Sam, please have a seat. NOTE Paragraph 00:07:20.577 --> 00:07:27.897 (Applause) NOTE Paragraph 00:07:27.897 --> 00:07:30.075 So Sam, why don't you tell these folks 00:07:30.075 --> 00:07:33.450 what it's like being affected with this condition called progeria? NOTE Paragraph 00:07:33.450 --> 00:07:37.258 Sam Burns: Well, progeria limits me in some ways. 00:07:37.258 --> 00:07:41.222 I cannot play sports or do physical activities, 00:07:41.222 --> 00:07:44.426 but I have been able to take interest in things 00:07:44.426 --> 00:07:47.405 that progeria, luckily, does not limit. 00:07:47.405 --> 00:07:49.962 But when there is something that I really do want to do 00:07:49.962 --> 00:07:52.979 that progeria gets in the way of, like marching band 00:07:52.979 --> 00:07:56.405 or umpiring, we always find a way to do it, 00:07:56.405 --> 00:07:59.922 and that just shows that progeria isn't in control of my life. NOTE Paragraph 00:07:59.922 --> 00:08:01.632 (Applause) NOTE Paragraph 00:08:01.632 --> 00:08:03.668 Francis Collins: So what would you like to say to researchers 00:08:03.668 --> 00:08:06.765 here in the auditorium and others listening to this? 00:08:06.765 --> 00:08:09.362 What would you say to them both about research on progeria 00:08:09.362 --> 00:08:11.248 and maybe about other conditions as well? NOTE Paragraph 00:08:11.248 --> 00:08:14.394 SB: Well, research on progeria has come so far 00:08:14.394 --> 00:08:16.636 in less than 15 years, 00:08:16.636 --> 00:08:21.005 and that just shows the drive that researchers can have 00:08:21.005 --> 00:08:24.423 to get this far, and it really means a lot 00:08:24.423 --> 00:08:27.674 to myself and other kids with progeria, 00:08:27.674 --> 00:08:30.498 and it shows that if that drive exists, 00:08:30.498 --> 00:08:33.099 anybody can cure any disease, 00:08:33.099 --> 00:08:37.046 and hopefully progeria can be cured in the near future, 00:08:37.046 --> 00:08:40.803 and so we can eliminate those 4,000 diseases 00:08:40.803 --> 00:08:43.810 that Francis was talking about. NOTE Paragraph 00:08:43.810 --> 00:08:46.939 FC: Excellent. So Sam took the day off from school today 00:08:46.939 --> 00:08:52.074 to be here, and he is — (Applause) -- 00:08:52.074 --> 00:08:56.890 He is, by the way, a straight-A+ student in the ninth grade 00:08:56.890 --> 00:08:58.223 in his school in Boston. 00:08:58.223 --> 00:09:00.424 Please join me in thanking and welcoming Sam. 00:09:00.424 --> 00:09:04.077 SB: Thank you very much. FC: Well done. Well done, buddy. 00:09:04.077 --> 00:09:15.895 (Applause) NOTE Paragraph 00:09:16.886 --> 00:09:18.602 So I just want to say a couple more things 00:09:18.602 --> 00:09:21.734 about that particular story, and then try to generalize 00:09:21.734 --> 00:09:24.230 how could we have stories of success 00:09:24.230 --> 00:09:27.743 all over the place for these diseases, as Sam says, 00:09:27.743 --> 00:09:30.262 these 4,000 that are waiting for answers. 00:09:30.262 --> 00:09:32.134 You might have noticed that the drug 00:09:32.134 --> 00:09:34.903 that is now in clinical trial for progeria 00:09:34.903 --> 00:09:36.667 is not a drug that was designed for that. 00:09:36.667 --> 00:09:39.529 It's such a rare disease, it would be hard for a company 00:09:39.529 --> 00:09:43.259 to justify spending hundreds of millions of dollars to generate a drug. 00:09:43.259 --> 00:09:45.419 This is a drug that was developed for cancer. 00:09:45.419 --> 00:09:47.584 Turned out, it didn't work very well for cancer, 00:09:47.584 --> 00:09:49.907 but it has exactly the right properties, the right shape, 00:09:49.907 --> 00:09:52.799 to work for progeria, and that's what's happened. 00:09:52.799 --> 00:09:56.027 Wouldn't it be great if we could do that more systematically? 00:09:56.027 --> 00:09:59.823 Could we, in fact, encourage all the companies that are out there 00:09:59.823 --> 00:10:01.661 that have drugs in their freezers 00:10:01.661 --> 00:10:03.863 that are known to be safe in humans 00:10:03.863 --> 00:10:06.155 but have never actually succeeded in terms 00:10:06.155 --> 00:10:09.011 of being effective for the treatments they were tried for? 00:10:09.011 --> 00:10:11.395 Now we're learning about all these new molecular pathways -- 00:10:11.395 --> 00:10:14.474 some of those could be repositioned or repurposed, 00:10:14.474 --> 00:10:16.873 or whatever word you want to use, for new applications, 00:10:16.873 --> 00:10:19.842 basically teaching old drugs new tricks. 00:10:19.842 --> 00:10:22.529 That could be a phenomenal, valuable activity. 00:10:22.529 --> 00:10:25.575 We have many discussions now between NIH and companies 00:10:25.575 --> 00:10:27.699 about doing this that are looking very promising. NOTE Paragraph 00:10:27.699 --> 00:10:30.313 And you could expect quite a lot to come from this. 00:10:30.313 --> 00:10:33.352 There are quite a number of success stories one can point to 00:10:33.352 --> 00:10:35.705 about how this has led to major advances. 00:10:35.705 --> 00:10:37.912 The first drug for HIV/AIDS 00:10:37.912 --> 00:10:39.641 was not developed for HIV/AIDS. 00:10:39.641 --> 00:10:42.159 It was developed for cancer. It was AZT. 00:10:42.159 --> 00:10:44.160 It didn't work very well for cancer, but became 00:10:44.160 --> 00:10:46.276 the first successful antiretroviral, 00:10:46.276 --> 00:10:48.848 and you can see from the table there are others as well. NOTE Paragraph 00:10:48.848 --> 00:10:52.492 So how do we actually make that a more generalizable effort? 00:10:52.492 --> 00:10:54.716 Well, we have to come up with a partnership 00:10:54.716 --> 00:10:57.576 between academia, government, the private sector, 00:10:57.576 --> 00:11:00.029 and patient organizations to make that so. 00:11:00.029 --> 00:11:01.679 At NIH, we have started this new 00:11:01.679 --> 00:11:04.879 National Center for Advancing Translational Sciences. 00:11:04.879 --> 00:11:08.494 It just started last December, and this is one of its goals. NOTE Paragraph 00:11:08.494 --> 00:11:09.935 Let me tell you another thing we could do. 00:11:09.935 --> 00:11:12.854 Wouldn't it be nice to be able to a test a drug 00:11:12.854 --> 00:11:15.225 to see if it's effective and safe 00:11:15.225 --> 00:11:17.326 without having to put patients at risk, 00:11:17.326 --> 00:11:19.879 because that first time you're never quite sure? 00:11:19.879 --> 00:11:22.030 How do we know, for instance, whether drugs are safe 00:11:22.030 --> 00:11:25.275 before we give them to people? We test them on animals. 00:11:25.275 --> 00:11:27.917 And it's not all that reliable, and it's costly, 00:11:27.917 --> 00:11:29.607 and it's time-consuming. 00:11:29.607 --> 00:11:32.470 Suppose we could do this instead on human cells. 00:11:32.470 --> 00:11:34.702 You probably know, if you've been paying attention 00:11:34.702 --> 00:11:36.002 to some of the science literature 00:11:36.002 --> 00:11:37.658 that you can now take a skin cell 00:11:37.658 --> 00:11:40.539 and encourage it to become a liver cell 00:11:40.539 --> 00:11:43.614 or a heart cell or a kidney cell or a brain cell for any of us. 00:11:43.614 --> 00:11:46.766 So what if you used those cells as your test 00:11:46.766 --> 00:11:49.711 for whether a drug is going to work and whether it's going to be safe? NOTE Paragraph 00:11:49.711 --> 00:11:53.942 Here you see a picture of a lung on a chip. 00:11:53.942 --> 00:11:57.463 This is something created by the Wyss Institute in Boston, 00:11:57.463 --> 00:12:00.638 and what they have done here, if we can run the little video, 00:12:00.638 --> 00:12:02.774 is to take cells from an individual, 00:12:02.774 --> 00:12:05.883 turn them into the kinds of cells that are present in the lung, 00:12:05.883 --> 00:12:07.688 and determine what would happen 00:12:07.688 --> 00:12:10.765 if you added to this various drug compounds 00:12:10.765 --> 00:12:13.230 to see if they are toxic or safe. 00:12:13.230 --> 00:12:15.501 You can see this chip even breathes. 00:12:15.501 --> 00:12:18.118 It has an air channel. It has a blood channel. 00:12:18.118 --> 00:12:19.821 And it has cells in between 00:12:19.821 --> 00:12:22.259 that allow you to see what happens when you add a compound. 00:12:22.259 --> 00:12:24.031 Are those cells happy or not? 00:12:24.031 --> 00:12:27.062 You can do this same kind of chip technology 00:12:27.062 --> 00:12:29.271 for kidneys, for hearts, for muscles, 00:12:29.271 --> 00:12:31.735 all the places where you want to see whether a drug 00:12:31.735 --> 00:12:34.016 is going to be a problem, for the liver. NOTE Paragraph 00:12:34.016 --> 00:12:37.064 And ultimately, because you can do this for the individual, 00:12:37.064 --> 00:12:39.278 we could even see this moving to the point 00:12:39.278 --> 00:12:42.719 where the ability to develop and test medicines 00:12:42.719 --> 00:12:45.905 will be you on a chip, what we're trying to say here is 00:12:45.905 --> 00:12:49.406 the individualizing of the process of developing drugs 00:12:49.406 --> 00:12:51.654 and testing their safety. NOTE Paragraph 00:12:51.654 --> 00:12:53.306 So let me sum up. 00:12:53.306 --> 00:12:55.566 We are in a remarkable moment here. 00:12:55.566 --> 00:12:57.669 For me, at NIH now for almost 20 years, 00:12:57.669 --> 00:13:00.270 there has never been a time where there was more excitement 00:13:00.270 --> 00:13:02.855 about the potential that lies in front of us. 00:13:02.855 --> 00:13:04.647 We have made all these discoveries 00:13:04.647 --> 00:13:07.012 pouring out of laboratories across the world. 00:13:07.012 --> 00:13:10.374 What do we need to capitalize on this? First of all, we need resources. 00:13:10.374 --> 00:13:13.929 This is research that's high-risk, sometimes high-cost. 00:13:13.929 --> 00:13:15.900 The payoff is enormous, both in terms of health 00:13:15.900 --> 00:13:18.780 and in terms of economic growth. We need to support that. 00:13:18.780 --> 00:13:21.081 Second, we need new kinds of partnerships 00:13:21.081 --> 00:13:23.302 between academia and government and the private sector 00:13:23.302 --> 00:13:26.649 and patient organizations, just like the one I've been describing here, 00:13:26.649 --> 00:13:30.229 in terms of the way in which we could go after repurposing new compounds. 00:13:30.229 --> 00:13:33.465 And third, and maybe most important, we need talent. 00:13:33.465 --> 00:13:35.606 We need the best and the brightest 00:13:35.606 --> 00:13:38.463 from many different disciplines to come and join this effort -- 00:13:38.463 --> 00:13:40.909 all ages, all different groups -- 00:13:40.909 --> 00:13:42.996 because this is the time, folks. 00:13:42.996 --> 00:13:46.621 This is the 21st-century biology that you've been waiting for, 00:13:46.621 --> 00:13:49.083 and we have the chance to take that 00:13:49.083 --> 00:13:51.573 and turn it into something which will, in fact, 00:13:51.573 --> 00:13:53.903 knock out disease. That's my goal. 00:13:53.903 --> 00:13:55.787 I hope that's your goal. 00:13:55.787 --> 00:13:58.467 I think it'll be the goal of the poets and the muppets 00:13:58.467 --> 00:14:00.476 and the surfers and the bankers 00:14:00.476 --> 00:14:02.754 and all the other people who join this stage 00:14:02.754 --> 00:14:04.504 and think about what we're trying to do here 00:14:04.504 --> 00:14:05.669 and why it matters. 00:14:05.669 --> 00:14:08.439 It matters for now. It matters as soon as possible. 00:14:08.439 --> 00:14:11.557 If you don't believe me, just ask Sam. NOTE Paragraph 00:14:11.557 --> 00:14:13.000 Thank you all very much. NOTE Paragraph 00:14:13.000 --> 00:14:17.831 (Applause)