WEBVTT 00:00:00.090 --> 00:00:02.430 The following content is provided under a Creative 00:00:02.430 --> 00:00:03.820 Commons license. 00:00:03.820 --> 00:00:06.030 Your support will help MIT OpenCourseWare 00:00:06.030 --> 00:00:10.120 continue to offer high quality educational resources for free. 00:00:10.120 --> 00:00:12.660 To make a donation or to view additional materials 00:00:12.660 --> 00:00:16.620 from hundreds of MIT courses, visit MIT OpenCourseWare 00:00:16.620 --> 00:00:17.992 at ocw.mit.edu. 00:00:20.473 --> 00:00:21.890 WILLIAM BONVILLIAN: I'm just going 00:00:21.890 --> 00:00:25.700 to do Venter very quickly since we went through this so 00:00:25.700 --> 00:00:27.680 thoroughly previously. 00:00:27.680 --> 00:00:32.580 But just a few wonderful snapshots, these are The Nature 00:00:32.580 --> 00:00:34.440 and the Science covers. 00:00:34.440 --> 00:00:38.640 So Venter did Science and Collins and the NIH Genome 00:00:38.640 --> 00:00:40.350 Project did Nature. 00:00:40.350 --> 00:00:42.830 Both published on the same day. 00:00:42.830 --> 00:00:46.470 It's kind of the truce that got arranged between the two sides. 00:00:46.470 --> 00:00:49.950 And there they are competing with each other. 00:00:49.950 --> 00:00:54.810 And then there's Collins on his Harley 00:00:54.810 --> 00:00:59.390 and Venter on one of his wild racing yachts. 00:00:59.390 --> 00:01:02.610 And Venter is only interested in sailing if it's 00:01:02.610 --> 00:01:04.440 dangerous is best I can figure. 00:01:04.440 --> 00:01:09.180 So and then there's this classic picture Venter in the business 00:01:09.180 --> 00:01:10.605 suit and wearing the lab coat. 00:01:10.605 --> 00:01:12.480 You know this is all about the contradictions 00:01:12.480 --> 00:01:14.190 in the model he's trying to pursue, 00:01:14.190 --> 00:01:17.100 that fundamental Solara contradiction. 00:01:17.100 --> 00:01:18.698 So these are just a few. 00:01:18.698 --> 00:01:20.490 AUDIENCE: Who's the dude on the motorcycle? 00:01:20.490 --> 00:01:22.240 WILLIAM BONVILLIAN: That's Francis Collins 00:01:22.240 --> 00:01:25.470 who's currently head of NIH, who's also a blues singer too, 00:01:25.470 --> 00:01:27.870 by the way. 00:01:27.870 --> 00:01:31.930 So both these folks are a little out there. 00:01:31.930 --> 00:01:33.180 They're both terrific talents. 00:01:33.180 --> 00:01:34.350 They're remarkable talents. 00:01:34.350 --> 00:01:35.940 We've talked at length about Venter. 00:01:35.940 --> 00:01:40.140 I think that for the purposes here, what I want to say 00:01:40.140 --> 00:01:46.440 is that, I put Venter in here as an example of someone who 00:01:46.440 --> 00:01:50.670 ran into all the contradictions in the life science innovation 00:01:50.670 --> 00:01:53.130 model, right? 00:01:53.130 --> 00:01:57.120 The fact that it was very hard for NIH 00:01:57.120 --> 00:01:59.280 to look outside of biology. 00:01:59.280 --> 00:02:01.050 It was all biologists all the time. 00:02:01.050 --> 00:02:04.530 Venter of course, was trained as a biologist 00:02:04.530 --> 00:02:07.740 but began moving into this computer territory, 00:02:07.740 --> 00:02:11.039 based upon some advances that Leroy Hood made before him 00:02:11.039 --> 00:02:14.670 and building on Hood's work and doing a lot of innovations 00:02:14.670 --> 00:02:17.310 like the genome shotgun approach that we 00:02:17.310 --> 00:02:21.720 talked about two weeks ago. 00:02:21.720 --> 00:02:24.270 He ran into all of the other series 00:02:24.270 --> 00:02:27.030 of these institutional problems at NIH 00:02:27.030 --> 00:02:29.310 as an innovation organization. 00:02:29.310 --> 00:02:32.460 That it became very hard for him to stand up 00:02:32.460 --> 00:02:38.520 a completely different pathway to technology advance 00:02:38.520 --> 00:02:40.740 and science advance. 00:02:40.740 --> 00:02:43.530 That the system was locked into a different route, 00:02:43.530 --> 00:02:46.450 and they were very unaccepting. 00:02:46.450 --> 00:02:49.780 In fact, ostracized him as he moved in a different kind 00:02:49.780 --> 00:02:50.470 of direction. 00:02:50.470 --> 00:02:52.990 So he ultimately leaves. 00:02:52.990 --> 00:02:55.620 And then we run into this change agent kind of idea. 00:02:55.620 --> 00:02:59.440 He becomes a competitor and forces change 00:02:59.440 --> 00:03:01.870 in the institution which he left as it 00:03:01.870 --> 00:03:06.070 tries to keep pace with him so as not to be embarrassed. 00:03:06.070 --> 00:03:08.710 Hence the NIH Genome Initiative. 00:03:08.710 --> 00:03:11.740 And you know it's just an illustration 00:03:11.740 --> 00:03:14.920 and a very personal kind of way, which is why I put it in here, 00:03:14.920 --> 00:03:17.560 of some of the larger institutional challenges 00:03:17.560 --> 00:03:19.730 that we've been talking about before. 00:03:19.730 --> 00:03:22.360 So let me leave the Venter model here 00:03:22.360 --> 00:03:28.330 and go into the chapter, chapter seven in our textbook. 00:03:31.580 --> 00:03:35.570 So it's not simply NIH, but it's the health care delivery 00:03:35.570 --> 00:03:41.030 system itself that has these legacy sector features. 00:03:41.030 --> 00:03:45.100 And as we talked about earlier on in class, 00:03:45.100 --> 00:03:47.810 the US is pretty good at standing up new technologies 00:03:47.810 --> 00:03:50.650 in open fields. 00:03:50.650 --> 00:03:52.527 It runs into real trouble trying to stand up 00:03:52.527 --> 00:03:54.610 technologies in these kind of established sectors. 00:03:54.610 --> 00:03:56.980 So in the life science territory, 00:03:56.980 --> 00:04:00.070 we did pretty well by creating this completely new 00:04:00.070 --> 00:04:03.670 biotech model which was a we were able to get 00:04:03.670 --> 00:04:05.140 a lot of advance out of. 00:04:05.140 --> 00:04:08.020 But when it comes back to fixing the whole health care delivery 00:04:08.020 --> 00:04:11.260 system, that's proven far more problematic. 00:04:11.260 --> 00:04:15.100 And you know that's been a major thorny political issue 00:04:15.100 --> 00:04:19.660 for three administrations in a row now. 00:04:19.660 --> 00:04:22.270 And they've each had rocky results 00:04:22.270 --> 00:04:25.000 trying to pursue that question of how we're going to organize 00:04:25.000 --> 00:04:26.980 health care delivery systems. 00:04:26.980 --> 00:04:30.790 So there's lots of legacy sector characteristics in health care 00:04:30.790 --> 00:04:34.240 delivery, perverse prices and price structures, 00:04:34.240 --> 00:04:36.640 established infrastructure and institutional structures 00:04:36.640 --> 00:04:41.530 like NIH, very powerful vested interests. 00:04:41.530 --> 00:04:44.470 We can see some of those in NIH. 00:04:44.470 --> 00:04:47.410 But we can certainly see them in other parts of the health care 00:04:47.410 --> 00:04:49.300 delivery system. 00:04:49.300 --> 00:04:51.710 They're sustained by public habits. 00:04:51.710 --> 00:04:52.210 Right? 00:04:52.210 --> 00:04:56.990 So it's very hard to tell Medicare patients 00:04:56.990 --> 00:05:03.980 that their full cost repayment system with no patient stake 00:05:03.980 --> 00:05:06.260 doesn't make a lot of economic sense. 00:05:06.260 --> 00:05:07.760 They're not going to be enthusiastic 00:05:07.760 --> 00:05:09.870 about the alternatives. 00:05:09.870 --> 00:05:14.780 So these are structured and sustained by public habits. 00:05:14.780 --> 00:05:15.280 Steph? 00:05:15.280 --> 00:05:18.763 AUDIENCE: Can you define no patient stake? 00:05:18.763 --> 00:05:20.930 WILLIAM BONVILLIAN: You know Medicare is a full cost 00:05:20.930 --> 00:05:22.640 reimbursement system. 00:05:22.640 --> 00:05:26.890 And it was organized for medicine 00:05:26.890 --> 00:05:31.150 as we knew it 30 or 40 years ago, right? 00:05:31.150 --> 00:05:33.730 It was a professional delivery system. 00:05:33.730 --> 00:05:37.690 And you would pay for the cost of the service, whatever 00:05:37.690 --> 00:05:39.280 that was. 00:05:39.280 --> 00:05:41.470 You would not pay for results, because the results 00:05:41.470 --> 00:05:43.565 in the medical system are too uncertain. 00:05:43.565 --> 00:05:45.790 It was very hard to figure out how 00:05:45.790 --> 00:05:52.450 to estimate a pay for results kind of outcomes oriented 00:05:52.450 --> 00:05:54.460 repayment system for these medical systems. 00:05:54.460 --> 00:06:00.130 So as a result, it became a fee for service, full payment 00:06:00.130 --> 00:06:02.710 system for the medical profession. 00:06:02.710 --> 00:06:05.080 And that created a huge incentive 00:06:05.080 --> 00:06:08.440 to run up costs for which the federal government would 00:06:08.440 --> 00:06:10.810 provide you full reimbursement, regardless 00:06:10.810 --> 00:06:12.940 of what the outcomes were. 00:06:12.940 --> 00:06:16.030 So trying to shift those economic signals 00:06:16.030 --> 00:06:19.660 has proven very problematic. 00:06:19.660 --> 00:06:21.910 You know, Obamacare attempted to change some of those, 00:06:21.910 --> 00:06:25.990 not without controversy within the medical professions. 00:06:25.990 --> 00:06:29.760 It's averse to change and innovation. 00:06:29.760 --> 00:06:31.680 The established knowledge base tends 00:06:31.680 --> 00:06:33.780 to get locked in for the professions that 00:06:33.780 --> 00:06:35.670 are participants here. 00:06:35.670 --> 00:06:38.290 There's a real problem with collective action. 00:06:38.290 --> 00:06:41.800 In other words, it's scattered amongst thousands 00:06:41.800 --> 00:06:44.530 of institutions and getting them to act collectively 00:06:44.530 --> 00:06:46.810 in a different kind of organizational model 00:06:46.810 --> 00:06:48.460 is not simple. 00:06:48.460 --> 00:06:51.750 And there are serious governmental and institutional 00:06:51.750 --> 00:06:52.650 problems here. 00:06:52.650 --> 00:06:54.360 So I won't go through the whole litany. 00:06:54.360 --> 00:06:59.070 But you begin to get a sense of how you can take the legacy 00:06:59.070 --> 00:07:03.280 sector analytical framework and apply it 00:07:03.280 --> 00:07:08.480 to a big economic sector that service based like health care. 00:07:08.480 --> 00:07:10.570 It's not just technology based like say energy, 00:07:10.570 --> 00:07:12.730 it's service based. 00:07:12.730 --> 00:07:14.350 But that analytical framework will 00:07:14.350 --> 00:07:17.770 work to a surprising extent for both kinds of sectors. 00:07:21.210 --> 00:07:22.343 And let me go into-- 00:07:22.343 --> 00:07:24.510 let me go out and get this one on the table as well, 00:07:24.510 --> 00:07:27.780 the PCAST, propelling innovation and drug discovery. 00:07:27.780 --> 00:07:31.290 It's a very important critique of the system 00:07:31.290 --> 00:07:33.330 that came out in 2012. 00:07:33.330 --> 00:07:36.450 PCAST is the President's Council of Advisors 00:07:36.450 --> 00:07:37.800 on Science and Technology. 00:07:40.540 --> 00:07:45.050 They advise the president on science and tech policy. 00:07:45.050 --> 00:07:48.488 We had a very strong PCAST under Eric Lander 00:07:48.488 --> 00:07:50.030 during the Obama administration a lot 00:07:50.030 --> 00:07:51.447 did a lot of breakthrough reports. 00:07:51.447 --> 00:07:54.830 I think frankly, this is one of the most significant. 00:07:54.830 --> 00:07:57.560 They really took a hard look at the health care innovation 00:07:57.560 --> 00:08:00.658 system and identified a lot of trouble, a lot of which we've 00:08:00.658 --> 00:08:01.700 been talking about today. 00:08:01.700 --> 00:08:04.310 But this really spelled a fair amount of it out. 00:08:04.310 --> 00:08:08.600 So the NIH budget doubled between '98 and 2003. 00:08:08.600 --> 00:08:12.030 It hasn't kept up with the inflation costs since then. 00:08:12.030 --> 00:08:14.533 But in parallel, we've had rising costs 00:08:14.533 --> 00:08:16.700 of clinical trials, which have now actually reached, 00:08:16.700 --> 00:08:21.870 according this report $1.8 billion per drug. 00:08:21.870 --> 00:08:24.420 There's a new patent cliff that's 00:08:24.420 --> 00:08:26.680 looming for pharmaceutical companies. 00:08:26.680 --> 00:08:28.290 So you may have noticed that farmers 00:08:28.290 --> 00:08:31.260 are busy merging and divesting themselves 00:08:31.260 --> 00:08:34.380 of their R&D operations. 00:08:34.380 --> 00:08:36.780 It looks like a problematic trend. 00:08:36.780 --> 00:08:38.539 So why is this? 00:08:38.539 --> 00:08:41.549 It's largely because of this upcoming patent cliff. 00:08:41.549 --> 00:08:44.550 Drugs with annual sales of $200 billion dollars 00:08:44.550 --> 00:08:51.060 will go off patent between 2010 and I think it's 2015 actually. 00:08:51.060 --> 00:08:53.160 So this has already been happening. 00:08:53.160 --> 00:08:54.730 And this has forced a restructuring 00:08:54.730 --> 00:08:57.720 and amongst the historic pharmaceuticals. 00:08:57.720 --> 00:09:00.580 Replacement revenues are not readily available. 00:09:00.580 --> 00:09:04.200 So hence this whole set of merger activities. 00:09:04.200 --> 00:09:06.540 And they've been, the pharmaceuticals 00:09:06.540 --> 00:09:10.990 have been curtailing their R&D as a result. Venture capital 00:09:10.990 --> 00:09:13.350 at the time this was written was in general decline 00:09:13.350 --> 00:09:15.960 for all sectors, including biopharma. 00:09:15.960 --> 00:09:19.950 Now frankly that's in significant part recovered now. 00:09:19.950 --> 00:09:21.390 That's now better than it was. 00:09:21.390 --> 00:09:25.470 This tends to be a cyclical kind of pattern for biotechs 00:09:25.470 --> 00:09:31.090 in the health care area. 00:09:31.090 --> 00:09:34.560 But at this particularly time, first time these C 00:09:34.560 --> 00:09:38.220 deals for biotechs were down really quite significantly. 00:09:38.220 --> 00:09:41.180 And so that's always an issue that we're 00:09:41.180 --> 00:09:43.473 going to have to confront. 00:09:43.473 --> 00:09:44.890 In other words, biotechs only work 00:09:44.890 --> 00:09:47.950 if the venture capital system is willing to be quite supportive. 00:09:47.950 --> 00:09:50.110 So ups and downs in the venture capital sector 00:09:50.110 --> 00:09:52.330 can have a pretty strong effect as to whether we 00:09:52.330 --> 00:09:55.990 get drugs emerging into the marketplace that we need. 00:09:55.990 --> 00:10:00.910 Despite R&D growth in past decades, drug output was flat 00:10:00.910 --> 00:10:02.710 and productivity was declining. 00:10:02.710 --> 00:10:05.380 And this report invented a concept 00:10:05.380 --> 00:10:09.010 called Eroom's law, which is the opposite of Moore's law. 00:10:09.010 --> 00:10:13.500 The cost of drug development doubles every nine years. 00:10:13.500 --> 00:10:16.130 And the results decline. 00:10:16.130 --> 00:10:19.740 So it's the opposite of Moore's law Martin? 00:10:19.740 --> 00:10:22.450 AUDIENCE: I was going to ask, so VCs function on a seven year 00:10:22.450 --> 00:10:24.500 time cycle for an exit? 00:10:24.500 --> 00:10:27.202 So are biotech VCs on a 20 year cycle? 00:10:27.202 --> 00:10:28.160 WILLIAM BONVILLIAN: No. 00:10:28.160 --> 00:10:28.780 No. 00:10:28.780 --> 00:10:31.870 But what they will do is they will 00:10:31.870 --> 00:10:33.750 go with different levels of funding, 00:10:33.750 --> 00:10:37.420 you know, A round, B round, C round, which they'll tie-- 00:10:37.420 --> 00:10:41.260 they'll benchmark often to the clinical trial process. 00:10:41.260 --> 00:10:44.470 So they'll be able to manage their risk in moving 00:10:44.470 --> 00:10:47.810 from one stage to another. 00:10:47.810 --> 00:10:50.110 And again, no other sector has thought of this. 00:10:50.110 --> 00:10:52.690 They haven't figured out an alternative model. 00:10:52.690 --> 00:10:55.120 The failure rate for new drugs in clinical trials 00:10:55.120 --> 00:10:56.780 is increasing. 00:10:56.780 --> 00:11:01.630 So as of 2003, that was 91% fail. 00:11:01.630 --> 00:11:04.090 Imagine trying to construct a profit model 00:11:04.090 --> 00:11:05.680 around a 91% failure rate. 00:11:05.680 --> 00:11:07.210 It's not simple. 00:11:07.210 --> 00:11:10.000 Can still be done, because the rewards can 00:11:10.000 --> 00:11:12.840 be so big through the blockbuster model. 00:11:12.840 --> 00:11:15.030 And the guarantee of monopoly rents to the patents 00:11:15.030 --> 00:11:15.863 that you get for it. 00:11:16.925 --> 00:11:20.820 AUDIENCE: [INAUDIBLE] Are things getting more stringent or are 00:11:20.820 --> 00:11:22.333 the diseases getting harder? 00:11:22.333 --> 00:11:23.750 WILLIAM BONVILLIAN: I think it's-- 00:11:23.750 --> 00:11:25.600 you know, what am I to say? 00:11:25.600 --> 00:11:30.270 And I don't think there are easy answers here on this, Max. 00:11:30.270 --> 00:11:33.020 I think that we've done the low hanging fruit. 00:11:33.020 --> 00:11:35.447 And the problem is getting much more complicated. 00:11:35.447 --> 00:11:36.780 Chris, you're nodding your head. 00:11:36.780 --> 00:11:38.520 Does that seem like a good answer to you? 00:11:38.520 --> 00:11:39.990 AUDIENCE: Yeah, I think definitely. 00:11:39.990 --> 00:11:41.490 Because like the next frontier seems 00:11:41.490 --> 00:11:43.420 to be like personalized medicine, 00:11:43.420 --> 00:11:46.740 which is a huge kind of new moving problem. 00:11:46.740 --> 00:11:50.290 Because a lot of the difficulties 00:11:50.290 --> 00:11:53.370 of kind of commercializing that and kind of making that viable. 00:11:53.370 --> 00:11:58.010 Although there's a lot of hype around it, which is great. 00:11:58.010 --> 00:11:59.980 WILLIAM BONVILLIAN: Right. 00:11:59.980 --> 00:12:01.440 AUDIENCE: Don't get too much data. 00:12:01.440 --> 00:12:04.115 It could be that maybe they're not doing as many? 00:12:04.115 --> 00:12:06.490 AUDIENCE: Yeah, well maybe now we're doing like way more. 00:12:06.490 --> 00:12:08.780 Or like, it's very easy. 00:12:08.780 --> 00:12:10.898 You know they say the worst lies are statistics. 00:12:10.898 --> 00:12:12.440 WILLIAM BONVILLIAN: Yes, that's true. 00:12:12.440 --> 00:12:14.680 And all of these could be lies. 00:12:14.680 --> 00:12:16.930 AUDIENCE: Well, it's just one way 00:12:16.930 --> 00:12:18.430 of looking at the problem, right? 00:12:18.430 --> 00:12:19.730 It's a way of showcasing it. 00:12:19.730 --> 00:12:22.820 But I would want to see the whole data to see why, 00:12:22.820 --> 00:12:23.930 the context behind it. 00:12:23.930 --> 00:12:24.290 WILLIAM BONVILLIAN: Yeah. 00:12:24.290 --> 00:12:26.630 I mean generally speaking, this report was well received. 00:12:26.630 --> 00:12:28.463 In other words, the experts in the community 00:12:28.463 --> 00:12:32.660 thought that this PCAST report was on to something, 00:12:32.660 --> 00:12:36.560 that they had identified some pretty critical problems. 00:12:36.560 --> 00:12:40.040 So time to market for drugs has also been growing. 00:12:40.040 --> 00:12:43.130 So eight years to market was 50 years ago 00:12:43.130 --> 00:12:46.430 is 14 years to market now. 00:12:46.430 --> 00:12:48.080 And the longer it takes, the more 00:12:48.080 --> 00:12:50.192 you eat up your monopoly rent period, 00:12:50.192 --> 00:12:52.400 which means you've got to make higher profits sooner, 00:12:52.400 --> 00:12:54.410 which means the short term charges for the drugs 00:12:54.410 --> 00:12:55.450 get higher. 00:12:55.450 --> 00:12:57.230 And you're driven and more and more 00:12:57.230 --> 00:12:59.090 towards a blockbuster recovery model. 00:12:59.090 --> 00:13:03.740 So this is problematic. 00:13:03.740 --> 00:13:07.220 And you know it particularly affects small companies 00:13:07.220 --> 00:13:11.030 and biotechs that can't really manage that long term risk 00:13:11.030 --> 00:13:13.050 period. 00:13:13.050 --> 00:13:15.050 And there's a gap between research and product 00:13:15.050 --> 00:13:17.900 development as well. 00:13:17.900 --> 00:13:19.577 And the whole advances in science 00:13:19.577 --> 00:13:21.410 are requiring different kind of models here. 00:13:21.410 --> 00:13:23.175 So now there's much more focus on-- we'll 00:13:23.175 --> 00:13:24.800 talk about convergence in a little bit. 00:13:24.800 --> 00:13:27.890 But much more focus on multidisciplinary teams 00:13:27.890 --> 00:13:32.960 rather than solo individual investigator ROI type results. 00:13:32.960 --> 00:13:36.530 You tend to have to cross over a series of fields now 00:13:36.530 --> 00:13:41.990 to get your medical advance out and that's more problematic. 00:13:41.990 --> 00:13:44.810 So ideas they propose, NCATS they 00:13:44.810 --> 00:13:47.300 cited, the translational medicine 00:13:47.300 --> 00:13:50.750 entity at NIH, that frankly has had trouble 00:13:50.750 --> 00:13:53.030 getting enough funding to really scale up 00:13:53.030 --> 00:13:58.190 to do what it needs to do, a DARPA type model, FDA exploring 00:13:58.190 --> 00:14:01.700 something called predictive toxicology, 00:14:01.700 --> 00:14:06.753 and predictive toxicity with a lab on a chip kind of approach. 00:14:06.753 --> 00:14:08.420 In other words, there's new technologies 00:14:08.420 --> 00:14:10.250 that could be breakthroughs here in trying 00:14:10.250 --> 00:14:12.720 to solve concept problems. 00:14:12.720 --> 00:14:15.410 So let me-- let's get through these 00:14:15.410 --> 00:14:18.470 I think we can just touch very briefly 00:14:18.470 --> 00:14:20.420 on Craig Venter and then kind of dig 00:14:20.420 --> 00:14:24.260 into the legacy sector reading, into the PCAST report. 00:14:27.420 --> 00:14:28.770 Just a quick question on Venter. 00:14:33.690 --> 00:14:37.200 Yeah, you got him, Good. 00:14:37.200 --> 00:14:39.360 AUDIENCE: I think Venter's discovery 00:14:39.360 --> 00:14:43.260 is one of the closest to [INAUDIBLE] ethical problems. 00:14:43.260 --> 00:14:45.480 And just now we had a discussion about how 00:14:45.480 --> 00:14:49.980 we want to speed up of all the research on antibiotics. 00:14:49.980 --> 00:14:58.290 So I want to ask, how do we balance this raising concern, 00:14:58.290 --> 00:15:01.040 by raising concerns of the public 00:15:01.040 --> 00:15:05.400 all this about related research and the need. 00:15:05.400 --> 00:15:12.600 There's a need for better drugs and better discoveries, 00:15:12.600 --> 00:15:18.255 in the sense that there is, like there 00:15:18.255 --> 00:15:22.320 is no mention the role of media in his discovery. 00:15:22.320 --> 00:15:27.820 Is that without the media pushing his results, 00:15:27.820 --> 00:15:35.910 pushing his discoveries, his team and his work 00:15:35.910 --> 00:15:40.440 wouldn't be approved by this parent organization. 00:15:40.440 --> 00:15:45.960 And the kind of discussion raised in public kind of 00:15:45.960 --> 00:15:48.678 gave him enough support to continue 00:15:48.678 --> 00:15:49.720 this [INAUDIBLE] process. 00:15:49.720 --> 00:15:53.280 So I would say what are the other ways to kind 00:15:53.280 --> 00:15:58.395 of balance this raising concerns and this need for better drugs? 00:16:01.895 --> 00:16:04.270 WILLIAM BONVILLIAN: It's a very interesting point, Luyao. 00:16:04.270 --> 00:16:08.780 The fact that Venter was able to mobilize media support, get 00:16:08.780 --> 00:16:12.560 them to understand the potential importance of these projects, 00:16:12.560 --> 00:16:17.660 and what the possibilities were, in effect help drive 00:16:17.660 --> 00:16:19.910 support for this whole genome initiative, 00:16:19.910 --> 00:16:23.030 whether it was Venter's or whether it was NIH's, 00:16:23.030 --> 00:16:24.890 it was a very powerful input. 00:16:24.890 --> 00:16:28.910 And we saw him and Collins on the cover of Time Magazine. 00:16:28.910 --> 00:16:31.730 That was just one example of the kind of media attention 00:16:31.730 --> 00:16:33.170 on this great scientific race. 00:16:35.680 --> 00:16:38.230 AUDIENCE: [INAUDIBLE] everyone was saying, 00:16:38.230 --> 00:16:41.620 everyone has their genetic information, 00:16:41.620 --> 00:16:43.390 everyone was concerned. 00:16:43.390 --> 00:16:47.600 And how can this-- 00:16:47.600 --> 00:16:52.040 I mean, the future will probably be more concerns. 00:16:52.040 --> 00:16:57.905 Is there any possible way to address this balance? 00:17:00.418 --> 00:17:02.710 AUDIENCE: So you're saying like change the structure so 00:17:02.710 --> 00:17:04.089 that somebody who has a good idea 00:17:04.089 --> 00:17:06.730 doesn't have to like completely go and fight 00:17:06.730 --> 00:17:08.135 the current structure for it? 00:17:10.750 --> 00:17:14.410 AUDIENCE: I want to say like how do we 00:17:14.410 --> 00:17:19.180 inform the public of this, the potential benefits that it 00:17:19.180 --> 00:17:24.430 brings, but also ensures that things are under-- 00:17:24.430 --> 00:17:25.750 AUDIENCE: Are actually worth-- 00:17:25.750 --> 00:17:29.280 yes I mean, I think a lot about like the cold fusion scandal. 00:17:29.280 --> 00:17:31.510 Where like they said they had it to the public 00:17:31.510 --> 00:17:33.280 so that they would get attention for it. 00:17:33.280 --> 00:17:35.552 But like the science wasn't super like sure. 00:17:35.552 --> 00:17:37.010 And so like there is a huge danger, 00:17:37.010 --> 00:17:38.350 especially in the scientific community, where 00:17:38.350 --> 00:17:40.017 you need to make sure something actually 00:17:40.017 --> 00:17:41.920 works and it's been tested by your peers 00:17:41.920 --> 00:17:43.750 before you go public, especially if it's 00:17:43.750 --> 00:17:48.670 a dramatic discovery, which I think the genomics project was. 00:17:48.670 --> 00:17:51.015 And so that is a very hard position to be in. 00:17:51.015 --> 00:17:52.390 At the same time, though, I think 00:17:52.390 --> 00:17:55.713 he leveraged a good amount of that. 00:17:55.713 --> 00:17:57.880 I think it's more about being a little more slightly 00:17:57.880 --> 00:18:00.250 or Machiavellian in understanding the structure 00:18:00.250 --> 00:18:02.080 and like who can kill you in terms 00:18:02.080 --> 00:18:04.505 of like credibility or other stuff. 00:18:04.505 --> 00:18:06.130 So he did a good job of actually making 00:18:06.130 --> 00:18:08.588 sure the science was good, kind of having some stakeholders 00:18:08.588 --> 00:18:09.970 just approve his stuff. 00:18:09.970 --> 00:18:11.530 But going to the media and saying, 00:18:11.530 --> 00:18:13.270 yo, this is why they're going to mess up 00:18:13.270 --> 00:18:15.340 and is why we're doing pretty well. 00:18:15.340 --> 00:18:17.258 And we're kind of a David versus Goliath. 00:18:17.258 --> 00:18:17.800 Check it out. 00:18:17.800 --> 00:18:20.510 It's pretty interesting. 00:18:20.510 --> 00:18:22.010 WILLIAM BONVILLIAN: We don't usually 00:18:22.010 --> 00:18:26.990 talk about the role of media in innovation and science policy. 00:18:26.990 --> 00:18:29.570 But you are right to point us in this direction. 00:18:29.570 --> 00:18:35.000 Because this was a highly public competition. 00:18:35.000 --> 00:18:36.920 I mean as I say here, it creates-- 00:18:40.360 --> 00:18:43.780 that competition could be viewed as just duplicative, right? 00:18:43.780 --> 00:18:46.600 Why are private and public resources 00:18:46.600 --> 00:18:50.030 in effect duplicating each other in this race? 00:18:50.030 --> 00:18:52.670 But on the other hand, it was incredibly creative 00:18:52.670 --> 00:18:55.910 and it spurred both sides to greatly accelerate and focus 00:18:55.910 --> 00:18:56.620 on the problems. 00:18:56.620 --> 00:19:01.820 So I think the duplicative research thesis really 00:19:01.820 --> 00:19:02.930 doesn't work here. 00:19:02.930 --> 00:19:04.640 But the other dimension you add here 00:19:04.640 --> 00:19:12.680 is, you know, how can innovators use public attention in helping 00:19:12.680 --> 00:19:14.120 to drive towards their goal. 00:19:14.120 --> 00:19:17.960 And we watched in the Boyer and Swanson 00:19:17.960 --> 00:19:24.830 case, how Swanson was able to mobilize media coverage 00:19:24.830 --> 00:19:28.250 and hold major press conferences when he had major announcements 00:19:28.250 --> 00:19:30.260 to make, he and Boyer had major announcements 00:19:30.260 --> 00:19:32.870 to make on their team. 00:19:32.870 --> 00:19:35.300 So it's a dimension that I don't think anybody's really 00:19:35.300 --> 00:19:37.758 spent a lot of time looking at in an organized kind of way. 00:19:37.758 --> 00:19:39.710 But it's a very interesting one. 00:19:39.710 --> 00:19:43.190 How does public support affect your ability 00:19:43.190 --> 00:19:44.795 to drive an innovation project? 00:19:48.517 --> 00:19:51.830 AUDIENCE: [INAUDIBLE] it seems to me 00:19:51.830 --> 00:19:54.680 that oftentimes, the general American public only 00:19:54.680 --> 00:19:56.900 gets really interested in the scientific topic 00:19:56.900 --> 00:20:01.970 when something's going really, really badly, like epidemic, is 00:20:01.970 --> 00:20:04.387 where people are like hm, biology and medicine 00:20:04.387 --> 00:20:04.970 are important. 00:20:04.970 --> 00:20:09.410 Or like you know, my favorite example of the Apollo 13 00:20:09.410 --> 00:20:11.892 disaster was when suddenly everyone cared about space, 00:20:11.892 --> 00:20:13.600 but only because people were about to die 00:20:13.600 --> 00:20:14.660 and it was exciting. 00:20:14.660 --> 00:20:18.740 So maybe some sort of response to the science communication 00:20:18.740 --> 00:20:21.920 problem would be getting information out there 00:20:21.920 --> 00:20:27.960 about the value of preventing the ratings grabbing disasters, 00:20:27.960 --> 00:20:28.870 I guess. 00:20:28.870 --> 00:20:31.130 AUDIENCE: It doesn't look as cool, though. 00:20:31.130 --> 00:20:33.610 AUDIENCE: [INAUDIBLE] of science fiction. 00:20:33.610 --> 00:20:35.990 That's kind of the role of science fiction. 00:20:35.990 --> 00:20:41.200 There's like a utility principle that I-- 00:20:41.200 --> 00:20:44.810 her name is-- can't remember her last name 00:20:44.810 --> 00:20:47.810 I took a course on bioethics last semester. 00:20:47.810 --> 00:20:49.310 It was a part of something called 00:20:49.310 --> 00:20:53.150 the [INAUDIBLE] seminars for public writing at Wellesley. 00:20:53.150 --> 00:20:57.730 And this woman who was, I guess, was coming in 00:20:57.730 --> 00:21:00.500 to speak with us about the role of media 00:21:00.500 --> 00:21:03.140 in establishing bioethics, and specifically 00:21:03.140 --> 00:21:04.610 about genetic engineering. 00:21:04.610 --> 00:21:07.630 And one of the points that she made to a question 00:21:07.630 --> 00:21:11.060 that I had asked was precisely that science fiction 00:21:11.060 --> 00:21:13.580 plays an enormous role in helping the public become 00:21:13.580 --> 00:21:16.430 comfortable with scientific advances far before the time 00:21:16.430 --> 00:21:20.240 that those scientific advances are even technically feasible. 00:21:20.240 --> 00:21:24.710 And so, maybe as the scientific community, or rather 00:21:24.710 --> 00:21:26.732 the scientific community should do a better job 00:21:26.732 --> 00:21:28.940 of involving themselves in the narrative storytelling 00:21:28.940 --> 00:21:31.340 process about what science is and has the potential 00:21:31.340 --> 00:21:35.270 to be in order to sort of set the stage 00:21:35.270 --> 00:21:38.730 for those scientific advancements when time comes. 00:21:38.730 --> 00:21:43.050 And there is I think a huge market, thinking 00:21:43.050 --> 00:21:46.850 about Martin's sort of business proclivity in children's books 00:21:46.850 --> 00:21:49.590 about science fiction, and the ways in which we, 00:21:49.590 --> 00:21:52.370 as maybe someday parents, right, could be right in reading 00:21:52.370 --> 00:21:55.070 these stories to our children at night, having them think 00:21:55.070 --> 00:21:58.207 about the future and that aspect of science 00:21:58.207 --> 00:22:00.290 communication and especially science communication 00:22:00.290 --> 00:22:02.300 to the younger generations which will inherit 00:22:02.300 --> 00:22:05.030 the kinds of innovations that we create today, 00:22:05.030 --> 00:22:07.280 are immensely important. 00:22:07.280 --> 00:22:09.080 But there's not a lot of storytellers 00:22:09.080 --> 00:22:11.150 who are equipped with the technical knowledge 00:22:11.150 --> 00:22:13.880 in order to communicate that adequately in a way 00:22:13.880 --> 00:22:15.710 that's palatable to children and it's also 00:22:15.710 --> 00:22:18.682 palatable to their parents and to the broader American market. 00:22:18.682 --> 00:22:20.390 So I think it's really important to think 00:22:20.390 --> 00:22:23.000 about this sort of storytelling dimensions of science 00:22:23.000 --> 00:22:26.660 communication, not just in the sense of, what's in the news, 00:22:26.660 --> 00:22:29.300 or what's going to be in the newspapers, 00:22:29.300 --> 00:22:35.210 but how we are orienting ourselves 00:22:35.210 --> 00:22:39.585 to make this an issue we care about socially. 00:22:39.585 --> 00:22:41.960 AUDIENCE: Yeah, I like the idea of preparing the stage so 00:22:41.960 --> 00:22:45.270 that people are receptive to advancements when they happen, 00:22:45.270 --> 00:22:47.130 and not just the scientist makes a discovery 00:22:47.130 --> 00:22:48.950 and then suddenly is trying to drum up support for something 00:22:48.950 --> 00:22:50.930 that they have to explain, A, why it's important first 00:22:50.930 --> 00:22:52.930 of all, and then, B, why what they've discovered 00:22:52.930 --> 00:22:54.675 is the relevant. 00:22:54.675 --> 00:22:55.550 You make good points. 00:22:55.550 --> 00:22:57.092 AUDIENCE: Do you remember who came up 00:22:57.092 --> 00:22:59.060 with the rockets, the original rocket that 00:22:59.060 --> 00:23:00.507 came from like Germany? 00:23:00.507 --> 00:23:01.340 AUDIENCE: Von Braun. 00:23:01.340 --> 00:23:01.890 AUDIENCE: Von Brian. 00:23:01.890 --> 00:23:03.200 Yeah, so that's a pretty good example. 00:23:03.200 --> 00:23:04.617 Because von Braun was having a lot 00:23:04.617 --> 00:23:07.220 of difficulty getting funding for the rockets originally. 00:23:07.220 --> 00:23:09.950 So what he did is he wrote a letter to Disney, 00:23:09.950 --> 00:23:11.230 because he was a foreigner. 00:23:11.230 --> 00:23:14.173 He was German, so like no one wanted him to actually do it. 00:23:14.173 --> 00:23:15.590 And they were kind of stifling him 00:23:15.590 --> 00:23:17.360 and they put him in a research facility 00:23:17.360 --> 00:23:18.740 where he wasn't really doing anything. 00:23:18.740 --> 00:23:19.532 So he wrote Disney. 00:23:19.532 --> 00:23:22.790 Disney does a whole thing on space [INAUDIBLE].. 00:23:22.790 --> 00:23:24.290 But I think it's an important aspect 00:23:24.290 --> 00:23:27.170 in terms of really, who are you stakeholders as a scientist. 00:23:27.170 --> 00:23:29.270 You don't have-- unless you're like a billionaire, 00:23:29.270 --> 00:23:30.230 and even if you are a billionaire, 00:23:30.230 --> 00:23:31.980 you don't have the power to manipulate-- 00:23:31.980 --> 00:23:33.300 you're not like the president. 00:23:33.300 --> 00:23:34.800 So you have to be able to figure out 00:23:34.800 --> 00:23:36.387 how you're going to persuade people 00:23:36.387 --> 00:23:38.720 and how you're going to use those different stakeholders 00:23:38.720 --> 00:23:42.950 in a very interesting-- well, in a very sort of pseudo 00:23:42.950 --> 00:23:46.210 Machiavellian but smart way, right? 00:23:46.210 --> 00:23:48.290 And how do you play the politics well? 00:23:48.290 --> 00:23:49.790 WILLIAM BONVILLIAN: So maybe there's 00:23:49.790 --> 00:23:52.490 an amendment to the legacy sector book 00:23:52.490 --> 00:23:54.740 that we've been using as our textbook, which 00:23:54.740 --> 00:24:00.260 is part of the role of change agents is to use the media 00:24:00.260 --> 00:24:01.340 and to do storytelling. 00:24:01.340 --> 00:24:03.260 Because this was a great story. 00:24:03.260 --> 00:24:06.140 This was an amazing story, which people in the United 00:24:06.140 --> 00:24:08.570 States and the world followed for a significant period 00:24:08.570 --> 00:24:11.170 of time, this great rivalry. 00:24:11.170 --> 00:24:13.810 And you know, Venter is almost made for media 00:24:13.810 --> 00:24:18.710 as a wonderful maverick, you know, fascinating character. 00:24:18.710 --> 00:24:19.980 And Collins is as well. 00:24:19.980 --> 00:24:22.940 So there was a powerful story to be told here 00:24:22.940 --> 00:24:25.880 and a great competition and a great race that 00:24:25.880 --> 00:24:28.340 made it a very powerful kind of media story 00:24:28.340 --> 00:24:30.390 for an extended period of time. 00:24:30.390 --> 00:24:34.460 So Louis, I had not anticipated this tangent of the course. 00:24:34.460 --> 00:24:36.882 But thanks for pulling us into it. 00:24:36.882 --> 00:24:38.840 So let me go on to the next couple of readings. 00:24:41.540 --> 00:24:44.210 So our textbook, who's got that? 00:24:44.210 --> 00:24:45.330 Chloe, all yours. 00:24:48.530 --> 00:24:51.080 AUDIENCE: So to set the stage for maybe the first question 00:24:51.080 --> 00:24:51.580 here. 00:24:51.580 --> 00:24:55.410 I went to a talk recently by a robotics entrepreneur, 00:24:55.410 --> 00:24:56.810 which seems unrelated. 00:24:56.810 --> 00:24:59.390 But I think he gave an interesting lesson 00:24:59.390 --> 00:25:01.652 as it relates, could relate to health care. 00:25:01.652 --> 00:25:03.860 He was talking about the problem he was dealing with, 00:25:03.860 --> 00:25:07.280 which was using robotics as an organizational tool 00:25:07.280 --> 00:25:08.870 in warehouses. 00:25:08.870 --> 00:25:10.850 And it wasn't interesting to me at first. 00:25:10.850 --> 00:25:13.225 But he made the problem pretty interesting after a while. 00:25:13.225 --> 00:25:15.980 But the way that he framed dealing with his problems 00:25:15.980 --> 00:25:20.660 was that he practice having the mindset of evaluating 00:25:20.660 --> 00:25:23.240 the problem at zero and infinity, as he said. 00:25:23.240 --> 00:25:28.245 Which basically translated to removing all constraints that 00:25:28.245 --> 00:25:30.370 were pre-existing on the system and then seeing you 00:25:30.370 --> 00:25:33.500 know if he had infinite space or infinite money or infinite 00:25:33.500 --> 00:25:37.010 labor, what he could accomplish or what his engineering 00:25:37.010 --> 00:25:38.090 solutions would be. 00:25:38.090 --> 00:25:41.540 And it was a really good way to sort of brainstorm 00:25:41.540 --> 00:25:43.110 an optimal solution to his problem. 00:25:43.110 --> 00:25:44.860 So I think it would be interesting to take 00:25:44.860 --> 00:25:48.300 the same approach to the inherent problems in health 00:25:48.300 --> 00:25:52.050 care delivery as a legacy sector. 00:25:52.050 --> 00:25:54.770 If we could-- like, you made the point 00:25:54.770 --> 00:25:59.780 that when you are recapping this chapter that our system is 00:25:59.780 --> 00:26:02.690 currently designed to suit medicine 00:26:02.690 --> 00:26:04.970 as it existed I guess 30 or 40 years ago, 00:26:04.970 --> 00:26:08.120 but not as it is moving into this exciting brave new world 00:26:08.120 --> 00:26:08.780 today. 00:26:08.780 --> 00:26:10.400 So if we could wipe the slate clean 00:26:10.400 --> 00:26:13.570 and not have any of the residual costs, 00:26:13.570 --> 00:26:16.760 like everyone started out healthily today. 00:26:16.760 --> 00:26:19.610 No one had any illness and we didn't have any residual costs 00:26:19.610 --> 00:26:21.470 or anything, like perfect ideal world, 00:26:21.470 --> 00:26:23.480 and we had to redesign our health care 00:26:23.480 --> 00:26:25.110 system from the ground up, and --I know 00:26:25.110 --> 00:26:26.360 this is a massive question. 00:26:26.360 --> 00:26:27.910 But I'm just interested in what you guys think 00:26:27.910 --> 00:26:28.950 that might look like. 00:26:28.950 --> 00:26:31.460 Like what would our innovation in that area 00:26:31.460 --> 00:26:33.830 look like we had no constraints. 00:26:33.830 --> 00:26:35.523 AUDIENCE: I mean obviously, you'd 00:26:35.523 --> 00:26:37.190 have a bunch of pharmaceutical companies 00:26:37.190 --> 00:26:39.310 would be producing infinite drugs, 00:26:39.310 --> 00:26:40.760 because they have tons of money. 00:26:40.760 --> 00:26:44.240 But from there, I think you'd have a significant bottleneck 00:26:44.240 --> 00:26:46.230 in terms of the FDA. 00:26:46.230 --> 00:26:48.620 So you'd have to expand that significantly. 00:26:48.620 --> 00:26:50.533 AUDIENCE: I don't necessarily mean like they 00:26:50.533 --> 00:26:51.700 have the infinite resources. 00:26:51.700 --> 00:26:53.660 It's just like if we were starting to [INAUDIBLE].. 00:26:53.660 --> 00:26:55.493 AUDIENCE: Create a completely perfect system 00:26:55.493 --> 00:26:57.843 for this time and the next 50 years, how would we do it? 00:26:57.843 --> 00:26:58.640 AUDIENCE: Yeah. 00:26:58.640 --> 00:26:59.360 AUDIENCE: I mean, you want me to give you 00:26:59.360 --> 00:27:01.700 an answer of how to structure or how to figure out 00:27:01.700 --> 00:27:03.890 what the process is? 00:27:03.890 --> 00:27:07.432 AUDIENCE: Just I guess, elements of what it might look like. 00:27:07.432 --> 00:27:08.890 AUDIENCE: Well, I think a lot of it 00:27:08.890 --> 00:27:10.010 falls in what Chris was talking about 00:27:10.010 --> 00:27:12.590 and what Bill was talking about, the personalized medicine 00:27:12.590 --> 00:27:13.347 component. 00:27:13.347 --> 00:27:14.930 That's kind of how they're touting it. 00:27:14.930 --> 00:27:17.060 It's the fundamental reorganization 00:27:17.060 --> 00:27:18.630 of health care delivery. 00:27:18.630 --> 00:27:22.397 And one of the ways in which they're-- 00:27:22.397 --> 00:27:24.230 I guess, my understanding is that the sector 00:27:24.230 --> 00:27:26.270 is purporting to make that happen is 00:27:26.270 --> 00:27:27.768 through additive manufacturing. 00:27:27.768 --> 00:27:29.060 So the 3D printing [INAUDIBLE]. 00:27:29.060 --> 00:27:32.148 AUDIENCE: I was just going to say that. 00:27:32.148 --> 00:27:33.690 WILLIAM BONVILLIAN: Well, 3D printing 00:27:33.690 --> 00:27:36.450 is going to be useful in a lot of health areas. 00:27:36.450 --> 00:27:38.760 So there's a new manufacturing institute 00:27:38.760 --> 00:27:41.080 that's organized around regenerative medicine 00:27:41.080 --> 00:27:42.930 and tissue engineering, for example, 00:27:42.930 --> 00:27:46.590 using 3D printing as one of the technologies they're looking at 00:27:46.590 --> 00:27:48.660 and combining that with synthetic biology. 00:27:48.660 --> 00:27:52.050 That's a very interesting and potentially very promising 00:27:52.050 --> 00:27:53.490 territory. 00:27:53.490 --> 00:27:58.430 As we'll get into in the next reading, that's not biology, 00:27:58.430 --> 00:27:59.130 right? 00:27:59.130 --> 00:28:02.130 That's a whole series of new engineering strands that are 00:28:02.130 --> 00:28:03.840 starting to enter , and IT strands, 00:28:03.840 --> 00:28:05.880 that are starting to enter this territory. 00:28:05.880 --> 00:28:10.650 And how does a system that's not organized around that, 00:28:10.650 --> 00:28:13.617 change to accommodate those strands? 00:28:13.617 --> 00:28:15.450 AUDIENCE: Well I think the container section 00:28:15.450 --> 00:28:18.780 of sort of the pharmaceutical industry 00:28:18.780 --> 00:28:21.295 and engineering, one of the most interesting articles 00:28:21.295 --> 00:28:23.670 that I read recently, was about the ways in which they're 00:28:23.670 --> 00:28:26.310 printing the pills in order to be better adapted 00:28:26.310 --> 00:28:35.160 to the person's absorption of the medicine. 00:28:35.160 --> 00:28:37.260 So they sort of-- 00:28:37.260 --> 00:28:39.720 they're starting to develop ways to gauge 00:28:39.720 --> 00:28:43.710 what a pill needs to look like for an individual to ingest it 00:28:43.710 --> 00:28:47.250 and also for the medicine to be delivered into their body. 00:28:47.250 --> 00:28:49.913 And They can't do that in mass manufacturing. 00:28:49.913 --> 00:28:51.830 They can only do that in personalized medicine 00:28:51.830 --> 00:28:53.250 through additives. 00:28:53.250 --> 00:28:54.965 So I thought that was pretty cool. 00:28:54.965 --> 00:28:57.090 So that would be an answer to your question, right? 00:28:57.090 --> 00:28:59.550 That exists and does not require an idealized world. 00:28:59.550 --> 00:29:03.120 It would just require the sort of commercialization process 00:29:03.120 --> 00:29:06.690 and then the scaling up of what exists potentially, 00:29:06.690 --> 00:29:09.367 if it's not to be thwarted by the legacy sector. 00:29:09.367 --> 00:29:11.700 WILLIAM BONVILLIAN: So the traditional production system 00:29:11.700 --> 00:29:17.700 for you know medicines is a batch processing system. 00:29:17.700 --> 00:29:20.910 You build a huge batch and you refine it 00:29:20.910 --> 00:29:22.980 so it has the perfect composition spread 00:29:22.980 --> 00:29:24.480 evenly throughout. 00:29:24.480 --> 00:29:25.620 And then you produce that. 00:29:25.620 --> 00:29:30.420 But that's frankly, not a modern manufacturing technology. 00:29:30.420 --> 00:29:33.540 So a continuous manufacturing process 00:29:33.540 --> 00:29:37.050 is much more flexible, potentially much more modular 00:29:37.050 --> 00:29:43.170 and adaptive to different components in that system, i.e. 00:29:43.170 --> 00:29:47.250 Different elements and a different molecular structures 00:29:47.250 --> 00:29:53.520 and different components within a particular drug or a pill. 00:29:53.520 --> 00:29:57.540 And 3D printing is a very interesting adaptive approach. 00:29:57.540 --> 00:30:02.070 So DARPA has been funding desktop pharmaceutical 00:30:02.070 --> 00:30:05.443 manufacturing with exactly that in mind. 00:30:05.443 --> 00:30:07.860 That we're going to have to move to personalized medicine. 00:30:07.860 --> 00:30:11.670 The military is going to have to have it for its own huge health 00:30:11.670 --> 00:30:15.210 care system, which is funded to the tune of about $50 billion 00:30:15.210 --> 00:30:20.820 a year, a major medical system. 00:30:20.820 --> 00:30:23.155 AUDIENCE: [INAUDIBLE] That's 10 arc reactors. 00:30:23.155 --> 00:30:25.470 I'll take it. 00:30:25.470 --> 00:30:28.050 WILLIAM BONVILLIAN: Well it's a very large medical system. 00:30:28.050 --> 00:30:30.870 And they're having to deal with changes and reforms 00:30:30.870 --> 00:30:32.880 in their own system, so they're moving 00:30:32.880 --> 00:30:35.748 on developing a whole new set of production technologies. 00:30:35.748 --> 00:30:37.290 And on that one, they've been working 00:30:37.290 --> 00:30:40.770 within an MIT team that's really quite interesting. 00:30:40.770 --> 00:30:44.100 But that again, NIH doesn't do this. 00:30:44.100 --> 00:30:46.260 That's not in NIH's territory. 00:30:46.260 --> 00:30:49.080 It happened that the military was interested in this. 00:30:49.080 --> 00:30:52.200 This new DARPA biological technologies office 00:30:52.200 --> 00:30:55.080 happens to be intrigued with those set of possibilities. 00:30:55.080 --> 00:31:00.850 But that's kind of outside the box of the existing system. 00:31:00.850 --> 00:31:02.750 AUDIENCE: So, plug and play manufacturing, 00:31:02.750 --> 00:31:05.352 plug and play personalized medicine? 00:31:05.352 --> 00:31:07.060 WILLIAM BONVILLIAN: Good phrasing, Chloe. 00:31:07.060 --> 00:31:07.893 AUDIENCE: Thank you. 00:31:12.562 --> 00:31:13.520 AUDIENCE: I don't know. 00:31:13.520 --> 00:31:15.020 I think we're going to have to focus 00:31:15.020 --> 00:31:18.647 on the new sexy technologies, focus more on the structure. 00:31:18.647 --> 00:31:21.230 Because I think like, say I have to use the technology that we 00:31:21.230 --> 00:31:22.772 have now, I would think about how I'd 00:31:22.772 --> 00:31:24.990 restructure the organization. 00:31:24.990 --> 00:31:28.310 What are the problems and are they caused by incentives? 00:31:28.310 --> 00:31:29.750 So I think this health care crisis 00:31:29.750 --> 00:31:33.030 isn't a problem of technology or even people or even doctors. 00:31:33.030 --> 00:31:35.270 I think it's an issue of incentives. 00:31:35.270 --> 00:31:37.460 So when I say I'm going to pay whatever you do, 00:31:37.460 --> 00:31:40.850 I'm like, oh well, let's stack it on, add more toppings. 00:31:40.850 --> 00:31:44.060 And like, I end up having a huge bill. 00:31:44.060 --> 00:31:46.520 And then also, as a generation, where now we 00:31:46.520 --> 00:31:48.060 have a huge kind of-- 00:31:48.060 --> 00:31:51.125 I don't-- we have a lot more elders than young people. 00:31:51.125 --> 00:31:53.000 And so we have to pay for these baby boomers. 00:31:53.000 --> 00:31:54.830 So it ignores that context. 00:31:54.830 --> 00:31:57.200 So we also might want to focus on probably 00:31:57.200 --> 00:31:58.850 like most of our costs are going to go 00:31:58.850 --> 00:32:01.582 to elders, not young people, because young people are fine. 00:32:01.582 --> 00:32:03.290 We have a huge portion of elders, so what 00:32:03.290 --> 00:32:04.600 are their diseases? 00:32:04.600 --> 00:32:05.660 How do they work? 00:32:05.660 --> 00:32:07.550 What are the main causes of those diseases? 00:32:07.550 --> 00:32:09.633 And how do I create my organization around that? 00:32:09.633 --> 00:32:12.050 Or maybe I just want to create a whole new medical system, 00:32:12.050 --> 00:32:13.550 focus on this segment because I know 00:32:13.550 --> 00:32:16.340 that they're going to be huge, you know, costs. 00:32:16.340 --> 00:32:18.920 And I can get you know, laws of economics 00:32:18.920 --> 00:32:20.800 by having such a large segment that's, 00:32:20.800 --> 00:32:22.815 like that whole system is personalized for them. 00:32:22.815 --> 00:32:25.190 And they have different needs than somebody who is young. 00:32:25.190 --> 00:32:28.600 Who can just take an Uber or something. 00:32:28.600 --> 00:32:29.270 Yeah. 00:32:29.270 --> 00:32:31.603 For them, you probably have to have somebody go directly 00:32:31.603 --> 00:32:34.220 to them, check how they're doing, check constant levels. 00:32:34.220 --> 00:32:37.200 So it's very different. 00:32:37.200 --> 00:32:40.090 And I think that could cut costs down. 00:32:40.090 --> 00:32:42.320 Or you could send somebody out to them. 00:32:42.320 --> 00:32:44.962 WILLIAM BONVILLIAN: Or use an IT system or use robotics. 00:32:44.962 --> 00:32:46.670 Those are all part of the menu that we're 00:32:46.670 --> 00:32:49.730 starting to think about Chloe, a closing 00:32:49.730 --> 00:32:52.535 thought about this reading on legacy sectors? 00:32:55.700 --> 00:32:57.440 AUDIENCE: Yeah. 00:32:57.440 --> 00:32:59.180 I think for me, it was really eye 00:32:59.180 --> 00:33:01.880 opening to see the part of the reading 00:33:01.880 --> 00:33:05.600 where you just listed the five or six specific 00:33:05.600 --> 00:33:11.090 characteristics of how health care delivery draws parallels 00:33:11.090 --> 00:33:12.020 to legacy sectors. 00:33:12.020 --> 00:33:14.930 And I think those are probably the areas where 00:33:14.930 --> 00:33:17.900 any sort of reformers or innovators 00:33:17.900 --> 00:33:19.670 would really hone in on and focus 00:33:19.670 --> 00:33:26.600 on lifting those restrictions to limber up the system a lot. 00:33:26.600 --> 00:33:27.740 WILLIAM BONVILLIAN: OK. 00:33:27.740 --> 00:33:29.150 Let's go to the PCAST report. 00:33:31.660 --> 00:33:39.870 AUDIENCE: Regarding this research, the drug development. 00:33:39.870 --> 00:33:42.120 We were just talking about how do 00:33:42.120 --> 00:33:47.190 we incentivize firms to take on more 00:33:47.190 --> 00:33:54.350 responsibility in developing some drugs that target minority 00:33:54.350 --> 00:33:57.470 groups or that don't have such a big market. 00:33:57.470 --> 00:34:02.980 And I want to introduce this model that Singapore has in 00:34:02.980 --> 00:34:08.159 incentivizing their small and medium sized firms to invest 00:34:08.159 --> 00:34:10.460 in R&D and trainings. 00:34:10.460 --> 00:34:16.580 What they do is, you can get a tax return 00:34:16.580 --> 00:34:22.120 or you can get a set amount of funds 00:34:22.120 --> 00:34:28.580 as long as you are investing in your employees 00:34:28.580 --> 00:34:35.480 or in any forms of training that can improve your productivity 00:34:35.480 --> 00:34:39.889 and improves your research in any form. 00:34:39.889 --> 00:34:43.130 And I think Singapore was able to do that because they 00:34:43.130 --> 00:34:48.020 are really small scale. 00:34:48.020 --> 00:34:52.010 And they also count on their small and medium sized 00:34:52.010 --> 00:34:55.889 businesses to thrive as the economy. 00:34:55.889 --> 00:34:58.640 But I want to know if this kind of model 00:34:58.640 --> 00:35:03.950 can be a good reference to develop the health care 00:35:03.950 --> 00:35:07.720 research sector in the US. 00:35:07.720 --> 00:35:11.067 If you have any-- 00:35:11.067 --> 00:35:13.400 WILLIAM BONVILLIAN: So the model Singapore is using then 00:35:13.400 --> 00:35:17.030 is to give an incentive to companies 00:35:17.030 --> 00:35:20.240 that are making significant investments in the training 00:35:20.240 --> 00:35:24.410 of their employees and their research teams 00:35:24.410 --> 00:35:27.440 to kind of significantly upgrade their skill sets. 00:35:27.440 --> 00:35:29.960 AUDIENCE: And they have give a lot of flexibility. 00:35:29.960 --> 00:35:33.140 As long as you can justify this amount of spending, 00:35:33.140 --> 00:35:38.960 or able to improve your productivity, 00:35:38.960 --> 00:35:41.548 they get this funding. 00:35:41.548 --> 00:35:43.590 WILLIAM BONVILLIAN: That's what you have to show. 00:35:43.590 --> 00:35:46.970 You have to show some kind of increased performance coming 00:35:46.970 --> 00:35:48.820 out of that. 00:35:48.820 --> 00:35:51.090 AUDIENCE: So what are your thoughts 00:35:51.090 --> 00:35:58.500 about this kind of approach that could kind of encourage a drug 00:35:58.500 --> 00:35:59.466 developments? 00:36:04.020 --> 00:36:05.770 WILLIAM BONVILLIAN: So that's interesting. 00:36:05.770 --> 00:36:09.790 So that's like a roamer technology talent approach 00:36:09.790 --> 00:36:12.490 into a discussion we've been having on institutional 00:36:12.490 --> 00:36:14.200 innovation organization. 00:36:14.200 --> 00:36:17.350 So it's again, you bring us an interesting piece 00:36:17.350 --> 00:36:19.880 of the puzzle. 00:36:19.880 --> 00:36:20.380 I like it. 00:36:29.660 --> 00:36:30.920 So let's go back to basics. 00:36:30.920 --> 00:36:32.690 Is there a talent need in this sector? 00:36:32.690 --> 00:36:34.010 Is there a talent shortage? 00:36:34.010 --> 00:36:37.880 Is there a talent enthusiasm issue 00:36:37.880 --> 00:36:41.970 here that we ought to be addressing as well? 00:36:41.970 --> 00:36:44.120 AUDIENCE: From what I've seen in my very limited 00:36:44.120 --> 00:36:47.580 biotech experience, it seems like there isn't 00:36:47.580 --> 00:36:48.870 really much of a shortage. 00:36:48.870 --> 00:36:52.890 It seems like there's a lot of enthusiasm around the field. 00:36:52.890 --> 00:36:55.890 And like, as has been a continuing 00:36:55.890 --> 00:36:59.070 theme this class, our good old American spirit 00:36:59.070 --> 00:37:02.520 and our entrepreneurial drive toward packing up and moving 00:37:02.520 --> 00:37:07.680 out West or in this case west is genetic engineering and all 00:37:07.680 --> 00:37:09.010 that good stuff. 00:37:09.010 --> 00:37:12.150 So I don't know if the drive is really the problem. 00:37:16.900 --> 00:37:18.890 AUDIENCE: I just think about-- 00:37:18.890 --> 00:37:24.390 I like to think about analogies. 00:37:24.390 --> 00:37:26.550 And often in urban planning, you know, 00:37:26.550 --> 00:37:29.398 people compare Scandinavian countries 00:37:29.398 --> 00:37:30.440 to the rest of the world. 00:37:30.440 --> 00:37:32.730 And say, well, why can't we be like them? 00:37:32.730 --> 00:37:34.260 And there's various factors that we 00:37:34.260 --> 00:37:36.177 don't take into consideration, the homogeneity 00:37:36.177 --> 00:37:38.168 of the population, their productive capacity, 00:37:38.168 --> 00:37:39.960 the resources they have accessible to them, 00:37:39.960 --> 00:37:42.210 the scale at which they're operating, 00:37:42.210 --> 00:37:44.545 maybe their colonial history and the ways in which they 00:37:44.545 --> 00:37:46.920 were able to sort of take advantage of some opportunities 00:37:46.920 --> 00:37:49.295 that we might not be able to as a country, et cetera. 00:37:49.295 --> 00:37:50.670 And so in this instance, I'd like 00:37:50.670 --> 00:37:54.960 to sort of invoke that and cross apply that argument to sort 00:37:54.960 --> 00:37:56.640 of Luyao's point about Singapore, 00:37:56.640 --> 00:37:59.440 and then to really utilize that to challenge what Max just 00:37:59.440 --> 00:37:59.940 said. 00:37:59.940 --> 00:38:03.510 Because I feel like we are situated 00:38:03.510 --> 00:38:07.200 in a very unique and incredible location 00:38:07.200 --> 00:38:09.240 for this kind of conversation. 00:38:09.240 --> 00:38:12.540 But where I'm from in Texas, the kinds of university systems 00:38:12.540 --> 00:38:14.238 and research system that exist there, 00:38:14.238 --> 00:38:15.780 these kinds of conversations probably 00:38:15.780 --> 00:38:17.370 don't happen to that extent. 00:38:17.370 --> 00:38:19.410 And the enthusiasm for commercialization 00:38:19.410 --> 00:38:24.130 is probably not to the same level. 00:38:24.130 --> 00:38:26.340 And so I think I would come back to what 00:38:26.340 --> 00:38:28.830 Max is saying specifically, by saying that perhaps we 00:38:28.830 --> 00:38:34.590 need to then bring something, in a way, usual socialist ways, 00:38:34.590 --> 00:38:39.510 to say that perhaps it is the kinds of talent that is missing 00:38:39.510 --> 00:38:41.880 or rather, people from marginalized groups 00:38:41.880 --> 00:38:48.250 may have a decidedly public good approach to research. 00:38:48.250 --> 00:38:50.848 And so they might not want to commercialize in the same ways 00:38:50.848 --> 00:38:52.890 that people who are being trained in institutions 00:38:52.890 --> 00:38:54.570 like MIT and Stanford are. 00:38:54.570 --> 00:38:57.017 And it is perhaps that it is precisely 00:38:57.017 --> 00:38:59.100 those people who the government should be funding, 00:38:59.100 --> 00:39:01.470 not major research universities that 00:39:01.470 --> 00:39:04.890 exist within a commercialization framework. 00:39:04.890 --> 00:39:07.590 Because those individuals are less 00:39:07.590 --> 00:39:10.350 likely to want to take the bigger piece of the pie 00:39:10.350 --> 00:39:12.660 and may potentially want to benefit and serve 00:39:12.660 --> 00:39:14.820 their communities much more, which 00:39:14.820 --> 00:39:17.840 could prove to be much more disruptive ultimately. 00:39:17.840 --> 00:39:21.320 WILLIAM BONVILLIAN: Who wants to take on Steph's economic model? 00:39:21.320 --> 00:39:21.820 [LAUGHING] 00:39:21.820 --> 00:39:23.485 Plenty of volunteers. 00:39:23.485 --> 00:39:25.610 AUDIENCE: One, you directly challenged me and you-- 00:39:25.610 --> 00:39:28.020 AUDIENCE: Please, please, please, please. 00:39:28.020 --> 00:39:30.650 AUDIENCE: Well, I mean, it's nice and idealistic. 00:39:30.650 --> 00:39:32.400 I love thinking that, OK, everyone is just 00:39:32.400 --> 00:39:33.710 going to work for free. 00:39:33.710 --> 00:39:35.127 AUDIENCE: Oh I don't think they're 00:39:35.127 --> 00:39:36.315 going to work for free, Max. 00:39:36.315 --> 00:39:37.440 That's not the insinuation. 00:39:37.440 --> 00:39:40.150 The insinuation is that the profit sharing is going to-- 00:39:40.150 --> 00:39:40.650 the. 00:39:40.650 --> 00:39:46.830 Profit sharing model is going to be much different I mean, 00:39:46.830 --> 00:39:48.300 I'm not going to [INAUDIBLE]. 00:39:48.300 --> 00:39:50.040 AUDIENCE: OK, so I understood it as they 00:39:50.040 --> 00:39:52.370 don't care about making big fish blockbuster drugs 00:39:52.370 --> 00:39:54.300 or we'll make like the drugs that are like [INAUDIBLE].. 00:39:54.300 --> 00:39:56.730 AUDIENCE: They can make the big fish blockbuster drugs. 00:39:56.730 --> 00:39:59.340 They just don't want to profit to that extent, which ends up 00:39:59.340 --> 00:40:00.540 being good for everyone. 00:40:00.540 --> 00:40:01.620 AUDIENCE: OK. 00:40:01.620 --> 00:40:04.200 So them not profiting [INAUDIBLE].. 00:40:04.200 --> 00:40:07.400 AUDIENCE: But the thing is, you could take the other side. 00:40:07.400 --> 00:40:08.910 The company's profiting means they 00:40:08.910 --> 00:40:12.270 can put that research-- put that money toward new drugs, 00:40:12.270 --> 00:40:14.190 as opposed to just develop one drug 00:40:14.190 --> 00:40:16.600 and then you basically break even. 00:40:16.600 --> 00:40:18.510 Then you're just like, what do I do now? 00:40:18.510 --> 00:40:20.010 AUDIENCE: I think the focus on drugs 00:40:20.010 --> 00:40:21.135 maybe also kind of limited. 00:40:21.135 --> 00:40:23.790 Like I guess we still want more drugs, that's fine. 00:40:23.790 --> 00:40:26.250 But I think they have drugs that are targeted 00:40:26.250 --> 00:40:29.830 towards like, maybe if-- 00:40:29.830 --> 00:40:31.830 I guess we talked about like a disease portfolio 00:40:31.830 --> 00:40:34.060 that they look at and it's very narrow. 00:40:34.060 --> 00:40:35.730 And so there's two things here. 00:40:35.730 --> 00:40:41.160 So I wonder if such proposals might help expand that disease 00:40:41.160 --> 00:40:41.730 portfolio. 00:40:41.730 --> 00:40:44.070 So you have people who care about more diseases 00:40:44.070 --> 00:40:47.610 and are able to do more research in sort of different areas. 00:40:47.610 --> 00:40:52.320 And then two, I wonder if those who actually subscribe 00:40:52.320 --> 00:40:58.330 to this sort of, let's say big pharma look kind of like we're 00:40:58.330 --> 00:41:01.990 in this age, FDA, drug an incremental advance type 00:41:01.990 --> 00:41:05.947 system, do they actually care to the extent 00:41:05.947 --> 00:41:07.280 that someone's trying to get at. 00:41:07.280 --> 00:41:08.920 Like are they actually interested 00:41:08.920 --> 00:41:12.550 in kind of solving this incremental advance problem 00:41:12.550 --> 00:41:15.490 or do they have a vested interest in what they're 00:41:15.490 --> 00:41:19.260 doing because they care about these sort of bigger fish, 00:41:19.260 --> 00:41:20.860 like kind of cure all drugs? 00:41:20.860 --> 00:41:23.110 But they don't have the funding opportunities and kind 00:41:23.110 --> 00:41:27.250 of the means to kind of get [INAUDIBLE].. 00:41:27.250 --> 00:41:29.460 AUDIENCE: The way institutions are specifically 00:41:29.460 --> 00:41:31.080 cited by the conversions reading, 00:41:31.080 --> 00:41:36.360 I think kind of really proved sort of where I'm coming from. 00:41:36.360 --> 00:41:39.030 They cited a Harvard, University of Texas 00:41:39.030 --> 00:41:41.370 at Austin, Carnegie Mellon, George Tech, U Chicago, 00:41:41.370 --> 00:41:43.450 and Tufts, obviously you know without saying, 00:41:43.450 --> 00:41:45.750 MIT, because most of the people on that commission 00:41:45.750 --> 00:41:48.600 had some relationship time or were researchers that graduated 00:41:48.600 --> 00:41:50.840 from MIT, were professors that MIT, 00:41:50.840 --> 00:41:53.640 were prominent political stakeholders at MIT. 00:41:53.640 --> 00:41:56.460 So you know it occurs to me that there if, it 00:41:56.460 --> 00:41:58.830 is that we you know really revere people like Craig 00:41:58.830 --> 00:42:03.980 Venter, and, if it is that the convergence reading, which 00:42:03.980 --> 00:42:06.820 we will be talking about soon enough, 00:42:06.820 --> 00:42:11.250 is putting a lot of weight on the potential of community 00:42:11.250 --> 00:42:13.470 colleges and of local institutions 00:42:13.470 --> 00:42:17.390 to train the workforce in the life sciences, 00:42:17.390 --> 00:42:19.890 why is it that we don't give them the same access to funding 00:42:19.890 --> 00:42:22.050 opportunities that we're giving you know, 00:42:22.050 --> 00:42:24.637 the sort of blockbuster research universities? 00:42:34.083 --> 00:42:35.750 AUDIENCE: I think the big thing is like, 00:42:35.750 --> 00:42:38.840 so it seems like a lot-- this is like a easy mental model 00:42:38.840 --> 00:42:39.642 for most people. 00:42:39.642 --> 00:42:41.850 What they do is they think about everything linearly. 00:42:41.850 --> 00:42:43.358 So the thing is like MIT, Harvard, 00:42:43.358 --> 00:42:45.650 and luxury institutions just have a huge conglomeration 00:42:45.650 --> 00:42:46.250 of people. 00:42:46.250 --> 00:42:47.750 So they're exponentially much better 00:42:47.750 --> 00:42:49.070 at solving certain problems. 00:42:49.070 --> 00:42:51.710 And they have the credibility to go and get funding 00:42:51.710 --> 00:42:53.990 from outside sources, whether it be government, 00:42:53.990 --> 00:42:58.580 whether it be VC, so it is this huge you know, 00:42:58.580 --> 00:43:01.310 very unproportional nature of the institutes 00:43:01.310 --> 00:43:03.620 and the way the system works. 00:43:03.620 --> 00:43:05.470 But there are some good things to that. 00:43:05.470 --> 00:43:08.690 What I worry more is like how do you create a new system 00:43:08.690 --> 00:43:12.290 to make all these smaller drugs and working on them 00:43:12.290 --> 00:43:13.370 more viable. 00:43:13.370 --> 00:43:15.770 So I think that's an interesting place for a startup, 00:43:15.770 --> 00:43:18.680 if you can figure out 3D printing of drugs 00:43:18.680 --> 00:43:21.470 or if you can figure out what is your kind 00:43:21.470 --> 00:43:24.325 of your competitive advantage in this problem. 00:43:24.325 --> 00:43:25.700 And what is the current structure 00:43:25.700 --> 00:43:27.830 for the company that focus on the big pharma. 00:43:27.830 --> 00:43:30.540 And how do you optimize for that. 00:43:30.540 --> 00:43:33.150 So what are so it's pretty much like Art of War. 00:43:33.150 --> 00:43:35.225 It's like, OK, they already have their strategy. 00:43:35.225 --> 00:43:37.100 They have to focus on doing business with us. 00:43:37.100 --> 00:43:39.142 And there's also called the intervention dilemma, 00:43:39.142 --> 00:43:41.780 which is like they have to make $5 billion 00:43:41.780 --> 00:43:43.340 in revenue off of this drug. 00:43:43.340 --> 00:43:45.280 I don't need to make $5 million in revenue. 00:43:45.280 --> 00:43:46.580 I'm a smaller fish. 00:43:46.580 --> 00:43:49.100 I could, with like $5 million, right? 00:43:49.100 --> 00:43:51.500 So like how do I create my systems so that I can make 00:43:51.500 --> 00:43:53.893 these kind of drugs and make a big company based 00:43:53.893 --> 00:43:55.310 on focusing on those smaller drugs 00:43:55.310 --> 00:43:57.120 that are important to society. 00:43:57.120 --> 00:43:59.240 That would be like the enterprise version. 00:43:59.240 --> 00:44:02.180 You can probably come up with some parallel using government 00:44:02.180 --> 00:44:04.520 funding and government support. 00:44:04.520 --> 00:44:07.735 And maybe it might be that FDA approval, being more lax, 00:44:07.735 --> 00:44:09.110 or depending on the drug, I would 00:44:09.110 --> 00:44:11.068 have to figure out which ones are the best ones 00:44:11.068 --> 00:44:13.500 to go after in the beginning. 00:44:13.500 --> 00:44:16.250 But I don't think, except making the argument 00:44:16.250 --> 00:44:19.200 that, oh how do we, you know, I don't want to say democratize, 00:44:19.200 --> 00:44:23.118 but why can't everybody like be in the pool? 00:44:23.118 --> 00:44:24.660 Especially when it's like those there 00:44:24.660 --> 00:44:26.160 are people that are Olympic swimmers 00:44:26.160 --> 00:44:27.283 and like institutes that-- 00:44:27.283 --> 00:44:29.450 I mean there are probably good high school swimmers, 00:44:29.450 --> 00:44:33.260 but like it just won't you won't get as much a run through 00:44:33.260 --> 00:44:34.090 of the same-- 00:44:34.090 --> 00:44:35.632 AUDIENCE: I guess it just concerns me 00:44:35.632 --> 00:44:38.750 that we pay so much lip service to the great groups model. 00:44:38.750 --> 00:44:42.770 And the great group model itself says that the drive for profit 00:44:42.770 --> 00:44:47.420 is not the greatest motivator to innovation. 00:44:47.420 --> 00:44:52.100 And if we are to consider that as true 00:44:52.100 --> 00:44:55.160 or to take that at face value in the ways in which the authors 00:44:55.160 --> 00:44:58.490 have presented their argument in the previous weeks, 00:44:58.490 --> 00:45:00.290 I don't know that the commercialization 00:45:00.290 --> 00:45:03.380 model for the life sciences is necessarily 00:45:03.380 --> 00:45:06.740 conducive to innovations in the ways in which we would 00:45:06.740 --> 00:45:08.240 hope that they would exist. 00:45:08.240 --> 00:45:09.782 WILLIAM BONVILLIAN: So, Steph, you're 00:45:09.782 --> 00:45:14.630 driving us towards obviously some truly big picture issues. 00:45:14.630 --> 00:45:21.920 Look, we have taken a pretty radical capitalist model 00:45:21.920 --> 00:45:26.520 to solving this problem of innovation in life science, 00:45:26.520 --> 00:45:27.260 right? 00:45:27.260 --> 00:45:29.450 It's pretty amazing that we have focused 00:45:29.450 --> 00:45:36.790 on a high risk, high reward system that's 00:45:36.790 --> 00:45:39.340 completely dependent upon you know, 00:45:39.340 --> 00:45:44.140 monopoly rents and major returns as the way in which we're 00:45:44.140 --> 00:45:46.570 going to do innovation in the life science territory. 00:45:46.570 --> 00:45:48.880 It's absolutely fascinating. 00:45:48.880 --> 00:45:51.010 How did we stumble into this? 00:45:51.010 --> 00:45:56.570 That wasn't what-- remember when we talked about Boyer 00:45:56.570 --> 00:45:59.960 and the conflicts he had with other UCSF faculty? 00:45:59.960 --> 00:46:04.280 When he went off to invent the biotech model with Swanson, 00:46:04.280 --> 00:46:06.960 he got a lot of flack for this. 00:46:06.960 --> 00:46:09.510 That was a radical departure. 00:46:09.510 --> 00:46:13.510 He was leaving a university based research system. 00:46:13.510 --> 00:46:16.650 But let's think back to the reasons why he was doing that. 00:46:16.650 --> 00:46:19.740 Because he wanted his technologies 00:46:19.740 --> 00:46:22.350 to scale up and be available and that was the option 00:46:22.350 --> 00:46:24.180 that he saw for being able to do that, 00:46:24.180 --> 00:46:26.072 so he teams up with Swanson. 00:46:26.072 --> 00:46:28.530 I don't think we're going to resolve these questions today. 00:46:28.530 --> 00:46:31.800 But it is a radical capitalist model. 00:46:31.800 --> 00:46:33.690 And as we've discussed at length today, 00:46:33.690 --> 00:46:35.790 there are gaps in that model, right? 00:46:35.790 --> 00:46:37.830 There's only some things that that model 00:46:37.830 --> 00:46:40.320 is going to be able to address given 00:46:40.320 --> 00:46:42.270 the structural limits that are coming into it, 00:46:42.270 --> 00:46:45.840 particularly the long term approval process 00:46:45.840 --> 00:46:47.730 that the FDA has to provide. 00:46:47.730 --> 00:46:50.670 So you know, drug companies hate the FDA 00:46:50.670 --> 00:46:53.670 because they have to spend seven years and $1.8 billion 00:46:53.670 --> 00:46:55.470 getting through their hurdles. 00:46:55.470 --> 00:47:00.450 But they also love FDA because it certifies their products 00:47:00.450 --> 00:47:02.070 and guarantees a market for them. 00:47:02.070 --> 00:47:04.890 So it's this odd love hate relationship. 00:47:04.890 --> 00:47:06.810 In some ways that's symptomatic of what we've 00:47:06.810 --> 00:47:09.690 got over this whole system. 00:47:09.690 --> 00:47:14.050 And it is a system under stress at this point. 00:47:14.050 --> 00:47:16.680 Luyao why don't you give us a closing point on this and then 00:47:16.680 --> 00:47:19.138 we'll go right into what I think the next part of the story 00:47:19.138 --> 00:47:19.920 is on convergence. 00:47:19.920 --> 00:47:23.400 Because I think it fits nicely with this. 00:47:23.400 --> 00:47:26.630 AUDIENCE: I realize we do focus on a lot of how to incentivize 00:47:26.630 --> 00:47:28.740 innovation in drug development. 00:47:28.740 --> 00:47:31.830 But I do think that you know we still 00:47:31.830 --> 00:47:37.380 have this scarcity problem with rising demand and limited 00:47:37.380 --> 00:47:38.650 supply. 00:47:38.650 --> 00:47:43.860 Why don't we also divert a little bit of focus 00:47:43.860 --> 00:47:47.130 on developing a healthy population. 00:47:47.130 --> 00:47:53.040 Can any form of drug development and kind of research 00:47:53.040 --> 00:47:55.740 that advance this process, so that we 00:47:55.740 --> 00:47:59.580 can reduce the unnecessary demand for certain type 00:47:59.580 --> 00:48:00.810 of health care. 00:48:00.810 --> 00:48:04.740 So that we can free up a bit of our funding and resources 00:48:04.740 --> 00:48:09.600 so that we can focus on the rest of the research programs. 00:48:09.600 --> 00:48:13.320 WILLIAM BONVILLIAN: And look, your point earlier, 00:48:13.320 --> 00:48:18.620 which we debated about, is there a talent problem here? 00:48:18.620 --> 00:48:22.792 Romer's prospector theory would tell us, 00:48:22.792 --> 00:48:24.500 you're going to get a lot more innovation 00:48:24.500 --> 00:48:26.850 if you put more well-trained prospectors on the problem, 00:48:26.850 --> 00:48:27.350 right? 00:48:27.350 --> 00:48:29.982 So I don't want us to kind of leave that point. 00:48:29.982 --> 00:48:31.940 I think there's an interesting underlying point 00:48:31.940 --> 00:48:35.235 you made in that area as well. 00:48:35.235 --> 00:48:37.235 All right, let's go on to the convergence study. 00:48:42.420 --> 00:48:44.170 The report is called "The Third Revolution 00:48:44.170 --> 00:48:46.295 - The Convergence Of Life, Physical and Engineering 00:48:46.295 --> 00:48:46.830 Sciences." 00:48:46.830 --> 00:48:50.400 And it came out of MIT in 2011. 00:48:50.400 --> 00:48:53.970 You know my office, the MIT Washington office, 00:48:53.970 --> 00:48:54.900 helped work on this. 00:48:54.900 --> 00:48:58.020 The project was led by Phil Sharpe and Bob Langer, 00:48:58.020 --> 00:49:00.420 somebody from engineering, somebody from biology, 00:49:00.420 --> 00:49:04.850 obviously two of MIT's all time greats. 00:49:04.850 --> 00:49:09.750 And the report tries to tell a story 00:49:09.750 --> 00:49:14.220 that the picture in the next advance wave, we're 00:49:14.220 --> 00:49:16.470 lacking a picture of the next wave of advance. 00:49:16.470 --> 00:49:19.260 The great thing the genomes piece gave 00:49:19.260 --> 00:49:22.080 us and we talked about Venter and the competition 00:49:22.080 --> 00:49:25.073 with Collins and the NIH, it gave us a story, 00:49:25.073 --> 00:49:26.490 it gave us a picture of what we're 00:49:26.490 --> 00:49:31.160 going to get for this massive investment in NIH. 00:49:31.160 --> 00:49:35.130 It enabled the public to see a story and be told a story 00:49:35.130 --> 00:49:38.220 and understand what the results were going to yield, right? 00:49:38.220 --> 00:49:41.910 We don't have a story for the life sciences 00:49:41.910 --> 00:49:46.380 that's out there now that has nearly the kind of power 00:49:46.380 --> 00:49:47.640 as that genomic story. 00:49:47.640 --> 00:49:50.580 We haven't figured out how to tell the next story. 00:49:50.580 --> 00:49:52.440 And that's part of the reason why 00:49:52.440 --> 00:49:55.900 we've got funding stagnation for NIH 00:49:55.900 --> 00:49:57.900 and the life sciences in general. 00:49:57.900 --> 00:50:00.540 So the doubling was led by the genomics revolution. 00:50:00.540 --> 00:50:02.053 NIH needs a new picture. 00:50:02.053 --> 00:50:03.720 So what are these different revolutions? 00:50:03.720 --> 00:50:06.750 So what this report argued was that really 00:50:06.750 --> 00:50:10.320 the kind of first revolution in recent time 00:50:10.320 --> 00:50:12.120 was the molecular biology revolution. 00:50:12.120 --> 00:50:15.930 And that was really the merger of physics and biology. 00:50:15.930 --> 00:50:21.360 So Max Delbruck comes out of the amazing pre-war German physics 00:50:21.360 --> 00:50:23.550 community. 00:50:23.550 --> 00:50:30.630 He works with Niels Bohr in Copenhagen 00:50:30.630 --> 00:50:33.540 as part of that amazing community that 00:50:33.540 --> 00:50:36.750 were living in Bohr's house, being trained by him. 00:50:36.750 --> 00:50:40.140 Bohr produces this amazing talent team 00:50:40.140 --> 00:50:42.970 and this is the second generation. 00:50:42.970 --> 00:50:45.690 This is you know Bohr and Einstein are 00:50:45.690 --> 00:50:48.450 an earlier generation and Marie Curie and so forth. 00:50:48.450 --> 00:50:50.130 This is the next generation out. 00:50:50.130 --> 00:50:52.020 How are they going to find their project? 00:50:52.020 --> 00:50:53.940 What's their project going to be? 00:50:53.940 --> 00:50:58.830 And Bohr kind of urges Delbruck, why don't you look at biology. 00:50:58.830 --> 00:51:01.680 We're coming along up with a lot of physics here. 00:51:01.680 --> 00:51:03.990 Is there a way of applying that to biology? 00:51:03.990 --> 00:51:08.340 And Delbruck does this and has to come to the United States. 00:51:08.340 --> 00:51:11.670 He has to flee Germany on the eve of the war. 00:51:11.670 --> 00:51:15.780 And in turn, Salvadore Luria, he is 00:51:15.780 --> 00:51:18.780 working with Enrico Fermi at the University of Rome. 00:51:18.780 --> 00:51:20.220 He's a medical doctor. 00:51:20.220 --> 00:51:22.200 He's trained in medicine. 00:51:22.200 --> 00:51:24.300 And he is fascinated with physics. 00:51:24.300 --> 00:51:27.450 So he goes to work for Fermi, working on particle physics 00:51:27.450 --> 00:51:28.330 issues. 00:51:28.330 --> 00:51:32.190 So these are two people that actually 00:51:32.190 --> 00:51:37.032 lead this whole molecular biology revolution, in part 00:51:37.032 --> 00:51:39.240 because they're doing this crossover thing that we've 00:51:39.240 --> 00:51:41.070 talked about before. 00:51:41.070 --> 00:51:43.890 They're taking physics and moving it into this new biology 00:51:43.890 --> 00:51:47.760 territory with a whole new raft of ideas 00:51:47.760 --> 00:51:52.120 that help mature and create all kinds of new thinking 00:51:52.120 --> 00:51:52.880 in biology. 00:51:52.880 --> 00:51:54.980 And it really leads to molecular biology, right? 00:51:57.490 --> 00:52:00.610 You know, the second revolution is really genome sequencing. 00:52:00.610 --> 00:52:02.840 We've talked a lot about that already. 00:52:02.840 --> 00:52:06.550 But essentially, that's another one of these crossovers, right? 00:52:06.550 --> 00:52:11.300 That's taking advances in computing 00:52:11.300 --> 00:52:13.960 and certain other kind of physical science areas 00:52:13.960 --> 00:52:16.600 and then bringing them into biology 00:52:16.600 --> 00:52:20.590 and creating a whole new set of applications in the biology 00:52:20.590 --> 00:52:23.980 field that are in turn transformative. 00:52:23.980 --> 00:52:27.160 So that's a second crossover. 00:52:27.160 --> 00:52:30.440 The third revolution-- here, by the way, 00:52:30.440 --> 00:52:33.220 are some of the earlier revolution leaders. 00:52:33.220 --> 00:52:37.540 So that's Salvadore Luria, one of MIT'S greats, 00:52:37.540 --> 00:52:38.770 Nobel Prize winner. 00:52:38.770 --> 00:52:41.050 That's Luria and Delbruck teamed up together 00:52:41.050 --> 00:52:46.720 on the back porch of I think some Long Island beach resort. 00:52:46.720 --> 00:52:48.430 Luria works in Cold Spring Harbor. 00:52:51.250 --> 00:52:54.490 But that's you know, that's an amazing talent team. 00:52:54.490 --> 00:52:57.640 And they really do create the intellectual underpinnings 00:52:57.640 --> 00:52:59.560 for molecular biology. 00:52:59.560 --> 00:53:02.110 That's Leroy Hood, inventor who you're familiar with. 00:53:02.110 --> 00:53:03.760 That's Eric Lander. 00:53:03.760 --> 00:53:07.330 They're leaders in the second revolution, 00:53:07.330 --> 00:53:12.340 the great genomic revolution, another crossover approach. 00:53:12.340 --> 00:53:15.730 And then we've got this whole new community. 00:53:15.730 --> 00:53:17.560 I've featured the MIT parts of it. 00:53:17.560 --> 00:53:21.400 But this revolution is happening at many other schools. 00:53:21.400 --> 00:53:24.640 But this is just the community that we're used to. 00:53:24.640 --> 00:53:26.890 So Phil Sharp on the left and Bob Langer, 00:53:26.890 --> 00:53:29.920 who are the leaders on this particular report. 00:53:29.920 --> 00:53:34.450 Tyler Jacks who leads the Koch Institute here. 00:53:34.450 --> 00:53:41.710 Paul Hammond, who is chairman of the biochemistry department, 00:53:41.710 --> 00:53:42.490 no. 00:53:42.490 --> 00:53:43.390 AUDIENCE: Chem E. 00:53:43.390 --> 00:53:45.557 WILLIAM BONVILLIAN: Chemical engineering department. 00:53:45.557 --> 00:53:48.310 But is doing an enormous amount of research in the life science 00:53:48.310 --> 00:53:49.720 side. 00:53:49.720 --> 00:53:53.050 Susan Hockfield is president at Sangeeta Bhatia, who's 00:53:53.050 --> 00:53:56.790 doing amazing work on cancer. 00:53:56.790 --> 00:54:03.610 You know, it's an incredible community of talent, 00:54:03.610 --> 00:54:07.120 again from a whole series of different kind of fields. 00:54:07.120 --> 00:54:10.090 It's another crossover. 00:54:10.090 --> 00:54:12.790 It's engineering and physical sciences 00:54:12.790 --> 00:54:16.630 and computational sciences entering into the life science 00:54:16.630 --> 00:54:19.930 space with a whole new set of disciplinary perspectives, 00:54:19.930 --> 00:54:23.620 a whole new set of systems perspectives, a whole new way 00:54:23.620 --> 00:54:26.110 of thinking about how to organize research. 00:54:26.110 --> 00:54:27.940 And so this is the MIT community. 00:54:27.940 --> 00:54:31.210 You could duplicate this at other schools as well. 00:54:31.210 --> 00:54:39.070 But it's a set of engineering tools are going to come here, 00:54:39.070 --> 00:54:43.360 but also a whole concept of engineering design comes here. 00:54:43.360 --> 00:54:48.160 So, life science systems tends to look at that complexity. 00:54:48.160 --> 00:54:53.900 They tend to look at complex systems 00:54:53.900 --> 00:54:58.100 and attempt to understand the elements in complex systems. 00:54:58.100 --> 00:55:00.800 That's the kind of way, the frame that biologists 00:55:00.800 --> 00:55:02.210 work from. 00:55:02.210 --> 00:55:04.450 Engineering works in a very different kind of way. 00:55:04.450 --> 00:55:07.370 It attempts to organize in a very hierarchical fashion 00:55:07.370 --> 00:55:09.650 and set priorities. 00:55:09.650 --> 00:55:11.420 Engineering design is a very different way 00:55:11.420 --> 00:55:12.450 of looking at the world. 00:55:12.450 --> 00:55:15.560 So these two fundamental different perspectives 00:55:15.560 --> 00:55:17.810 now have the opportunity of coming together here, 00:55:17.810 --> 00:55:21.920 for what becomes actually a very different kind of research 00:55:21.920 --> 00:55:22.480 model. 00:55:22.480 --> 00:55:33.320 So there will be new knowledge bases here that 00:55:33.320 --> 00:55:34.820 come about as a result of this. 00:55:34.820 --> 00:55:38.030 Just as genomics gave us a whole new knowledge base, 00:55:38.030 --> 00:55:41.270 just as molecular biology gave us a whole new knowledge base, 00:55:41.270 --> 00:55:44.390 the convergence of these different fields 00:55:44.390 --> 00:55:46.890 is going to create a new knowledge base. 00:55:46.890 --> 00:55:50.120 But convergence is somewhat different. 00:55:50.120 --> 00:55:54.620 Because it's also, particularly through the engineering side, 00:55:54.620 --> 00:55:58.610 it could lead us to a whole new set of therapeutic advances. 00:55:58.610 --> 00:56:02.120 So new technologies shift over from engineering 00:56:02.120 --> 00:56:05.810 in areas like imaging sensors, nanotechnology, simulation 00:56:05.810 --> 00:56:09.800 modeling, probability, these are all kind 00:56:09.800 --> 00:56:12.590 of engineering led sides of things that can now 00:56:12.590 --> 00:56:15.690 walk into the biology space. 00:56:15.690 --> 00:56:19.970 So this report at MIT kind of laid a lot of the groundwork. 00:56:19.970 --> 00:56:22.940 And frankly Susan Hockfield saw the promise 00:56:22.940 --> 00:56:26.060 of what these folks were writing about 00:56:26.060 --> 00:56:30.470 and created you know just, up the road from us 00:56:30.470 --> 00:56:33.260 created the Koch Institute, so that we 00:56:33.260 --> 00:56:35.810 were walking the walk at the same time we 00:56:35.810 --> 00:56:37.000 were talking the talk here. 00:56:37.000 --> 00:56:39.230 So that Koch Institute was under way 00:56:39.230 --> 00:56:41.660 before this report was even finished, 00:56:41.660 --> 00:56:44.600 because it was just so clear that this was an incredibly 00:56:44.600 --> 00:56:48.590 promising set of new research opportunities that were going 00:56:48.590 --> 00:56:52.820 to create a lot of real breakthrough spaces 00:56:52.820 --> 00:56:55.380 in the life sciences. 00:56:55.380 --> 00:56:59.450 So there's a whole series of strands 00:56:59.450 --> 00:57:01.520 that we had already seen, that you could 00:57:01.520 --> 00:57:04.430 call convergence-like strands, so synthetic biology 00:57:04.430 --> 00:57:09.050 and nano biology and systems biology, bioinformatics, 00:57:09.050 --> 00:57:11.570 computational biology, tissue engineering, these 00:57:11.570 --> 00:57:16.010 were all kind of strands at MIT and in life sciences generally. 00:57:16.010 --> 00:57:18.840 And the idea of convergence was, ah, these things 00:57:18.840 --> 00:57:20.330 are doing similar things. 00:57:20.330 --> 00:57:22.850 We can understand this in a larger kind of way 00:57:22.850 --> 00:57:25.250 and take more creative advantage of it. 00:57:25.250 --> 00:57:28.760 So will convergence play a role in the medical costs 00:57:28.760 --> 00:57:31.970 problems that we've been talking about? 00:57:31.970 --> 00:57:34.640 We talked about the lack of incentives for cost controls 00:57:34.640 --> 00:57:37.280 in the system. 00:57:37.280 --> 00:57:47.950 So far, we've been thinking about health care as a, 00:57:47.950 --> 00:57:51.880 like rearranging the kind of financial plumbing, right? 00:57:51.880 --> 00:57:54.640 Could we create different kinds of cost structures and cost 00:57:54.640 --> 00:57:56.105 incentives and so forth. 00:57:56.105 --> 00:57:57.730 There is another potential answer here, 00:57:57.730 --> 00:57:59.688 and it's back to the prospector theory in a way 00:57:59.688 --> 00:58:01.650 that you were suggesting. 00:58:01.650 --> 00:58:04.530 Maybe there are innovation answers here too, right? 00:58:04.530 --> 00:58:06.480 In other words, if you get a whole series 00:58:06.480 --> 00:58:09.300 of innovation based advances, that 00:58:09.300 --> 00:58:11.020 can tackle a lot of these problems. 00:58:11.020 --> 00:58:16.260 So for example, you know, NIH working away 00:58:16.260 --> 00:58:18.690 in supporting life science research 00:58:18.690 --> 00:58:23.460 really enabled huge progress against heart disease, which 00:58:23.460 --> 00:58:25.860 you know, is breathtaking and really moved 00:58:25.860 --> 00:58:30.690 heart disease down a notch from a nightmare killer 00:58:30.690 --> 00:58:33.030 to much more manageable health problem. 00:58:33.030 --> 00:58:37.690 And that's occurred in the last 25 years. 00:58:37.690 --> 00:58:40.170 If you do that in a number of areas, 00:58:40.170 --> 00:58:42.430 you can really start to affect the whole kind of cost 00:58:42.430 --> 00:58:42.930 structure. 00:58:42.930 --> 00:58:47.520 And particularly, could you have healthier aging? 00:58:47.520 --> 00:58:50.790 So one part of the dilemma for the current demographics 00:58:50.790 --> 00:58:53.400 challenge that's going to be upon you, 00:58:53.400 --> 00:58:56.730 is keeping my generation in the workforce 00:58:56.730 --> 00:59:02.280 longer with returns that are going into society. 00:59:02.280 --> 00:59:04.560 Can you make me and my generation 00:59:04.560 --> 00:59:08.790 work longer, generating returns that get distributed to all? 00:59:08.790 --> 00:59:10.890 That would solve a lot of problems. 00:59:10.890 --> 00:59:13.780 That helps us, rather than walking off a cliff, 00:59:13.780 --> 00:59:17.290 it helps it manage much more of a curve. 00:59:17.290 --> 00:59:20.820 So if we could do that, that would be powerful. 00:59:20.820 --> 00:59:23.730 And may be that some of these convergence space technologies 00:59:23.730 --> 00:59:26.340 can really be significant enablers in ways 00:59:26.340 --> 00:59:27.940 that we kind of never saw before. 00:59:27.940 --> 00:59:32.400 So that's an innovation-based policy approach 00:59:32.400 --> 00:59:37.810 to a profound kind of societal challenge. 00:59:37.810 --> 00:59:40.120 So there's a whole series of policy steps 00:59:40.120 --> 00:59:43.570 that the report argues need to be taken. 00:59:43.570 --> 00:59:47.320 We need to get across NIH stovepipes. 00:59:47.320 --> 00:59:50.980 NIH, which is all biology all the time on most days, 00:59:50.980 --> 00:59:53.440 needs to be encouraged to be able to look at and fund 00:59:53.440 --> 00:59:54.730 other fields. 00:59:54.730 --> 00:59:56.410 It's hard for NIH to do it because it's 00:59:56.410 --> 00:59:58.120 composed of biologists. 00:59:58.120 --> 01:00:01.180 If it analyzes proposals that involve complex engineering, 01:00:01.180 --> 01:00:03.580 how does it do the analysis? 01:00:03.580 --> 01:00:07.190 How does it have multidisciplinary peer review 01:00:07.190 --> 01:00:08.590 systems? 01:00:08.590 --> 01:00:11.380 Is it able to encourage RO1s that 01:00:11.380 --> 01:00:15.760 have multiple PIs, not sole single PIs, that 01:00:15.760 --> 01:00:20.320 represent a series of different fields and disciplines. 01:00:20.320 --> 01:00:23.380 It's how do we do education and convergence? 01:00:23.380 --> 01:00:28.120 So we still have stove piped disciplinary fields, 01:00:28.120 --> 01:00:29.890 and they're producing a lot of talent. 01:00:29.890 --> 01:00:32.120 But how do they get educated in these other fields 01:00:32.120 --> 01:00:33.650 so they can take advantage of it? 01:00:33.650 --> 01:00:36.190 Do we need a new kind of approach 01:00:36.190 --> 01:00:37.630 in life science education? 01:00:37.630 --> 01:00:40.550 And what are the of common language features going to be? 01:00:40.550 --> 01:00:41.050 Steph. 01:00:41.050 --> 01:00:43.000 AUDIENCE: I feel like the report was really 01:00:43.000 --> 01:00:46.180 missing an element about the jobs in the convergence field, 01:00:46.180 --> 01:00:49.170 because there's not really many job opportunities for people 01:00:49.170 --> 01:00:51.910 who are trained in multidisciplinary 01:00:51.910 --> 01:00:52.900 understandings. 01:00:52.900 --> 01:00:55.357 And I feel like there would be an enormous insecurity 01:00:55.357 --> 01:00:57.940 or uncertainty for those people who are interested in pursuing 01:00:57.940 --> 01:01:00.148 the really innovative fields if they don't understand 01:01:00.148 --> 01:01:02.617 what the actual next step is after a university education. 01:01:02.617 --> 01:01:03.700 WILLIAM BONVILLIAN: Right. 01:01:03.700 --> 01:01:04.960 There's no question about it. 01:01:04.960 --> 01:01:05.860 This is a dilemma. 01:01:05.860 --> 01:01:07.840 And look, this has been a dilemma for a while 01:01:07.840 --> 01:01:09.760 for bioengineering departments. 01:01:09.760 --> 01:01:11.440 I think we're getting out of that phase. 01:01:11.440 --> 01:01:13.780 I think those are starting to really kind of take off, 01:01:13.780 --> 01:01:17.140 in part because this model is taking off. 01:01:17.140 --> 01:01:19.060 But we don't have clear pathways. 01:01:19.060 --> 01:01:21.670 So I mean the model is going to continue to be that you're 01:01:21.670 --> 01:01:24.130 going to be a biologist. 01:01:24.130 --> 01:01:28.540 But can you get access to a series of other fields? 01:01:28.540 --> 01:01:30.940 Maybe you're an engineer but you get significant access 01:01:30.940 --> 01:01:33.430 to a series of medical related fields as well. 01:01:33.430 --> 01:01:39.290 Can we adjust our training system and modify it so that-- 01:01:39.290 --> 01:01:41.890 there was a talented staffer in my office who said look, 01:01:41.890 --> 01:01:43.750 we we're going to need a new language here 01:01:43.750 --> 01:01:45.525 in life science innovation. 01:01:45.525 --> 01:01:47.650 We're going to need kind of our convergence Creole, 01:01:47.650 --> 01:01:50.080 a mix of different languages from different fields 01:01:50.080 --> 01:01:52.090 so that people are going to be able to speak 01:01:52.090 --> 01:01:54.520 across these disciplinary lines and understand things 01:01:54.520 --> 01:01:56.620 across these disciplinary lines. 01:01:56.620 --> 01:01:59.980 That could be that could be pretty important. 01:01:59.980 --> 01:02:02.740 You know that's the heart of this report. 01:02:02.740 --> 01:02:06.730 MIT team subsequently went on in a much broader based 01:02:06.730 --> 01:02:08.830 report that went across many institutions. 01:02:08.830 --> 01:02:12.700 And this past year, did a report looking at what could we 01:02:12.700 --> 01:02:13.810 get from convergence? 01:02:13.810 --> 01:02:16.240 In other words, what other promising convergence areas 01:02:16.240 --> 01:02:18.970 and what might be obtained from them in an attempt 01:02:18.970 --> 01:02:21.280 to get a much more strategic approach to convergence? 01:02:21.280 --> 01:02:23.890 Not just say convergence is neat, 01:02:23.890 --> 01:02:25.570 which is kind of what this report did. 01:02:25.570 --> 01:02:28.230 But really get a strategy together. 01:02:28.230 --> 01:02:29.380 I'm partly guilty. 01:02:29.380 --> 01:02:31.660 But actually get a strategy together 01:02:31.660 --> 01:02:33.897 around what territories in convergence 01:02:33.897 --> 01:02:35.230 might be particularly promising. 01:02:35.230 --> 01:02:38.240 So I urge you to take a look at that more recent report. 01:02:38.240 --> 01:02:42.240 That was much more widely shared across institutions. 01:02:42.240 --> 01:02:44.130 Luyao, some questions for us. 01:02:46.660 --> 01:02:49.920 AUDIENCE: I think one of the most relevant discussions 01:02:49.920 --> 01:02:53.670 of the [INAUDIBLE] would be what are the possible features 01:02:53.670 --> 01:02:58.350 that we could expect in university level that 01:02:58.350 --> 01:03:00.960 will be probably encouraging this kind of confidence 01:03:00.960 --> 01:03:03.670 to take place? 01:03:03.670 --> 01:03:04.170 Yes. 01:03:04.170 --> 01:03:07.085 AUDIENCE: I mean, we have the whole liberal arts college. 01:03:07.085 --> 01:03:08.460 It's essentially the liberal arts 01:03:08.460 --> 01:03:12.060 for science and engineering model. 01:03:12.060 --> 01:03:17.380 AUDIENCE: But why is it currently not-- 01:03:17.380 --> 01:03:19.450 like since they're proposing this convergence 01:03:19.450 --> 01:03:25.330 of researchers, so why is it not happening 01:03:25.330 --> 01:03:30.440 with all this students with multidisciplinary backgrounds, 01:03:30.440 --> 01:03:35.020 why are they not currently working together 01:03:35.020 --> 01:03:36.790 across disciplines? 01:03:36.790 --> 01:03:40.120 AUDIENCE: I would say, in no particular reference 01:03:40.120 --> 01:03:42.760 to our institution at Wellesley. 01:03:42.760 --> 01:03:45.850 I work a lot with multidisciplinary stakeholders 01:03:45.850 --> 01:03:49.690 for the incubator program that I am facilitating currently. 01:03:49.690 --> 01:03:53.770 And a big concern that faculty members have 01:03:53.770 --> 01:03:57.160 is that they, one, by the administration 01:03:57.160 --> 01:03:59.600 are not facilitated to do collaborations. 01:03:59.600 --> 01:04:03.190 And two, that a lot of people feel 01:04:03.190 --> 01:04:09.400 like having an entrepreneurship or a sort of innovation 01:04:09.400 --> 01:04:12.220 for the purpose of commercialization model 01:04:12.220 --> 01:04:16.780 goes against the spirit of a liberal arts education. 01:04:16.780 --> 01:04:18.460 So I feel like in that sense, you know, 01:04:18.460 --> 01:04:25.110 research universities are very much well-designed 01:04:25.110 --> 01:04:27.580 to sort of adopt more of a liberal arts model 01:04:27.580 --> 01:04:30.000 than I think liberal arts colleges are designed 01:04:30.000 --> 01:04:32.110 to adopt more of a stem model. 01:04:32.110 --> 01:04:34.960 But you know, perhaps somewhere in there 01:04:34.960 --> 01:04:41.830 is a model that can arise about multidisciplinary coordination. 01:04:41.830 --> 01:04:44.260 But it would require facilitation 01:04:44.260 --> 01:04:46.270 by the administration to the extent 01:04:46.270 --> 01:04:48.010 to which I think MIT does a really 01:04:48.010 --> 01:04:53.850 great job of facilitating that at the institutional level. 01:04:53.850 --> 01:04:56.850 AUDIENCE: The rise of kind of these collaborative 01:04:56.850 --> 01:04:59.340 projects, particularly culminating 01:04:59.340 --> 01:05:01.470 in your senior year, I think capstone projects 01:05:01.470 --> 01:05:04.950 are a great opportunity to start encouraging 01:05:04.950 --> 01:05:07.860 these collaborative efforts. 01:05:07.860 --> 01:05:09.910 I think Course Two does this pretty well 01:05:09.910 --> 01:05:13.290 with their 2.009 Mechancial Engineering 01:05:13.290 --> 01:05:15.000 kind of product design course. 01:05:15.000 --> 01:05:18.150 But the piece that I would take from that 01:05:18.150 --> 01:05:19.650 is at the beginning of the semester, 01:05:19.650 --> 01:05:21.300 they have you list all of your skills. 01:05:21.300 --> 01:05:24.630 And they separate you out based on kind of backgrounds 01:05:24.630 --> 01:05:27.510 and then form teams that are like inherently collaborative, 01:05:27.510 --> 01:05:30.240 so you don't have sort of lumpiness and all the students 01:05:30.240 --> 01:05:34.100 that are interested, or have particular backgrounds 01:05:34.100 --> 01:05:36.540 in product design, they're not all on the same team. 01:05:36.540 --> 01:05:38.490 They kind of spread it out. 01:05:38.490 --> 01:05:42.505 And then I think if it be an interesting exercise 01:05:42.505 --> 01:05:46.570 to have MIT do sort of a school engineering capstone project. 01:05:46.570 --> 01:05:48.240 And sort of elect into a course where 01:05:48.240 --> 01:05:51.690 you have people that are interested in tissue 01:05:51.690 --> 01:05:54.690 engineering, but they come from the biology department, 01:05:54.690 --> 01:05:56.640 the chemical engineering department, 01:05:56.640 --> 01:05:59.250 and bioengineering department, and they work together 01:05:59.250 --> 01:06:02.610 to kind of formalize this. 01:06:02.610 --> 01:06:04.990 AUDIENCE: In my impression, there's kind of two models. 01:06:04.990 --> 01:06:07.650 Either we train more multidisciplinary students 01:06:07.650 --> 01:06:12.000 like, for me, like my major is philosophy, politics, 01:06:12.000 --> 01:06:13.110 and economics. 01:06:13.110 --> 01:06:16.200 But I'm not actually in [INAUDIBLE].. 01:06:16.200 --> 01:06:19.020 So then the other option is to have 01:06:19.020 --> 01:06:21.180 like a lot of very focused students 01:06:21.180 --> 01:06:23.170 and bring them together to work as a team. 01:06:23.170 --> 01:06:26.590 Which model do you think will be more effective in addressing 01:06:26.590 --> 01:06:27.938 health care change? 01:06:27.938 --> 01:06:29.480 AUDIENCE: I think great groups model. 01:06:29.480 --> 01:06:30.593 [INAUDIBLE] 01:06:30.593 --> 01:06:32.010 AUDIENCE: You definitely like want 01:06:32.010 --> 01:06:34.890 people that like, what's the difference between-- 01:06:34.890 --> 01:06:36.480 AUDIENCE: It's the deep generalist. 01:06:36.480 --> 01:06:37.700 That's what they were calling them. 01:06:37.700 --> 01:06:38.242 AUDIENCE: No. 01:06:38.242 --> 01:06:39.990 Well, you need one person who can gets it 01:06:39.990 --> 01:06:41.310 for every single one of the issues. 01:06:41.310 --> 01:06:43.290 But then you need somebody who-- you need somebody who is 01:06:43.290 --> 01:06:43.860 obsessed-- 01:06:43.860 --> 01:06:45.025 [INTERPOSING VOICES] 01:06:45.025 --> 01:06:46.650 AUDIENCE: Yo, I read every single book. 01:06:46.650 --> 01:06:48.780 I read two, three books outside this course. 01:06:48.780 --> 01:06:50.480 You know, I know this and this and this. 01:06:50.480 --> 01:06:52.260 And the person is just like, like, 01:06:52.260 --> 01:06:54.427 knows so much that like, it's just like you hit them 01:06:54.427 --> 01:06:56.370 with like, any word and they're inspired. 01:06:56.370 --> 01:06:58.658 So like, you want-- 01:06:58.658 --> 01:07:01.200 but you definitely need somebody who knows it and understands 01:07:01.200 --> 01:07:02.010 how to lead really. 01:07:02.010 --> 01:07:02.510 Well. 01:07:02.510 --> 01:07:05.640 Because really great engineers tend to not 01:07:05.640 --> 01:07:07.227 be able to communicate greatly. 01:07:07.227 --> 01:07:09.060 So you have to be able to figure out and ask 01:07:09.060 --> 01:07:12.283 what questions to ask that they won't tell you or figure out 01:07:12.283 --> 01:07:13.950 what they're not telling you, especially 01:07:13.950 --> 01:07:16.230 when hitting deadlines. 01:07:16.230 --> 01:07:18.060 And then another big part with groups, 01:07:18.060 --> 01:07:20.190 with these kind of groups, is to figure out 01:07:20.190 --> 01:07:21.440 where each person stands. 01:07:21.440 --> 01:07:24.720 So it's like, oh, I don't think you're doing a great job. 01:07:24.720 --> 01:07:26.460 Or I know you have this deadline. 01:07:26.460 --> 01:07:27.580 You might may not make it. 01:07:27.580 --> 01:07:28.260 I know you're stressed. 01:07:28.260 --> 01:07:29.010 Don't be stressed. 01:07:29.010 --> 01:07:31.483 I'll check in at the 70% mark. 01:07:31.483 --> 01:07:33.900 How are you doing and then we'll figure it out from there. 01:07:33.900 --> 01:07:35.850 But I don't know, I think is more 01:07:35.850 --> 01:07:37.740 interesting to the research and the media lab 01:07:37.740 --> 01:07:38.670 kind of does that. 01:07:38.670 --> 01:07:40.087 But I don't know if it's been done 01:07:40.087 --> 01:07:41.880 for very, very hard research. 01:07:41.880 --> 01:07:45.128 What I would consider super, like solving 01:07:45.128 --> 01:07:46.170 like a very hard problem. 01:07:49.448 --> 01:07:51.240 WILLIAM BONVILLIAN: It's all these problems 01:07:51.240 --> 01:07:55.920 are now at hand, as we start to seriously pursue 01:07:55.920 --> 01:07:58.020 this convergence model, exactly how we're 01:07:58.020 --> 01:07:59.317 going to cope with this. 01:07:59.317 --> 01:08:00.900 Susan Hockfield used to talk about it. 01:08:00.900 --> 01:08:03.430 And other people have talked about it too, T-shaped people. 01:08:03.430 --> 01:08:05.520 In other words, people with a deep disciplinary 01:08:05.520 --> 01:08:09.060 die, but capable of operating across fields as well. 01:08:09.060 --> 01:08:13.470 And that may well be a pretty key feature here. 01:08:13.470 --> 01:08:15.210 And then combining that community so 01:08:15.210 --> 01:08:17.470 it's able to communicate with each other. 01:08:17.470 --> 01:08:19.540 But you draw on a series of different fields. 01:08:19.540 --> 01:08:21.569 So what the organizational model is going to be 01:08:21.569 --> 01:08:23.399 is really critical here. 01:08:23.399 --> 01:08:26.279 Because again as we've talked about two classes ago, 01:08:26.279 --> 01:08:28.845 innovation, you know, happens with people. 01:08:28.845 --> 01:08:30.970 It's not these institutional organizational models. 01:08:30.970 --> 01:08:33.137 And how do you optimize the opportunities for people 01:08:33.137 --> 01:08:34.290 to be creative? 01:08:34.290 --> 01:08:37.060 So that's upon this model, right? 01:08:37.060 --> 01:08:39.630 Koch Institute is spending a lot of time thinking about this. 01:08:39.630 --> 01:08:42.540 But actually Koch Institute is only one part 01:08:42.540 --> 01:08:44.069 of the convergence going on at MIT. 01:08:44.069 --> 01:08:45.810 Something like 130 engineers are now 01:08:45.810 --> 01:08:48.450 working a significant amount of their time 01:08:48.450 --> 01:08:52.010 at I'm MIT on the life science side. 01:08:52.010 --> 01:08:53.430 And that's not unique. 01:08:53.430 --> 01:08:56.970 That's going on at a lot of institutions now. 01:08:56.970 --> 01:08:58.710 And NIH is a problem here. 01:08:58.710 --> 01:09:01.080 Because it hasn't caught up to be 01:09:01.080 --> 01:09:02.939 able to manage that kind of transition 01:09:02.939 --> 01:09:04.439 and embrace these different fields. 01:09:04.439 --> 01:09:06.569 So that's the big funder. 01:09:06.569 --> 01:09:09.583 And how do we bring that institution along? 01:09:09.583 --> 01:09:11.250 AUDIENCE: I think my big concern though, 01:09:11.250 --> 01:09:13.740 is like having all academics, like, yeah, we're 01:09:13.740 --> 01:09:15.569 getting T people but, let's define 01:09:15.569 --> 01:09:18.540 T like somebody who is like a grad student 01:09:18.540 --> 01:09:20.310 or undergrad at MIT. 01:09:20.310 --> 01:09:22.170 Like I don't know if the best answer 01:09:22.170 --> 01:09:24.720 is to have a great group of Ts. 01:09:24.720 --> 01:09:25.805 You know, I want some As. 01:09:25.805 --> 01:09:26.430 I want some Bs. 01:09:26.430 --> 01:09:27.430 I want some accents. 01:09:27.430 --> 01:09:30.450 I want some question marks. 01:09:30.450 --> 01:09:33.450 Because like, I would use all sorts, like the background, 01:09:33.450 --> 01:09:33.950 right? 01:09:33.950 --> 01:09:34.950 Because it is the difference between, 01:09:34.950 --> 01:09:37.640 oh, I can make a really good like bistro sandwich versus I 01:09:37.640 --> 01:09:40.710 need to make 100, 1,000, 100,000. 01:09:40.710 --> 01:09:42.359 And I need to have the financials 01:09:42.359 --> 01:09:44.700 for all of the stuff, which is like McDonald's, right? 01:09:44.700 --> 01:09:45.870 So ideally, I would want somebody 01:09:45.870 --> 01:09:47.600 who's already been in the field, like somebody 01:09:47.600 --> 01:09:48.725 who has faculty experience. 01:09:48.725 --> 01:09:51.090 Because I might start to build out a certain way. 01:09:51.090 --> 01:09:53.729 But they have just a big scene phenomena, right? 01:09:53.729 --> 01:09:55.810 Like a great example is, originally 01:09:55.810 --> 01:09:58.640 GE, when they were putting out the electrical lens, 01:09:58.640 --> 01:10:00.110 there was this guy named Steinmetz. 01:10:00.110 --> 01:10:04.230 He was this hunchback immigrant that couldn't even 01:10:04.230 --> 01:10:06.510 speak English when he got to the country, 01:10:06.510 --> 01:10:09.790 and didn't really have a great-- do you have Steinmetz on there? 01:10:09.790 --> 01:10:10.140 WILLIAM BONVILLIAN: I don't. 01:10:10.140 --> 01:10:11.723 AUDIENCE: It would be cool if you did. 01:10:11.723 --> 01:10:15.060 But, yeah, but he was just like this very, very kind 01:10:15.060 --> 01:10:18.402 of somebody who would never even be at MIT. 01:10:18.402 --> 01:10:19.860 Or like the Wright brothers, right? 01:10:19.860 --> 01:10:21.690 That it was just they had the practice experience 01:10:21.690 --> 01:10:23.773 that, oh, I think I can do this and trying it out. 01:10:23.773 --> 01:10:26.580 Also like just because of, as in academics, 01:10:26.580 --> 01:10:28.400 there's always a flaw in any organization. 01:10:28.400 --> 01:10:29.940 And you have to figure out, especially in business, 01:10:29.940 --> 01:10:31.560 you look at, oh, how can I expose their flaw 01:10:31.560 --> 01:10:32.970 and they're never going to be able to go here 01:10:32.970 --> 01:10:33.762 because of the way. 01:10:33.762 --> 01:10:34.920 This is their blind spot. 01:10:34.920 --> 01:10:37.020 And so like, you can definitely, like what 01:10:37.020 --> 01:10:38.490 happened at the Wright brothers, where it's like, 01:10:38.490 --> 01:10:40.323 I can't work on this problem because there's 01:10:40.323 --> 01:10:42.360 no perfect theory and it's too much of a risk. 01:10:42.360 --> 01:10:43.900 And I'm already 50 years old. 01:10:43.900 --> 01:10:47.303 And I'm not trying to risk my reputation 01:10:47.303 --> 01:10:48.720 and make my friends make fun of me 01:10:48.720 --> 01:10:50.190 because that's too uncomfortable. 01:10:50.190 --> 01:10:52.720 And I've already kind of like gone my way. 01:10:52.720 --> 01:10:53.880 And there might be somebody that's like, you know, 01:10:53.880 --> 01:10:55.380 it's pretty crazy but I'm just going 01:10:55.380 --> 01:10:57.060 to try it and see what happens. 01:10:57.060 --> 01:10:58.980 And I think in that paper, there is a quote, where it's like, 01:10:58.980 --> 01:11:00.772 what's the point of research if you already 01:11:00.772 --> 01:11:02.030 know what's going to happen? 01:11:02.030 --> 01:11:03.680 But the way the system is, sometimes 01:11:03.680 --> 01:11:05.270 it incentivizes you to just do like, 01:11:05.270 --> 01:11:06.590 oh, I know this is never going to-- 01:11:06.590 --> 01:11:07.800 I don't know if it's going to be perfect. 01:11:07.800 --> 01:11:09.717 But I know it's pretty much going to work out. 01:11:09.717 --> 01:11:10.880 And I think that's a very-- 01:11:10.880 --> 01:11:13.213 OK, as like somebody who likes capitalism, 01:11:13.213 --> 01:11:14.630 I think that's a huge opportunity. 01:11:14.630 --> 01:11:16.755 Because like, you don't just make your company look 01:11:16.755 --> 01:11:17.760 at all the blind spots. 01:11:17.760 --> 01:11:20.120 But I think that's going to be one of the reasons 01:11:20.120 --> 01:11:23.280 why these models don't succeed. 01:11:23.280 --> 01:11:25.430 And it will be big failure too. 01:11:25.430 --> 01:11:26.360 If they fail, right? 01:11:26.360 --> 01:11:27.380 Because of you like you said. 01:11:27.380 --> 01:11:28.422 You spent all this money. 01:11:28.422 --> 01:11:29.950 You got all these smart people. 01:11:29.950 --> 01:11:31.642 And there can be a huge failure there. 01:11:31.642 --> 01:11:33.100 WILLIAM BONVILLIAN: So let's close. 01:11:33.100 --> 01:11:35.450 I attempted to put the convergence reading last 01:11:35.450 --> 01:11:37.630 because it's basically a positive. 01:11:37.630 --> 01:11:40.400 In other words, there are huge innovation opportunities 01:11:40.400 --> 01:11:43.610 that are at hand that we're starting to move on. 01:11:43.610 --> 01:11:46.400 So despite all the problems in the innovation system 01:11:46.400 --> 01:11:49.490 in this sector and all of its organizational gaps, 01:11:49.490 --> 01:11:51.810 something really interesting is starting to happen. 01:11:51.810 --> 01:11:55.320 So let me close with a comment from Elias Zerhouni, who 01:11:55.320 --> 01:11:59.210 is the Director of NIH before Francis Collins. 01:11:59.210 --> 01:12:02.060 He writes, "As science grows more complex, 01:12:02.060 --> 01:12:05.690 it is also converging on a set of unifying principles 01:12:05.690 --> 01:12:08.450 that link apparently disparate diseases 01:12:08.450 --> 01:12:11.060 through common biological pathways 01:12:11.060 --> 01:12:12.830 and therapeutic approaches. 01:12:12.830 --> 01:12:17.240 Today NIH research needs to reflect this new reality." 01:12:17.240 --> 01:12:19.930 So I think that's our innovation organization task here, 01:12:19.930 --> 01:12:22.830 I think summarized nicely in a couple of sentences 01:12:22.830 --> 01:12:24.170 from Zerhouni. 01:12:24.170 --> 01:12:29.140 A closing thought, Luyao? 01:12:29.140 --> 01:12:31.840 AUDIENCE: Well, I do think this reading sends a very positive 01:12:31.840 --> 01:12:35.350 message that we will need to search 01:12:35.350 --> 01:12:40.930 for a holistic organization that kind of get our resources 01:12:40.930 --> 01:12:42.490 and tackle these problems. 01:12:42.490 --> 01:12:45.190 Still, I also feel like we are not 01:12:45.190 --> 01:12:48.190 addressing the problem of kind of, 01:12:48.190 --> 01:12:52.450 instead of tackling all these diseases, why don't we prepare, 01:12:52.450 --> 01:12:55.090 like kind of advocate this population to be 01:12:55.090 --> 01:13:00.832 more healthy, to encourage them to have a healthier lifestyle. 01:13:00.832 --> 01:13:03.040 WILLIAM BONVILLIAN: Preventative medicine rather than 01:13:03.040 --> 01:13:04.760 just repair jobs. 01:13:04.760 --> 01:13:05.345 Right, right. 01:13:05.345 --> 01:13:06.220 An important thought. 01:13:06.220 --> 01:13:07.260 AUDIENCE: And I do think-- 01:13:07.260 --> 01:13:08.140 WILLIAM BONVILLIAN: And there aren't incentives 01:13:08.140 --> 01:13:10.150 in this system particularly to do that either, 01:13:10.150 --> 01:13:12.600 which is problematic.