0:00:00.723,0:00:02.899
So let me ask for a show of hands.

0:00:02.899,0:00:07.091
How many people here are over the age of 48?

0:00:07.091,0:00:09.972
Well, there do seem to be a few.

0:00:09.972,0:00:12.147
Well, congratulations,

0:00:12.147,0:00:16.017
because if you look at this particular slide of U.S. life expectancy,

0:00:16.017,0:00:19.115
you are now in excess of the average life span

0:00:19.115,0:00:21.902
of somebody who was born in 1900.

0:00:21.902,0:00:25.436
But look what happened in the course of that century.

0:00:25.436,0:00:27.098
If you follow that curve,

0:00:27.098,0:00:29.712
you'll see that it starts way down there.

0:00:29.712,0:00:32.181
There's that dip there for the 1918 flu.

0:00:32.181,0:00:34.603
And here we are at 2010,

0:00:34.603,0:00:37.659
average life expectancy of a child born today, age 79,

0:00:37.659,0:00:39.555
and we are not done yet.

0:00:39.555,0:00:40.890
Now, that's the good news.

0:00:40.890,0:00:42.731
But there's still a lot of work to do.

0:00:42.731,0:00:44.365
So, for instance, if you ask,

0:00:44.365,0:00:47.091
how many diseases do we now know

0:00:47.091,0:00:49.150
the exact molecular basis?

0:00:49.150,0:00:52.708
Turns out it's about 4,000, which is pretty amazing,

0:00:52.708,0:00:54.944
because most of those molecular discoveries

0:00:54.944,0:00:57.609
have just happened in the last little while.

0:00:57.609,0:01:00.905
It's exciting to see that in terms of what we've learned,

0:01:00.905,0:01:03.012
but how many of those 4,000 diseases

0:01:03.012,0:01:05.360
now have treatments available?

0:01:05.360,0:01:07.248
Only about 250.

0:01:07.248,0:01:10.006
So we have this huge challenge, this huge gap.

0:01:10.006,0:01:12.586
You would think this wouldn't be too hard,

0:01:12.586,0:01:14.112
that we would simply have the ability

0:01:14.112,0:01:17.138
to take this fundamental information that we're learning

0:01:17.138,0:01:20.283
about how it is that basic biology teaches us

0:01:20.283,0:01:22.185
about the causes of disease

0:01:22.185,0:01:25.211
and build a bridge across this yawning gap

0:01:25.211,0:01:27.591
between what we've learned about basic science

0:01:27.591,0:01:29.086
and its application,

0:01:29.086,0:01:32.343
a bridge that would look maybe something like this,

0:01:32.343,0:01:35.955
where you'd have to put together a nice shiny way

0:01:35.955,0:01:38.923
to get from one side to the other.

0:01:38.923,0:01:41.523
Well, wouldn't it be nice if it was that easy?

0:01:41.523,0:01:43.668
Unfortunately, it's not.

0:01:43.668,0:01:46.259
In reality, trying to go from fundamental knowledge

0:01:46.259,0:01:48.923
to its application is more like this.

0:01:48.923,0:01:50.838
There are no shiny bridges.

0:01:50.838,0:01:52.490
You sort of place your bets.

0:01:52.490,0:01:54.451
Maybe you've got a swimmer and a rowboat

0:01:54.451,0:01:55.975
and a sailboat and a tugboat

0:01:55.975,0:01:57.703
and you set them off on their way,

0:01:57.703,0:02:00.367
and the rains come and the lightning flashes,

0:02:00.367,0:02:01.881
and oh my gosh, there are sharks in the water

0:02:01.881,0:02:03.902
and the swimmer gets into trouble,

0:02:03.902,0:02:05.486
and, uh oh, the swimmer drowned

0:02:05.486,0:02:08.698
and the sailboat capsized,

0:02:08.698,0:02:10.399
and that tugboat, well, it hit the rocks,

0:02:10.399,0:02:13.039
and maybe if you're lucky, somebody gets across.

0:02:13.039,0:02:15.028
Well, what does this really look like?

0:02:15.028,0:02:17.082
Well, what is it to make a therapeutic, anyway?

0:02:17.082,0:02:20.083
What's a drug? A drug is made up

0:02:20.083,0:02:22.408
of a small molecule of hydrogen, carbon,

0:02:22.408,0:02:24.659
oxygen, nitrogen, and a few other atoms

0:02:24.659,0:02:26.882
all cobbled together in a shape,

0:02:26.882,0:02:29.259
and it's those shapes that determine whether, in fact,

0:02:29.259,0:02:32.572
that particular drug is going to hit its target.

0:02:32.572,0:02:34.795
Is it going to land where it's supposed to?

0:02:34.795,0:02:37.951
So look at this picture here -- a lot of shapes dancing around for you.

0:02:37.951,0:02:40.338
Now what you need to do, if you're trying to develop

0:02:40.338,0:02:41.795
a new treatment for autism

0:02:41.795,0:02:44.014
or Alzheimer's disease or cancer

0:02:44.014,0:02:45.806
is to find the right shape in that mix

0:02:45.806,0:02:48.723
that will ultimately provide benefit and will be safe.

0:02:48.723,0:02:51.890
And when you look at what happens to that pipeline,

0:02:51.890,0:02:53.391
you start out maybe with thousands,

0:02:53.391,0:02:55.033
tens of thousands of compounds.

0:02:55.033,0:02:57.182
You weed down through various steps

0:02:57.182,0:02:58.565
that cause many of these to fail.

0:02:58.565,0:03:01.905
Ultimately, maybe you can run a clinical trial with four or five of these,

0:03:01.905,0:03:04.947
and if all goes well, 14 years after you started,

0:03:04.947,0:03:06.958
you will get one approval.

0:03:06.958,0:03:08.988
And it will cost you upwards of a billion dollars

0:03:08.988,0:03:11.132
for that one success.

0:03:11.132,0:03:14.436
So we have to look at this pipeline the way an engineer would,

0:03:14.436,0:03:15.644
and say, "How can we do better?"

0:03:15.644,0:03:18.321
And that's the main theme of what I want to say to you this morning.

0:03:18.321,0:03:20.134
How can we make this go faster?

0:03:20.134,0:03:23.199
How can we make it more successful?

0:03:23.199,0:03:24.540
Well, let me tell you about a few examples

0:03:24.540,0:03:26.796
where this has actually worked.

0:03:26.796,0:03:29.747
One that has just happened in the last few months

0:03:29.747,0:03:33.457
is the successful approval of a drug for cystic fibrosis.

0:03:33.457,0:03:35.111
But it's taken a long time to get there.

0:03:35.111,0:03:39.713
Cystic fibrosis had its molecular cause discovered in 1989

0:03:39.713,0:03:42.041
by my group working with another group in Toronto,

0:03:42.041,0:03:44.176
discovering what the mutation was in a particular gene

0:03:44.176,0:03:45.804
on chromosome 7.

0:03:45.804,0:03:47.842
That picture you see there?

0:03:47.842,0:03:49.945
Here it is. That's the same kid.

0:03:49.945,0:03:53.289
That's Danny Bessette, 23 years later,

0:03:53.289,0:03:54.568
because this is the year,

0:03:54.568,0:03:57.006
and it's also the year where Danny got married,

0:03:57.006,0:04:00.063
where we have, for the first time, the approval by the FDA

0:04:00.063,0:04:03.800
of a drug that precisely targets the defect in cystic fibrosis

0:04:03.800,0:04:05.738
based upon all this molecular understanding.

0:04:05.738,0:04:07.162
That's the good news.

0:04:07.162,0:04:10.791
The bad news is, this drug doesn't actually treat all cases of cystic fibrosis,

0:04:10.791,0:04:13.000
and it won't work for Danny, and we're still waiting

0:04:13.000,0:04:15.335
for that next generation to help him.

0:04:15.335,0:04:18.530
But it took 23 years to get this far. That's too long.

0:04:18.530,0:04:20.223
How do we go faster?

0:04:20.223,0:04:22.921
Well, one way to go faster is to take advantage of technology,

0:04:22.921,0:04:25.585
and a very important technology that we depend on

0:04:25.585,0:04:27.881
for all of this is the human genome,

0:04:27.881,0:04:30.469
the ability to be able to look at a chromosome,

0:04:30.469,0:04:33.139
to unzip it, to pull out all the DNA,

0:04:33.139,0:04:36.089
and to be able to then read out the letters in that DNA code,

0:04:36.089,0:04:38.170
the A's, C's, G's and T's

0:04:38.170,0:04:41.441
that are our instruction book and the instruction book for all living things,

0:04:41.441,0:04:42.955
and the cost of doing this,

0:04:42.955,0:04:45.610
which used to be in the hundreds of millions of dollars,

0:04:45.610,0:04:47.523
has in the course of the last 10 years

0:04:47.523,0:04:49.922
fallen faster than Moore's Law, down to the point

0:04:49.922,0:04:53.929
where it is less than 10,000 dollars today to have your genome sequenced, or mine,

0:04:53.929,0:04:57.728
and we're headed for the $1,000 genome fairly soon.

0:04:57.728,0:04:59.054
Well, that's exciting.

0:04:59.054,0:05:02.864
How does that play out in terms of application to a disease?

0:05:02.864,0:05:05.144
I want to tell you about another disorder.

0:05:05.144,0:05:07.456
This one is a disorder which is quite rare.

0:05:07.456,0:05:10.224
It's called Hutchinson-Gilford progeria,

0:05:10.224,0:05:13.529
and it is the most dramatic form of premature aging.

0:05:13.529,0:05:17.312
Only about one in every four million kids has this disease,

0:05:17.312,0:05:20.672
and in a simple way, what happens is,

0:05:20.672,0:05:23.373
because of a mutation in a particular gene,

0:05:23.373,0:05:26.040
a protein is made that's toxic to the cell

0:05:26.040,0:05:28.337
and it causes these individuals to age

0:05:28.337,0:05:30.921
at about seven times the normal rate.

0:05:30.921,0:05:34.064
Let me show you a video of what that does to the cell.

0:05:34.064,0:05:37.199
The normal cell, if you looked at it under the microscope,

0:05:37.199,0:05:40.088
would have a nucleus sitting in the middle of the cell,

0:05:40.088,0:05:43.967
which is nice and round and smooth in its boundaries

0:05:43.967,0:05:45.722
and it looks kind of like that.

0:05:45.722,0:05:47.586
A progeria cell, on the other hand,

0:05:47.586,0:05:50.688
because of this toxic protein called progerin,

0:05:50.688,0:05:52.972
has these lumps and bumps in it.

0:05:52.972,0:05:55.987
So what we would like to do after discovering this

0:05:55.987,0:05:57.839
back in 2003

0:05:57.839,0:06:01.057
is to come up with a way to try to correct that.

0:06:01.057,0:06:04.145
Well again, by knowing something about the molecular pathways,

0:06:04.145,0:06:06.144
it was possible to pick

0:06:06.144,0:06:08.761
one of those many, many compounds that might have been useful

0:06:08.761,0:06:10.222
and try it out.

0:06:10.222,0:06:12.797
In an experiment done in cell culture

0:06:12.797,0:06:14.839
and shown here in a cartoon,

0:06:14.839,0:06:17.533
if you take that particular compound

0:06:17.533,0:06:20.789
and you add it to that cell that has progeria,

0:06:20.789,0:06:23.010
and you watch to see what happened,

0:06:23.010,0:06:25.972
in just 72 hours, that cell becomes,

0:06:25.972,0:06:28.240
for all purposes that we can determine,

0:06:28.240,0:06:30.082
almost like a normal cell.

0:06:30.082,0:06:34.423
Well that was exciting, but would it actually work in a real human being?

0:06:34.423,0:06:37.808
This has led, in the space of only four years

0:06:37.808,0:06:41.309
from the time the gene was discovered to the start of a clinical trial,

0:06:41.309,0:06:43.506
to a test of that very compound.

0:06:43.506,0:06:45.469
And the kids that you see here

0:06:45.469,0:06:48.031
all volunteered to be part of this,

0:06:48.031,0:06:49.492
28 of them,

0:06:49.492,0:06:52.587
and you can see as soon as the picture comes up

0:06:52.587,0:06:55.969
that they are in fact a remarkable group of young people

0:06:55.969,0:06:57.388
all afflicted by this disease,

0:06:57.388,0:06:59.637
all looking quite similar to each other.

0:06:59.637,0:07:01.311
And instead of telling you more about it,

0:07:01.311,0:07:05.297
I'm going to invite one of them, Sam Berns from Boston,

0:07:05.297,0:07:07.730
who's here this morning, to come up on the stage

0:07:07.730,0:07:09.950
and tell us about his experience

0:07:09.950,0:07:11.860
as a child affected with progeria.

0:07:11.860,0:07:15.918
Sam is 15 years old. His parents, Scott Berns and Leslie Gordon,

0:07:15.918,0:07:18.039
both physicians, are here with us this morning as well.

0:07:18.039,0:07:20.577
Sam, please have a seat.

0:07:20.577,0:07:27.897
(Applause)

0:07:27.897,0:07:30.075
So Sam, why don't you tell these folks

0:07:30.075,0:07:33.450
what it's like being affected with this condition called progeria?

0:07:33.450,0:07:37.258
Sam Burns: Well, progeria limits me in some ways.

0:07:37.258,0:07:41.222
I cannot play sports or do physical activities,

0:07:41.222,0:07:44.426
but I have been able to take interest in things

0:07:44.426,0:07:47.405
that progeria, luckily, does not limit.

0:07:47.405,0:07:49.962
But when there is something that I really do want to do

0:07:49.962,0:07:52.979
that progeria gets in the way of, like marching band

0:07:52.979,0:07:56.405
or umpiring, we always find a way to do it,

0:07:56.405,0:07:59.922
and that just shows that progeria isn't in control of my life.

0:07:59.922,0:08:01.632
(Applause)

0:08:01.632,0:08:03.668
Francis Collins: So what would you like to say to researchers

0:08:03.668,0:08:06.765
here in the auditorium and others listening to this?

0:08:06.765,0:08:09.362
What would you say to them both about research on progeria

0:08:09.362,0:08:11.248
and maybe about other conditions as well?

0:08:11.248,0:08:14.394
SB: Well, research on progeria has come so far

0:08:14.394,0:08:16.636
in less than 15 years,

0:08:16.636,0:08:21.005
and that just shows the drive that researchers can have

0:08:21.005,0:08:24.423
to get this far, and it really means a lot

0:08:24.423,0:08:27.674
to myself and other kids with progeria,

0:08:27.674,0:08:30.498
and it shows that if that drive exists,

0:08:30.498,0:08:33.099
anybody can cure any disease,

0:08:33.099,0:08:37.046
and hopefully progeria can be cured in the near future,

0:08:37.046,0:08:40.803
and so we can eliminate those 4,000 diseases

0:08:40.803,0:08:43.810
that Francis was talking about.

0:08:43.810,0:08:46.939
FC: Excellent. So Sam took the day off from school today

0:08:46.939,0:08:52.074
to be here, and he is — (Applause) --

0:08:52.074,0:08:56.890
He is, by the way, a straight-A+ student in the ninth grade

0:08:56.890,0:08:58.223
in his school in Boston.

0:08:58.223,0:09:00.424
Please join me in thanking and welcoming Sam.

0:09:00.424,0:09:04.077
SB: Thank you very much. FC: Well done. Well done, buddy.

0:09:04.077,0:09:15.895
(Applause)

0:09:16.886,0:09:18.602
So I just want to say a couple more things

0:09:18.602,0:09:21.734
about that particular story, and then try to generalize

0:09:21.734,0:09:24.230
how could we have stories of success

0:09:24.230,0:09:27.743
all over the place for these diseases, as Sam says,

0:09:27.743,0:09:30.262
these 4,000 that are waiting for answers.

0:09:30.262,0:09:32.134
You might have noticed that the drug

0:09:32.134,0:09:34.903
that is now in clinical trial for progeria

0:09:34.903,0:09:36.667
is not a drug that was designed for that.

0:09:36.667,0:09:39.529
It's such a rare disease, it would be hard for a company

0:09:39.529,0:09:43.259
to justify spending hundreds of millions of dollars to generate a drug.

0:09:43.259,0:09:45.419
This is a drug that was developed for cancer.

0:09:45.419,0:09:47.584
Turned out, it didn't work very well for cancer,

0:09:47.584,0:09:49.907
but it has exactly the right properties, the right shape,

0:09:49.907,0:09:52.799
to work for progeria, and that's what's happened.

0:09:52.799,0:09:56.027
Wouldn't it be great if we could do that more systematically?

0:09:56.027,0:09:59.823
Could we, in fact, encourage all the companies that are out there

0:09:59.823,0:10:01.661
that have drugs in their freezers

0:10:01.661,0:10:03.863
that are known to be safe in humans

0:10:03.863,0:10:06.155
but have never actually succeeded in terms

0:10:06.155,0:10:09.011
of being effective for the treatments they were tried for?

0:10:09.011,0:10:11.395
Now we're learning about all these new molecular pathways --

0:10:11.395,0:10:14.474
some of those could be repositioned or repurposed,

0:10:14.474,0:10:16.873
or whatever word you want to use, for new applications,

0:10:16.873,0:10:19.842
basically teaching old drugs new tricks.

0:10:19.842,0:10:22.529
That could be a phenomenal, valuable activity.

0:10:22.529,0:10:25.575
We have many discussions now between NIH and companies

0:10:25.575,0:10:27.699
about doing this that are looking very promising.

0:10:27.699,0:10:30.313
And you could expect quite a lot to come from this.

0:10:30.313,0:10:33.352
There are quite a number of success stories one can point to

0:10:33.352,0:10:35.705
about how this has led to major advances.

0:10:35.705,0:10:37.912
The first drug for HIV/AIDS

0:10:37.912,0:10:39.641
was not developed for HIV/AIDS.

0:10:39.641,0:10:42.159
It was developed for cancer. It was AZT.

0:10:42.159,0:10:44.160
It didn't work very well for cancer, but became

0:10:44.160,0:10:46.276
the first successful antiretroviral,

0:10:46.276,0:10:48.848
and you can see from the table there are others as well.

0:10:48.848,0:10:52.492
So how do we actually make that a more generalizable effort?

0:10:52.492,0:10:54.716
Well, we have to come up with a partnership

0:10:54.716,0:10:57.576
between academia, government, the private sector,

0:10:57.576,0:11:00.029
and patient organizations to make that so.

0:11:00.029,0:11:01.679
At NIH, we have started this new

0:11:01.679,0:11:04.879
National Center for Advancing Translational Sciences.

0:11:04.879,0:11:08.494
It just started last December, and this is one of its goals.

0:11:08.494,0:11:09.935
Let me tell you another thing we could do.

0:11:09.935,0:11:12.854
Wouldn't it be nice to be able to a test a drug

0:11:12.854,0:11:15.225
to see if it's effective and safe

0:11:15.225,0:11:17.326
without having to put patients at risk,

0:11:17.326,0:11:19.879
because that first time you're never quite sure?

0:11:19.879,0:11:22.030
How do we know, for instance, whether drugs are safe

0:11:22.030,0:11:25.275
before we give them to people? We test them on animals.

0:11:25.275,0:11:27.917
And it's not all that reliable, and it's costly,

0:11:27.917,0:11:29.607
and it's time-consuming.

0:11:29.607,0:11:32.470
Suppose we could do this instead on human cells.

0:11:32.470,0:11:34.702
You probably know, if you've been paying attention

0:11:34.702,0:11:36.002
to some of the science literature

0:11:36.002,0:11:37.658
that you can now take a skin cell

0:11:37.658,0:11:40.539
and encourage it to become a liver cell

0:11:40.539,0:11:43.614
or a heart cell or a kidney cell or a brain cell for any of us.

0:11:43.614,0:11:46.766
So what if you used those cells as your test

0:11:46.766,0:11:49.711
for whether a drug is going to work and whether it's going to be safe?

0:11:49.711,0:11:53.942
Here you see a picture of a lung on a chip.

0:11:53.942,0:11:57.463
This is something created by the Wyss Institute in Boston,

0:11:57.463,0:12:00.638
and what they have done here, if we can run the little video,

0:12:00.638,0:12:02.774
is to take cells from an individual,

0:12:02.774,0:12:05.883
turn them into the kinds of cells that are present in the lung,

0:12:05.883,0:12:07.688
and determine what would happen

0:12:07.688,0:12:10.765
if you added to this various drug compounds

0:12:10.765,0:12:13.230
to see if they are toxic or safe.

0:12:13.230,0:12:15.501
You can see this chip even breathes.

0:12:15.501,0:12:18.118
It has an air channel. It has a blood channel.

0:12:18.118,0:12:19.821
And it has cells in between

0:12:19.821,0:12:22.259
that allow you to see what happens when you add a compound.

0:12:22.259,0:12:24.031
Are those cells happy or not?

0:12:24.031,0:12:27.062
You can do this same kind of chip technology

0:12:27.062,0:12:29.271
for kidneys, for hearts, for muscles,

0:12:29.271,0:12:31.735
all the places where you want to see whether a drug

0:12:31.735,0:12:34.016
is going to be a problem, for the liver.

0:12:34.016,0:12:37.064
And ultimately, because you can do this for the individual,

0:12:37.064,0:12:39.278
we could even see this moving to the point

0:12:39.278,0:12:42.719
where the ability to develop and test medicines

0:12:42.719,0:12:45.905
will be you on a chip, what we're trying to say here is

0:12:45.905,0:12:49.406
the individualizing of the process of developing drugs

0:12:49.406,0:12:51.654
and testing their safety.

0:12:51.654,0:12:53.306
So let me sum up.

0:12:53.306,0:12:55.566
We are in a remarkable moment here.

0:12:55.566,0:12:57.669
For me, at NIH now for almost 20 years,

0:12:57.669,0:13:00.270
there has never been a time where there was more excitement

0:13:00.270,0:13:02.855
about the potential that lies in front of us.

0:13:02.855,0:13:04.647
We have made all these discoveries

0:13:04.647,0:13:07.012
pouring out of laboratories across the world.

0:13:07.012,0:13:10.374
What do we need to capitalize on this? First of all, we need resources.

0:13:10.374,0:13:13.929
This is research that's high-risk, sometimes high-cost.

0:13:13.929,0:13:15.900
The payoff is enormous, both in terms of health

0:13:15.900,0:13:18.780
and in terms of economic growth. We need to support that.

0:13:18.780,0:13:21.081
Second, we need new kinds of partnerships

0:13:21.081,0:13:23.302
between academia and government and the private sector

0:13:23.302,0:13:26.649
and patient organizations, just like the one I've been describing here,

0:13:26.649,0:13:30.229
in terms of the way in which we could go after repurposing new compounds.

0:13:30.229,0:13:33.465
And third, and maybe most important, we need talent.

0:13:33.465,0:13:35.606
We need the best and the brightest

0:13:35.606,0:13:38.463
from many different disciplines to come and join this effort --

0:13:38.463,0:13:40.909
all ages, all different groups --

0:13:40.909,0:13:42.996
because this is the time, folks.

0:13:42.996,0:13:46.621
This is the 21st-century biology that you've been waiting for,

0:13:46.621,0:13:49.083
and we have the chance to take that

0:13:49.083,0:13:51.573
and turn it into something which will, in fact,

0:13:51.573,0:13:53.903
knock out disease. That's my goal.

0:13:53.903,0:13:55.787
I hope that's your goal.

0:13:55.787,0:13:58.467
I think it'll be the goal of the poets and the muppets

0:13:58.467,0:14:00.476
and the surfers and the bankers

0:14:00.476,0:14:02.754
and all the other people who join this stage

0:14:02.754,0:14:04.504
and think about what we're trying to do here

0:14:04.504,0:14:05.669
and why it matters.

0:14:05.669,0:14:08.439
It matters for now. It matters as soon as possible.

0:14:08.439,0:14:11.557
If you don't believe me, just ask Sam.

0:14:11.557,0:14:13.000
Thank you all very much.

0:14:13.000,0:14:17.831
(Applause)