Roger Stein: A bold new way to fund drug research

47,831 views ・ 2014-01-07

TED


Please double-click on the English subtitles below to play the video.

00:12
So this is a picture of my dad and me, at the beach in Far Rockaway,
0
12639
6515
00:19
or actually Rockaway Park.
1
19178
1462
00:20
I'm the one with the blond hair.
2
20664
2666
00:23
My dad's the guy with the cigarette.
3
23354
1942
00:25
It was the 60's.
4
25320
1151
00:26
A lot of people smoked back then.
5
26495
1638
00:28
In the summer of 2009, my dad was diagnosed with lung cancer.
6
28157
3517
00:34
Cancer is one of those things that actually touches everybody.
7
34302
3723
00:38
If you're a man in the US,
8
38049
2628
00:40
you've got about a one in two chance
9
40701
2046
00:42
of being diagnosed with cancer during your lifetime.
10
42771
2935
00:45
If you're a woman, you've got about a one in three chance
11
45730
2785
00:48
of being diagnosed with cancer.
12
48539
1669
00:51
Everybody knows somebody who's been diagnosed with cancer.
13
51025
3134
00:55
Now, my dad's doing better today,
14
55355
1580
00:56
and part of the reason for that is that he was able to participate in the trial
15
56959
5033
01:02
of an experimental new drug that happened to be specially formulated
16
62016
3233
01:05
and very good for his particular kind of cancer.
17
65273
2253
01:07
There are over 200 kinds of cancer.
18
67550
1773
01:10
And what I want to talk about today
19
70660
2260
01:12
is how we can help more people like my dad,
20
72944
2872
01:15
because we have to change the way we think about raising money
21
75840
2954
01:18
to fund cancer research.
22
78818
1394
01:21
So a while after my dad was diagnosed,
23
81273
1934
01:23
I was having coffee with my friend Andrew Lo.
24
83231
2775
01:26
He's the head of the Laboratory for Financial Engineering at MIT,
25
86407
3266
01:29
where I also have a position,
26
89697
1938
01:31
and we were talking about cancer.
27
91659
1602
01:33
And Andrew had been doing his own bits of research,
28
93285
2394
01:35
and one of the things that he had been told
29
95703
2094
01:37
and that he'd learned from studying the literature
30
97821
4445
01:42
was that there's actually a big bottleneck.
31
102290
2081
01:44
It's very difficult to develop new drugs,
32
104395
2140
01:46
and the reason it's difficult to develop new drugs
33
106559
2360
01:48
is because in the early stages of drug development,
34
108943
2421
01:51
the drugs are very risky, and they're very expensive.
35
111388
2505
01:53
So Andrew asked me if I'd want to maybe work with him a bit,
36
113917
2873
01:56
work on some of the math and the analytics
37
116814
2110
01:58
and see if we could figure out something we could do.
38
118948
2537
02:02
Now I'm not a scientist.
39
122116
1469
02:03
You know, I don't know how to build a drug.
40
123609
2048
02:05
And none of my coauthors, Andrew Lo or Jose-Maria Fernandez or David Fagnan --
41
125681
4895
02:10
none of those guys are scientists either.
42
130600
2715
02:13
We don't know the first thing about how to make a cancer drug.
43
133339
3029
02:16
But we know a little bit about risk mitigation
44
136828
2199
02:19
and a little bit about financial engineering,
45
139051
2116
02:21
and so we started thinking, what could we do?
46
141191
2108
02:24
I'm going to tell you about some work
47
144079
1810
02:25
we've been doing over the last couple years
48
145913
2085
02:28
that we think could fundamentally change the way
49
148022
2271
02:30
research for cancer and lots of other things gets done.
50
150317
2873
02:33
We want to let the research drive the funding,
51
153750
2389
02:36
not the other way around.
52
156163
1552
02:38
So in order to get started,
53
158446
1294
02:39
let me tell you how you get a drug financed.
54
159764
2123
02:41
Imagine that you're in your lab -- you're a scientist, you're not like me --
55
161911
4401
02:46
and you've developed a new compound
56
166336
1972
02:48
that you think might be therapeutic for somebody with cancer.
57
168332
3856
02:52
Well, what you do is, you test in animals, you test in test tubes,
58
172212
3656
02:55
but there's this notion of going from the bench to the bedside,
59
175892
3060
02:58
and in order to get from the bench, the lab, to the bedside, to the patients,
60
178976
3652
03:02
you've got to get the drug tested.
61
182652
1830
03:04
And the way the drug gets tested
62
184506
1604
03:06
is through a series of, basically, experiments,
63
186134
2221
03:08
through these large, they're called trials,
64
188379
2031
03:10
that they do to determine whether the drug is safe
65
190434
2355
03:12
and whether it works and all these things.
66
192813
2013
03:14
So the FDA has a very specific protocol.
67
194850
2080
03:16
In the first phase of this testing,
68
196954
1691
03:18
which is called testing for toxicity, it's called Phase I.
69
198669
3609
03:22
In the first phase, you give the drug to healthy people
70
202675
2694
03:25
and you see if it actually makes them sick.
71
205393
2337
03:28
In other words, are the side effects just so severe
72
208578
2394
03:30
that no matter how much good it does,
73
210996
1788
03:32
it's not going to be worth it?
74
212808
1458
03:34
Does it cause heart attacks, kill people, liver failure?
75
214290
2691
03:37
And it turns out, that's a pretty high hurdle.
76
217005
2153
03:39
About a third of all drugs drop out at that point.
77
219182
2362
03:41
In the next phase, you test to see if the drug's effective,
78
221663
2859
03:44
and you give it to people with cancer
79
224546
1790
03:46
and you see if it makes them better.
80
226360
1731
03:48
And that's also a higher hurdle. People drop out.
81
228115
2318
03:50
And in the third phase, you test it on a very large sample,
82
230457
2806
03:53
and you're trying to determine what the right dose is,
83
233287
3108
03:56
is it better than what's available today? If not, then why build it?
84
236419
3202
03:59
When you're done with all that,
85
239891
1944
04:01
what you have is a very small percentage of drugs
86
241960
2485
04:04
that start the process actually come out the other side.
87
244469
2826
04:07
So those blue bottles -- those blue bottles save lives,
88
247319
3714
04:11
and they're also worth billions, sometimes billions a year.
89
251057
3060
04:14
So now here's a question:
90
254800
1575
04:16
if I were to ask you, for example,
91
256399
3242
04:19
to make a one-time investment of, say, 200 million dollars
92
259665
3965
04:23
to buy one of those bottles,
93
263654
1644
04:25
so 200 million dollars up front, one time, to buy one of those bottles,
94
265322
3352
04:28
I won't tell you which one it is,
95
268698
1616
04:30
and in 10 years, I'll tell you whether you have one of the blue ones.
96
270338
3901
04:34
Does that sound like a good deal for anybody?
97
274263
2131
04:36
No. No, right?
98
276418
1238
04:37
And of course, it's a very, very risky trial position,
99
277680
2534
04:40
and that's why it's very hard to get funding,
100
280238
2142
04:42
but to a first approximation, that's actually the proposal.
101
282404
2796
04:45
You have to fund these things from the early stages on.
102
285224
2610
04:47
It takes a long time.
103
287858
1267
04:49
So Andrew said to me, he said,
104
289149
3417
04:52
"What if we stop thinking about these as drugs?
105
292590
2446
04:55
What if we start thinking about them as financial assets?"
106
295060
2786
04:57
They've got really weird payoff structures and all that,
107
297870
2644
05:00
but let's throw everything we know about financial engineering at them.
108
300538
3340
05:03
Let's see if we can use all the tricks of the trade
109
303902
2460
05:06
to figure out how to make these drugs work as financial assets.
110
306386
3347
05:10
Let's create a giant fund.
111
310344
1446
05:12
In finance, we know what to do with assets that are risky.
112
312289
2744
05:15
You put them in a portfolio and you try to smooth out the returns.
113
315057
3112
05:18
So we did some math, and it turned out you could make this work,
114
318193
3043
05:21
but in order to make it work, you need about 80 to 150 drugs.
115
321260
3432
05:24
Now the good news is, there's plenty of drugs
116
324716
2165
05:26
that are waiting to be tested.
117
326905
1460
05:28
We've been told that there's a backlog of about 20 years of drugs
118
328389
3933
05:32
that are waiting to be tested but can't be funded.
119
332346
2453
05:34
In fact, that early stage of the funding process,
120
334823
2397
05:37
that Phase I and preclinical stuff,
121
337244
1972
05:39
that's actually, in the industry, called the Valley of Death
122
339240
2869
05:42
because it's where drugs go to die.
123
342133
1737
05:43
It's very hard to for them to get through there,
124
343894
2254
05:46
and of course, if you can't get through there,
125
346172
2227
05:48
you can't get to the later stages.
126
348423
1634
05:50
So we did this math, and we figured out, OK,
127
350081
2052
05:52
well, you need about 80 to, say, 150, or something like that, drugs.
128
352157
3298
05:55
And then we did a little more math, and we said, OK,
129
355767
2786
05:58
well, that's a fund of about three to 15 billion dollars.
130
358577
3262
06:01
So we kind of created a new problem by solving the old one.
131
361863
3484
06:05
We got rid of the risk, but now we need a lot of capital,
132
365905
2683
06:08
and you can only get that kind of capital in the capital markets.
133
368612
3111
06:11
Venture capitalists and philanthropies don't have it.
134
371747
2493
06:14
But we have to figure out how to get people in the capital markets,
135
374264
3207
06:17
who traditionally don't invest in this, to want to invest in this stuff.
136
377495
3492
06:21
So again, financial engineering was helpful here.
137
381011
2318
06:23
Imagine the megafund starts empty,
138
383353
1800
06:25
and what it does is it issues some debt and some equity,
139
385177
4009
06:29
and that generates cash flow.
140
389210
1396
06:30
That cash flow is used, then, to buy that big portfolio of drugs that you need,
141
390964
3745
06:34
and those drugs start working their way through that approval process,
142
394733
3313
06:38
and each time they go through a phase of approval,
143
398070
2391
06:40
they gain value.
144
400485
1195
06:41
Most of them don't make it, but a few of them do,
145
401704
2358
06:44
and with the ones that gain value, you can sell some,
146
404086
2506
06:46
and when you sell them,
147
406616
1238
06:47
you have money to pay the interest on those bonds,
148
407878
2338
06:50
but also to fund the next round of trials.
149
410240
2051
06:52
It's almost self-funding.
150
412315
1225
06:53
You do that for the course of the transaction,
151
413564
2280
06:55
and when you're done, you liquidate the portfolio,
152
415868
2429
06:58
pay back the bonds, and you can give the equity holders a nice return.
153
418321
3322
07:01
That was the theory, and we talked about it,
154
421667
2175
07:03
we did a bunch of experiments,
155
423866
1470
07:05
and then we said, let's really try to test it.
156
425360
2168
07:07
We spent the next two years doing research.
157
427552
2884
07:10
We talked to hundreds of experts in drug financing and venture capital.
158
430915
4368
07:15
We talked to people who have developed drugs.
159
435721
2252
07:17
We talked to pharmaceutical companies.
160
437997
1835
07:19
We actually looked at the data for drugs,
161
439856
2830
07:22
over 2,000 drugs that had been approved or denied or withdrawn,
162
442710
4172
07:26
and we also ran millions of simulations.
163
446906
2351
07:30
And all that actually took a lot of time.
164
450230
2530
07:32
But when we were done, we found something that was sort of surprising.
165
452784
3293
07:36
It was feasible to structure that fund
166
456101
1826
07:37
such that when you were done structuring it,
167
457951
2092
07:40
you could actually produce low-risk bonds that would be attractive to bond holders,
168
460067
3939
07:44
that would give you yields of about five to eight percent,
169
464030
2740
07:46
and you could produce equity
170
466794
1465
07:48
that would give equity holders about a 12 percent return.
171
468283
2695
07:51
Now those returns aren't going to be attractive to a venture capitalist.
172
471002
3382
07:54
They want to make those big bets
173
474408
1533
07:55
and get those billion dollar payoffs.
174
475965
1793
07:57
But it turns out there are lots of other folks that would be interested.
175
477995
3438
08:01
That's right in the investment sweet spot of pension funds and 401(k) plans
176
481457
3579
08:05
and all this other stuff.
177
485060
1298
08:06
So we published some articles in the academic press,
178
486687
2472
08:09
in medical journals, in finance journals.
179
489183
4815
08:14
But it wasn't until we actually got the popular press interested in this
180
494022
3445
08:17
that we began to get some traction.
181
497491
2205
08:20
We wanted to do more than just make people aware of it.
182
500360
2596
08:22
We wanted people to get involved.
183
502980
1580
08:24
So we took all of our computer code and made that available online
184
504584
3113
08:27
under an open-source license to anybody that wanted it.
185
507721
2688
08:30
And you guys can download it today
186
510433
1695
08:32
if you want to run your own experiments to see if this would work.
187
512152
3127
08:35
And that was really effective,
188
515303
1485
08:36
because people that didn't believe our assumptions
189
516812
2364
08:39
could try their own and see how it would work.
190
519200
2151
08:41
Now there's an obvious problem, which is,
191
521375
1986
08:43
is there enough money in the world to fund this?
192
523385
2275
08:45
I've told you there's enough drugs, but is there enough money?
193
525684
2917
08:48
There's 100 trillion dollars of capital
194
528625
1906
08:50
currently invested in fixed-income securities.
195
530555
2994
08:54
That's a hundred thousand billion.
196
534632
1833
08:58
There's plenty of money.
197
538320
1607
08:59
(Laughter)
198
539951
3006
09:04
But we realized it's more than just money that's required.
199
544088
2743
09:06
We had to get people motivated, involved,
200
546855
2000
09:08
and get them to understand this.
201
548879
1650
09:10
And we started thinking about all the different things that could go wrong.
202
550775
3535
09:14
What are all the challenges that might get in the way?
203
554334
2862
09:17
And we had a long list.
204
557220
1220
09:18
We assigned a bunch of people, including ourselves,
205
558464
2979
09:21
different pieces of this problem.
206
561467
1586
09:23
And we said, could you start a work stream on credit risk?
207
563910
2811
09:26
Could you start a work stream on the regulatory aspects?
208
566745
2645
09:29
Could you start a work stream on how you would manage so many projects?
209
569414
4033
09:33
And we had all these experts get together and do these different work streams,
210
573471
3672
09:37
and then we held a conference.
211
577167
1848
09:39
The conference was held over this past summer.
212
579039
2230
09:41
It was an invitation-only conference.
213
581293
1780
09:43
It was sponsored by the American Cancer Society
214
583097
2250
09:45
and done in collaboration with the National Cancer Institute.
215
585371
3409
09:48
We had experts from every field we thought would be important,
216
588804
2958
09:51
including the government, and people that run research centers,
217
591786
2997
09:54
and for two days they heard the reports
218
594807
2437
09:57
from those five work streams, and talked about it.
219
597268
2592
09:59
It was the first time the people who could make this happen
220
599884
2838
10:02
sat across the table from each other and had these conversations.
221
602746
3067
10:05
Now these conferences, it's typical to have a dinner,
222
605837
3497
10:09
and at that dinner, you get to know each other,
223
609358
2772
10:12
sort of like what we're doing here.
224
612154
1741
10:13
I happened to look out the window,
225
613919
1646
10:15
and hand on my heart,
226
615589
1296
10:16
on the night of this conference -- it was the summertime --
227
616909
2812
10:19
and that's what I saw, a double rainbow.
228
619745
2244
10:22
So I'd like to think it was a good sign.
229
622013
1943
10:24
Since the conference, we've got people working between Paris and San Francisco,
230
624082
4201
10:28
lots of different folks working on this
231
628307
1869
10:30
to try to see if we can really make it happen.
232
630200
2451
10:32
We're not looking to start a fund, but we want somebody else to do this.
233
632932
3403
10:36
Because, again, I'm not a scientist.
234
636359
2170
10:38
I can't build a drug.
235
638553
1879
10:40
I'm never going to have enough money to fund even one of those trials.
236
640456
3626
10:44
But all of us together, with our 401(k)s,
237
644443
2825
10:47
with our 529 plans, with our pension plans,
238
647292
2568
10:49
all of us together can actually fund hundreds of trials
239
649884
2883
10:52
and get paid well for doing it
240
652791
1584
10:54
and save millions of lives like my dad.
241
654399
2747
10:57
Thank you.
242
657170
1151
10:58
(Applause)
243
658345
5575
About this website

This site will introduce you to YouTube videos that are useful for learning English. You will see English lessons taught by top-notch teachers from around the world. Double-click on the English subtitles displayed on each video page to play the video from there. The subtitles scroll in sync with the video playback. If you have any comments or requests, please contact us using this contact form.

https://forms.gle/WvT1wiN1qDtmnspy7