Latest / The BioHub - by Avetix / Episode 204 - Samir Ounzain - Co-founder & CEO Haya Therapeutics

Transcript

Episode 204 - Samir Ounzain - Co-founder & CEO Haya Therapeutics · The BioHub - by Avetix

• 0:06
  So welcome back to another episode of the Bio Hub and
• 0:09
  Avetics podcast. Today, I'm joined by Samir
• 0:12
  Unzen, Co founder and CEO of Higher Therapeutics.
• 0:17
  Since founding Higher, he has led the company from academic
• 0:20
  discovery to a well capitalized biotech platform, raising more
• 0:23
  than $100 million and securing a major strategic collaboration
• 0:27
  with Li Lily. In this conversation, we talk
• 0:30
  about the founding story behind Hire, the challenges of building
• 0:33
  the company in the early days, how they've been approaching AI
• 0:37
  and multiomics, and what's next as they prepare to enter the
• 0:41
  clinic. Pleasure to have you, Samir.
• 0:43
  How are you? Very well.
• 0:45
  Thank you for the invitation. Really looking forward to the
• 0:48
  discussion. Yeah, it's been a while in the
• 0:50
  works, this one, so really excited for it.
• 0:54
  I I want to start at the beginning.
• 0:56
  So what what originally inspired you to found Hire and what was
• 1:01
  the big vision from day one? Yeah.
• 1:05
  So hire was really a consequence of my my life's academic work.
• 1:10
  So I very much was always obsessed with understanding what
• 1:14
  seems like a simple question, but it's actually a very, very
• 1:17
  complex question that has potentially a huge impact on
• 1:20
  medicine, which is how does your genome speak to the environment?
• 1:25
  So if you imagine your genetic material, your DNA, it's really
• 1:29
  the the code, the software that allows you to respond to the
• 1:33
  environment, to develop and to adapt.
• 1:35
  And unfortunately, most of the common and chronic diseases that
• 1:40
  are plaguing society, those diseases that are really
• 1:44
  limiting human health span are diseases caused by your
• 1:48
  environment and common genetic variation.
• 1:51
  And that ultimately integrates converges on your genome and,
• 1:57
  and a particular region of your genome that I'd spent my whole
• 2:00
  career studying. So this region of the genome is
• 2:03
  called what's called junk DNA. Then it 10 years after that it,
• 2:08
  it started to be called the dark matter of the genome or the dark
• 2:12
  genome. We like to call it the
• 2:14
  regulatory genome. And the academic work that I've
• 2:18
  been done unlocking and unmasking this, this biology in
• 2:21
  the context of cardiovascular disease and heart failure in
• 2:24
  particular, was really the stimulus to to make the decision
• 2:28
  to start translating this from scientific curiosity and
• 2:31
  discovery into a potential tractable causal drug
• 2:35
  development process that could really, we believe, change the
• 2:39
  Natural History of common and chronic disease.
• 2:41
  So that was really the division from the outset which was
• 2:45
  unlocking this biology might actually provide the industry
• 2:48
  with a tractable way to make common and chronic disease
• 2:52
  something that is disease modifiable.
• 2:56
  Yeah, it's super exciting. And you describe, let's just
• 2:59
  talk about that term you use for the regulatory genome as as dark
• 3:03
  matter of biology. But for those less familiar,
• 3:06
  what, what does that actually mean and why?
• 3:08
  Why is it so important? So the term dark matter really
• 3:13
  originated at the beginning of the century when we when we
• 3:16
  sequenced or published the first draft of the human genome in
• 3:19
  2001 actually, when obviously this was a huge 15 year project
• 3:24
  to to essentially decode the book of life, understand all of
• 3:28
  your genetic material, find all of the genes and obviously genes
• 3:33
  equal phenotypes, equal disease. That was the the the working
• 3:37
  hypothesis that if we can decode all of your genetic material,
• 3:41
  all of your genes will be able to solve all disease.
• 3:43
  And there was kind of a really big surprise, which was we
• 3:47
  sequenced the human genome, and then we asked what percentage of
• 3:50
  your human genome actually codes for genes, and it turned out it
• 3:54
  was less than two percent. 1.8% of your genome is actually
• 3:58
  what's technically called protein coding.
• 4:00
  So that was the definition of the gene at the time.
• 4:04
  And furthermore, those genes, about 20,000 of them are
• 4:09
  basically the same in number, function and complement as a
• 4:15
  fruit fly or a worm. So this was kind of an
• 4:18
  intellectual, I would say shock that happened to, to molecular
• 4:22
  biology that was really crystallized by the full draft
• 4:25
  sequence of the human genome, which was less than 2% of the
• 4:29
  genome is actually protein coding it's genes.
• 4:32
  And the genes we have are basically the same as a family
• 4:34
  or a worm. So the obvious question is,
• 4:37
  well, why are we different to a family or a worm?
• 4:41
  And what is that 98% of the genome doing now?
• 4:44
  At the time it was called junk DNA.
• 4:46
  It was considered as evolutionary trash that had
• 4:48
  accumulated in the genome and wasn't doing anything.
• 4:51
  But obviously post the human genome being sequenced, we
• 4:56
  started to change the terminology thing.
• 4:58
  Maybe it's having some regulatory function.
• 4:59
  Maybe it's doing something really important that makes us
• 5:02
  human and allows us to adapt the environment and is maybe really
• 5:05
  important in disease. But we don't quite know what
• 5:07
  it's doing. And that's why it it, it was
• 5:11
  given this name, the dark matter of the genome, like dark matter
• 5:14
  in the universe, it's central to the fabric of reality of the
• 5:17
  universe. But we don't know what it does.
• 5:20
  And the same kind of metaphor was applied to to the human
• 5:23
  genome. Now over the last 2025 years,
• 5:27
  we've started to really understand what it's doing.
• 5:29
  So many of your audience folks will be familiar with the
• 5:33
  concept of epigenetics, how non DNA based changes to your genome
• 5:41
  can influence how you respond to the environment.
• 5:45
  Most of the epigenetics happens in the dark matter of the
• 5:48
  genome. The dark matter of the genome is
• 5:49
  really think of it as the information processing core of
• 5:53
  your genetic material. It's how your genome speaks to
• 5:56
  the environment to adapt and to dictate the behaviour of cells,
• 6:00
  tissues and ultimately the the Organism.
• 6:04
  Now what about disease? So during my PhD studies
• 6:07
  actually, which I studied my PhD in in 2004, post the genome
• 6:12
  being sequenced, the community started running the so-called
• 6:16
  genome wide association studies, which is taking thousands of
• 6:20
  people with the disease, thousands of people that are
• 6:23
  healthy and looking for common genetic variation that
• 6:26
  predisposes to that disease risk.
• 6:29
  So they're called GWAS for sure. And we had another kind of
• 6:32
  shock. And it's always the same #98%.
• 6:35
  It turns out that 98% of the genetic variation that
• 6:39
  predisposes you to or fine tunes your risk to common and chronic
• 6:43
  disease, it's not found in protein coding regions.
• 6:47
  It's not found in your genes. It's found in the non coding or
• 6:50
  the dark matter of the genome. So there's these two things that
• 6:53
  have emerged. How you respond to the
• 6:55
  environment via a process of epigenetics is hard wired or
• 6:59
  processed in the dark matter of the genome.
• 7:01
  And most of the genetic risk that fine tunes your
• 7:04
  predisposition to these common and chronic diseases is found in
• 7:08
  the dark matter of the genome. So maybe we should start
• 7:10
  thinking about what is the dark matter of the genome doing in
• 7:13
  terms of disease initiation, disease progression, disease
• 7:16
  driving, and ultimately how does it influence field function and
• 7:20
  survive outcomes in common and chronic disease.
• 7:23
  And that really is the thesis that underpins everything we've
• 7:27
  been doing at HIRE over the last six years.
• 7:32
  Yeah, I love your journey from where you've gone to to where
• 7:34
  you are now. And then there's this added
• 7:36
  aspect of AI machine learning, which we're actually talking
• 7:42
  about a little bit before we got on.
• 7:44
  They're obviously getting huge attention right now.
• 7:47
  How do you think your approach is genuinely different from many
• 7:50
  of the other companies in in the space?
• 7:54
  Sure. So, so obviously I, I've kind of
• 7:57
  framed that we're unlocking 98% of the genome that was
• 8:00
  considered dark. And and actually what we're
• 8:03
  unlocking within that region of the genome, it's a biomolecule
• 8:08
  RNA that doesn't act as a messenger for protein, but acts
• 8:11
  as an information processor. So it's a completely novel type
• 8:14
  of RNA from your dark genome or the dark matter of the genome
• 8:17
  that transacts these information processing events between the
• 8:21
  outside world, the environment, the inside world, your genome
• 8:25
  and how that in an emergent way controls your cellular
• 8:28
  behaviour. Now this requires a suite of
• 8:35
  multimodal omic based approaches.
• 8:38
  So post the human genome, the the, the post genome era, all of
• 8:42
  these accelerating genomic technologies have emerged that
• 8:45
  allow us to start looking at the activity of your whole genome at
• 8:48
  different levels. At the level of the RNA output
• 8:51
  of the whole genome, not just messenger RNA, but all this
• 8:54
  regulatory RNA at the level of how the epigenome is regulated
• 8:59
  or is active. So epigenetics at the level of
• 9:02
  the three-dimensional topology and confirmation of your genome.
• 9:05
  Your genome inside a cell isn't a linear sequence, it's a 3
• 9:08
  dimensional topological structure that is changing and
• 9:11
  adapting all of the time. Now to unlock this biology, you
• 9:15
  have to have a multi layered holistic assessment of all of
• 9:20
  that activity. So how do you do that?
• 9:23
  Well, you need the input high quality multi modal, multi omic
• 9:27
  input data that captures all of those layers of regulation.
• 9:31
  And then the only option that you have actually is to use AI,
• 9:35
  machine learning and other very complex data science approaches,
• 9:38
  approaches to deconviolute and find patterns in that
• 9:42
  information. So actually Hire is a fully AI
• 9:46
  enabled company from a drug discovery and drug development
• 9:49
  perspective, because without using cutting edge AI
• 9:52
  approaches, we wouldn't be able to deconvolute that biology.
• 9:55
  So what differentiates us in terms of how we use AI?
• 9:58
  Well, we're not using AI to generate molecules against known
• 10:03
  targets or known pathways, or to unlock protein structure or
• 10:08
  unlock aspects of known biology. We actually use AI to completely
• 10:11
  unlock new biology. So our AI enabled platform
• 10:15
  allows us to unlock how the dark genome, how these regulatory
• 10:20
  RNAs in an emergent way control cellular identity and cellular
• 10:25
  behaviour. And then finally and most
• 10:28
  importantly, what we need the AI to help us do is to identify
• 10:32
  within those emergent networks that control cellular
• 10:35
  behaviours, what is the tractable causal novel target
• 10:40
  that fine tunes those responses. So our AI enabled platform
• 10:45
  doesn't just unlock new biology, but it identifies first in
• 10:48
  biology targets. So all of the targets in the
• 10:51
  higher pipeline are first in biology.
• 10:53
  They were unknown, they were novel.
• 10:56
  So I think when we look at the AI ecosystem in terms of drug
• 10:59
  discovery, drug development, I think the greatest test of, of
• 11:04
  AI going forward will be can you use AI to unlock new biology and
• 11:09
  identify completely novel first in biology targets that don't
• 11:12
  even exist. And last but not least, can you
• 11:16
  actually drug them? Can you make therapeutic
• 11:19
  products that you can translate into clinic against this first
• 11:22
  in biology space? And and that's why I'm, I'm very
• 11:26
  happy to say that we've been doing it higher for the last six
• 11:28
  years and we're translating that into clinic.
• 11:33
  I think that answer was really important as well because I feel
• 11:36
  like AI has been this buzzword in the industry for the last 2-3
• 11:40
  years. And you know, the amount of
• 11:43
  companies say, yes, we use AI, but really like you can, you can
• 11:47
  say use AI, but really talking about it in that level of detail
• 11:50
  about how using AI and how you're thinking about it is the
• 11:53
  difference. Your platform combines AI,
• 11:58
  multiomics, Human Genetics and and RNA engineering.
• 12:03
  Also, how is how important is the integration when it comes to
• 12:06
  understanding disease at the level of cell state?
• 12:11
  You know, garbage in, garbage out.
• 12:12
  OK, it's kind of a cliche, but it's so true.
• 12:15
  So if you really want to unlock this biology, the quality of the
• 12:20
  input data to build your models to unlock it is everything.
• 12:23
  So we invest a lot of resources at hire.
• 12:26
  We are, I like to say we're a product focused platform company
• 12:30
  and I'll I'll touch on that in a moment.
• 12:32
  But we invest a lot of resources in how do we build the data
• 12:34
  sets, these multi modal layered multiomic genomic data sets to
• 12:41
  unlock the regulatory genome to build these access.
• 12:44
  So the quality, the paired sampling from patient biopsies
• 12:49
  to animal models to vitro systems where we deploy on that
• 12:52
  same biological material at the same quality standard, these
• 12:55
  multi layered, multi omic approaches, it's every it's
• 12:59
  absolutely everything in terms of the models that we can then
• 13:01
  build to unlock this biology of the regulatory genome and to
• 13:04
  identify first in biology targets.
• 13:07
  So I think that is absolutely critical from an integration
• 13:10
  piece. Having disparate types of
• 13:12
  multiomic data from different sources, not all captured in the
• 13:15
  same material doesn't allow you to build the same quality of
• 13:18
  models to start unlocking this biology.
• 13:21
  But you know, I know we're talking about AI, but I really
• 13:23
  want to just emphasize one thing higher.
• 13:27
  Everything we do on the discovery, the multiomics and
• 13:30
  the AI side serves one purpose where we also leverage data
• 13:33
  science and AI, which is what is the causal pathobiology that
• 13:38
  drives feel, function and survive outcomes in the
• 13:41
  patients. You know, we're interested in
• 13:43
  common and chronic disease. Common and chronic disease has
• 13:45
  become very apparent that single proteins and pathways as single
• 13:51
  broken parts for targeting and not really moving the needle.
• 13:55
  So if you really look at common and chronic disease and ask the
• 13:58
  question, why does that person with heart failure feel bad,
• 14:02
  function badly and has pretty poor outcomes, The, the, the
• 14:06
  causal unit of that, of those fuel function and survive
• 14:09
  outcomes isn't a pathway or a gene or a protein.
• 14:13
  The causal unit of those outcomes, it's cellular
• 14:15
  behaviour within the environment of the heart.
• 14:18
  So if you really frame the, the, the target as cell states, so
• 14:24
  reprogramming in a causal way, the cellular states that govern
• 14:27
  tissue architecture, that then governs how you feel, how you
• 14:30
  function on survive. If that's your focus, then you
• 14:33
  need to use AI all the way from the patient all the way to
• 14:35
  target discovery to unlock a tractable target that allows you
• 14:40
  to induce that cellular reprogramming.
• 14:43
  So we always build backwards from the pathophysiology and the
• 14:47
  path of biology that underpins feel, function and survive.
• 14:50
  That's what we're obsessed about.
• 14:52
  Why do people not feel well, not function well, and not survive
• 14:55
  with these diseases and then build backwards to the genome?
• 14:59
  So we build backwards from patient need and forwards with
• 15:02
  what we call biological truth or biological causality.
• 15:05
  Let's embrace the complexity of the biology to illicit disease
• 15:10
  modifying effects that ultimately will impact fuel
• 15:13
  function and survive. Yeah, Yeah.
• 15:17
  It's really exciting. And you you've talked about this
• 15:19
  a little bit as well that you've gone from being a postdoc
• 15:23
  researcher to now becoming ACEO of a venture backed biotech.
• 15:28
  What was that transition like? And also what what surprised you
• 15:31
  most in that transition? I mean, it's a big transition.
• 15:38
  I mean, what I guess some of the key perspectives is as an
• 15:44
  academic scientist, you you kind of have an open-ended objective
• 15:48
  which is to discover new biology and make discoveries.
• 15:51
  Transitioning into the CEO of a drug discovery and drug
• 15:54
  development company, my focus was building backwards from the
• 15:58
  patient, the product and the outcomes.
• 16:00
  So that was a complete reframing of there is a goal and objective
• 16:04
  of hire, which is to develop safe, accessible, needed and
• 16:07
  effective medicines in these common and chronic diseases as
• 16:10
  fast as possible. OK, So very, very different
• 16:14
  framing from my academic career or my academic training, which
• 16:17
  is let's learn more about this biology.
• 16:21
  So you really move from asking what is interesting to what
• 16:24
  matters most for the patient. And the other I think big, big
• 16:29
  change was in academia, I think capital is usually constrained
• 16:35
  and the most important capital is often ideas and and
• 16:37
  knowledge. Whereas in biotech the scarcest
• 16:41
  resource, the scarcest capital is time.
• 16:45
  Time is everything. So you go from maybe serially
• 16:49
  executing to parallel processing.
• 16:51
  You know, you really what is important versus what is not
• 16:55
  important. These are kind of basic things,
• 16:57
  but are very different between kind of my academic career and
• 17:00
  my entrepreneurial career as ACEO.
• 17:04
  And last but not least, academia is very much, I would say
• 17:07
  somewhat a solo enterprise, a solo journey.
• 17:10
  Whereas being ACEO and founder of of HIRE, it's actually all
• 17:15
  about the team. I basically my most important
• 17:18
  job is just to hire the right talent and unlock that talent.
• 17:22
  So I'm really a people amplifier, a people facilitator.
• 17:26
  It's all about the team, it's all about the collective.
• 17:28
  It's setting that kind of no star vision.
• 17:30
  What is the objective, safe, accessible, needing and
• 17:33
  effective medicines as fast as possible, bring in a team,
• 17:37
  unlock that team to deliver on that.
• 17:39
  So they, I think, are the big differences between an academic
• 17:42
  mindset and ACEO mindset. How, how quickly do you think it
• 17:46
  took you to really realise that? I think it I was, I would say
• 17:58
  prepared for that mindset shift because before making the jump
• 18:03
  and leap with my Co founder Daniel Blessing, CSO of hire,
• 18:07
  who was also finishing his PhD at that time, it was a big
• 18:11
  decision for both of us to leave academia.
• 18:13
  My dream had always been to be a professor.
• 18:16
  I had some tenure track opportunities to set up my own
• 18:18
  lab. You know, this was a life
• 18:20
  changing decision both for myself and for Daniel.
• 18:22
  So we actually did quite a bit of work before making the
• 18:26
  decision. And some of the work I did,
• 18:28
  which is why I love to be on podcasts like yours, is podcasts
• 18:33
  and YouTube videos around biotech building and what it
• 18:37
  takes to succeed. What are the mistakes I consumed
• 18:41
  for probably a year and a half before making the decision to to
• 18:45
  found hire. I consumed everything I could
• 18:46
  find online about biotech venture, biotech building,
• 18:50
  lessons learned, mistakes not to be repeated.
• 18:53
  So as I went into it, I think I had a framing that 90% of the
• 18:58
  success of this venture is going to be about the talent, the
• 19:00
  people and the culture that allows us to deliver on the
• 19:04
  opportunity and the vision of this technology and these
• 19:07
  products. So, so I think thanks, you know,
• 19:13
  to folks like yourself for putting out this content because
• 19:16
  it did help me frame how we were going to build the organization,
• 19:21
  how we're going to architect the culture and how we were going to
• 19:24
  establish an operating model that we believed would be the
• 19:27
  most effective to deliver on, on these objectives and these
• 19:30
  milestones. I think that's really
• 19:33
  interesting because I, I spoke about that mindset when we first
• 19:35
  met and, and I think the exact question I asked you was who,
• 19:39
  who was your mentor or did you have mentors as you, as you came
• 19:42
  through? And the answer was, well, no.
• 19:45
  I just utilized, you know, books, podcasts.
• 19:49
  And I think that's, that's really powerful because you can
• 19:52
  really utilize that, especially in this day and age, everyone's
• 19:55
  putting out content and there's these little golden Nuggets of
• 19:58
  things that people can consume. So it's it's awesome.
• 20:02
  Just. You go ahead.
• 20:04
  Now I would just add that of course, once we started, then
• 20:07
  there was a lot of experienced folks that did provide
• 20:10
  mentorship, whether it was advisors at the board level.
• 20:13
  And where they become really important, I believe is kind of
• 20:17
  filtering and triaging the crowdsourcing, because when you
• 20:21
  Crowdsource, it's kind of a deluge of information.
• 20:23
  And I think the key, the key lesson or the key aspect of
• 20:29
  crowdsourcing information that is really important is knowing
• 20:32
  what to triage, what to keep and what not to keep.
• 20:36
  Yeah. And I, and, you know, I think I,
• 20:37
  I, I did say to you, one of the quotes I love is, you know,
• 20:40
  smart people learn from their own mistakes.
• 20:43
  Wise people learn from the mistakes of others.
• 20:46
  And I think part of being wise is to be able to triage some of
• 20:50
  that crowd sourced information. Yeah, definitely.
• 20:55
  Let's just go a little bit more into detail about the build of
• 20:58
  the company, especially with a, a new scientific approach, which
• 21:02
  we've we've been talking about what what were some of the
• 21:05
  biggest challenges in the early days of hire.
• 21:09
  So I think in the early days, obviously you have your value
• 21:14
  proposition, your scientific vision, how you think your
• 21:17
  innovation is going to change medicine.
• 21:19
  But then you need to obviously build a team, the right type of
• 21:23
  talent in the right type of ecosystems to deliver on that
• 21:27
  vision. OK.
• 21:28
  So we're working in RNA therapeutics, genomics first in
• 21:33
  biology approaches, we needed to build the ecosystem suitable for
• 21:37
  us, which just so happened to be a Swiss US model.
• 21:41
  So we were born in Switzerland, but in terms of genomics, RNA
• 21:45
  therapeutics, there was one particular ecosystem where there
• 21:48
  was an exceptional amount of talent that was very valuable to
• 21:50
  our mission, which was S California, San Diego, great hub
• 21:54
  for RNA genomics, etcetera. So, so we built that from day
• 21:59
  one. We said we're going to be a
• 22:01
  Swiss American Enterprise where we're capturing talent and value
• 22:05
  from those two ecosystems. So that was a challenge, you
• 22:08
  know, as starting that and, and and convincing that type of
• 22:12
  talent to to join a very early stage company.
• 22:15
  But then the third to hire and build the team and the talent to
• 22:19
  execute on that vision. You need capital, you need
• 22:22
  resources. OK.
• 22:24
  And so probably the biggest challenge at the beginning for
• 22:26
  hire was securing any capital. You know, we we really went out
• 22:31
  there to the market in 2018 with this idea, you know, me and my
• 22:35
  Co founder, we kind of went all in.
• 22:37
  We, we abandoned our academic careers and said we're going to
• 22:41
  build hire. We didn't raise any money until
• 22:44
  essentially the beginning of 21. There was COVID in the middle
• 22:48
  and there's a few things, but you know, it was essentially a 2
• 22:52
  1/2 year with not a single dime in the company.
• 22:56
  And part of the reason for that is we were going out to the
• 22:59
  world somewhat naively saying we're developing a first in
• 23:03
  biology, RNA therapeutic to combat heart failure.
• 23:07
  Now the world in 2018 and 19 was just getting to grips with
• 23:12
  genetic medicines, oligonucleotides and and RNA.
• 23:15
  It was in it was kind of obsessed with rare diseases and
• 23:18
  oncology and the idea of developing a first in biology
• 23:21
  RNA medicine for heart failure was, I would say not the most
• 23:26
  attractive value proposition to venture capital.
• 23:29
  So, so that was the biggest challenge translating the
• 23:32
  biology, translating the opportunity in the indication
• 23:34
  space to to bring the resources into the company to allow us to
• 23:40
  build a team and execute on provision.
• 23:43
  Yeah, amazing. And you've now got to a point
• 23:48
  where you've got some really major milestones coming up,
• 23:52
  particularly moving into the clinic, right?
• 23:54
  You know what, what are you excited about in the next sort
• 23:57
  of year to to 18 months? Yeah.
• 24:00
  So, you know, for me, everything we've done at Higher so far is a
• 24:06
  means to an end. And the end is truly tractable
• 24:11
  causal disease modifying therapies in common chronic
• 24:14
  disease. That's ultimately the only thing
• 24:18
  we're focused on doing. And I think the first step on
• 24:21
  that path is translating this biology, taking your first
• 24:25
  molecule, your first asset into clinic.
• 24:28
  So over the next 12 to 18 months, not only are we going to
• 24:31
  be transitioning into the clinic, but we're going to start
• 24:34
  having some little, some little peaks.
• 24:37
  First of all around safety and tolerability with our
• 24:40
  investigational molecule. But ultimately over the next two
• 24:44
  years, we'll start to get some insights if we can actually in
• 24:48
  an RNA guided fashion drive cellular reprogramming in, for
• 24:53
  example, cardiac fibrosis. Now this is obviously a huge
• 24:57
  moment and not just for hire, but I believe for the field
• 24:59
  we're taking a cellular reprogramming approach in to
• 25:03
  patients using the regulatory genome as the substrate for that
• 25:08
  approach. But I think what it will do is
• 25:11
  we haven't talked a lot about it here, but hire, we're product
• 25:14
  focused. But on the front end we have I
• 25:17
  think a pioneering drug discovery and drug development
• 25:20
  platform that unlocks this biology, identifies novel
• 25:22
  targets and in a tailored fashion delivers oligonucleotide
• 25:27
  therapies against those targets to translate into clinic.
• 25:30
  Now as soon as we start to demonstrate safety, tolerability
• 25:34
  and potential signals for proof of mechanism, it unlocks our
• 25:38
  whole platform into other areas. And I think this is what makes
• 25:42
  it a very exciting next 24 months for hire, which is
• 25:46
  translating this biology into clinic and potentially unlocking
• 25:49
  the full power of our platform. Because ultimately we want to
• 25:53
  unlock the power of the regulatory genome as fast as
• 25:55
  possible in these common and chronic disease areas where I
• 25:59
  think there's a huge unmet need. And there's a huge, there's a
• 26:01
  huge requirement actually for changing the lens by which we
• 26:05
  approach these diseases, changing the framing.
• 26:09
  And I think the next 12 to 24 months from higher will
• 26:12
  hopefully start changing some of the convictions in the industry
• 26:16
  around how we should approach these type of diseases.
• 26:19
  Yeah, I can't wait to see it for sure.
• 26:22
  It's been awesome learning about what you guys are doing.
• 26:25
  And that brings us to the final question, which the question
• 26:29
  asked every one of our guests. If you could have dinner with
• 26:33
  any scientist or innovator, past or present, who would it be and
• 26:37
  why? Yeah, I mean, I, I have so many
• 26:41
  names here, but, but actually very recently we lost one of
• 26:45
  the, The Pioneers and, and the legends of our whole industry,
• 26:49
  Craig Venter. And I never, unfortunately I
• 26:52
  never had the opportunity to, to speak to him in person or have
• 26:58
  dinner with him. And I, and I would have loved
• 26:59
  to, to have dinner with Craig Venter.
• 27:02
  I actually read his autobiography, I think it was
• 27:04
  2000 and seven, 2008 when it came out.
• 27:06
  And it actually had a big, I didn't realise at the time
• 27:08
  because I was so obsessed about academic path, but it had a big
• 27:12
  impact in my thinking, you know, 10 years later when I started to
• 27:17
  think about founding higher. And you know, when you read his
• 27:19
  autobiography, there was some really a lot of things resonated
• 27:23
  with his vision, his ambition, his, I would say, ability to
• 27:30
  maybe challenge convictions in terms of how we think about
• 27:34
  biology. You know, I, I personally think,
• 27:36
  you know, everything we're doing at hiring the whole kind of post
• 27:39
  genomics era, you know, we're standing on the shoulders of, of
• 27:42
  people like Craig Benser. He, he fundamentally changed how
• 27:45
  we think about biology. He was really helped launch the
• 27:49
  whole genomic era and I think what I admire most about him
• 27:53
  that came across as an autobiography in the interviews
• 27:55
  I've seen with him and what he actually achieved is his ability
• 27:58
  to really challenge assumptions. He really positioned biology
• 28:03
  front and centre as an information science and I think
• 28:07
  what we're doing ultimately is standing on the shoulders of
• 28:10
  pioneers like him. So I think the next frontier is
• 28:13
  really from moving away from reading the genome to actually
• 28:18
  understand how the genome speaks, how the genome regulates
• 28:22
  cell states, and then translating that into tractable
• 28:26
  genetic medicines. And I think Craig Venter really
• 28:30
  helped give birth to the whole field of genomic science that is
• 28:35
  the foundation of everything we do at HIRE.
• 28:38
  Love that answer. It's tied it all together as
• 28:40
  well which perfect. Thank you so much for coming on
• 28:44
  Samir. It's been a pleasure.
• 28:46
  Thank you for the invitation. Really, really appreciate the
• 28:48
  chance to tell you a little bit about our story.
• 28:51
  No, it's been so good and everyone please like and
• 28:54
  subscribe to the bio hub and and thanks so much again, some here.
• 28:58
  Thank you.