Latest / The BioHub - by Avetix / Episode 244 - Erik Wiklund & Thomas Hansen - CEO & CTO of Circio

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Episode 244 - Erik Wiklund & Thomas Hansen - CEO & CTO of Circio · The BioHub - by Avetix

• 0:06
  Eric Thomas, welcome to the Bio Hub podcast.
• 0:09
  Pleasure to feature you guys. Thanks for coming on.
• 0:13
  Thanks, Callum. Thanks.
• 0:15
  Yeah, I appreciate you guys taking the time.
• 0:18
  If you forgive the. Pun I want to circle back to the
• 0:22
  beginning with you guys because I know you've been on a roll so
• 0:24
  far in 2026. But I think it's always useful
• 0:28
  context to yeah, maybe for listeners tuning in who haven't
• 0:32
  heard of Circle before and to get to know you guys a little
• 0:35
  bit and about your own journeys in biotech.
• 0:37
  But also how the company formed and and various different guises
• 0:42
  under which it's, yeah, made its way to to the here and now.
• 0:47
  Basically, yeah. So Thomas and I, we used to be
• 0:50
  colleagues in the lab back in the late 2000s.
• 0:54
  We, we worked in the same lab during our PhDs and we were just
• 1:01
  serendipitously, I would say stumbled across circular RNA.
• 1:04
  We were looking for something else is one of those stories
• 1:07
  where control didn't work as expected and turned out it was a
• 1:12
  circular RNA. And now we, we run circular RNA
• 1:17
  companies. So that's, that's a bit of fun
• 1:19
  and of course it adds to the whole motivation to try and get
• 1:23
  this to, to work. And I think maybe Thomas, you
• 1:27
  are probably better positioned to to talk about a bit about
• 1:30
  that science and what we did back those days.
• 1:33
  Yeah, it was quite a quite an interesting, interesting period
• 1:37
  for both of us, I believe. But I mean for me in particular,
• 1:41
  you know, now, you know, thinking back is of course the
• 1:43
  other day you were thinking back at 911.
• 1:45
  You vividly remember that day, right?
• 1:47
  And for me, similarly, when we sort of stumbled upon that
• 1:50
  circle RNA that one day where it all sort of made sense and you
• 1:54
  could put in some of that genetic data that you had and
• 1:58
  your, your, your lab work, it all sort of came together.
• 2:02
  And it's a, it's a circle RNA we're looking at.
• 2:05
  And I think that was quite an interesting thing.
• 2:08
  And I had a had again, I was lucky enough to have one of
• 2:11
  those episodes once more in my academic life that was sort of a
• 2:15
  continuation of the work that Eric and I did.
• 2:17
  And and we can come back to that potentially.
• 2:19
  But but yeah, that sort of spawned a lot of a lot of work.
• 2:23
  And basically my whole academic career was was based on that one
• 2:27
  lucky day in the office essentially.
• 2:31
  Yeah. So, and here we are, you know,
• 2:33
  basically in a sense trying to leverage that know how and all
• 2:38
  that information that we were gathering back in the days to
• 2:42
  develop these what we believe is what we expect to be much better
• 2:50
  gene therapy approaches and cell therapy approaches.
• 2:53
  I guess you could say that that day in the lab you're referring
• 2:57
  to I think maybe was 2008 or 2009.
• 3:01
  That's now that that directly led to these acquisitions this
• 3:04
  year by, for example, Eli Lilly acquiring or not therapeutics
• 3:07
  for $2.4 billion. It's a consequence of that
• 3:11
  particular discovery on that day.
• 3:13
  I mean, there's been some pretty good examples of science going
• 3:16
  in unexpected directions in history, right?
• 3:19
  And leading to some pretty good outcomes, you know, some
• 3:22
  experiments set up in a certain way that delivered different
• 3:26
  results that, you know, resulted in massive breakthroughs.
• 3:29
  So yeah. Nice, cool.
• 3:32
  And how did you guys come about forming a company around that
• 3:36
  day in the lab? Like can you talk to us a little
• 3:39
  bit about the inception of the of the business side of things?
• 3:44
  I was, I was running or working in the company that was
• 3:48
  developing immunotherapies and one of those immunotherapies was
• 3:53
  an oncolytic virus. So a virus that they would
• 3:56
  inject into a tumour and it will kill the cancer cells.
• 4:00
  And that was really hot fashionable technology back in
• 4:04
  2010, 2015. But come 2020, there were more
• 4:09
  concerns around that approach. And we had relatively good data,
• 4:14
  but there were small data sets. And the next step of our program
• 4:18
  had a price ticket over about $50 million we would need to
• 4:22
  raise to, to move forward in the clinic.
• 4:25
  And that became really difficult as this modality was going a bit
• 4:29
  out of fashion and the the financing window of during COVID
• 4:34
  on close towards late 2021. So we were scrambling a bit to
• 4:38
  figure out what we do now. And then in parallel here, these
• 4:42
  circular net companies like Orna, which I mentioned before
• 4:45
  and also flagship launched a circular net company, Laurent
• 4:48
  with big financing rounds made me think, look, I have this
• 4:51
  story. I know all the people in the Sir
• 4:53
  Carne space and, and Thomas and I worked together in the past.
• 4:56
  Sophie felt here we have access to some key talent in this area,
• 5:01
  which is now super hot. And one of the reasons we were
• 5:07
  struggling a bit with this oncolytic adenovirus is that we
• 5:10
  couldn't get enough of the, the, the genetic payload it was
• 5:13
  carrying expressed. So then I had this idea, well,
• 5:16
  what how about engineering our virus to express the gene via
• 5:21
  circular RNA? That should allow us to get more
• 5:22
  durable and higher gene expression because the, the
• 5:25
  circular RNA is, is much longer half life than than mRNA.
• 5:30
  So I convinced the, the, the board of the company to park
• 5:34
  those old programs. We still had sufficient capital
• 5:37
  that we could could develop a novel system and, and platform.
• 5:42
  So we decided to do that. Let's let's park the whole
• 5:44
  stuff, build something new. And I called Thomas, took a few
• 5:49
  phone calls to convince him to to come on board.
• 5:51
  I don't think this would have been doable without without
• 5:53
  Thomas. But then Thomas joined together
• 5:55
  with one of his, the people from his lab.
• 5:57
  And yeah, we basically started in 2022 with the aim initially
• 6:01
  to, to build an oncolidic adenovirus platform expressing
• 6:07
  circle RNA, which then subsequently evolved into what
• 6:10
  we're doing now, which is the gene therapy in vivo CAR The
• 6:14
  that that's how we ended up moving into the space.
• 6:18
  Oh, nice. OK.
• 6:19
  And then that's when the name change happened right after you
• 6:23
  came in Thomas 2023 thereabouts, right.
• 6:26
  And based on the platform, obviously.
• 6:29
  Yeah, I think it was still toggle back for yeah, 1 1/2
• 6:33
  years roughly I think from when I joined the company and then we
• 6:36
  name changed and was it May 23? Something like that, yeah.
• 6:41
  Branded as Sergio, namely, I think of the.
• 6:45
  Name it looks forward and backwards or SERC for SERC,
• 6:49
  that's the SERC IO immunotherapy.
• 6:51
  That's what we used to. We used to do and we're still
• 6:54
  somewhat involved in and therefore.
• 6:55
  Sergio, Yeah, yeah, yeah, yeah. Nice.
• 6:58
  I think, I mean, there's sometimes more complicated
• 7:02
  explanations for company names when I ask that question, but I
• 7:06
  mean, for you guys, it just makes a lot of sense.
• 7:09
  So, yeah, sounds good. Cool.
• 7:12
  And then, I mean, you've touched on a few of the reasons already,
• 7:14
  Eric, in terms of why circular RNA platform you guys have.
• 7:20
  Yeah, it's potentially interesting in that world of
• 7:23
  gene therapy and in vivo cell therapy.
• 7:26
  But do you want to give a high level and maybe this is one for
• 7:28
  for you Thomas of sort of why it is so relevant and and what are
• 7:34
  some of the benefits that you get from yeah, from circular RNA
• 7:38
  as opposed to like you said, there are, you know, more
• 7:40
  traditional approaches do. You want to take that one,
• 7:43
  Thomas on the. Certainly.
• 7:46
  On the biochemical advantages of circular RNA.
• 7:49
  Yeah, maybe before I get into that, I mean, I think, you know,
• 7:52
  classically if you want to, I don't know how much of the
• 7:55
  audience are are familiar with classical molecular biology, but
• 7:58
  but you know, typically if you want to increase expression of
• 8:01
  your whatever gene you may have, you typically try to find a more
• 8:05
  potent promoter. So you basically get more RNA
• 8:08
  transcribed and that typically also which sort of translates
• 8:12
  into higher protein expression. But I think you know what's
• 8:15
  what's clever with circules approaches like we, we, we
• 8:19
  approach this from the different sort of from the different
• 8:22
  direction that we, I mean, we can also tap into more potent
• 8:26
  promoters, but we look at the decay of the RNA instead.
• 8:29
  So if and that has an equal contribution to the to the
• 8:34
  expression. In the end, though, instead of
• 8:35
  increasing the production rate, we decrease the decay rate.
• 8:39
  And we do so by namely using a circular RNA instead of our
• 8:42
  linear messenger RNA. So it's well established that
• 8:45
  the circlon A is much more stable in a cell.
• 8:48
  So it's resistant to the typical decay mechanism you have inside
• 8:53
  cells that constantly turn over your messenger and A and and
• 8:57
  therefore it just stays around for a long period of time.
• 8:59
  And you can imagine if you keep transcribing and and and
• 9:02
  producing circlon A, it just accumulates.
• 9:05
  So it gets it get to what we call a higher steady state.
• 9:08
  So the sort of the end point level of expression once you
• 9:13
  have an equal production and decay rate that's much higher
• 9:16
  for circle RNA's than for M RNA's.
• 9:19
  So that's, so that's the basic concept.
• 9:22
  And of course this also requires an optimization.
• 9:24
  How do we how does this we still need to have a good production
• 9:27
  rate, right. So this is stuff we are
• 9:29
  optimizing to, to make sure that our our platform is, is is
• 9:34
  producing efficiently. And then we just basically
• 9:36
  leverage the the intrinsic stability of the circular RNA
• 9:41
  design to achieve that that much higher gene expression compared
• 9:45
  to conventional gene expression platforms.
• 9:48
  Basically, so if we tie that into sort of therapeutic use, we
• 9:53
  can leverage this advantage for a variety of purposes, for
• 9:56
  example, with AAV gene therapy. The biggest problem you have
• 9:59
  with AAV gene therapy today is that it's hard to get enough
• 10:03
  gene expression. You you need to give really high
• 10:05
  doses and they're not particularly potent.
• 10:07
  So what we do is we just switch in our circular RNA based
• 10:10
  expression system and by doing that we can substantially
• 10:14
  increase the the gene expression from the same amount of of
• 10:17
  vector. I think our our latest version
• 10:19
  circuit VEC Generation 5 has shown that it can improve gene
• 10:24
  expression by more than two hundredfold in vivo.
• 10:28
  So if that translates later into primates and then the clinical
• 10:31
  setting, it would entail that you could drop down the dose of
• 10:35
  the AV gene therapy potentially by 200 fold and this would be
• 10:39
  huge for that field. That would mean you can
• 10:42
  substantially improve the toxicity profile.
• 10:45
  You could treat many more diseases and you can make them
• 10:47
  lower cost because they would be more straightforward to
• 10:49
  manufacture. Yeah, I'm going to, yeah.
• 10:53
  I mean it could bring out some assets that have been put on the
• 10:56
  scrapheap for toxicity related issues, right.
• 10:59
  There's been number a number of assets, yeah, that have been
• 11:04
  disregarded for that exact reason.
• 11:06
  Or you simply couldn't get enough gene expression to
• 11:09
  correct the defect. Well, now suddenly you're in
• 11:11
  play. You could use Circouac.
• 11:12
  So we, we believe at least according to the data we have,
• 11:16
  admittedly it's early stage, this could be a disruptive
• 11:20
  technology for, for gene therapy that will be incorporated in
• 11:23
  every gene therapy in in the future because it's such a
• 11:26
  potent way to increase the gene expression.
• 11:29
  Yeah, nice. OK.
• 11:30
  And then on the cell therapy side of things, I know you're
• 11:33
  looking at the platform there as well.
• 11:36
  Like I said before, I'm I'm a failed scientist, so you'll have
• 11:40
  to go light on the detail. But yeah, what are the key
• 11:42
  differences there? And and yeah, where do you see
• 11:45
  the value potentially on that side of things?
• 11:47
  So today there is a lot of excitement around in vivo cell
• 11:51
  therapy approach. So classic cell therapy, which
• 11:55
  is ex vivo ontologist cell therapy.
• 11:59
  In this case, you, you have a patient, you take the blood from
• 12:03
  the patient and harvest the patient's immune cells, T cells,
• 12:07
  and then you engineer those T cells X vivo.
• 12:10
  So outside of the patient, they're propagated up and then
• 12:14
  you give them back to the patient and then they've been
• 12:16
  engineered to attack the cancer cell.
• 12:18
  Now this is shown to be tremendously potent in
• 12:23
  particularly in in blood cancers, liquid cancers where
• 12:26
  there's no solid tumor. This works really well.
• 12:28
  However, as you can hear from how I described this, this is
• 12:31
  very complicated and it can also be a big strain on the patient's
• 12:36
  immune system. And it's very high cost because
• 12:38
  you need to keep the patient also in hospital for 2-3 weeks
• 12:41
  or or more. So this type of therapy is not
• 12:45
  really broadly used and it's mainly used in younger,
• 12:47
  healthier patients that can tolerate it.
• 12:50
  So what's happening now is that we're starting to see technology
• 12:54
  emerge, in particular the mRNA technology as well as LMP
• 12:59
  delivery technology that allows you to do this engineering of
• 13:02
  the T cells inside of the patients instead of having to
• 13:05
  remove the, the blood and the, the, the T cells outside of the
• 13:08
  patients. So this substantially simplifies
• 13:12
  things. If you could just give an
• 13:13
  injection and that's sufficient to to engineer these cells.
• 13:18
  And there has been a number of large transactions in this area,
• 13:24
  like all the big pharma's are moving into this at the at the
• 13:26
  moment. What's interesting if you look
• 13:29
  at the dynamic in that space is you have two major alternatives.
• 13:33
  1 is to use LMP formulated RNA. Now when you do that you can get
• 13:37
  expression of the construct you're delivering, usually a
• 13:40
  CAR, a chimeric antigen receptor for one to two days for the mRNA
• 13:46
  or maybe a week or a little bit more if you use circular RNA.
• 13:50
  So it's quite short lived and that probably has potential to
• 13:56
  work really well for autoimmune disease where you just need to
• 14:00
  main manage the the problematic cell population in the patient,
• 14:06
  but not necessarily eliminated in cancer, you need to get rid
• 14:11
  of all the cancer cells. And we believe currently that
• 14:14
  that short expression window is not sufficient to to deal with
• 14:19
  cancer. So the RNA based in vivo CAR is
• 14:22
  all basically developed for for autoimmune disease.
• 14:25
  Now if you want more permanent expressions, your current only
• 14:27
  alternative is to use a lentiviral vector system.
• 14:31
  This is the same as uses the X vivo strategy.
• 14:33
  Now this works well, but the problem is that these
• 14:36
  lentiviruses that integrate permanently into the patient's
• 14:39
  genome. So then you can never reverse
• 14:42
  it. It's integrated.
• 14:43
  I will stick there forever. And also that integration has a
• 14:47
  risk of triggering secondary cancers.
• 14:50
  So this is irreversible and potentially toxic, but it's the
• 14:54
  only tool available if you want to use this kind of in vivo
• 14:58
  approach for cancer patients at the moment.
• 15:02
  Now with our gene expression system using CIRC RNA, we can
• 15:05
  get expression for three to six months on a single dose.
• 15:09
  So that's position this technology right in the middle.
• 15:11
  You get a new therapeutic window where it's much more durable
• 15:15
  than the RNA. It can probably deal with the
• 15:19
  with cancer more effectively because of that increased
• 15:22
  durability, but it's still transient, it's reversible and
• 15:27
  it can be re dosed so you can control it better and avoid the
• 15:30
  safety concerns of the integrating approach.
• 15:33
  So we're really excited about this, this opportunity as a
• 15:37
  completely novel system to use in an illegal car.
• 15:43
  Yeah, for sure. I can imagine you've had a lot
• 15:45
  of interest on the partnering side with you know with those
• 15:49
  high level stats I guess guys in in terms of people reaching out.
• 15:53
  I mean, I was curious how you guys are thinking about the like
• 15:58
  long term direction of of the company, like whether you are
• 16:02
  going to be, you know, platform based as it were moving forward
• 16:06
  doing many different partnerships with, you know,
• 16:09
  biotechs looking to solve this problem for their own assets.
• 16:12
  Or whether you guys are thinking, hey, you know, we'll
• 16:14
  we'll have a nice mix of both where we look at bringing
• 16:18
  something through of our own, but also keep the keep the
• 16:21
  partnering route alive. Are you able to share any detail
• 16:24
  on that front or where you are with things on on that front or?
• 16:30
  So our, our the technology is a true platform.
• 16:34
  It can be deployed broadly and we therefore enter numerous
• 16:40
  collaborations which have different purposes.
• 16:42
  In some cases, it's to test complementary technology to see
• 16:46
  whether we can make Circuit better or work in a novel area.
• 16:52
  In some cases, there is a partner who would like to use
• 16:54
  Circuit for their own systems, so we simply just give them
• 16:57
  access to the technology. This broaden our opportunity
• 17:00
  landscape and helps us generate data in, in novel settings.
• 17:04
  And thirdly, eventually of course, it'd be great to enter
• 17:08
  partnering deals with big pharma and, and get our technology out
• 17:11
  licensed and and earn funding from from that kind of of
• 17:16
  source. We, we've now we've raised
• 17:21
  capital over several rounds this year.
• 17:24
  The the 1st 2 financings we did were focused on platform
• 17:28
  development, So not necessarily having a concrete therapeutic
• 17:32
  program in mind, but just developing our technology
• 17:35
  platform and then extending our cash runway.
• 17:38
  So the company that we have funding, we have funding for the
• 17:41
  next 4 years at least. And then we've done 2 subsequent
• 17:45
  fundraisings. 1 is towards gene therapy, which I described first
• 17:49
  for this is an AV gene therapy. We're probably going to move
• 17:52
  forward in the genetic heart disease and that leg is funded
• 17:57
  all the way to the, to a clinical trial and clinical
• 18:00
  data, which we aim to deliver in, in that time frame 2930.
• 18:05
  And then the fundraising we did this week, which was another $20
• 18:08
  million or that's oriented towards the evil car program.
• 18:13
  So now we can really, we can really advance on that.
• 18:16
  And I think on both of these programs, we're now doing
• 18:18
  numerous collaborations and and maybe you want to, you know, to
• 18:21
  talk, talk us through Thomas how we're thinking about technology
• 18:25
  collaborations both on our gene and cell therapy programs.
• 18:29
  I think we're we're, you know, open to all sorts of of
• 18:33
  collaboration at this stage. So we're really trying to
• 18:35
  explore all avenues. So I think for the from the AB
• 18:38
  perspective, obviously you know, we are in charge of the AB
• 18:42
  genome basically the, the, the, the DNA that goes into the AB.
• 18:47
  So that would be the cirquevig obviously expressing the circle
• 18:50
  RNA. But we are not experts in in, in
• 18:52
  the AB sort of what you call the surface the capsid of the AB.
• 18:56
  So we're doing some collaboration with other
• 18:57
  companies that have AB capsid that with a suitable tropism for
• 19:02
  you know, a tissue of interest. So it will go more effectively
• 19:05
  to that tissue. So that's, that's one Ave. we're
• 19:07
  exploring trying to identify the best possible capsid out there
• 19:11
  that hopefully will act in A at least an additive way with our
• 19:16
  circuit cassette. So that's I think that's
• 19:19
  probably sort of the main direction with the gene therapy
• 19:22
  to identify the best combination of an AV capsid and and our
• 19:26
  circuit cassette for the more non viral cell therapy
• 19:29
  approaches. I think there we have probably
• 19:34
  quite a some more, what do you call it, some more strings to
• 19:38
  play. So there we need the delivery
• 19:40
  system which is typically used for LMP, but there's also some
• 19:43
  non LMP systems out there that we are eager to test similar to
• 19:47
  AB, they will have different tropism, different different
• 19:51
  effectiveness getting your cargo into the cell.
• 19:55
  And since this is adna that we're delivering, we also
• 19:57
  looking into different DNA formats that would then go into
• 20:00
  that LMP. Still, the DNA sequence will be
• 20:03
  the surf back, but there's different ways you can.
• 20:06
  Put that into adna. So we have, you know, double
• 20:08
  stranded DNA, you have single stranded DNA, you have things
• 20:11
  that people refer to as doggy bone and anoplasmid.
• 20:13
  So we are, we're testing all of this basically and see what
• 20:16
  works best in our hands and what, what seems to have the
• 20:19
  best effect in our, in our mouse model.
• 20:22
  So in, in, in combination with different LMP formulations.
• 20:25
  So as you can see already now we are, we're looking at 3
• 20:28
  dimensions. So that's quite a space to
• 20:30
  cover, but we are letting data sort of guide us through this
• 20:34
  and and and see where it takes us.
• 20:37
  They were particularly excited about the the promise of single
• 20:41
  stranded DNA and we're looking at the build really next
• 20:46
  generation gene and cell therapy vectors that are single stranded
• 20:50
  DNA based expressing A circular RNA.
• 20:53
  So this should make them immune quiet, high expressing and
• 20:57
  really long lasting. And they were doing some, some
• 21:02
  interesting collaborations with US and and Europe based cutting
• 21:07
  edge biotechs that are are in this particular space of of
• 21:10
  single stranded DNA. And given we're now well
• 21:14
  capitalised, we might also consider at some point looking
• 21:18
  at an M&A or in licensing or such technology into circle.
• 21:24
  OK, nice. And thanks for breaking that
• 21:26
  down. I know we we wanted to touch on
• 21:27
  the financing side as well, Alex.
• 21:29
  So it's always useful to hear exactly what those are funding
• 21:33
  those different, different projects, I guess internally.
• 21:37
  Yeah, So, yeah, so yeah, that's super helpful.
• 21:40
  I mean, obviously it's a big shift coming for the business,
• 21:44
  right, over the next 3-4 years. Like you said, you've got runway
• 21:47
  till 2030 now. What do you think the biggest
• 21:51
  shift is going to be going from that sort of, yeah, platform
• 21:54
  company with a lot of internal R&D capabilities into
• 21:58
  transitioning to possibly a clinical stage company within
• 22:01
  that time you obviously mentioned on the cardiology side
• 22:04
  of things, Yeah. How do you begin to plan for
• 22:07
  that I guess? Yeah.
• 22:09
  I just go around and talk about the grand vision, right as being
• 22:11
  the the CEO. But I think Thomas, you have to
• 22:14
  operate operationalise this. Yeah.
• 22:16
  I mean it's a good question. I mean we, yeah, we are, we are,
• 22:21
  I mean basically discussing this internally as we speak.
• 22:27
  So how, how do we best approach this?
• 22:28
  Of course, we have a pretty advanced at least from a sort
• 22:31
  of, but still early, but pretty advanced program in heart.
• 22:37
  We have a lot of data. So we are quite confident in our
• 22:41
  technology when it comes to, to expression in heart from an AV.
• 22:47
  So there we're identifying what would be the best indication
• 22:49
  currently going forward. We have a shortlist.
• 22:51
  So, so the question is now how many indications in that
• 22:55
  shortlist do we have the capacity to basically address in
• 22:58
  a preclinical model? And, and then based on that
• 23:02
  data, what do we then see as the best candidate sort of moving
• 23:07
  forward into an Ind and, and and hopefully a clinical first in
• 23:12
  sort of first our first clinical candidate.
• 23:14
  So I think that's at least so we have I think we are quite mature
• 23:18
  in our thinking when it comes to our cardiac program.
• 23:21
  As you may know, we're also looking at ABS and in the eye in
• 23:24
  the CNS think they are our thinking is a little more tied
• 23:28
  up to what would a potential partner be interested in, in
• 23:31
  seeing here what we don't understand our technology to the
• 23:34
  same extent. We just know that it is vastly
• 23:37
  superior to to the benchmark. But where would that then be
• 23:40
  applicable then for instance, say subretinal disorder?
• 23:45
  So we need to we need to do more homework there.
• 23:49
  And we, we have a, we have a study that actually initiated
• 23:51
  this week that hopefully will help us or guide us as to what
• 23:56
  would be a good ophthalmology indication for us.
• 24:01
  So, yeah, so again, we need to look at the data we are, we are
• 24:04
  working on on a lot of different in a lot of different directions
• 24:08
  towards this. But but yeah, so, so hopefully
• 24:12
  within the next, you know, half a year we will at least for the
• 24:16
  cardiac program be much better positioned to to have a yeah.
• 24:23
  Makes sense? To see a picture of where we
• 24:24
  need to where we should go. Yeah.
• 24:27
  I think it's the interesting thing about having a technology
• 24:31
  platform that has so many potential benefits across so
• 24:35
  many different areas that like you said, Thomas, you're not,
• 24:39
  you can't be experts in everything, right, Indication
• 24:41
  wise, I guess it just heightens the importance of the
• 24:44
  partnerships you're looking at externally to, to guide you on
• 24:48
  that, which is. Yeah, interesting.
• 24:51
  Nice. Well, look, it seems like it's
• 24:54
  going to be a really exciting few years ahead.
• 24:58
  I guess final question in terms of like certainly next year, is
• 25:01
  there anything that? You've.
• 25:03
  You're forecasting the 27. That'll be a real sort of litmus
• 25:07
  test of, you know, the guys are on track.
• 25:09
  They're really nailing it. Is there anything that we should
• 25:11
  be keeping an eye out for? In general, I think what
• 25:15
  differentiates us from the classic biotechs by being a
• 25:21
  preclinical platform company now with two fully funded separate
• 25:26
  programs, it means we have a large number of readouts, we
• 25:32
  have lots of experiments going on continuously.
• 25:35
  So here you have a high frequency news flow, many
• 25:40
  milestones coming up and because it's all early stage, you're not
• 25:46
  necessarily required to communicate negative data.
• 25:49
  Not all of this is going to work, but we have plenty of
• 25:52
  areas we're exploring that something most likely is is
• 25:55
  going to pan out really well. And we already have, like Thomas
• 25:57
  said, we're quite advanced and very confident in our heart
• 26:00
  data. Now this contrasts to your
• 26:02
  classic biotech which has a therapeutic candidate in the
• 26:05
  clinic that requires a lot of capital to bring it through.
• 26:09
  The clinical development can often be a long time between
• 26:12
  data points and you may be looking at an interim data
• 26:15
  point, which is really not conclusive.
• 26:19
  So in this sense, I think it's maybe it's easier and more fun
• 26:22
  to follow a preclinical biotech like us if you're if you're a
• 26:26
  shareholder and and then in, in parallel, I would say this
• 26:31
  platform strategy with two programs that, that lead.
• 26:35
  It also means it's less binary and less risk because there is
• 26:42
  always something to fall back on.
• 26:45
  And we've been fortunate enough now to to raise almost $90
• 26:50
  million this year. So that means also that the
• 26:52
  capital constraint is taking out to the picture.
• 26:54
  We're not facing any financial risk either.
• 26:58
  So that's kind of the overall picture.
• 27:00
  Then I think of course, there are concrete important
• 27:03
  milestones to to look out for. And then yeah, maybe Thomas, you
• 27:06
  want to you want to highlight some of the the big milestones
• 27:10
  from from your perspective? No, I mean you know, I'm AI like
• 27:15
  of course the platform kind of things.
• 27:17
  This is where I have, I think I understand how it works and
• 27:22
  molecular biology. So as Eric was hinting to
• 27:24
  before, we are working on a circuit version 5.
• 27:27
  We have reasons to believe that that would be extremely powerful
• 27:31
  and hard, but how that would work in the CNS or in in the
• 27:36
  eye? We, we, we have not tested that
• 27:39
  yet. So I'm very eager to to set up
• 27:41
  that experiment to see will that translate all those benefits we
• 27:46
  see in the heart? Will that translate to other,
• 27:47
  other avenues? So that would be 111 key data
• 27:51
  point that I'm interested in of course, but but then at the same
• 27:54
  time we are setting up some more preclinical models that will
• 28:00
  mimic, you know, the human genetic heart diseases that's
• 28:04
  out there. And and I think that would be
• 28:06
  the maybe the lack most tests that you were talking about.
• 28:09
  Can we see that those reduction that we are that we are
• 28:12
  claiming? I mean, we see that from just
• 28:14
  me, a gene expression, but how would that translate into where
• 28:17
  you can actually read out a, a phenotype such as arrhythmia of
• 28:21
  the heart or the left ventricular ejection fraction?
• 28:24
  All that stuff that you that we, that we aim to rescue are
• 28:28
  correct. So, so that we hope to those
• 28:31
  data points for these some models will will, will have that
• 28:37
  next year. So, so that I'm very excited
• 28:41
  about when, when we are finally ready to, to do that
• 28:44
  experiments. It takes a while to set up these
• 28:46
  mice models. So that's why it's a, it's a
• 28:48
  little from, from you decide on, on let's, let's try to address
• 28:53
  this, you know, specific genetic, you know, disease until
• 29:00
  you have a mouse model that's up and running and, and until you
• 29:02
  have your, your circrec ABS and all that, it takes a little
• 29:05
  while. So that's why, but we are on
• 29:07
  track and, and this is we're expecting this to read out early
• 29:10
  next year when we finally have it all together.
• 29:14
  So that will be very interesting.
• 29:17
  And then of course. We have a, we have a
• 29:19
  collaboration with one of the major top five pharma companies.
• 29:24
  So they're, they're funding, they're funding a research
• 29:27
  program using Cirquevic in, in gene therapy targeted at CNS.
• 29:32
  And this is ongoing at the moment.
• 29:35
  There will be, we have in vivo experiments running.
• 29:38
  There should be data from this collaboration next year and here
• 29:42
  we're of course dealing with a with a large partner.
• 29:45
  So there is some bureaucracy processes there, but hoping
• 29:48
  we'll be able to release data from that collaboration next
• 29:50
  year and idea I said that that works well that may trigger a
• 29:53
  licensing transaction immediately following.
• 29:58
  Nice, cool. Lots to look forward to.
• 30:01
  Look guys. Thanks for coming on.
• 30:03
  I've really appreciated the chance to learn more about the
• 30:05
  platform and it seems like you guys have, yeah, a lot to
• 30:09
  deliver on in the next few years.
• 30:11
  So yeah, looking forward to staying in touch and tracking
• 30:13
  the progress. Thanks.
• 30:17
  Yeah, thanks for the. Great.
• 30:18
  Thanks. Thanks for having us on, Callum.
• 30:21
  No, no worries. Pleasure.
• 30:23
  All right, We'll see each other soon.