1 00:00:00,090 --> 00:00:02,430 The following content is provided under a Creative 2 00:00:02,430 --> 00:00:03,820 Commons license. 3 00:00:03,820 --> 00:00:06,030 Your support will help MIT OpenCourseWare 4 00:00:06,030 --> 00:00:10,120 continue to offer high quality educational resources for free. 5 00:00:10,120 --> 00:00:12,660 To make a donation or to view additional materials 6 00:00:12,660 --> 00:00:16,620 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:16,620 --> 00:00:17,992 at ocw.mit.edu. 8 00:00:20,473 --> 00:00:21,890 WILLIAM BONVILLIAN: I'm just going 9 00:00:21,890 --> 00:00:25,700 to do Venter very quickly since we went through this so 10 00:00:25,700 --> 00:00:27,680 thoroughly previously. 11 00:00:27,680 --> 00:00:32,580 But just a few wonderful snapshots, these are The Nature 12 00:00:32,580 --> 00:00:34,440 and the Science covers. 13 00:00:34,440 --> 00:00:38,640 So Venter did Science and Collins and the NIH Genome 14 00:00:38,640 --> 00:00:40,350 Project did Nature. 15 00:00:40,350 --> 00:00:42,830 Both published on the same day. 16 00:00:42,830 --> 00:00:46,470 It's kind of the truce that got arranged between the two sides. 17 00:00:46,470 --> 00:00:49,950 And there they are competing with each other. 18 00:00:49,950 --> 00:00:54,810 And then there's Collins on his Harley 19 00:00:54,810 --> 00:00:59,390 and Venter on one of his wild racing yachts. 20 00:00:59,390 --> 00:01:02,610 And Venter is only interested in sailing if it's 21 00:01:02,610 --> 00:01:04,440 dangerous is best I can figure. 22 00:01:04,440 --> 00:01:09,180 So and then there's this classic picture Venter in the business 23 00:01:09,180 --> 00:01:10,605 suit and wearing the lab coat. 24 00:01:10,605 --> 00:01:12,480 You know this is all about the contradictions 25 00:01:12,480 --> 00:01:14,190 in the model he's trying to pursue, 26 00:01:14,190 --> 00:01:17,100 that fundamental Solara contradiction. 27 00:01:17,100 --> 00:01:18,698 So these are just a few. 28 00:01:18,698 --> 00:01:20,490 AUDIENCE: Who's the dude on the motorcycle? 29 00:01:20,490 --> 00:01:22,240 WILLIAM BONVILLIAN: That's Francis Collins 30 00:01:22,240 --> 00:01:25,470 who's currently head of NIH, who's also a blues singer too, 31 00:01:25,470 --> 00:01:27,870 by the way. 32 00:01:27,870 --> 00:01:31,930 So both these folks are a little out there. 33 00:01:31,930 --> 00:01:33,180 They're both terrific talents. 34 00:01:33,180 --> 00:01:34,350 They're remarkable talents. 35 00:01:34,350 --> 00:01:35,940 We've talked at length about Venter. 36 00:01:35,940 --> 00:01:40,140 I think that for the purposes here, what I want to say 37 00:01:40,140 --> 00:01:46,440 is that, I put Venter in here as an example of someone who 38 00:01:46,440 --> 00:01:50,670 ran into all the contradictions in the life science innovation 39 00:01:50,670 --> 00:01:53,130 model, right? 40 00:01:53,130 --> 00:01:57,120 The fact that it was very hard for NIH 41 00:01:57,120 --> 00:01:59,280 to look outside of biology. 42 00:01:59,280 --> 00:02:01,050 It was all biologists all the time. 43 00:02:01,050 --> 00:02:04,530 Venter of course, was trained as a biologist 44 00:02:04,530 --> 00:02:07,740 but began moving into this computer territory, 45 00:02:07,740 --> 00:02:11,039 based upon some advances that Leroy Hood made before him 46 00:02:11,039 --> 00:02:14,670 and building on Hood's work and doing a lot of innovations 47 00:02:14,670 --> 00:02:17,310 like the genome shotgun approach that we 48 00:02:17,310 --> 00:02:21,720 talked about two weeks ago. 49 00:02:21,720 --> 00:02:24,270 He ran into all of the other series 50 00:02:24,270 --> 00:02:27,030 of these institutional problems at NIH 51 00:02:27,030 --> 00:02:29,310 as an innovation organization. 52 00:02:29,310 --> 00:02:32,460 That it became very hard for him to stand up 53 00:02:32,460 --> 00:02:38,520 a completely different pathway to technology advance 54 00:02:38,520 --> 00:02:40,740 and science advance. 55 00:02:40,740 --> 00:02:43,530 That the system was locked into a different route, 56 00:02:43,530 --> 00:02:46,450 and they were very unaccepting. 57 00:02:46,450 --> 00:02:49,780 In fact, ostracized him as he moved in a different kind 58 00:02:49,780 --> 00:02:50,470 of direction. 59 00:02:50,470 --> 00:02:52,990 So he ultimately leaves. 60 00:02:52,990 --> 00:02:55,620 And then we run into this change agent kind of idea. 61 00:02:55,620 --> 00:02:59,440 He becomes a competitor and forces change 62 00:02:59,440 --> 00:03:01,870 in the institution which he left as it 63 00:03:01,870 --> 00:03:06,070 tries to keep pace with him so as not to be embarrassed. 64 00:03:06,070 --> 00:03:08,710 Hence the NIH Genome Initiative. 65 00:03:08,710 --> 00:03:11,740 And you know it's just an illustration 66 00:03:11,740 --> 00:03:14,920 and a very personal kind of way, which is why I put it in here, 67 00:03:14,920 --> 00:03:17,560 of some of the larger institutional challenges 68 00:03:17,560 --> 00:03:19,730 that we've been talking about before. 69 00:03:19,730 --> 00:03:22,360 So let me leave the Venter model here 70 00:03:22,360 --> 00:03:28,330 and go into the chapter, chapter seven in our textbook. 71 00:03:31,580 --> 00:03:35,570 So it's not simply NIH, but it's the health care delivery 72 00:03:35,570 --> 00:03:41,030 system itself that has these legacy sector features. 73 00:03:41,030 --> 00:03:45,100 And as we talked about earlier on in class, 74 00:03:45,100 --> 00:03:47,810 the US is pretty good at standing up new technologies 75 00:03:47,810 --> 00:03:50,650 in open fields. 76 00:03:50,650 --> 00:03:52,527 It runs into real trouble trying to stand up 77 00:03:52,527 --> 00:03:54,610 technologies in these kind of established sectors. 78 00:03:54,610 --> 00:03:56,980 So in the life science territory, 79 00:03:56,980 --> 00:04:00,070 we did pretty well by creating this completely new 80 00:04:00,070 --> 00:04:03,670 biotech model which was a we were able to get 81 00:04:03,670 --> 00:04:05,140 a lot of advance out of. 82 00:04:05,140 --> 00:04:08,020 But when it comes back to fixing the whole health care delivery 83 00:04:08,020 --> 00:04:11,260 system, that's proven far more problematic. 84 00:04:11,260 --> 00:04:15,100 And you know that's been a major thorny political issue 85 00:04:15,100 --> 00:04:19,660 for three administrations in a row now. 86 00:04:19,660 --> 00:04:22,270 And they've each had rocky results 87 00:04:22,270 --> 00:04:25,000 trying to pursue that question of how we're going to organize 88 00:04:25,000 --> 00:04:26,980 health care delivery systems. 89 00:04:26,980 --> 00:04:30,790 So there's lots of legacy sector characteristics in health care 90 00:04:30,790 --> 00:04:34,240 delivery, perverse prices and price structures, 91 00:04:34,240 --> 00:04:36,640 established infrastructure and institutional structures 92 00:04:36,640 --> 00:04:41,530 like NIH, very powerful vested interests. 93 00:04:41,530 --> 00:04:44,470 We can see some of those in NIH. 94 00:04:44,470 --> 00:04:47,410 But we can certainly see them in other parts of the health care 95 00:04:47,410 --> 00:04:49,300 delivery system. 96 00:04:49,300 --> 00:04:51,710 They're sustained by public habits. 97 00:04:51,710 --> 00:04:52,210 Right? 98 00:04:52,210 --> 00:04:56,990 So it's very hard to tell Medicare patients 99 00:04:56,990 --> 00:05:03,980 that their full cost repayment system with no patient stake 100 00:05:03,980 --> 00:05:06,260 doesn't make a lot of economic sense. 101 00:05:06,260 --> 00:05:07,760 They're not going to be enthusiastic 102 00:05:07,760 --> 00:05:09,870 about the alternatives. 103 00:05:09,870 --> 00:05:14,780 So these are structured and sustained by public habits. 104 00:05:14,780 --> 00:05:15,280 Steph? 105 00:05:15,280 --> 00:05:18,763 AUDIENCE: Can you define no patient stake? 106 00:05:18,763 --> 00:05:20,930 WILLIAM BONVILLIAN: You know Medicare is a full cost 107 00:05:20,930 --> 00:05:22,640 reimbursement system. 108 00:05:22,640 --> 00:05:26,890 And it was organized for medicine 109 00:05:26,890 --> 00:05:31,150 as we knew it 30 or 40 years ago, right? 110 00:05:31,150 --> 00:05:33,730 It was a professional delivery system. 111 00:05:33,730 --> 00:05:37,690 And you would pay for the cost of the service, whatever 112 00:05:37,690 --> 00:05:39,280 that was. 113 00:05:39,280 --> 00:05:41,470 You would not pay for results, because the results 114 00:05:41,470 --> 00:05:43,565 in the medical system are too uncertain. 115 00:05:43,565 --> 00:05:45,790 It was very hard to figure out how 116 00:05:45,790 --> 00:05:52,450 to estimate a pay for results kind of outcomes oriented 117 00:05:52,450 --> 00:05:54,460 repayment system for these medical systems. 118 00:05:54,460 --> 00:06:00,130 So as a result, it became a fee for service, full payment 119 00:06:00,130 --> 00:06:02,710 system for the medical profession. 120 00:06:02,710 --> 00:06:05,080 And that created a huge incentive 121 00:06:05,080 --> 00:06:08,440 to run up costs for which the federal government would 122 00:06:08,440 --> 00:06:10,810 provide you full reimbursement, regardless 123 00:06:10,810 --> 00:06:12,940 of what the outcomes were. 124 00:06:12,940 --> 00:06:16,030 So trying to shift those economic signals 125 00:06:16,030 --> 00:06:19,660 has proven very problematic. 126 00:06:19,660 --> 00:06:21,910 You know, Obamacare attempted to change some of those, 127 00:06:21,910 --> 00:06:25,990 not without controversy within the medical professions. 128 00:06:25,990 --> 00:06:29,760 It's averse to change and innovation. 129 00:06:29,760 --> 00:06:31,680 The established knowledge base tends 130 00:06:31,680 --> 00:06:33,780 to get locked in for the professions that 131 00:06:33,780 --> 00:06:35,670 are participants here. 132 00:06:35,670 --> 00:06:38,290 There's a real problem with collective action. 133 00:06:38,290 --> 00:06:41,800 In other words, it's scattered amongst thousands 134 00:06:41,800 --> 00:06:44,530 of institutions and getting them to act collectively 135 00:06:44,530 --> 00:06:46,810 in a different kind of organizational model 136 00:06:46,810 --> 00:06:48,460 is not simple. 137 00:06:48,460 --> 00:06:51,750 And there are serious governmental and institutional 138 00:06:51,750 --> 00:06:52,650 problems here. 139 00:06:52,650 --> 00:06:54,360 So I won't go through the whole litany. 140 00:06:54,360 --> 00:06:59,070 But you begin to get a sense of how you can take the legacy 141 00:06:59,070 --> 00:07:03,280 sector analytical framework and apply it 142 00:07:03,280 --> 00:07:08,480 to a big economic sector that service based like health care. 143 00:07:08,480 --> 00:07:10,570 It's not just technology based like say energy, 144 00:07:10,570 --> 00:07:12,730 it's service based. 145 00:07:12,730 --> 00:07:14,350 But that analytical framework will 146 00:07:14,350 --> 00:07:17,770 work to a surprising extent for both kinds of sectors. 147 00:07:21,210 --> 00:07:22,343 And let me go into-- 148 00:07:22,343 --> 00:07:24,510 let me go out and get this one on the table as well, 149 00:07:24,510 --> 00:07:27,780 the PCAST, propelling innovation and drug discovery. 150 00:07:27,780 --> 00:07:31,290 It's a very important critique of the system 151 00:07:31,290 --> 00:07:33,330 that came out in 2012. 152 00:07:33,330 --> 00:07:36,450 PCAST is the President's Council of Advisors 153 00:07:36,450 --> 00:07:37,800 on Science and Technology. 154 00:07:40,540 --> 00:07:45,050 They advise the president on science and tech policy. 155 00:07:45,050 --> 00:07:48,488 We had a very strong PCAST under Eric Lander 156 00:07:48,488 --> 00:07:50,030 during the Obama administration a lot 157 00:07:50,030 --> 00:07:51,447 did a lot of breakthrough reports. 158 00:07:51,447 --> 00:07:54,830 I think frankly, this is one of the most significant. 159 00:07:54,830 --> 00:07:57,560 They really took a hard look at the health care innovation 160 00:07:57,560 --> 00:08:00,658 system and identified a lot of trouble, a lot of which we've 161 00:08:00,658 --> 00:08:01,700 been talking about today. 162 00:08:01,700 --> 00:08:04,310 But this really spelled a fair amount of it out. 163 00:08:04,310 --> 00:08:08,600 So the NIH budget doubled between '98 and 2003. 164 00:08:08,600 --> 00:08:12,030 It hasn't kept up with the inflation costs since then. 165 00:08:12,030 --> 00:08:14,533 But in parallel, we've had rising costs 166 00:08:14,533 --> 00:08:16,700 of clinical trials, which have now actually reached, 167 00:08:16,700 --> 00:08:21,870 according this report $1.8 billion per drug. 168 00:08:21,870 --> 00:08:24,420 There's a new patent cliff that's 169 00:08:24,420 --> 00:08:26,680 looming for pharmaceutical companies. 170 00:08:26,680 --> 00:08:28,290 So you may have noticed that farmers 171 00:08:28,290 --> 00:08:31,260 are busy merging and divesting themselves 172 00:08:31,260 --> 00:08:34,380 of their R&D operations. 173 00:08:34,380 --> 00:08:36,780 It looks like a problematic trend. 174 00:08:36,780 --> 00:08:38,539 So why is this? 175 00:08:38,539 --> 00:08:41,549 It's largely because of this upcoming patent cliff. 176 00:08:41,549 --> 00:08:44,550 Drugs with annual sales of $200 billion dollars 177 00:08:44,550 --> 00:08:51,060 will go off patent between 2010 and I think it's 2015 actually. 178 00:08:51,060 --> 00:08:53,160 So this has already been happening. 179 00:08:53,160 --> 00:08:54,730 And this has forced a restructuring 180 00:08:54,730 --> 00:08:57,720 and amongst the historic pharmaceuticals. 181 00:08:57,720 --> 00:09:00,580 Replacement revenues are not readily available. 182 00:09:00,580 --> 00:09:04,200 So hence this whole set of merger activities. 183 00:09:04,200 --> 00:09:06,540 And they've been, the pharmaceuticals 184 00:09:06,540 --> 00:09:10,990 have been curtailing their R&D as a result. Venture capital 185 00:09:10,990 --> 00:09:13,350 at the time this was written was in general decline 186 00:09:13,350 --> 00:09:15,960 for all sectors, including biopharma. 187 00:09:15,960 --> 00:09:19,950 Now frankly that's in significant part recovered now. 188 00:09:19,950 --> 00:09:21,390 That's now better than it was. 189 00:09:21,390 --> 00:09:25,470 This tends to be a cyclical kind of pattern for biotechs 190 00:09:25,470 --> 00:09:31,090 in the health care area. 191 00:09:31,090 --> 00:09:34,560 But at this particularly time, first time these C 192 00:09:34,560 --> 00:09:38,220 deals for biotechs were down really quite significantly. 193 00:09:38,220 --> 00:09:41,180 And so that's always an issue that we're 194 00:09:41,180 --> 00:09:43,473 going to have to confront. 195 00:09:43,473 --> 00:09:44,890 In other words, biotechs only work 196 00:09:44,890 --> 00:09:47,950 if the venture capital system is willing to be quite supportive. 197 00:09:47,950 --> 00:09:50,110 So ups and downs in the venture capital sector 198 00:09:50,110 --> 00:09:52,330 can have a pretty strong effect as to whether we 199 00:09:52,330 --> 00:09:55,990 get drugs emerging into the marketplace that we need. 200 00:09:55,990 --> 00:10:00,910 Despite R&D growth in past decades, drug output was flat 201 00:10:00,910 --> 00:10:02,710 and productivity was declining. 202 00:10:02,710 --> 00:10:05,380 And this report invented a concept 203 00:10:05,380 --> 00:10:09,010 called Eroom's law, which is the opposite of Moore's law. 204 00:10:09,010 --> 00:10:13,500 The cost of drug development doubles every nine years. 205 00:10:13,500 --> 00:10:16,130 And the results decline. 206 00:10:16,130 --> 00:10:19,740 So it's the opposite of Moore's law Martin? 207 00:10:19,740 --> 00:10:22,450 AUDIENCE: I was going to ask, so VCs function on a seven year 208 00:10:22,450 --> 00:10:24,500 time cycle for an exit? 209 00:10:24,500 --> 00:10:27,202 So are biotech VCs on a 20 year cycle? 210 00:10:27,202 --> 00:10:28,160 WILLIAM BONVILLIAN: No. 211 00:10:28,160 --> 00:10:28,780 No. 212 00:10:28,780 --> 00:10:31,870 But what they will do is they will 213 00:10:31,870 --> 00:10:33,750 go with different levels of funding, 214 00:10:33,750 --> 00:10:37,420 you know, A round, B round, C round, which they'll tie-- 215 00:10:37,420 --> 00:10:41,260 they'll benchmark often to the clinical trial process. 216 00:10:41,260 --> 00:10:44,470 So they'll be able to manage their risk in moving 217 00:10:44,470 --> 00:10:47,810 from one stage to another. 218 00:10:47,810 --> 00:10:50,110 And again, no other sector has thought of this. 219 00:10:50,110 --> 00:10:52,690 They haven't figured out an alternative model. 220 00:10:52,690 --> 00:10:55,120 The failure rate for new drugs in clinical trials 221 00:10:55,120 --> 00:10:56,780 is increasing. 222 00:10:56,780 --> 00:11:01,630 So as of 2003, that was 91% fail. 223 00:11:01,630 --> 00:11:04,090 Imagine trying to construct a profit model 224 00:11:04,090 --> 00:11:05,680 around a 91% failure rate. 225 00:11:05,680 --> 00:11:07,210 It's not simple. 226 00:11:07,210 --> 00:11:10,000 Can still be done, because the rewards can 227 00:11:10,000 --> 00:11:12,840 be so big through the blockbuster model. 228 00:11:12,840 --> 00:11:15,030 And the guarantee of monopoly rents to the patents 229 00:11:15,030 --> 00:11:15,863 that you get for it. 230 00:11:16,925 --> 00:11:20,820 AUDIENCE: [INAUDIBLE] Are things getting more stringent or are 231 00:11:20,820 --> 00:11:22,333 the diseases getting harder? 232 00:11:22,333 --> 00:11:23,750 WILLIAM BONVILLIAN: I think it's-- 233 00:11:23,750 --> 00:11:25,600 you know, what am I to say? 234 00:11:25,600 --> 00:11:30,270 And I don't think there are easy answers here on this, Max. 235 00:11:30,270 --> 00:11:33,020 I think that we've done the low hanging fruit. 236 00:11:33,020 --> 00:11:35,447 And the problem is getting much more complicated. 237 00:11:35,447 --> 00:11:36,780 Chris, you're nodding your head. 238 00:11:36,780 --> 00:11:38,520 Does that seem like a good answer to you? 239 00:11:38,520 --> 00:11:39,990 AUDIENCE: Yeah, I think definitely. 240 00:11:39,990 --> 00:11:41,490 Because like the next frontier seems 241 00:11:41,490 --> 00:11:43,420 to be like personalized medicine, 242 00:11:43,420 --> 00:11:46,740 which is a huge kind of new moving problem. 243 00:11:46,740 --> 00:11:50,290 Because a lot of the difficulties 244 00:11:50,290 --> 00:11:53,370 of kind of commercializing that and kind of making that viable. 245 00:11:53,370 --> 00:11:58,010 Although there's a lot of hype around it, which is great. 246 00:11:58,010 --> 00:11:59,980 WILLIAM BONVILLIAN: Right. 247 00:11:59,980 --> 00:12:01,440 AUDIENCE: Don't get too much data. 248 00:12:01,440 --> 00:12:04,115 It could be that maybe they're not doing as many? 249 00:12:04,115 --> 00:12:06,490 AUDIENCE: Yeah, well maybe now we're doing like way more. 250 00:12:06,490 --> 00:12:08,780 Or like, it's very easy. 251 00:12:08,780 --> 00:12:10,898 You know they say the worst lies are statistics. 252 00:12:10,898 --> 00:12:12,440 WILLIAM BONVILLIAN: Yes, that's true. 253 00:12:12,440 --> 00:12:14,680 And all of these could be lies. 254 00:12:14,680 --> 00:12:16,930 AUDIENCE: Well, it's just one way 255 00:12:16,930 --> 00:12:18,430 of looking at the problem, right? 256 00:12:18,430 --> 00:12:19,730 It's a way of showcasing it. 257 00:12:19,730 --> 00:12:22,820 But I would want to see the whole data to see why, 258 00:12:22,820 --> 00:12:23,930 the context behind it. 259 00:12:23,930 --> 00:12:24,290 WILLIAM BONVILLIAN: Yeah. 260 00:12:24,290 --> 00:12:26,630 I mean generally speaking, this report was well received. 261 00:12:26,630 --> 00:12:28,463 In other words, the experts in the community 262 00:12:28,463 --> 00:12:32,660 thought that this PCAST report was on to something, 263 00:12:32,660 --> 00:12:36,560 that they had identified some pretty critical problems. 264 00:12:36,560 --> 00:12:40,040 So time to market for drugs has also been growing. 265 00:12:40,040 --> 00:12:43,130 So eight years to market was 50 years ago 266 00:12:43,130 --> 00:12:46,430 is 14 years to market now. 267 00:12:46,430 --> 00:12:48,080 And the longer it takes, the more 268 00:12:48,080 --> 00:12:50,192 you eat up your monopoly rent period, 269 00:12:50,192 --> 00:12:52,400 which means you've got to make higher profits sooner, 270 00:12:52,400 --> 00:12:54,410 which means the short term charges for the drugs 271 00:12:54,410 --> 00:12:55,450 get higher. 272 00:12:55,450 --> 00:12:57,230 And you're driven and more and more 273 00:12:57,230 --> 00:12:59,090 towards a blockbuster recovery model. 274 00:12:59,090 --> 00:13:03,740 So this is problematic. 275 00:13:03,740 --> 00:13:07,220 And you know it particularly affects small companies 276 00:13:07,220 --> 00:13:11,030 and biotechs that can't really manage that long term risk 277 00:13:11,030 --> 00:13:13,050 period. 278 00:13:13,050 --> 00:13:15,050 And there's a gap between research and product 279 00:13:15,050 --> 00:13:17,900 development as well. 280 00:13:17,900 --> 00:13:19,577 And the whole advances in science 281 00:13:19,577 --> 00:13:21,410 are requiring different kind of models here. 282 00:13:21,410 --> 00:13:23,175 So now there's much more focus on-- we'll 283 00:13:23,175 --> 00:13:24,800 talk about convergence in a little bit. 284 00:13:24,800 --> 00:13:27,890 But much more focus on multidisciplinary teams 285 00:13:27,890 --> 00:13:32,960 rather than solo individual investigator ROI type results. 286 00:13:32,960 --> 00:13:36,530 You tend to have to cross over a series of fields now 287 00:13:36,530 --> 00:13:41,990 to get your medical advance out and that's more problematic. 288 00:13:41,990 --> 00:13:44,810 So ideas they propose, NCATS they 289 00:13:44,810 --> 00:13:47,300 cited, the translational medicine 290 00:13:47,300 --> 00:13:50,750 entity at NIH, that frankly has had trouble 291 00:13:50,750 --> 00:13:53,030 getting enough funding to really scale up 292 00:13:53,030 --> 00:13:58,190 to do what it needs to do, a DARPA type model, FDA exploring 293 00:13:58,190 --> 00:14:01,700 something called predictive toxicology, 294 00:14:01,700 --> 00:14:06,753 and predictive toxicity with a lab on a chip kind of approach. 295 00:14:06,753 --> 00:14:08,420 In other words, there's new technologies 296 00:14:08,420 --> 00:14:10,250 that could be breakthroughs here in trying 297 00:14:10,250 --> 00:14:12,720 to solve concept problems. 298 00:14:12,720 --> 00:14:15,410 So let me-- let's get through these 299 00:14:15,410 --> 00:14:18,470 I think we can just touch very briefly 300 00:14:18,470 --> 00:14:20,420 on Craig Venter and then kind of dig 301 00:14:20,420 --> 00:14:24,260 into the legacy sector reading, into the PCAST report. 302 00:14:27,420 --> 00:14:28,770 Just a quick question on Venter. 303 00:14:33,690 --> 00:14:37,200 Yeah, you got him, Good. 304 00:14:37,200 --> 00:14:39,360 AUDIENCE: I think Venter's discovery 305 00:14:39,360 --> 00:14:43,260 is one of the closest to [INAUDIBLE] ethical problems. 306 00:14:43,260 --> 00:14:45,480 And just now we had a discussion about how 307 00:14:45,480 --> 00:14:49,980 we want to speed up of all the research on antibiotics. 308 00:14:49,980 --> 00:14:58,290 So I want to ask, how do we balance this raising concern, 309 00:14:58,290 --> 00:15:01,040 by raising concerns of the public 310 00:15:01,040 --> 00:15:05,400 all this about related research and the need. 311 00:15:05,400 --> 00:15:12,600 There's a need for better drugs and better discoveries, 312 00:15:12,600 --> 00:15:18,255 in the sense that there is, like there 313 00:15:18,255 --> 00:15:22,320 is no mention the role of media in his discovery. 314 00:15:22,320 --> 00:15:27,820 Is that without the media pushing his results, 315 00:15:27,820 --> 00:15:35,910 pushing his discoveries, his team and his work 316 00:15:35,910 --> 00:15:40,440 wouldn't be approved by this parent organization. 317 00:15:40,440 --> 00:15:45,960 And the kind of discussion raised in public kind of 318 00:15:45,960 --> 00:15:48,678 gave him enough support to continue 319 00:15:48,678 --> 00:15:49,720 this [INAUDIBLE] process. 320 00:15:49,720 --> 00:15:53,280 So I would say what are the other ways to kind 321 00:15:53,280 --> 00:15:58,395 of balance this raising concerns and this need for better drugs? 322 00:16:01,895 --> 00:16:04,270 WILLIAM BONVILLIAN: It's a very interesting point, Luyao. 323 00:16:04,270 --> 00:16:08,780 The fact that Venter was able to mobilize media support, get 324 00:16:08,780 --> 00:16:12,560 them to understand the potential importance of these projects, 325 00:16:12,560 --> 00:16:17,660 and what the possibilities were, in effect help drive 326 00:16:17,660 --> 00:16:19,910 support for this whole genome initiative, 327 00:16:19,910 --> 00:16:23,030 whether it was Venter's or whether it was NIH's, 328 00:16:23,030 --> 00:16:24,890 it was a very powerful input. 329 00:16:24,890 --> 00:16:28,910 And we saw him and Collins on the cover of Time Magazine. 330 00:16:28,910 --> 00:16:31,730 That was just one example of the kind of media attention 331 00:16:31,730 --> 00:16:33,170 on this great scientific race. 332 00:16:35,680 --> 00:16:38,230 AUDIENCE: [INAUDIBLE] everyone was saying, 333 00:16:38,230 --> 00:16:41,620 everyone has their genetic information, 334 00:16:41,620 --> 00:16:43,390 everyone was concerned. 335 00:16:43,390 --> 00:16:47,600 And how can this-- 336 00:16:47,600 --> 00:16:52,040 I mean, the future will probably be more concerns. 337 00:16:52,040 --> 00:16:57,905 Is there any possible way to address this balance? 338 00:17:00,418 --> 00:17:02,710 AUDIENCE: So you're saying like change the structure so 339 00:17:02,710 --> 00:17:04,089 that somebody who has a good idea 340 00:17:04,089 --> 00:17:06,730 doesn't have to like completely go and fight 341 00:17:06,730 --> 00:17:08,135 the current structure for it? 342 00:17:10,750 --> 00:17:14,410 AUDIENCE: I want to say like how do we 343 00:17:14,410 --> 00:17:19,180 inform the public of this, the potential benefits that it 344 00:17:19,180 --> 00:17:24,430 brings, but also ensures that things are under-- 345 00:17:24,430 --> 00:17:25,750 AUDIENCE: Are actually worth-- 346 00:17:25,750 --> 00:17:29,280 yes I mean, I think a lot about like the cold fusion scandal. 347 00:17:29,280 --> 00:17:31,510 Where like they said they had it to the public 348 00:17:31,510 --> 00:17:33,280 so that they would get attention for it. 349 00:17:33,280 --> 00:17:35,552 But like the science wasn't super like sure. 350 00:17:35,552 --> 00:17:37,010 And so like there is a huge danger, 351 00:17:37,010 --> 00:17:38,350 especially in the scientific community, where 352 00:17:38,350 --> 00:17:40,017 you need to make sure something actually 353 00:17:40,017 --> 00:17:41,920 works and it's been tested by your peers 354 00:17:41,920 --> 00:17:43,750 before you go public, especially if it's 355 00:17:43,750 --> 00:17:48,670 a dramatic discovery, which I think the genomics project was. 356 00:17:48,670 --> 00:17:51,015 And so that is a very hard position to be in. 357 00:17:51,015 --> 00:17:52,390 At the same time, though, I think 358 00:17:52,390 --> 00:17:55,713 he leveraged a good amount of that. 359 00:17:55,713 --> 00:17:57,880 I think it's more about being a little more slightly 360 00:17:57,880 --> 00:18:00,250 or Machiavellian in understanding the structure 361 00:18:00,250 --> 00:18:02,080 and like who can kill you in terms 362 00:18:02,080 --> 00:18:04,505 of like credibility or other stuff. 363 00:18:04,505 --> 00:18:06,130 So he did a good job of actually making 364 00:18:06,130 --> 00:18:08,588 sure the science was good, kind of having some stakeholders 365 00:18:08,588 --> 00:18:09,970 just approve his stuff. 366 00:18:09,970 --> 00:18:11,530 But going to the media and saying, 367 00:18:11,530 --> 00:18:13,270 yo, this is why they're going to mess up 368 00:18:13,270 --> 00:18:15,340 and is why we're doing pretty well. 369 00:18:15,340 --> 00:18:17,258 And we're kind of a David versus Goliath. 370 00:18:17,258 --> 00:18:17,800 Check it out. 371 00:18:17,800 --> 00:18:20,510 It's pretty interesting. 372 00:18:20,510 --> 00:18:22,010 WILLIAM BONVILLIAN: We don't usually 373 00:18:22,010 --> 00:18:26,990 talk about the role of media in innovation and science policy. 374 00:18:26,990 --> 00:18:29,570 But you are right to point us in this direction. 375 00:18:29,570 --> 00:18:35,000 Because this was a highly public competition. 376 00:18:35,000 --> 00:18:36,920 I mean as I say here, it creates-- 377 00:18:40,360 --> 00:18:43,780 that competition could be viewed as just duplicative, right? 378 00:18:43,780 --> 00:18:46,600 Why are private and public resources 379 00:18:46,600 --> 00:18:50,030 in effect duplicating each other in this race? 380 00:18:50,030 --> 00:18:52,670 But on the other hand, it was incredibly creative 381 00:18:52,670 --> 00:18:55,910 and it spurred both sides to greatly accelerate and focus 382 00:18:55,910 --> 00:18:56,620 on the problems. 383 00:18:56,620 --> 00:19:01,820 So I think the duplicative research thesis really 384 00:19:01,820 --> 00:19:02,930 doesn't work here. 385 00:19:02,930 --> 00:19:04,640 But the other dimension you add here 386 00:19:04,640 --> 00:19:12,680 is, you know, how can innovators use public attention in helping 387 00:19:12,680 --> 00:19:14,120 to drive towards their goal. 388 00:19:14,120 --> 00:19:17,960 And we watched in the Boyer and Swanson 389 00:19:17,960 --> 00:19:24,830 case, how Swanson was able to mobilize media coverage 390 00:19:24,830 --> 00:19:28,250 and hold major press conferences when he had major announcements 391 00:19:28,250 --> 00:19:30,260 to make, he and Boyer had major announcements 392 00:19:30,260 --> 00:19:32,870 to make on their team. 393 00:19:32,870 --> 00:19:35,300 So it's a dimension that I don't think anybody's really 394 00:19:35,300 --> 00:19:37,758 spent a lot of time looking at in an organized kind of way. 395 00:19:37,758 --> 00:19:39,710 But it's a very interesting one. 396 00:19:39,710 --> 00:19:43,190 How does public support affect your ability 397 00:19:43,190 --> 00:19:44,795 to drive an innovation project? 398 00:19:48,517 --> 00:19:51,830 AUDIENCE: [INAUDIBLE] it seems to me 399 00:19:51,830 --> 00:19:54,680 that oftentimes, the general American public only 400 00:19:54,680 --> 00:19:56,900 gets really interested in the scientific topic 401 00:19:56,900 --> 00:20:01,970 when something's going really, really badly, like epidemic, is 402 00:20:01,970 --> 00:20:04,387 where people are like hm, biology and medicine 403 00:20:04,387 --> 00:20:04,970 are important. 404 00:20:04,970 --> 00:20:09,410 Or like you know, my favorite example of the Apollo 13 405 00:20:09,410 --> 00:20:11,892 disaster was when suddenly everyone cared about space, 406 00:20:11,892 --> 00:20:13,600 but only because people were about to die 407 00:20:13,600 --> 00:20:14,660 and it was exciting. 408 00:20:14,660 --> 00:20:18,740 So maybe some sort of response to the science communication 409 00:20:18,740 --> 00:20:21,920 problem would be getting information out there 410 00:20:21,920 --> 00:20:27,960 about the value of preventing the ratings grabbing disasters, 411 00:20:27,960 --> 00:20:28,870 I guess. 412 00:20:28,870 --> 00:20:31,130 AUDIENCE: It doesn't look as cool, though. 413 00:20:31,130 --> 00:20:33,610 AUDIENCE: [INAUDIBLE] of science fiction. 414 00:20:33,610 --> 00:20:35,990 That's kind of the role of science fiction. 415 00:20:35,990 --> 00:20:41,200 There's like a utility principle that I-- 416 00:20:41,200 --> 00:20:44,810 her name is-- can't remember her last name 417 00:20:44,810 --> 00:20:47,810 I took a course on bioethics last semester. 418 00:20:47,810 --> 00:20:49,310 It was a part of something called 419 00:20:49,310 --> 00:20:53,150 the [INAUDIBLE] seminars for public writing at Wellesley. 420 00:20:53,150 --> 00:20:57,730 And this woman who was, I guess, was coming in 421 00:20:57,730 --> 00:21:00,500 to speak with us about the role of media 422 00:21:00,500 --> 00:21:03,140 in establishing bioethics, and specifically 423 00:21:03,140 --> 00:21:04,610 about genetic engineering. 424 00:21:04,610 --> 00:21:07,630 And one of the points that she made to a question 425 00:21:07,630 --> 00:21:11,060 that I had asked was precisely that science fiction 426 00:21:11,060 --> 00:21:13,580 plays an enormous role in helping the public become 427 00:21:13,580 --> 00:21:16,430 comfortable with scientific advances far before the time 428 00:21:16,430 --> 00:21:20,240 that those scientific advances are even technically feasible. 429 00:21:20,240 --> 00:21:24,710 And so, maybe as the scientific community, or rather 430 00:21:24,710 --> 00:21:26,732 the scientific community should do a better job 431 00:21:26,732 --> 00:21:28,940 of involving themselves in the narrative storytelling 432 00:21:28,940 --> 00:21:31,340 process about what science is and has the potential 433 00:21:31,340 --> 00:21:35,270 to be in order to sort of set the stage 434 00:21:35,270 --> 00:21:38,730 for those scientific advancements when time comes. 435 00:21:38,730 --> 00:21:43,050 And there is I think a huge market, thinking 436 00:21:43,050 --> 00:21:46,850 about Martin's sort of business proclivity in children's books 437 00:21:46,850 --> 00:21:49,590 about science fiction, and the ways in which we, 438 00:21:49,590 --> 00:21:52,370 as maybe someday parents, right, could be right in reading 439 00:21:52,370 --> 00:21:55,070 these stories to our children at night, having them think 440 00:21:55,070 --> 00:21:58,207 about the future and that aspect of science 441 00:21:58,207 --> 00:22:00,290 communication and especially science communication 442 00:22:00,290 --> 00:22:02,300 to the younger generations which will inherit 443 00:22:02,300 --> 00:22:05,030 the kinds of innovations that we create today, 444 00:22:05,030 --> 00:22:07,280 are immensely important. 445 00:22:07,280 --> 00:22:09,080 But there's not a lot of storytellers 446 00:22:09,080 --> 00:22:11,150 who are equipped with the technical knowledge 447 00:22:11,150 --> 00:22:13,880 in order to communicate that adequately in a way 448 00:22:13,880 --> 00:22:15,710 that's palatable to children and it's also 449 00:22:15,710 --> 00:22:18,682 palatable to their parents and to the broader American market. 450 00:22:18,682 --> 00:22:20,390 So I think it's really important to think 451 00:22:20,390 --> 00:22:23,000 about this sort of storytelling dimensions of science 452 00:22:23,000 --> 00:22:26,660 communication, not just in the sense of, what's in the news, 453 00:22:26,660 --> 00:22:29,300 or what's going to be in the newspapers, 454 00:22:29,300 --> 00:22:35,210 but how we are orienting ourselves 455 00:22:35,210 --> 00:22:39,585 to make this an issue we care about socially. 456 00:22:39,585 --> 00:22:41,960 AUDIENCE: Yeah, I like the idea of preparing the stage so 457 00:22:41,960 --> 00:22:45,270 that people are receptive to advancements when they happen, 458 00:22:45,270 --> 00:22:47,130 and not just the scientist makes a discovery 459 00:22:47,130 --> 00:22:48,950 and then suddenly is trying to drum up support for something 460 00:22:48,950 --> 00:22:50,930 that they have to explain, A, why it's important first 461 00:22:50,930 --> 00:22:52,930 of all, and then, B, why what they've discovered 462 00:22:52,930 --> 00:22:54,675 is the relevant. 463 00:22:54,675 --> 00:22:55,550 You make good points. 464 00:22:55,550 --> 00:22:57,092 AUDIENCE: Do you remember who came up 465 00:22:57,092 --> 00:22:59,060 with the rockets, the original rocket that 466 00:22:59,060 --> 00:23:00,507 came from like Germany? 467 00:23:00,507 --> 00:23:01,340 AUDIENCE: Von Braun. 468 00:23:01,340 --> 00:23:01,890 AUDIENCE: Von Brian. 469 00:23:01,890 --> 00:23:03,200 Yeah, so that's a pretty good example. 470 00:23:03,200 --> 00:23:04,617 Because von Braun was having a lot 471 00:23:04,617 --> 00:23:07,220 of difficulty getting funding for the rockets originally. 472 00:23:07,220 --> 00:23:09,950 So what he did is he wrote a letter to Disney, 473 00:23:09,950 --> 00:23:11,230 because he was a foreigner. 474 00:23:11,230 --> 00:23:14,173 He was German, so like no one wanted him to actually do it. 475 00:23:14,173 --> 00:23:15,590 And they were kind of stifling him 476 00:23:15,590 --> 00:23:17,360 and they put him in a research facility 477 00:23:17,360 --> 00:23:18,740 where he wasn't really doing anything. 478 00:23:18,740 --> 00:23:19,532 So he wrote Disney. 479 00:23:19,532 --> 00:23:22,790 Disney does a whole thing on space [INAUDIBLE].. 480 00:23:22,790 --> 00:23:24,290 But I think it's an important aspect 481 00:23:24,290 --> 00:23:27,170 in terms of really, who are you stakeholders as a scientist. 482 00:23:27,170 --> 00:23:29,270 You don't have-- unless you're like a billionaire, 483 00:23:29,270 --> 00:23:30,230 and even if you are a billionaire, 484 00:23:30,230 --> 00:23:31,980 you don't have the power to manipulate-- 485 00:23:31,980 --> 00:23:33,300 you're not like the president. 486 00:23:33,300 --> 00:23:34,800 So you have to be able to figure out 487 00:23:34,800 --> 00:23:36,387 how you're going to persuade people 488 00:23:36,387 --> 00:23:38,720 and how you're going to use those different stakeholders 489 00:23:38,720 --> 00:23:42,950 in a very interesting-- well, in a very sort of pseudo 490 00:23:42,950 --> 00:23:46,210 Machiavellian but smart way, right? 491 00:23:46,210 --> 00:23:48,290 And how do you play the politics well? 492 00:23:48,290 --> 00:23:49,790 WILLIAM BONVILLIAN: So maybe there's 493 00:23:49,790 --> 00:23:52,490 an amendment to the legacy sector book 494 00:23:52,490 --> 00:23:54,740 that we've been using as our textbook, which 495 00:23:54,740 --> 00:24:00,260 is part of the role of change agents is to use the media 496 00:24:00,260 --> 00:24:01,340 and to do storytelling. 497 00:24:01,340 --> 00:24:03,260 Because this was a great story. 498 00:24:03,260 --> 00:24:06,140 This was an amazing story, which people in the United 499 00:24:06,140 --> 00:24:08,570 States and the world followed for a significant period 500 00:24:08,570 --> 00:24:11,170 of time, this great rivalry. 501 00:24:11,170 --> 00:24:13,810 And you know, Venter is almost made for media 502 00:24:13,810 --> 00:24:18,710 as a wonderful maverick, you know, fascinating character. 503 00:24:18,710 --> 00:24:19,980 And Collins is as well. 504 00:24:19,980 --> 00:24:22,940 So there was a powerful story to be told here 505 00:24:22,940 --> 00:24:25,880 and a great competition and a great race that 506 00:24:25,880 --> 00:24:28,340 made it a very powerful kind of media story 507 00:24:28,340 --> 00:24:30,390 for an extended period of time. 508 00:24:30,390 --> 00:24:34,460 So Louis, I had not anticipated this tangent of the course. 509 00:24:34,460 --> 00:24:36,882 But thanks for pulling us into it. 510 00:24:36,882 --> 00:24:38,840 So let me go on to the next couple of readings. 511 00:24:41,540 --> 00:24:44,210 So our textbook, who's got that? 512 00:24:44,210 --> 00:24:45,330 Chloe, all yours. 513 00:24:48,530 --> 00:24:51,080 AUDIENCE: So to set the stage for maybe the first question 514 00:24:51,080 --> 00:24:51,580 here. 515 00:24:51,580 --> 00:24:55,410 I went to a talk recently by a robotics entrepreneur, 516 00:24:55,410 --> 00:24:56,810 which seems unrelated. 517 00:24:56,810 --> 00:24:59,390 But I think he gave an interesting lesson 518 00:24:59,390 --> 00:25:01,652 as it relates, could relate to health care. 519 00:25:01,652 --> 00:25:03,860 He was talking about the problem he was dealing with, 520 00:25:03,860 --> 00:25:07,280 which was using robotics as an organizational tool 521 00:25:07,280 --> 00:25:08,870 in warehouses. 522 00:25:08,870 --> 00:25:10,850 And it wasn't interesting to me at first. 523 00:25:10,850 --> 00:25:13,225 But he made the problem pretty interesting after a while. 524 00:25:13,225 --> 00:25:15,980 But the way that he framed dealing with his problems 525 00:25:15,980 --> 00:25:20,660 was that he practice having the mindset of evaluating 526 00:25:20,660 --> 00:25:23,240 the problem at zero and infinity, as he said. 527 00:25:23,240 --> 00:25:28,245 Which basically translated to removing all constraints that 528 00:25:28,245 --> 00:25:30,370 were pre-existing on the system and then seeing you 529 00:25:30,370 --> 00:25:33,500 know if he had infinite space or infinite money or infinite 530 00:25:33,500 --> 00:25:37,010 labor, what he could accomplish or what his engineering 531 00:25:37,010 --> 00:25:38,090 solutions would be. 532 00:25:38,090 --> 00:25:41,540 And it was a really good way to sort of brainstorm 533 00:25:41,540 --> 00:25:43,110 an optimal solution to his problem. 534 00:25:43,110 --> 00:25:44,860 So I think it would be interesting to take 535 00:25:44,860 --> 00:25:48,300 the same approach to the inherent problems in health 536 00:25:48,300 --> 00:25:52,050 care delivery as a legacy sector. 537 00:25:52,050 --> 00:25:54,770 If we could-- like, you made the point 538 00:25:54,770 --> 00:25:59,780 that when you are recapping this chapter that our system is 539 00:25:59,780 --> 00:26:02,690 currently designed to suit medicine 540 00:26:02,690 --> 00:26:04,970 as it existed I guess 30 or 40 years ago, 541 00:26:04,970 --> 00:26:08,120 but not as it is moving into this exciting brave new world 542 00:26:08,120 --> 00:26:08,780 today. 543 00:26:08,780 --> 00:26:10,400 So if we could wipe the slate clean 544 00:26:10,400 --> 00:26:13,570 and not have any of the residual costs, 545 00:26:13,570 --> 00:26:16,760 like everyone started out healthily today. 546 00:26:16,760 --> 00:26:19,610 No one had any illness and we didn't have any residual costs 547 00:26:19,610 --> 00:26:21,470 or anything, like perfect ideal world, 548 00:26:21,470 --> 00:26:23,480 and we had to redesign our health care 549 00:26:23,480 --> 00:26:25,110 system from the ground up, and --I know 550 00:26:25,110 --> 00:26:26,360 this is a massive question. 551 00:26:26,360 --> 00:26:27,910 But I'm just interested in what you guys think 552 00:26:27,910 --> 00:26:28,950 that might look like. 553 00:26:28,950 --> 00:26:31,460 Like what would our innovation in that area 554 00:26:31,460 --> 00:26:33,830 look like we had no constraints. 555 00:26:33,830 --> 00:26:35,523 AUDIENCE: I mean obviously, you'd 556 00:26:35,523 --> 00:26:37,190 have a bunch of pharmaceutical companies 557 00:26:37,190 --> 00:26:39,310 would be producing infinite drugs, 558 00:26:39,310 --> 00:26:40,760 because they have tons of money. 559 00:26:40,760 --> 00:26:44,240 But from there, I think you'd have a significant bottleneck 560 00:26:44,240 --> 00:26:46,230 in terms of the FDA. 561 00:26:46,230 --> 00:26:48,620 So you'd have to expand that significantly. 562 00:26:48,620 --> 00:26:50,533 AUDIENCE: I don't necessarily mean like they 563 00:26:50,533 --> 00:26:51,700 have the infinite resources. 564 00:26:51,700 --> 00:26:53,660 It's just like if we were starting to [INAUDIBLE].. 565 00:26:53,660 --> 00:26:55,493 AUDIENCE: Create a completely perfect system 566 00:26:55,493 --> 00:26:57,843 for this time and the next 50 years, how would we do it? 567 00:26:57,843 --> 00:26:58,640 AUDIENCE: Yeah. 568 00:26:58,640 --> 00:26:59,360 AUDIENCE: I mean, you want me to give you 569 00:26:59,360 --> 00:27:01,700 an answer of how to structure or how to figure out 570 00:27:01,700 --> 00:27:03,890 what the process is? 571 00:27:03,890 --> 00:27:07,432 AUDIENCE: Just I guess, elements of what it might look like. 572 00:27:07,432 --> 00:27:08,890 AUDIENCE: Well, I think a lot of it 573 00:27:08,890 --> 00:27:10,010 falls in what Chris was talking about 574 00:27:10,010 --> 00:27:12,590 and what Bill was talking about, the personalized medicine 575 00:27:12,590 --> 00:27:13,347 component. 576 00:27:13,347 --> 00:27:14,930 That's kind of how they're touting it. 577 00:27:14,930 --> 00:27:17,060 It's the fundamental reorganization 578 00:27:17,060 --> 00:27:18,630 of health care delivery. 579 00:27:18,630 --> 00:27:22,397 And one of the ways in which they're-- 580 00:27:22,397 --> 00:27:24,230 I guess, my understanding is that the sector 581 00:27:24,230 --> 00:27:26,270 is purporting to make that happen is 582 00:27:26,270 --> 00:27:27,768 through additive manufacturing. 583 00:27:27,768 --> 00:27:29,060 So the 3D printing [INAUDIBLE]. 584 00:27:29,060 --> 00:27:32,148 AUDIENCE: I was just going to say that. 585 00:27:32,148 --> 00:27:33,690 WILLIAM BONVILLIAN: Well, 3D printing 586 00:27:33,690 --> 00:27:36,450 is going to be useful in a lot of health areas. 587 00:27:36,450 --> 00:27:38,760 So there's a new manufacturing institute 588 00:27:38,760 --> 00:27:41,080 that's organized around regenerative medicine 589 00:27:41,080 --> 00:27:42,930 and tissue engineering, for example, 590 00:27:42,930 --> 00:27:46,590 using 3D printing as one of the technologies they're looking at 591 00:27:46,590 --> 00:27:48,660 and combining that with synthetic biology. 592 00:27:48,660 --> 00:27:52,050 That's a very interesting and potentially very promising 593 00:27:52,050 --> 00:27:53,490 territory. 594 00:27:53,490 --> 00:27:58,430 As we'll get into in the next reading, that's not biology, 595 00:27:58,430 --> 00:27:59,130 right? 596 00:27:59,130 --> 00:28:02,130 That's a whole series of new engineering strands that are 597 00:28:02,130 --> 00:28:03,840 starting to enter , and IT strands, 598 00:28:03,840 --> 00:28:05,880 that are starting to enter this territory. 599 00:28:05,880 --> 00:28:10,650 And how does a system that's not organized around that, 600 00:28:10,650 --> 00:28:13,617 change to accommodate those strands? 601 00:28:13,617 --> 00:28:15,450 AUDIENCE: Well I think the container section 602 00:28:15,450 --> 00:28:18,780 of sort of the pharmaceutical industry 603 00:28:18,780 --> 00:28:21,295 and engineering, one of the most interesting articles 604 00:28:21,295 --> 00:28:23,670 that I read recently, was about the ways in which they're 605 00:28:23,670 --> 00:28:26,310 printing the pills in order to be better adapted 606 00:28:26,310 --> 00:28:35,160 to the person's absorption of the medicine. 607 00:28:35,160 --> 00:28:37,260 So they sort of-- 608 00:28:37,260 --> 00:28:39,720 they're starting to develop ways to gauge 609 00:28:39,720 --> 00:28:43,710 what a pill needs to look like for an individual to ingest it 610 00:28:43,710 --> 00:28:47,250 and also for the medicine to be delivered into their body. 611 00:28:47,250 --> 00:28:49,913 And They can't do that in mass manufacturing. 612 00:28:49,913 --> 00:28:51,830 They can only do that in personalized medicine 613 00:28:51,830 --> 00:28:53,250 through additives. 614 00:28:53,250 --> 00:28:54,965 So I thought that was pretty cool. 615 00:28:54,965 --> 00:28:57,090 So that would be an answer to your question, right? 616 00:28:57,090 --> 00:28:59,550 That exists and does not require an idealized world. 617 00:28:59,550 --> 00:29:03,120 It would just require the sort of commercialization process 618 00:29:03,120 --> 00:29:06,690 and then the scaling up of what exists potentially, 619 00:29:06,690 --> 00:29:09,367 if it's not to be thwarted by the legacy sector. 620 00:29:09,367 --> 00:29:11,700 WILLIAM BONVILLIAN: So the traditional production system 621 00:29:11,700 --> 00:29:17,700 for you know medicines is a batch processing system. 622 00:29:17,700 --> 00:29:20,910 You build a huge batch and you refine it 623 00:29:20,910 --> 00:29:22,980 so it has the perfect composition spread 624 00:29:22,980 --> 00:29:24,480 evenly throughout. 625 00:29:24,480 --> 00:29:25,620 And then you produce that. 626 00:29:25,620 --> 00:29:30,420 But that's frankly, not a modern manufacturing technology. 627 00:29:30,420 --> 00:29:33,540 So a continuous manufacturing process 628 00:29:33,540 --> 00:29:37,050 is much more flexible, potentially much more modular 629 00:29:37,050 --> 00:29:43,170 and adaptive to different components in that system, i.e. 630 00:29:43,170 --> 00:29:47,250 Different elements and a different molecular structures 631 00:29:47,250 --> 00:29:53,520 and different components within a particular drug or a pill. 632 00:29:53,520 --> 00:29:57,540 And 3D printing is a very interesting adaptive approach. 633 00:29:57,540 --> 00:30:02,070 So DARPA has been funding desktop pharmaceutical 634 00:30:02,070 --> 00:30:05,443 manufacturing with exactly that in mind. 635 00:30:05,443 --> 00:30:07,860 That we're going to have to move to personalized medicine. 636 00:30:07,860 --> 00:30:11,670 The military is going to have to have it for its own huge health 637 00:30:11,670 --> 00:30:15,210 care system, which is funded to the tune of about $50 billion 638 00:30:15,210 --> 00:30:20,820 a year, a major medical system. 639 00:30:20,820 --> 00:30:23,155 AUDIENCE: [INAUDIBLE] That's 10 arc reactors. 640 00:30:23,155 --> 00:30:25,470 I'll take it. 641 00:30:25,470 --> 00:30:28,050 WILLIAM BONVILLIAN: Well it's a very large medical system. 642 00:30:28,050 --> 00:30:30,870 And they're having to deal with changes and reforms 643 00:30:30,870 --> 00:30:32,880 in their own system, so they're moving 644 00:30:32,880 --> 00:30:35,748 on developing a whole new set of production technologies. 645 00:30:35,748 --> 00:30:37,290 And on that one, they've been working 646 00:30:37,290 --> 00:30:40,770 within an MIT team that's really quite interesting. 647 00:30:40,770 --> 00:30:44,100 But that again, NIH doesn't do this. 648 00:30:44,100 --> 00:30:46,260 That's not in NIH's territory. 649 00:30:46,260 --> 00:30:49,080 It happened that the military was interested in this. 650 00:30:49,080 --> 00:30:52,200 This new DARPA biological technologies office 651 00:30:52,200 --> 00:30:55,080 happens to be intrigued with those set of possibilities. 652 00:30:55,080 --> 00:31:00,850 But that's kind of outside the box of the existing system. 653 00:31:00,850 --> 00:31:02,750 AUDIENCE: So, plug and play manufacturing, 654 00:31:02,750 --> 00:31:05,352 plug and play personalized medicine? 655 00:31:05,352 --> 00:31:07,060 WILLIAM BONVILLIAN: Good phrasing, Chloe. 656 00:31:07,060 --> 00:31:07,893 AUDIENCE: Thank you. 657 00:31:12,562 --> 00:31:13,520 AUDIENCE: I don't know. 658 00:31:13,520 --> 00:31:15,020 I think we're going to have to focus 659 00:31:15,020 --> 00:31:18,647 on the new sexy technologies, focus more on the structure. 660 00:31:18,647 --> 00:31:21,230 Because I think like, say I have to use the technology that we 661 00:31:21,230 --> 00:31:22,772 have now, I would think about how I'd 662 00:31:22,772 --> 00:31:24,990 restructure the organization. 663 00:31:24,990 --> 00:31:28,310 What are the problems and are they caused by incentives? 664 00:31:28,310 --> 00:31:29,750 So I think this health care crisis 665 00:31:29,750 --> 00:31:33,030 isn't a problem of technology or even people or even doctors. 666 00:31:33,030 --> 00:31:35,270 I think it's an issue of incentives. 667 00:31:35,270 --> 00:31:37,460 So when I say I'm going to pay whatever you do, 668 00:31:37,460 --> 00:31:40,850 I'm like, oh well, let's stack it on, add more toppings. 669 00:31:40,850 --> 00:31:44,060 And like, I end up having a huge bill. 670 00:31:44,060 --> 00:31:46,520 And then also, as a generation, where now we 671 00:31:46,520 --> 00:31:48,060 have a huge kind of-- 672 00:31:48,060 --> 00:31:51,125 I don't-- we have a lot more elders than young people. 673 00:31:51,125 --> 00:31:53,000 And so we have to pay for these baby boomers. 674 00:31:53,000 --> 00:31:54,830 So it ignores that context. 675 00:31:54,830 --> 00:31:57,200 So we also might want to focus on probably 676 00:31:57,200 --> 00:31:58,850 like most of our costs are going to go 677 00:31:58,850 --> 00:32:01,582 to elders, not young people, because young people are fine. 678 00:32:01,582 --> 00:32:03,290 We have a huge portion of elders, so what 679 00:32:03,290 --> 00:32:04,600 are their diseases? 680 00:32:04,600 --> 00:32:05,660 How do they work? 681 00:32:05,660 --> 00:32:07,550 What are the main causes of those diseases? 682 00:32:07,550 --> 00:32:09,633 And how do I create my organization around that? 683 00:32:09,633 --> 00:32:12,050 Or maybe I just want to create a whole new medical system, 684 00:32:12,050 --> 00:32:13,550 focus on this segment because I know 685 00:32:13,550 --> 00:32:16,340 that they're going to be huge, you know, costs. 686 00:32:16,340 --> 00:32:18,920 And I can get you know, laws of economics 687 00:32:18,920 --> 00:32:20,800 by having such a large segment that's, 688 00:32:20,800 --> 00:32:22,815 like that whole system is personalized for them. 689 00:32:22,815 --> 00:32:25,190 And they have different needs than somebody who is young. 690 00:32:25,190 --> 00:32:28,600 Who can just take an Uber or something. 691 00:32:28,600 --> 00:32:29,270 Yeah. 692 00:32:29,270 --> 00:32:31,603 For them, you probably have to have somebody go directly 693 00:32:31,603 --> 00:32:34,220 to them, check how they're doing, check constant levels. 694 00:32:34,220 --> 00:32:37,200 So it's very different. 695 00:32:37,200 --> 00:32:40,090 And I think that could cut costs down. 696 00:32:40,090 --> 00:32:42,320 Or you could send somebody out to them. 697 00:32:42,320 --> 00:32:44,962 WILLIAM BONVILLIAN: Or use an IT system or use robotics. 698 00:32:44,962 --> 00:32:46,670 Those are all part of the menu that we're 699 00:32:46,670 --> 00:32:49,730 starting to think about Chloe, a closing 700 00:32:49,730 --> 00:32:52,535 thought about this reading on legacy sectors? 701 00:32:55,700 --> 00:32:57,440 AUDIENCE: Yeah. 702 00:32:57,440 --> 00:32:59,180 I think for me, it was really eye 703 00:32:59,180 --> 00:33:01,880 opening to see the part of the reading 704 00:33:01,880 --> 00:33:05,600 where you just listed the five or six specific 705 00:33:05,600 --> 00:33:11,090 characteristics of how health care delivery draws parallels 706 00:33:11,090 --> 00:33:12,020 to legacy sectors. 707 00:33:12,020 --> 00:33:14,930 And I think those are probably the areas where 708 00:33:14,930 --> 00:33:17,900 any sort of reformers or innovators 709 00:33:17,900 --> 00:33:19,670 would really hone in on and focus 710 00:33:19,670 --> 00:33:26,600 on lifting those restrictions to limber up the system a lot. 711 00:33:26,600 --> 00:33:27,740 WILLIAM BONVILLIAN: OK. 712 00:33:27,740 --> 00:33:29,150 Let's go to the PCAST report. 713 00:33:31,660 --> 00:33:39,870 AUDIENCE: Regarding this research, the drug development. 714 00:33:39,870 --> 00:33:42,120 We were just talking about how do 715 00:33:42,120 --> 00:33:47,190 we incentivize firms to take on more 716 00:33:47,190 --> 00:33:54,350 responsibility in developing some drugs that target minority 717 00:33:54,350 --> 00:33:57,470 groups or that don't have such a big market. 718 00:33:57,470 --> 00:34:02,980 And I want to introduce this model that Singapore has in 719 00:34:02,980 --> 00:34:08,159 incentivizing their small and medium sized firms to invest 720 00:34:08,159 --> 00:34:10,460 in R&D and trainings. 721 00:34:10,460 --> 00:34:16,580 What they do is, you can get a tax return 722 00:34:16,580 --> 00:34:22,120 or you can get a set amount of funds 723 00:34:22,120 --> 00:34:28,580 as long as you are investing in your employees 724 00:34:28,580 --> 00:34:35,480 or in any forms of training that can improve your productivity 725 00:34:35,480 --> 00:34:39,889 and improves your research in any form. 726 00:34:39,889 --> 00:34:43,130 And I think Singapore was able to do that because they 727 00:34:43,130 --> 00:34:48,020 are really small scale. 728 00:34:48,020 --> 00:34:52,010 And they also count on their small and medium sized 729 00:34:52,010 --> 00:34:55,889 businesses to thrive as the economy. 730 00:34:55,889 --> 00:34:58,640 But I want to know if this kind of model 731 00:34:58,640 --> 00:35:03,950 can be a good reference to develop the health care 732 00:35:03,950 --> 00:35:07,720 research sector in the US. 733 00:35:07,720 --> 00:35:11,067 If you have any-- 734 00:35:11,067 --> 00:35:13,400 WILLIAM BONVILLIAN: So the model Singapore is using then 735 00:35:13,400 --> 00:35:17,030 is to give an incentive to companies 736 00:35:17,030 --> 00:35:20,240 that are making significant investments in the training 737 00:35:20,240 --> 00:35:24,410 of their employees and their research teams 738 00:35:24,410 --> 00:35:27,440 to kind of significantly upgrade their skill sets. 739 00:35:27,440 --> 00:35:29,960 AUDIENCE: And they have give a lot of flexibility. 740 00:35:29,960 --> 00:35:33,140 As long as you can justify this amount of spending, 741 00:35:33,140 --> 00:35:38,960 or able to improve your productivity, 742 00:35:38,960 --> 00:35:41,548 they get this funding. 743 00:35:41,548 --> 00:35:43,590 WILLIAM BONVILLIAN: That's what you have to show. 744 00:35:43,590 --> 00:35:46,970 You have to show some kind of increased performance coming 745 00:35:46,970 --> 00:35:48,820 out of that. 746 00:35:48,820 --> 00:35:51,090 AUDIENCE: So what are your thoughts 747 00:35:51,090 --> 00:35:58,500 about this kind of approach that could kind of encourage a drug 748 00:35:58,500 --> 00:35:59,466 developments? 749 00:36:04,020 --> 00:36:05,770 WILLIAM BONVILLIAN: So that's interesting. 750 00:36:05,770 --> 00:36:09,790 So that's like a roamer technology talent approach 751 00:36:09,790 --> 00:36:12,490 into a discussion we've been having on institutional 752 00:36:12,490 --> 00:36:14,200 innovation organization. 753 00:36:14,200 --> 00:36:17,350 So it's again, you bring us an interesting piece 754 00:36:17,350 --> 00:36:19,880 of the puzzle. 755 00:36:19,880 --> 00:36:20,380 I like it. 756 00:36:29,660 --> 00:36:30,920 So let's go back to basics. 757 00:36:30,920 --> 00:36:32,690 Is there a talent need in this sector? 758 00:36:32,690 --> 00:36:34,010 Is there a talent shortage? 759 00:36:34,010 --> 00:36:37,880 Is there a talent enthusiasm issue 760 00:36:37,880 --> 00:36:41,970 here that we ought to be addressing as well? 761 00:36:41,970 --> 00:36:44,120 AUDIENCE: From what I've seen in my very limited 762 00:36:44,120 --> 00:36:47,580 biotech experience, it seems like there isn't 763 00:36:47,580 --> 00:36:48,870 really much of a shortage. 764 00:36:48,870 --> 00:36:52,890 It seems like there's a lot of enthusiasm around the field. 765 00:36:52,890 --> 00:36:55,890 And like, as has been a continuing 766 00:36:55,890 --> 00:36:59,070 theme this class, our good old American spirit 767 00:36:59,070 --> 00:37:02,520 and our entrepreneurial drive toward packing up and moving 768 00:37:02,520 --> 00:37:07,680 out West or in this case west is genetic engineering and all 769 00:37:07,680 --> 00:37:09,010 that good stuff. 770 00:37:09,010 --> 00:37:12,150 So I don't know if the drive is really the problem. 771 00:37:16,900 --> 00:37:18,890 AUDIENCE: I just think about-- 772 00:37:18,890 --> 00:37:24,390 I like to think about analogies. 773 00:37:24,390 --> 00:37:26,550 And often in urban planning, you know, 774 00:37:26,550 --> 00:37:29,398 people compare Scandinavian countries 775 00:37:29,398 --> 00:37:30,440 to the rest of the world. 776 00:37:30,440 --> 00:37:32,730 And say, well, why can't we be like them? 777 00:37:32,730 --> 00:37:34,260 And there's various factors that we 778 00:37:34,260 --> 00:37:36,177 don't take into consideration, the homogeneity 779 00:37:36,177 --> 00:37:38,168 of the population, their productive capacity, 780 00:37:38,168 --> 00:37:39,960 the resources they have accessible to them, 781 00:37:39,960 --> 00:37:42,210 the scale at which they're operating, 782 00:37:42,210 --> 00:37:44,545 maybe their colonial history and the ways in which they 783 00:37:44,545 --> 00:37:46,920 were able to sort of take advantage of some opportunities 784 00:37:46,920 --> 00:37:49,295 that we might not be able to as a country, et cetera. 785 00:37:49,295 --> 00:37:50,670 And so in this instance, I'd like 786 00:37:50,670 --> 00:37:54,960 to sort of invoke that and cross apply that argument to sort 787 00:37:54,960 --> 00:37:56,640 of Luyao's point about Singapore, 788 00:37:56,640 --> 00:37:59,440 and then to really utilize that to challenge what Max just 789 00:37:59,440 --> 00:37:59,940 said. 790 00:37:59,940 --> 00:38:03,510 Because I feel like we are situated 791 00:38:03,510 --> 00:38:07,200 in a very unique and incredible location 792 00:38:07,200 --> 00:38:09,240 for this kind of conversation. 793 00:38:09,240 --> 00:38:12,540 But where I'm from in Texas, the kinds of university systems 794 00:38:12,540 --> 00:38:14,238 and research system that exist there, 795 00:38:14,238 --> 00:38:15,780 these kinds of conversations probably 796 00:38:15,780 --> 00:38:17,370 don't happen to that extent. 797 00:38:17,370 --> 00:38:19,410 And the enthusiasm for commercialization 798 00:38:19,410 --> 00:38:24,130 is probably not to the same level. 799 00:38:24,130 --> 00:38:26,340 And so I think I would come back to what 800 00:38:26,340 --> 00:38:28,830 Max is saying specifically, by saying that perhaps we 801 00:38:28,830 --> 00:38:34,590 need to then bring something, in a way, usual socialist ways, 802 00:38:34,590 --> 00:38:39,510 to say that perhaps it is the kinds of talent that is missing 803 00:38:39,510 --> 00:38:41,880 or rather, people from marginalized groups 804 00:38:41,880 --> 00:38:48,250 may have a decidedly public good approach to research. 805 00:38:48,250 --> 00:38:50,848 And so they might not want to commercialize in the same ways 806 00:38:50,848 --> 00:38:52,890 that people who are being trained in institutions 807 00:38:52,890 --> 00:38:54,570 like MIT and Stanford are. 808 00:38:54,570 --> 00:38:57,017 And it is perhaps that it is precisely 809 00:38:57,017 --> 00:38:59,100 those people who the government should be funding, 810 00:38:59,100 --> 00:39:01,470 not major research universities that 811 00:39:01,470 --> 00:39:04,890 exist within a commercialization framework. 812 00:39:04,890 --> 00:39:07,590 Because those individuals are less 813 00:39:07,590 --> 00:39:10,350 likely to want to take the bigger piece of the pie 814 00:39:10,350 --> 00:39:12,660 and may potentially want to benefit and serve 815 00:39:12,660 --> 00:39:14,820 their communities much more, which 816 00:39:14,820 --> 00:39:17,840 could prove to be much more disruptive ultimately. 817 00:39:17,840 --> 00:39:21,320 WILLIAM BONVILLIAN: Who wants to take on Steph's economic model? 818 00:39:21,320 --> 00:39:21,820 [LAUGHING] 819 00:39:21,820 --> 00:39:23,485 Plenty of volunteers. 820 00:39:23,485 --> 00:39:25,610 AUDIENCE: One, you directly challenged me and you-- 821 00:39:25,610 --> 00:39:28,020 AUDIENCE: Please, please, please, please. 822 00:39:28,020 --> 00:39:30,650 AUDIENCE: Well, I mean, it's nice and idealistic. 823 00:39:30,650 --> 00:39:32,400 I love thinking that, OK, everyone is just 824 00:39:32,400 --> 00:39:33,710 going to work for free. 825 00:39:33,710 --> 00:39:35,127 AUDIENCE: Oh I don't think they're 826 00:39:35,127 --> 00:39:36,315 going to work for free, Max. 827 00:39:36,315 --> 00:39:37,440 That's not the insinuation. 828 00:39:37,440 --> 00:39:40,150 The insinuation is that the profit sharing is going to-- 829 00:39:40,150 --> 00:39:40,650 the. 830 00:39:40,650 --> 00:39:46,830 Profit sharing model is going to be much different I mean, 831 00:39:46,830 --> 00:39:48,300 I'm not going to [INAUDIBLE]. 832 00:39:48,300 --> 00:39:50,040 AUDIENCE: OK, so I understood it as they 833 00:39:50,040 --> 00:39:52,370 don't care about making big fish blockbuster drugs 834 00:39:52,370 --> 00:39:54,300 or we'll make like the drugs that are like [INAUDIBLE].. 835 00:39:54,300 --> 00:39:56,730 AUDIENCE: They can make the big fish blockbuster drugs. 836 00:39:56,730 --> 00:39:59,340 They just don't want to profit to that extent, which ends up 837 00:39:59,340 --> 00:40:00,540 being good for everyone. 838 00:40:00,540 --> 00:40:01,620 AUDIENCE: OK. 839 00:40:01,620 --> 00:40:04,200 So them not profiting [INAUDIBLE].. 840 00:40:04,200 --> 00:40:07,400 AUDIENCE: But the thing is, you could take the other side. 841 00:40:07,400 --> 00:40:08,910 The company's profiting means they 842 00:40:08,910 --> 00:40:12,270 can put that research-- put that money toward new drugs, 843 00:40:12,270 --> 00:40:14,190 as opposed to just develop one drug 844 00:40:14,190 --> 00:40:16,600 and then you basically break even. 845 00:40:16,600 --> 00:40:18,510 Then you're just like, what do I do now? 846 00:40:18,510 --> 00:40:20,010 AUDIENCE: I think the focus on drugs 847 00:40:20,010 --> 00:40:21,135 maybe also kind of limited. 848 00:40:21,135 --> 00:40:23,790 Like I guess we still want more drugs, that's fine. 849 00:40:23,790 --> 00:40:26,250 But I think they have drugs that are targeted 850 00:40:26,250 --> 00:40:29,830 towards like, maybe if-- 851 00:40:29,830 --> 00:40:31,830 I guess we talked about like a disease portfolio 852 00:40:31,830 --> 00:40:34,060 that they look at and it's very narrow. 853 00:40:34,060 --> 00:40:35,730 And so there's two things here. 854 00:40:35,730 --> 00:40:41,160 So I wonder if such proposals might help expand that disease 855 00:40:41,160 --> 00:40:41,730 portfolio. 856 00:40:41,730 --> 00:40:44,070 So you have people who care about more diseases 857 00:40:44,070 --> 00:40:47,610 and are able to do more research in sort of different areas. 858 00:40:47,610 --> 00:40:52,320 And then two, I wonder if those who actually subscribe 859 00:40:52,320 --> 00:40:58,330 to this sort of, let's say big pharma look kind of like we're 860 00:40:58,330 --> 00:41:01,990 in this age, FDA, drug an incremental advance type 861 00:41:01,990 --> 00:41:05,947 system, do they actually care to the extent 862 00:41:05,947 --> 00:41:07,280 that someone's trying to get at. 863 00:41:07,280 --> 00:41:08,920 Like are they actually interested 864 00:41:08,920 --> 00:41:12,550 in kind of solving this incremental advance problem 865 00:41:12,550 --> 00:41:15,490 or do they have a vested interest in what they're 866 00:41:15,490 --> 00:41:19,260 doing because they care about these sort of bigger fish, 867 00:41:19,260 --> 00:41:20,860 like kind of cure all drugs? 868 00:41:20,860 --> 00:41:23,110 But they don't have the funding opportunities and kind 869 00:41:23,110 --> 00:41:27,250 of the means to kind of get [INAUDIBLE].. 870 00:41:27,250 --> 00:41:29,460 AUDIENCE: The way institutions are specifically 871 00:41:29,460 --> 00:41:31,080 cited by the conversions reading, 872 00:41:31,080 --> 00:41:36,360 I think kind of really proved sort of where I'm coming from. 873 00:41:36,360 --> 00:41:39,030 They cited a Harvard, University of Texas 874 00:41:39,030 --> 00:41:41,370 at Austin, Carnegie Mellon, George Tech, U Chicago, 875 00:41:41,370 --> 00:41:43,450 and Tufts, obviously you know without saying, 876 00:41:43,450 --> 00:41:45,750 MIT, because most of the people on that commission 877 00:41:45,750 --> 00:41:48,600 had some relationship time or were researchers that graduated 878 00:41:48,600 --> 00:41:50,840 from MIT, were professors that MIT, 879 00:41:50,840 --> 00:41:53,640 were prominent political stakeholders at MIT. 880 00:41:53,640 --> 00:41:56,460 So you know it occurs to me that there if, it 881 00:41:56,460 --> 00:41:58,830 is that we you know really revere people like Craig 882 00:41:58,830 --> 00:42:03,980 Venter, and, if it is that the convergence reading, which 883 00:42:03,980 --> 00:42:06,820 we will be talking about soon enough, 884 00:42:06,820 --> 00:42:11,250 is putting a lot of weight on the potential of community 885 00:42:11,250 --> 00:42:13,470 colleges and of local institutions 886 00:42:13,470 --> 00:42:17,390 to train the workforce in the life sciences, 887 00:42:17,390 --> 00:42:19,890 why is it that we don't give them the same access to funding 888 00:42:19,890 --> 00:42:22,050 opportunities that we're giving you know, 889 00:42:22,050 --> 00:42:24,637 the sort of blockbuster research universities? 890 00:42:34,083 --> 00:42:35,750 AUDIENCE: I think the big thing is like, 891 00:42:35,750 --> 00:42:38,840 so it seems like a lot-- this is like a easy mental model 892 00:42:38,840 --> 00:42:39,642 for most people. 893 00:42:39,642 --> 00:42:41,850 What they do is they think about everything linearly. 894 00:42:41,850 --> 00:42:43,358 So the thing is like MIT, Harvard, 895 00:42:43,358 --> 00:42:45,650 and luxury institutions just have a huge conglomeration 896 00:42:45,650 --> 00:42:46,250 of people. 897 00:42:46,250 --> 00:42:47,750 So they're exponentially much better 898 00:42:47,750 --> 00:42:49,070 at solving certain problems. 899 00:42:49,070 --> 00:42:51,710 And they have the credibility to go and get funding 900 00:42:51,710 --> 00:42:53,990 from outside sources, whether it be government, 901 00:42:53,990 --> 00:42:58,580 whether it be VC, so it is this huge you know, 902 00:42:58,580 --> 00:43:01,310 very unproportional nature of the institutes 903 00:43:01,310 --> 00:43:03,620 and the way the system works. 904 00:43:03,620 --> 00:43:05,470 But there are some good things to that. 905 00:43:05,470 --> 00:43:08,690 What I worry more is like how do you create a new system 906 00:43:08,690 --> 00:43:12,290 to make all these smaller drugs and working on them 907 00:43:12,290 --> 00:43:13,370 more viable. 908 00:43:13,370 --> 00:43:15,770 So I think that's an interesting place for a startup, 909 00:43:15,770 --> 00:43:18,680 if you can figure out 3D printing of drugs 910 00:43:18,680 --> 00:43:21,470 or if you can figure out what is your kind 911 00:43:21,470 --> 00:43:24,325 of your competitive advantage in this problem. 912 00:43:24,325 --> 00:43:25,700 And what is the current structure 913 00:43:25,700 --> 00:43:27,830 for the company that focus on the big pharma. 914 00:43:27,830 --> 00:43:30,540 And how do you optimize for that. 915 00:43:30,540 --> 00:43:33,150 So what are so it's pretty much like Art of War. 916 00:43:33,150 --> 00:43:35,225 It's like, OK, they already have their strategy. 917 00:43:35,225 --> 00:43:37,100 They have to focus on doing business with us. 918 00:43:37,100 --> 00:43:39,142 And there's also called the intervention dilemma, 919 00:43:39,142 --> 00:43:41,780 which is like they have to make $5 billion 920 00:43:41,780 --> 00:43:43,340 in revenue off of this drug. 921 00:43:43,340 --> 00:43:45,280 I don't need to make $5 million in revenue. 922 00:43:45,280 --> 00:43:46,580 I'm a smaller fish. 923 00:43:46,580 --> 00:43:49,100 I could, with like $5 million, right? 924 00:43:49,100 --> 00:43:51,500 So like how do I create my systems so that I can make 925 00:43:51,500 --> 00:43:53,893 these kind of drugs and make a big company based 926 00:43:53,893 --> 00:43:55,310 on focusing on those smaller drugs 927 00:43:55,310 --> 00:43:57,120 that are important to society. 928 00:43:57,120 --> 00:43:59,240 That would be like the enterprise version. 929 00:43:59,240 --> 00:44:02,180 You can probably come up with some parallel using government 930 00:44:02,180 --> 00:44:04,520 funding and government support. 931 00:44:04,520 --> 00:44:07,735 And maybe it might be that FDA approval, being more lax, 932 00:44:07,735 --> 00:44:09,110 or depending on the drug, I would 933 00:44:09,110 --> 00:44:11,068 have to figure out which ones are the best ones 934 00:44:11,068 --> 00:44:13,500 to go after in the beginning. 935 00:44:13,500 --> 00:44:16,250 But I don't think, except making the argument 936 00:44:16,250 --> 00:44:19,200 that, oh how do we, you know, I don't want to say democratize, 937 00:44:19,200 --> 00:44:23,118 but why can't everybody like be in the pool? 938 00:44:23,118 --> 00:44:24,660 Especially when it's like those there 939 00:44:24,660 --> 00:44:26,160 are people that are Olympic swimmers 940 00:44:26,160 --> 00:44:27,283 and like institutes that-- 941 00:44:27,283 --> 00:44:29,450 I mean there are probably good high school swimmers, 942 00:44:29,450 --> 00:44:33,260 but like it just won't you won't get as much a run through 943 00:44:33,260 --> 00:44:34,090 of the same-- 944 00:44:34,090 --> 00:44:35,632 AUDIENCE: I guess it just concerns me 945 00:44:35,632 --> 00:44:38,750 that we pay so much lip service to the great groups model. 946 00:44:38,750 --> 00:44:42,770 And the great group model itself says that the drive for profit 947 00:44:42,770 --> 00:44:47,420 is not the greatest motivator to innovation. 948 00:44:47,420 --> 00:44:52,100 And if we are to consider that as true 949 00:44:52,100 --> 00:44:55,160 or to take that at face value in the ways in which the authors 950 00:44:55,160 --> 00:44:58,490 have presented their argument in the previous weeks, 951 00:44:58,490 --> 00:45:00,290 I don't know that the commercialization 952 00:45:00,290 --> 00:45:03,380 model for the life sciences is necessarily 953 00:45:03,380 --> 00:45:06,740 conducive to innovations in the ways in which we would 954 00:45:06,740 --> 00:45:08,240 hope that they would exist. 955 00:45:08,240 --> 00:45:09,782 WILLIAM BONVILLIAN: So, Steph, you're 956 00:45:09,782 --> 00:45:14,630 driving us towards obviously some truly big picture issues. 957 00:45:14,630 --> 00:45:21,920 Look, we have taken a pretty radical capitalist model 958 00:45:21,920 --> 00:45:26,520 to solving this problem of innovation in life science, 959 00:45:26,520 --> 00:45:27,260 right? 960 00:45:27,260 --> 00:45:29,450 It's pretty amazing that we have focused 961 00:45:29,450 --> 00:45:36,790 on a high risk, high reward system that's 962 00:45:36,790 --> 00:45:39,340 completely dependent upon you know, 963 00:45:39,340 --> 00:45:44,140 monopoly rents and major returns as the way in which we're 964 00:45:44,140 --> 00:45:46,570 going to do innovation in the life science territory. 965 00:45:46,570 --> 00:45:48,880 It's absolutely fascinating. 966 00:45:48,880 --> 00:45:51,010 How did we stumble into this? 967 00:45:51,010 --> 00:45:56,570 That wasn't what-- remember when we talked about Boyer 968 00:45:56,570 --> 00:45:59,960 and the conflicts he had with other UCSF faculty? 969 00:45:59,960 --> 00:46:04,280 When he went off to invent the biotech model with Swanson, 970 00:46:04,280 --> 00:46:06,960 he got a lot of flack for this. 971 00:46:06,960 --> 00:46:09,510 That was a radical departure. 972 00:46:09,510 --> 00:46:13,510 He was leaving a university based research system. 973 00:46:13,510 --> 00:46:16,650 But let's think back to the reasons why he was doing that. 974 00:46:16,650 --> 00:46:19,740 Because he wanted his technologies 975 00:46:19,740 --> 00:46:22,350 to scale up and be available and that was the option 976 00:46:22,350 --> 00:46:24,180 that he saw for being able to do that, 977 00:46:24,180 --> 00:46:26,072 so he teams up with Swanson. 978 00:46:26,072 --> 00:46:28,530 I don't think we're going to resolve these questions today. 979 00:46:28,530 --> 00:46:31,800 But it is a radical capitalist model. 980 00:46:31,800 --> 00:46:33,690 And as we've discussed at length today, 981 00:46:33,690 --> 00:46:35,790 there are gaps in that model, right? 982 00:46:35,790 --> 00:46:37,830 There's only some things that that model 983 00:46:37,830 --> 00:46:40,320 is going to be able to address given 984 00:46:40,320 --> 00:46:42,270 the structural limits that are coming into it, 985 00:46:42,270 --> 00:46:45,840 particularly the long term approval process 986 00:46:45,840 --> 00:46:47,730 that the FDA has to provide. 987 00:46:47,730 --> 00:46:50,670 So you know, drug companies hate the FDA 988 00:46:50,670 --> 00:46:53,670 because they have to spend seven years and $1.8 billion 989 00:46:53,670 --> 00:46:55,470 getting through their hurdles. 990 00:46:55,470 --> 00:47:00,450 But they also love FDA because it certifies their products 991 00:47:00,450 --> 00:47:02,070 and guarantees a market for them. 992 00:47:02,070 --> 00:47:04,890 So it's this odd love hate relationship. 993 00:47:04,890 --> 00:47:06,810 In some ways that's symptomatic of what we've 994 00:47:06,810 --> 00:47:09,690 got over this whole system. 995 00:47:09,690 --> 00:47:14,050 And it is a system under stress at this point. 996 00:47:14,050 --> 00:47:16,680 Luyao why don't you give us a closing point on this and then 997 00:47:16,680 --> 00:47:19,138 we'll go right into what I think the next part of the story 998 00:47:19,138 --> 00:47:19,920 is on convergence. 999 00:47:19,920 --> 00:47:23,400 Because I think it fits nicely with this. 1000 00:47:23,400 --> 00:47:26,630 AUDIENCE: I realize we do focus on a lot of how to incentivize 1001 00:47:26,630 --> 00:47:28,740 innovation in drug development. 1002 00:47:28,740 --> 00:47:31,830 But I do think that you know we still 1003 00:47:31,830 --> 00:47:37,380 have this scarcity problem with rising demand and limited 1004 00:47:37,380 --> 00:47:38,650 supply. 1005 00:47:38,650 --> 00:47:43,860 Why don't we also divert a little bit of focus 1006 00:47:43,860 --> 00:47:47,130 on developing a healthy population. 1007 00:47:47,130 --> 00:47:53,040 Can any form of drug development and kind of research 1008 00:47:53,040 --> 00:47:55,740 that advance this process, so that we 1009 00:47:55,740 --> 00:47:59,580 can reduce the unnecessary demand for certain type 1010 00:47:59,580 --> 00:48:00,810 of health care. 1011 00:48:00,810 --> 00:48:04,740 So that we can free up a bit of our funding and resources 1012 00:48:04,740 --> 00:48:09,600 so that we can focus on the rest of the research programs. 1013 00:48:09,600 --> 00:48:13,320 WILLIAM BONVILLIAN: And look, your point earlier, 1014 00:48:13,320 --> 00:48:18,620 which we debated about, is there a talent problem here? 1015 00:48:18,620 --> 00:48:22,792 Romer's prospector theory would tell us, 1016 00:48:22,792 --> 00:48:24,500 you're going to get a lot more innovation 1017 00:48:24,500 --> 00:48:26,850 if you put more well-trained prospectors on the problem, 1018 00:48:26,850 --> 00:48:27,350 right? 1019 00:48:27,350 --> 00:48:29,982 So I don't want us to kind of leave that point. 1020 00:48:29,982 --> 00:48:31,940 I think there's an interesting underlying point 1021 00:48:31,940 --> 00:48:35,235 you made in that area as well. 1022 00:48:35,235 --> 00:48:37,235 All right, let's go on to the convergence study. 1023 00:48:42,420 --> 00:48:44,170 The report is called "The Third Revolution 1024 00:48:44,170 --> 00:48:46,295 - The Convergence Of Life, Physical and Engineering 1025 00:48:46,295 --> 00:48:46,830 Sciences." 1026 00:48:46,830 --> 00:48:50,400 And it came out of MIT in 2011. 1027 00:48:50,400 --> 00:48:53,970 You know my office, the MIT Washington office, 1028 00:48:53,970 --> 00:48:54,900 helped work on this. 1029 00:48:54,900 --> 00:48:58,020 The project was led by Phil Sharpe and Bob Langer, 1030 00:48:58,020 --> 00:49:00,420 somebody from engineering, somebody from biology, 1031 00:49:00,420 --> 00:49:04,850 obviously two of MIT's all time greats. 1032 00:49:04,850 --> 00:49:09,750 And the report tries to tell a story 1033 00:49:09,750 --> 00:49:14,220 that the picture in the next advance wave, we're 1034 00:49:14,220 --> 00:49:16,470 lacking a picture of the next wave of advance. 1035 00:49:16,470 --> 00:49:19,260 The great thing the genomes piece gave 1036 00:49:19,260 --> 00:49:22,080 us and we talked about Venter and the competition 1037 00:49:22,080 --> 00:49:25,073 with Collins and the NIH, it gave us a story, 1038 00:49:25,073 --> 00:49:26,490 it gave us a picture of what we're 1039 00:49:26,490 --> 00:49:31,160 going to get for this massive investment in NIH. 1040 00:49:31,160 --> 00:49:35,130 It enabled the public to see a story and be told a story 1041 00:49:35,130 --> 00:49:38,220 and understand what the results were going to yield, right? 1042 00:49:38,220 --> 00:49:41,910 We don't have a story for the life sciences 1043 00:49:41,910 --> 00:49:46,380 that's out there now that has nearly the kind of power 1044 00:49:46,380 --> 00:49:47,640 as that genomic story. 1045 00:49:47,640 --> 00:49:50,580 We haven't figured out how to tell the next story. 1046 00:49:50,580 --> 00:49:52,440 And that's part of the reason why 1047 00:49:52,440 --> 00:49:55,900 we've got funding stagnation for NIH 1048 00:49:55,900 --> 00:49:57,900 and the life sciences in general. 1049 00:49:57,900 --> 00:50:00,540 So the doubling was led by the genomics revolution. 1050 00:50:00,540 --> 00:50:02,053 NIH needs a new picture. 1051 00:50:02,053 --> 00:50:03,720 So what are these different revolutions? 1052 00:50:03,720 --> 00:50:06,750 So what this report argued was that really 1053 00:50:06,750 --> 00:50:10,320 the kind of first revolution in recent time 1054 00:50:10,320 --> 00:50:12,120 was the molecular biology revolution. 1055 00:50:12,120 --> 00:50:15,930 And that was really the merger of physics and biology. 1056 00:50:15,930 --> 00:50:21,360 So Max Delbruck comes out of the amazing pre-war German physics 1057 00:50:21,360 --> 00:50:23,550 community. 1058 00:50:23,550 --> 00:50:30,630 He works with Niels Bohr in Copenhagen 1059 00:50:30,630 --> 00:50:33,540 as part of that amazing community that 1060 00:50:33,540 --> 00:50:36,750 were living in Bohr's house, being trained by him. 1061 00:50:36,750 --> 00:50:40,140 Bohr produces this amazing talent team 1062 00:50:40,140 --> 00:50:42,970 and this is the second generation. 1063 00:50:42,970 --> 00:50:45,690 This is you know Bohr and Einstein are 1064 00:50:45,690 --> 00:50:48,450 an earlier generation and Marie Curie and so forth. 1065 00:50:48,450 --> 00:50:50,130 This is the next generation out. 1066 00:50:50,130 --> 00:50:52,020 How are they going to find their project? 1067 00:50:52,020 --> 00:50:53,940 What's their project going to be? 1068 00:50:53,940 --> 00:50:58,830 And Bohr kind of urges Delbruck, why don't you look at biology. 1069 00:50:58,830 --> 00:51:01,680 We're coming along up with a lot of physics here. 1070 00:51:01,680 --> 00:51:03,990 Is there a way of applying that to biology? 1071 00:51:03,990 --> 00:51:08,340 And Delbruck does this and has to come to the United States. 1072 00:51:08,340 --> 00:51:11,670 He has to flee Germany on the eve of the war. 1073 00:51:11,670 --> 00:51:15,780 And in turn, Salvadore Luria, he is 1074 00:51:15,780 --> 00:51:18,780 working with Enrico Fermi at the University of Rome. 1075 00:51:18,780 --> 00:51:20,220 He's a medical doctor. 1076 00:51:20,220 --> 00:51:22,200 He's trained in medicine. 1077 00:51:22,200 --> 00:51:24,300 And he is fascinated with physics. 1078 00:51:24,300 --> 00:51:27,450 So he goes to work for Fermi, working on particle physics 1079 00:51:27,450 --> 00:51:28,330 issues. 1080 00:51:28,330 --> 00:51:32,190 So these are two people that actually 1081 00:51:32,190 --> 00:51:37,032 lead this whole molecular biology revolution, in part 1082 00:51:37,032 --> 00:51:39,240 because they're doing this crossover thing that we've 1083 00:51:39,240 --> 00:51:41,070 talked about before. 1084 00:51:41,070 --> 00:51:43,890 They're taking physics and moving it into this new biology 1085 00:51:43,890 --> 00:51:47,760 territory with a whole new raft of ideas 1086 00:51:47,760 --> 00:51:52,120 that help mature and create all kinds of new thinking 1087 00:51:52,120 --> 00:51:52,880 in biology. 1088 00:51:52,880 --> 00:51:54,980 And it really leads to molecular biology, right? 1089 00:51:57,490 --> 00:52:00,610 You know, the second revolution is really genome sequencing. 1090 00:52:00,610 --> 00:52:02,840 We've talked a lot about that already. 1091 00:52:02,840 --> 00:52:06,550 But essentially, that's another one of these crossovers, right? 1092 00:52:06,550 --> 00:52:11,300 That's taking advances in computing 1093 00:52:11,300 --> 00:52:13,960 and certain other kind of physical science areas 1094 00:52:13,960 --> 00:52:16,600 and then bringing them into biology 1095 00:52:16,600 --> 00:52:20,590 and creating a whole new set of applications in the biology 1096 00:52:20,590 --> 00:52:23,980 field that are in turn transformative. 1097 00:52:23,980 --> 00:52:27,160 So that's a second crossover. 1098 00:52:27,160 --> 00:52:30,440 The third revolution-- here, by the way, 1099 00:52:30,440 --> 00:52:33,220 are some of the earlier revolution leaders. 1100 00:52:33,220 --> 00:52:37,540 So that's Salvadore Luria, one of MIT'S greats, 1101 00:52:37,540 --> 00:52:38,770 Nobel Prize winner. 1102 00:52:38,770 --> 00:52:41,050 That's Luria and Delbruck teamed up together 1103 00:52:41,050 --> 00:52:46,720 on the back porch of I think some Long Island beach resort. 1104 00:52:46,720 --> 00:52:48,430 Luria works in Cold Spring Harbor. 1105 00:52:51,250 --> 00:52:54,490 But that's you know, that's an amazing talent team. 1106 00:52:54,490 --> 00:52:57,640 And they really do create the intellectual underpinnings 1107 00:52:57,640 --> 00:52:59,560 for molecular biology. 1108 00:52:59,560 --> 00:53:02,110 That's Leroy Hood, inventor who you're familiar with. 1109 00:53:02,110 --> 00:53:03,760 That's Eric Lander. 1110 00:53:03,760 --> 00:53:07,330 They're leaders in the second revolution, 1111 00:53:07,330 --> 00:53:12,340 the great genomic revolution, another crossover approach. 1112 00:53:12,340 --> 00:53:15,730 And then we've got this whole new community. 1113 00:53:15,730 --> 00:53:17,560 I've featured the MIT parts of it. 1114 00:53:17,560 --> 00:53:21,400 But this revolution is happening at many other schools. 1115 00:53:21,400 --> 00:53:24,640 But this is just the community that we're used to. 1116 00:53:24,640 --> 00:53:26,890 So Phil Sharp on the left and Bob Langer, 1117 00:53:26,890 --> 00:53:29,920 who are the leaders on this particular report. 1118 00:53:29,920 --> 00:53:34,450 Tyler Jacks who leads the Koch Institute here. 1119 00:53:34,450 --> 00:53:41,710 Paul Hammond, who is chairman of the biochemistry department, 1120 00:53:41,710 --> 00:53:42,490 no. 1121 00:53:42,490 --> 00:53:43,390 AUDIENCE: Chem E. 1122 00:53:43,390 --> 00:53:45,557 WILLIAM BONVILLIAN: Chemical engineering department. 1123 00:53:45,557 --> 00:53:48,310 But is doing an enormous amount of research in the life science 1124 00:53:48,310 --> 00:53:49,720 side. 1125 00:53:49,720 --> 00:53:53,050 Susan Hockfield is president at Sangeeta Bhatia, who's 1126 00:53:53,050 --> 00:53:56,790 doing amazing work on cancer. 1127 00:53:56,790 --> 00:54:03,610 You know, it's an incredible community of talent, 1128 00:54:03,610 --> 00:54:07,120 again from a whole series of different kind of fields. 1129 00:54:07,120 --> 00:54:10,090 It's another crossover. 1130 00:54:10,090 --> 00:54:12,790 It's engineering and physical sciences 1131 00:54:12,790 --> 00:54:16,630 and computational sciences entering into the life science 1132 00:54:16,630 --> 00:54:19,930 space with a whole new set of disciplinary perspectives, 1133 00:54:19,930 --> 00:54:23,620 a whole new set of systems perspectives, a whole new way 1134 00:54:23,620 --> 00:54:26,110 of thinking about how to organize research. 1135 00:54:26,110 --> 00:54:27,940 And so this is the MIT community. 1136 00:54:27,940 --> 00:54:31,210 You could duplicate this at other schools as well. 1137 00:54:31,210 --> 00:54:39,070 But it's a set of engineering tools are going to come here, 1138 00:54:39,070 --> 00:54:43,360 but also a whole concept of engineering design comes here. 1139 00:54:43,360 --> 00:54:48,160 So, life science systems tends to look at that complexity. 1140 00:54:48,160 --> 00:54:53,900 They tend to look at complex systems 1141 00:54:53,900 --> 00:54:58,100 and attempt to understand the elements in complex systems. 1142 00:54:58,100 --> 00:55:00,800 That's the kind of way, the frame that biologists 1143 00:55:00,800 --> 00:55:02,210 work from. 1144 00:55:02,210 --> 00:55:04,450 Engineering works in a very different kind of way. 1145 00:55:04,450 --> 00:55:07,370 It attempts to organize in a very hierarchical fashion 1146 00:55:07,370 --> 00:55:09,650 and set priorities. 1147 00:55:09,650 --> 00:55:11,420 Engineering design is a very different way 1148 00:55:11,420 --> 00:55:12,450 of looking at the world. 1149 00:55:12,450 --> 00:55:15,560 So these two fundamental different perspectives 1150 00:55:15,560 --> 00:55:17,810 now have the opportunity of coming together here, 1151 00:55:17,810 --> 00:55:21,920 for what becomes actually a very different kind of research 1152 00:55:21,920 --> 00:55:22,480 model. 1153 00:55:22,480 --> 00:55:33,320 So there will be new knowledge bases here that 1154 00:55:33,320 --> 00:55:34,820 come about as a result of this. 1155 00:55:34,820 --> 00:55:38,030 Just as genomics gave us a whole new knowledge base, 1156 00:55:38,030 --> 00:55:41,270 just as molecular biology gave us a whole new knowledge base, 1157 00:55:41,270 --> 00:55:44,390 the convergence of these different fields 1158 00:55:44,390 --> 00:55:46,890 is going to create a new knowledge base. 1159 00:55:46,890 --> 00:55:50,120 But convergence is somewhat different. 1160 00:55:50,120 --> 00:55:54,620 Because it's also, particularly through the engineering side, 1161 00:55:54,620 --> 00:55:58,610 it could lead us to a whole new set of therapeutic advances. 1162 00:55:58,610 --> 00:56:02,120 So new technologies shift over from engineering 1163 00:56:02,120 --> 00:56:05,810 in areas like imaging sensors, nanotechnology, simulation 1164 00:56:05,810 --> 00:56:09,800 modeling, probability, these are all kind 1165 00:56:09,800 --> 00:56:12,590 of engineering led sides of things that can now 1166 00:56:12,590 --> 00:56:15,690 walk into the biology space. 1167 00:56:15,690 --> 00:56:19,970 So this report at MIT kind of laid a lot of the groundwork. 1168 00:56:19,970 --> 00:56:22,940 And frankly Susan Hockfield saw the promise 1169 00:56:22,940 --> 00:56:26,060 of what these folks were writing about 1170 00:56:26,060 --> 00:56:30,470 and created you know just, up the road from us 1171 00:56:30,470 --> 00:56:33,260 created the Koch Institute, so that we 1172 00:56:33,260 --> 00:56:35,810 were walking the walk at the same time we 1173 00:56:35,810 --> 00:56:37,000 were talking the talk here. 1174 00:56:37,000 --> 00:56:39,230 So that Koch Institute was under way 1175 00:56:39,230 --> 00:56:41,660 before this report was even finished, 1176 00:56:41,660 --> 00:56:44,600 because it was just so clear that this was an incredibly 1177 00:56:44,600 --> 00:56:48,590 promising set of new research opportunities that were going 1178 00:56:48,590 --> 00:56:52,820 to create a lot of real breakthrough spaces 1179 00:56:52,820 --> 00:56:55,380 in the life sciences. 1180 00:56:55,380 --> 00:56:59,450 So there's a whole series of strands 1181 00:56:59,450 --> 00:57:01,520 that we had already seen, that you could 1182 00:57:01,520 --> 00:57:04,430 call convergence-like strands, so synthetic biology 1183 00:57:04,430 --> 00:57:09,050 and nano biology and systems biology, bioinformatics, 1184 00:57:09,050 --> 00:57:11,570 computational biology, tissue engineering, these 1185 00:57:11,570 --> 00:57:16,010 were all kind of strands at MIT and in life sciences generally. 1186 00:57:16,010 --> 00:57:18,840 And the idea of convergence was, ah, these things 1187 00:57:18,840 --> 00:57:20,330 are doing similar things. 1188 00:57:20,330 --> 00:57:22,850 We can understand this in a larger kind of way 1189 00:57:22,850 --> 00:57:25,250 and take more creative advantage of it. 1190 00:57:25,250 --> 00:57:28,760 So will convergence play a role in the medical costs 1191 00:57:28,760 --> 00:57:31,970 problems that we've been talking about? 1192 00:57:31,970 --> 00:57:34,640 We talked about the lack of incentives for cost controls 1193 00:57:34,640 --> 00:57:37,280 in the system. 1194 00:57:37,280 --> 00:57:47,950 So far, we've been thinking about health care as a, 1195 00:57:47,950 --> 00:57:51,880 like rearranging the kind of financial plumbing, right? 1196 00:57:51,880 --> 00:57:54,640 Could we create different kinds of cost structures and cost 1197 00:57:54,640 --> 00:57:56,105 incentives and so forth. 1198 00:57:56,105 --> 00:57:57,730 There is another potential answer here, 1199 00:57:57,730 --> 00:57:59,688 and it's back to the prospector theory in a way 1200 00:57:59,688 --> 00:58:01,650 that you were suggesting. 1201 00:58:01,650 --> 00:58:04,530 Maybe there are innovation answers here too, right? 1202 00:58:04,530 --> 00:58:06,480 In other words, if you get a whole series 1203 00:58:06,480 --> 00:58:09,300 of innovation based advances, that 1204 00:58:09,300 --> 00:58:11,020 can tackle a lot of these problems. 1205 00:58:11,020 --> 00:58:16,260 So for example, you know, NIH working away 1206 00:58:16,260 --> 00:58:18,690 in supporting life science research 1207 00:58:18,690 --> 00:58:23,460 really enabled huge progress against heart disease, which 1208 00:58:23,460 --> 00:58:25,860 you know, is breathtaking and really moved 1209 00:58:25,860 --> 00:58:30,690 heart disease down a notch from a nightmare killer 1210 00:58:30,690 --> 00:58:33,030 to much more manageable health problem. 1211 00:58:33,030 --> 00:58:37,690 And that's occurred in the last 25 years. 1212 00:58:37,690 --> 00:58:40,170 If you do that in a number of areas, 1213 00:58:40,170 --> 00:58:42,430 you can really start to affect the whole kind of cost 1214 00:58:42,430 --> 00:58:42,930 structure. 1215 00:58:42,930 --> 00:58:47,520 And particularly, could you have healthier aging? 1216 00:58:47,520 --> 00:58:50,790 So one part of the dilemma for the current demographics 1217 00:58:50,790 --> 00:58:53,400 challenge that's going to be upon you, 1218 00:58:53,400 --> 00:58:56,730 is keeping my generation in the workforce 1219 00:58:56,730 --> 00:59:02,280 longer with returns that are going into society. 1220 00:59:02,280 --> 00:59:04,560 Can you make me and my generation 1221 00:59:04,560 --> 00:59:08,790 work longer, generating returns that get distributed to all? 1222 00:59:08,790 --> 00:59:10,890 That would solve a lot of problems. 1223 00:59:10,890 --> 00:59:13,780 That helps us, rather than walking off a cliff, 1224 00:59:13,780 --> 00:59:17,290 it helps it manage much more of a curve. 1225 00:59:17,290 --> 00:59:20,820 So if we could do that, that would be powerful. 1226 00:59:20,820 --> 00:59:23,730 And may be that some of these convergence space technologies 1227 00:59:23,730 --> 00:59:26,340 can really be significant enablers in ways 1228 00:59:26,340 --> 00:59:27,940 that we kind of never saw before. 1229 00:59:27,940 --> 00:59:32,400 So that's an innovation-based policy approach 1230 00:59:32,400 --> 00:59:37,810 to a profound kind of societal challenge. 1231 00:59:37,810 --> 00:59:40,120 So there's a whole series of policy steps 1232 00:59:40,120 --> 00:59:43,570 that the report argues need to be taken. 1233 00:59:43,570 --> 00:59:47,320 We need to get across NIH stovepipes. 1234 00:59:47,320 --> 00:59:50,980 NIH, which is all biology all the time on most days, 1235 00:59:50,980 --> 00:59:53,440 needs to be encouraged to be able to look at and fund 1236 00:59:53,440 --> 00:59:54,730 other fields. 1237 00:59:54,730 --> 00:59:56,410 It's hard for NIH to do it because it's 1238 00:59:56,410 --> 00:59:58,120 composed of biologists. 1239 00:59:58,120 --> 01:00:01,180 If it analyzes proposals that involve complex engineering, 1240 01:00:01,180 --> 01:00:03,580 how does it do the analysis? 1241 01:00:03,580 --> 01:00:07,190 How does it have multidisciplinary peer review 1242 01:00:07,190 --> 01:00:08,590 systems? 1243 01:00:08,590 --> 01:00:11,380 Is it able to encourage RO1s that 1244 01:00:11,380 --> 01:00:15,760 have multiple PIs, not sole single PIs, that 1245 01:00:15,760 --> 01:00:20,320 represent a series of different fields and disciplines. 1246 01:00:20,320 --> 01:00:23,380 It's how do we do education and convergence? 1247 01:00:23,380 --> 01:00:28,120 So we still have stove piped disciplinary fields, 1248 01:00:28,120 --> 01:00:29,890 and they're producing a lot of talent. 1249 01:00:29,890 --> 01:00:32,120 But how do they get educated in these other fields 1250 01:00:32,120 --> 01:00:33,650 so they can take advantage of it? 1251 01:00:33,650 --> 01:00:36,190 Do we need a new kind of approach 1252 01:00:36,190 --> 01:00:37,630 in life science education? 1253 01:00:37,630 --> 01:00:40,550 And what are the of common language features going to be? 1254 01:00:40,550 --> 01:00:41,050 Steph. 1255 01:00:41,050 --> 01:00:43,000 AUDIENCE: I feel like the report was really 1256 01:00:43,000 --> 01:00:46,180 missing an element about the jobs in the convergence field, 1257 01:00:46,180 --> 01:00:49,170 because there's not really many job opportunities for people 1258 01:00:49,170 --> 01:00:51,910 who are trained in multidisciplinary 1259 01:00:51,910 --> 01:00:52,900 understandings. 1260 01:00:52,900 --> 01:00:55,357 And I feel like there would be an enormous insecurity 1261 01:00:55,357 --> 01:00:57,940 or uncertainty for those people who are interested in pursuing 1262 01:00:57,940 --> 01:01:00,148 the really innovative fields if they don't understand 1263 01:01:00,148 --> 01:01:02,617 what the actual next step is after a university education. 1264 01:01:02,617 --> 01:01:03,700 WILLIAM BONVILLIAN: Right. 1265 01:01:03,700 --> 01:01:04,960 There's no question about it. 1266 01:01:04,960 --> 01:01:05,860 This is a dilemma. 1267 01:01:05,860 --> 01:01:07,840 And look, this has been a dilemma for a while 1268 01:01:07,840 --> 01:01:09,760 for bioengineering departments. 1269 01:01:09,760 --> 01:01:11,440 I think we're getting out of that phase. 1270 01:01:11,440 --> 01:01:13,780 I think those are starting to really kind of take off, 1271 01:01:13,780 --> 01:01:17,140 in part because this model is taking off. 1272 01:01:17,140 --> 01:01:19,060 But we don't have clear pathways. 1273 01:01:19,060 --> 01:01:21,670 So I mean the model is going to continue to be that you're 1274 01:01:21,670 --> 01:01:24,130 going to be a biologist. 1275 01:01:24,130 --> 01:01:28,540 But can you get access to a series of other fields? 1276 01:01:28,540 --> 01:01:30,940 Maybe you're an engineer but you get significant access 1277 01:01:30,940 --> 01:01:33,430 to a series of medical related fields as well. 1278 01:01:33,430 --> 01:01:39,290 Can we adjust our training system and modify it so that-- 1279 01:01:39,290 --> 01:01:41,890 there was a talented staffer in my office who said look, 1280 01:01:41,890 --> 01:01:43,750 we we're going to need a new language here 1281 01:01:43,750 --> 01:01:45,525 in life science innovation. 1282 01:01:45,525 --> 01:01:47,650 We're going to need kind of our convergence Creole, 1283 01:01:47,650 --> 01:01:50,080 a mix of different languages from different fields 1284 01:01:50,080 --> 01:01:52,090 so that people are going to be able to speak 1285 01:01:52,090 --> 01:01:54,520 across these disciplinary lines and understand things 1286 01:01:54,520 --> 01:01:56,620 across these disciplinary lines. 1287 01:01:56,620 --> 01:01:59,980 That could be that could be pretty important. 1288 01:01:59,980 --> 01:02:02,740 You know that's the heart of this report. 1289 01:02:02,740 --> 01:02:06,730 MIT team subsequently went on in a much broader based 1290 01:02:06,730 --> 01:02:08,830 report that went across many institutions. 1291 01:02:08,830 --> 01:02:12,700 And this past year, did a report looking at what could we 1292 01:02:12,700 --> 01:02:13,810 get from convergence? 1293 01:02:13,810 --> 01:02:16,240 In other words, what other promising convergence areas 1294 01:02:16,240 --> 01:02:18,970 and what might be obtained from them in an attempt 1295 01:02:18,970 --> 01:02:21,280 to get a much more strategic approach to convergence? 1296 01:02:21,280 --> 01:02:23,890 Not just say convergence is neat, 1297 01:02:23,890 --> 01:02:25,570 which is kind of what this report did. 1298 01:02:25,570 --> 01:02:28,230 But really get a strategy together. 1299 01:02:28,230 --> 01:02:29,380 I'm partly guilty. 1300 01:02:29,380 --> 01:02:31,660 But actually get a strategy together 1301 01:02:31,660 --> 01:02:33,897 around what territories in convergence 1302 01:02:33,897 --> 01:02:35,230 might be particularly promising. 1303 01:02:35,230 --> 01:02:38,240 So I urge you to take a look at that more recent report. 1304 01:02:38,240 --> 01:02:42,240 That was much more widely shared across institutions. 1305 01:02:42,240 --> 01:02:44,130 Luyao, some questions for us. 1306 01:02:46,660 --> 01:02:49,920 AUDIENCE: I think one of the most relevant discussions 1307 01:02:49,920 --> 01:02:53,670 of the [INAUDIBLE] would be what are the possible features 1308 01:02:53,670 --> 01:02:58,350 that we could expect in university level that 1309 01:02:58,350 --> 01:03:00,960 will be probably encouraging this kind of confidence 1310 01:03:00,960 --> 01:03:03,670 to take place? 1311 01:03:03,670 --> 01:03:04,170 Yes. 1312 01:03:04,170 --> 01:03:07,085 AUDIENCE: I mean, we have the whole liberal arts college. 1313 01:03:07,085 --> 01:03:08,460 It's essentially the liberal arts 1314 01:03:08,460 --> 01:03:12,060 for science and engineering model. 1315 01:03:12,060 --> 01:03:17,380 AUDIENCE: But why is it currently not-- 1316 01:03:17,380 --> 01:03:19,450 like since they're proposing this convergence 1317 01:03:19,450 --> 01:03:25,330 of researchers, so why is it not happening 1318 01:03:25,330 --> 01:03:30,440 with all this students with multidisciplinary backgrounds, 1319 01:03:30,440 --> 01:03:35,020 why are they not currently working together 1320 01:03:35,020 --> 01:03:36,790 across disciplines? 1321 01:03:36,790 --> 01:03:40,120 AUDIENCE: I would say, in no particular reference 1322 01:03:40,120 --> 01:03:42,760 to our institution at Wellesley. 1323 01:03:42,760 --> 01:03:45,850 I work a lot with multidisciplinary stakeholders 1324 01:03:45,850 --> 01:03:49,690 for the incubator program that I am facilitating currently. 1325 01:03:49,690 --> 01:03:53,770 And a big concern that faculty members have 1326 01:03:53,770 --> 01:03:57,160 is that they, one, by the administration 1327 01:03:57,160 --> 01:03:59,600 are not facilitated to do collaborations. 1328 01:03:59,600 --> 01:04:03,190 And two, that a lot of people feel 1329 01:04:03,190 --> 01:04:09,400 like having an entrepreneurship or a sort of innovation 1330 01:04:09,400 --> 01:04:12,220 for the purpose of commercialization model 1331 01:04:12,220 --> 01:04:16,780 goes against the spirit of a liberal arts education. 1332 01:04:16,780 --> 01:04:18,460 So I feel like in that sense, you know, 1333 01:04:18,460 --> 01:04:25,110 research universities are very much well-designed 1334 01:04:25,110 --> 01:04:27,580 to sort of adopt more of a liberal arts model 1335 01:04:27,580 --> 01:04:30,000 than I think liberal arts colleges are designed 1336 01:04:30,000 --> 01:04:32,110 to adopt more of a stem model. 1337 01:04:32,110 --> 01:04:34,960 But you know, perhaps somewhere in there 1338 01:04:34,960 --> 01:04:41,830 is a model that can arise about multidisciplinary coordination. 1339 01:04:41,830 --> 01:04:44,260 But it would require facilitation 1340 01:04:44,260 --> 01:04:46,270 by the administration to the extent 1341 01:04:46,270 --> 01:04:48,010 to which I think MIT does a really 1342 01:04:48,010 --> 01:04:53,850 great job of facilitating that at the institutional level. 1343 01:04:53,850 --> 01:04:56,850 AUDIENCE: The rise of kind of these collaborative 1344 01:04:56,850 --> 01:04:59,340 projects, particularly culminating 1345 01:04:59,340 --> 01:05:01,470 in your senior year, I think capstone projects 1346 01:05:01,470 --> 01:05:04,950 are a great opportunity to start encouraging 1347 01:05:04,950 --> 01:05:07,860 these collaborative efforts. 1348 01:05:07,860 --> 01:05:09,910 I think Course Two does this pretty well 1349 01:05:09,910 --> 01:05:13,290 with their 2.009 Mechancial Engineering 1350 01:05:13,290 --> 01:05:15,000 kind of product design course. 1351 01:05:15,000 --> 01:05:18,150 But the piece that I would take from that 1352 01:05:18,150 --> 01:05:19,650 is at the beginning of the semester, 1353 01:05:19,650 --> 01:05:21,300 they have you list all of your skills. 1354 01:05:21,300 --> 01:05:24,630 And they separate you out based on kind of backgrounds 1355 01:05:24,630 --> 01:05:27,510 and then form teams that are like inherently collaborative, 1356 01:05:27,510 --> 01:05:30,240 so you don't have sort of lumpiness and all the students 1357 01:05:30,240 --> 01:05:34,100 that are interested, or have particular backgrounds 1358 01:05:34,100 --> 01:05:36,540 in product design, they're not all on the same team. 1359 01:05:36,540 --> 01:05:38,490 They kind of spread it out. 1360 01:05:38,490 --> 01:05:42,505 And then I think if it be an interesting exercise 1361 01:05:42,505 --> 01:05:46,570 to have MIT do sort of a school engineering capstone project. 1362 01:05:46,570 --> 01:05:48,240 And sort of elect into a course where 1363 01:05:48,240 --> 01:05:51,690 you have people that are interested in tissue 1364 01:05:51,690 --> 01:05:54,690 engineering, but they come from the biology department, 1365 01:05:54,690 --> 01:05:56,640 the chemical engineering department, 1366 01:05:56,640 --> 01:05:59,250 and bioengineering department, and they work together 1367 01:05:59,250 --> 01:06:02,610 to kind of formalize this. 1368 01:06:02,610 --> 01:06:04,990 AUDIENCE: In my impression, there's kind of two models. 1369 01:06:04,990 --> 01:06:07,650 Either we train more multidisciplinary students 1370 01:06:07,650 --> 01:06:12,000 like, for me, like my major is philosophy, politics, 1371 01:06:12,000 --> 01:06:13,110 and economics. 1372 01:06:13,110 --> 01:06:16,200 But I'm not actually in [INAUDIBLE].. 1373 01:06:16,200 --> 01:06:19,020 So then the other option is to have 1374 01:06:19,020 --> 01:06:21,180 like a lot of very focused students 1375 01:06:21,180 --> 01:06:23,170 and bring them together to work as a team. 1376 01:06:23,170 --> 01:06:26,590 Which model do you think will be more effective in addressing 1377 01:06:26,590 --> 01:06:27,938 health care change? 1378 01:06:27,938 --> 01:06:29,480 AUDIENCE: I think great groups model. 1379 01:06:29,480 --> 01:06:30,593 [INAUDIBLE] 1380 01:06:30,593 --> 01:06:32,010 AUDIENCE: You definitely like want 1381 01:06:32,010 --> 01:06:34,890 people that like, what's the difference between-- 1382 01:06:34,890 --> 01:06:36,480 AUDIENCE: It's the deep generalist. 1383 01:06:36,480 --> 01:06:37,700 That's what they were calling them. 1384 01:06:37,700 --> 01:06:38,242 AUDIENCE: No. 1385 01:06:38,242 --> 01:06:39,990 Well, you need one person who can gets it 1386 01:06:39,990 --> 01:06:41,310 for every single one of the issues. 1387 01:06:41,310 --> 01:06:43,290 But then you need somebody who-- you need somebody who is 1388 01:06:43,290 --> 01:06:43,860 obsessed-- 1389 01:06:43,860 --> 01:06:45,025 [INTERPOSING VOICES] 1390 01:06:45,025 --> 01:06:46,650 AUDIENCE: Yo, I read every single book. 1391 01:06:46,650 --> 01:06:48,780 I read two, three books outside this course. 1392 01:06:48,780 --> 01:06:50,480 You know, I know this and this and this. 1393 01:06:50,480 --> 01:06:52,260 And the person is just like, like, 1394 01:06:52,260 --> 01:06:54,427 knows so much that like, it's just like you hit them 1395 01:06:54,427 --> 01:06:56,370 with like, any word and they're inspired. 1396 01:06:56,370 --> 01:06:58,658 So like, you want-- 1397 01:06:58,658 --> 01:07:01,200 but you definitely need somebody who knows it and understands 1398 01:07:01,200 --> 01:07:02,010 how to lead really. 1399 01:07:02,010 --> 01:07:02,510 Well. 1400 01:07:02,510 --> 01:07:05,640 Because really great engineers tend to not 1401 01:07:05,640 --> 01:07:07,227 be able to communicate greatly. 1402 01:07:07,227 --> 01:07:09,060 So you have to be able to figure out and ask 1403 01:07:09,060 --> 01:07:12,283 what questions to ask that they won't tell you or figure out 1404 01:07:12,283 --> 01:07:13,950 what they're not telling you, especially 1405 01:07:13,950 --> 01:07:16,230 when hitting deadlines. 1406 01:07:16,230 --> 01:07:18,060 And then another big part with groups, 1407 01:07:18,060 --> 01:07:20,190 with these kind of groups, is to figure out 1408 01:07:20,190 --> 01:07:21,440 where each person stands. 1409 01:07:21,440 --> 01:07:24,720 So it's like, oh, I don't think you're doing a great job. 1410 01:07:24,720 --> 01:07:26,460 Or I know you have this deadline. 1411 01:07:26,460 --> 01:07:27,580 You might may not make it. 1412 01:07:27,580 --> 01:07:28,260 I know you're stressed. 1413 01:07:28,260 --> 01:07:29,010 Don't be stressed. 1414 01:07:29,010 --> 01:07:31,483 I'll check in at the 70% mark. 1415 01:07:31,483 --> 01:07:33,900 How are you doing and then we'll figure it out from there. 1416 01:07:33,900 --> 01:07:35,850 But I don't know, I think is more 1417 01:07:35,850 --> 01:07:37,740 interesting to the research and the media lab 1418 01:07:37,740 --> 01:07:38,670 kind of does that. 1419 01:07:38,670 --> 01:07:40,087 But I don't know if it's been done 1420 01:07:40,087 --> 01:07:41,880 for very, very hard research. 1421 01:07:41,880 --> 01:07:45,128 What I would consider super, like solving 1422 01:07:45,128 --> 01:07:46,170 like a very hard problem. 1423 01:07:49,448 --> 01:07:51,240 WILLIAM BONVILLIAN: It's all these problems 1424 01:07:51,240 --> 01:07:55,920 are now at hand, as we start to seriously pursue 1425 01:07:55,920 --> 01:07:58,020 this convergence model, exactly how we're 1426 01:07:58,020 --> 01:07:59,317 going to cope with this. 1427 01:07:59,317 --> 01:08:00,900 Susan Hockfield used to talk about it. 1428 01:08:00,900 --> 01:08:03,430 And other people have talked about it too, T-shaped people. 1429 01:08:03,430 --> 01:08:05,520 In other words, people with a deep disciplinary 1430 01:08:05,520 --> 01:08:09,060 die, but capable of operating across fields as well. 1431 01:08:09,060 --> 01:08:13,470 And that may well be a pretty key feature here. 1432 01:08:13,470 --> 01:08:15,210 And then combining that community so 1433 01:08:15,210 --> 01:08:17,470 it's able to communicate with each other. 1434 01:08:17,470 --> 01:08:19,540 But you draw on a series of different fields. 1435 01:08:19,540 --> 01:08:21,569 So what the organizational model is going to be 1436 01:08:21,569 --> 01:08:23,399 is really critical here. 1437 01:08:23,399 --> 01:08:26,279 Because again as we've talked about two classes ago, 1438 01:08:26,279 --> 01:08:28,845 innovation, you know, happens with people. 1439 01:08:28,845 --> 01:08:30,970 It's not these institutional organizational models. 1440 01:08:30,970 --> 01:08:33,137 And how do you optimize the opportunities for people 1441 01:08:33,137 --> 01:08:34,290 to be creative? 1442 01:08:34,290 --> 01:08:37,060 So that's upon this model, right? 1443 01:08:37,060 --> 01:08:39,630 Koch Institute is spending a lot of time thinking about this. 1444 01:08:39,630 --> 01:08:42,540 But actually Koch Institute is only one part 1445 01:08:42,540 --> 01:08:44,069 of the convergence going on at MIT. 1446 01:08:44,069 --> 01:08:45,810 Something like 130 engineers are now 1447 01:08:45,810 --> 01:08:48,450 working a significant amount of their time 1448 01:08:48,450 --> 01:08:52,010 at I'm MIT on the life science side. 1449 01:08:52,010 --> 01:08:53,430 And that's not unique. 1450 01:08:53,430 --> 01:08:56,970 That's going on at a lot of institutions now. 1451 01:08:56,970 --> 01:08:58,710 And NIH is a problem here. 1452 01:08:58,710 --> 01:09:01,080 Because it hasn't caught up to be 1453 01:09:01,080 --> 01:09:02,939 able to manage that kind of transition 1454 01:09:02,939 --> 01:09:04,439 and embrace these different fields. 1455 01:09:04,439 --> 01:09:06,569 So that's the big funder. 1456 01:09:06,569 --> 01:09:09,583 And how do we bring that institution along? 1457 01:09:09,583 --> 01:09:11,250 AUDIENCE: I think my big concern though, 1458 01:09:11,250 --> 01:09:13,740 is like having all academics, like, yeah, we're 1459 01:09:13,740 --> 01:09:15,569 getting T people but, let's define 1460 01:09:15,569 --> 01:09:18,540 T like somebody who is like a grad student 1461 01:09:18,540 --> 01:09:20,310 or undergrad at MIT. 1462 01:09:20,310 --> 01:09:22,170 Like I don't know if the best answer 1463 01:09:22,170 --> 01:09:24,720 is to have a great group of Ts. 1464 01:09:24,720 --> 01:09:25,805 You know, I want some As. 1465 01:09:25,805 --> 01:09:26,430 I want some Bs. 1466 01:09:26,430 --> 01:09:27,430 I want some accents. 1467 01:09:27,430 --> 01:09:30,450 I want some question marks. 1468 01:09:30,450 --> 01:09:33,450 Because like, I would use all sorts, like the background, 1469 01:09:33,450 --> 01:09:33,950 right? 1470 01:09:33,950 --> 01:09:34,950 Because it is the difference between, 1471 01:09:34,950 --> 01:09:37,640 oh, I can make a really good like bistro sandwich versus I 1472 01:09:37,640 --> 01:09:40,710 need to make 100, 1,000, 100,000. 1473 01:09:40,710 --> 01:09:42,359 And I need to have the financials 1474 01:09:42,359 --> 01:09:44,700 for all of the stuff, which is like McDonald's, right? 1475 01:09:44,700 --> 01:09:45,870 So ideally, I would want somebody 1476 01:09:45,870 --> 01:09:47,600 who's already been in the field, like somebody 1477 01:09:47,600 --> 01:09:48,725 who has faculty experience. 1478 01:09:48,725 --> 01:09:51,090 Because I might start to build out a certain way. 1479 01:09:51,090 --> 01:09:53,729 But they have just a big scene phenomena, right? 1480 01:09:53,729 --> 01:09:55,810 Like a great example is, originally 1481 01:09:55,810 --> 01:09:58,640 GE, when they were putting out the electrical lens, 1482 01:09:58,640 --> 01:10:00,110 there was this guy named Steinmetz. 1483 01:10:00,110 --> 01:10:04,230 He was this hunchback immigrant that couldn't even 1484 01:10:04,230 --> 01:10:06,510 speak English when he got to the country, 1485 01:10:06,510 --> 01:10:09,790 and didn't really have a great-- do you have Steinmetz on there? 1486 01:10:09,790 --> 01:10:10,140 WILLIAM BONVILLIAN: I don't. 1487 01:10:10,140 --> 01:10:11,723 AUDIENCE: It would be cool if you did. 1488 01:10:11,723 --> 01:10:15,060 But, yeah, but he was just like this very, very kind 1489 01:10:15,060 --> 01:10:18,402 of somebody who would never even be at MIT. 1490 01:10:18,402 --> 01:10:19,860 Or like the Wright brothers, right? 1491 01:10:19,860 --> 01:10:21,690 That it was just they had the practice experience 1492 01:10:21,690 --> 01:10:23,773 that, oh, I think I can do this and trying it out. 1493 01:10:23,773 --> 01:10:26,580 Also like just because of, as in academics, 1494 01:10:26,580 --> 01:10:28,400 there's always a flaw in any organization. 1495 01:10:28,400 --> 01:10:29,940 And you have to figure out, especially in business, 1496 01:10:29,940 --> 01:10:31,560 you look at, oh, how can I expose their flaw 1497 01:10:31,560 --> 01:10:32,970 and they're never going to be able to go here 1498 01:10:32,970 --> 01:10:33,762 because of the way. 1499 01:10:33,762 --> 01:10:34,920 This is their blind spot. 1500 01:10:34,920 --> 01:10:37,020 And so like, you can definitely, like what 1501 01:10:37,020 --> 01:10:38,490 happened at the Wright brothers, where it's like, 1502 01:10:38,490 --> 01:10:40,323 I can't work on this problem because there's 1503 01:10:40,323 --> 01:10:42,360 no perfect theory and it's too much of a risk. 1504 01:10:42,360 --> 01:10:43,900 And I'm already 50 years old. 1505 01:10:43,900 --> 01:10:47,303 And I'm not trying to risk my reputation 1506 01:10:47,303 --> 01:10:48,720 and make my friends make fun of me 1507 01:10:48,720 --> 01:10:50,190 because that's too uncomfortable. 1508 01:10:50,190 --> 01:10:52,720 And I've already kind of like gone my way. 1509 01:10:52,720 --> 01:10:53,880 And there might be somebody that's like, you know, 1510 01:10:53,880 --> 01:10:55,380 it's pretty crazy but I'm just going 1511 01:10:55,380 --> 01:10:57,060 to try it and see what happens. 1512 01:10:57,060 --> 01:10:58,980 And I think in that paper, there is a quote, where it's like, 1513 01:10:58,980 --> 01:11:00,772 what's the point of research if you already 1514 01:11:00,772 --> 01:11:02,030 know what's going to happen? 1515 01:11:02,030 --> 01:11:03,680 But the way the system is, sometimes 1516 01:11:03,680 --> 01:11:05,270 it incentivizes you to just do like, 1517 01:11:05,270 --> 01:11:06,590 oh, I know this is never going to-- 1518 01:11:06,590 --> 01:11:07,800 I don't know if it's going to be perfect. 1519 01:11:07,800 --> 01:11:09,717 But I know it's pretty much going to work out. 1520 01:11:09,717 --> 01:11:10,880 And I think that's a very-- 1521 01:11:10,880 --> 01:11:13,213 OK, as like somebody who likes capitalism, 1522 01:11:13,213 --> 01:11:14,630 I think that's a huge opportunity. 1523 01:11:14,630 --> 01:11:16,755 Because like, you don't just make your company look 1524 01:11:16,755 --> 01:11:17,760 at all the blind spots. 1525 01:11:17,760 --> 01:11:20,120 But I think that's going to be one of the reasons 1526 01:11:20,120 --> 01:11:23,280 why these models don't succeed. 1527 01:11:23,280 --> 01:11:25,430 And it will be big failure too. 1528 01:11:25,430 --> 01:11:26,360 If they fail, right? 1529 01:11:26,360 --> 01:11:27,380 Because of you like you said. 1530 01:11:27,380 --> 01:11:28,422 You spent all this money. 1531 01:11:28,422 --> 01:11:29,950 You got all these smart people. 1532 01:11:29,950 --> 01:11:31,642 And there can be a huge failure there. 1533 01:11:31,642 --> 01:11:33,100 WILLIAM BONVILLIAN: So let's close. 1534 01:11:33,100 --> 01:11:35,450 I attempted to put the convergence reading last 1535 01:11:35,450 --> 01:11:37,630 because it's basically a positive. 1536 01:11:37,630 --> 01:11:40,400 In other words, there are huge innovation opportunities 1537 01:11:40,400 --> 01:11:43,610 that are at hand that we're starting to move on. 1538 01:11:43,610 --> 01:11:46,400 So despite all the problems in the innovation system 1539 01:11:46,400 --> 01:11:49,490 in this sector and all of its organizational gaps, 1540 01:11:49,490 --> 01:11:51,810 something really interesting is starting to happen. 1541 01:11:51,810 --> 01:11:55,320 So let me close with a comment from Elias Zerhouni, who 1542 01:11:55,320 --> 01:11:59,210 is the Director of NIH before Francis Collins. 1543 01:11:59,210 --> 01:12:02,060 He writes, "As science grows more complex, 1544 01:12:02,060 --> 01:12:05,690 it is also converging on a set of unifying principles 1545 01:12:05,690 --> 01:12:08,450 that link apparently disparate diseases 1546 01:12:08,450 --> 01:12:11,060 through common biological pathways 1547 01:12:11,060 --> 01:12:12,830 and therapeutic approaches. 1548 01:12:12,830 --> 01:12:17,240 Today NIH research needs to reflect this new reality." 1549 01:12:17,240 --> 01:12:19,930 So I think that's our innovation organization task here, 1550 01:12:19,930 --> 01:12:22,830 I think summarized nicely in a couple of sentences 1551 01:12:22,830 --> 01:12:24,170 from Zerhouni. 1552 01:12:24,170 --> 01:12:29,140 A closing thought, Luyao? 1553 01:12:29,140 --> 01:12:31,840 AUDIENCE: Well, I do think this reading sends a very positive 1554 01:12:31,840 --> 01:12:35,350 message that we will need to search 1555 01:12:35,350 --> 01:12:40,930 for a holistic organization that kind of get our resources 1556 01:12:40,930 --> 01:12:42,490 and tackle these problems. 1557 01:12:42,490 --> 01:12:45,190 Still, I also feel like we are not 1558 01:12:45,190 --> 01:12:48,190 addressing the problem of kind of, 1559 01:12:48,190 --> 01:12:52,450 instead of tackling all these diseases, why don't we prepare, 1560 01:12:52,450 --> 01:12:55,090 like kind of advocate this population to be 1561 01:12:55,090 --> 01:13:00,832 more healthy, to encourage them to have a healthier lifestyle. 1562 01:13:00,832 --> 01:13:03,040 WILLIAM BONVILLIAN: Preventative medicine rather than 1563 01:13:03,040 --> 01:13:04,760 just repair jobs. 1564 01:13:04,760 --> 01:13:05,345 Right, right. 1565 01:13:05,345 --> 01:13:06,220 An important thought. 1566 01:13:06,220 --> 01:13:07,260 AUDIENCE: And I do think-- 1567 01:13:07,260 --> 01:13:08,140 WILLIAM BONVILLIAN: And there aren't incentives 1568 01:13:08,140 --> 01:13:10,150 in this system particularly to do that either, 1569 01:13:10,150 --> 01:13:12,600 which is problematic.