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I'm endlessly fascinated by people who are doing

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of the Founders Podcast. Each

1:00

week, David gives you ideas you can use

1:02

in your work revealed by the life stories

1:04

of history's greatest entrepreneurs. He connects

1:06

the legacies of the likes of Kobe Bryant to

1:09

Estee Lauder to Winston Churchill and the list goes

1:11

on. He recently put out

1:13

a new favorite episode on Anna Wintour. Founders

1:16

Podcast will undoubtedly light a spark of ambition

1:18

and aid all of us in our quest

1:20

towards our life's work. David

1:22

isn't stopping anytime soon. In fact, if you

1:24

ask him, he's just begotten. I'm

1:26

proud that Founders is part of the Colossus network and

1:28

you can find a link to this treasure trove in

1:30

the show notes. Hello

1:35

and welcome everyone. I'm Patrick O'Shaughnessy and this

1:37

is Invest Like the Best. This

1:40

show is an open-ended exploration of markets,

1:42

ideas, stories, and strategies that will help

1:44

you better invest both your time and

1:46

your money. Invest Like the

1:48

Best is part of the Colossus family

1:50

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1:53

our podcasts, including edited transcripts, show notes,

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and other resources to keep learning at

1:57

joincolossus.com. Patrick

2:00

O'Shaughnessy is the CEO of Positive

2:02

Time. All opinions expressed by

2:04

Patrick and podcast guests are solely their

2:07

own opinions and do not receive the

2:09

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2:11

podcast is for informational purposes only and

2:13

should not be relied upon as a

2:15

basis for investment decisions. Clients

2:17

of Positive Time may maintain positions

2:19

in the securities discussed in this podcast.

2:22

To learn more, visit

2:24

psuheap.com. My

2:29

guest today is Alex Stillford. Alex

2:31

is the founder of Convoke, a software

2:34

platform to help streamline drug development and

2:36

commercialization. He has also been writing

2:38

frequent blog posts on the biotech industry since 2019,

2:40

keeping a pulse

2:42

on the direction of innovation. He

2:44

joined me today to talk about the history

2:46

of the pharmaceutical industry and what's becoming possible

2:48

in medicine in the coming years. Alex

2:51

helped break down the complexities of investing

2:53

in new drug development, breakthroughs in gene

2:56

therapy on the horizon, and the dance

2:58

between timely progress and restrictive regulation. This

3:01

industry has a ton to unpack and Alex

3:03

thoughtfully lays out the landscape. Please

3:05

enjoy my conversation with Alex Stillford. Alex,

3:10

we're going to spend a ton of time

3:12

today to give out pharmaceuticals. Not just the

3:14

industry, some of the companies, but the process

3:16

by which the world has created incredible

3:19

technologies for our health and also the

3:21

impediments to creating more of them in

3:24

the future. Could you give

3:26

your version of just a

3:28

sweeping overview of the

3:30

process and evolution of how we find drugs?

3:33

Alex Stillford, MD Yeah. So, it

3:35

starts with academic research. This

3:37

is mostly funded by government research and

3:39

it occurs in universities. So, the

3:42

NIH is the biggest vendor of biomedical

3:44

research in the world and they support

3:47

scientists trying to understand the basic science

3:49

of how diseases work. What are

3:51

the mechanics of diabetes? What are the mechanics of

3:53

heart disease? What are the

3:55

different genes, proteins involved in those

3:58

pathological processes? to

4:00

some extent, how could we modulate

4:03

those processes to affect disease? But

4:05

that's only really a small part of the

4:07

story. So once you have an idea for

4:09

how you might intervene a disease, you then

4:11

have to do a lot of work to

4:14

convert it into a drug. So these initial

4:16

hypothesis is to use these target specific proteins,

4:18

RNA, whatever, so you can modulate to affect

4:20

the disease. I've then taken

4:22

up by mostly small profit

4:26

biotech companies who then do

4:28

all the hard work of turning this into

4:30

a drug, using a molecule that can modulate

4:32

these targets, either inhibit it or

4:34

increase the activity in a certain way.

4:37

And then running everything through

4:39

first these in vitro systems,

4:42

just basically cells in a Petri dish, then

4:44

up to animal models, so testing them in

4:46

mice, monkey models of disease

4:48

potentially. Once it's safe enough, then you

4:50

can go into human trials. You

4:53

have a steadily escalating sequence of trials

4:55

starting from phase one, which is just

4:57

basic safety trials in human volunteers and

4:59

dosing. Phase two, we try to

5:01

get a sense of initial efficacy of the drug. And

5:04

then phase three, which confirms the efficacy and

5:06

safety to the extent we can. And then

5:08

you have to go to the regulators, FDA,

5:11

EMMA, the MDA, and they'll

5:13

review the package of information and make a

5:15

determination for whether or not the drug can

5:17

be marketed for a certain specific use case,

5:19

which is called an indication. That

5:22

long chain produces the things we use. What

5:25

was so interesting to me reading all your

5:27

work was the rate of discovery and

5:29

the rate of development seems to have changed

5:31

a lot. You read about this guy, Paul

5:34

Jansen, maybe you can describe who he was,

5:36

but it seemed like in the early days

5:38

of this research, a single person or lab

5:40

could produce dozens and dozens of things that

5:43

get used for a long time. And now,

5:45

I think he said somewhere in there, like

5:47

the average researcher will not work on anything

5:49

that ever gets to production and use in

5:52

trials or practice. So give us a history

5:54

of how efficient we are at finding new

5:56

stuff and what the important timeline points are

5:58

in that development. So, if we

6:01

go back to, let's say, the end of

6:03

the 19th century, where you have

6:05

a really nascent pharmaceutical industry. So,

6:07

you have pharmaceutical companies that came

6:10

out of apocasaries, which were selling

6:12

extracts of plants like you might

6:14

still get today, naturalistic medicine, and

6:16

extracts of organs. So, there

6:18

was this idea that nature produces some bounty of

6:20

molecules that are somewhat useful to treat health. But

6:23

we don't really know what parts of

6:25

the natural extracts actually treat health. So,

6:27

you have some companies that are extracting

6:29

just those natural products and selling them

6:31

in extracts of cyberscranes, whatever. And then

6:33

you have another set of companies that

6:35

came out of the chemicals industry, so

6:37

the dye industry. And around when

6:39

our ability to understand chemistry

6:42

and use chemistry to develop our

6:44

own molecules was developing to

6:46

this back in the 1900s, some

6:48

of these dye manufacturers were figuring

6:50

out ways to just adapt dye

6:52

chemicals and use them for therapeutic purposes.

6:55

Those companies eventually became more sophisticated

6:57

at the types of chemical

7:00

manipulations we could do, the type of extraction we could

7:02

do, and purification of these natural materials. And

7:04

that started to evolve into what

7:06

became the pharmaceutical industry around the

7:08

early 20th century. So,

7:11

then you had some

7:13

point, the golden age of antibiotics

7:16

between the two world wars became

7:18

the first great success story of the

7:21

pharmaceutical industry. So, moving away from products

7:23

like these natural extracts that didn't really

7:25

work in most cases towards antibiotics, which

7:27

had an obvious and noticeable effect on

7:30

the ability to cure these infectious diseases.

7:32

And a lot of these antibiotics were found

7:35

through just experiments. Go into the wild, find

7:37

soil samples, find drugs in these soil samples

7:39

from bacteria, and would purify them. And

7:43

that worked for a while, limited

7:45

to infectious diseases for the most part. And

7:47

then in the sort of 40s and 50s, you

7:50

start getting people like Jenssen

7:52

coming about. So, Jenssen

7:55

was a Belgian doctor. He trained in

7:57

medicine. And his dad was actually

7:59

one of these... importers of some

8:01

of these natural products, organ extracts. Janssen

8:04

recalled when he went to school that

8:06

in medical school, his contemporaries were making

8:08

fun of him or being part of

8:10

a family who sold these sort of

8:12

ineffective organ extracts. And

8:14

so Janssen had a bit of a chip on his shoulder, I think, from that. And

8:17

he wanted to figure out, adapt, how

8:19

can we actually improve on some

8:21

of these compounds that nature has given us? So he

8:23

went to the US for a while. He realized, I

8:25

think, when in 1948, there were some

8:28

labs that were springing up that were trying

8:30

to do more rational drug design, trade

8:32

their own new drugs rather than just take what existed

8:34

already in nature. And he brought that idea back to

8:36

Belgium and started his own lab in 1952 and started

8:38

trying to tinker

8:42

with natural compounds and use some of the

8:45

emerging tools of chemistry to just adapt some

8:47

of these existing compounds that had been discovered

8:49

already and find new uses for them. So

8:52

the 50s, there's a lot of opportunity in

8:54

the sense that you had these newly evolving

8:56

molecular tools, and you had a lot of

8:58

space to apply those tools to

9:00

discover new useful compounds. So you started with these

9:02

functional compounds that we found from nature. We had

9:05

a lot of starting points, morphine,

9:07

pettitine, and atropine, that could

9:09

be tinkered around with to find useful

9:11

things that are close to those natural

9:13

compounds, but not exactly the same. There's

9:15

some slightly different effects. And you had

9:17

an emerging suite of molecular tools. And

9:19

then you fast forward over time, the

9:21

kind of low-hanging fruit have, to some

9:24

extent, been exhausted with a lot of

9:26

those molecular tools were developed in that

9:28

period of time. So the pharmaceutical industry,

9:30

as it previously existed, has been declining

9:32

in its efficacy of finding new products

9:35

because you've exhausted the

9:37

opportunities to develop new drugs because we've

9:39

tinkered around a lot of what

9:42

is easy to find. And then you

9:44

have in the 80s, the biotech industry

9:46

arose. So just as you have this

9:48

declining pharmaceutical industry, which is small molecules,

9:51

medicinal chemistry, you have this rising

9:53

biotech industry that's built

9:56

on this idea that you can get bacteria to

9:58

produce any kind of product. of

10:00

arbitrary protein by inserting it into its DNA and

10:02

that's how you get a lot of those things like the

10:05

humanized insulin, the monofilamptibodies, there's all

10:07

these like biotech products that are now

10:09

somewhat still in the sentence. So

10:11

you have sort of two curves overlapping.

10:13

The fall off of the kind of

10:15

exhaustion of low-hanging fruits in traditional pharmaceuticals

10:18

and then the growth in what we

10:20

can do with biotechnology tools like recombinant

10:22

DNA and more recently CRISPR things like that. Yeah,

10:24

it'd be interesting to zoom all the way to

10:27

now and talk a little bit

10:29

about the best contenders for the

10:31

next explosion like what we saw in

10:34

small molecule pharma. What is the low-hanging

10:36

fruit of today? I love hearing you

10:38

talk about biologics or anything else that

10:40

you think about the major categories of

10:42

discovery or new enabling technologies that will

10:44

allow us to do more faster and

10:46

maybe have another explosion like what we

10:48

saw early in the industry's history. If

10:51

you look at the history of technologies in

10:54

biotech, it takes a long time to

10:57

commercialize something. Going back to the idea of people

10:59

discovering targets in academia, as soon as you have

11:01

an idea for a target, it takes something like

11:03

20 years for that to turn into a drug.

11:05

The best of time period 20 years plus

11:08

minus 10 is fairly consistent of how long

11:10

it takes to translate this sort of idea

11:12

of how you might treat a disease and

11:14

so much of what goes into commercializing and

11:16

developing a drug is turning it from this

11:18

idea into a product that is actually manufacturable,

11:20

scalable, doesn't trigger all these unwanted side effects

11:22

and many of these things you don't know

11:24

about until you've tried it out in humans.

11:26

Monoclonals now are the top

11:28

selling drug in the world, Keytruda,

11:31

previously with Humira. They're both monoclonal

11:33

antibodies and now the processes to

11:35

develop, produce, and sell those

11:37

monoclonal antibodies at scale are pretty established. It

11:39

took a long time to get there. I

11:41

think if you want to answer the question,

11:43

what is the next set of technologies that

11:45

are going to be really impactful in the

11:47

next 10, 20 years you need to look at

11:50

what is just nascent today

11:53

and getting approved. So things

11:55

like gene therapies, we have a few gene therapies

11:57

that have been approved. So-so, gentlemen, the treatment for

11:59

spiciness. It's just really devastating infant

12:01

neuromuscular disease. The patients who have that

12:03

disease would die before they are one

12:05

year old and the most serotype. But

12:08

now they can be seemingly almost cured

12:10

with these gene therapies and other modern

12:12

therapeutics. And that's one example of

12:14

an early success story, but we haven't had many

12:16

other examples of gene therapies being commercially successful. But

12:19

once you have one launch on success, you

12:21

can then iterate and refine the processes for

12:23

developing these drugs and you will have down

12:25

the lines future successes. So I think another

12:27

10 years, another 20 years, we'll see just

12:29

the process for developing manufacturing gene therapies.

12:32

All those things get worked out and

12:34

they'll become applied to many more conditions

12:36

at a larger scale. Also things like

12:38

cell therapies. So there's some

12:41

type of drug called CAR T-cell, which

12:43

is way more complicated than just a

12:45

typical pill. Immusals extract it from your

12:47

body. They're flown to a manufacturing site.

12:49

They're gene edited to have this sequence

12:52

protein inserted into their membranes that binds

12:54

a specific type of protein found on

12:56

cancer cells and it's reinfused into the

12:58

body and then it goes and eliminates

13:00

these pathogenic blood cancer cells. And

13:03

that's an extremely complicated manufacturing process.

13:06

You're taking these cells out as a human,

13:08

you're flying them to another country probably, you're

13:11

gene editing them and then you're flying them back and you have

13:13

to do that in a very short amount of time. One

13:16

because the viability of the product and

13:18

two because these patients have very severe types of

13:20

cancer and if you don't get it fast enough,

13:22

they're going to die. There's a lot of kinks

13:24

in those processes that need to be ironed out

13:27

before these technologies become scalable

13:29

beyond the niche use cases. One interesting

13:31

example is this company Bristol Myers Square,

13:33

they bought a company called CellGene who

13:35

are pioneers in this type of CAR

13:38

T-cell therapy. And they have

13:40

the same amount of employees working on delivering

13:42

these compounds as they have crazy patients. So

13:44

something like 4,000 each. The

13:46

high touch process expertise and scale you

13:49

need to deliver these super complex next

13:51

generation therapies like CAR T's, like gene

13:53

therapies that are somewhat personalized is just

13:56

way beyond traditional pharma where

13:58

you make a pill in a factory and then you put the the pill

14:00

on the shelf, you just have a pharmacist give it out.

14:03

So I think we're going to see a

14:05

lot more of these type of process-like products

14:07

that are way beyond just a standard pill

14:09

and it's much more

14:11

a whole complex, large,

14:14

the actual drug itself is a complex

14:16

product. There's also a whole process around

14:18

the drug of delivering it to patients in

14:20

a timely fashion, manufacturing,

14:22

which is very complicated. Another good

14:24

example is radiopharmaceuticals. So a lot

14:26

of companies have been investing in

14:28

this lately, buying up biotechs, developing

14:30

these drugs. And these are compounds

14:32

where you have a targeting element,

14:35

let's say cancer, and then you have a

14:37

radionucleotide, which emits radiation. You infuse these drugs,

14:39

it binds the cancer, it emits radiation, the

14:41

radiation kills the cancer cell in a targeted

14:44

way. And these are really complicated to deliver

14:47

because half-life assignment of these radioactive compounds is

14:49

something like seven days. You can manufacture and

14:51

deliver it to the patient within a few

14:53

days before they lose efficacy. So

14:55

these complex therapies are

14:57

one way of farmers trying

14:59

to build up moats to

15:02

distribution of products. Can you

15:04

say a few words? There's four categories there that

15:06

we'll focus on, three that are terms that I

15:08

think people have heard, but they may not know

15:10

exactly what it actually means. So

15:13

the first is monocle antibodies or

15:15

biologics. The second is gene

15:17

therapy. The third is cell therapy. Can

15:19

you just describe what those mean and

15:21

what they're doing as categories of drugs?

15:24

So I'll start with this antibody, right? So when

15:26

you get infected with some sort

15:28

of virus or bacteria, your

15:30

immune system will generate antibodies against

15:33

the invading threat. And these

15:36

antibodies are specific to foreign

15:38

proteins. Your body's always generating all the

15:40

time all these antibodies. And

15:43

when a virus infects you, then

15:45

if you have an existing antibody that

15:47

binds that virus, it'll multiply within the body, and

15:49

then it will bind to viral copies

15:52

and it will stop the infection by

15:54

just binding and neutralizing these viruses or

15:56

bacteria. And that was recognized

15:59

as Antibodies

16:01

can bind proteins on these

16:04

foreign microbes. It can

16:06

also probably be useful for binding

16:08

other molecules or other proteins

16:11

in the body that aren't necessarily foreign

16:13

but may impact disease in certain ways. So we can

16:15

use it to knock out proteins that we would want

16:17

to knock out to treat diseases. So

16:20

one example is

16:22

Humira, which is until recently the top selling

16:24

drug in the world, something like 20 billion in revenue.

16:26

It's an antibody that binds to a protein

16:29

called TNS alpha, which is

16:31

an inflammatory protein. People

16:33

who have arthritis in these inflammatory conditions

16:36

will have an excess of this protein

16:38

that just causes an immune response that

16:40

leads to inflammation and joint pain. If

16:43

you can put in this antibody that binds this

16:45

protein, takes it out, then you can treat some

16:48

of these diseases that are pathologically too many of

16:50

a certain protein. And if you want to treat cancer,

16:53

cancers have a different composition of proteins

16:55

on the surface than normal cells, you

16:58

can put in antibodies that

17:00

bind those cancer cells and kill them

17:02

specifically. So where the monoclonal comes in

17:04

is that a while back

17:06

there was some scientists figured out that

17:08

you could take these immune cells that

17:10

were producing antibodies and combine them

17:14

with a cancer cell essentially and

17:16

immortalize the cells that produces antibodies.

17:18

That's how they make them just

17:20

scalable, manufacturing process. You have these

17:24

cells that are artificially immortalized that produce the

17:26

antibody anyway, and they just produce tons and

17:28

tons of this antibody product that you can

17:31

just grow it up into a bioreactor vat

17:33

and then up to a certain level grow it and

17:35

then you skim off the antibodies. Gene therapy

17:38

is this idea that you can insert

17:40

genes into the body to compensate

17:42

for effective genes, which you have something

17:44

like this spinal muscular atrophy example where

17:47

patients who have that disease, they lack

17:49

a functional copy of a gene called

17:51

SMN1, which produces a

17:53

protein that your motor neurons need to

17:56

stay alive essentially. And if

17:58

you can package up that gene

18:00

and deliver it into a cell, the cells

18:02

that are missing this functional copy will then

18:04

produce the protein. You can restore that function.

18:07

That seems like quite a promising approach. But

18:09

then, of course, the difficulty with fixing these

18:11

mutations is actually getting the gene into

18:13

the cells. So a lot of the

18:15

innovation around gene therapy was figuring out how to deliver

18:18

genes to cells. And the approach that

18:20

we've landed on now in the industry, the

18:22

dominant approach is to use a virus,

18:25

essentially. So what Christmas is doing

18:27

is making specific let's

18:30

say cuts of the DNA to

18:32

either eliminate genes that we

18:34

want to eliminate or potentially down the

18:36

line to insert new functional copies of

18:39

genes. And then cell

18:41

therapy is this idea that you

18:43

can edit cells, immune cells in

18:45

the body, to program them in a way to

18:47

do something that you want them to do. They

18:49

might otherwise be inclined to do so. You

18:52

take immune cells that are very good at killing other cells, they're very

18:54

good at killing foreign cells and

18:56

microbes. But the body has

18:58

a lot of systems to prevent immune cells

19:00

from killing your own cells. So

19:02

what cell therapy is doing is taking

19:04

out some of these immune cells and

19:07

treating them so they'll bind and recognize

19:10

pathogenic cells like cancer cells that

19:12

may look to the unedited immune system

19:15

similar to a normal cell and then

19:17

redirecting the immune system towards removing

19:20

those pathogenic cells. You tell

19:22

me a little bit about the

19:24

potential speed of all

19:26

of this and the problems that slow

19:28

things down. I was really struck by

19:30

the AIDS example, your post, and

19:32

also just by this, I think it's Moore's Law backwards,

19:35

or E-R talk

19:40

about that episode with the AIDS epidemic and the frustration with those that

19:42

could have benefited from some drugs and just the slow

19:44

process of being able to get them and E-R and

19:47

the things that are slowing down the

19:49

iteration speed cycling of, can we make

19:51

a discovery? We've got patients in need.

19:53

How do we shorten the time between

19:55

a discovery and an implementation? The

19:58

big tension in how you

20:00

regulate drugs is how

20:02

do you balance the need to develop

20:04

drugs quickly while also keeping patients who

20:06

are participating in clinical trauma safe and

20:09

keeping people who take the drugs when it

20:11

comes to market safe. So it's really

20:13

difficult I think for regulators to find

20:15

an appropriate balance and it's almost an

20:17

impossible problem because if you come down

20:19

too hard on drugs and on

20:21

manufacturers and make them jump to too many hoops,

20:24

then it's just going to be uneconomical to develop

20:26

drugs and there's going to be a massive invisible

20:28

graveyard of people who could

20:30

have been saved had the drug come to

20:32

market faster but it was blocked by various

20:34

regulatory processes. But then if you're too lenient

20:36

on drug makers, some of them will take

20:39

advantage and will push drugs onto market that

20:41

shouldn't have been there. They may be acting

20:43

completely good intentions and they'll test according to

20:45

the process that regulatory agency demands and bring

20:47

the drug to market and it turns out

20:49

later that it causes some harm that we

20:51

could have foreseen if you had more rigorous

20:53

testing. So there's

20:56

no one size, that's all solution

20:58

to fix this tension. So

21:00

you have to always make some compromises

21:02

and coming back to the issue about

21:04

we're running out of low hanging fruit,

21:07

one of the biggest problems is that

21:09

we already have so many good drugs

21:11

for many conditions. So if

21:14

you have diabetes or something, then

21:16

we have insulin, we have many

21:18

varieties of insulin, we have the

21:21

GLP1 drugs now, we have a number

21:23

of other drugs that treat diabetes effectively

21:25

and they're very effective drugs. So it's

21:27

hard to make the case that you

21:29

need to bring another drug to

21:31

market with great haste to treat large

21:33

number of patients who are being well

21:35

treated by current drugs. So

21:38

under that situation, you

21:40

probably as a regulator are inclined to

21:42

put really high burdens on drug makers.

21:45

So you have to absolutely demonstrate that anything you bring to market

21:47

is really safe, you have to jump through all these tubes, you

21:49

can do mortality studies, all these really onerous

21:51

requirements. On the other hand, if

21:53

you have a disease where there's no effective standard

21:55

of care and patients are

21:58

dying or suffering greatly. from

22:00

their condition, then it makes

22:02

sense to potentially relax the regulatory burdens.

22:04

Compared to the AIDS issue, when you

22:07

had the AIDS epidemic, there

22:09

were a number of drugs that seemed to

22:11

be promising. The pharmaceutical

22:13

companies were developing at

22:15

speed, like they did with COVID, right? A

22:17

lot of companies were developing all their anti-COVID

22:19

drugs and vaccines. And the AIDS patients were

22:22

quite rightly saying that we're going to die

22:25

anyway. So we have a death sentence. You

22:27

should let us try these drugs

22:29

and see if they're effective because

22:32

the alternative is death. And

22:34

the FDA, until that time, didn't

22:37

really have a process to deal

22:39

with these differences in need between

22:41

conditions. So they would treat

22:43

diabetes similar to how they would treat

22:45

AIDS. So after a lot

22:47

of protesting by the AIDS community and

22:49

a lot of patient advocacy, the FDA

22:51

ended up speeding a number of these

22:54

compounds through development, so AZT and

22:56

DDI to the drugs. These turned out to

22:58

not be particularly effective drugs,

23:00

but you could argue that potentially by

23:03

starting off that process of defining a pathway

23:05

for how you treat AIDS, getting some drugs

23:07

on the market, getting some initial revenue, you'd

23:09

then spurred the process to iterate and invent

23:11

drugs that are now very effective. So now

23:13

there are very effective treatments for AIDS. This

23:16

initial speeding of the regulatory process during

23:18

the AIDS epidemic turned into

23:20

what became the accelerated approval pathway.

23:23

And that is for drugs that have

23:25

seen promising in disease a very high

23:27

unmet need like AIDS was, like

23:29

a lot of cancers are. Drugs can get

23:32

through regulators with much

23:34

less evidence than they can in other conditions

23:36

like diabetes where the standards might be higher.

23:38

So now what you've seen in

23:40

the industry is regulatory arbitrage, where a lot

23:43

of companies are investing in things that are

23:45

easier to get past the regulators. You don't

23:47

need to do all these really huge phase

23:49

three trials because there isn't a good standard

23:51

care, where there's not so many patients. So

23:54

you see a lot of this investment in

23:56

cancers and greater genetic diseases.

23:58

The requirement for evidence is... lower and

24:00

the need is greater. One of the things that

24:02

you write a lot about is just

24:04

the discovery process itself. And there's a

24:06

very clever video game analogy to Super

24:08

Mario and what we can learn about

24:10

speed running video games and relating that

24:12

to the process of discovering things that

24:14

might be useful in the pharma world

24:16

and the biotech world. Maybe you

24:18

flush that idea out of just your interest in

24:21

the process of discovery itself and what we can

24:23

learn from other domains like games. Yeah,

24:25

so I think if you look at

24:27

how things get discovered in the pharma

24:29

industry, it's often very

24:31

serendipitous. So you'll have

24:34

these stories of people tinkering away on

24:36

some idea they think is promising for

24:38

years and years and then eventually

24:40

that becomes a drug. So like the GLP ones

24:42

is a good example where you

24:45

had decades of the scientists at

24:47

Novo Nordisk pushing for this idea,

24:49

tinkering around the boundaries of GLPs

24:52

and trying to figure out how to

24:54

actually take this idea from just

24:57

a concept to an actual drug. So

25:00

I think one way to help

25:02

make the industry more effective is to

25:05

try and find ways of

25:08

promoting that artful tinkering

25:10

because we just haven't proven to be very

25:12

good at developing drugs

25:15

to a pre-described positioning if you

25:17

like. The really important and powerful drugs

25:19

get discovered from this bottom-up process where

25:21

you have scientists who are really passionate

25:23

about an idea, they push through their

25:25

management saying you should stop this, it's

25:28

clearly not working and they try to

25:30

find ways to work on this without

25:32

management finding out. They try to

25:34

get resources wherever they can and champion

25:37

despair, loss of opposition and then eventually

25:39

it turns into this drug that becomes

25:41

a mega blockbuster and helps a huge

25:43

amount of patients and then the management

25:45

thinks they'll say that we do it

25:47

all along or something in retrospect. But

25:49

you can't prospectively figure out what drugs

25:51

are going to be really impactful a

25:53

lot of the time. So

25:55

I think it's quite dangerous in an industry

25:57

like pharma which is so innovation. driven

26:00

and science driven to impose these

26:02

top-down, pre-described notions

26:05

of what people should invest in, you're very

26:07

likely to be wrong. Until a few years

26:09

ago, I think Novo Nordisk was regarded as

26:11

a pretty boring European

26:13

pharma company, not very innovative,

26:16

just doing insulin. But now

26:18

they're like the 16th

26:20

biggest company in the world or something and

26:22

everyone's excited about the GLPs. You would have

26:24

necessarily predicted that would come out of Novo

26:27

Nordisk, but Novo Nordisk was able to produce

26:29

GLPs because they had this really long-standing interest

26:31

in a specific area, edible diseases, they support

26:33

the scientists there and they've just been tinkering

26:36

in this area for a long time. So

26:39

to come back to the food moray

26:41

idea, that post is about this

26:43

idea that one of the

26:45

ways you might productively use

26:47

technologies like artificial intelligence and

26:49

simulation tooling is to have

26:52

an intuition about what areas of

26:55

science might be promising and then

26:57

use automation to do scalable

27:00

tinkering around that nucleus of

27:02

an idea. So the example

27:04

in Super Mario was that there is this idea

27:07

that you could do a specific type of

27:09

jump to get through a level in Super

27:11

Mario much faster than people have been able

27:13

to do so far, but no one knew

27:15

it was possible. So until a software developer

27:17

developed this tool called Scattershot, which simulates millions

27:20

and millions of Marios jumping up this level in

27:22

every possible configuration of jumps and starting positions, he

27:24

managed to figure out there actually was a way

27:26

to get up to the top of the castle.

27:28

They were trying to jump up and finish a

27:30

level much faster than previously possible. It's changed this

27:33

whole way that speedrunning is approached for that game.

27:35

Speedrunning itself is probably out of interest to that

27:37

many people, but the general principle I think is

27:39

interesting that you have this intuition

27:41

that something is possible. For Jansen, when he

27:44

was developing his drugs, he had an intuition

27:46

that you could improve this compound

27:49

pethidine to make

27:51

a better drug, but he didn't exactly know

27:53

how to modulate it to make it the

27:55

best drug it could be. He was just

27:57

tinkering around. So if you could use technologies

28:00

to automated scientists to feel the

28:02

process of tinkering, trying out solutions,

28:04

testing them, then you can tighten

28:07

the feedback loop around the

28:09

industry and maybe develop drugs a lot faster

28:11

than you might otherwise be. I think

28:13

one of the big problems with pharmaceuticals and industry is

28:16

that the iteration speed is super low

28:18

compared to something like tech. Like in

28:20

tech, if you have

28:22

an idea for a product, you can

28:25

spin up a demo in

28:27

a few days and you

28:29

can show it to some customers, you

28:31

can get some feedback on that, you

28:33

can then iterate it, layer instantly in

28:35

code effectively, and then show the next demo to

28:37

customers, get more feedback, you can do these A, B

28:39

tests at massive scale. You can move super fast and

28:41

learn very quickly. But pharma is

28:44

a very far domain to learn quickly

28:46

because everything takes so long. Clinical

28:48

trials will take around 10 years to get

28:51

everything through clinical trials. Even

28:53

do animal experiments, it takes weeks

28:55

or months to do animal experiments in certain ways.

28:58

Making all these chemicals takes a long time. You're

29:00

testing even in the very earliest stages. So

29:03

you learn at a very slow rate about

29:05

what works. And if you could

29:07

just find ways to speed that up across

29:09

the whole process, then you'll do a

29:12

lot to, I think, address some of these

29:14

problems with the pharma industry like drug

29:16

prices, just a huge healthcare

29:18

spend, things like that. I was

29:20

AI-figured to all this, obviously alpha-holed within

29:22

the mainstream consciousness. I think Jensen Wong,

29:24

the CEO of N videos, recently talking

29:27

about how much explosion of

29:29

activity there is using AI in the

29:31

world of medicine and biology. So talk

29:34

about the role that this new set

29:36

of tools might play in both discovery

29:39

and speed and tinkering

29:41

and all these different ideas. I

29:43

think I'm short-term, pessimistic, long-term optimistic

29:46

when it comes to AI for

29:48

drug discovery. And I'm much

29:50

more optimistic in the short-term about using AI

29:52

to automate some of the processes along with

29:54

drug development, not necessarily the designing of drugs

29:57

itself, but all the steps to bring a

29:59

drug to mind. market. So if

30:01

we start with drug discovery, like finding a drug, I

30:04

think it's just very difficult to see

30:06

how AI is anything more than just

30:08

another tool in the expanding toolbox that

30:10

drug developers use to produce and

30:13

test molecules. AlphaFold's an

30:15

example where now you can predict the

30:17

protein structure from a protein sequence, which

30:19

is a problem that we didn't know

30:21

how to do to a high degree

30:23

of accuracy until before AlphaFold. And

30:26

a lot of people are saying, oh, AlphaFold is

30:28

going to revolutionize drug discovery. But

30:30

in reality, it's just one little piece

30:32

of the hugely complex puzzle where sometimes

30:34

you'll have situations where you don't know

30:36

the structure of a protein you're trying

30:38

to make a drug against. And

30:40

AlphaFold can be a helpful starting point for that

30:42

specific circumstance. But

30:45

actually, AlphaFold has problems

30:47

with the very granular

30:49

predictions of the configuration

30:51

of the sites where drugs might bind. So

30:53

it's not completely accurate. So we need more

30:55

accurate models for it to be really useful

30:57

for drug discovery. And

30:59

to improve AlphaFold, we need more

31:01

data. It's just taken a long time to collect

31:03

all this data. So AlphaFold has trained on

31:06

decades of protein structures that have

31:08

been collected painstakingly by researchers growing

31:10

crystals in dark rooms. It's

31:12

definitely speeded up parts of

31:15

that process, but a very small part.

31:17

And I think the way AI is going to impact

31:19

the pharma industry looks similar to that. A lot of

31:21

little tools that are going to be very

31:24

useful and accurate, but they're going to take a long time

31:26

to deploy and figure out how best to use them and

31:28

work them into the process. One

31:30

thing I should also mention is that there's this

31:32

idea that drug discovery is rate limited

31:34

by our ability to design

31:36

molecules and design new drugs. And

31:39

that's not really true. Our ability

31:41

is much more rate limited by the downstream

31:44

clinical development and

31:46

testing these drugs and gathering information. We

31:49

need to actually justify approving these drugs

31:51

for sale and working them. While

31:53

I'm optimistic about over the

31:56

long term using things like AI to

31:58

predict what drugs might be

32:01

effective to do drug discovery and

32:03

drug candidate selection. I'm

32:05

much more optimistic about using AI in the

32:07

short term to help drugs to market faster

32:09

than they otherwise might have been. So things

32:12

like can we use AI

32:14

to help prepare

32:16

regulatory documents quicker? Can

32:18

we use AI to do things like

32:21

help companies figure out what

32:23

the markets for their drugs are and prioritize what

32:25

opportunities they should invest in? How can we use

32:27

AI to help identify patients who might be a

32:30

good fit to enroll in a certain trial? So

32:32

things like trial enrollment just takes away

32:35

too much time. So just

32:37

better tooling to identify patients who might

32:39

be a good fit for a trial

32:41

and enrolling them seems pretty impactful. If

32:44

you were just a czar of this

32:46

entire universe and godlike powers, what two

32:49

or three changes would you

32:51

make regulatory, technology-wise, industry structure?

32:53

Anything that you could change.

32:56

What would you change that you think would

32:58

most benefit patient outcomes or the overall

33:00

system? I would make regulatory

33:03

changes. I think I would want to

33:05

formalize the idea that different diseases require

33:07

different standards of evidence and then pre-publishing

33:09

the degree of evidence that we require

33:12

for each different disease. So something like

33:14

diabetes should have quite a high standard

33:16

of evidence. So you should have to

33:19

enroll very large numbers of patients and

33:21

test mortality because we have

33:23

extremely good drugs. And in other cases,

33:25

we should have a lot of diseases where our standard of

33:28

care is not as effective. We should

33:30

formalize this idea of smaller trials, maybe approved drugs

33:33

with a weaker standard of

33:36

evidence. And we are seeing some moves

33:38

towards that with rare diseases. So

33:40

you have a lot of these conditions that

33:42

are too small to really be able

33:44

to tell. They maybe have

33:46

too few patients, but they may have too few patients plus the disease

33:49

evolves over too long of a time for you to reasonably

33:52

be able to figure out the standard

33:54

drugs are really working to any high

33:56

level of confidence. So I think in those conditions,

33:58

you'll have to be able to say... Look,

34:00

we're just not going to be able to determine

34:02

this in a trial. We have to, based on

34:04

some principle of the reasons, evidence we've

34:07

collected about how the drug works in animals

34:09

and maybe some initial human testing and

34:11

some preliminary biomarker evidence, we can then

34:13

approve this drug and roll it out

34:15

and then collect information more rigorously once

34:18

on the market. Because you're just never

34:20

going to get drugs for certain diseases,

34:22

just uneconomical to achieve this vision of

34:24

hyper-personalized medicine where you're

34:26

developing a drug for everyone's individual

34:28

condition and biology without some ability

34:30

to make inferences about what is

34:32

likely to work based on early

34:34

evidence and genetics and reasoning

34:36

about the mechanism of action. So

34:39

I think the second thing I would do

34:41

is just be much more rigorous about data

34:43

collection post-marketing. So you have a lot of

34:45

drugs that go on to the market and

34:47

then they're approved and then we're not really

34:49

following up sufficiently to be able to

34:51

tell us they really work. So there's been some instances

34:53

of drugs that have got this extolibrated approval on

34:56

some fairly flimsy evidence but they seem promising.

34:58

But then the company is meant to do

35:00

a confirmatory trial to determine, okay, actually, we

35:02

have a good reason to believe this drug

35:05

is going to work but we

35:07

don't actually know that until we've collected this evidence

35:09

over five, ten years or whatever. So

35:12

companies drag their heels on doing the confirmatory

35:14

study. I think the regulators should be much

35:16

more stringent in forcing companies to collect that

35:18

data and report it and take it out

35:21

of the market. That is a combination of

35:23

being more open to approving

35:25

drugs based on a lower standard evidence when

35:27

there's no good treatments and when it's just

35:29

unfeasible to do with standard clinical trial coupled

35:31

with more rigorous data collection and then taking

35:34

things off the market when they're not working.

35:36

I think that will help increase the sort of range of learning

35:38

in the regulatory process. Surrogates as

35:40

well, I think, are really important. So

35:42

surrogates are measures like biomarkers that you

35:45

can use as a proxy to tell whether a

35:48

disease is being treated effectively or not. And so

35:50

for cancer, what you really care about when someone

35:52

has cancer is stopping them from dying but you

35:54

can get a proxy for that by looking at

35:56

the size of the tumor and it's not always

35:59

a perfect correlation. you shrink the tumor.

36:01

Most of the time, that's a good

36:03

thing. Sometimes you shrink the tumor, but it

36:05

doesn't actually help the patient survive longer in the

36:07

end. So you're exposing them to toxicity and

36:09

you're not helping them survive. So it's actually you

36:11

doing that harm with that drug. The idea

36:13

of a surrogate is that you have a

36:16

measure like tumor shrinkage that

36:19

predicts benefit down the line and that

36:21

helps you develop a drug much faster. A lot

36:23

of drugs have been, not to market, much faster

36:25

in cancer than they otherwise would have

36:27

been many very effective drugs because they've

36:29

used these surrogate markers of efficacy like

36:31

tumor shrinkage. But it's harder to

36:33

apply this principle in other conditions like

36:36

more complex chronic conditions where you don't

36:38

have an objective measure that's as simple

36:40

as does a tumor get smaller or

36:42

not. If you're treating a disease that's

36:45

eventually fatal, if you see that this

36:47

biological response happens early on, then that's

36:49

very highly predictive of improved survival down

36:51

the line. And just finding more of

36:53

these surrogates is a really, I think,

36:55

valuable activity that something like the NIH

36:58

and government agencies should invest a lot

37:00

more money in developing because as soon

37:02

as you have an established surrogate that

37:04

the regulators could use, you really speed

37:06

up how quickly you can iterate on

37:09

developing drugs. You can take it to the clinic, you can

37:11

see if it works in the surrogate, and then you can decide whether

37:13

or not to continue or discontinue based on that. You don't have to

37:15

wait five, 10 years to just

37:17

survive or not. So something like aging, we're

37:20

never going to get drugs to prolong

37:22

people's lives until we figure

37:25

out a surrogate for aging. Can

37:27

you talk about RCTs, randomized controlled

37:29

trials, and something like vitamin D?

37:31

I love the idea that this

37:33

tool, RCTs, is an incredibly important

37:35

process that's helped us learn a

37:37

lot. What, if any, limitations are

37:39

there in your mind to RCTs

37:41

and learning? So the big

37:44

limitation to RCTs is they

37:46

take time, they're complex to run, and then people

37:48

don't want to be in the control group, essentially.

37:50

So you have to expose patients to something that

37:52

they don't want to take really because you're entering

37:55

an RCT for a drug, you want the active

37:57

drug. But maybe take a step back. So the

37:59

idea behind... an RCT is that

38:02

you have patients

38:04

or people who are going to take

38:06

some intervention and you want to figure

38:08

out does this intervention really work

38:11

or not. And you have to find some

38:13

way of assigning the intervention randomly because if

38:15

you rely on anything other than randomness, there's

38:17

going to be some bias that creeps into

38:19

the study. Let's say you have this

38:21

new drug and you take it to doctors and you say,

38:23

I want to test this new drug, then the doctors

38:27

may be more inclined to give

38:29

the drug to patients who are sicker, who

38:31

really need it. And they're less inclined to give

38:33

the experimental drug to patients who seem like they're

38:35

going to recover anyway. So if you just do

38:38

that non-random allocation, you're going to see that

38:40

people who take the drug die at a much higher

38:42

rate or have a much worse illness than people who

38:44

don't take the drug because you have

38:46

this selection bias of doctors giving the drug to

38:48

people who are less likely to have a good

38:51

outcome. You need to find a

38:53

way to eliminate those biases and you can do that

38:55

with just total randomization. And so coming back to the

38:57

vitamin D example, one thing in

39:00

data and retrospective data that

39:02

looks at vitamin D levels

39:04

and outcomes is that

39:06

you see people with low vitamin D levels

39:08

have generally worse health outcomes.

39:10

So they have high rates of mortality,

39:13

they have cardiovascular disease. I think it's

39:15

probably been correlated with every bad thing

39:17

you can probably get. And there's a

39:20

natural inference there that we should just

39:22

supplement vitamin D to the people

39:24

who are low levels and then they'll have less

39:26

rates of all these bad diseases. So then people

39:28

do the trials and they do

39:30

a randomized control trial when they assign

39:33

people randomly to groups and they

39:35

give supplementation of vitamin D and then they

39:37

measure outcomes in all these things like mortality

39:39

and health. And then what they find is that

39:42

actually vitamin D has very

39:44

minimal or no effect on both of these

39:46

outcomes, even though some of the

39:49

data from retrospective analysis looks pretty strong. So

39:51

how did that happen? What

39:53

you find out is something like vitamin D

39:55

is actually a marker for poor health in

39:57

general. So vitamin D is produced by your

39:59

boss. body in response to sunlight.

40:02

And if you are ill

40:04

or poor health generally, you're

40:06

less likely to go outside or you

40:09

may be less mobile in general, so you may

40:11

get less exposure to sun.

40:13

And then you'll see the correlation between just

40:15

general poor health and vitamin D. It's like

40:17

with pushups. So they say, if you can

40:19

do less than 10 pushups, then you're much

40:21

more likely to die. Not literally the ability to

40:23

do pushups determines how likely you are to die

40:25

is just being able to do pushups

40:28

is a decent proxy marker for general health.

40:30

So RCTs are just the most

40:33

effective way we know of eliminating all

40:35

these biases that you don't know exist

40:38

from the start. Because otherwise, it's just everything

40:40

you try gets really confounded and

40:42

you don't even know how it's confounded. Can

40:45

you imagine an alternative or do you

40:47

think this will be and needs to

40:49

be the method by which we learn

40:51

truth and efficacy across medical interventions? So

40:54

I think RCTs are always going

40:56

to be the gold standard. And

40:59

I think when we can do RCTs

41:01

recently, we should do them because

41:03

they just eliminate so many of these

41:05

problems that you're just unaware of. That's the biggest

41:07

issue if you control for the sources of bias

41:09

that you think are likely to exist. There'll be

41:11

hidden sources of bias that you just can't control

41:13

for. So I think we want

41:15

to keep RCTs as a control inventory method.

41:17

But there are other tools in

41:19

these instances where RCTs aren't feasible that have a lot

41:21

of promise. So things like if

41:23

you do a single arm study, which is

41:26

just one treatment group, and you compare the

41:28

progression of a disease with a

41:30

matched digital twin of these patients, you

41:32

can train with information from natural history

41:35

data. We can make a good assumption

41:37

about the rate at which these patients would

41:39

have progressed had they not had

41:41

the drug. So there's been a few approvals

41:43

that have used some of these matched natural

41:46

history data as an alternative cohort. And those

41:48

have been in cases where there's been too

41:50

few patients to really do largely

41:52

randomized trials. And then you have

41:54

these instances of super rare diseases where you

41:57

may have just a handful of patients

41:59

in the US, for instance, is the

42:02

same possible thrown RCT and have any

42:04

reasonable statistical outcome. You need to use

42:06

things like digital twins or

42:08

surrogates, single arm trials, you

42:10

can't use RCTs. There's a lot of innovation

42:13

in this digital twin idea and natural history

42:15

cohorts. Obviously, a big driver

42:17

of all this is profits. We

42:19

talked about how people don't like pharma,

42:22

in part because they charge so much

42:24

for really valuable drugs and they make

42:26

big profits. And that bothers people as

42:28

it relates to their health. But profits

42:30

and revenue drive. The motive

42:32

for discovery and the US has always been a

42:34

leader here. Can you talk about

42:36

what you've learned about blockbuster drugs specifically? You

42:39

can define what you mean by a blockbuster

42:41

drug, but it seems like the biggest drugs

42:43

that represent, there's sort of a mini power

42:45

law here that the top handful

42:47

of drugs represent a huge percent of the

42:49

entire industry's revenue. So talk about the business

42:51

side here, the distribution of revenue, the role

42:54

of blockbuster drugs, the good, the bad, the

42:56

ugly. Love to hear what you've learned there.

42:58

Yeah. So a blockbuster drug is a drug

43:00

that makes a billion dollars in annual revenue.

43:03

So blockbusters are really have an

43:05

outside influence in the pharmaceutical industry

43:07

because they account for a

43:10

huge proportion of the overall revenue

43:12

of the industry. Well, the way

43:14

the economics operate is pretty analogous

43:16

to venture capital and these long

43:19

tail models where you have many

43:21

losers who don't make much money or don't recoup the

43:23

money that has invested in them and

43:25

a few really huge winners that will go

43:28

off and generate billions of billions

43:30

and billions of dollars and pay for all

43:32

the failures many times over. So the industry

43:34

is in many ways worth

43:36

investing in for investors and

43:38

for companies because there's always this potential

43:41

of hitting it really big and having a mega

43:43

blockbuster like Humira or Kritrudar, the COVID vaccines that

43:46

generate tens of billions of dollars in revenue a

43:48

year. Because if you look at all the drugs

43:50

that are getting launched, it's sad that you have

43:52

all this work that goes into developing a drug

43:54

and it takes me 10 years to

43:56

get it to market or 12 years to get it to market. And

43:59

it... launches and then it stops. 55%

44:03

of drugs that launch, that get to the left process,

44:05

they don't even recoup enough money to

44:07

pay the average development cost of

44:09

a drug. And then you

44:11

have a very small number of drugs, blockbusters, that

44:14

30% to 40% of revenue of the

44:16

whole industry is made by these blockbuster drugs, which

44:19

are quite a small fraction of all

44:21

drugs. But when I look at the numbers, something

44:23

like 170 blockbuster

44:25

drugs that were actively

44:28

generating revenue, and then the

44:30

whole long tail of other drugs is

44:32

making very small sums relative to blockbusters.

44:35

So because the industry operates like

44:37

a lottery type model where you just want

44:39

to really hit a big, you have a

44:41

venture capital, it distorts all the incentives. So

44:44

you see a similar thing in

44:46

biotech investments that you do with VCs

44:48

where the farmer is only really interested

44:50

in developing a drug if it can

44:52

potentially become one of these blockbusters. Because

44:54

anything less than that is just unlikely

44:56

to recoup the investment. So you get

44:58

a lot of these small markets that

45:00

patients really would like to have drugs

45:02

for. They could be really beneficial and we

45:04

know that we could potentially develop a drug against this

45:06

disease. We have a good understanding of

45:09

the mechanism, like many small genetic diseases, but it's

45:11

just not worth big farmer's time or even biotech's

45:13

time to invest in developing these drugs because the

45:15

economics don't work out. Even if

45:18

you get to the whole process of developing this drug and

45:20

it works and you get to clinical trials, you

45:23

maybe make a few tens of

45:25

millions a year and that's just absolutely not worth it because it

45:27

will cost you 100, 200, 300

45:29

million just to get the drug through the whole development process.

45:32

So there's a lot of, I

45:34

think, alpha that could be unlocked

45:36

in making the process much cheaper

45:39

in how drugs are tested and validated because

45:41

it would mean that it's actually

45:43

worth developing all these drugs that are not

45:45

worth developing. What do you

45:48

think about drug pricing as a key variable

45:50

in all of this big equation that blockbuster

45:52

drugs are driven by a price times an

45:54

amount? What drug pricing sometimes comes up

45:56

all the time as predatory

45:59

or strange or... or the US system

46:01

subsidizing a lot of the rest of

46:03

the world. We make an outsize percent

46:05

of the discoveries and you want to be

46:07

compensated for that. It seems like a very complicated equation.

46:10

So what, if anything, have you learned

46:12

about drug pricing that you feel is different than

46:14

the norm or how would you change things? So

46:17

I think drug pricing is truly difficult. One thing I

46:19

guess to say about the US is the US has

46:21

by far the highest prices in the world. I think

46:23

people recognize that. It's about twice as high as Europe

46:26

on a net price basis. And the

46:28

US market for drugs is something like

46:30

40% of the global market and it's something like 60%

46:33

by revenue of newer drugs. So drugs

46:35

launched in the past 10 years or so. So

46:37

the US, it's kind of where outside share of

46:40

the revenue that drug companies make. And part of

46:42

that is because the US system is, for the

46:44

most part, some of this is changing, but it

46:46

is mostly a free pricing system where you can charge

46:48

whatever you want. So when you're pricing

46:50

a drug for the US market, you'll often, as

46:52

a company, just try to charge whatever you think

46:54

you could get away with, what if you think the market

46:56

will bear. And in a reasonable world where

46:58

all the incentives are aligned, what the

47:00

market will bear is close to the actual

47:03

value of that drug. In healthcare,

47:05

you have all these strange incentives in markets that don't

47:07

function properly. But in the rest of the world, you

47:09

have systems where you

47:12

have governments doing the

47:14

negotiation on behalf of the population.

47:16

So they'll make assessments of the value

47:18

of a drug based on metrics like how

47:20

many quality adjusted life years does this drug

47:22

give us, what's the benefit of this drug

47:24

over the existing standard of care, and how

47:26

much better is it, and how much for

47:28

price premium can we give it on the

47:30

existing drugs. So both systems have flaws. I

47:32

don't think it's possible to find a perfect

47:34

system to satisfy everyone. If you look

47:37

at the profit margins of drug makers, they make

47:39

something like 10% to 20% profit

47:41

after you take out the cost. And the growth margins are something like

47:43

80%. Really,

47:46

that's a distorted picture of the

47:48

actual profitability of

47:51

drug companies because the distortions between

47:53

when R&D expenses are paid and when the drug revenue comes

47:56

in. So it's not necessarily a good picture of how much

47:58

of a profit you take out of the drug. drug

48:00

companies getting for their investment. And if you look at

48:02

how much drug companies are getting for their investment, actually,

48:05

most of them are pretty near zero or very

48:08

low return on invested capital. The

48:10

industry as a whole is not that great of a business. It's

48:12

very much like a lottery model that

48:14

is just type of lottery that's

48:16

attractive to people with biomedical PhDs,

48:18

where most people are not

48:21

making that much money, but

48:23

a small number of drugs and companies are

48:25

making supernormal profits. And you can

48:27

point to the examples of people

48:29

who have very high prices and making supernormal

48:31

profits like Humira, 20 billion seems

48:34

like an incredible amount of money for a drug

48:36

to make. And I think if you

48:38

look at that isolated example, you can say,

48:40

just seems unreasonable that anyone is making 20

48:42

billion off this drug per year. It's way

48:45

more than what someone should reasonably

48:47

get for producing this drug. But you

48:49

have to think more about the system

48:51

of incentives. You want to be worth

48:53

investing in developing drugs. And so the

48:55

incentive needs to be very strong. And

48:57

because such a small number of drugs

48:59

produce so much of the revenue, like

49:02

a real Pareto distribution in revenue,

49:04

you need these lottery winners

49:07

to make it worthwhile investing in the

49:09

system, which is an

49:11

unfortunate reality that because drug development

49:13

is so inefficient, you need high

49:15

prices. And you need these

49:17

superblockbusters to make the economics work out.

49:19

When you're doing R&D, you're paying money

49:22

that you have now for revenues in

49:24

10 years. So from a temporarily discounted

49:26

point of view, you need to think

49:28

you're going to get a really huge

49:30

amount of money in the future

49:32

to be worthwhile you paying hundreds of millions of dollars

49:34

in the next few years. I think

49:37

it's a really difficult problem. If you put

49:39

pressure too much on drug prices, you can

49:41

very easily remove the incentives people to invest

49:43

in the industry. Quite a fragile ecosystem that's

49:45

built up once you start cutting down the

49:47

tall poppies, you actually

49:50

are removing a lot of

49:52

the incentives to even develop any

49:54

drugs. And there's just a few things that

49:56

you can do outside of damage by cutting

49:58

down even just a small

50:00

number of these huge lottery winners,

50:02

if you like. You have to accept a bit of

50:05

a trade-off where you are trading off

50:07

innovation for how much you're spending on

50:09

drugs. What about prevention?

50:12

We've talked entirely about

50:14

interventions where something bad happens

50:16

in the body and we've

50:18

developed ways of treating that thing or making

50:20

it better. What about similar

50:23

research that could go into, whether

50:25

it's lifestyle or other things that we

50:27

do before these bad things happen to

50:30

us that would prevent them from happening

50:32

in the first place? Any thoughts

50:34

on that side of the ledger? Yeah,

50:36

I think prevention is difficult. People

50:38

in the US often say that the

50:40

reason why the US doesn't invest in

50:42

prevention is because a lot of people

50:44

are on these employee insurance plans and

50:47

there's a lot of insurance plan turnover.

50:50

So any insurance company that invests in

50:52

prevention for its members is unlikely to

50:54

reap the benefits of those

50:56

investments when they switch to another employer and

50:58

get a different plan. But the

51:01

problem is that if you look at other countries

51:03

that do have these nationalized healthcare systems like the

51:05

NHS, other European countries, they actually don't

51:07

invest in that much in prevention either

51:09

even though they should in theory because it's a

51:12

whole nationalized health system. They should be

51:14

wanting to invest in those things. So is the

51:16

problem really that there's a lack of supply of

51:19

preventative treatments or is it really there's a lack

51:21

of demand for preventative treatments? And

51:24

I feel like the issue is probably more

51:26

on the demand side where people unfortunately don't

51:29

have a strong demand for preventative

51:31

medicine and they don't take up

51:33

preventative medicine in many cases when

51:35

it's offered to them. So it's

51:37

the whole thing with the argument

51:39

about othempic and why don't

51:41

people just practice diet and exercise. That's

51:44

a more sustainable way of dealing with obesity. The

51:46

reality is that people just don't do that. So

51:48

they would rather just take othempic when

51:50

they are obese and have that treat

51:52

their obesity. So I think this whole

51:55

consumer attitude shift that needs to happen

51:57

before preventative medicine ever becomes really established

51:59

needs to be demanded. So you think

52:01

about like the healthcare system, who is really the

52:03

end consumer of pharmaceuticals and healthcare system? I think

52:05

you can make the argument that it's actually healthy

52:08

people who are enrolling in the plans and are

52:10

paying the majority of the money that goes into

52:12

the system. And they want that

52:14

system to represent value for money for when they

52:16

do get sick. So you have to have

52:19

the people who are healthy have

52:22

a greater demand for products of

52:24

prevention that then incentivize companies

52:26

to invest in them. And that's a really important

52:28

thing to do is to provide the services. I'd love

52:30

you to talk about two extremes. The

52:32

things that have you in this entire

52:34

world, the most excited for the future,

52:36

and the things that have you the

52:38

most worried. I think what I'm

52:40

most excited about is not a specific technology,

52:43

but more this Cambrian explosion

52:46

if you like those modalities

52:48

and biotech. So

52:50

for a while, I think

52:52

back maybe 10, 20 years ago,

52:54

there were that many different types of different treatment

52:57

classes. And since the rise

52:59

of biotech, you have all these different ways

53:01

of treating disease that are becoming established and

53:04

have a lot of potential. So you

53:06

have CRISPR, RNA interference, these CAR T

53:08

cells we talked about, base editing, different

53:10

variations of CRISPR. Our ability to control

53:13

our biology is getting much

53:15

better than it was even just a

53:18

few decades ago. So

53:20

this increase in our ability to control

53:22

our own biology and intervene in biological

53:24

systems and very precisely

53:26

use these molecular scaffolds to push

53:28

a system into a desirable state

53:31

is really interesting. And just

53:33

this flywheel of improving on the

53:35

modalities that we do have. So

53:38

you look at something like CRISPR, we just got our

53:40

first CRISPR-approved therapy in the UK and the US. But

53:43

CRISPR is already in some ways becoming a bit of an

53:45

outdated technology. You have a lot of investment in what's next.

53:47

So instead of making cuts, you're going to have these

53:50

base editors that are changing specific

53:52

letters in the DNA sequence. And

53:55

that's a more effective way of precisely treating

53:57

certain genetic diseases than making cuts at CRISPR.

54:00

cuts. And then maybe after these base editors you

54:02

have prime editing, it's even more versatile in the

54:04

type of edits it can make. And you have

54:06

different types of editing. So just a lot of improvements

54:09

in tooling for how we intervene in

54:11

biological systems to treat diseases and

54:14

seemingly like an increase in the rate

54:16

at which we're developing these tools and

54:18

applying them to the clinic to solve

54:20

biological problems and to treat diseases. Like

54:23

general explosion in technology, our ability to

54:25

control systems is really interesting. I'm interested

54:27

in just general new business models of

54:30

the industry. So one

54:32

trend you've seen over the past, I

54:35

think it started probably in the 2000s,

54:37

is this greater externalization research.

54:40

So pharma companies, big pharma companies used to do

54:42

a lot of research in-house. They've

54:44

mostly externalized that to smaller biotechs

54:47

that are venture capital funded. So

54:49

the big pharma companies are really

54:51

just like commercialization machines all the time. And

54:53

they do maintain some research labs, but the

54:55

purpose of the research labs is in many

54:57

cases just to validate external opportunities and test

54:59

them in-house and have the expertise to actually

55:01

meaningfully evaluate these external opportunities they're going to

55:04

bring in. Maybe you'll see greater and greater

55:06

externalization of more and more functions. Now

55:08

we have innovation R&D externalized to

55:10

little biotechs. We have running

55:13

clinical trials being externalized to these

55:15

clinical research organizations that

55:17

specialize in that. You can argue whether that's been

55:19

a good thing or not, but it's just a

55:21

hard externalization trend and you have more and more

55:23

just pieces being externalized. Every commercial analysis

55:25

will be externalized in the future as

55:28

well. So you may end up with

55:30

pharma companies just being very specific IP

55:32

holding companies, plus just

55:34

managing the finances of actually selling and distributing

55:36

these drugs. Every little piece of

55:38

the ecosystem is focused on some specific component

55:40

of the process and maybe that

55:42

will help to be more efficient. So I'm interested in

55:45

that. I think you can argue that it

55:47

may not be helpful. Externalization hasn't been completely helpful.

55:49

There's been some trade-offs there. But there's

55:51

a lot of redundancy in the industry. And if

55:54

you're in a biotech who's getting ready to

55:56

launch, people having to build up redundant capacity

55:58

to run and execute trials in an efficient

56:00

way. And then what I'm worried about is

56:03

there's a genuine worry that it's

56:05

not going to be worthwhile

56:07

for a lot of companies to continue

56:10

to invest in developing new drugs. So

56:12

I'm worried that if we don't get

56:14

the balance, rewards innovation and incentives to

56:17

innovation, we'll end up with a system

56:19

where companies will decide, okay, I'm not

56:21

actually incentivized to really invest in doing

56:24

meaningful fundamental research and spending 30 years

56:27

tinkering on some opportunity that may

56:29

eventually bear fruit. I'm actually just

56:31

going to market the drugs I already have

56:33

that are old. I'm going to invest in

56:35

drugs that are very hard to copy. So

56:38

even though once they do go generic, no one's going

56:40

to be able to copy me. So things like these

56:42

radio, radio fair treats, talk about Cartes, they

56:45

have a whole process of sojourn with them, so they're

56:47

quite hard to copy. And then you're just going to

56:49

sit on existing treatments and try to milk

56:51

them as much as possible and use

56:53

tricks and techniques to extend the patent life

56:56

and make it hard as a copy and

56:58

try and extract as much value for as long as possible. And

57:00

maybe not invest in this fundamental

57:03

research that really drives meaningful products

57:05

and has this element of unpredictability to it.

57:08

One thing I think is quite dangerous in

57:10

the pharma industry is just this over-reliance on

57:12

an accounting type mindset. Rather than a recognition

57:15

that there's a science-driven industry, there's

57:17

an innovation-driven industry, the commercial

57:20

parts of big pharma should be in

57:22

service to the R&D portion of the

57:24

organization or the ecosystem. And

57:27

you should really be hiring good

57:29

people with good intuitions about what's worth

57:31

developing and letting those scientists tinker

57:33

for as long as they need to on some

57:36

of these really challenging ideas at the forefront of

57:38

what is possible and give them enough time, 10 or 20

57:40

years, for these ideas to mature into

57:43

actual products and then you can commercialize them.

57:45

And if the returns innovation are down too

57:47

much and maybe you get too many fully

57:49

managerial positions who are focused on the accounting

57:51

aspect of it, there's one of milk-existing products

57:53

and copy the big ones that we don't

57:55

have an existing market, then you're not going

57:57

to get a kind of meaningful process like...

58:00

like Keytruda, these immuno-oncology drugs, the GLP-1

58:02

agonists that we're seeing now. So

58:04

yeah, that's one thing I worry about. So just a

58:06

totally fascinating overview of one of the most

58:08

interesting parts of the world and of the

58:10

business world, of just the technology and innovation

58:13

world. I've so appreciated everything you've written and

58:15

sharing so much here with us today. In

58:18

these interviews, I always ask the same traditional closing question.

58:20

What is the kindest thing that anyone's ever done for

58:22

you? I think this has a

58:24

pretty obvious answer in my case. Many

58:26

people have done many kind things for me, right?

58:28

I think I just have to go with probably

58:30

what is the most common answer, which is my

58:33

parents have always been an unending well of support.

58:35

And they bailed me out of many low points

58:37

in my life. So I'm very thankful for that.

58:39

And now while other people have done me many

58:41

kindnesses, nothing really compares to what

58:43

my parents have done. Alex, thank you

58:45

so much for your time. Thanks. If

58:49

you enjoyed this episode, check out

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59:28

Thank you.