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And welcome everyone, to another
Smart Money Circle episode. 

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I'm Adam Sarhan. 
With me today is Martin Brenner,

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who's the CEO and the CSO, the 
chief scientific officer at 

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IBIO. 
Martin, thank you so much for 

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taking the time and welcome to 
the Smart Money Circle. 

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Thank you, Adam. 
I really appreciate being here 

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today with you. 
It's a pleasure. 

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So, Martin, I always like to 
begin. 

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Can you please tell us your 
story and how you got to where 

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you are today? 
There's a a long story and a 

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convoluted story. 
So I my CV is not exactly what 

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you would call linear. 
I started actually out trying my

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hand at becoming an electrical 
engineer and wouldn't this 

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didn't work really well. 
I studied veterinary medicine 

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and halfway through my studies I
met my professor for 

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pharmacology who ignited kind of
a fire in me for drug discovery 

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and making medicines. 
And ever since I've been 

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pursuing that career, first in 
large pharma. 

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I was in R&D departments of four
of the major companies, Ileli, 

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Pfizer, AstraZeneca and Merck, 
and then jumped into biotech at 

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which at the time sounded like 
a, a more, you know, exciting 

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opportunity for me after 15 
years in big pharma. 

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And have, you know, ever been in
biotech since trying to actually

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combine novel technologies with 
a very empirical field of drug 

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discovery, which is always a 
challenge. 

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It's a job that is very humbling
because every experiment around 

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the corner can be the last one 
for your program. 

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So it's not for the faint of 
heart to be in biotech. 

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I love that and I usually that's
it's a good Darwin type wedding 

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out the weak and the strong and 
the ones that can adapt and 

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survive. 
So I'm glad that you're here and

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thank you for the work you do. 
So Martin, next question for you

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is tell us about your business 
please, your story, your 

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competitive advantages, anywhere
you want to go? 

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Absolutely. 
So Ibio is first and foremost a 

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really cool turn around story. 
When I took over the reins of 

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Ibio together with my CFO, 
Philippe Duran, the company was 

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a CDMO. 
We actually had a plant based 

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expression system. 
So we literally grew tobacco 

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plants or you know, cousins of 
the tobacco plant in in a 

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facility extracting molecules, 
among other things antibodies. 

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And I was originally brought on 
to build a biotech arm that 

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would feed programs into this 
manufacturing site. 

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While it was really challenging 
to do the manufacturing and we 

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ultimately decided to to walk 
away from it, the biotech sector

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actually has taken off quite 
nicely. 

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We were in a lucky position to 
acquire the assets of a a 

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company called Rubric 
Therapeutics. 

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It was a pioneering company that
one of the first companies that 

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used AI in antibody discovery. 
And the only downfall for Rubric

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was they were a few years ahead 
of their time. 

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And so funding got really 
complicated. 

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And so we brought this 
outstanding team down to from 

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the Bay Area to San Diego and we
started to kind of build upon 

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what they've done. 
So we are staying far, far away 

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from calling ourselves an AI 
company. 

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What we are is really we're 
very, very good in integrating 

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AI technologies in the current 
lab flow. 

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And as you might know, there's 
two sorts of companies, one that

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are more focused on the 
developability of drugs. 

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So we make antibodies and you 
know, if you want to make a 

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medicine out of this, they have 
to fulfill certain requirements 

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that make them drug like. 
And there's some companies that 

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are good at that and there's 
other companies that are going 

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full in silico. 
So they design molecules by AI, 

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but they usually run into a ball
when it comes to developability 

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because they don't look at all 
as drugs. 

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And we have found a niche for us
in between there. 

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So we can make really hard to 
make molecules, but at the same 

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time they look like developable 
drugs. 

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And we're a few months away from
moving the first molecule into 

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clinical development. 
Our initial focus is cardio 

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metabolic disease and that's a 
little bit of a coming home area

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for me. 
And that was my research area 

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for the 1st 15 years of my 
career. 

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It's a pretty complex space, but
we have focused on that. 

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And what makes us so different 
in the space is we have not like

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everybody else gone into the GLP
ones. 

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We have actually accepted the 
GLP ones will be a cornerstone 

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of obesity treatment. 
And from that point on, we 

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actually looked at what do they 
leave open in patient care and 

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these are the areas we're trying
to cover with with our 

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antibodies. 
Wow, I love that. 

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So for the audience, just to 
help them understand what you're

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doing is that you're using AI to
develop molecules to help 

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antibody discoveries and create 
a platform that can really help 

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accelerate that that discovery. 
Is that a good way of 

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summarizing? 
It that that is a really good 

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way of saying this. 
And you know, you have to solve 

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multiple problems on that way 
from an idea to having actually 

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an antibody that works in in an 
animal model or in humans. 

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And So what we've done was we 
created multiple layers that 

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will help us, right? 
I, I hear a lot of people 

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saying, oh, we made an AI drug. 
It's, it's a little silly to say

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that because it takes about 
10,000 steps to make a medicine 

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and we enable three to four with
AI. 

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That doesn't really make it an 
AI drug, but it actually allows 

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us to do things we couldn't do 
before. 

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And I think this is where, where
I draw the line. 

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If you can create a molecule 
with the help of AI, you 

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couldn't imagine before, then 
you have something if you just 

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make a molecule that I can find 
with my traditional way of 

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making drugs, what good is it to
use user model, right? 

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And so we have really focused on
can we create antibodies against

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targets that are usually 
considered undruggable. 

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And we have two great examples 
where we are, at least to our 

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knowledge, the only company in 
the world that is has an 

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antibody against two of these 
targets. 

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That is important. 
And we have also proven, like 

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you said, the speed of of 
development. 

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We have our our first program, 
we moved it from a paper 

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exercise where we just 
strategize what that molecule 

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would look like all the way to a
so-called development candidate.

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That's when you make the 
decision. 

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Yes, that's the molecule we 
actually want to move forward in

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clinical development. 
That took us seven months. 

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That is a process that can take 
up to two years. 

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So it is helpful, it does help, 
but you know, AI is not yet 

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there to help us in assessing 
safety, not yet there in helping

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us to assess efficacy in humans,
but we're likely going to get 

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there at one point. 
But so far we've really focused 

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on this early part of discovery 
where AI really can help us and 

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has demonstrated really clearly 
that it can help make you make 

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better drugs or make drugs that 
have been impossible to make 

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before. 
Wow, I love that. 

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OK, Thank you for your work, by 
the way, and it's a good segue 

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to my next question, Doctor 
Brenner. 

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Let's talk about risk 
management. 

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How do you handle risk and what 
are some mistakes you see people

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make with respect to risk 
management? 

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So we're in biotech. 
So we're anyway a high risk 

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environment if you will. 
What is what is really 

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challenging is making the right 
decisions, not throwing good 

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money after bad. 
And we start to de risk our 

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programs, if you will, in a way 
where we we always start with 

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the final product at the end. 
And that means is there a 

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patient that will benefit from 
the molecule we're designing? 

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If that's the case, that's step 
one. 

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That's the most important one. 
But you also have to think 

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about, is there a physician that
will prescribe this drug? 

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Is there a payer that ultimately
will pay for this drug? 

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And so then we work our way 
backwards through phase three 

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development. 
Is it feasible? 

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Are there endpoints that we can 
register? 

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And this is basically how we're 
working all the way back to the 

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very beginning. 
So we're never starting a 

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program unless we have a clear 
path of this could actually 

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become a medicine that helps 
people and that de risks you a 

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lot. 
There's always risk in in, you 

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know, the mechanism might not 
work. 

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The molecule in itself might 
have, you know, a part that is 

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toxic that we don't know 
upfront. 

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But if you clear that path to to
a ultimately a product, a 

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medicine, it de risks you 
dramatically. 

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But I would lie if I if I'd say,
you know, we have this all under

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control. 
Luck plays a large role in what 

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we do and that's why we use an 
attrition model as well, right. 

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So if you start three or four 
programs, up to six programs, 

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you almost guarantee that one 
will hit the clinical 

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development. 
But it it could be up to 60% 

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attrition in, in early 
preclinical models. 

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So far we had zero. 
So our model is a little, we're 

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now in a in a good position to 
not have enough money, but many 

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programs that are very 
successful and very positive. 

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So it becomes a luxury problem. 
Which ones do we actually 

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prioritize in this case? 
I love that. 

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OK, next question for you. 
Let's go towards the timeless 

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advice here. 
What is What are some timeless 

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lessons you've learned along the
way that you'd like to share 

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with the audience? 
I think the, the one thing 

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we've, we've learned over the 
last four years, if you will, is

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that resilience is an absolutely
necessary factor for a team that

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works in biotech. 
We were multiple times close to 

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having to shut down. 
None of us gave up, neither the 

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board nor the leadership team 
nor any of our team members in 

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the R&D team. 
We all believed we we have 

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something that is really 
valuable and that actually 

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brought us through these really 
dark times. 

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So it's a, it's a fact of 
biotech life that you have to go

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through these dark times. 
If you do not have the 

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resilience, if you cannot take 
punches on a daily basis, this 

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is probably not the right 
environment. 

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So this resilience is kind of 
the key part. 

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There's a lot of other things, 
you know, you need to have the 

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right like minded people. 
You have to rally behind one 

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common mission and goal. 
All of these are important, but 

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ultimately, if you can't sustain
these assaults on on on your 

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programs on a daily basis, 
you're not going to make it. 

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Yeah, that's a great point. 
OK. 

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Other side of that would be 
timeless mistakes. 

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What are some timeless mistakes 
you've learned along the way, 

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and how do you learn from them 
so you don't repeat them? 

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So one of the biggest 
misconceptions and, and mistakes

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that our industry in general has
made is trying to de risk 

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programs by going after things 
that they already we already 

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know and, and know as a fact. 
What this breeds is a is a 

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so-called me too environment 
where everybody's doing the same

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thing. 
And then ultimately you 

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differentiate your molecules not
because they're helping patients

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better. 
You're differentiating because 

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you have a better marketing 
team. 

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To my knowledge, this has never 
worked long time for any 

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pharmaceutical company to be 
really kind of that the risk, 

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right? 
It is very hard for larger 

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companies to change, you know, 
tracks and reinvent themselves. 

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And sometimes it's necessary, 
right? 

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If just because one mechanism 
works doesn't mean you have to 

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do the same mechanism all over 
again and try to kind of make 

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things fit. 
That's usually the time when you

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have a successful truck out 
there that you should reinvent 

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yourself. 
Really kind of think about 

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what's the next big challenge 
you want to solve and that 

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inherently has not been done. 
And, and I think this is one of 

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the biggest mistakes we have. 
Once you have basically money 

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coming in from a successful 
drug, that's the time to really 

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kind of put your eyesights on 
the next frontier. 

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And it is very hard because that
usually requires change, and 

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change is is hard for everybody,
and specifically the larger the 

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organization. 
Yeah, it makes perfect sense. 

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OK, let's talk about leadership.
As a leader used to work at big 

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pharma companies and that had 
other leaders there, and now 

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you're the leader here. 
What makes a great leader, and 

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what are some lessons you'd like
to share with the audience about

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leadership? 
So first of all, in my eyes, a 

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great leader always trusts and 
relies on the team. 

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I have never seen a single smart
person making a better decision 

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than a whole group of really 
smart people. 

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As smart as the individual might
be, a group of smart people 

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always makes better decisions. 
I think that is a cornerstone of

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leadership. 
The second is that you need to 

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invest into your team, into 
mentoring, into developing your 

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team. 
We have a huge effort at Ibio. 

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We literally built the R&D team 
around 3, pretty junior, but 

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absolutely brilliant talents. 
And our job is to actually kind 

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of grow them into vice president
C-Suite roles over time. 

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And yes, we do share a lot with 
them. 

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There's a strategy should be 
owned by the entire company, not

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just by a leadership team. 
So they're part of devising the 

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strategy. 
They're part of devising our 

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messaging to the outside and by 
including them in the scenario 

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planning in how does AC Suite 
make decisions? 

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I wish somebody had opened that 
treasure chest for me when I got

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for the first time in AC Suite. 
And and you know, I had to learn

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the hard way, but you know, if 
you have smart people that 

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actually soak up information, 
why would you withhold that? 

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So there's a huge effort and I 
bio to really kind of develop 

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talent. 
And this is independent of 

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level. 
We always hire people that have 

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the potential to grow 
significantly in their careers. 

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And it's our job to kind of 
provide them with the right 

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folder, if you will feed the 
right information so that they 

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can actually utilize that to, to
further their careers. 

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And if, you know, two or three 
of our team members go out, 

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start their own companies at one
point because they're all 

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entrepreneurial thinking, then 
we know we've done our job well.

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OK, I love that. 
And then let's talk about 

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adversity being successful. 
You have to handle adversity 

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different overcome obstacles. 
How do you handle adversity? 

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What are some obstacles you had 
to overcome along the way? 

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And anything along those lines 
please. 

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So I think you know, as a, as a 
small company pivoting into a 

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completely new area, you have to
overcome the, the notion that 

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you know, you have no 
credibility, you have to 

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establish your credibility. 
And I think one of the key 

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points for us to be successful 
was that we never over promised 

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things, right? 
So if, if people can hold you to

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your word, if you deliver what 
you say, you will deliver, that 

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might not initially be something
where people say, oh, you could 

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do this faster. 
Of course, you know, if if all 

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the stars align, you can do 
things faster. 

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But being realistic about this 
is really important. 

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It's also important to be 
realistic about science, right? 

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So a lot of people just 
misinterpret or misrepresent, 

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not in a, in a negative way, not
in the way that they want to, 

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you know, manipulate, you know, 
public opinion. 

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But sometimes we just don't know
enough about the science to make

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big assessments of, of how 
things work. 

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I get asked all the time, how 
much weight loss do you expect? 

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How much this do you expect? 
Well, if I knew I would 

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probably, you know, not working,
I would be retired by that time.

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But it's, it's really important 
to kind of remain true and 

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remain actually sticking with 
the facts that you have. 

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Yes, people ask us to speculate 
and, and, but it's important to 

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kind of mention at this point, 
we're speculating. 

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We don't have the data to 
support this. 

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So there's issues that if you 
overplay your hands, that can 

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backfire very easily. 
So we're always trying to remind

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ourselves what our messaging is 
so that we're not basically 

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giving guidance that that we 
cannot support with facts. 

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Yeah, that's great. 
Thank you for that. 

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I take notes as people speak and
I was taking a lot of notes as 

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you're speaking. 
So thank you for sharing a lot 

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of great stuff here. 
Final question for you, Doctor 

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Brenner, what is the best piece 
of advice you'd like to give the

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audience or your 30 year old 
self? 

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Go into biotech, Foster. 
No, I think you know, what is 

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absolutely critical is that you 
love what you do. 

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I keep telling people I have the
best job in the world. 

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Doesn't mean that 100% of the 
tasks I have to do are bringing 

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me pleasure, but I truly have 
the best job in the world. 

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I can interact every single day 
with people on a Sunday night. 

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I don't feel bad coming to work 
Monday morning. 

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I'm excited to come to work 
Monday morning everyday. 

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I'm not here because I have to 
travel a lot and can't interact 

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with my team. 
I feel a little sad because I 

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really enjoy working with smart 
people that have a goal that you

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know, have the same passion that
I have. 

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And I, I can highly encourage 
people if if making medicines is

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your passion, it's a greatly 
rewarding job. 

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If your passion is more on the 
financial side, if, if money is 

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your main driver, don't go in 
biotech. 

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00:15:45,000 --> 00:15:47,200
You're going to be disappointed 
to run a hedge fund that's 

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00:15:47,200 --> 00:15:50,040
that's way more, way more 
conducive for your goals. 

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But it is a fantastic job if you
can live with kind of the 

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setbacks. 
But you know, if you hit it one 

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time and if you make a medicine 
that changes people's lives, 

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this is all over what we're 
working for. 

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Yeah, I love it. 
Congrats on your success. 

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Thank you so much for coming on 
the show. 

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Hopefully we'll have you on 
again soon. 

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00:16:07,800 --> 00:16:10,520
This is absolutely fantastic. 
Thank you so much for having me,

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Adam.