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0:31

Hey

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y'all. I'm Sam Sanders, and I wanna tell y'all

0:33

a little bit about a new podcast that I

0:35

co host. Called Vivec. But first,

0:37

gotta introduce my cohost. Hey,

0:39

y'all. I'm Syed Jones, and I'm Zach

0:41

Stafford. On Vivec, the three of us

0:43

talk about everything. From Beyonce,

0:46

to political violence, to which

0:48

candy is the gayest

0:49

candy, more of an ass. Just just tune

0:51

in. Yeah. We literally talk about any and

0:53

everything on our show. Absolutely. Join

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our group chat, come to life, follow

0:58

and listen to Vybeq wherever you get your

1:00

podcast.

1:06

I got an email from a parent of a

1:08

child. Who is

1:10

going progressively blind. This

1:12

parent writes to me and says, look, my child is

1:15

an amazing student and she's doing

1:17

so well, but she's going to lose vision.

1:20

It's going to happen. We don't know when. Can

1:22

you stop it?

1:24

Everyday, doctor Theodore EARNoff's inbox

1:26

is flooded with emails like that one,

1:29

and they put him in a difficult position

1:32

because he has the technology to

1:34

stop it Theodore is a professor

1:37

at UC Berkeley and a leading

1:39

researcher in the field of genomic therapies.

1:42

He develops medications that

1:44

change people's DNA

1:46

to cure genetic diseases, like

1:48

the one described in that email.

1:50

We are not just sitting here hand wringing at

1:53

the fault that's in our stars. We

1:55

can actually fly to the stars and touch them and

1:57

manipulate them. But having the ability

2:00

to do something and actually doing

2:02

it are two very different things.

2:08

Medicine has always suffered from a

2:10

problem called the no do

2:12

gap. It's the difference between what

2:14

we actually do for our patients and

2:16

what we could do. Given all that

2:18

we know. Breakthroughs in biomedicine

2:21

are allowing doctors to do things

2:23

they could never do before. But

2:26

sometimes these advances don't

2:28

fit into our financial or regulatory

2:30

systems. That means it can take a

2:33

long time for patients to

2:35

actually benefit, time that

2:37

many of them don't have to spare.

2:40

The National Institutes of Health invest

2:42

more than forty billion dollars in

2:44

biomedical research each year.

2:47

And the private sector in the US spends

2:49

more than twice that.

2:51

Clearly, we value these discoveries.

2:54

Why is it so hard to use them?

2:58

From the Freakonomics Radio Network, this

3:00

is FreakonomicsMD. I'm

3:02

BabuJena. Today on the show,

3:05

we'll talk about the promise of lifesaving

3:08

genetic treatments. But

3:09

first, how can we find the people

3:11

who might benefit from them? What

3:13

if artificial intelligence could examine

3:16

the fingerprints, the breadcrumbs that patients

3:18

leave throughout the healthcare system.

3:21

And feed our earn off will tell us how

3:23

editing the human genome can

3:25

cure disease and why his

3:27

answers to those desperate emails aren't

3:29

so straightforward. Our ability

3:32

to engineer these CRISPR medicines has

3:34

far outpaced

3:36

how these medicines are actually built,

3:38

tested, and put into human beings.

3:54

I'm Garv Single. I'm a physician and computer

3:56

scientist, studied artificial intelligence

3:58

and robotics, and ultimately

4:01

became a doctor to see patients. When you

4:03

say you have a background in robotics, does

4:05

that just mean you used to play with Legos Yeah. No.

4:07

Actually, I helped build a Lego

4:09

based team of autonomous soccer playing

4:11

robots as an undergraduate. And

4:13

they were in the World Cup recently? They were in

4:15

the Robo Cup. Actually, we played

4:17

at Carnegie Mellon and got

4:19

destroyed by robots that had seven

4:22

wheels. We had two wheels and, you know,

4:24

lost Are you joking or is this I'm a hundred

4:26

percent serious. When

4:28

he's not playing with Legos, my friend

4:30

Gaurav, spends his time using computer

4:33

science to solve health care problems.

4:36

Most recently, he was the chief data officer

4:38

of a company called Foundation Medicine,

4:41

which develops tests that diagnose cancer

4:43

patients with specific genetic mutations.

4:47

Now he sees patients at Brigham and Women's

4:49

Hospital in Boston and

4:51

advises other companies that are using

4:53

big data in artificial intelligence

4:56

to solve problems in medicine. Artificial

4:59

intelligence in the doctor's office may

5:02

sound as science fiction as say

5:04

soccer playing robots

5:05

but the fact is that artificial

5:08

intelligence already permeates

5:10

our lives. Credit

5:15

card companies have been using artificial intelligence

5:17

to help map risk scores. As

5:19

part of your credit evaluation, Spotify

5:22

uses art official intelligence to make personalized

5:24

recommendations, things like Google Photos

5:27

have the ability to match photos of

5:29

my children all the way from when they were born

5:31

till now when they're nine and seven, that's

5:34

incredible. The metric for a long

5:36

time has been, can computers do

5:38

things as well as humans? But

5:40

you see places like this task of

5:42

matching infant pictures to childhood pictures

5:45

where computers outperform humans. And

5:48

once you cross that threshold, you get

5:50

to real opportunity where computers could complement

5:52

humans. So when we get to medicine, I

5:54

think this becomes particularly relevant. What

5:56

are computers and artificial intelligence good at?

5:58

Two things at least. Number one,

6:01

pattern matching. Number two, doing

6:03

things very quickly. So where those two things

6:05

are important, there may be a real role for

6:07

computers and artificial intelligence. One example

6:09

where that's the case is diagnosing strokes.

6:12

When a patient has a stroke, part of

6:15

the blood flow to their brain has been blocked.

6:17

And every minute that goes by,

6:19

more and more neurons die. If you

6:21

wait too long, That brain tissue has already

6:24

died, and in fact opening up the blood vessel

6:26

no longer has any

6:27

benefit. When a patient shows up at

6:29

the emergency room with a suspected stroke,

6:31

they need to get treatment fast.

6:34

But first, to confirm what

6:36

kind of stroke they had, those

6:38

patients usually get a CT

6:40

scan. Which has to be read by

6:42

a radiologist. That's a very busy

6:44

environment.

6:45

And maybe the case that that CT scan of

6:47

the head very important, very time

6:49

sensitive, is in a queue of equally

6:51

important and equally urgent scans that

6:53

that radiologist has to read. So

6:56

it may take five minutes, ten minutes, twenty

6:58

minutes for that radiologist to review it to see if it

7:00

has a stroke, only after which can that

7:02

patient be evaluated and hopefully

7:04

treated if it's in the time window for treatment.

7:07

One place that artificial intelligence has already had an

7:09

impact is analyzing scans

7:11

of these head CTs in the emergency room

7:13

faster than radiologists ever could. One

7:16

example is a company called VIS AI that

7:18

has an FDA approved algorithm

7:20

for detecting stroke in the emergency room. It

7:22

lives on the scanner of hospitals all over the

7:24

country. I should note that this

7:27

AI is one of the companies that Gaurav

7:29

has consulted with. Every time he

7:31

scan is taken of someone's head, That

7:33

algorithm runs on that scan and

7:35

determines if it believes there's a stroke

7:38

there. If stroke is detected by

7:40

the algorithm, a radiologist immediately

7:42

reviews it determines if that patient

7:44

has indeed had a stroke and rushes

7:46

the next steps for

7:47

intervention. The result of this has been

7:49

faster detection of strokes, often by

7:51

a dozen minutes or more, stroke is

7:53

really, really common. Are there examples where

7:56

this technology is being deployed

7:58

in areas where the diseases are

8:01

much less common, what we might call rare

8:03

diseases? I

8:04

think rare diseases are the next frontier

8:06

for computational diagnostics. They're

8:09

not diseases that most providers see

8:11

every day by deaf mission and

8:13

as a result believed to be highly

8:15

underdiagnosed. Meaning, for

8:17

some of these conditions, the subset

8:19

of people who know that they have the condition

8:22

is a small minority of the people who actually

8:24

have the condition. A common expression that

8:26

we probably both heard in training is when

8:28

you hear hoof beats, think horses,

8:31

not zebras that the rare diseases one

8:33

thinks about later. On the flip

8:35

side of that, If you're a patient with a

8:37

rare disease, that can be a very frustrating

8:39

experience. It can mean going from

8:41

doctor to doctor from proposed diagnosis

8:44

to proposed diagnosis alitany

8:46

of tests and evaluations and treatments,

8:49

all without any benefit while you're

8:51

on what is often termed a diagnostic odyssey

8:54

That can take months, it can take years, it can

8:56

take lots of expense and heartache

8:58

and frustration. If there were

9:00

a way to make that diagnosis earlier,

9:03

That could be tremendously beneficial to the

9:05

patient, to the health system, avoiding

9:07

all this unnecessary work and getting on the right treatment

9:10

sooner. The

9:13

US government defines rare diseases

9:15

as those that affect fewer than two hundred

9:18

thousand people in the country. Some

9:20

affect only a handful of people. But

9:23

the word rare can be misleading when

9:25

talking about rare diseases because

9:27

there are more than seven thousand of them.

9:30

Taken altogether, more than thirty

9:32

million people in the United States

9:34

have been diagnosed with a rare disease.

9:37

That's around ten percent of the population.

9:39

So improving how we

9:41

find and care for those patients could

9:44

have a really big

9:45

impact. What if artificial intelligence

9:47

could examine the fingerprints, the breadcrumbs

9:50

that patients leave throughout the healthcare

9:52

system as part of their routine care through

9:54

the radiology imaging they've gotten, the

9:57

EKGs that have been done, the lab

9:59

work they've had done, what if it were

10:01

possible for AI to interpret

10:03

that in the background passively without

10:05

anybody even needing to think about the rare

10:07

disease and have proactively brought it up

10:10

and alert the patient or the physician that,

10:12

hey, this is something that you might want to keep an

10:14

eye out for. I noticed this pattern.

10:16

That feels like an incredibly powerful area,

10:18

and I believe there's real examples where that's now

10:24

One example might be looking at an EKG.

10:27

There's a lot more signal in EKG than

10:29

just Have you had heart attack or not?

10:31

It's incredibly rich analysis of the

10:33

electrical conduction of the heart.

10:36

And in fact, there are now a number of different

10:38

research scientists across the country that

10:40

have demonstrated that if you give

10:42

a computer sufficient training in analyzing

10:45

an EKG, Using artificial intelligence,

10:47

a computer can predict which

10:49

subset of patients is more

10:51

likely to have one of these rare conditions

10:54

Don't you worry about false positives in this sort

10:56

of scenario? That's a great point. Artificial intelligence

10:58

isn't perfect, like any screening test.

11:01

It flags people who may have

11:03

the condition, and then they can go get the

11:05

confirmatory testing. The problem is today,

11:08

we're not flagging enough people who are at

11:10

risk and as a

11:11

result, the majority of people with some of these

11:13

conditions don't know they have it. Theodore

11:15

Urnoff, the geneticist we heard from earlier,

11:18

is all too familiar with the diagnostic

11:20

odyssey that Gorev

11:22

described. It's heartbreaking. But

11:24

the good news is a key technology advance

11:26

has happened literally in the past five years

11:29

that I think is an enormous

11:31

call to action for pretty much the entirety

11:33

of the biomedical community.

11:35

Theodore isn't talking about artificial

11:37

intelligence. He's talking about

11:39

an advance in the next step on

11:41

the path to

11:42

diagnosis. The one that would come

11:44

after a patient is flagged by AI

11:47

as being at risk for rare disease.

11:50

Genetic testing. The

11:52

first complete sequence of the human genome

11:55

was obtained two decades ago. It

11:57

took about a decade in three billion dollars.

11:59

Today, there's a room on the UC Berkeley

12:01

campus and on the UCSF campus across

12:03

the bay or at Stanford. large

12:05

number of places where you can walk into the door

12:08

and a technician will take one of those little

12:10

swabs, you know, the ones you use for COVID tests

12:12

-- Mhmm. -- and swirl them in

12:14

your mouth or your nose. And twenty

12:16

four hours later, you can get a link to

12:18

your complete genetic sequence. If

12:20

you ask me when was a graduate student,

12:23

when the mid nineties at the Brown, do

12:25

you think we'll ever get to a time where

12:27

we could do that in a day, I'd go, oh, come on.

12:29

We'd more likely move faster than the speed

12:31

of light. I mean, if here we are, This isn't

12:34

some hypothetical of something that will exist in

12:36

two thousand and thirty three. This exists in

12:38

January twenty twenty three. So

12:40

the technologies to read DNA at an incredible

12:43

rate are here and they got so

12:45

much cheaper. And so

12:47

these folks who described to me

12:49

the harrowing odyssey of, like, what's wrong with

12:51

my child? Should not

12:53

suffer. We as a society. We

12:56

as a species. Owe

12:58

it to folks in such predicament to

13:01

develop and deploy a scalable

13:03

solution of rapid genetic

13:06

diagnosis.

13:07

AI could serve a lot of other functions in

13:09

healthcare, including helping

13:11

design new treatments, helping predict which treatment

13:13

is better for which

13:14

patient. But the idea of screening

13:16

for rare diseases or diagnosing undiagnosed

13:19

conditions, these feel absolutely

13:22

here and out. And as Gaurav

13:24

said earlier, artificial intelligence

13:26

is already being used in emergency

13:28

rooms to instantaneously

13:30

screen for a more common disease,

13:32

stroke. You're improving

13:35

patient's lives by decreasing morbidity

13:37

from stroke in a way that saves

13:39

payers money. And increases

13:42

the number of procedures that are done in a fee for

13:44

service environment. And so you have

13:46

incentive alignment between payers

13:48

and healthcare systems to do what's

13:50

in the best interest of patients. Implementation

13:53

is not trivial. You have to deploy software

13:55

algorithms workflow tools behind

13:57

the firewall of hundreds and thousands

14:00

of health systems. You have to get providers

14:02

to use the tools. You have to get them to make decisions

14:04

based on them. There are a lot of hurdles that

14:06

have to be overcome. And yet, when

14:08

incentives are aligned, that can happen.

14:12

The question is what will be required for the

14:14

example of detecting rare cardiovascular

14:16

condition from EKGs to become

14:18

real? The payer is caught in a bind here

14:21

because if we screen more for rare conditions,

14:23

we identify more patients who will

14:25

need to have expensive treatments. On

14:27

the other hand, you have an entire industry

14:29

that's developing novel medicines for these patients

14:32

who can't get the medicine today because they

14:34

don't even know they have the condition that's

14:36

being treated. Theodore Urnoff

14:38

is among the scientists developing those

14:41

novel medicines.

14:43

But his work comes with its own set

14:45

of economic challenges. We

14:47

owe it to the patients and the families to

14:49

aggressively

14:50

build a new framework. That's

14:53

after the break. I'm Bob Pujena,

14:55

and this is FreakonomicsMD.

15:05

This podcast is supported by Sonder

15:07

Mind. Listen, we all have

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o h per terms. Hey

16:05

y'all. I'm Sam Sanders, and I wanna tell y'all

16:07

a little bit about a new podcast that I

16:09

co host called ViveCheck. But first,

16:11

gotta introduce my cohosts. Hey,

16:13

y'all. I'm sorry, Jones. And I'm the next

16:15

effort. On Vivec, the three of us

16:18

talk about everything. From Beyonce

16:20

to political violence, to which

16:22

candy is the gayest

16:23

candy, more of an ass. Just just tune

16:26

in. Yeah. We literally talk about any and

16:28

everything on our show. Absolutely. Join

16:30

our group chat, come to life, follow

16:32

and listen to Vybeq wherever you get your

16:34

podcast.

16:39

What is DNA? Explain it in a way

16:41

that someone who doesn't have a medical background

16:43

would understand. Asking a geneticist

16:45

what's DNA is like asking an

16:47

astronomer, what's a star. You

16:49

know, it's a bowl of light. Before

16:53

the break, I mentioned that doctor Fyodor

16:55

Urnoff is developing treatments

16:57

for genetic diseases. But

16:59

first, let's take step back What

17:02

is a genetic disease? And

17:04

seriously, what's DNA? Like,

17:06

what is it? Really? It's

17:09

a molecule, so it consists of atoms

17:11

just like everything else. And

17:14

it has two remarkable properties that

17:16

pretty much no other molecule has.

17:19

It can carry in it

17:21

genetic information, just like a piece of

17:23

paper, can carry a sentence.

17:26

DNA can carry genetic

17:28

sentences. In

17:29

UNI, it carries twenty thousand

17:31

such genetic constructions, which were called genes.

17:33

But the other property of DNA, which

17:36

inspired me to devote my life to it,

17:38

is that it's conceptually and

17:40

molecularly

17:41

straightforward to make a copy of it.

17:45

But that copying process isn't

17:47

foolproof. As DNA reproduces

17:50

itself again and again, sometimes

17:53

there are little typos or mutations in

17:55

the genetic instructions it

17:57

holds. That's the basis of

17:59

a process which we call evolution that

18:02

gave us this wonderful constellation

18:04

of bacteria, animals,

18:07

plants, you and I. If

18:09

DNA never changed, you

18:11

and I would still be little microbes

18:14

floating around in the primordial soup.

18:16

So this intrinsic ability

18:18

of DNA

18:19

to change is the basis of

18:22

life paradoxically. Some

18:24

of these changes have beneficial outcomes,

18:27

like a mutation that occurred around

18:29

five thousand years ago, that allowed

18:31

humans to digest lactose

18:34

for the first time. I'm happy I

18:36

inherited that one, but sometimes

18:38

the outcomes of a typo in genetic instructions

18:41

can change our lives for the worse.

18:44

Those are what we call genetic diseases.

18:47

One of the more common ones you may have heard

18:49

of is sickle cell disease. Around

18:52

one hundred thousand Americans suffer

18:54

from it. So we classify it

18:57

as a rare disease. Even

18:59

though it's the most common inherited

19:01

blood disorder in the country and

19:03

affects millions of people worldwide.

19:07

People who inherit sickle cell disease can't

19:09

form normal red blood cells that

19:11

carry oxygen. Instead, they

19:14

produce red blood cells that are rigid

19:16

and sickle shaped like a crescent. That

19:19

deformity causes extreme pain

19:22

episodes that puts sickle cell patients

19:24

in the hospital on a regular

19:26

basis. It also delays

19:28

normal development in children, damages

19:31

joints, nerves, and organs, and

19:33

often causes strokes. All

19:36

of these bad outcomes are the result

19:38

of just one letter out

19:40

of six billion in the genome

19:43

being

19:43

flipped. One tiny typos

19:45

spreads its devastating effects

19:47

through the entire body, And even with

19:50

the best healthcare, our fellow Americans

19:52

with sickle cell disease, their lifespan is

19:54

around the mid

19:54

forties. So it takes away

19:57

decades from your life.

20:01

Until now, the only way to

20:03

cure sickle cell disease was with

20:05

a bone marrow transplant. But

20:07

the procedure is not for everyone.

20:09

It can be difficult to find a well matched

20:12

donor and bone marrow transplants

20:14

are really hard on the body, especially

20:17

as patients get older. What if

20:19

instead of replacing the patients faulty

20:22

bone marrow, doctors could actually

20:24

fix the typo in the patient's

20:27

own bone marrow. That would be

20:29

a much safer procedure and

20:31

eliminate the need for a well matched donor,

20:33

meaning anyone with the disease

20:36

could potentially be cured. Thanks

20:39

to a revolutionary gene editing technology

20:41

called CRISPR, scientists like

20:44

Theodore are now doing exactly

20:46

that.

20:52

The first thing to note about CRISPR is it's one

20:54

of those acronyms. Where

20:56

what the acronym stands for is not

20:58

useful to know because it doesn't tell you anything

21:00

about what it does. And there are great examples

21:03

to the country, let's say, school, right, or

21:05

self contained underwater breathing apparatus. If

21:07

you know what the acronym stands for, you're like aha.

21:10

But CRISPR stands for,

21:11

mhmm, clustered regularly into space

21:14

short palindromic repeats, and your audience is welcome

21:16

to forget that immediately.

21:17

Or say it a hundred times, it'll help you go to sleep,

21:19

or become the least popular person

21:21

at a social gathering. Honey, I know what

21:23

CRISPR stands for. So I'm

21:26

sitting in a recording studio at

21:28

school or journalism of the University of California,

21:30

Berkeley, where I'm a professor, and

21:32

probably the single biggest discovery

21:35

in biomedicine of the past quarter

21:37

century. Was made here on the

21:39

Berkeley campus. That

21:40

discovery was made by the biochemist, Jennifer

21:43

Downner. Who, together with the Manuel

21:45

Charpentier, won the twenty

21:47

twenty Nobel Prize in Chemistry.

21:50

To be clear, Jennifer and

21:52

Emmanuel didn't create CRISPR.

21:55

CRISPR is not a fancy new lab machine.

21:58

It's a microbial defense mechanism.

22:01

It consists of just two molecules, an

22:03

enzyme that acts as a pair of DNA

22:05

scissors and a special

22:07

piece of genetic material that

22:09

tells the enzyme where in the

22:11

DNA to cut, and it's

22:14

billions of years old. Early

22:16

in the history of life, bacteria

22:18

evolved, crisper, to fight

22:20

off parasites that could attack

22:22

and kill them. It's basically little

22:25

molecular machine that carries

22:27

in it a memory of a previous

22:29

attack by a genetic invader, a

22:31

snippet of the offender's genetic material,

22:34

like a law enforcement officer with a most

22:36

wanted poster with a picture of somebody suspected

22:39

of a crime. And it literally

22:41

matches every piece of DNA it sees.

22:43

Do you have a match to this twenty

22:46

letter word that I'm carrying inside

22:48

me? If yes, I'll cut you on the tray.

22:50

If not, have a nice day. Jennifer

22:53

and Emmanuel's big discovery was

22:55

not that CRISPR exists. What

22:57

they discovered was something that CRISPR

22:59

can

22:59

do. So it turns out that you can

23:02

put CRISPR inside human cells, which

23:04

seems insane. This thing comes from

23:06

bacteria, which are billions of years apart from

23:08

us evolutionarily. You can take CRISPR,

23:10

you can give it a twenty letter match

23:13

to a human gene that's broken, and

23:15

it'll fix it. We don't understand

23:17

why it's been so successful in this

23:20

incredible new environment, but

23:22

we're grateful to mother nature

23:24

and, of course, to Jennifer and Emmanuel for

23:26

having the insight, that you can

23:28

program. This is the keyword.

23:31

CRISPR can be programmed.

23:33

Not only have scientists wielded CRISPR's

23:36

innate destructive function to

23:38

eliminate toxic genes, but

23:40

they've also come up with ways to make

23:42

CRISPR serve a constructive function.

23:45

That is to precisely alter

23:47

just one letter in the DNA to

23:50

repair a gene rather than getting rid

23:52

of it altogether. Genesis

23:54

made use of that function to

23:57

develop a cure for sickle cell

23:59

disease. Which is currently the first

24:01

CRISPR based therapeutic up for

24:03

approval by the FDA.

24:05

This is a great example of the

24:07

ways in which we humans have

24:09

borrowed from mother nature and then elaborated

24:11

on her

24:12

inventions. And we wouldn't

24:14

be talking about this if this hasn't be used on

24:16

people. CRISPR isn't the first approach

24:18

to gene therapy. There are several approved

24:21

medications that use modified viruses

24:24

to deliver disease treating genetic

24:26

material into a patient's cells.

24:28

But CRISPR cures are

24:30

the first to edit the genome

24:33

itself. So far, CRISPR

24:35

has been used to treat genetic diseases of

24:37

the bloodstream, liver, eye,

24:39

and immune system. For others,

24:42

like those affecting the lungs, brain,

24:44

and kidneys, scientists haven't

24:46

yet figured out how to get CRISPR

24:48

into enough of the organ to

24:50

actually heal it. So to be

24:53

clear, CRISPR is still far

24:55

from a cure all. But as

24:57

new techniques and technologies to deliver

24:59

CRISPR are developed, more

25:02

and more organs will come online.

25:04

Theodor expects the lungs to be next.

25:09

Having the power to cure genetic diseases

25:12

by editing the human genome is

25:14

a dream come true for geneticist. But

25:17

when it comes to using that power

25:19

to help people, the story gets

25:21

more

25:22

complicated. Many a time

25:24

when parents of children with severe genetic

25:26

disease send me an out

25:28

saying doctor Urnoff, is there anything you

25:30

can do? If they are willing to share

25:32

what the mutation is. I can load

25:35

it into some software in my computer, which is available

25:37

to all I wanna be clear on some proprietary UC

25:39

Berkeley software. And you

25:41

can basically engineer if you know what

25:43

you're doing at CRISPR to fix that mutation.

25:46

For many diseases, that Engineered

25:48

CRISPR on my computer screen can become

25:51

a vial with that CRISPR that we

25:53

can pretty quickly test for whether

25:55

it can repair the defect safely

25:57

and effectively. Start to finish.

25:59

If you know what you're doing, it'll take well under

26:01

a year. So do I write back to the

26:03

parents and say, hi, and guess what? No.

26:07

And here's why. Engineering the

26:09

medicine is the first step

26:11

of probably a four

26:13

year process to protect patients

26:15

from faulty medicines. I wanna

26:17

really emphasize, I'm not sitting here and saying,

26:20

get rid of the loss to protect patients

26:22

from faulty medicines. But going

26:24

through that four year process just to get to the clinic

26:26

takes anywhere between eight million dollars to ten million dollars

26:29

for one disease. If

26:31

the disease is relatively prevalent like sickle

26:34

cell disease and then if they charge

26:36

what is currently being charged for these types of medicines,

26:38

which is one to two to three million

26:40

dollars a patient. I can see

26:42

why a company would invest years and millions

26:44

to build a medicine. Okay. Now I get this

26:46

email from somebody and they have to children

26:48

and both children have this change and

26:50

it's unclear that anybody else on planet

26:52

Earth has that genetic

26:53

change. So who exactly is going to spend four

26:56

years and ten million dollars building a medicine

26:58

that's gonna be used to treat two kids.

27:03

Many of these diseases are individually so

27:05

rare that they do not form a

27:07

viable commercial proposition under the

27:09

current system. We need

27:12

to face the remarkable reality

27:14

that our ability to engineer these

27:16

CRISPR medicines has far

27:19

outpaced how these medicines

27:21

are actually built, tested, and put into

27:23

human beings. We have never had

27:25

a technology like CRISPR. We

27:28

owe it to the patients and the families to

27:31

aggressively build a new

27:33

framework to provide these

27:35

medicines to these

27:36

individuals. It scares me

27:38

because it's one thing to say

27:41

to somebody that we don't have a treatment

27:43

because the biology doesn't exist. To

27:45

provide that treatment. It's another thing to say that

27:47

we don't have a treatment because there's

27:49

not sufficient commercial incentive to develop

27:52

that treatment, to evaluate it, to test it.

27:54

To market it or that we

27:56

have a set of regulatory policies

27:59

that aren't adept enough to

28:01

recognize that There are some patients

28:03

with some diseases who literally

28:06

months matter in terms of getting

28:08

access to care. We want to get medicines to

28:11

people faster. But we wanna make sure that

28:13

we do so in a way that's safe. And

28:15

the FDA is really tasked with managing

28:18

that speed, safety trade off, but

28:20

Of course, that trade off should change when

28:22

the parameters change. Right? So if you have

28:24

a new technology that

28:26

will allow for personalized intervention

28:29

in people with life threatening diseases

28:31

for which early treatment

28:34

really does matter, we should be able

28:36

to create a regulatory pathway that would allow

28:38

for that. And then there's the other bucket of

28:40

our, well, how do we pay for that? That commercialization issue

28:42

is equally important. I cannot

28:44

improve on what you just say. I'll just add

28:47

one point. Many genetic

28:49

diseases are diagnosed in

28:51

human beings. At a

28:53

stage where current technology. And

28:56

I emphasize current because our

28:58

field is moving very fast. Current

29:01

technology is essentially powerless.

29:04

By the time we are looking at that human being

29:06

in a clinic, it's too late.

29:09

A really profound and poignant example

29:12

is the disease that killed Woody Guthrie,

29:14

which is Huntington's disease. It's

29:17

a broken gene. It's actually a toxic

29:19

gene, which is basically killing

29:21

the brain. And by the time people develop

29:24

symptoms, parts of their brain are

29:26

just gone, and we don't have a technology

29:28

that can bring it back.

29:30

Remember the email from the very beginning

29:32

of the episode that Theodore received

29:35

from the parent of a girl going progressively

29:37

blind. We don't know if we'll be

29:39

able to turn back time

29:41

and bring back vision. To one

29:44

hundred percent But at the very least if we

29:46

can diagnose early enough. If

29:48

we can intervene at the genetic level before

29:50

it gets worse, I can tell

29:52

you that the patient

29:53

community, the vast worldwide community of folks

29:55

with genetic disease, will applaud.

29:58

This brings us back to artificial intelligence,

30:00

and its role in catching these rare

30:02

genetic diseases as early

30:04

as possible. Here's Gaurav

30:06

Single again. Now that they're effective

30:09

treatments, it feels more important than

30:11

ever that we use techniques like this

30:13

to make sure that people who have this condition know they

30:15

have it so they can get

30:16

treated. And

30:17

in Theodore's eyes, this is a two

30:19

way street. I think we as a community

30:21

owe it to the folks

30:24

out there whose genetic changes we're identifying

30:26

is potentially dangerous or disease driving.

30:29

To make sure that our ability to address

30:31

those changes actionably in the clinic

30:33

catches up to how quickly we can identify them.

30:38

Suppose that we can solve the economic puzzle,

30:42

What's your big picture ideal

30:44

vision? A world where

30:46

genetic disease is diagnosed early

30:49

in a way that's so affordable that

30:51

health insurance just covers it. And then

30:53

in cases where that's appropriate, the

30:56

CRISPR medicine is manufactured and

30:59

administered to that individual in

31:01

a way that is scalable,

31:04

affordable, and does not involve

31:07

years and millions. Really

31:09

having, I don't know what we're gonna

31:11

call them, CRISPR clinics. Today,

31:13

I'm wearing glasses. It's a way to

31:16

correct my myopia. Do I see

31:18

a future where CRISPR is deployed to repair

31:20

genetic defects in a way that's relatively

31:22

commonplace? I do. That's

31:25

world I want to live in. So

31:28

how can we solve the economic puzzle?

31:31

How can we make developing cures

31:33

for rare genetic diseases

31:34

profitable, and accessing

31:37

those cures affordable. Financing

31:40

ends up being a tremendous roadblock but

31:42

with the right kind of financing, it actually

31:44

ends up accelerating

31:46

our ability to treat these patients. And

31:49

what's it like to receive a crisper

31:51

cure. It all amounted to a

31:53

small syringe of DNA that took

31:55

about thirty seconds to infuse.

31:58

And my life changed completely.

32:02

That's coming up next week on Freakonomics.

32:05

In the meantime, let us know what you thought

32:07

about the show. I'm at boppu at

32:09

Freakonomics dot com. That's

32:11

BAPU at freakonomix dot

32:14

com. That's it for today.

32:16

I'd like to thank my guests this week, doctor

32:18

Gaurav Singhal and doctor

32:20

Fyodor

32:20

Urnoff. And thanks to you,

32:23

of course, for listening. Fri

32:25

Freakonomics MD is part of the

32:27

Freakonomics Radio Network, which

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also includes Freakonomics Radio.

32:32

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33:01

This episode was produced by Julie

33:03

Canfor and Claire Boudreaux bitch. It

33:05

was mixed by Eleanor Osbourne. Our

33:07

executive team is Neil Carruth, Gabriel

33:10

Roth, and Steven Dubner, original

33:12

music composed by Luis SCARRA. As

33:15

always, thanks for listening.

33:24

For the last twenty two years,

33:26

every time I talk to my biology

33:29

colleagues, I'm always happy

33:31

that I did a PhD in Freakonomics. But

33:34

when I'm talking to

33:35

you, I'm thinking to myself, Doug, on it,

33:37

why didn't I do something different twenty

33:40

three years ago, between you are an

33:42

MD. Right?

33:42

I'm an MD and a PG and Yeah.

33:44

Both. Wow.

33:46

That's two lives in one. Yeah. Exactly.

33:49

Yeah. I don't

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