0:00

I'm Matt Pillar , host of the Business of Biotech podcast

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but not seeing my face , maybe you

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haven't heard that we've launched a new Business

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for the flagship pioneering community

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. It's a veritable breeding

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ground for biotech innovation and

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the organization has given us generous access

0:52

to its partner CEOs itself

0:54

a who's who of biotech entrepreneurship

0:57

. Less than a year into

0:59

his role as CEO at Ampersand

1:01

Biomedicines , Jason Gardner

1:03

offers a case in point . He

1:06

joined Ampersand on the heels of

1:08

his leadership of the rise and ultimate

1:10

wind down of Magenta Therapeutics

1:12

, that tenure preceded by

1:14

a lengthy career that started at Chiron

1:16

and saw a long stay at GSK

1:19

. Jason's seen some

1:21

things , experienced the highs and

1:23

the lows , and come back for more , demonstrating

1:26

the kind of fortitude required of a leader

1:28

in this tough business . At

1:31

Ampersand , he's working on a tech-driven

1:33

platform that aims to redefine how

1:35

we identify local precision

1:37

medicine targets and develop navigable

1:40

therapeutics to address those targets

1:42

pluggable therapeutics to address those targets . I'm Matt Piller , this

1:44

is the Business of Biotech , and

1:51

I was honored to sit down with Jason in San . Francisco

1:53

to discuss this heady stuff and a whole lot more . Let's give it a

1:55

listen . Jason Gardner , CEO at Ampersand . Before we

1:57

get into learning about Ampersand , I

1:59

want to rewind a little bit and get to

2:01

know you . It's

2:03

always interesting to me when an

2:05

exec leaves the

2:07

at least perceived comforts

2:09

of a company like GSK , where you were

2:12

for 11 years to

2:15

jump into sort of a biotech . You

2:18

did that , notably leading Magenta Therapeutics

2:21

, after 11 years at GSK . So

2:24

the fundamental question is what motivated

2:26

you to make that move ?

2:28

Yes . So first of all , my clients would have been

2:30

no my pleasure to be here . So

2:33

, reflecting back at that time and that decision

2:35

, there were really three memories

2:38

we had . So first , the science

2:40

the science of transplant

2:43

medicine was really coming

2:45

together , with a number of academic

2:48

labs and new programs

2:50

coming out . It was a great opportunity to

2:52

potentially build a company around it

2:54

. Second was the

2:57

impact of the patients . So transplants

2:59

are curative . Transplants

3:02

are really not done as widely

3:04

as they could be because it's quite hard for

3:07

patients to go through it . The initial transplant and swab

3:09

regenerative was built to solve with

3:11

this new site . And the third

3:13

was the team people . So

3:15

one of the people who's co-founded with me

3:17

was my boss

3:19

post-op at

3:21

Harvard , david Skadden , and then

3:23

both Atlas Ventures

3:25

and Third Rock Ventures were interested in

3:28

science , in building center

3:30

companies , and so we were

3:32

able to bring them together and build

3:35

one company and that became Magenta

3:37

Therapeutics . So that combination

3:39

and we'll talk a bit more about that in

3:41

the future , a future decision-making In

3:44

the science , the impact on patients and

3:46

the people to me it was very important

3:48

and so , even though I wasn't looking to leave GST

3:51

, that was the end of the stream of motivation

3:54

.

3:55

There's got to be and I ask

3:58

this question often and sometimes I get

4:00

honest there's

4:02

got to be , you know , at least some

4:04

degree of trepidation when

4:06

you make a move like that . I mean people

4:08

spend their entire careers at companies like gsk

4:10

, comfortably especially in

4:12

leadership positions like you are in . I mean , yes

4:14

, you know it wasn't like there was the end

4:17

of a career trajectory there , right ? So

4:19

walk me through that , like speaking to uh

4:21

folks who may be considering making such a move

4:23

how do you ? how do you ? rationalize it

4:25

. And how did your wife take it , jason ?

4:30

so so I've been in biotech

4:32

before gsk , spent eight years

4:34

first here at chiron

4:36

and as a junior

4:38

scientist and then at bridger's okay

4:41

in new york the most senior site , so

4:43

I knew a bit about Vita and life

4:45

in a smaller company and again

4:47

I'd be very happy and I had great

4:50

opportunities at GSK . But this was

4:52

a moment for me to really look at those

4:54

three principles the science , the

4:57

impact on patients and the team and I just felt

4:59

doing that in a new company

5:01

startup environment would be impactful

5:05

and also a challenge to

5:07

me personally in the next chapter of my career

5:09

, so

5:11

that dinner table conversation

5:13

with my wife was

5:16

very interesting

5:18

and I got very clear

5:20

feedback that if this was the

5:22

moment , this is what I should do . Yeah

5:25

.

5:27

So , yeah , the support is

5:29

fundamental .

5:31

And you're incredibly . Can I just double click on that ? So

5:33

when I was building Magenta

5:36

, and then similarly now at Appas and I talk

5:38

about that we got employees

5:40

. Our employees , families and their

5:42

friends and their networks are really part of our extended

5:45

team and we rely on them

5:47

. Sometimes , you

5:49

know , when the days are long and there are ups and

5:51

downs and twists and turns in our industry

5:53

and our work . It's often

5:55

our families and our friends who provide that support . So

5:57

I always thank our

5:59

team members to ensure the big

6:02

bounce to the to that

6:04

team , that extended team , yeah , yeah .

6:08

Another transition that I wanted to get

6:10

your perspective on would be I mean , you mentioned

6:13

that you started out as a . You're

6:15

a research scientist , yes , yeah . At

6:18

what point along the road

6:20

did you begin to develop

6:22

an interest in , in leadership

6:25

and business development ?

6:26

and , yeah , so basically

6:29

I , you know , I went into biotech and into the industry

6:31

principally because I loved science

6:34

, but I really wanted to make medicines and

6:36

then I just thought that was the best way to do

6:38

that . Um , and so learning

6:41

the science of the industry , which is really

6:43

the foundation of our industry , was

6:45

terrific . We were talking

6:47

about how it became medicine , so

6:49

I was very fortunate in that world with great teams

6:52

. And then , naturally , I

6:54

was fortunate to get leadership opportunities , leading

6:56

projects first and then leading teams and

6:58

then building teams and then building units

7:00

over the years . And

7:03

then the business side just came along . So I

7:05

started setting up budgets , setting

7:07

up partnerships , knowing how to do

7:09

that , learning , adapting and

7:12

thinking strategically . And

7:16

then , in Sioux , I learned a lot of the business from people who I worked with , from

7:18

my doing and delivering , and

7:20

I think that's a very important way to learn a lot

7:22

in school and a lot both

7:25

on the science and the business side , but doing

7:27

that to practice and doing that with

7:29

great people who you can learn from

7:31

, and being humble . Being humble

7:34

and also knowing that everything we do here in

7:36

the industry is as a team , being

7:39

able to communicate well , listen

7:41

, learn , ask . It's been

7:43

. I think I've been very lucky to

7:46

have those opportunities and I'm excited

7:48

. Oh , we're doing that , yeah .

7:51

Yeah , very good . Was there a particular

7:53

point where you

7:56

know , as you assume those responsibilities

7:59

, those leadership responsibilities , was there a particular

8:01

point , where was it ? You know a

8:03

light bulb moment , like I want to run

8:05

the show .

8:07

Yeah , so that's an interesting

8:10

question . I

8:13

believe in

8:15

, you know , my values

8:17

will be around investing potions

8:19

and I was using science to do that in

8:22

a leadership role . That was in

8:24

battle and

8:28

then to me if I could make more of it . I never

8:30

woke up one day and thought I have to be a CEO , I

8:32

want to be a CEO . It was more of

8:34

a natural progression

8:37

and

8:44

for me , I was also very fortunate to have a number of influential

8:46

mentors and still do in my career , both at GSK , both beyond

8:48

GSK and until today

8:51

in the venture world and in the biotech

8:53

world . I believe

8:55

firmly that those types of experienced

8:58

mentors , those folks who have

9:00

been CEOs or been leaders , provide

9:03

a huge amount of feedback into

9:05

our system , even though they're not

9:07

the most operational roles

9:10

. That's incredibly important . You look at the diaspora

9:12

, certainly in the Boston biotech

9:15

community , of companies that have

9:17

become great companies and

9:19

team members who have gone on to do great things

9:21

in other companies and then beyond

9:24

. It's an amazing network

9:26

, yeah , and it's one of the reasons I believe

9:28

the venture

9:30

firms and the investors like Blacksheet

9:32

, have talent-based

9:36

networks to be able to access and tap

9:38

into that , and so , as

9:41

a CEO , I remember

9:44

one of my mentors , right took

9:47

the role . Well was recreated and profounded . Magenta

9:49

told me the CEO

9:51

is the loneliest job in the world . It

9:54

is the time where you find out how

9:57

you can make hard decisions

9:59

. But it doesn't need to be . You

10:02

have to have an experienced board , a team

10:04

around you and then so see , those networks can be

10:06

very helpful .

10:09

Did you take advantage of any formalized

10:11

network opportunities

10:13

, or was it more

10:15

of an informal ?

10:17

Oh no , it's more informal and

10:20

I would say , you know , I think folks

10:23

have been very generous with that time , which , with

10:25

the the years , and I also like to

10:27

network with people as well and share experiences

10:29

. And in today's

10:31

flagship world as well , there is a very

10:34

vibrant cross-CEO and

10:36

cross-company within

10:38

the flagship ecosystem

10:41

. Right , lots of sharing of experiences

10:45

and ideas as well . It's

10:49

a great model . We'll talk more about it Company

10:51

creation . It's also a great model of

10:53

network for company building and

10:56

company execution as well . That interplay

10:59

Sure . So

11:01

I think networking and the networking skills and I tell

11:03

my children who are teenagers about

11:06

that and what you learn in college is going to be

11:08

important . What you then do with that is going to

11:10

be important in your careers . But who and

11:12

how you work and building

11:14

those networks over the years is

11:17

going to be important for whatever career

11:19

choice you're in , particularly

11:21

in finance .

11:22

Entirely . I mean , I could go along on stories

11:24

about this . My son just started , uh

11:26

, college this past fall , you

11:29

know , and he's he's of this mindset where he just

11:31

thinks he has to have everything mapped out and

11:33

I'm like , but you can map it out all you want

11:35

, it's not going to play out the way you but , but

11:37

it's it's who , what and and

11:39

how right you interact . So , um

11:42

, I want to get the get

11:44

the story behind your arrival on

11:46

the scene at flagship . So you're , you're

11:48

only six or so months on the

11:50

scene right now . Five , five , okay

11:53

, so not even six yet , yeah , so

11:55

so new there . Uh , you joined flagship

11:57

sort of on the heels of magenta's wind

12:00

down yes , that'd be the appropriate way to put

12:02

it . So how did that come to pass ?

12:05

Yeah , so basically I was thinking

12:08

about my next role and the

12:10

way I could impact patients and science

12:12

and really bring my

12:14

experience to an organization

12:16

. So I got to do the flagship team

12:19

. I had several members that were

12:21

ready to pull beyond the EU Medicine

12:24

was a great capacity as well the

12:26

rest of the folks and

12:29

we started a conversation about

12:31

you know what could be

12:33

an interesting opportunity for

12:36

somebody to dive in on . And

12:38

I got to know a back of Asia

12:40

and the Ampersand team

12:42

and I

12:45

went on the cda . Uh , I

12:48

read the website and I listened

12:50

to lots of the flagship uh

12:53

interviews , uh on youtube

12:55

as well . So really don't understand about

12:57

what it's like to work , that good diligence

12:59

as folks , people . But I went on the

13:02

cd and I put her and saw the

13:05

under seven data , the science

13:07

, and then it's blown away and I

13:09

thought this is what I have to do next and what's

13:11

wrong if it's about what company's doing . Yeah , the

13:13

platform . But I really wanted to

13:16

build a platform company

13:18

. Just the business optionality

13:20

, I think the opportunities of ships

13:23

would we've talked about that In this

13:25

current capital markets . It's

13:27

very powerful . I

13:30

like the impact of this platform on how it's

13:32

an architectural and then the team

13:34

. So the team was terrific

13:37

and the flagship model and the expertise

13:39

the flagship raised , so it checked

13:41

those three boxes for me and

13:44

so it was a very , in the end

13:46

, straightforward decision . I'm about to say the

13:48

dinner table conversation this time , and

13:51

then I have to rely on my wife . It was

13:53

, uh , very straightforward yeah , very

13:55

nice .

13:56

Yeah , half percent formed up officially

13:59

in 21 . Yes

14:01

, so , uh , obviously

14:04

it was before your arrival on the scene

14:06

. But give me a little bit of the backstory

14:08

into how and why .

14:11

Yeah , so this is really , I think , very important

14:13

about the flagship model . So

14:16

the science comes from flagship

14:19

explorations into emerging

14:21

areas of big

14:24

breaking science . So three years ago , if

14:26

you wind the clock back , was

14:33

really when big biology , big data , ai was starting to emerge

14:35

on super biotech . So

14:37

flagship creates companies

14:39

around . Audacious questions right . Questions

14:43

that other investors

14:45

would look at and ask

14:48

why we've asked that question Right . And

14:50

so you know , that

14:53

can be incredibly powerful , thinking

14:58

about what could happen next . And the question

15:00

for Amberson basically was what

15:02

if we could

15:04

program smarter medicines

15:07

to act and work only

15:09

where they're needed in the body , right ? So

15:12

this is a question that has challenged

15:14

the industry since the start . We

15:18

make many medicines that

15:20

work and we give

15:23

them to patients . The vast majority

15:25

of them do not specifically target

15:28

exactly where they need to be . So I'll give you an example

15:30

Steroids Steroids

15:33

widely . There's a neutral information Steroids

15:37

work . Steroids work across lots of tissues . It

15:39

causes lots of side effects and we wish

15:41

we could just target them specifically to where they're needed

15:44

, right . So there's lots of other types of medicines that

15:47

could also apply that . So with that

15:49

question , how do

15:51

you do that ? Well , with big data

15:53

and AI starting to emerge , three years

15:55

ago , basically , they built the company

15:58

around this challenge of creating

16:00

an addressed map , molecular

16:02

anatomy of the human body

16:05

, tissue , cellular

16:07

and protein level , understanding

16:10

all the potential targets that

16:12

you could then use to send medicines

16:15

exactly to . Whatever

16:17

you do , it programmed biologics

16:19

. So basically they're going to drug

16:22

and say it's in the kidney

16:24

and then make

16:26

an antibody just to bind to that

16:28

address and then attach

16:30

the antibody to an active drug that

16:33

you then send to the kidney and it only works

16:35

in the kidney . You already know the drug's active

16:38

, maybe an approved drug , right . But

16:40

it could be a drug that's already been there suddenly but didn't work

16:42

out because it didn't have the right potency levels

16:45

or it didn't have the safety effects . But you can then

16:47

apply this address map across

16:49

very different tissues and diseases

16:52

right as a platform . So

16:55

that was three years ago , in the first

16:57

year , starting to assemble all these publicly

16:59

available data sets , start learning algorithms

17:02

to to particular targets , and then

17:04

, over the next two years , developed

17:07

even bigger data sets , both public

17:09

as well as proprietary data

17:11

sets , probably in the flagship ecosystem

17:13

, and then started to generate

17:16

data because the algorithms

17:18

and the machine learning can actually design the molecules

17:20

on the computer Such

17:23

that you can then test them on the computer

17:25

in the machine learning

17:28

and then you can test

17:30

a handful in the lab and you can do

17:32

that within months , which

17:34

would normally take years and

17:36

frankly , would

17:38

still have a high risk of whether the biology would

17:40

work . And

17:43

I'll just take a step back Today

17:45

at the start of clinical

17:47

development . 90%

17:49

of the drugs start

17:52

clinical development with FAPE Right . So

17:54

imagine if you're a sports

17:56

player , right , and you're at 90%

17:59

or 10% batting

18:01

average of the baseball player right , it just wouldn't

18:03

work . So one

18:05

of the reasons that that number is so

18:07

high , the federated rate , is because our ability

18:10

to select the targets and send those to the

18:12

right places , the biodistribution of our medicines

18:14

, has never been possible

18:17

to make it precise a program without

18:20

this type of platform , possible to make

18:22

it precise a program without this type of platform . So that was the that really . I mean the foundational

18:24

science in this platform

18:27

, the address map , machine

18:29

learning , the algorithms , the ability to get started

18:31

to drugs within months , not years . And

18:37

then I started to look at the data and the early projects and talk about someone

18:39

and it was really exciting , and so

18:41

I knew this would be a big story . The vision

18:44

and the vision that you could actually

18:46

show and understand that it was working

18:48

very quickly , even in pre-clinical

18:51

studies . And then the opportunities across all

18:53

sets of diseases , the opportunities

18:55

for partnerships and , I think , the opportunities

18:57

to make a massive impact that would

18:59

not have been possible before .

19:00

Yeah , I think the opportunity to make a massive impact that would not have been possible before . Yeah

19:02

, when you started talking about

19:04

sort of the idea and

19:06

you said the

19:09

investment community , the traditional investment

19:11

community , might you know , look

19:13

at that wide-eyed right , raise an eyebrow

19:15

, perhaps Flagship strikes me

19:17

as interesting in its

19:19

commitment to

19:21

the exploratory . I don't know , there's probably

19:24

a more eloquent way of saying that , but it's

19:26

unique and I wonder how it's rationalized

19:29

, like , what's your perception on that ? How

19:31

do you rationalize

19:33

funding , especially

19:37

in this economy when there are so many , so many

19:39

uh vcs ? Are

19:41

, you know , tranching deals

19:43

based on outcomes , civic

19:46

milestones , like and if it doesn't happen

19:48

, it's , it's over ? Like you're not . You're

19:51

talking about putting significant resources

19:53

behind big concepts

19:56

. I'm just curious how , like what ?

19:58

how would you actually so it's

20:01

a great question and from my perspective

20:03

, it really is best

20:06

answered in through

20:08

the lens of the platform . Do you really believe

20:10

you have a platform and you're building a platform

20:12

company , or are

20:15

you really interested in building an asset in

20:17

a company with one or two assets ? So

20:19

in order to have a platform , right

20:22

, I believe as a business , you'd

20:24

have to set out

20:26

milestones to

20:28

demonstrate that you have a platform . So what does it take

20:31

to build a platform ? And

20:33

this was interesting as I got to know Flagship and

20:35

got on the CDA and I remember

20:37

visiting Ampersand , talking

20:39

to some of the scientists . It

20:41

was really great , very exciting . And then

20:43

that night one of

20:46

the flagship recruitment team members called me and said

20:48

hey , jason , could you come in to flagship tomorrow ? We'd like

20:50

to give a presentation on the efficient For the

20:52

company . We'll talk about platform building approach

20:54

with Archer . So

20:56

in the CEO

20:59

world , you have to be ready to hustle

21:01

and respond and react . So

21:03

we had that conversation I remember talking

21:05

about and Flagship

21:07

, the Flagship partnership , are , you

21:09

know , one of the ultimate platform-building company

21:12

groups . So

21:14

showing that you have a platform , that you can

21:16

do this multiple times , that

21:18

you can do it on demand , right

21:21

, and you really can do

21:23

something that's not been done

21:26

before . And then if

21:28

you really… that's principle one

21:30

Are you going to link back on company show

21:33

that you can do it multiple times From

21:35

a preclinical perspective . Do you have the right

21:37

data set ? Then you can pick

21:40

projects that can become your portfolio

21:42

. You can pick projects that you can partner

21:45

with potential partner partner

21:47

companies as well . But

21:50

if you really take the time and the emphasis

21:52

and the investment and just reach in

21:54

, knowing that you've got a platform and you're

21:56

recruiting people who want to build platform companies

21:58

, versus just saying , yeah

22:01

, of course we want to get to that , yes , of course we

22:03

want to , it doesn't matter . But

22:05

spending the time to ensure that

22:07

that mindset and that culture is

22:09

built in and starts to come out

22:11

, it's incredibly important . So

22:14

it's not just from an investor perspective , it's also

22:16

from a team . Yeah , yeah

22:18

.

22:18

Yeah , when you stepped

22:21

into the role at

22:23

Ampersand what

22:25

five months

22:28

ago . You're well aware

22:30

of the environment

22:34

, the financial environment

22:36

, when you take that position , take that

22:38

position . How did you personally rationalize

22:41

that ? Yes , stepping

22:43

into a startup ?

22:45

Yes , right , a

22:48

pretty rocky time yes , so

22:50

you know the capital market's been turbulent

22:53

, as you're living to , for some time

22:55

, both in the private

22:57

as well as public markets , and sector

22:59

macro forces and

23:02

sector-specific forces

23:04

. Having learned a lot

23:06

from the Vigento experience as

23:08

a private company CEO and a public

23:10

company CEO , it was very helpful

23:13

for me to rationalize

23:15

that . But in my opinion at

23:17

the time , at the time

23:19

, my view was that that

23:21

type of turbulence actually

23:24

selects for

23:26

the best science . It selects for the best opportunities

23:28

and the best teams , and so

23:30

I found it relatively

23:33

, in my opinion , straightforward to filter

23:35

and think about that . And

23:38

so when I got some pleasure from Ampersand

23:40

, it was a blow away like amazing opportunity

23:42

. And so when I got some of their flagship and Alparsad , it was a blow away like amazing opportunity . And

23:46

then so the world around

23:48

became very still and

23:50

I think Yubar refers to it as the poly

23:52

crisis right , that

23:54

drives innovation , it

23:57

drives opportunities , and for me it

23:59

was actually relatively straightforward , actually

24:01

relatively straightforward , seeing the science and

24:04

those you know principles I talked about the

24:06

opportunity for patients and seeing , yeah

24:08

, all right , so let's talk about , you

24:10

know , the , the , the team

24:13

, the science .

24:14

Those were probably the first and easiest

24:16

boxes to check . Yes , uh

24:19

, the , the patient impact . I want to , I

24:21

want to have a conversation around the

24:23

platform , dig into the platform

24:26

and sort of translate that to . She

24:30

flicks away . But the intended patient

24:33

impact you talked briefly

24:35

about the address navigate

24:38

, determine and trademarked

24:41

and platform . Um

24:43

, give us a little

24:45

more color on that . Yes , yes

24:47

.

24:47

So basically , the three

24:50

pillars to platform the address map

24:52

first , right . So that's that big data set

24:54

. They were constantly curating , constantly

24:57

updating and increasing . So we have um

24:59

lots data over

25:02

a terabyte of data , which is over 100

25:06

billion data points across disease

25:10

, across different OX , across

25:12

different proteins , and so mapping tissues

25:14

and cells that exquisite well

25:16

Together

25:19

with an algorithm you can use to predict addresses

25:21

has constantly been updated in a team of computational

25:24

monitors and a team of scientists

25:26

who work very closely within the flagship intelligence

25:30

team of the cloud system . So a lot of really

25:33

heavy-duty data

25:35

and scientists . So then

25:37

the second pillar

25:40

is the navigators of building these molecules in Silicode

25:42

, which I think is really being a massive

25:45

step forward for the industry over

25:48

the last several years , and the

25:50

ability to do that and to convert basically

25:53

what would have taken years into months to

25:55

design molecules and fine-tune

25:57

them . So in our case we've

25:59

got hand bodies , sort of like an antibody

26:02

targeting piece and an active drug

26:04

. We can fine-tune both of those between silicon

26:06

, so we can take an approved drug and make it better

26:09

, not just by telling it's

26:11

the right place , but by optimizing its properties

26:13

yeah , good , so you can do that

26:15

and then the determinant

26:18

piece which . The determinant piece which is when we make a few of these molecules

26:20

, we can test them in the lab . So we have lab-based

26:22

scientists . Lab-based

26:24

scientists run these studies and

26:26

then the data that come out of these studies very quickly

26:29

feed back into the platform through

26:31

machine learning and the platform becomes

26:33

even more powerful and more active . So

26:37

that's a true existence . Now we also

26:40

last week announced we acquired an

26:42

antibody-distilled platform company

26:44

in

26:47

. Europe , yeah . So Abcheck

26:49

yeah , that's right . So the

26:51

reason we did that was because we

26:54

were starting to see that science emerged from the

26:56

M-cell platform and we knew

26:58

that we would need to make antibody

27:01

binding . And AdCheck

27:05

had the experience , the capabilities

27:07

, to be able to do that with

27:10

full stack discovery

27:12

technologies in the lab

27:14

, including cutting

27:16

edge technologies like micro fluidics

27:19

and functional antibody screening , so you can

27:21

go end to end to generate

27:23

ample grade antibodies . They have a huge

27:25

amount of experience . So on day

27:27

one we became an

27:30

integrated antibody . The story company can

27:32

now take a really cool platform early

27:35

science and start to make medicines

27:37

and then took us on it very

27:39

different . So we basically not

27:41

only double the size of the company , but it became

27:43

a different company because of that

27:46

and really accelerated the development

27:48

. So somewhat

27:50

unusual for a startup company

27:52

to be acquiring a platform company right

27:54

. Often happens the other way around

27:58

and I give lots of credit

28:01

to Flagship and the Ampersand . They've

28:03

been shipped together going through that as

28:05

a team and it's

28:07

going to be exciting for you for this year . Yeah , actually

28:09

.

28:12

A bunch of follow-up questions there , but I want to start with

28:14

one of the boxes that you had to check and that

28:16

was the people . And in a company

28:18

such as you're describing , it's

28:21

a common refrain that you

28:23

know the marriage and I see it getting

28:25

better I want your perspective on it . The

28:28

marriage of computational biology and

28:31

computational anything right

28:33

, like that's the tech folks and

28:35

the science folks it's a

28:37

difficult one to

28:39

officiate , right . So

28:41

I've heard , you know , I've heard multiple

28:44

approaches . I've talked to biotech CEOs

28:47

who are in this space and in

28:49

the AI space who've talked about

28:51

, you know , throwing them in a room together , making

28:53

sure they're in close proximity and they start speaking

28:55

the same language as a starting

28:57

point . I mean , what are your thoughts on

28:59

that ? Like , how are you making

29:02

sure that that box , those people

29:04

, are the rare breed that

29:06

can walk both sides

29:08

of that line right , or at

29:11

least work together on other side of that line

29:13

?

29:14

Yeah , so we start with the business . So

29:16

we are one company , right , I've

29:19

got an amp , a sound , a chair , a vision , style

29:21

, rates and so on , and so you know having

29:23

our team on site , we

29:26

now have two sites , right , but certainly

29:28

together in the same room talking

29:30

about a science . But also it

29:32

works both ways , this communication , dialogue

29:35

. So it's also

29:37

about the computational biology team

29:39

sharing what they're doing and our lab-based

29:41

scientists sharing what they're doing and listening to

29:43

each other , and those are two different

29:45

styles . But

29:47

I think you're right in terms of alluding to the different

29:50

backgrounds and different cultures . If

29:52

you're in front of the computer doing all your work on the

29:54

computer every day , yeah . If you're

29:56

a scientist in the lab , the pen it's

29:59

almost two different models , right

30:01

. I think it's part of my

30:03

job , part of the leadership team's job , to

30:05

make sure that we are able to communicate

30:08

within the company as well and

30:10

share that knowledge and

30:12

share that culture together . I

30:15

think another thing you have to put on the table and say

30:17

look , this could be a challenge . So

30:20

let's A recognize that , b think

30:22

about how we continue to communicate

30:24

transparently and share information

30:27

.

30:27

Yeah , when you look at the talent

30:30

pool that's available to you to serve

30:32

those needs , are you seeing change

30:34

there in terms of , like , what academia

30:37

is putting out ? You know people who

30:39

are well-versed in both

30:41

languages , understanding the value of computational

30:44

, you know coming in outside side and vice versa . Like

30:47

, do you ? You know I mean what ? When the concept

30:49

began , there really wasn't , I'm

30:51

assuming , an academia , uh

30:54

, an academic path

30:56

toward being a computational biologist

30:59

.

30:59

yes , so I think what we're

31:01

seeing now my

31:04

sense , strong sense is we're actually seeing folks

31:06

on the comp biology side moving

31:09

into their second or third roles in

31:11

industry , so seeing

31:13

people join earlier stage companies

31:15

, bringing more experience now from

31:18

the industry , together with the epidemic track

31:20

, well , those kind of roles . And

31:22

so I think there's a very interesting maturation

31:25

of the talent pool happening

31:28

right now very quickly , and

31:31

I think some of that is being accelerated as

31:33

well by a number of the

31:35

company changes that

31:38

we've seen in the sector over

31:40

the last year . So

31:42

that's also

31:44

happening in the biology side as well . And

31:47

I think that's helping right in

31:50

terms of how we recruit , you know . Think about talent

31:52

development .

31:53

Yeah , Another

31:55

common refrain around AI and

31:58

ML is that , like you

32:00

know , there are obvious benefits and applications

32:03

happening in terms of speed of

32:05

discovery and speed of design efficiency

32:07

. But a question

32:09

that comes up often in my conversation is

32:12

what the next application for

32:14

these technologies is . When

32:17

you enter the clinic

32:19

, like because that's

32:21

where the expenses mount , that's

32:24

where failure happens

32:26

quickly . Sometimes not so

32:29

quickly . Failure happens after a lot of money's

32:31

invested . Do you have any thoughts on that ? Like

32:33

where , uh , where a company like ampersand

32:36

as it continues out

32:38

sort of the clinical continuum where

32:40

it might be able to leverage some of these technologies

32:43

to improve efficiencies ? Yes , beyond

32:46

discovery yeah .

32:47

So you know , I think there's a couple of areas

32:50

. Uh , it's a great question and I do think there's a couple of areas . It's a great question and

32:52

I do think it's a very important question

32:54

for us in industry . Going

32:56

back to that 90% failure rate number yes

32:59

, so how do we take clinical data and

33:01

early translational medicine data and feedback

33:03

into our

33:05

user data ? It was like a continuum

33:07

of our life-based data . How

33:10

can we learn quickly from our medicines

33:12

early in the clinic

33:15

versus late in development ? And

33:17

so I actually see a natural opportunity

33:19

to start applying MSR to

33:22

our platform and to start machine learning

33:24

with more molecules , I mean in

33:26

the clinic , and we definitely intend to

33:28

do that . I mean I know it's

33:30

within the flagship ecosystem that

33:33

pioneering intelligent is so

33:35

essential . We are a

33:37

machine learning team within

33:39

flagship and across flagship . That is a

33:41

huge area . I'm excited

33:44

about the

33:46

announcement this week with the Samsung

33:48

partnership flagship . There's some

33:50

of that element within that

33:53

partnership and clinical trial centers

33:55

.

33:56

Yeah , can

33:58

you share anything in terms of

34:00

like what a pipeline

34:02

development plan might look like ? Right

34:04

?

34:05

Yeah , so that is very exciting

34:07

. So , as I mentioned , you know building out a platform first

34:09

, and then we have a built-in fiber platform and

34:12

then we have projects that are

34:14

ongoing . We have around

34:19

a dozen active projects going on

34:21

across different diseases

34:23

. What I will tell you first of all is

34:25

all of those projects have big

34:27

and iconic propositions clinically . So

34:30

we've really directed the platform early on

34:32

Hard , hard

34:34

problems , not easy , small

34:37

step changes , modified

34:39

molecule safety but can

34:42

we improve both problem-seeking and

34:44

safety with the same type of hidden molecules

34:47

in the state , right for molecules

34:49

on new drugs that are not being used in

34:52

a broader patient population as possible ? So

34:54

I will say at this point , we are

34:57

seeing

34:59

some really nice data in autoimmune

35:01

potatoes , immuno-oncology

35:04

. Can we target

35:06

tumors more effectively ? Avoiding

35:10

side effects is a very powerful mechanism . So

35:12

we're seeing some very nice data there . So

35:15

we'll talk more about the portfolio

35:17

as it evolves , but we think

35:20

about it from our own core portfolio

35:22

and then also what we do in partnerships . There

35:24

are certain areas disease areas where

35:27

it would be incredibly

35:30

synergistic to apply the Amsterdam platform and

35:32

run for some to develop into

35:34

those areas with a parliament that brings

35:37

up expertise , potentially , brings thoughts that

35:39

they want to optimise and send to

35:41

the right place . So it's natural areas

35:45

with laughing interest . We're already having some partnership

35:47

discussions during this week

35:49

in Chattanooga yeah , I spoke to our meeting , so it seems a lot more . Similar interest we're already having

35:51

some scholarship discussions during this week yeah , meeting , so

35:54

it seems a lot more a

35:56

platform company has all sorts of optionality

35:59

in terms of partnerships , exits

36:01

, mergers .

36:03

You know , optionality is

36:05

like the name of the game . Do

36:08

you run sort of as

36:10

you know , obviously you're in early

36:12

but looking

36:14

forward ? Are you running kind of fast and loose and looking

36:17

for opportunities like wherever they might present

36:19

themselves ? Or do you have some sort of an idea , like you

36:21

know , this is a platform that we could outsource

36:23

or this is a platform that we could handily

36:25

stick around forever spinning off products

36:28

that we , then that we is

36:31

it opportunity presents

36:33

itself when you realize it , when you see it .

36:35

Yeah , so

36:39

you spend a lot of time thinking about partnerships

36:41

and initial

36:43

partnerships . Early partnerships for

36:46

a platform company are very important

36:48

, first of all because they have

36:50

the opportunity to validate it , and

36:53

then second is to build out projects and

36:56

programs that potentially wouldn't do . There

36:58

are categories to come , so

37:01

we spend a lot of time planning

37:03

, through storage , how they could

37:05

look so that we are

37:07

, I would say , ready

37:09

and prepared for opportunities , versus

37:13

being loose and unprepared .

37:16

I should have known the response I was going to get to that .

37:21

But maximizing optionality is

37:23

important . I would say there's no type of partnership

37:26

, there's no type of deal

37:28

, structure or therapy together that we

37:30

would not consider the platform

37:32

support . That's it . The question

37:34

is really , in terms

37:37

of quantity and quality , what we decide

37:39

to do internally in that next term .

37:43

Is there , like you know , all

37:46

indicators not all indicators , but many indicators

37:48

are kind of pointing to a return

37:50

on the markets , right , Like things are starting to look

37:52

better . In

37:54

a situation like yours , when you're looking

37:57

for and open to early deals , early

38:00

platform deals , is there

38:02

a more ideal sort of market condition

38:04

for that kind of thing and a less ideal

38:06

market condition in a less ideal market condition , would it be a scenario

38:08

where you might go you know what , we're

38:10

ready , but

38:14

we could probably strike some better deals if it's six months from now and some of

38:16

these indicators like inflation and

38:20

the merger , m&a

38:22

markets continue to look good and deals are

38:24

looking better ? We're going to kind of hold our cards

38:27

here a bit . Right

38:29

. Is that a ? fair question to ask . Well

38:31

, I think it's important .

38:32

I mean , I think that I'm looking at it a different way , matt

38:34

which is you know what do you control and

38:37

what do you not . So we

38:39

all control the map forces , things that are in

38:42

the market places where we do

38:44

control that . So you

38:46

can think about these potential

38:48

partnerships when and

38:50

how it was before . That's why it's

38:52

important . But I would like to take a step back

38:54

because as we think about this platform

38:57

, you must have

38:59

seen the explosion

39:02

of antibody drug conjugate

39:04

partnerships , approvals

39:06

, M&A acquisition . Antibody

39:09

drug conjugates are really the

39:12

core starting point of what we do

39:14

at Anvisa . Except we're not

39:16

just working in tumors . Using

39:18

three classes of pain loads toxic

39:20

pain loads and 11 tumor

39:22

target , we're actually able to deliver

39:25

different types of molecules and to

39:27

adjust to multiple tissues . So

39:30

this is like the next generation of

39:32

antibody drug conjugates . So this whole

39:35

concept of targeting drugs to the right place

39:37

is what we need . We see ADC

39:39

as being a food principle

39:41

at that , but in a very small

39:44

target space . That's

39:46

intolerance . So we have close to 700

39:48

targets across different

39:50

tissues . Yeah , that would be dreadful . So

39:54

when you think about , you

39:56

know , the weight of interest in ADCs , the

39:59

types of market conditions , I find that very

40:01

interesting . From a microsector

40:03

perspective within biota , that

40:06

obviously is very different from macro

40:08

forces . I think we could talk about

40:10

high rate pressure and

40:13

how it plays a role in the industry at

40:15

large and how innovation

40:18

, high

40:20

quality science and location

40:23

impact with platform

40:25

companies are incredibly powerful .

40:30

What's the next big step for Eppers ? Obviously

40:34

you're here . That's

40:37

a big step when you

40:39

get back to your day-to-day . This

40:43

year is going to be exciting .

40:44

We're going to see more science emerge from the platform

40:46

. Selecting our first

40:49

set of lead projects to move forward , to

40:52

develop candidates this concludes

40:54

the milestone and

40:56

then executing on partnerships . We

40:58

have a lot to do this year . We just acquired a company Obviously

41:01

did that last week , so building

41:04

out the antibody discovery capabilities

41:06

and lots of execution

41:08

. We are in discussions with both partners

41:10

and investors about the next

41:12

chapter . It's all built on science and the story and

41:14

about it proposition . There's

41:16

a lot going on .

41:17

It's great yeah , it is a

41:20

lot . Your plate is full . Um , another

41:23

question about that , uh , that acquisition

41:25

um , whenever

41:28

you acquire a company , obviously it's it's disruptive

41:31

. It's good I was , I mean there's yeah , it's there's

41:33

value , but it's also disruptive , uh

41:36

, you know , to the , to the mission you were . You were

41:38

on prior to the acquisition . So

41:40

give us some color on how you're sort

41:43

of managing through the integration of this company

41:45

, maybe even some uh illustration of what

41:47

that integration is going to look like in terms of

41:49

physical space and

41:51

integrating people .

41:53

Sure . So we had a head

41:55

start with Amtrak because we had

41:57

already been working on a

42:00

distillery campaign . So

42:02

we had that relationship . We've lapsed

42:04

it and then we

42:07

were already applied to success . So we've already been

42:09

partners here . We had toici together . So

42:12

both groups working together , pushing

42:14

about , you know , decision-making communication

42:16

. So they're based in the Czech Republic , so

42:19

obviously , time to engagement , they

42:22

are used to . We are used to working across

42:24

time zones but working by video

42:27

conferences , but , you know , really

42:29

playing that forward and can be overly

42:31

communicating . And so

42:33

starting to lay out objectives

42:36

and plans and

42:39

it's actually it's early days , it's

42:41

going well so far , but planning for

42:44

what could go wrong before

42:46

it happens , and then being

42:49

able to communicate , I

42:52

think it's incredibly important from a culture perspective

42:54

, and so that's something

42:56

that we're working on together , powered

43:00

for this year and beyond . And also

43:02

I will add that within the flagship ecosystem

43:04

, that capability is also very powerful

43:06

being able to generate our

43:09

own antibodies , not just at Ampersand but also

43:11

together with other flagship

43:13

companies as well

43:15

. I'm going to see this

43:18

platform really accelerate

43:20

next year or two . What

43:23

haven't I covered .

43:25

If I were a veteran interviewer , what should I

43:27

have asked you that ?

43:28

I didn't . I think we covered the full landscape here . I think we covered the full

43:31

landscape here .

43:33

Yeah , we covered a lot of culture .

43:40

You either have something great or you're white , yeah

43:43

and that I get asked

43:46

a lot is what

43:48

keeps you awake at night .

43:49

It's a good question . Yeah , it's always a combination of data and people . Data and people keeps you awake at night . It's a good question .

43:51

Yeah , it's always a combination of data and people .

43:54

Data and people keep you up at night .

43:56

It's always , constantly You're

43:58

thinking about what will the days look like ? How

44:01

do you recruit for the

44:03

best talent ? How do we compete ? How

44:06

do you build a company ? The combination of

44:08

that is any co-occurring

44:10

CEO . How do you build a company ? The combination of that is any co-occurring

44:13

CEO is thinking about

44:15

that . I'm not going to tell you that .

44:18

When you say data the data

44:20

that keeps you up at night Be more specific .

44:23

What's the next data set look like . Are

44:25

we on track ?

44:27

Not the machine learning , not

44:29

the data sets that you're using in the application

44:31

.

44:32

So the data sets for machine learning . Actually

44:34

, when we see the data in the lab on

44:36

these molecules that come out of the M-SAM platform

44:39

, we almost cannot lose

44:41

Because , on the one hand , the

44:43

data from the lab experiments could

44:45

be positive right , and the molecules would

44:48

. So , if they're being designed , that's

44:50

cool , that's great , great for the platform

44:52

, great for the potential partner . If

44:55

they don't work , that's actually hugely

44:57

valuable because that's its platform

44:59

and the machine like

45:01

that uses training data sets . They're like gold

45:04

, yeah , both positive and negative . So

45:06

as a machine learning platform , your platform gets stronger

45:08

, both on positive and negative data

45:10

. The result of the experiments have been run with the

45:12

right controls .

45:14

Yeah , that negative data , the failure data

45:16

, the stuff that doesn't work . Is that largely

45:19

proprietary ?

45:19

I was just having a conversation Purposefully . Yeah , we

45:25

actually like to make a couple molecules that we

45:29

think are not going to work as well , to test

45:31

the negatives and all hypotheses . Yeah , other

45:33

than just having a no treatment

45:36

, if you like control , we have to make a molecule

45:38

that is designed not to work as well . Yeah

45:40

, I've been trying to look , that's interesting

45:43

.

45:44

I was talking to someone earlier today who was talking

45:46

about a panel presentation here

45:48

at this event that addressed that

45:50

issue that you know , in the public

45:52

domain there's plenty of success

45:55

data right , yes , To pull from

45:57

, but there's very little in the way of , you

45:59

know , deep failure data and that was acknowledged

46:02

as a problem as AI and Apple

46:04

become more pervasive in the process

46:06

. Yeah , I agree , I agree , pervasive in

46:08

the process .

46:08

Yeah , I agree . I think

46:11

the underreporting of negative data

46:13

has been with us as

46:15

an industry for a long time . I

46:17

think from a culture perspective in terms of building

46:20

a company , recognizing

46:22

the importance of getting to no-go decision

46:24

products through negative data is

46:26

really tough . I think

46:28

the best companies are able to recognize and

46:31

celebrate that and understand

46:33

how that can be a high quality , very

46:35

impactful piece . But now you know

46:37

to go back to the platform machine like these

46:39

incredible ones have high quality data

46:42

set feeding back in and the know-how

46:44

that goes with that .

46:45

You you

46:47

framed that question that I should have asked is

46:49

what , what keeps me up at night , which you know

46:51

as sort of a , a negative

46:53

, negative intonation , like a concerned intonation

46:56

are the things

46:58

that keep you up at night in a concerned way , the

47:00

same things that excite you ?

47:02

oh sure yeah , absolutely

47:04

, and so you know , I think . So . Just to go

47:06

back to this piece of um , you

47:08

know , living without ambiguity and

47:10

optionality can actually drive

47:13

, I think , great innovation , progress

47:15

, finding solutions quickly and

47:18

flagship

47:20

this is called paranoid optimism

47:23

Avert the turn . So

47:25

it exists on a daily basis . Package

47:28

in the CEO world and

47:30

it comes to that point where it's just

47:32

you know you have to be able to

47:34

. I remember mentors

47:36

over the years telling me spend time

47:38

predicting the future , because

47:41

if you're not as a CEO , nobody else

47:43

is going to do that as

47:46

effectively . And trying

47:48

to do that and also being proud of it kind

47:50

of both optimistic in the role is

47:53

very challenging , but super exciting it is

47:56

. And so sometimes we get

47:58

exciting data and positive data where

48:00

we least expect it , and the opposite can be true

48:02

as well in the industry . But early

48:05

discovery platform companies at

48:07

this time , and I think within the Flash ecosystem

48:09

is a great

48:12

place to be yeah , that's terrific , all

48:14

right .

48:14

So personal question , because paranoia and

48:16

optimism can both be unhealthy

48:18

if taken

48:20

in too large a dose . What would

48:22

you do to personally like , what do

48:24

you do to decompress

48:27

, to make sure that you're you're not taking your

48:29

paranoia and your and your

48:31

height home with you

48:33

and to and to keep your , keep your , you

48:35

know your sanity ?

48:36

Yeah , simple . Turn up my phone and do it all the

48:39

time . Yeah , start that

48:41

. Everything else is straightforward

48:43

. Very good , yeah .

48:45

Well , I appreciate the time . This has been very insightful and

48:47

I enjoyed meeting with you . I thank you for spending

48:49

. I know there's a lot of demand for

48:51

your time , so I'm honored

48:53

that you spent some of it . Oh question Matt . Yeah , I really

48:56

enjoyed it , thank you .

48:56

Nice to talk to you . Good to meet you , thanks .

49:08

I'm Matt Pillar and you just listened to the Business of

49:10

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49:14

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49:27

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49:29

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49:33

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