Latest / The BioHub - by Avetix / Episode 245 - Michael Fenn CBO Bexorg

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Episode 245 - Michael Fenn CBO Bexorg · The BioHub - by Avetix

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Hi, everyone. Welcome to another episode of
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the Biohub podcast. Today I'm joined by Michael
0:11
Fenn, Chief Business Officer at Bexorg.
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Michael's background spans biotech investing, company
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building, and corporate development, most recently as a
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venture partner, ISO Ventures, and previously at Dew Point
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Therapeutics. Vexorg is building a human brain
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pharmacology platform really designed to improve translation
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in CNS drug development and help advance better neurological
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medicines. In this episode, we'll discuss
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Michael's move from the investor to operator, what drew him to
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Vexorg, and how the company is approaching some of the biggest
0:44
challenges in neuroscience. Mike, welcome to the Bio
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podcast. Yeah.
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Thanks, Amber. Thanks for having me.
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I really appreciate it. No.
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Problem and maybe start it off then can you tell the listeners
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more about your background and really what convinced you to go
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over and join bexorg? Yeah, sure.
1:02
So I mean, my, my background's a little circuitous.
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I think many, many peoples are. So I, I actually started as a,
1:08
an academic, I have a PhD in biomedical engineering and I
1:12
started as an, my academic career as an assistant professor
1:15
down in Florida and, you know, running a lab as, as any
1:18
professor might, but got really interested in, in
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entrepreneurship. We actually spun a company out
1:23
of my lab kind of early on around some spectroscopy and
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kind of early machine learning work we were doing.
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That company ultimately went to Y Combinator and ultimately got
1:32
acquired. And so I think my batting
1:34
average for grants and company formation were about the same.
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At that point, I decided I wanted to never write a grant
1:40
again if I could avoid it and, and really make the jump, you
1:44
know, from my, my tenure track role into a, a more, you know,
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into company building. And so moved up to Boston.
1:50
I actually worked at Harvard for a while building their building
1:54
Harvard innovation labs in their biotech incubator, which kind of
1:57
really allowed me to get integrated into the, the biotech
1:59
incubator, which was a lot of great relationships help, you
2:02
know, kind of work on and, and, and build the foundations of a
2:05
number of, of companies coming out of the Harvard and hospital
2:09
ecosystem there, which was really exciting.
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Learned a lot was working on a company of my own, you know, at
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the time, an ophthalmology with a couple of colleagues.
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But then I was approached by a a really pretty well known drug
2:21
hunter who was building a company called Viewpoint
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Therapeutics at the time. Big platform, exciting science,
2:27
condensate biology. It was new.
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This was in the heyday, of course, also when it was easy to
2:32
raise money back in 2020. And he convinced me to instead
2:36
come join him at Dew Point. I, I did that for, I worked at
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Dew Point for about 5 years, kind of heading up a number of
2:44
different roles within corporate business development, including
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external innovation to investor relations, working on
2:52
partnerships and licensing, etcetera, kind of all across the
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board. And really got to watch this
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company grow, you know, from this scientific concept early on
3:01
to becoming now a, a clinical stage company.
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So really great experience, you know, just a talented, A
3:07
talented bunch of scientists in particular.
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So that was, you know, I think really influential also and, and
3:12
kind of how to take science and translate, especially really,
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you know, new novel science and, and concepts and translate that
3:20
into something that can actually generate and make new medicines
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for patients, which is ultimately, of course, our, our
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goal in our industry. So from there, I, I, after
3:30
leaving dew point, I've also been an investor with ISO
3:33
venture. So kind of sitting on both sides
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of the table, you know, would love to kind of discuss my
3:37
perspective on, on that, being able to see things from kind of
3:40
these two different angles or multiple different angles,
3:43
because I think those, those perspectives overlap and also
3:47
they, they may also conflict sometimes.
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And so I think I'm understanding how those are viewed and we'll
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talk more about CNS in particular, kind of how risks
3:56
are assessed in those in those ways from those different
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perspectives. So kind of giving me a really
4:01
unique way of looking at things in, in biotech.
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But since then, I, you know, invested in, in a number of
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different companies at ISO, we have a fund that's entirely
4:10
focused actually on CNS therapeutics, which is how I
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originally got introduced to bexorg.
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Actually our, our attorney, we have an attorney that we use at
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ISO that also is the same attorney at Bexorg through work.
4:23
And, and we got introduced initially that way.
4:27
And then subsequently after hearing about Bexorg, I was
4:30
actually connected to a good friend of mine who was at who's
4:33
at Biohave and leads their head of drug discovery over there who
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mentioned that I really need to meet this company working on
4:38
brains. So this is kind of the second
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time I'd heard about this company working on brains, you
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know, another big exciting platform.
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At the time I was really focused on kind of single asset
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companies too. But but the really interesting
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thing about this platform is its ability to recapitulate human
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brain pharmacology like no other platform.
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And, and this really got me interested, especially from some
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of the investments that we were currently evaluating.
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So after meeting the CEO Zavonomir and the team, I kind
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of started working with them on some, some licensing efforts,
5:12
some more consulting role. But it was just so compelling
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and just the platform, how exciting it is.
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And I think the potential it has to change the really the
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trajectory and our ability to develop CNS therapeutics.
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It was just, I had such conviction in this platform.
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I've decided I decided to join full time and a couple of weeks
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ago alongside Bruce Carr, who joined as our CSO, he came over
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from Biohaven. I, I also joined as Chief
5:41
business office. So I'm really excited to be here
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at Bexorg and and to share more about the about the Bexorg story
5:47
and platform. Yeah, and I'm really excited to
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talk a little bit more about Bexorg and and the CNS market
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overall as well. But looking back across your
5:55
career, what experiences have shaped you and how you think
5:59
about building biotech companies today?
6:02
Yeah, I, I think it's really changed and evolved, right?
6:05
I mean, I think starting out as an academic, right, you're often
6:08
just really excited about the science, right?
6:11
Which of course is foundational to, you know, pretty much
6:13
everything we do. But I think one thing that I
6:16
that I learned and, and really maybe the hard way in several
6:19
cases was that science is great, but it has to really have an
6:24
application that meets a need, right?
6:27
It addresses a problem or a question that needs to be
6:30
answered to eventually develop something that can be a product,
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right. And, and this is in our case, in
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our industry, and I'll probably refer back to this, in our
6:40
industry, the currency of value are, are drugs or therapeutics
6:44
in patients and ideally better therapeutics, right, in patients
6:48
that are proven upon what we already have.
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And so I think that was something that understanding
6:52
that science, really interesting science can be a great
6:56
foundation, but that you have to be able to take that science and
7:00
identify somewhere that you can address a problem or an unmet
7:03
need in a differentiated or a better way than otherwise can
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be. And I think that kind of
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understanding a value proposition and the concept of
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developing a commercial product, having a viable path to
7:15
commercialization and translation of that science, I
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think was probably the, one of the most profound things to
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really learn coming from the academic background.
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I think also the, the part of that, I mean, piece of that is,
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is risk and, and understanding how to assess and evaluate risk
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and also value, right? So kind of that balance of risk
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reward or risk and value. I think that's kind of another
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thing that's been really key to my career and, and kind of the
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the path that I've been on is, you know, how we look at risk,
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especially in a space like CNS and and neuroscience where
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historically this has been probably the riskiest, most
7:54
challenging space. Definitely.
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And for people that are discovering bexhorg for the
7:59
first time, then what problem is the company trying to solve and
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why is it being so difficult for the field to solve until now?
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Yeah, so I mean, there's, there's of course been a number
8:09
of of challenges in CNS drug discovery, CNS drug development,
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right. I, I think though, you know, one
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of the, the biggest challenges is we just have not ever really
8:18
had access to the human brain. And the human brain is, you
8:22
know, someone say the most complex thing out there in in
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the universe, right? And we just do not understand
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that that degree of complexity and really appreciate, I think
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how complex and, and difficult it is.
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So I think the the one thing that has has lacked one of the
8:39
biggest gaps really has been translational models and being
8:44
able to go from preclinical to clinical with improve
8:48
probabilities of success or improve predictability of
8:52
jumping from that preclinical space to the clinical.
8:55
Of course, there's many challenges also in the clinical
8:58
space, right, where these are often diseases that are highly
9:01
complex because of the nature of the brain.
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The clinical studies that are run are often highly
9:07
heterogeneous along with the disease themselves.
9:10
These trials are typically very long.
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The endpoints in particular are very squishy.
9:15
So often times it's hard to understand if if an endpoint is
9:18
actually correlating to clinical improvement or clinical benefit.
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And there's also just been lag and things like biomarkers,
9:25
right, Like in other therapeutic areas like oncology, there's,
9:29
you know, where we've we've kind of had established biomarkers
9:32
for some time now. Only recently have we started to
9:35
see some maturation of, of biomarkers in the CNS space.
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That's something where I think Baxor is something that we
9:41
really want to focus on, you know, as what we're calling
9:44
ourselves a precision neurology company, taking learnings from
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these areas like oncology and being able to translate them
9:51
into the CNS space. So being able to identify
9:55
biomarkers of the brain in the Baxor human perfused human brain
10:00
models and then being able to translate that to the clinic to
10:03
improve those probabilities of success and discharge some of
10:06
that risk prior to going in the clinic, we think this is
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ultimately where we can provide the most value.
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Yeah. And so you've just described
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Bexorg as that precision neurology company.
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What does that mean to you? And how would you say Bexorg
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approaches differ from traditional CNS development?
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Yeah, well, I mean, we have a, we have a whole intact human
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brain. These, these are our donor
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brains. They're they come from, from
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donors, sometimes with disease or not, they've been donated.
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And then they're they're connected to this, what we call
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a brain X rig. And this allows for the brain to
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be fully reperfused. And so in this case, we have a
10:44
whole intact brain that has the blood brain barrier intact, the
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full vasculature, it produces its own CSF.
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You have all of the cells and tissues and architecture that
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you would in an entirely intact branch.
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And it's human. So all of the targets that you
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would be trying to address are human targets.
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They're not animal, they're not rodent or you know, in HP
11:08
targets, right? These are the actual proteins or
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other targets that you would actually be looking to address.
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This is real human biology. And with that, you get real
11:17
human brain pharmacology. And so I think this is something
11:20
that that's very unique to Baxora could be able to do.
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And in terms of the precision neurology piece, it's kind of a
11:27
multiple multifaceted, right, where we have the biological
11:31
precision, as I just mentioned, because these are actually the
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human targets as they exist in the human brain.
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We have the anatomical and, and really delivery precision in
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terms of being able to get things where they need to go in
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the right regions and the right tissues and the right actually
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even down to the cellular level, because again, this is in the
11:49
human brain. So we have that true
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architecture and, and, and an anatomical structure that you
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would have a human brain. And then I think also really
11:58
translational precision. And again, this is something I
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think that is that's most important because this allows us
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to bridge that gap from preclinical to clinical were
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really the highest risk, right. The the the biggest amount of
12:09
money is, is is put at risk and spent in the clinic.
12:13
Being able to improve those probabilities of success by
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being able to have access to, you know, blood, you know, blood
12:20
in this case the perfuse actual human CSF, right?
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We can do imaging, we can do pharmacodynamic readouts of all
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different types. We can evaluate target
12:30
engagement and PKPD, right. But again, we have access to the
12:34
brain tissue, the CSF in the blood that allows us to identify
12:38
and really validate biomarkers that can truly guide this
12:42
translational process. And I think that's where that
12:44
precision piece really comes in to be able to identify the right
12:48
patients, potentially the right the subtypes of patients or
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subgroups of patients, identifying other criteria that
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would allow us to or even endpoints right that allow us to
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to design the right trial to demonstrate proof of concept of
13:04
a given program. Well, that's absolutely
13:07
incredible. And with CNS drug development,
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then it's historically had one of the highest failure rates in
13:14
biotech. What do you see as the biggest
13:16
reasons behind that? Yeah.
13:18
I mean, I, I think it kind of goes back to what we were just
13:20
talking about, right, That the challenges have been in CNS.
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One, these are highly complex diseases.
13:27
You look at something like Alzheimer's disease, you look at
13:30
Parkinson's disease or or sporadic ALS.
13:34
We one, we don't truly understand what's driving the
13:37
pathology. It's often a very complex,
13:40
there's kind of multiple things that are driving that underlying
13:44
pathology and we don't fully understand what that is.
13:47
There's also heterogeneous populations of patients.
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We look at Alzheimer's disease. I don't think that's truly just
13:54
one disease, right? This is kind of like oncology
13:58
right back back in the day, right?
13:59
Cancer isn't just one disease, many, many thousands potentially
14:03
diseases. I think in this case, when you
14:05
look at say Alzheimer's for for instance, there's many different
14:08
patient populations or subpopulations or subgroups
14:10
within Alzheimer's disease. And so being able to stratify
14:15
those different groups has historically been very difficult
14:17
to do and which group should get which drugs right and with which
14:21
drugs would you know would those patients most likely be
14:24
responders to. And again also I think how we
14:28
measure success in the clinic in terms of the endpoints that are
14:31
used, these are often you look at ALS as an example, you look
14:36
at Alzheimer's, things that the the types of endpoints that are
14:38
used are, are often not very objective that were
14:43
quantitative. And again, they can be quite
14:45
squishy and that has also been quite challenging to address.
14:48
But I think really what it comes back to is the translational
14:52
gap. We just have never really in CNS
14:54
had good translational models. You look at again, referring to
14:58
oncology where we have, you know, organoids, right, we have
15:02
a number of different models, right, that actually can
15:05
recapitulate reasonably well that pathology of a tumor,
15:09
right? We just have never had that.
15:12
But you look at the, the types of translational models, whether
15:15
that's or animal models or IPS, CS or brain organoids, whatever
15:19
those are in CNS, they just, they do not fully recapitulate
15:23
that complexity of biology, that complexity of anatomy and that
15:26
complexity of disease. And so again, this is where I
15:28
think Bexorg and our and our brain platform really allows us
15:32
to be able to to really overcome this translational gap.
15:37
Yeah, definitely. And I think as well, a lot of
15:40
companies right now are describing themselves as
15:43
platform companies. What makes Pexor's platform
15:46
fundamentally different in that sense?
15:48
Yeah. I mean, you know, I think just
15:50
just to speak kind of on the concept of of platforms, right.
15:53
I mean, you build a platform in biotech to ultimately again,
15:59
drugs are the currency of value. So you build a platform to make
16:03
better drugs, whether it's faster, cheaper or like in our
16:07
case with a higher probability of success, especially in an
16:10
area where that has been pretty, pretty limited.
16:13
And so I, I think in our case, our platform offers something
16:16
that's truly unique. And we, we've demonstrated this
16:18
I think on multiple fronts, both internally with our own
16:20
programs, but especially with some of our partner programs,
16:23
right, that we are able to remove risk, right.
16:26
We're able to de risk programs, we're able to drive these clinic
16:30
or these programs forward into the clinic in, in reducing kind
16:34
of that that risk that that might have been present
16:37
otherwise without our platform. And the goal here is to be able
16:40
to do this repeatedly and to do it repeatedly at scale.
16:44
And this actually speaks to the business model that that we're
16:47
creating effects where we can speak more on that.
16:49
But I think the, the type of, of data, the type of experiments
16:52
that we're able to run in this human brain, addressing things
16:55
like target engagement in a human brain, addressing things
16:59
like dose and optimizing dose delivery in an actual whole
17:03
human brain. We're able to do these things
17:05
again, in the human context, in the human disease context that
17:09
no one else is able to do. And this really allows us again,
17:11
to address that translational gap.
17:13
And the platform concept is to produce not a drug, but mini
17:17
drugs. And so being able to address
17:19
that translational gap repeatedly over and over again,
17:23
finding very specific what we call platform asset fit, right,
17:26
forgiven drug, being able to identify what is that unique
17:30
thing or things that we can differentiate and address or
17:34
enable for a given program or a given asset that really no one
17:37
else can that gives us kind of that unique.
17:39
Edge, yeah. And beyond the technology
17:43
itself, that and what has been the biggest engineering or
17:46
operational challenge in making that platform work at scale?
17:50
Wow. Yeah, No, I mean, I, I have to
17:52
give a lot of credit to our engineering team, to our AI and
17:57
data science team, to our CEO, really, Zebotomir, you know, he,
18:02
he actually developed this. It originally came out of a
18:05
Nature paper that he published, you know, over half a decade ago
18:09
where he was using pig brains, but demonstrating that the
18:13
brain, when you remove the blood and the oxygen nutrients,
18:16
doesn't, doesn't die immediately.
18:18
It's actually quite a gradual death of the cells and the
18:22
tissue itself. So I think that was one really
18:24
unique enabling discovery. But what he was able to do and
18:27
now through our engineering team to do this, you know, really at
18:29
scale and, and with a very high degree of rigor and precision
18:33
and validation is be able to then keep these brains in that
18:37
kind of intact state and functional state where you have
18:40
that, that biology, the molecular biology, the cellular
18:44
biology, the architecture, the tissue architecture, etcetera,
18:47
all remains intact and viable, right.
18:51
And so I think the engineering challenges there of the
18:53
perfusion rig that have been developed, there's over 150 kind
18:56
of proprietary components that have been developed to be able
18:59
to perfuse these brains and keep them viable for the period over
19:03
which we evaluate drugs and and conduct these experiments.
19:07
So I think that's been one piece that that's really profound, not
19:10
only the hardware engineering, but also the software
19:13
engineering. Each brain that we run on the
19:15
rig generates a TB plus of data, which is, which is an enormous
19:20
amount of data. We've run over 400 brains, I
19:22
believe now at this point. So we're really just generating
19:25
huge one just to be able to handle that data, to manage the
19:28
data, to be able to analyze it and then actually turn that into
19:32
something that's that's actionable.
19:34
I think this has been this has been also a big challenge.
19:37
I have to give a lot of credit to our AI team and what we call
19:40
Neurolens. This is the AI component of our
19:42
platform. And this really is what
19:45
ultimately the output of that Neurolens and the data that's
19:47
generated and processed through the AI capabilities is what it,
19:51
what provides these kind of decision grade actionable
19:55
insights that we're able to get. And, and now been demonstrated,
19:58
I think for instance, with our, our partner program with Bio
20:01
Haven, where this can actually make a difference in terms of
20:05
clinical decision making. Again, where the real value lies
20:08
with the real risk lies. And I think that's been, you
20:11
know, something I, I really want to highlight.
20:14
And then also not only with the brains on the rigs, once
20:17
they're, when they're being perfused, once they're done
20:19
being perfused, we can take those brains after the, the
20:22
brain acts as we call that brain acts platform component.
20:25
And then we can move that to, to brain slices where we actually
20:28
take the brain and we slice the brain up into organotypic brain
20:32
slices. And we can use those further to
20:34
conduct additional experiments over a longer time frame, really
20:38
providing kind of that longitudinal piece of the story
20:40
as well. So chronic dosing and things
20:42
like that can be done as well. So we've really tried to kind of
20:46
address end to end along the drug discovery value chain as
20:50
well as across kind of the different areas in terms of
20:53
modalities, right? And kind of the different
20:56
questions that you might ask for a given molecule that would go
20:58
into the brain, really the ability to address all these
21:01
questions that are truly enabling.
21:03
Yeah. And you just touched on a lot of
21:06
the AI part within that workflow, but where does it
21:10
create the most value at this point, do you think?
21:13
Yeah, I mean, it kind of a a couple different places.
21:17
It creates value. I, I think, you know, everything
21:19
from, you know, we can identify and validate potentially novel
21:23
targets kind of starting at the beginning.
21:26
But we're really focused on on assets and how they actually the
21:30
pharmacology of these drugs in the brain.
21:32
And so I think being able to get information such as I mentioned
21:36
Biomars, right, being able to use the the AI, the neural lens
21:40
component to be able to validate or identify at least first a
21:44
novel biomarker and then use that data and the AI component
21:48
to validate the biomarker. And then use that to also
21:51
identify potentially how we would construct our clinical
21:54
development plan, what patient population we might want to
21:58
identify using that data to optimize dose and dose selection
22:03
and driving forward, you know, our understanding of PKPD target
22:09
engagement really kind of across the board is, is where I think
22:14
the, the AI is able to kind of add the most value.
22:17
I think it's the biomarker output though, in my mind, if
22:20
that's really most exciting. And we have I think some
22:23
examples here that we're working on of really being able to to
22:27
understand the way that we can use this data to make actionable
22:32
insights about the clinical studies that will conduct and
22:36
having those being a biomarker driven and that understanding
22:39
coming from the data that we did that we generate.
22:42
Amazing. And there's often, you know
22:45
debate around platform companies versus single asset companies.
22:49
How do you think about that discussion in the market today?
22:52
Yeah. I mean again, I'll refer back to
22:54
it, right. So the the currency of value in
22:57
our industry is drugs. So, so drugs and patients in
23:00
particular and and getting drugs to market right that are you
23:04
know that improve patient outcomes.
23:06
So the platform should be developing new assets, new drugs
23:12
that go into patients, and it should enable this in some way.
23:16
Some improvement, right? Some better, some better
23:20
pharmacology, some better profile of whatever ultimately
23:23
that that drug is to be as a product.
23:26
Developing a platform, building a platform just for the sake of
23:28
building a platform. I, I, I, it's hard for me to
23:30
understand the value of that. Now, it may generate data for
23:34
the sake of generating data that could be informative in other
23:37
ways in including the development of drugs.
23:41
But I think ultimately, if you're going to develop a
23:44
platform, you have to show that the platform is able to create,
23:48
generate better drugs or inform the process of drug development
23:52
in a way that otherwise cannot to improve that probability of
23:54
success or improve the speed at which you do things or improve,
23:58
reduce the cost, right, in some way.
24:01
But I think, you know, the ultimate output of any platform
24:04
needs to be drugs. And that really is the focus.
24:07
So it's, it's kind of, I think intertwined in that way.
24:09
And we need to look at, you know, how does a platform enable
24:13
the therapeutic development and downstream, what value is that
24:17
platform adding? And importantly, can it do it
24:19
repeatedly? The platform can only do
24:21
something once, like what is it really a platform, right?
24:25
And so I think that's, you know, I think that's one question to
24:27
really ask, but but ultimately the platform needs to be able to
24:31
produce drugs or really better drugs than otherwise, you know,
24:36
what other platforms, tools, technologies that we might have.
24:40
And so how would you or how do you balance investing in the
24:43
platform while keeping that therapeutic progress front and
24:47
center? Yeah.
24:49
So again, I mean, show me your platform, show me why it's
24:52
differentiated, but in the context of why is it generating
24:56
a unique asset that otherwise someone else is not able and
24:59
what? And again, that asset, that
25:01
drug, that program needs to be, needs to have a profile that is
25:07
an improvement on what is existing in terms of the
25:10
standard of care, whatever that improvement, better efficacy,
25:13
improve safety or tox profile, right?
25:15
Maybe it's more convenient, whatever that might be, right.
25:18
But there needs to be some improvement that that platform
25:22
is providing to the drug program or programs that you're working
25:25
on, right? And so the way that we look at
25:27
this again is, you know, OK, you have a differentiated platform
25:31
that's really neat, that's really exciting.
25:33
But show me proof. The proof is in the pudding.
25:36
The pudding there in this case are the drugs, the programs, the
25:38
assets that you're working on. And so we do really look at, you
25:41
know, if you have, if you have a program, what did the pro, what
25:45
did that platform add? What did the platform do that
25:48
otherwise no one else could have done.
25:50
And so then that gives us the conviction, if you can
25:52
demonstrate that you've enabled a drug program or programs,
25:57
right, either wholly owned or through a partner, right, that
26:01
that platform can actually add value otherwise.
26:03
And that gives us a conviction that it's worth allocating
26:06
capital to the platform itself. Again, also with the
26:09
consideration of can it do it multiple times, otherwise it's
26:13
well you have an asset and that asset might be a great asset,
26:16
but let's invest in the asset, right.
26:18
And so I think that's really how we think about it.
26:20
I think if you look at it more broadly, right, investors of
26:23
historian, we as an investor you can value an asset, you can
26:27
calculate the risk adjusted net present value, an asset or a
26:31
templated process for doing that.
26:33
I don't know how to do that for a platform.
26:35
Actually, if I look at a platform often times, right, I
26:38
may just discount that that value.
26:39
It's just a, it's really a cost center and it's just an expense
26:42
and less you can show that that platform adds value in a way
26:47
again, improving the probability of success, reducing the time it
26:51
takes drugs to an equity market or reducing the cost at which
26:55
that takes, right? And I think, but you have to
26:58
show that, right? It has to be rated in my mind
27:03
again, I think the value generating data that's
27:06
biologically rich and informative, but I, I, I think
27:09
ultimately it has to be informative towards, towards the
27:13
end goal, which is developing better drugs.
27:16
Yeah. And I think some really good
27:17
insights there as well. And I think from your end,
27:21
having spent both, having spent time on both sides of the table,
27:25
how has your perspective on risk changed since moving from that
27:29
venture side into an operating role?
27:32
Yeah. I mean, so you know it in a, in
27:35
the in venture you you're really looking at risk kind of at, you
27:39
know, in, in at 2 levels, right. You're evaluating a company that
27:42
you might invest in and you're evaluating the risk of that
27:45
company individually, intrinsically, what is the risk
27:48
of that company? What is the, the risk and
27:51
reward, the value of their platform?
27:53
How do those, how do those balance right.
27:56
But also at one level higher, which is at the portfolio level.
27:59
And so where does that risk reward profile of that given
28:03
company fit within your portfolio and ensuring that
28:06
that's also, you know, balanced or you know, really is, is
28:09
directional towards where where you want that portfolio to go.
28:13
And I, and I think that's, that's something that's, that's,
28:15
that's unique, but also similar to if you're at a company and
28:18
you're developing a platform that has multiple assets and
28:22
you're developing A pipeline and multiple assets, again, for each
28:25
individual asset or program, what's the risk of that program?
28:28
What's the potential upside that you have of that program?
28:31
And how does that fit into your pipeline or portfolio within the
28:34
company? And so I think there's actually
28:35
good overlap there. I think though, at a company and
28:39
that operating role with that perspective, you're really it's
28:42
more the day-to-day, what are the risk of the day-to-day
28:45
decisions, right? What are the challenges that
28:47
that this presents? And what, how do I have to weigh
28:50
these risks from kind of the day-to-day operations versus
28:53
kind of the long term corporate objectives, right?
28:56
Where again, and you know, as an investor, you're, you're looking
28:59
at risk in terms of how does this fit within My Portfolio?
29:02
How does this fit with, you know, you know, improving my
29:07
internal rate of return, right, my returns overall, right, where
29:10
you know, what, what is the upside versus what is the risk?
29:13
I'm willing to, to take on in terms of doing so, I think
29:16
that's really, you know, kind of kind of one of the differences.
29:18
But also again, there's some similarity there.
29:21
You know what I think also as an investor, you have to have a, a
29:26
thesis around which you invest and and where do you have a
29:31
unique edge where you can kind of discharge some of that risk
29:35
by having some unique insight or better understanding or
29:39
potentially even adding some value by being an investor in
29:42
that company in some way in addition to the capital.
29:45
I think really, you know that that's an important piece,
29:48
important piece of the calculus as well.
29:50
Yeah. And I think for some of the
29:51
listeners out there that might be founders, what would the best
29:54
founders do to build trust with those investors, you know,
29:57
particular in a challenging area like CNS?
30:01
Yeah, you know, so, so one especially in the early stages,
30:06
the way that that we at ISO and I think many investors at the
30:09
early stages, I think one of the top criteria, one of the first
30:13
criteria and maybe most important that we evaluate is
30:15
the founders themselves. And and really are, are these
30:19
founders, is this team the best team to do the job that they're
30:24
setting out to do? Do they have the experience, do
30:27
they have the chemistry to work together right?
30:29
Do they, do they understand and have a strategy and an objective
30:33
to reach a particular objective or milestone that is truly
30:36
meaningful? They understand what that is.
30:38
I think another thing that, you know, as an investor that that
30:40
we look at in the founders and the founding team is, you know,
30:43
do they recognize that they're things that they don't know,
30:46
right? Do they do they know that
30:48
there's unknowns that they may not even identify or know,
30:51
right? And, and do they know and are
30:53
they willing to learn? Are they willing to listen or
30:55
are they willing to go out and find the right people who can
30:58
address those things that are unknown to them, right?
31:01
I think that's extremely important.
31:03
Founders who are willing to learn, willing to kind of be
31:07
flexible in their view and find the people who can be
31:12
complementary to their skill sets.
31:14
Those are the types of founders that we really look at.
31:16
And that's, you know, one of the top criteria.
31:18
So I would say, you know, having that kind of willingness to
31:21
continue to always be learning, you know, have that, have a view
31:25
where you start. What do they say?
31:27
Strong beliefs, but loosely held, right is something that we
31:30
like to see, right, Where given new information, right, those
31:33
views may change, right. But it's finding the right
31:35
people to provide that insight, that information that's very
31:39
important. Yeah.
31:41
I think also additionally, you know, as an investor, again, if
31:44
if you're building a platform company, it's understanding
31:47
what, what is value, truly understanding what is value.
31:50
And again I'll go back to it again, which is therapeutics
31:52
drugs are what is, you know, the currency of a value in our
31:55
industry. I think understanding that is
31:57
very important. I think also understanding how
32:00
you generate that value and what is a meaningful value inflection
32:04
and how to reach that without, you know, truly having a, a, a
32:07
viable plan and being in, in a part of that plan, being very
32:11
capital efficient. I think that's something that's
32:14
extremely important. We've learned that, you know,
32:16
over the past, you know, decade or really since kind of coming
32:19
out of 2020 when maybe people weren't as capital efficient,
32:22
capital was much more abundant. I think nowadays, you know,
32:26
being able to run lean, be capital efficient, make smart
32:29
decisions and really focusing on what that value inflecting
32:33
milestone is and that path to get there.
32:35
That's something that we very much value and and look for when
32:38
we invest in companies. Yeah.
32:41
And I think we kind of touched on this earlier, but oncology
32:44
has benefited enormously from that precision medicine angle
32:48
over the past decade. Obviously you've touched on
32:51
precision medicine within Bexorg, but what lessons do you
32:55
think the wider neuroscience market can take from that
32:58
evolution? Yeah, I mean, that's a really
33:00
interesting, you know, this is something really interesting.
33:03
This goes back to the concept of of precision neurology.
33:06
You know, what can we learn from the other therapeutic areas
33:09
where we've seen, you know, success and and kind of
33:12
improvements and success and particularly in the clinic.
33:17
And I think, you know, biomarkers are, are one thing
33:20
that's really important. I think in oncology we've really
33:23
seen, you know, over the past two decades, just a real
33:26
maturation and biomarkers and also in our ability to identify
33:32
and stratify patients and select for patients that who would be a
33:36
particularly strong responders for a drug given whatever the
33:40
mechanism of that drug is, given whatever the underlying
33:43
foundational pathology is in in the case of of of the disease.
33:47
I think these are things that we can really learn from oncology
33:50
and translate to CNS. And this is really one of the
33:52
things that we're trying to kind of really foundation of what
33:55
we're doing at Baxillord, right, which is how can we understand
33:59
that disease in an actual human brain?
34:02
What are the, the biomarkers that are potentially produced?
34:06
What are the, what's being modulated in a human brain,
34:09
right, that can actually then be observed in the clinic?
34:12
And how and with what degree of confidence can we observe that
34:16
in our model in the lab and be able to have certainty or some
34:20
degree of certainty that we should also see that thing being
34:23
modulated in the clinic? And then how predictive is that
34:27
thing that is being modulated of a meaningful clinically
34:30
meaningful drug response, right? And so that kind of line of
34:34
thinking, that line of logic is, is I think really important.
34:37
You see this in oncology and this is something that we really
34:39
hope to bring, you know, to the CNS space by by utilizing whole
34:44
human brains. And then if you were thinking
34:47
about the next 5 to 10 years, what developments would give you
34:50
the most optimism for the future of CNS therapeutics?
34:54
Yeah. I mean, I think, I think being
34:55
able to better understand the underlying biology of disease, I
35:00
think being able to stratify patients and understanding that
35:03
diseases, especially neurodegenerative diseases and
35:06
even neuropsychiatric diseases are likely not one single
35:09
disease, right? When you say schizophrenia or we
35:12
say Alzheimer's disease, these are likely a kind of a spectrum
35:17
of multiple different diseases, right?
35:20
And I think that's one piece, being able to really stratify
35:24
these patient populations, understanding what's driving the
35:28
pathology of each of these populations and being able to
35:31
then use that to link to what is the best medication, what is the
35:36
best drug that would then provide the best response and
35:40
those given patients. I think that's one piece that's
35:42
really important. I think, I think one of the
35:44
other things, and this is something that that really
35:46
actually got me most excited about Baxter is, is being able
35:48
to get so, so my background is in is actually drug delivery,
35:53
using nano materials for drug delivery.
35:55
And so getting things to where they need to go in the body,
35:58
right, is very important. It's, it's, I would say one of
36:00
the grand challenges, if not the grand challenge of, of much of
36:02
genetic medicine and kind of some of the more, you know,
36:05
advanced modalities. You know, I, I think what's,
36:08
what's really particularly important with CNS is getting
36:12
the drugs to where they need to go in the brain.
36:14
And there's kind of a hierarchy to that.
36:16
One is just getting the drug to the brain.
36:19
And so you got to, to be able to model that you have to have a
36:22
human brain. We have this human brain effects
36:23
or even something I think that's really unique.
36:25
But there's, there's more to that.
36:27
There's another, there's several more layers to that hierarchy,
36:30
which is, yes, you can get the drug to the brain.
36:32
Can you get the right exposure in the brain?
36:35
That's kind of part of the number 2.
36:36
Can you then get target engagement in the brain?
36:38
Where in the brain are you getting target engagement?
36:41
So there's likely certain regions, tissues or even cells
36:45
that you need to specifically target and get that target
36:49
engagement that the right exposure at that tissue or even
36:52
cellular level. And then even once to the cells,
36:56
are you getting that drug to the right place within the cells,
36:58
right? That for for instance, with
36:59
oligotherapies like AS OS and siRNA, even that intracellular
37:03
trafficking, right or the ABS getting the drug once inside the
37:07
cell to the right place, it needs to be.
37:09
So I think kind of this, this delivery to the brain and kind
37:12
of across those layers or of different resolution is, is
37:16
probably one of the biggest challenges that needs to be
37:19
addressed. And I think fear of the next 5
37:20
to 10 years, this is something that we'll start to address.
37:23
I, I think quite, quite well. We're optimistic back story that
37:26
we have a, you know, we have the tools to be able to, to really
37:28
address this, especially for things like AAV gene therapies
37:32
using unique novel brain shuttles, right?
37:35
For some of the large molecules I mentioned Olga therapies where
37:39
with these modality really offer unique advantages, but they
37:43
don't have that advantage unless you can get it to where it needs
37:45
to go. So I'm very hopeful and very
37:47
optimistic that that's something here for the next 5 to 10 years
37:50
that we as an industry and we as a company will really be able to
37:53
address that I think will be that I think will be a big, a
37:55
big lift overall to the space. Yeah.
37:59
And I think it'll be really exciting to see some of those
38:02
advancements both from Beck Sorg and in the wider CNS market.
38:05
As you know, we get to that kind of fast 10 year.
38:09
But yeah, in terms of this podcast, we're into the closing
38:13
question now and that is the same for everyone that joins us
38:16
at the Biohub. And so, if you could invite any
38:18
scientist or innovator, past or present, to dinner, who would it
38:22
be and why? Yeah, well, I, I, I thought
38:24
about this question a bit, you know, and I had a couple of
38:27
people in mind. You know, I thought Bob Swanson
38:31
was one person. You know, of course he's founder
38:33
of Genentech and really took the risk on recombinant DNAI mean,
38:37
he really not just founding Genentech, but really kind of
38:39
founded the industry of biotech, right?
38:41
He was willing to kind of take the risk and and almost in a
38:45
contrarian sense, right, with a with an academic Co founder,
38:48
right, taking science, you know, kind of as a theme, right, and
38:50
translating that from the lab into a, you know, commercial
38:53
product, Natma a very successful company.
38:56
I think having, you know, him having that vision to do that, I
38:59
think it's really profound. You know, I, there was a, you
39:03
know, if we're talking about people alive, there was a few
39:05
other people as, and Bob Duggan is a person that I, I'd really
39:08
be interested in meeting. You know, I think his willing,
39:11
willingness to make contrarian bets.
39:13
That's something I'm very interested in is make contrarian
39:16
bets, but doing so with kind of very rigorous systematic logic,
39:21
right? Being willing to allocate the
39:23
capital also charge that risk in a meaningful way and with that
39:28
kind of another person and that maybe I'll just you know, I knew
39:30
yes, one, but there's there's several.
39:32
I'd say Bob Nelson from arch ventures is, is probably at the
39:35
top of my list, to be honest, you know, talking about taking
39:39
kind of wild ideas, translating from lab, taking contrarian, you
39:43
know, that's looking at risk in a way.
39:47
But I think being systematic and identifying where, yes, this is
39:50
a big contrarian bet, This is a big risk, But this could really
39:53
change kind of the course of, you know, whatever disease or
39:56
the therapeutic area that might be and allocating the really,
40:00
truly significant capital to really try and and deep
40:03
discharge that risk. I think in my mind that that's
40:06
something that's that's quite you see this cross arches
40:08
portfolio, you know, in the different companies that you
40:11
know that that Bob Nelson has been involved in and everything
40:14
from, you know, even alumina. Right, Nylum to there's many,
40:18
many companies he's willing to try things that I think many
40:22
others won't and really provide and allocate the meaningful
40:25
amount of capital to really give it the right and really give it
40:27
a go, right. And and I really respect that.
40:30
I, I think that's, I think that's phenomenal.
40:33
I think we need more of that, honestly, in our industry.
40:36
I think that's something we're hopefully trying to do with
40:37
Bexorg, you know, really, really taking a, you know, a big risk,
40:41
but in a calculated way with something that is truly novel
40:44
and we think enabling, meeting, meeting the challenges and needs
40:47
where where they're at. So yeah.
40:49
Some really good picks there. I, I love it.
40:51
Well, thanks so much for joining us, Mark.
40:53
It's been a great conversation, and good luck with everything at
40:55
Bexorg. Absolutely, thanks so much for
40:58
having me.