How Do We Build Better Medicines Without Relying on Animal Testing? with Dr. Yassen Abbas from CN Bio

In this episode of Pulse, Aldo sits down with Dr. Yassen Abbas, Biology Group Leader at CN Bio, to explore how organ-on-a-chip technology is transforming the future of biotechnology, drug discovery, and personalised medicine.

They discuss how these sophisticated human tissue models are helping researchers predict drug safety and effectiveness more accurately than traditional laboratory methods, why AI is becoming an essential partner in pharmaceutical research, and how advances in synthetic biology and genomics are bringing us closer to truly personalised healthcare.

From the changing regulatory landscape to the promise of multi-organ systems, rare disease research, and the convergence of engineering, biology, and artificial intelligence, this is a conversation about the technologies that could redefine how new medicines are developed.

Sincerely hope you will enjoy this conversation with Dr. Yassen Abbas.

You can find out more about CN Bio here: https://cn-bio.com/

Watch this episode on YouTube: https://www.youtube.com/@AlphaWireHQ

This episode was brought together by AlphaWire: https://alphawire.xyz/

Transcript

SPEAKER_01 0:00

What is going to be more predictive? Most drugs in the drug development portfolio fail. And it costs a lot of money to develop a compound, a drug that gets into the clinic and begins to treat diseases. So drug discovery companies are investing in more predictive models.

SPEAKER_00 0:19

Welcome to Pulse by Alpha Wire, the podcast where science and education meet cutting-edge technology and artificial intelligence. My name is Aldo de Pop, and each week I sit down with innovators, thinkers, and doers who are working to change our world for the better. Together we explore their journeys, uncover the lessons they've learned, and take an entrepreneurial pulse that drives them on their path to success. How do we build better medicines without relying on animal testing? In this episode of Pulse, I sit down with Dr. Yasin Abbas, biology group leader at CN Bio. And together we discuss how organ on a chip technology is transforming the future of biotechnology, drug discovery, and personalized medicine. We discuss how these sophisticated human tissue models are helping researchers predict drug safety and effectiveness more accurately than traditional laboratory methods, why AI is becoming an essential partner in pharmaceutical research, and how advances in synthetic biology and genomics are bringing us closer to truly personalized healthcare. From the changing regulatory landscape to the promise of multi-organ systems, rare disease research and the convergence of engineering, biology, and artificial intelligence, this is a conversation about the technologies that could redefine how new medicines are developed. Sincerely hope you will enjoy my conversation with Dr. Yasin Abbas. Yes, and we are live with another interview, and we are going to take a deep dive in the exciting world of biotechnology and alternatives to animal testing and loads of research. And I have here with me an expert, an absolute expert, Dr. Yassen Abbas, who is biology group leader at CN Bio. A very good day to you, Jason. How are you?

SPEAKER_01 2:31

I'm well, thank you. How are you doing?

SPEAKER_00 2:33

Yes, I'm I'm doing very well. I'm uh full disclosure, I just came back from holidays yesterday. So I'm I'm in I'm having a day that I'm just getting back into things, and I have so many things to take care of. I don't know if you know the feeling, but there's like, oh my goodness, I've gotta tie all these ends together. But I did have one um light point on my agenda, which is this interview with you. So let's dive in. So, Yassen, you were just saying you're from Glasgow, so you're Scottish from Glasgow, and I believe you did your PhD in Edinburgh, so you are familiar with the country that I am in. Is that correct?

SPEAKER_01 3:12

Yeah. Yeah, I am. So I'm I was born in Glasgow, grew up in Glasgow. I did I did my undergrad in um chemical engineering uh in Edinburgh, and I then did a PhD uh in Cambridge, um, focus in on biotechnology, bioengineering. Uh so my my background is essentially bringing together life sciences together with engineering and applying engineering techniques to problem solve biological questions. But I also spent time in between my undergrad and my PhD at the European Space Agency based in the Netherlands, working in the life support department, uh, designing and working on experiments that were due to go to the moon. And that was a really nice first job before starting my PhD, applying some of those bioengineering techniques to design experiments to answer some fundamental biological questions. So my background really is using it, my engineering background to apply um solutions to biological problems.

SPEAKER_00 4:15

Wow, that's that's already amazing. So we we can just dive into your work at uh European space agency, is that what it's called? Or you recognize my accent, you detect. I do, I do, yeah.

SPEAKER_01 4:28

Yeah, I I lived in Leiden for for one year.

SPEAKER_00 4:32

Okay, yeah. We're not gonna talk about the Dutch, because that's an all episode altogether. Uh but it's very interesting that your field and your work as a group biology leader touches so many things, and that is also the uh the impression that I got when I looked at the site of CN Bio. It is so many things, so many good things. So just for the listener to understand, can you give me a quick pitch as to what CN Bio exactly does?

SPEAKER_01 4:59

Sure. So CN Bio is a bioengineering company, and we develop what is called organ on a chip technology. So for your listeners who've never heard of organ on a chip, what we're trying to do is recreate aspects of human biology in the lab. So currently, if you want to model an organ or a disease, there's many different aspects that you'd want to consider. And what we do at CM Bio is develop solutions that are able to culture cells from a specific organ or a specific disease in an environment that helps to make these cells more functional. So a good example is the liver. The liver is composed of many different cell types. The main one is called primary humidoparocytes. And if you wanted to culture primary humidoparocytes with these cells taken from a human liver, additionally, you would have taken these cells and put them on a 2D culture dish. So these are essentially pieces of plastic where you add cells on top and they form a very thin monolayer. Now, with this traditional practice, you can culture these cells for a maximum of 24, 40, maybe even pushing to 72 hours. What we do at CN Bio, we create these fluidic systems and these bioengineering scaffolds whereby you can add cells such as primary human hepatocytes into these scaffolds. They form these micro tissues as they converge and adhere together, and we apply fluidics or recirculating fluidics to mimic the circulation of blood. And what this does is it essentially keeps these cells in function much, much longer in the lab. So going from two to three days to between two weeks, three weeks, or even longer. So that does, it enables you to understand and model human diseases, to be able to test drugs that are currently in the pharmaceutical drug development portfolio in ways that you weren't able to do before. And what we also do is we link different organs together. So we have a multi-organ platform that can link tissues from, as an example, the gut and the liver, if you're wanting to understand how a drug is absorbed by the human body and then subsequently metabolized by the human body. And then we also create bits of software computational modeling that takes the data we generate in these organ inner chip systems to then predict how that drug might behave in humans.

SPEAKER_00 7:29

That's incredibly fascinating. And I hope I understood it all correctly. My first question would be like, obviously, it's very innovative. What did people use before CNBio was around?

SPEAKER_01 7:43

Yeah, well, the liver example is a really good one. And our sort of main focus has been the liver, but we also sort of have other focuses on the gut, the lung, and combining these different organs together. So as I said, um primarily people use these more traditional methods of culturing cells on plastic, on a dish. And these cells quickly lose their function after being isolated from a human liver, as you might expect. The other traditional model is also animals. So historically, animals have been heavily used in pharmaceutical research to model human beings. But animals are useful and they're a really important part of the drug development process. But a dog is not a human. A rat is not a human. A monkey is not a human. And that goes to the biology as much as anything else, as much as the physiology. A good example in the liver is that the enzymes that are expressed in a rat and a dog, the expression profile and a phenotypic profile is very different compared to what exists in humans. And there's other good examples, such as rats are a model and have been used as a model to investigate reproduction, but rats cannot menstruate. Whereas in pharmaceutical research and academic research, we use animals such as rats to model diseases of reproduction. So there are kind of really fundamental differences between human biology and biology that exists in certain animal models.

SPEAKER_00 9:13

Just so I understand it correctly, it is all real biodata, right? It's not synthetic. It's how you grow it that makes it unique.

SPEAKER_01 9:22

Exactly. So we use cells. So we use primarily we use cells isolated from human beings, from patients. So there are approaches that can use cell lines. So for those for those listeners who are not familiar with what a cell line is, an immortalized cell line is when you might take a certain batch of cells from a growing cancer and immortalize them so you can continuously passage them over time. And you have these cells which are continuously passaged for years, even decades. But what we use, we use cells that are directly isolated from human patients. So these cells are the most relevant to human biology, and they are as well the most functional because they're directly isolated from a human organ or a tissue.

SPEAKER_00 10:10

Taking you back to the very beginning, so you joined a company in 2020.

SPEAKER_01 10:14

Yeah.

SPEAKER_00 10:15

I can imagine this is like very rapidly evolving, and there are you know there are loads of things happening. You mentioned computational biology. I can also imagine that the market might be very rapidly advancing. So in the six years that you've been there, have you seen many things changing very quickly?

SPEAKER_01 10:33

That's a good question. And I think in any sort of cutting-edge science, like you always see changes. And I I've seen changes within a single year. But there have been a few things that have really changed and moved the needle in this field. The primary one was in 2022 when the US government introduced what was called the FDA Modernization Act. And in this FDA Modernization Act, essentially said that in the drug development portfolio plan, it removed a requirement for animal testing. So before this, before 2022, there was a requirement under US law that was monitored by the FDA that essentially every drug that went through efficacy testing or safety testing had to be used with animals. But this law removed that mandate to encourage what's called non-animal methodologies as part of the mix. And I really saw quite a stark change in the market whereby pharmaceutical companies who hadn't really invested in organ and the chip or newer, sort of more predictive human biology who could have used the more traditional models, began to take more of an interest. We began to sort of hear from groups in certain pharmaceutical companies who had a bit of interest before, but a regulator shift meant that the field was beginning to wake up to the broader requirements by regulators. And i if you had that regulator push, then the people who are developing the drugs then begin to act and then begin to shift their focus as well.

SPEAKER_00 12:14

Yeah, I can imagine that's a game changer. And I like the terminology organ on a chip. I mean it it sounds very oversimplified. It does, but it does work. Yeah. Because it for me it unlocks this idea of compute as well. Because it's kind of like by night I'm a I'm a podcast uh uh interviewer, but by day I run uh a biotech company in synthetic biology, and it's a lot about computing data, right? You know, condensing human information to such a level so that it becomes a piece of code, so you can apply it on many levels and you can understand it on many levels. So translating an organ onto a chip would allow us to do so. Is that what is meant with it, or am I completely interpreting it the wrong way?

SPEAKER_01 13:04

Well, I think you're half right. I mean, I think probably the the person who invented this word organ in a chip didn't really have the foresight of what organ in a chip would look like. So the very first organ in the chips were essentially the size of a USB stick. And essentially the first sort of iteration was a fluidic channel with an inlet and an outlet, and inside that fluidic channel, you would have a hydrogel, and then that hydrogel contains some cells. And then you would flow cell culture media in one inlet in a channel below, and then another fluidic path would be cell culture media on the other. And then you could add a drug, you could add nutrients to you could model drug transport, immune cell transport. That was really the first sort of organ on a chip, and it looked like a chip. Our technology looks nothing like that traditional organ in a chip. It's it's it's very different, and the way sort of we engineered the fluidics is very different. But having said that, the future of it that I think organ in the chip will take is the creation of more human-relevant data to inform models such as AI in drug discovery. I mean, AI has really transformed many fields and it's beginning to transform many fields, but particularly in drug discovery. Every single pharma company is investing in AI. But what's really important, as you know, with AI is that the data that's inputted in these models is really crucial for the success. And a lot of the data that these models are currently being used in the pharmaceutical sector is quite mixed. So some of it is sort of historic animal data, more traditional 2D data. And what will ensure the success of these AI models is more predictive quality human in vitro data. So that's where I think one of the future paths that OrganIner Chip will take is the generation of high-quality human-focused data that will feed into the pipeline of these AI models.

SPEAKER_00 15:11

This is a nice segue into what we always look at with this podcast is like, do you consider yourself to be working against AI, or like does an AI an accelerator to your business? Or uh in previous conversations I was talking to you know someone who was doing something in space and they were sending things into space as a product. And then I said, Well, who are your competitors? And I said, Well, weirdly enough, now people are looking at AI because it it it forges the idea that the product is in space, right? It's a forgery because it's not real, but AI can give you this design effect, and I appreciate this is a completely different business. But there's always this scary definition of AI whereby people think like, okay, it's not gonna give us what what we want it to have, because AI is just this very bad shortcut that us humans just sometimes crave. Do you consider there to be such a model kind of in your business if if if we're not careful, or do you think it's all it's all good?

SPEAKER_01 16:19

Well, I think the question is perhaps more wider within with AI within the pharmaceutical discovery business. And I think the question is what is going to be more most predictive? So a really sort of easy fact is that most drugs in the drug development portfolio fail. Not every drug that goes in at the beginning ever reaches the clinic, ever reaches market. And it costs a lot of money to develop a compound, a drug that gets into the clinic and begins to treat diseases. And drugs are becoming a lot more expensive than they ever used to be. I mean, it can cost a single treatment for a single person up to a million dollars or even more. So drug discovery companies are investing in more predictive models. If a drug company can reduce their attrition rates by 5, 10, 15%, that could be potentially transformative to their business model. So hence the investment in AI, hence the investment in organ and a chip. Now, if your question is, do I think one technology will win out on the other? I don't think so. I think that I think they're quite different, and I think the the technologies sort of answer different questions. I don't think we're ever going to replace real lab experiments in science because I think there's nothing better than data that you can generate in an actual cell. Nevertheless, I think AI is proving to be and will be transformational. I mean, we've already seen certain biotech companies that are have been formed that are just using, predominantly just using AI to develop their compound from ideation into the point at which it enters into the clinic. And those companies had had I I mean one company, Althos Lab, I think, had um more than a billion dollars in investment. So it's extraordinary sums of money going into this. But what I what I do think is that by using technologies such as the organ and the chip, we can better inform these these AI models. Um I think that there is a kind of a really nice complementary space that more predictive human models can inform these more advanced AI models. And together we might see progress in the attrition rates uh when it comes to the development of drugs.

SPEAKER_00 18:33

Yeah, it it reminds me of some wisdom shared with me uh with uh you know people working at MIT. So so you know on a side note, we we sometimes work with MIT in January. We did we did a course there, and but when I say we, it's it's where I am in Scotland, and there's a there's this great program for entrepreneurs, and I got the opportunity with a grant to go to Boston and work at MIT, and then they're all about AI and how AI can you know in half an hour you can build a business and a business plan, and and you're all on the AI hype. But at the same time, they do acknowledge that the AI is only gonna be as good as the input that you give it. Because if you ask it silly questions or give it silly information, the outcome will be even sillier, right? Or or it will hallucinate or kind of all those things. And so, and specifically when we look at biotech and when we look at drug discovery and drug development, you need to be very, very sure that the input that you give it is solid and is real and is something that you can build upon, because if it isn't, then it's going to lead to very diluted and wrong output later on. So I guess that's where OOC can be such a solution. And and OOC, by the way, I know that's the abbreviation for organ on a chip, I believe. Um as I've been told. But so am I correct to understand that it is all about ensuring that you give AI the right input and that that is kind of what you guys are focusing on on a very high granular level when it comes to understanding ourselves?

SPEAKER_01 20:14

Yeah, I mean, in in one part, I think we make models for our customers, and our customers are largely biotechs and pharma companies. And if you speak to these pharma companies, and I was in a conference a couple of weeks ago in Basel, and there was an entire session focusing on AI. A lot of them said that they are developing these AI models and they're working with people such as Google or Anthropic. And what they need is quite high-quality, large data sets. You know, we have customers who are who are looking to use the data generated in in the organ inner chip models to then inform the AI models. I think as another way to think about this is that AI and machine learning more generally, I think can inform the data and help translate the data that we are able to generate in our models. One of our assays that we are currently developing and we has been quite established, is our drug-induced liver injury assay. And it is able to model the liver and better understand how a drug may be toxic at a specific concentration. But there are biomarkers within this assay that are that can be elevated upon the administration of a drug at a certain concentration, and we can measure the same biomarkers that a doctor might want to measure in a patient if they're looking for liver injury. But one of the challenges is how do you interpret that data and then translate it in a human context? How do you go from what's happening in the lab and to say, hey, this drug looks toxic in the lab? Will this drug be equally as toxic? And at what concentration is that drug going to be toxic in it? Patient. One of the roots is by using machine learning, we can better understand that data that comes from the organ in the chip, and we can be we can potentially be able to better translate and assign a risk to that data. And then to also that drug. So I think machine learning and AI can work in two ways. One, you can take the data generated from organ in the chip and then feed that into more complex AI models. But equally, you can apply machine learning to the data you generate from organ in a chip to better translate that data to be able to feed into clinical predictions.

SPEAKER_00 22:35

Very interesting. One thing I sometimes think about is the AI models used. And I promise, this is the last question about AI because I think there's loads more to what you're doing. But I was just wondering on kind of your your ideas on this. When data goes into AI and understanding the model that is being used, up to which level do you do you feel is that relevant? Do you feel that we and by we I mean as humans need to better understand the AI models before we give it to data? Because again, I go to this example of whether it's enriching the data, so better understanding the data that you have, or whether it is doing new interpretation with the data with an existing AI model. What do you feel should be the rules of the game when it comes to the AI model itself? Um because I don't think there is one at the moment. I think there are multiple players in the market, like one fairly closed and another one more open. You can look under the hood at any time. Do you feel that it is relevant in any way for you? Yeah, I that's a good question.

SPEAKER_01 23:49

I mean, are we at the stage in which there's a lot of positive talk about it? Um and how much benefit are we are we seeing from these models? I think the benefit comes from when we have computational engineers or computational scientists designing the machine learning and AI tools who really do understand these biological systems. Because I I think if you were to sort of just feed data into a generic AI machine learning model, I'm not sure how useful that is. I think you really need to be able to understand the data because the data can be complicated. And I've heard this a lot, right? So computational biologists, one of their biggest frustrations is often the quality of the data and not being able to really understand how that data is generated in a real-world experiment. And I think if you can get the experimental scientists talking with the computational scientists, then I think that's when you have a really synergistic approach. Because making an AI model for the sake of making an AI model won't really produce valuable insights. If that model really supplements and is able to really translate the experimental data, then I think you can have potentially something powerful and can really better inform the sorts of decision making and early drug discovery.

SPEAKER_00 25:09

Yeah. They always tell me like we're just at the very beginning of this journey when it comes to all these models. Yeah, it's scary, yeah, yeah. But many will come and many will go, and you know, we we don't know, and some will be reinvented, and it's a bubble in the market, or it isn't, and so so it can keep you busy for hours. I'm gonna move on because I feel I want to do justice to C and Bio and the great work that you do. I want to go to the place where you are right now, because that is Cambridge. And of course, in the United Kingdom, it's very much on a pedestal because there's so much happening there. It's it's this relatively small city, I would say, a small town, but it has such an immense wealth in terms of scientific knowledge and specifically, I believe, even in the life sciences. Is that also a hype, or is that really true, Jassen?

SPEAKER_01 26:00

That is really true. What's amazing about Cambridge is that you have both a world-renowned and strong university with a really good reputation, but also a biotech and tech sector that surrounds the university. So in in every direction, you've essentially got a science park. Um so CN Bio sits in what's called the Cambridge Science Park in the north of the city. But there's also science parks in the center of town, um, in the south of Cambridge, next to Adam Brooks Hospital. And what what you see is often a collaboration between academics and scientists with the tech sector and biotech sector that surrounds the university. And a good example is that we currently have an intern with us at CN Bio who has just graduated from Cambridge University in their undergrad. So we've also got access to talent close by, and we've got some really nice relationships with certain academics within the university. Also, Cambridge is situated close to both Oxford and London. And we have some really good collaborators in London, and it's that sort of triangle which creates an ecosystem that you can tap into. It's easy to sort of meet people in person, especially when you're doing collaborations or meeting with customers. And I think all of that is really valuable. And the last thing is also talent. I think having this ecosystem close by means you can access people who come out of the university or come out of other uh industry jobs, and the the recruitment process means that you have good quality people that come through the door when you're recruiting. So it's the mix of the ecosystem, but also the quality of the people that surround Cambridge, which makes uh this place a really good place to do to do science, to build companies, to grow companies, all of the above.

SPEAKER_00 27:55

Yeah. No, it sounds like a wonderful place. As a well, I am British, but as someone who didn't go to university in the UK, I I always hear this referral to the Golden Triangle, which is you mentioned it as well, Oxford, Cambridge, and London, which is always referred to in admiration, but also sometimes a little bit of envy. And uh being here in the northeast of Scotland, um, but also looking at you know, city where you're from, Glasgow and Edinburgh, which also is host to I think a great life science uh sector, and they're really trying to put themselves on the map. They're kind of always trying to emulate that specific success. There's no question to what I'm saying, but what I'm I kind of say is like for Scotland, I say, well, believe in your own kind of talents and success and and do it the Scottish way, and you know, you shouldn't continuously refer to this Golden Triangle, even though there's many reasons why you should. It's also like working on what you have yourself, your own resources and whatever. Do you feel, and and I'm asking you because you you yeah, you grew up in Glasgow, do you feel that there's a there's too much of an outflux from Scotland to uh something like the Golden Triangle, or do you think it's actually quite okay?

SPEAKER_01 29:09

I think like the Cambridge Oxford London does attract people from all over the country. Before the UK was in as part of the EU, like there's a really strong presence of EU researchers here. Um I think naturally when you've got clusters, clusters tend to attract talent from all over the UK. But you're right, I mean Glasgow, Edinburgh has a is a really strong life science sector. I mean, science in Scotland and science all over the UK. There's great universities all over. There isn't just the universities that sit around the Golden Triangle. But but I think ecosystems are important and clusters are really important. And it's for that reason of talent and technical people being able to live in a place, particularly in the biotech and tech sector, it's not unusual for a company to grow and then be bought and then there are redundancies, or or or if a company, for whatever reason, runs out of funding, the ability to sort of move from one company to another is really important. So if you have a biotech tech company in a city of no other biotechs or tech companies, if that company goes out of business, where does that scientist then go? So it's a nice place to sort of bring up a family and call home because you've got options.

SPEAKER_00 30:25

It's true. And I lived uh over a decade in London and I miss it dearly, but I but but I also see that if you overfocus on a cluster, as you say, that that might go to the detriment of other talents and resources that are elsewhere in the country that is the United Kingdom. What is next for CN Bio? I mean, it's been a tremendous journey. Well, when was the company founded, by the way? Because you joined in 2020, when was it founded?

SPEAKER_01 30:52

I think technically it was founded around 2014. The company's story actually originates from a DARPA grant. So if I get that the full acronym, but that there was essentially this really nice grant to create what's called a human on a chip back in 2014. And this was only a year or two after the birth or the proper birth of Oregon on a chip. And there were it's essentially a competition between the Veace Institute at Harvard and MIT. And C and Bio, being a British-based company, was then one of MIT's partners. So after the DARPA grant ended, CN Bio took on a lot of the IP from um MIT. So then we adopted um a lot of that technology with the sole purpose of commercializing that technology. So we've been going for a while now, but certainly growing quite nicely since the early 2020s.

SPEAKER_00 31:50

Nice. And you've made tremendous strides. What is next for C and bio? I mean, if there is any next, like do you want to get better? You mentioned the liver, and then you mentioned kind of other organs that you look at. Are you going in-depth that way, better understanding every single organ that a human has, or what is kind of the next level for you guys?

SPEAKER_01 32:10

Yeah, I mean, as a scientist, I'd love to sort of model every single organ and then bring multiple different organs together. But I think for us, it's important to have a focus in on a number of key applications. And our focus here in bio has been looking at drug toxicity and drug pharmacokinetics. But beyond that, what we're working on actively is integrating elements of the immune system into our organ and the chip models. I mean, it goes without saying, in in every disease in the human body, in every in every tissue, in every organ, the immune system plays a really important role. And newer drug therapeutics such as antibodies target the immune system. So as a developer of models that aim to better understand human biology, it's really important that we're able to integrate elements of the immune system into our models. So that's one of the things that we're working on quite closely. The other is computational modeling. So as I mentioned earlier, how do we take the data that we generate using our models to better translate into clinical predictions of drug safety and drug pharmacokinetics, but also drug efficacy? So the immune system and computational modeling are sort of two main avenues which we're we're also focusing in on at the moment.

SPEAKER_00 33:26

Pharmacogenetics, I think, is such an extremely interesting area, and there's there's still so much that I think should be unlocked because it's based on, and correct me if I'm wrong, you're more of an expert than I am, but it's based on kind of the idea of you know who we are genetically, right? And that kind of our genome is the blueprint to who we are, and that kind of a lot can be found as to which drugs should be developed for our genome, and that we can from there on also develop precision medicine and kind of do all those things. But there are also people that say, well, it's not only genetics, right? It's also who you are phenotypically, and it's also how you've lived as a person, and there's also loads of other uh information relevant. So sometimes people say, well, it's not genetics, it's cellular information that we need. I'm not really sure. Sometimes it it shifts the conversation. I'm not sure where we are today, but where are you on that? So is pharmacogenetics the the be all and end all, or is there more to it?

SPEAKER_01 34:30

And sorry if I oversimplify, but uh yeah, I I mean it it's it's a good question, and I don't think there's a really simple answer to it. I think it is important, but I think it's also really challenging to study in an in vitro setting. So the question is how how do you really gain a broad overview when you do experiments of of a human population, right? Like that that's that's really, really difficult, particularly if you're working on organ in a chip or in vitro type work with human cells. We're obtaining these cells from human donors. And as much as I'd love to have a cocktail of like a hundred donors, a thousand donors, ten thousand donors, practically that's really, really difficult. So often you'll do experiments on between one donor to five donors. So the question is, does does that three or four or five donors really capture the broad spectrum of human genetics of human phenotype? Probably not. A really, really good example is in metabolism, right? So how I will metabolize a certain drug might be very different to how you metabolize a certain drug. Um, for reasons that we're made up very differently, phenotypically, genetically, ethnically as well. There are certain metabolizing enzymes in the human liver which are very polymorphic, which means amongst a human population, I might have a really high activity, so I'm a high metabolizer of a certain drug, and you might be a low metabolizer. And that has implications when understanding the pharmacokinetics of a compound. So therefore, it's harder to predict dosage safety efficacy. Yeah. I mean, th the way pharma companies get around it is by having a pooled donor set of five donors, ten donors. But even then, it's still quite small scale. I mean, there's also questions of are we really capturing in human trials the diversity in the human population? Traditionally, a a lot of trials, especially early early trials, are dominated by young men and not capturing the differences in sex and ethnicity. It's a good question, but very complicated and not very simple answer that that you have challenges really early in drug discovery, but also later in clinical trials.

SPEAKER_00 36:46

Yeah. Well, one thing is certain that hasn't been debunked. You know, it's still very relevant and more information. That's very often that sometimes, oh, it's not so relevant or whatever. It's like, well, actually it is. And it's true that other information can be brought into the mix to better understand that, but it's not that it's been debunked that it's completely futile or that it is wrong. That's that's absolutely not. So I wanted to tap into synthetic, that what is synthetic. Uh, and I I appreciate C and Bio isn't, uh, but in computational biology, there are all these things happening. I believe the Wellcome Trust gave one million dollars to building a synthetic human genome. Um it might even also be an organization in let me say the Golden Triangle, it might be in Cambridge, I'm not sure. But what they're doing is they're building the human DNA from scratch outside of a human body. And so you would argue that if this continues to go, we're able to emulate everything, understand everything without even touching a human body. Right? Where are you on that when you look at those things? Do you feel that that is progress?

SPEAKER_01 37:55

I take it with a bit of caution. I think often you sort of hear about uh hear about these sort of strategies, and I think once you sort of dig a bit deeper, there's often sort of key context of use questions, and there are sort of questions which align on a specific scientific problem. And therefore you can sort of bring in synthetic biology or these sort of sort of questions of synthetic genomics, and those two can be aligned. A lot of it really depends, I guess, on that context of use and can you use that specific piece of technology to sort of answer that broad scientific question. So I sort of take it with a a pinch of salt um in terms of sort of the broad claims that are made about these sorts of technologies. But but nonetheless, it'd be really interesting to see where this path takes us.

SPEAKER_00 38:42

Yeah. You need to build a bit of hype around it, right? Whether it's gonna work, yes or no. If you're doing the project, you need to kind of say, well, here's what it's going to do. Yeah. It still remains to be seen whether it's it's gonna do anything, right? But I do think I do like the thought of it that if you're able to simulate those things, it it would be able to use it in the right in the right way. But again, it's also dependent on the use case. Like, I mean, there there are many ifs and buts there. Is there one thing? Last question, and uh, and then we'll go to our our very last ones. But is there one thing kind of in science that you are very excited about, in the life science, that you said, well, I might not be into this business, but here's what I'm very excited about. Here's here's something new.

SPEAKER_01 39:22

Well, the one thing that I think is really exciting is the treatment of rare diseases. I mean, people listening to this podcast may have heard from different news resources over the number of years the amazing advances of gene therapy. Gene therapy in the past five, ten, fifteen years, we've seen an immense progress. Diseases that were completely untreatable, undruggable, can now be not always cured, but certainly a treatment, and there's hope for many people with rare diseases. And this isn't a rare disease, but diseases such as sickle cell and phallassemia, these broad hematological diseases that that exist, have never really had a cure, but we now have gene therapies which have come on the market or certainly advanced in clinical trials, which may not offer a full cure, but really make the diseases manageable to these patients. So both in rare diseases but also diseases like phallassemia and sickle cell disease, we've seen huge advances. I mean I think the challenges are the cost of these diseases, the cost of these therapeutics to treat these diseases are immense. So you're talking figures of excess a million dollars, a million pounds to treat one single patient. Accessibility questions are really important because can everyone access these treatments? Probably not, probably only in Western democracies and in Western economies. But certainly the real transformation in how we treat some of these diseases previously that have been untreatable.

SPEAKER_00 40:57

Yeah, and and I mean when it's initially invented then the cost is always tremendous. But the cost you know what we've learned is that the cost comes down quickly, right? Once we are able to bring it to scale. So that we should persevere, right? And having a treatment for something is is better than having none at all, you know, regardless of the cost, I would say. But yeah. Yeah, no, I that's that's something to be very excited about. And by gene treatment, do you mean is that by default editing?

SPEAKER_01 41:29

The term gene therapy can mean a number of different things. But a good example is taking a patient's bone marrow or stem cells and then inserting new genetic information that can then sort of change the way the protein is expressed in the human body. And in in it's difficult to sort of give you one answer of how this technology works because it's quite specific and dependent, very much dependent on a specific disease.

SPEAKER_00 41:56

Yeah.

SPEAKER_01 41:57

But because it's so complicated, there isn't one size solution for us for every disease, large amounts of money has to go in. And often we're talking about maybe tens of people in a specific country. But yeah, I mean, you asked the question, I sort of have to take a deep breath before I answer it. That's a good question. So what are you most proud of? Well, what am I most proud of? I think gaining a PhD degree from Cambridge. I think for me personally, that's a really great sense of pride. It was great to see my parents there during graduation as well, um, from a from a really good university. Um, but also I'm also proud of the work I'm doing now, right? I mean, like I'd say the work that we're doing at C and Bio is impactful. And we're working on a technology which, in some senses, we are trying to disrupt how these sorts of experiments are currently done in the pharmaceutical field. Um, but rather than competing against animals, um, I think the technology can add value to existing experiments and reduce the number of animals in the short term but longer term potential replacement. And if we're able to produce more predictive biology and more predictive data sets, then I think that's that's amazing. That's something to really be proud of.

SPEAKER_00 43:13

Nice, there's a lot to be proud of. And it reminds me, PhD at Cambridge. Um, the last time I was in Cambridge as a tourist, as a complete tourist, and the owner of the Airbnb, he was watching over a little dog that was there. And he said the owner isn't there because she's just written a book, and it's it's doing well. At the time it was, you know, still growing. But that was Tara Westover with her book Educated. And so I was friends with there was a he's uh it's written she's written this book and he showed me the book and I said, Oh, I I hope it does well and whatever. And off it goes, being this absolute bestseller and an amazing book. And I, you know, from what I've seen of Tara Westover, she's an amazing person. But that's a Cambridge PhD for you, right?

SPEAKER_01 43:57

So that's you know Yeah, I I remember reading that book during during Lockdown and then the surprise that you went from um was a base in Utah, and then at the end she reaches Cambridge, and it was sort of like the most amazing contrast between her upbringing, her childhood to where she ended up.

SPEAKER_00 44:16

Yeah, she had no formal education before, like, and she just you know, her parents were like, we're not teaching our kids anything, and she craved for it, so she kind of rebelled against her parents wanting an education, and then yeah, ended up getting her PhD from Cambridge, and yeah, it's a beautiful story, yeah. So uh just reminding me because she said PhD at Cambridge. Uh Justin, thank you so much. Uh my two very last questions. Questions that I ask all of my guests. One is about morning routines, because I've developed myself into a bit of an amateur researcher into what people do with their mornings. So I actually ask each and every guest on my podcast, do you have a morning routine? And if so, do you care to share?

SPEAKER_01 45:03

I mean, I do have a morning routine, but I'm afraid it's very boring and probably not very interesting, which involves waking up a bit grumpy, and then taking my son to nursery. So I've got a two-year-old, so the the morning routine often involves a negotiation between him him wanting to play toys and me helping him to brush his teeth, getting himself ready, walking to nursery, and then into work. I think it's a very sort of a familiar routine with parents of young children.

SPEAKER_00 45:31

I know that well, I've got a seven-year-old now, but we've we've we've been through this specifically the negotiation part, my daughter to eat her breakfast and do all the things that she has to do, and then not doing that yourself, and then you know, but also as they say, blink and it's over. Because you know, if I remembered she was only two, five years ago, and it's already like now she's seven, and I'm like, no, stop. I don't want you to grow too quickly. I want to enjoy this. A very good morning routine and stick with it. That sounds great.

SPEAKER_01 46:04

And I should mention that the the morning routine also involves a lot of cycling because being in Cambridge, important to cycle.

SPEAKER_00 46:12

You can go around on a on a bicycle, that's also exactly yeah. And then for my very last question, for people listening in who are inspired about your career and your career path and the work you do for CN Bio, has there been any reading or any other type of content that has inspired you on your path?

SPEAKER_01 46:33

I'm gonna mention a film which was a book, um, but I I very much watched the film first um before before looking at the book. Um it's a film called I'll be very honest. And it's a film called The Martian. You may have heard the heard about it. It's it's starring Matt Damon, and in The Martian, Matt Damon's character is stuck on Mars after his crew leaves after problems and a storm um in the Mars. And in that book, he had to really problem solve his way out of the predicament he found himself. And I think it's a really nice book that shows that there is a solution to most scientific problems, and I think it kind of summarizes really nicely the determination and the grit that you need to do well and to succeed in in any sort of scientific discovery. Um, but also I've got interest in space, um, and I like to sort of keep that interest so it's a nice sort of relatable piece of context and and a nice interest of mine.

SPEAKER_00 47:36

Nice, and it's a beautiful recommendation. And yes, I I agree with you. The the grit and resilience is second to none in the movie, right? The fact that he he manages to find a solution on Mars all by himself with nothing that works, or you know, I don't know. It was extremely difficult and dire, and and he manages so incredibly.

SPEAKER_01 47:55

Yeah, I think I think if if anyone can aim for 1% of his resilience, then I think he'd be doing very, very well.

SPEAKER_00 48:01

Yes. Thank you so much for your time. I'm sure we're going to hear a lot more about you and your great work at CNBio. Thanks so much, Dr. Yassen Abbas. Thank you so much. You've listened to Pulse by Alphawire, produced by Natalie Piles and Amela Faisal, with great music, The Optimist, written by Holly Hamill, performed and produced by Alo. Episodes hosted weekly by me, Aldo DePop.