Susan Solomon: The promise of research with stem cells

95,334 views ・ 2012-09-13

TED


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

00:00
Translator: Joseph Geni Reviewer: Morton Bast
0
0
7000
00:16
So, embryonic stem cells
1
16381
2989
00:19
are really incredible cells.
2
19370
3300
00:22
They are our body's own repair kits,
3
22670
2788
00:25
and they're pluripotent, which means they can morph into
4
25458
2964
00:28
all of the cells in our bodies.
5
28422
2432
00:30
Soon, we actually will be able to use stem cells
6
30854
2688
00:33
to replace cells that are damaged or diseased.
7
33542
2985
00:36
But that's not what I want to talk to you about,
8
36527
2431
00:38
because right now there are some really
9
38958
2678
00:41
extraordinary things that we are doing with stem cells
10
41636
3946
00:45
that are completely changing
11
45582
1607
00:47
the way we look and model disease,
12
47189
2899
00:50
our ability to understand why we get sick,
13
50088
2519
00:52
and even develop drugs.
14
52607
2431
00:55
I truly believe that stem cell research is going to allow
15
55038
4313
00:59
our children to look at Alzheimer's and diabetes
16
59351
4505
01:03
and other major diseases the way we view polio today,
17
63856
4391
01:08
which is as a preventable disease.
18
68247
3201
01:11
So here we have this incredible field, which has
19
71448
3223
01:14
enormous hope for humanity,
20
74671
4387
01:19
but much like IVF over 35 years ago,
21
79058
3520
01:22
until the birth of a healthy baby, Louise,
22
82578
2334
01:24
this field has been under siege politically and financially.
23
84912
5069
01:29
Critical research is being challenged instead of supported,
24
89981
4272
01:34
and we saw that it was really essential to have
25
94253
4360
01:38
private safe haven laboratories where this work
26
98613
3531
01:42
could be advanced without interference.
27
102144
2830
01:44
And so, in 2005,
28
104974
2531
01:47
we started the New York Stem Cell Foundation Laboratory
29
107505
2612
01:50
so that we would have a small organization that could
30
110117
3589
01:53
do this work and support it.
31
113706
3312
01:57
What we saw very quickly is the world of both medical
32
117018
3385
02:00
research, but also developing drugs and treatments,
33
120403
3376
02:03
is dominated by, as you would expect, large organizations,
34
123779
3713
02:07
but in a new field, sometimes large organizations
35
127492
3119
02:10
really have trouble getting out of their own way,
36
130611
2168
02:12
and sometimes they can't ask the right questions,
37
132779
2436
02:15
and there is an enormous gap that's just gotten larger
38
135215
3356
02:18
between academic research on the one hand
39
138571
3211
02:21
and pharmaceutical companies and biotechs
40
141782
2701
02:24
that are responsible for delivering all of our drugs
41
144483
3266
02:27
and many of our treatments, and so we knew that
42
147749
2390
02:30
to really accelerate cures and therapies, we were going
43
150139
3946
02:34
to have to address this with two things:
44
154085
2807
02:36
new technologies and also a new research model.
45
156892
3222
02:40
Because if you don't close that gap, you really are
46
160114
3759
02:43
exactly where we are today.
47
163873
1607
02:45
And that's what I want to focus on.
48
165480
1667
02:47
We've spent the last couple of years pondering this,
49
167147
3550
02:50
making a list of the different things that we had to do,
50
170697
2391
02:53
and so we developed a new technology,
51
173088
2631
02:55
It's software and hardware,
52
175719
1251
02:56
that actually can generate thousands and thousands of
53
176970
3503
03:00
genetically diverse stem cell lines to create
54
180473
3170
03:03
a global array, essentially avatars of ourselves.
55
183643
3787
03:07
And we did this because we think that it's actually going
56
187430
3434
03:10
to allow us to realize the potential, the promise,
57
190864
3415
03:14
of all of the sequencing of the human genome,
58
194279
3080
03:17
but it's going to allow us, in doing that,
59
197359
2504
03:19
to actually do clinical trials in a dish with human cells,
60
199863
5008
03:24
not animal cells, to generate drugs and treatments
61
204871
4159
03:29
that are much more effective, much safer,
62
209030
3249
03:32
much faster, and at a much lower cost.
63
212279
3256
03:35
So let me put that in perspective for you
64
215535
2384
03:37
and give you some context.
65
217919
1416
03:39
This is an extremely new field.
66
219335
4832
03:44
In 1998, human embryonic stem cells
67
224167
2832
03:46
were first identified, and just nine years later,
68
226999
3512
03:50
a group of scientists in Japan were able to take skin cells
69
230511
4305
03:54
and reprogram them with very powerful viruses
70
234816
3195
03:58
to create a kind of pluripotent stem cell
71
238011
4242
04:02
called an induced pluripotent stem cell,
72
242253
2090
04:04
or what we refer to as an IPS cell.
73
244343
3008
04:07
This was really an extraordinary advance, because
74
247351
3198
04:10
although these cells are not human embryonic stem cells,
75
250549
2544
04:13
which still remain the gold standard,
76
253093
1794
04:14
they are terrific to use for modeling disease
77
254887
3470
04:18
and potentially for drug discovery.
78
258357
2730
04:21
So a few months later, in 2008, one of our scientists
79
261087
3040
04:24
built on that research. He took skin biopsies,
80
264127
3200
04:27
this time from people who had a disease,
81
267327
2028
04:29
ALS, or as you call it in the U.K., motor neuron disease.
82
269355
2914
04:32
He turned them into the IPS cells
83
272269
1698
04:33
that I've just told you about, and then he turned those
84
273967
2686
04:36
IPS cells into the motor neurons that actually
85
276653
2704
04:39
were dying in the disease.
86
279357
1461
04:40
So basically what he did was to take a healthy cell
87
280818
3019
04:43
and turn it into a sick cell,
88
283837
1714
04:45
and he recapitulated the disease over and over again
89
285551
3558
04:49
in the dish, and this was extraordinary,
90
289109
3360
04:52
because it was the first time that we had a model
91
292469
2248
04:54
of a disease from a living patient in living human cells.
92
294717
4188
04:58
And as he watched the disease unfold, he was able
93
298905
3120
05:02
to discover that actually the motor neurons were dying
94
302025
3011
05:05
in the disease in a different way than the field
95
305036
2127
05:07
had previously thought. There was another kind of cell
96
307163
2494
05:09
that actually was sending out a toxin
97
309657
2201
05:11
and contributing to the death of these motor neurons,
98
311858
2511
05:14
and you simply couldn't see it
99
314369
1358
05:15
until you had the human model.
100
315727
1790
05:17
So you could really say that
101
317517
2667
05:20
researchers trying to understand the cause of disease
102
320184
3906
05:24
without being able to have human stem cell models
103
324090
4152
05:28
were much like investigators trying to figure out
104
328242
2760
05:31
what had gone terribly wrong in a plane crash
105
331002
3241
05:34
without having a black box, or a flight recorder.
106
334243
3997
05:38
They could hypothesize about what had gone wrong,
107
338240
2602
05:40
but they really had no way of knowing what led
108
340842
3112
05:43
to the terrible events.
109
343954
2172
05:46
And stem cells really have given us the black box
110
346126
4183
05:50
for diseases, and it's an unprecedented window.
111
350309
3968
05:54
It really is extraordinary, because you can recapitulate
112
354277
3245
05:57
many, many diseases in a dish, you can see
113
357522
3247
06:00
what begins to go wrong in the cellular conversation
114
360769
3536
06:04
well before you would ever see
115
364305
2424
06:06
symptoms appear in a patient.
116
366729
2536
06:09
And this opens up the ability,
117
369265
2523
06:11
which hopefully will become something that
118
371788
2814
06:14
is routine in the near term,
119
374602
2647
06:17
of using human cells to test for drugs.
120
377249
4146
06:21
Right now, the way we test for drugs is pretty problematic.
121
381395
5464
06:26
To bring a successful drug to market, it takes, on average,
122
386859
3318
06:30
13 years — that's one drug —
123
390177
2186
06:32
with a sunk cost of 4 billion dollars,
124
392363
3388
06:35
and only one percent of the drugs that start down that road
125
395751
4867
06:40
are actually going to get there.
126
400618
2248
06:42
You can't imagine other businesses
127
402866
2125
06:44
that you would think of going into
128
404991
1449
06:46
that have these kind of numbers.
129
406440
1755
06:48
It's a terrible business model.
130
408195
1802
06:49
But it is really a worse social model because of
131
409997
3989
06:53
what's involved and the cost to all of us.
132
413986
3328
06:57
So the way we develop drugs now
133
417314
3752
07:01
is by testing promising compounds on --
134
421066
3200
07:04
We didn't have disease modeling with human cells,
135
424266
1880
07:06
so we'd been testing them on cells of mice
136
426146
3464
07:09
or other creatures or cells that we engineer,
137
429610
3667
07:13
but they don't have the characteristics of the diseases
138
433277
3061
07:16
that we're actually trying to cure.
139
436338
2336
07:18
You know, we're not mice, and you can't go into
140
438674
3046
07:21
a living person with an illness
141
441720
2418
07:24
and just pull out a few brain cells or cardiac cells
142
444138
2928
07:27
and then start fooling around in a lab to test
143
447066
2289
07:29
for, you know, a promising drug.
144
449355
3561
07:32
But what you can do with human stem cells, now,
145
452916
3585
07:36
is actually create avatars, and you can create the cells,
146
456501
4337
07:40
whether it's the live motor neurons
147
460838
1967
07:42
or the beating cardiac cells or liver cells
148
462805
3010
07:45
or other kinds of cells, and you can test for drugs,
149
465815
4109
07:49
promising compounds, on the actual cells
150
469924
3125
07:53
that you're trying to affect, and this is now,
151
473049
3631
07:56
and it's absolutely extraordinary,
152
476680
2814
07:59
and you're going to know at the beginning,
153
479494
3156
08:02
the very early stages of doing your assay development
154
482650
3744
08:06
and your testing, you're not going to have to wait 13 years
155
486394
3389
08:09
until you've brought a drug to market, only to find out
156
489783
3319
08:13
that actually it doesn't work, or even worse, harms people.
157
493102
5056
08:18
But it isn't really enough just to look at
158
498158
4340
08:22
the cells from a few people or a small group of people,
159
502498
3782
08:26
because we have to step back.
160
506280
1644
08:27
We've got to look at the big picture.
161
507924
1851
08:29
Look around this room. We are all different,
162
509775
3136
08:32
and a disease that I might have,
163
512911
2740
08:35
if I had Alzheimer's disease or Parkinson's disease,
164
515651
2877
08:38
it probably would affect me differently than if
165
518528
3766
08:42
one of you had that disease,
166
522294
1641
08:43
and if we both had Parkinson's disease,
167
523935
4345
08:48
and we took the same medication,
168
528280
2268
08:50
but we had different genetic makeup,
169
530548
2747
08:53
we probably would have a different result,
170
533295
2285
08:55
and it could well be that a drug that worked wonderfully
171
535580
3731
08:59
for me was actually ineffective for you,
172
539311
3579
09:02
and similarly, it could be that a drug that is harmful for you
173
542890
4692
09:07
is safe for me, and, you know, this seems totally obvious,
174
547582
4302
09:11
but unfortunately it is not the way
175
551884
2728
09:14
that the pharmaceutical industry has been developing drugs
176
554612
3186
09:17
because, until now, it hasn't had the tools.
177
557798
3986
09:21
And so we need to move away
178
561784
2292
09:24
from this one-size-fits-all model.
179
564076
2954
09:27
The way we've been developing drugs is essentially
180
567030
3177
09:30
like going into a shoe store,
181
570207
1379
09:31
no one asks you what size you are, or
182
571586
2283
09:33
if you're going dancing or hiking.
183
573869
2210
09:36
They just say, "Well, you have feet, here are your shoes."
184
576079
2808
09:38
It doesn't work with shoes, and our bodies are
185
578887
3600
09:42
many times more complicated than just our feet.
186
582487
3472
09:45
So we really have to change this.
187
585959
2541
09:48
There was a very sad example of this in the last decade.
188
588500
5184
09:53
There's a wonderful drug, and a class of drugs actually,
189
593684
2648
09:56
but the particular drug was Vioxx, and
190
596332
2680
09:59
for people who were suffering from severe arthritis pain,
191
599012
4376
10:03
the drug was an absolute lifesaver,
192
603388
3392
10:06
but unfortunately, for another subset of those people,
193
606780
5080
10:11
they suffered pretty severe heart side effects,
194
611860
4769
10:16
and for a subset of those people, the side effects were
195
616629
2728
10:19
so severe, the cardiac side effects, that they were fatal.
196
619357
3897
10:23
But imagine a different scenario,
197
623254
4042
10:27
where we could have had an array, a genetically diverse array,
198
627296
4302
10:31
of cardiac cells, and we could have actually tested
199
631598
3626
10:35
that drug, Vioxx, in petri dishes, and figured out,
200
635224
5081
10:40
well, okay, people with this genetic type are going to have
201
640305
3744
10:44
cardiac side effects, people with these genetic subgroups
202
644049
5000
10:49
or genetic shoes sizes, about 25,000 of them,
203
649049
5144
10:54
are not going to have any problems.
204
654193
2760
10:56
The people for whom it was a lifesaver
205
656953
2615
10:59
could have still taken their medicine.
206
659568
1677
11:01
The people for whom it was a disaster, or fatal,
207
661245
4386
11:05
would never have been given it, and
208
665631
2091
11:07
you can imagine a very different outcome for the company,
209
667722
2583
11:10
who had to withdraw the drug.
210
670305
2768
11:13
So that is terrific,
211
673073
2816
11:15
and we thought, all right,
212
675889
1834
11:17
as we're trying to solve this problem,
213
677723
2759
11:20
clearly we have to think about genetics,
214
680482
2197
11:22
we have to think about human testing,
215
682679
2834
11:25
but there's a fundamental problem,
216
685513
1579
11:27
because right now, stem cell lines,
217
687092
2699
11:29
as extraordinary as they are,
218
689791
1710
11:31
and lines are just groups of cells,
219
691501
1744
11:33
they are made by hand, one at a time,
220
693245
4332
11:37
and it takes a couple of months.
221
697577
2224
11:39
This is not scalable, and also when you do things by hand,
222
699801
4366
11:44
even in the best laboratories,
223
704167
1543
11:45
you have variations in techniques,
224
705710
3161
11:48
and you need to know, if you're making a drug,
225
708871
3181
11:52
that the Aspirin you're going to take out of the bottle
226
712052
1898
11:53
on Monday is the same as the Aspirin
227
713950
2440
11:56
that's going to come out of the bottle on Wednesday.
228
716390
2081
11:58
So we looked at this, and we thought, okay,
229
718471
3791
12:02
artisanal is wonderful in, you know, your clothing
230
722262
3152
12:05
and your bread and crafts, but
231
725414
2944
12:08
artisanal really isn't going to work in stem cells,
232
728358
2983
12:11
so we have to deal with this.
233
731341
2390
12:13
But even with that, there still was another big hurdle,
234
733731
3920
12:17
and that actually brings us back to
235
737651
3564
12:21
the mapping of the human genome, because
236
741215
2384
12:23
we're all different.
237
743599
2711
12:26
We know from the sequencing of the human genome
238
746310
2832
12:29
that it's shown us all of the A's, C's, G's and T's
239
749142
2557
12:31
that make up our genetic code,
240
751699
2468
12:34
but that code, by itself, our DNA,
241
754167
4269
12:38
is like looking at the ones and zeroes of the computer code
242
758436
4599
12:43
without having a computer that can read it.
243
763035
2825
12:45
It's like having an app without having a smartphone.
244
765860
3288
12:49
We needed to have a way of bringing the biology
245
769148
3884
12:53
to that incredible data,
246
773032
2209
12:55
and the way to do that was to find
247
775241
3115
12:58
a stand-in, a biological stand-in,
248
778356
2687
13:01
that could contain all of the genetic information,
249
781043
4025
13:05
but have it be arrayed in such a way
250
785068
2528
13:07
as it could be read together
251
787596
2864
13:10
and actually create this incredible avatar.
252
790460
3256
13:13
We need to have stem cells from all the genetic sub-types
253
793716
3704
13:17
that represent who we are.
254
797420
2952
13:20
So this is what we've built.
255
800372
2760
13:23
It's an automated robotic technology.
256
803132
3320
13:26
It has the capacity to produce thousands and thousands
257
806452
2608
13:29
of stem cell lines. It's genetically arrayed.
258
809060
4239
13:33
It has massively parallel processing capability,
259
813299
3749
13:37
and it's going to change the way drugs are discovered,
260
817048
3320
13:40
we hope, and I think eventually what's going to happen
261
820368
3835
13:44
is that we're going to want to re-screen drugs,
262
824203
2199
13:46
on arrays like this, that already exist,
263
826402
2491
13:48
all of the drugs that currently exist,
264
828893
1871
13:50
and in the future, you're going to be taking drugs
265
830764
2911
13:53
and treatments that have been tested for side effects
266
833675
2872
13:56
on all of the relevant cells,
267
836547
2303
13:58
on brain cells and heart cells and liver cells.
268
838850
3153
14:02
It really has brought us to the threshold
269
842003
3329
14:05
of personalized medicine.
270
845332
2214
14:07
It's here now, and in our family,
271
847546
4441
14:11
my son has type 1 diabetes,
272
851987
2938
14:14
which is still an incurable disease,
273
854925
2648
14:17
and I lost my parents to heart disease and cancer,
274
857573
3442
14:21
but I think that my story probably sounds familiar to you,
275
861015
3733
14:24
because probably a version of it is your story.
276
864748
4230
14:28
At some point in our lives, all of us,
277
868978
3944
14:32
or people we care about, become patients,
278
872922
2736
14:35
and that's why I think that stem cell research
279
875658
2609
14:38
is incredibly important for all of us.
280
878267
3383
14:41
Thank you. (Applause)
281
881650
3668
14:45
(Applause)
282
885318
7108
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