Latest / The BioHub - by Avetix / Episode 139 - Tess Bevers - CEO & Founder - Novogaia

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Episode 139 - Tess Bevers - CEO & Founder - Novogaia · The BioHub - by Avetix

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  So today I'm joined by Tess Bevers, the CEO and founder of
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  Mobagaya, a company using AI and mass spectrometry, to discover
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  new Masons. From from By Tess.
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  Welcome to By the Papers. Thanks, Amber.
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  And So what would you say really LED you to start Mobagaya and
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  funks on those molecules from nature?
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  It was the big moment really came when I realised that the
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  technology kind of finally caught up with the opportunity
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  that has been a big, big bottleneck in the past years to
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  be screened nature systematically and find new
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  chemistry from nature systematically.
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  And so on one hand, it's definitely my passion for
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  nature, for biology, the the beauty that I see in in how
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  nature's able to design the best solutions, but also
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  understanding of that with AI and modern last photography
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  technologies, we can string nature in a more systematic way.
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  And that bottle, Mcgooley, unlocks the new era of finding
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  new drugs for nature. And I see.
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  And why are fungi such an interesting source of the
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  mercury? I would say so.
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  Overall, many, many parts of nature are still very valuable
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  to to screen and we've screened very little of it so far.
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  But specifically fungi are exciting me a lot because they
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  are also eukaryotes like Hughes and they have an ability to make
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  a wide range of diapers molecules.
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  They have been the least explored of many other parts of
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  nature, even though it's the largest Canadian of all canoes
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  in nature. And we also see now advancements
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  in their culture and technologies and ways of how we
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  can actually work with fungi that now make it also much
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  easier to to screen fungi than it has been before.
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  And what's being the the biggest bottleneck in a natural public?
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  Discovery previously there have been overall the the bowl neck
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  in general has been like accessing the chemistry from
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  nature and prioritize the amount to focus on thing.
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  There have been over the years have been different like phases
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  of, of bottlenecks that we've been able to, to to bypass.
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  So in the in the early days, it's been way more about like
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  how to actually culture the the microbes or, or different parts
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  of nature. That was more like early days of
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  natural part of drug discovery. From the 80s onwards, it's been
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  more, the culturing was we were getting better and better to
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  culturing, but we had a big issue of, of rediscovering the
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  same molecules over and over again.
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  So the replication issue where big pharma spent a lot of money
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  trying to find new molecules in nature and make federal
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  financial structures that in the era of like genomics.
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  So from the 2000s to 2015, a little bit later, we could
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  finally see that there's many, many more molecules that each
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  micro can make. But then the question was like,
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  how do we actually express those compounds?
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  How do we like are able to get our have a hand on these
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  molecules that are like cryptic or or usually not expressed by
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  these organisms? And now we're really entering
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  since a couple of years now this, this era of more like
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  modern natural point vertical discovery, which is way more
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  reliant on mass cryptometry. Is it adding factor to
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  prioritize molecules, but still the ball and like is very much
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  that we see that there's so much chemistry out there, but like
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  how do we how do we prioritize which ones are actually feasible
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  for drug development and have the activity that we're looking
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  for? And what's changed more recently
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  with mass spec and AI that makes that?
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  Possible. My SO mass spec is a technology
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  that's been around for like many decades, but in the past it was
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  mainly more used to like identify compounds that we
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  already know, that we have already seen before.
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  So we know the spectrum that is linked to a specific chemistry
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  and we can use that to parse the new data to advertise the
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  molecules that we know is in the SAP.
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  Yes or no. And because one of the very
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  difficult thing to do in mass spec in the past was doing more
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  on targeted aspectrometry, which means that you actually are not
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  looking for a very specific chemistry, but you just screen
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  everything that's there and try to determine what the chemical
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  structures are of like novel compounds.
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  And we've become better and better at doing that.
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  And AI has been a huge facilitator and accelerant on
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  that front as well, because a lot of from every single screen,
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  there's so much mass spec data in the past, you know, chemists
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  just look at the, at the Spectra, try to identify the
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  structures from expertise, expert knowledge, But there's,
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  you know, just that many Spectra that a single person can, can
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  look at at the same time. And so the fact that now we can
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  build these models that have seen so many different types of
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  Spectra and are starting to understand the language of like
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  mass spectrometry and use that to identify all the cures that
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  can ever read, see before. That is allowing mass
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  spectrometry to not not be like a detection tool only, but even
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  like a discovery tool and and thus help us with the
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  prioritization of focus at a. High level, how does your model
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  turn data into actual molecules? So the the Nova Gaia is built on
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  and like computational pipeline that very much looks at mass
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  spectrometry data from different cultures, in our case from fungi
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  and passes that through through the pipeline to identify certain
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  chemical features in these mass Spectra that can help us
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  prioritize whether or not a new molecule we find you've been
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  interested in drug or not. So we, this is like a paralyzed
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  pipeline, right? On one hand, we have culturing
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  of the strains and doing activity screens against a very
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  specific targets that were inside the beds.
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  But at the same time, we've gathered mass spectrometry data
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  and it tells us, OK, from all these active hits that we're
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  finding, we also know the chemical properties of these
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  hits. And thus you should only be
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  focusing on these this subset for actual free development.
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  So one of the big issues and that you've seen a natural
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  product is that you get so many active hits and sometimes you
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  know companies just have to choose a couple of top hits that
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  look most potent and just develop those without having any
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  information about the kind of properties of these molecules.
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  And the big issue for Nash products is that toxicity issues
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  or for develop developability issues.
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  And so having this mass spectral signal where we can already
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  identify the chemical properties, and linking that to
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  the activity makes us choose which of the active compounds
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  are actually the ones most worth pursuing, further developments
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  highlights. OK.
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  Now that's that's really interesting.
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  What would you say your strategy is for building, you know, the
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  guys in the air? From now so I'm very I mean a
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  very strong believer that you need to be laser focused and
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  biotech. I think the fields it has this
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  beautiful sigh of being very like sciency and discovery.
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  But at the end of the day, the real value lies in way more like
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  later stage than the core compounds.
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  And in the early days, I'm incredibly grateful for like our
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  early on investors right who are, who are taking the risk and
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  are able to are giving us a chance to be building this up.
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  But we need to be laser focused on actually getting molecules
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  further along the pipeline as soon as we can.
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  So on one hand we're building Gaia 2, a second version of Gaia
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  one, which will accelerate the pipeline and the process.
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  But most of our folks company is very much our first like hit
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  discovery screens following those hits against very specific
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  targets in the unity and getting those compounds as far away in
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  the background as we can. So a lot of my day-to-day is
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  also around like speaking to senior leaders in in big pharma
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  to understand how they think and how we can best position
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  ourselves for first partnerships.
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  OK, well. That's that's really
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  interesting. And and I know that you're going
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  to a move San Francisco from London and what's really driving
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  that move for you? We start in London.
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  It's an incredible place to be building a biotech.
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  I do see like a huge uptake in the vents in companies popping
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  up in London and London. Just being a good buyer to cover
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  us there as a whole, especially for you and Oxford as one year.
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  And that being said, we've spent more and more of our time in San
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  Francisco the last year here especially.
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  And every time we we came over there, there was definitely like
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  a big uptake in like just it's easier because it's such more of
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  a cons like a kind of like condensed ecosystem.
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  It's much easier to like meet the right people that can
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  accelerate, you know, the Nets introduction and, and are very
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  much in this, in this startup mindset.
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  So I think for us, it's always been a huge accelerate.
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  And remember we were over there and caught us more and more
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  excited. It's to continue to get the
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  company there. Yeah, definitely.
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  And I think you know the UK ecosystem is starting to
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  flourish and beats the event. S Francisco.
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  And finally for the info past, if you could have dinner like
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  any innovative or Santi's past and present, who would it be and
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  why? I am big.
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  I'm personally very, very inspired and a big fan of people
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  who are like just technically excellent in their fields.
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  So like, let's love this talk, but actually like scientifically
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  and on engineering scale, just like the best.
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  And so one, one person that comes up a lot is Tom Nash, the
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  famous professor Princeton password 20/20/15.
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  I believe he, you know, it's one of the founding fathers of a lot
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  of thoughts behind game theory and about strategy and
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  competition. And just like this mix between
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  these like, you know, seeing the world and the beauty of the
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  world, but at the same time try and understand the real like
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  maths and principles behind it. And yeah, always I think it
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  would be a beautiful conversation if if I put it in
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  me possible. Nice, great.
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  Well, thanks so much for coming into the office today tests and
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  doing my Bible podcast live. It was great for you to man.
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  Thanks. Thank you and everyone like us
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  too.