Latest / The BioHub - by Avetix / Episode 152 - Nazli Azimi - Co-Founder & CEO - Therna Bio

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Episode 152 - Nazli Azimi - Co-Founder & CEO - Therna Bio · The BioHub - by Avetix

• 0:06
  Welcome to another episode of the Bio Hub podcast.
• 0:09
  Today I'm joined by Nazli Azimi, Co Founder and CEO of Therna
• 0:14
  Biosciences, a company building AI driven platforms to design
• 0:18
  programmable RNA medicines. Nazli has previously founded and
• 0:23
  scaled 2 biotech companies through to acquisition and is
• 0:26
  now focused on combining deep biological expertise with
• 0:29
  artificial intelligence to rethink how RNA therapeutics are
• 0:33
  designed. In today's conversation, we'll
• 0:36
  explore the evolution of AI in drug discovery, what
• 0:39
  differentiates donors approach, and where the future of RNA
• 0:42
  therapeutics is heading. Naslie, welcome to the podcast.
• 0:46
  Thank you, Amber. I'm excited to be here.
• 0:49
  Amazing. And to start, I'd love to give
• 0:51
  the listeners a bit of context on your background.
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  And you've built and exited 2 biotech companies already.
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  What led you to start Thona and move into the AIRNA space?
• 1:02
  Yes, maybe I'd start by giving a little background about myself.
• 1:06
  I'm a scientist by training. I did my postdoc at the NIH and
• 1:11
  worked on immunology. I was fortunate enough that as
• 1:15
  part of the lab that I was working on at doing my postdoc,
• 1:19
  we discovered a very important cytokine at that time.
• 1:23
  So it created a lot of opportunities for all the
• 1:27
  members of the lab, including myself.
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  So we delved into characterizing this newly discovered cytokine
• 1:36
  and figuring out what what are its function.
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  And you know, a lot of publication and good discoveries
• 1:43
  came out of it. And that cytokine Illinois 15
• 1:46
  turned out to be a really important regulator of the
• 1:50
  immune system. So that as part of that journey
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  I started, I started questioning what are the diseases that could
• 2:02
  potentially be affected by regulation, by dysregulation of
• 2:05
  this cytokine. And I published on that.
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  And then it turned out that it's really important cytokine, one
• 2:14
  of the master regulators of the immune system.
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  And in certain diseases there is a dysregulation in production of
• 2:22
  the cytokine. So the obvious next question in
• 2:25
  my mind was what, what do we do? You know, if I want to block
• 2:29
  that cytokine, it's overproduction so I can have so
• 2:34
  it would have some therapeutic effect in these diseases that
• 2:39
  are driven by the overproduction of the cytokine.
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  Why do I do that? And that was the beginning of
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  how did my previous biotech companies started.
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  I came up with a very novel way of targeting and blocking these
• 2:55
  cytokines in the context of these diseases.
• 2:59
  And then I started, you know, developing that 1st, the concept
• 3:03
  of it that day, NIH and later on when I transitioned to
• 3:06
  entrepreneurship. So the, the thing that's was
• 3:11
  interesting in my journey, when I reflect back now, is that why
• 3:15
  I felt compelled to do that. It was, you know, I, I was
• 3:20
  never, I was never brought up to think about entrepreneurship,
• 3:25
  never had any desire. I just, all I wanted to do is
• 3:28
  just to do research and publish paper and give lectures.
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  And I did pretty good good at that while I did my postdoc at
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  the NIH. But one thing that became clear
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  to me is that my, my brain worked in a very direct and goal
• 3:47
  oriented fashion. I had, I was focused when I was
• 3:51
  setting up an experiment. This is the experiment I want to
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  do. This is the answer I want to get
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  and I was always able to bring it into a conclusion, to
• 4:01
  meaningful conclusion. Yes or no or you know, cannot
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  continue on this. A lot of brilliant scientists,
• 4:10
  they follow their desire different paths and they just
• 4:15
  wander around wherever their curiosity takes in, which is
• 4:18
  amazing. We definitely need those
• 4:20
  peoples. But my brain was more, I want to
• 4:23
  see a conclusion in the work that I'm doing.
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  And then I realized that this is this mentality is very well
• 4:30
  suited for taking this concept, scientific concept and turn it
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  into an application like this is what it means, the discovery.
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  So what I'm going to do with that, that type of question,
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  that type of transition. So I realized that in order for
• 4:50
  me to do that, maybe I need to pursue entrepreneurship.
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  After a night, I I got a job at Fritz Hutchinson as a faculty
• 5:00
  member and I was there. But, you know, it was a moment
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  for me to realize, do I want to be in academia or is it time for
• 5:10
  me to move on to something else? And I chose the latter.
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  It was a really hard decision for me to leave academia, but I
• 5:20
  I felt that I can have a better impact.
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  I can finish the job that I started at the NIH of I
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  discovered this. We or our group discovered this
• 5:30
  cytokine. I discovered the connection of
• 5:33
  the cytokine to some diseases. Now I want to see if I block
• 5:37
  that cytokine, can I see some therapeutic effect?
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  So I started the companies around that very interesting
• 5:45
  learning curve, loved it, loved the whole experience.
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  I'm, I'm, I'm happy that we managed to take that the, the,
• 5:56
  the, the, the strategy, the medicines that we created into
• 5:59
  clinical trial and show efficacy and, and all that good stuff.
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  When you were first thinking about Ferna, what was the key
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  problem in RNA therapeutics or drug discovery that you felt
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  wasn't being solved properly? Yeah, when when I met the
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  scientific Co founders of of Therna, what became clear to me
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  was the potential that an approach that we are pursuing at
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  Therna could solve. Because if you think about it,
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  RNA has been overlooked. Understanding the RNA biology
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  has been overlooked as part of the central dogma being genetic
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  material in the DNA, transferring the information
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  through the RNA into protein. The focus has always been let's
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  figure out the genetic material, the DNA, the efforts on the
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  sequencing in the human genome progress, Human Genome Project
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  and many other efforts figuring out what is the genetic
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  component of of life is made of. The focus has been a lot on that
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  and also on the protein because life, the functionality of life
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  is exerted through protein. So a lot of effort has been done
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  of what the proteins are, how do they behave, what's destruction,
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  structure, function, relationship of the protein, but
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  not much effort on what is this transition transitional molecule
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  called mRNA is doing. And in the past few decades that
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  scientists started figuring out and just asking this question,
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  what's what's in it? You know, are there any
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  uniqueness about properties in there on the RNA are the ways
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  that the expression of any given gene has been controlled through
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  the through the mRNA. And my conversation with with
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  Honey and the focus on the RNA resonated with me because when I
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  was at the NIH, one of the project that we heavily focused
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  on when we discovered the cytokine R15 was on relational
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  control on this cytokines mRNA. Because this particular mRNA is
• 8:21
  heavily controlled not at the chain level surprisingly, but at
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  the mRNA level. So I knew there are many levers
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  that one can pull and push in order to control the expression
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  level at the RNA. So this is this was something
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  that I realized that has been missing just because of the lack
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  of understanding. The whole industry doesn't think
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  about it that that well that deeply.
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  And I hope that with the work that you're doing, implementing
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  some AI, we could shed some light into our new biology in
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  more systematic way so we can leverage it for further drug
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  development and discovery. And there has been a huge wave
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  of AI drug discovery companies over the past few years.
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  Where do you think that first generation fell short?
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  Yes, very good question. It goes back to how much do you
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  know about the biology of the RNA?
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  Because when you talk about the AI and you talk about the
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  morally, it all comes down to data.
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  If you have enough data, you can build any model.
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  The question is, is that model good enough?
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  Is that model is doing something appropriately?
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  Can it have the right prediction?
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  If you don't, if you don't train your model or if you don't build
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  your model based on appropriate data, it's going to be garbage
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  in garbage ads. So the core advantage to be the
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  main advantage or the OR the fundamental advantage that we
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  have at Turner is that the root of the company goes back into
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  biology and AI. And I cannot emphasize it enough
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  because if you don't understand the biology of the RNA, you
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  don't ask the right question, you don't set up the proper
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  experiments, you don't generate the right data.
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  And then again, you have a model that it's a model which is not a
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  good model. And the benefit, our advantage
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  is that the legacy of the company goes for many, many
• 10:43
  decades on the fundamental work that our founders, our
• 10:47
  scientific founders have conducted an understanding what
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  mRNA is, what are the elements that control its regulation, in
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  what context, in what tissue, in what organ, what are the
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  elements that are affecting its three-dimensional structure.
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  Any possible scenario that could affect the RNA regulation, we
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  have taken into consideration when we set up our own
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  experiments and these experiments are different set of
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  readouts, different set of outputs with tremendous amount
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  of data that are proprietary to us.
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  So we can interrogate mRNA, you can interrogate RNA at every
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  potential angle, every potential, every potential
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  question that one can have, what could possibly have to
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  understand the RNA and its relationship within the cell
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  with other proteins with the DNA that it's get translated from.
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  So all of that collectively makes a tremendous amount of
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  proprietary data that it's unique to us and it's relevant,
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  it's biologically relevant because now you can build your
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  model based on your data with the confidence that the good
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  with the with the proper ask input, you can have a reliable
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  output, whatever that output is going to be, that you can be
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  reliable. The first generation of the AI
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  companies that came out, probably very likely it lacked
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  this fundamental biological knowledge.
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  Therefore, there are models, there are robust models, but the
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  output is not necessarily reflective of the biology or
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  grounded in biology. So not not relevant.
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  And this is the problem that you're trying to solve by
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  building a comprehensive set of datas that are rooted in
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  biological system, validated in biological system.
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  So we are actually our lab is mostly our company is mostly wet
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  lab, meaning people are actually doing experiments, taking those
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  data, giving it to their AI scientists who are doing the
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  data build out training, remodeling and building and
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  training the models. So we have confidence that the
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  output is going to be meaningful and biologically relevant.
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  That's a great insight into how Ferner is building things
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  differently when it comes to that biological setting.
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  And so you are building a platform that can design those
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  RNA molecules with very specific properties.
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  What does that actually look like in practice?
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  Yes. So when we have this different
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  models and each of them have a different functionality based on
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  the questions that we had asked during setting up the
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  experiments, getting that lab data, laboratory data and
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  feeding them into the Model 1 of the questions.
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  Is that, for example, is there a way that you can make an RNA to
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  be on in certain cells or tissue and off in others?
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  In other words, is it possible, for example, for us to make an
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  RNA that is tissue specific? You can say, you know, I want, I
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  want my RNA to be only on or expressed in my in muscles, but
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  not in anywhere else. Is that possible?
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  So we have built models to address that specifically.
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  And it's a process where you set up this experiments because you
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  understand the biology, you set up this experiments and then you
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  come and collect this data and you feed them into the into your
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  models in a setting that we call it lab in the loop.
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  So, generation of the data goes into the AI model, trains it,
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  send us some output, that output get tested and then data goes
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  back into the model, retrain it and this thing can go on and on.
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  So you can have that those asks, you can have those proper
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  characteristic as part of the model and then build this
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  different models together. And what we have done at Derna
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  is that we built different models on tissue specificity.
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  We have different model to control the longevity of the
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  RNARNA is short lived by nature because it's job is to naturally
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  take the information from DNA to protein and then disappear.
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  That's, that's what it was intended to do.
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  But if you're thinking about use RNA as a, as a medicine, you
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  want it to be long lasting. So how can you do that?
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  So a tremendous amount of work work has gone into doing
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  experiments to address that. So we can leverage specific
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  properties to make the OM RNA long lasting.
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  So there's a model for that. There's a model that can address
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  the splicing within the RNA. It's a property that different
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  part of RNA can be spliced up and stitched together.
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  You can have a prediction on that.
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  A model that talks about, you know, how the the
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  three-dimensional structure of the RNA could affect its, it's
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  transcription, translation, and regulation.
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  So all of that stacked into on top of each other.
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  And then at the end you can ask basically to have an mRNA that
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  is bespoke. You can ask an mRNA and say I
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  want to have an mRNA to be only specific and expressed or on in
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  T cells but nowhere else in the body.
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  So it can be cell or tissue specific.
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  And I want it to last for three days and you can incorporate it
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  that and I want it to be expressed at such and such
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  efficiency. So I want it to be translated
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  into protein at this level you can find to that.
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  So all of these are properties that you can demand the model,
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  our model to make that for you. And the outcome, the output is
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  actually is an mRNA with this specific properties that you can
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  take and you can test it in the lab and to see whether to see
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  whether the the mRNA has this programmable components in it.
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  And that's something that we can think about.
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  Could it be an medicine to treat such and such diseases or not.
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  So that's, that's what part of the equation.
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  On the other side again because we have built this model that we
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  call it RNA intelligence model, it's an AI system.
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  We have this RNA intelligence knowledge.
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  We can also leverage it to target the mRNA with modalities,
• 18:42
  well established modalities, oligonochlorides, antisense
• 18:47
  oligonochloride, Asos or SIRNAS that can that can control the
• 18:53
  expression at the mRNA level. You, they can target it to
• 18:58
  silence the mRNA expression or you can use them to up regulate
• 19:03
  or fine tune the mRNA expression.
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  Again, that insight of where and how where to target the mRNA and
• 19:12
  how to design this ASO or SIRNAS also is derived from our RNA
• 19:18
  intelligence model. So we can do both.
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  We can use our RNA intelligence model as a way to as a
• 19:29
  generative model to make programmable mRNA with the
• 19:33
  properties that we want. We can also design ASO and SI
• 19:38
  RNA that are identified and designed based on our RNA
• 19:45
  intelligence model. So it's a very, it's a the most
• 19:50
  comprehensive a model out there that allows to make any kind of
• 19:56
  RNA medicine for any purpose that is needed based on based on
• 20:02
  patient's need. And I think the most interesting
• 20:06
  thing about the model and your platform is that you're already
• 20:09
  having some real world impact. Can you tell the listeners about
• 20:12
  the example that you shared with me about designing that RNA
• 20:16
  molecule for a pulmonary fibrosis patient?
• 20:19
  Yes, you know the the objective, the whole purpose of this
• 20:25
  building, the platform, this RNA intelligence platform is to
• 20:28
  apply towards making new medicines.
• 20:32
  And the way that we believe we at Ferno are uniquely positioned
• 20:37
  to do that is because we have all the capabilities that are
• 20:42
  needed for making these medicines.
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  It starts with the biological understanding of the RNA, the AI
• 20:53
  capabilities to build these models and having the right
• 20:58
  talents and experience in how drug development works and the
• 21:03
  drug developments are all these three components.
• 21:07
  The biological understanding, the AI modelings and the drug
• 21:11
  developments, they're all hand in hand, work hand in hand, work
• 21:16
  hand in hand from the beginning, from the day one in order for us
• 21:22
  to think about how we can make these new medicines.
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  So at Eternal we, we are doing, we are making medicines for the
• 21:33
  large populations and also for patients who are most in needs.
• 21:40
  And this is the patients that you're referring to who had the
• 21:43
  long fibrosis. But let me spend a little time
• 21:46
  talk about the most standard drug development part and that
• 21:54
  is figuring out where the need is, where is the unmet medical
• 22:00
  need is. So for that we look at some
• 22:05
  diseases for the larger populations and whether the
• 22:11
  current medicines are serving them.
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  And if not, is there something that they can do with our
• 22:19
  technology to address those unmet medical needs?
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  The population is usually large enough that we can think that if
• 22:28
  you make this medicine, this many people could benefit from
• 22:32
  that. And that's how we think about it
• 22:36
  from day one. There is an unmet medical need.
• 22:39
  We need a medicine to have these properties.
• 22:42
  OK. Let's go and see whether we can
• 22:43
  make that medicine or not because there are enough people,
• 22:48
  there are enough patients suffering from that disease that
• 22:53
  once the medicine is made, we can actually run the clinical
• 22:57
  trials. And the clinical trial is
• 23:00
  basically is an experiment where you take your medicine and you
• 23:04
  give it to some patients and you ask the question, are you
• 23:07
  benefiting from taking that medicine?
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  If does this medicine have any therapeutic benefit for you, for
• 23:15
  this patient population or not, That's the purpose of the
• 23:18
  clinical trial. The size and the design of it
• 23:22
  totally depends on the disease and how big is the population.
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  So we are doing that for some diseases that we're going to,
• 23:32
  we're going to disclose hopefully later this year.
• 23:35
  But then you come across some patients that have this very
• 23:42
  rare genetic or genetic problem that causes the disease.
• 23:52
  We refer to them as ultra rare patients.
• 23:55
  They have ultra rare diseases, ultra rare mutations or for
• 24:00
  short. And A1A patient has a
• 24:03
  dysfunction in a gene that is very unique to that patient.
• 24:09
  So what do we do with that? If these patient population are
• 24:15
  not part of the larger group, does it mean that we cannot make
• 24:20
  any medicine for that for them? So far it has been the case
• 24:26
  because there has to be some commercial monetary benefit for
• 24:33
  making bed, for making medicines.
• 24:35
  All these pharmaceutical companies, they're really doing,
• 24:39
  they're doing an amazing job working for the patients.
• 24:43
  But at the same time it's a business that needs to, that
• 24:47
  needs to make sense. And for the patients who are
• 24:51
  suffering from these ultra rare diseases, that monetary benefit
• 24:56
  and reward is just not there. So it has been literally
• 25:00
  impossible to serve them, to help them.
• 25:05
  So what our platform can offer to them and did offer to this
• 25:11
  patient who had lung fibrosis is that they they came to us and
• 25:16
  the patient is suffering from the severe form of lung
• 25:19
  fibrosis, very, very critical condition and they want a
• 25:24
  medicine as fast as possible for this patient.
• 25:29
  And what we have to offer is that this RNA intelligence AI
• 25:35
  platform that allows us to make medicine for this individual,
• 25:41
  for a patient in a very time efficient manner.
• 25:47
  Usually traditionally the time that takes to make this
• 25:52
  medicines for large population are in the years like 3 years,
• 25:57
  two to three years until you have a molecule that you can
• 26:00
  test it in in animals and then take it into human to see
• 26:04
  whether this thing is work or not.
• 26:07
  So our model allows us to do that, allows us to make medicine
• 26:13
  that were not even possible before in an unprecedented
• 26:18
  speed. So for this particular patient,
• 26:21
  it was less than the actual molecule design was in 2-3 days
• 26:27
  and the whole testing it in the context of the lab in the loop
• 26:31
  was completed in less than three months.
• 26:33
  So this is very, very important because a patient who comes and
• 26:38
  this patient is suffering from a serious severe ailment, how do
• 26:43
  we help that patient? You need to make a medicine that
• 26:46
  works for that patient but also in a very time efficient manner
• 26:52
  as fast as possible. And this is something that we
• 26:55
  were able to do for this patient.
• 26:57
  And now that that medicine is going through the pre clinical
• 27:02
  testing as fast as possible and we're hopeful that the patient
• 27:06
  can be dosed and benefit from this medicine in, in few months.
• 27:13
  And we could see the impact of our work in real time in, in a
• 27:19
  matter of months to save a patient's life.
• 27:24
  And it's extremely rewarding for me for everyone at Derna to be
• 27:29
  able to do that, not only in a like more traditional drug
• 27:34
  development that we're doing for the larger population, but also
• 27:38
  for these patients, for this and one individuals who are
• 27:42
  suffering from this ultra rare diseases.
• 27:46
  Because if we don't, who would? Who would serve these patients?
• 27:53
  And how do you think that that approach could be scaled to
• 27:56
  reach a broader group of patients who need personalized
• 27:59
  treatments? I I think about it Amber as like
• 28:03
  how is it? How, how we can make this
• 28:06
  accessible to a larger group of individuals, each of them
• 28:12
  suffering from a very unique and ultra rare genetic disease.
• 28:18
  And we have been approached especially since we had that
• 28:21
  announcement almost every week with different groups, with
• 28:26
  different individuals, parents who have their baby diagnosed
• 28:32
  with the lethal, with the, with the deadly genetic disease.
• 28:38
  And I ask that question, how, how we can help them because
• 28:43
  some they have the resources, majority don't.
• 28:46
  So who, who is, who is going to come up with the, with the
• 28:52
  system, with the mechanism that we can, we can serve these
• 28:56
  patients. Our view at Thermal is that we
• 29:00
  have these unique capabilities that can make these medicines
• 29:05
  that were not even possible few years ago and we can do it
• 29:11
  really fast. And again, time is of the
• 29:13
  essence for these patients and we can offer this, this life
• 29:19
  saving treatment for these patients.
• 29:23
  But the fundamental question is that how the how it can be
• 29:29
  supported, how this work can be supported because there are
• 29:32
  costs associated with it. And although our view at Thermal
• 29:37
  is that we're not looking at this opportunity as commercial
• 29:40
  opportunities because it's just they're not, this is just for a
• 29:44
  patient saving their lives. A medicine for one patient just
• 29:49
  cannot be a commercial opportunity.
• 29:51
  And that's not our intention. But we need to cover our cost.
• 29:55
  So we are willing to do that as part of our commitment to our
• 30:01
  community to serve them and we continue to do that.
• 30:06
  But how can we think about in a system where we can reach these
• 30:15
  patients who do not have the resources, maybe a minority of
• 30:19
  have the resources to provide them with this, with this life
• 30:23
  saving treatments. And I don't have an answer for
• 30:26
  that. And I think this is something
• 30:28
  that we as the community of the life science researcher, as a
• 30:35
  community of the drug developer need to answer it and think
• 30:40
  about it as whether we can come up with the mechanism to do it.
• 30:45
  Should it be a government, private public relationship
• 30:52
  support? Could it be a nonprofit system
• 30:56
  that supports it? I don't know.
• 30:59
  But I intend to explore it to see whether we can come up with
• 31:04
  a strategy that next time when a patient comes to us, a dad or
• 31:08
  mom comes to us and they say their child have this disease.
• 31:13
  And if we know that our platform can help that child, I can say
• 31:18
  yes, in spite the fact that they don't have the resources to
• 31:23
  support the cost associated with making this medicines.
• 31:27
  And this is something that we as a community of within the life
• 31:33
  science research ecosystem and also drug development, biotech,
• 31:38
  pharma industry, we have to think about it because now we're
• 31:43
  at the stage that the technology can answer that question.
• 31:47
  We just have the capability now it comes down to how to come up
• 31:52
  with the resources to make it a reality.
• 31:54
  Yeah. And such an important topic
• 31:56
  across across the market as well.
• 31:59
  But maybe thinking about further forward.
• 32:03
  Yeah, thinking further forward with RNA therapeutics.
• 32:07
  You know, they had a huge moment with RNA within vaccines.
• 32:11
  But where do you see the field moving over the next 5 to 10
• 32:15
  years? Yeah, RNA definitely is just
• 32:20
  finding its place in the spectrum of the modalities for
• 32:26
  for drugs from small molecules to biologics.
• 32:32
  Now gene therapies, they're finding, finding their footing.
• 32:35
  So is the RNA medicines and RNA medicine.
• 32:40
  If you categorize them into mrnas, we have to successfully
• 32:44
  the mRNA vaccine and also ASOS and SI RNAs which target the RNA
• 32:50
  and control the gene expression at the RNA level.
• 32:53
  They're definitely they're having, they're having the
• 32:55
  moment and then maybe you start with the ASOSIRNA side which
• 33:01
  target the RNA. I think the next generation of
• 33:04
  those molecule are going to be the ones that are going to up
• 33:09
  regulate the expression of the gene at the RNA level and also
• 33:14
  the capabilities of fine tuning that we've gotten really good at
• 33:19
  knocking down the expression, basically shutting down the
• 33:23
  expression at the RNA level. Number of them has been approved
• 33:27
  in the market, they're doing really well.
• 33:30
  But for capabilities of these Asos and srna to up regulate
• 33:36
  their, the gene expression is something that you're working on
• 33:40
  and there are like amazing opportunities that you're trying
• 33:43
  to explore. I think that would be the next
• 33:45
  generation on that front on the mRNA level, still a lot to go.
• 33:51
  It's the it's still in this in my view, it's still in their
• 33:55
  infancy. We certainly saw the potential
• 33:58
  in the mRNA vaccine. But the next frontier will be
• 34:02
  how you're going to treat the chronic diseases, not, you know,
• 34:07
  targeting broader group of the tissues, broader group of cells.
• 34:13
  Because of that, it is very much dependent on the delivery
• 34:17
  component, which is a big challenge.
• 34:21
  But there's tremendous amount of resources on time and talent is
• 34:25
  going into that. So I'm very hopeful that finally
• 34:29
  there will be a breakthrough on what would be a delivery system
• 34:33
  that could target the cargo, cargo being the mRNA to the the
• 34:39
  tissue or the cells that that we need this mrnas at a lot of
• 34:49
  acquisition and MNA happened. If you have few, few of them in
• 34:53
  the past, past year around the targeting of the immune cells
• 35:00
  for the in vivo gene editing where the cargo is the mRNA,
• 35:07
  there are there are benefits of doing that because the mRNA
• 35:11
  doesn't integrate permanently into the host genome.
• 35:15
  It does its job and it just disappears.
• 35:18
  So there's less chance of the side effect.
• 35:21
  It could be programmed as we discussed with technologies like
• 35:26
  ours to where it needs to go. For example, you want it to go
• 35:30
  only to be in the T cells, in the immune cells, in the muscle
• 35:34
  cells. You can we can program the mRNA
• 35:38
  to be active only in that, in that tissue or cells that that
• 35:43
  matters. These are the technologies that
• 35:45
  we have. What we think it's going to be
• 35:48
  the future at the at Therna is that we add a targeted delivery,
• 35:55
  which we are working with, with the technology that in a
• 36:01
  collaboration with Upenn, a targeted delivery with the mRNA
• 36:07
  that is programmed. These two combine together, they
• 36:12
  can provide an elegant and exquisite way to deliver the
• 36:17
  medicine to where it matters and where it intend to be.
• 36:21
  I don't think that the targeted delivery alone would be the
• 36:24
  answer. I don't think the programmable
• 36:28
  mRNA would be the answer. But these two together, they
• 36:32
  would be the in vivo editing or the in vivo therapies, the 2.0
• 36:39
  that there's so much hope and excitement for it.
• 36:43
  And I think that's going to be the future.
• 36:45
  And we certainly working towards that our focus our technologies
• 36:50
  on the cargo by making the programmable mRNA.
• 36:57
  But at the same time, we're partnering with groups such as
• 37:01
  Upenn or Expert in this targeted delivery.
• 37:04
  Combining these two together, I think you can achieve amazing
• 37:08
  things with the next generations of the RNA medicines.
• 37:12
  Yeah. No, that's a really great
• 37:13
  insight into the future of RNA medicines there.
• 37:16
  And to round off our episode then with everything that we we
• 37:19
  ask every guest, if you could have dinner with any scientist
• 37:23
  or innovator, past or present, who would that be and why?
• 37:27
  You know, amongst all your questions, all the questions to
• 37:31
  think about, this one took most of my time because there's so
• 37:35
  many people that I want to meet and chat with them.
• 37:38
  But I finally landed on Leonardo da Vinci.
• 37:44
  And the reason for that is that to me, he seemed like the a
• 37:50
  complete man, a true, I mean, he was the Renaissance man, but
• 37:54
  true in every sense because he was a thinker, he was an artist,
• 38:02
  he was a scientist and innovator.
• 38:06
  And I admire that. I, I, I don't call myself an
• 38:11
  artist, but I've painted for many, many years is one of my
• 38:15
  joys and loves in my life. And I see how that artistic
• 38:23
  brain of mine can help impact my scientific mind and vice versa.
• 38:31
  So that comprehensive and well-rounded individuality that
• 38:41
  Leonardo da Vinci present, it's something that I admire, that I
• 38:46
  admire and I, I personally inspire it to be.
• 38:49
  It's, it's ultimate man, it's ultimate human being.
• 38:53
  And I would have loved the opportunity to sit down and chat
• 38:57
  with him and get an insight into how does he bring operates and
• 39:02
  how does he see the world and just learn from him.
• 39:08
  I would have loved that. Nice.
• 39:11
  Well, thank you so much for taking the time today, Naslie,
• 39:15
  and joining us on the Biohub podcast.
• 39:17
  Thank you. It was great fun.
• 39:19
  A lot of good question and I enjoyed it.
• 39:22
  Thank you. And for all of our listeners,
• 39:25
  please like and subscribe.