Designing Next-Gen Biomaterials: Peptide Hydrogels in 3D Culture & Regenerative Platforms

0:05
Good afternoon, everyone, and welcome to today's thought leadership session designing next generation and biomaterials, peptide hydrogels and 3G culture and regenerative platforms.

0:15
My name is Rachel and I'm delighted to guide you through today's session where we'll explore how advances in biomolecular engineering are transforming the future of tissue engineering, regenerative medicine and organoid technology.

0:27
We're honoured to be joined today by our thought leader, Professor Aline Miller, Professor of Biomolecular Engineering and Chief Scientific Officer of Unit M at the University of Manchester.

0:39
Professor Miller is a pioneer in peptide self assembly and biomaterials innovation.

0:45
Over the course of her career, she has shaped the field of animal free tunable peptide hydraulics for 3D cell culture, bio printing and translational medicine.

0:54
Professor Miller, thank you so much for joining us today.

0:57
Oh, it's an absolute pleasure.

0:58
Thank you.

1:00
So to begin, could you share your journey into biomolecular engineering and what initially sparked your interest in peptides, self assembly and biomaterials?

1:09
Yeah, no, absolutely.

1:10
So I'm a chemist by training, so that's my first degree.

1:14
And then I meandered into doing a PhD in polymer science.

1:18
And I was really intrigued in terms of how polymers self assemble, how they come together, structure themselves to make materials for lots of different applications, probably more materials or ******** materials applications.

1:31
And then I transitioned into a postdoc.

1:34
I was a fellow down in Cambridge and that was at the time where protein misfolding was starting to come through.

1:41
So this is probably 2025 years ago and I kind of used the polymer knowledge in terms of self assembly that I had developed to that point and applied it to protein folding, unfolding, how proteins unfolded and then aggregated together to form higher ordered hierarchical structures.

2:01
And I used to get frustrated because, you know, proteins are big lung molecules made-up of lots of amino acids.

2:08
And it sometimes when you unfolded them, different parts of that molecule would stick to different parts of another molecule.

2:14
So you'd never quite get the same thing twice.

2:17
And so it was frustrating a sort of a chemist, a materials chemist trying to sort of get that batch, that reproducibility when you're trying to look at material properties and thinking about applications.

2:30
So that's when I started thinking about, right, if we chopped the protein down into very small controllable fragments, then maybe we can get controllable materials from them.

2:42
So that sort of coincided with me starting a lectureship at then the University of Manchester Science and Technology.

2:50
So you missed and I went and come for the engineering department because I'm very keen on doing applied research and making it matter effectively.

3:01
So applied lots of self assembling techniques to these shorts and peptide sequences.

3:05
And luckily at the time when I was starting, we got into a well funded department who bought me a peptide synthesiser and gave me a PhD student and that's how we started.

3:17
Started the research quite a number of years ago.

3:21
And then of course, you must merged with the Victoria University of Manchester back in 2004.

3:27
So yeah, stayed in Manchester for the remainder of my career to date.

3:33
Well, yeah, very interesting how you've got to this point.

3:38
So you wear multiple hats across academia and innovation leadership.

3:44
How do you balance scientific discovery with entrepreneurial translation, such as your work with Manchester Biogel and Mola Farmer?

3:53
I'm sorry.

3:54
Yeah.

3:54
Well, brilliant.

3:55
So in terms of those activities, so back in 2013, you know, as academics, do, you go around and give talks at various conferences?

4:08
And we started going round and people would ask to use our hydrogels because we're able to make controllable hydrogels from self assembly building blocks from peptides.

4:19
And because the peptides were made from amino acids, they are inherently biocompatible and biodegradable because they just breakdown through hydrolysis.

4:31
People were asking them for using them as scaffolds for cell culture, for less probably organised growth at that time, but as scaffolds for tissue regeneration applications.

4:43
And we met somebody who was a business development officer on one of our big EU projects that we were involved in.

4:51
And he said, so why don't I come and help you set up a company from this research that you're doing?

4:57
So myself, my collaborator and Guild, the business development person, we set up and Manchester Bio Gel back in 2013 and it kind of grew out from there.

5:07
And we went on a of a 1011 year journey with that company, making all the mistakes that you can make, terms of shared dilution, hiring the wrong people, all sorts of different effects along the way.

5:19
But we survived.

5:20
We grew over 10 years, we grew to a team of 10.

5:24
We scaled up the manufacturing and we had a global distribution network.

5:28
And I went to time out from academia to go run the company.

5:33
And then the university after 3-4 years said, are you coming back or are you not?

5:38
And so I decided that my heart was still in sort of research and translation.

5:43
So I went back to the university and I've taken on various roles and now over the past few years to help other people get excited about doing something similar to really make that tangible impact from the research they do, Whether that be through collaborating with a business or, you know, a company to get that to solve a challenge that the company's having or to look at commercialising and generating that tangible societal or economic impact and the work that they do through start up or spin out creation.

6:15
Oh yeah, no, very fascinating how those all those collaborations can work together.

6:20
So now on to more specific science questions.

6:27
What makes peptide based hydrogels particularly well suited for 3D cell culture compared to traditional scaffolds like collagen or Machu gel?

6:39
So I think peptides make the perfect scaffold.

6:44
I would say that not unbiased, but peptides themselves are inherently biocompatible just because they're made-up of natural amino acids.

6:52
I mean, you can do lots of synthesis on them.

6:56
You can do D amino acids to get some slightly different structures or other things, but we use natural amino acids because they're accepted in the body.

7:06
And also with the they will break down.

7:09
So just to hydrolysis in terms of the actual molecule themselves, so they can be well accepted.

7:19
And so translationally wise, that's a positive, but also they actually, you've got a small molecule.

7:26
So it's my finger.

7:27
You can control the chemistries of this.

7:29
So we typically work with peptides that are about 6 to 12 amino acids in length.

7:35
People do different things that, you know, there's di Tri peptides or longer peptides.

7:40
And then you can control the chemistry of the side groups that you have.

7:44
So we can control sort of the hydrophilicity, the electrostatic interactions ππ interactions to control how one molecule sits on top of the other, then sits on top of the other and forms these sort of fibrillar structures.

7:57
And so you can control sort of the properties of that fibre both from a chemical or biochemical perspective, but also from a physical perspective.

8:09
So you can control how flexible that fibre is, how sticky it is, will it aggregate with another fibre that's next to it.

8:15
And that all influences how you control the mechanical properties.

8:19
So how stiff that material is.

8:21
And of course that is perfect.

8:25
That three-dimensional fibrillar structure is perfect in terms of replicating the structure of natural extracellular matrix.

8:34
And I think about it's sort of a three-dimensional climbing frame that cells can go into ground on and grow and you can vary the properties depending on the cell type that you're looking at, of course.

8:46
So you know, muscle cells like something very different to bone cells, so neuronal cells.

8:50
So you can tailor the properties according to the cell type.

8:54
Why is it better I think than collagen or metro gel?

8:59
I think it combines the properties of such that synthetic system that you can really control and get batch to batch variability in a large scale.

9:08
So that sort of helps negate the some of the issues with sort of collagen, also some of the issues with mate to gel.

9:15
You know, everybody knows mate to gel.

9:17
You know, it's a decellularized genre that's grown in mice, so you never get the same thing twice.

9:22
And you know, they say that by a big batch of mate to gel to do your one set of experiments for that very reason.

9:29
But also ethically, you know what The thing is that 10 million nice or something a few years ago anyway, 10 million nice or cold every year just to make Metrogel, which is a consumable.

9:41
So can we have an A synthetic alternative for all those good reasons as well as they're being clinically translatable and relatable, right?

9:49
Yeah, no.

9:50
Fascinating.

9:52
And so how do you tune the physical and biochemical properties of your hydrogels to support specific cell types or differentiation pathways like, sorry, excuse me, like meth and shell stem cells for bone repair?

10:08
Yeah.

10:08
So you can do various things.

10:10
So 1 is looking at the mechanical properties.

10:12
So you can vary, you know, the stiffness of the hydrogel.

10:15
You can have something that's very soft brain like tissue or you can go quite which it's so it varies between probably maybe 50 pascals.

10:30
Anything lower, you know, it's very much fluid like watered like all the way up to probably 1020 thousand kilopascals, which is quite stiff.

10:38
It's very much like Jelly that you would eat and consume, although don't eat your gels, but also you need the gels to be malleable enough that one you can inject the gels or you can print the gels or you can get the gels into the bottom of a well, a cell culture plate.

11:04
And also so you can mix the jet, the cells homogeneously within that three-dimensional matrix.

11:11
Various tricks to do so.

11:14
One is controlling the mechanical properties so you can induce.

11:17
So some stem cells will respond and change a phenotype depending on the mechanical that you have.

11:24
The other way is to think about cell recognition sequences and incorporation of growth factors, for example.

11:31
So cell recognition sequences are easy because you have your peptide, you've got your peptide building blocks forming the fibre.

11:38
You just chemically when you're doing your peptides sequence, just add a few more amino acids on and a specific sequence that will direct the behaviour of the cell.

11:48
So everybody use RGD or I KB A/B GFOGER and you know, if you're going to try to replicate the sequences of fibronectin or laminin or collagen, for example, and then you just dope in a small percentage of your functional one.

12:04
And then you've got that integrated within your 3D network.

12:09
The other thing to do is just physically mix in growth factors, obviously synthetic growth factors focusing on animal free, and you can just physically mix them into your 3D matrix or into your cell culture media as well and try to get back to sort of help and control sense cell behaviour.

12:35
I have to say it's still very much a plug and play.

12:39
I think we've got to the stage where we can control and design the material.

12:44
But if you give me a brand new cell type, I still want to try a few different materials to work at, which is best.

12:51
Because certainly we haven't got to the stage where we're able to look at the sound like this specific formulation is going to be the one also controlling electrostatics so it can control the charge on the surface of the peptide as well.

13:08
That is a really good factor.

13:10
And it always amazes me that most cells from our experience like positive or neutral scaffolds, The liver cells love negative scaffolds.

13:21
We learned that the hard way.

13:24
Try whenever Great, thank you Could you also explain the importance of using animal free chemically defined systems for building reproducible and ethical 3D culture platforms?

13:37
Yeah, So I think I'm a big advocate of moving to animal freecell culture, obviously for ethical reasons, but also as we try to push the understanding of tissue diseases and or organoid models and a materials chemist, not a cell biologist.

14:00
So we push it so far in our collaborators to do the clever biological pathway analysis.

14:07
But for us, it's very much looking to reduce the number of animals used for just standard consumables.

14:16
So it matrix is 1 very important component.

14:19
But we touched on growth factors.

14:21
But if you look then at all the components that are in assays, for example, they still contain a lot of animal components.

14:29
So we need to look at all of those and make the synthetic alternatives.

14:34
And that capability is out there now in terms of the actual synthesis of all the different components.

14:41
Just need to work out on a cost basis and how to make that accessible and work out all the protocols for people to use them in sort of a routine drug discovery applications.

14:54
So ethically really important, but also if you think about the whole drug discovery pipeline in terms of looking and identifying new therapeutic molecules to treat XYZ diseases, you want to do all those drug screening tests on systems that are as close to human behaviour as you can get.

15:19
So you find the right drop and identify the one that is going to be efficacy very early and trying to do and develop 3D human models in vitro to replace the use of animal testing in drug discovery is obviously then a knock on benefit from working at all the different synthetic components.

15:44
So you can get that ethical reduce the number of millions of animals that are used every year in animal in drug testing, but also get more representative behaviour of drugs in humans.

15:59
And that's before we touch on things like personalised medicines and getting the cocktails of medicines right for individuals, for example, in cancer treatments and getting that right from the get go rather than a little bit of trial and error as you move through your chemo cycles, that kind of thing.

16:16
Yeah, OK, great.

16:18
And so I want to move on to organoid systems and regenerative interfaces.

16:26
So although organoids aren't your exclusive focus, your hydrogels have been used to support organised formation.

16:34
What are the key advantages that they bring in this context?

16:38
Yeah, so I think one it's about the reproducibility of the organised and trying to encourage 3 dimensional environment for the organised.

16:52
So in terms of using a matrix to help support the organised growth and then that builds in, you're trying to mimic more close face of human behaviour and getting the cell biological pathways within your organoid to be as close to human behaviour as you possibly can get them.

17:14
The other aspect for me is thinking about sort of the high throughput nature of the drug screening.

17:22
So you can use the hydrogel not just to support the growth and development of the organoid, but also to deposit, you know, single organoids on a multi well plate.

17:32
So you can do a high throughput screening and in sort of a Marshall viable way.

17:42
And oh, and I had another one that I've forgotten now.

17:45
What was the other reason just then thinking about that's it trying to accelerate the use of organised and the adoption of organoids to replace the use of animals and dog discovery.

18:03
And thinking about biopreservation and transportation of these organoids from CRO or from a manufacturer to ACRO.

18:15
Using components within the hydrogel to help remove the cryopreservation step so you can just transport it from temperature.

18:25
And there's a lot of work going on in that area now to help just reduce costs of these things and facilitate adoption.

18:34
So it's easy, it's cheap, it's reproducible and it's accessible.

18:40
Yeah, definitely.

18:42
So how do you see synthetic biomaterials bridging the gap between basic organised research and applications in personalised medicine or regenerative repair?

18:55
So I think in personalised medicines then it's thinking about having that ease of use.

19:03
So if we can make these organoids that, you know, a manufacturer might make the organoids in the hydrogel, you can ship them to the hospital, the clinician, it's easy for them to use the organised all grown, it's just off the shelf.

19:18
Then they can mix or the hydrogel, the system is all set up ready to go.

19:22
Then they mix the patient cells so they can take a biopsy, mix the patient's cells in the gel, grow the organoid there.

19:30
Then they can test the cocktail gloves on them.

19:34
It just makes things go a lot quicker.

19:37
Whether that wouldn't be the clinician doing that, but it'd be that, you know, the hospital team doing a lot of that.

19:44
So just making things easy and accessible, the protocols all set out so things are reliable and reproducible.

19:53
In terms of regenerative medicine, then you're looking at things like potentially growing organised, but potentially then growing tissue cells or even cell therapies.

20:06
So then you're using the hydrogel sort of as the carrier and support to potentially?

20:10
Grow and put down the matrix for the tissue that you're looking to replace.

20:14
But these things are injectable, they're sprayable.

20:17
So you can put them at the target site and they will stay at the target site for a set period of time depending on how long you want the hydrogel there or the cells to be delivered there.

20:28
And then they're long enough that they start to put down their own matrix and replace the tissues that the site that you need them to.

20:36
Whether that be we've done quite a bit personally, we've done quite a bit of work around cardiac repair, cartilage repair, neuronal discord, those types of application areas.

20:48
Yeah, OK, great.

20:51
So we want to move now to bioprinting and next Gen platforms.

20:56
3D bioprinting has been advancing rapidly.

21:00
How well do your peptide hydrogels function as bio inks and what specific technical challenges do they help overcome?

21:07
Yeah.

21:08
So bio printing's sort of been around for quite a few years now and it's progressing really well.

21:15
And there's lots of different inks out there in terms of being able to print 3 dimensional structures with cells or prints 3 dimensional structures and then infiltrate that with cells.

21:29
And what I see the peptide hydrogels being able to do is printing and doing all the good things that you need them to be sheer thinning.

21:43
So you can print quite intricate structures with them with cells embedded.

21:49
And then they've got all the positive USPS unique selling points in terms of them being biocompatible and being there to support the cells.

22:04
But then also once the cells start to lay down their own matrix and start to mature, then the gels, the peptide gels will either degrade away over time or sometimes they'll be ingested by the cells themselves or there's a mixture of the two.

22:21
So the gels will not stay around forever.

22:25
They will be there long enough to do the supporting job for the cells, but will then disappear over time.

22:33
So I think that's kind of one of the key USPS of them.

22:36
Or you can print structures within other polymer matrices to do sort of composite structures or if you're looking to do reels of organ or 3 dimensional MV2 structures which are really mimicking in vivo.

22:56
So you can then do things like just go into a structure that's already made and print out vascular structure to you really start to get the complexity of your 3D system to another level.

23:13
Great.

23:13
Yeah.

23:14
So considering that, what role do you envision from modular hydrogel systems and scaling up complex tissue models, especially those requiring vascularisation or layered structures?

23:28
I think there's, you know, I think the ability to print is out there already.

23:37
There's lots of groups that are looking at sort of these sort of complex multi component models, which is really exciting.

23:43
I think even that at some point you need to ask yourself how complex a system do I need to make in vitro to replicate what is in vivo.

23:58
So how far do I need to go?

24:00
What is the question I'm trying to answer as my 3D model, Therefore point is good enough.

24:08
But yeah, there's lots of groups that are working on doing quite clever, intricate, elegant work in that area that we've seen.

24:19
We don't do so much of that.

24:21
We're sort of more on the 3D printing side, but yeah, so there's lots of things out there at the moment.

24:29
Yeah, no, it's exciting time.

24:32
And so looking ahead, what developments in 3D cell culture or regenerative medicine are you personally most excited by over the next 5 to 10 years?

24:42
So I'm really excited by the fact that there seems to be a bit of momentum now about the animal free and really pushing that to become a reality.

24:53
I mean, I've kind of been talking about it for quite a number of years now, but I think it's only now that I start to feel that there's real momentum in terms of it's not just the scaffold, but all the other bits that go into it and all the development of everything that you need to do, you know, your drug discovery testing.

25:11
And it's not just good enough to say, well, I'm doing it on an organoid and rather than on a rabbit.

25:18
It's looking at all the components that you're using to deliver and build that organoid.

25:23
So I feel that there's real momentum in there in terms of the technology, the accessibility of all the components that are able, you know, to buy and purchase and all the sort of synthetic routes to making these things are there now.

25:37
But also sort of on the government side, there's real appetite to try to push this and you just look at the US in terms of the developments and these out requirement for animal testing as well.

25:52
So I think it's coming at it from both angles.

25:54
So that's super exciting.

25:57
But also the opportunities that this is now also opening up on that personalised medicine front is exciting for me personally.

26:09
And I can see some of the opportunities that it's going to begin to unlock to help treat an ageing population.

26:16
As I get older, I start to think about this more.

26:20
So yeah, I think that those are the two things that I see has been exciting.

26:26
Yeah, No, definitely.

26:28
Well, thank you, Professor Miller, for those inspiring insights into the rapidly evolving world of biomaterials and regenerative engineering.

26:37
This leads us perfectly into our panel discussion on advancing drug discovery, specifically animal free innovation in organoids and 3D cell culture.

26:47
I will now hand things over to you to moderate the discussion.

26:50
So perhaps you can start by introducing the panellists or perhaps the panellists can introduce themselves.

26:57
Oh, brilliant.

26:57
Thank you very much.

26:58
This is looking forward to is to have a good discussion with the three excellent panellists that we have here today.

27:06
So I think what would be good to get us started is that we go round everyone in turn.

27:11
You spend a couple of minutes just introducing yourselves what you're working on and how what you're doing sort of fits into this wider discussion.

27:20
And should we start with you that Billy key you're just pressed on my screen?

27:25
Yes.

27:26
So my name is Patrika Lodimo.

27:28
My background is Physiology, genetics, oncology.

27:31
I'm between places, I'm between Athens, Frankfurt and US and my research is focusing in regenerative medicine.

27:45
And the two actually project that I'm currently working is 3D organ printing using mesenchymal stem cells and how you can actually use AI in regenerative medicine and all, you know, the things that they come out with AI and how we can actually make it as a tool for us.

28:08
Yeah, thank you.

28:09
That's a really good addition to the discussion looping in AI.

28:14
Excellent.

28:15
Marina, you want to go next?

28:18
Hello, my name is Marina Panamarova and I'm a cellular modelling specialist at the SENO Institute.

28:25
I work within cellular operations, which helps to translate early discovery research from our faculty groups and large scale industrially robust pipelines, and my focus is on developing and scaling patient arrived and IPS derived organoated complex models.

28:43
And my motivation essentially working in organ woods and complex cell models is that in my early academic career in my PhD and postdoc, I frequently work with animal models, even in cases that were and well suited for studying human specific disease.

29:07
That experience really made me realise how much more we can achieve what scientifically and ethically, just like you mentioned previously, Eileen, by developing human relevant cellular models that better represent real tissue and organ biology.

29:23
Excellent, good.

29:24
Thank you Andy.

29:26
Hi, I'm Andy Virendra Vijaya.

29:29
My background is in biochemical engineering and metabolic engineering.

29:33
I'm not working on the nutrition space, trying to bring the 3D organoid technology as a standard platform for screening nutritional compounds, nutritional bioactives.

29:44
So in that sense, it is extremely important to be able to have a high throughput and scalable process to produce these organoids.

29:53
At the same time, we also need to care about the quality of organoids so we can screen all these compounds in a more efficient way and being transitioned into from 2D to 3D culture.

30:04
At the same time, trying to remove all the animal derived components certainly helps in the process to be able to generate good quality of these cells.

30:12
And it's nice to have you all.

30:16
Brilliant.

30:17
Thanks very much everyone.

30:19
So we've got an excellent mix of backgrounds and areas of expertise.

30:24
So I'll kick things off in terms of first question.

30:30
So we, I kind of talked a little bit about how I think it's, you know, all these different things are in the art of the possible now in terms of where we are.

30:39
But what are some of the challenges and barriers from moving this forward in terms of getting the sort of drug discovery whole process and pipeline shifted over to animal free systems?

30:53
I don't know who wants to kick off.

30:56
I'm going to start, I'm going to start.

30:58
I think the major challenges that we have today, the variability and the performance differences for the things that we use.

31:07
And for me, it's very actually, very important to actually have a supply chain that is standardised and we can actually have, you know, validation results and we can reproduce them.

31:19
You know, viability, especially when it comes to regenerative medicine, viability plays an important role.

31:24
So we really need to have this, all these things, all these standards, and also very important regulatory and documentation burden.

31:34
This is right now an issue.

31:37
I know that FDA the last thing, especially when it comes to animal Moulder, if they want to go, you know move out of the animal models and goes a different direction.

31:46
But they face a lot of fish here and it's definitely a challenging time in US right now in FDA.

31:53
And also when it comes to cost and how we can actually incorporate that, especially when you are, I love in a university because I wear a hat and I'm also a professor in a university.

32:08
So that always is challenging of how you can actually take your work from outside from your lab.

32:14
And you know, the cost is something that is very important.

32:18
And also lastly, the challenges I will say about the characterization imported CSA also very important when it comes to, you know, all these such as regenerative medicine, all these applications that we actually want to do in order to stay patients care.

32:38
And I will give to the other speakers to actually add, I can add something I think in terms of switching from animal to drive components and animal free, You know, more often we are facing the fact that it needs to be replaced by a single recombinant proteins or growth factors, right?

33:02
And as a standard in the industry, this part is still poorly understood.

33:07
For example, there are different type of expression system to produce these type of growth factors.

33:12
You have the plan expression system, we have the equal expression system, we have other Macmillan expression systems and that they bio efficacy and bio activity of these components is not well understood yet.

33:25
So there is no one to one replacement people need to test.

33:29
But I would still argue that it is better to switch to this animal chemically defined media because you know when working with a primary cell type, we already have the donor to donor variability.

33:47
So if we have variability from the serum FBS, then it's going to increase multi factorial the variability we have from all our experiments.

33:58
So it is always better to start with a not so efficient system with a chemically defined media and then working slowly by improving the efficacy of these components.

34:14
And again, there's still many things that us as a practitioners, researchers trying to understand how we can transfer that efficiently.

34:22
I don't think anyone has a single recipe in that sense.

34:28
Yeah, I come Marina.

34:31
Yeah, I think we already had two very excellent responses.

34:36
But I would just like to support Vasiliki in the aspect of I see the major challenge as a high cost of running these complex cell models.

34:51
Everything that has to do with 3D modelling is expensive.

34:56
The models themselves are expensive, the mattress is expensive, the media is expensive.

35:03
And if you need to make your assay to the scale to get your robust read out, you essentially need to run more experiments to get that clear cut yes or no results.

35:19
So everything adds up.

35:22
And even if you are an organisation that is completely, you know, flush with cash, but you still need to do a cost benefit analysis of, you know, having spent all this cash.

35:36
Does it answer your question and is there a more effective way of doing it?

35:41
Right.

35:41
So I'm hoping that as new technologies are going to come in the market, there is also going to be some sort of a reduction in the cost that is associated with mass production of these various elements that hopefully will help researchers to embrace this technology more.

36:10
No, I think that's a really good point.

36:12
And yeah, I think in an institution that we have some cash, but we're not flush with cash, it is a real issue and a blocker.

36:22
How do we overcome that?

36:24
Like as researchers, do we just wait until the technology is available that, you know, it's fully scalable and therefore the cost comes down?

36:35
Or is there the things that we could do?

36:37
And just a general question, I think it's collaboration and try to find funds from outside.

36:47
It will be the key solution because sometimes, you know, I have the idea, but I'm missing a part, but you have the part that I'm missing.

36:55
So if you collaborate, it will be more difficult, easy for us to find somebody to support or even get an EU grant or if you are based in, you know, the European Union or in any other country, all the grants that you can get.

37:09
But I think collaboration and speak actually loud that how beneficial will be that?

37:17
And I think this is the key.

37:20
Is it working?

37:21
If you ask me, not all the time.

37:24
We're facing a lot of issue with trust collaboration, setting data.

37:30
You know, even if you go to a conference and speak, most of us we do not serve that, you know, our data specific would just give them an idea.

37:39
So I think if we understand that collaboration will actually will be the key, especially now that we want to have this move and actually we need the money with all this that's happening in the world, maybe will be the key solution.

37:55
I'm not sure.

37:57
I'm trying to pause in that direction, but I think maybe this is the key.

38:03
Yeah, I think that's a really good point.

38:06
Any thoughts from anyone else or I think when the confidence in the fact that a 3D models contribute something that 2D models simply cannot there, I think, you know, they feel in general might move more towards supporting 3D culture.

38:31
And I'm hoping that as other, you know, as companies that manufacture all these various reagents continue producing more and more rather than being this super niche market that only a few researchers here and there use, I hope that is going to bring the overall costs down.

38:55
Yeah, I think that's a good point.

38:59
Maybe I can come to you Andy with sort of another question and things that, you know, we've talked a lot about all the different components that we've put into these organoids models in terms of scaffolds, cross factors, medias and thinking about the assays and things.

39:17
We haven't really got sort of a gold standard.

39:19
Everybody has their own favourite material or their own favourite cocktail of ingredients to use.

39:27
How do we move towards, and this is a massive question, but how do we move towards finding that gold standard?

39:35
And then once we, you know, we think we've got something, how do we actually know it's successful in terms of replicating that human model?

39:47
I think that's a very good question.

39:50
The success criteria should be that we generate good quality of cells or organoids that has the Physiology mimicking what is inside human body or any Organism body, right?

40:04
So that would be like the gold standard, meaning whatever we put inside there, you know there are about 20 components in the basal media plus at least 10 in the supplements.

40:17
It's going to take ages to fully understand what are the function of its, of the ingredients.

40:23
We know there's a textbook of media, the media optimization, but then we need to understand there's also interaction between these components.

40:31
There's a chemical interaction between this component when you put in together.

40:35
There's also interaction within the components and the cells.

40:39
And it's going to take a lot of time to understand and to come up with sort of a, OK, this is like a single gold solution that we need to use everywhere.

40:48
But I want to say though, people always need to start from somewhere, right?

40:52
If we have a project, if we start to do something, we need to start from somewhere.

40:56
What it means is more often we are facing a challenge and we need to take a pragmatic solutions, which means whatever we can study, we study.

41:07
Whatever is there, it needs to be there.

41:09
But then at the end of the day, the goal standard as I mentioned, is still trying to mimic what is inside the human body or the Organism body that we want to mimic.

41:18
Once we can do that, I think we have achieved it even though we might have five extra components there.

41:24
I mean, that would be the next step if we want to talk about achieving a cheaper cost, right?

41:31
That means we need to play a bit with the concentrations, removing what we don't need to have and just really do a targeted component.

41:40
But then that will be the next step.

41:43
Yeah, I think that's a really good and something for us to think about.

41:49
I just, I'm going to come back to you, Vasiliki.

41:52
You mentioned in your intro that you're looking and looping in AI and all the opportunities that growing field can bring to 3D organoids and drift discovery.

42:07
What are the opportunities and how far can we adopt AI to push the work that we're doing further along?

42:14
Yeah, great question.

42:18
I come to like AI as a professor not at all when my student using it to you know to reply to my assignment.

42:26
But when it comes to research, I really like it because it makes it can make I'm a stem cell expert.

42:34
So for me, it's very important to use the AI to have the ability to get, you know, is that person suitable for confrontation just to give you an exam.

42:46
Can I actually have any graph man?

42:47
What the things that I need when we're when I do it by hand or when I will do it by full cytometry or all the other things, all the other techniques takes time.

42:57
So AI, it will be a very powerful tool that can help us even in 3D culture, which now it's something that I try to, you know, to put in and see how it works.

43:10
The only issue that I face, and this is a personal, my personal opinion, because when it comes to stem cells, the data is limited.

43:20
So right now the limitations of AI when it comes to use it in a genetic medicine is that this amazing tool is built to use a lot of data in order to give you the results that you want.

43:36
So right now I'm trying to find, you know, algorithms I'm working.

43:41
I have collaborate with a person in Greece that actually is using AI for the last 10 years, which I didn't know that was actually something out there in order for me to find a way to use those algorithms in my small data and make it more efficient.

44:00
If we can actually do that because it's a huge opportunity.

44:04
Can you imagine the safe when it comes to patient care, to safety, for therapies, to, you know, to have my, I'm trying to 3D printing organs.

44:15
Can you imagine how easy it would be even to have my scaffolds, you know, ready because I'm the scientist, you know, I can, you know, have my pattern to produce nothing gamma stem cells for adipose, but I need somebody to fix me mascarpone in order, you know, to generate this thing.

44:30
How easy will it be?

44:32
Yeah, for that.

44:33
So for me, it's a huge opportunity.

44:36
And I think it would be nice if scientists that because it's something new for us, especially when it comes to regenerative medicine, It would be nice if we start setting a little bit of data to make it easy with everybody of how we can actually use this powerful tool.

44:53
For me, it's going to be cute and it's going to be very helpful.

44:59
And, but we really need to find the ways and also standards of how we can not compromise with the data, the ethical issue that comes with all the patients data, you know, all the GDPR and all these things.

45:13
So yes, science is good, but we really always need to think about standards and ethics and how we can make it signatures as a tool in our for our advantage.

45:25
If I'm may add to the use of AI and computational modelling and kind of loop it in to the gold standard that we are discussing here, right.

45:36
So many of you might have heard about the big project called Human Cell Atlas that give generates this huge amount of data about the composition of human tissues and human organs.

45:52
That gives us as cellular modelling experts a huge amount of information about how this golden standard that we all aim for aim to achieve should be built and what cells should be included and how they should be located next to each other.

46:11
However, what this data set doesn't provide is how these do these cells interact with each other, right?

46:19
And to understand that interaction, this is where I think AI and computational modelling can be really powerful.

46:29
And if we had the models like this to predict this cellular interactions, we would know how we would like our models to be built to be able to reproduce this tissue and that organ in a way that would be a representative of genuine human tissue.

46:57
Yeah, I think that's a really good point.

47:00
And thinking about, you know, it's quite a chasm still to move through till we get to everything just being designed are tested in silico and a whole heap of regulatory pathways.

47:17
We need to think about in that journey as well.

47:21
But just thinking about, you know, Andy, you mentioned that you focus around sort of the CD screening and really pushing that to being high throughput.

47:30
So hopefully, you know, we can build up some of the data sets as well as doing lots of testing in the interval.

47:35
And what are some of the current challenges that you face as you're trying to do more quickly?

47:42
What are some of the opportunities, you know, maybe sort of horizon scanning moving forward to move from a classical cell culture to a high throughput one?

47:52
It means we also use all those robotic arms, liquid handler automation systems that we have.

48:00
At the same time, the culture system is to be also monitored and controlled properly, right?

48:07
This is not a very classical cell culture system that we put just in the incubators which control all the physical parameters that we can measure like pH, the salt, oxygen, all the things.

48:18
So I think not having a initial scientific data and background about that is really challenging.

48:28
That means we need to do a design of experiment from the scratch, meaning we try to understand how sensitive each of these parameters are to the cell culture.

48:39
And second time is also understanding how these robotics platform can take over human job, right?

48:48
Human tends to do it.

48:50
There is a little bit variables from one person to another, from one time to another.

48:55
But when you have a standardised system, you start to understand that OK, probably the changes is not due to the variation or the errors in the human, but potentially this is due to the culture age.

49:09
These are the things that is less explored by the community, right?

49:13
How a, for example, a pre important stem cell age passage number can influence the outcome?

49:20
This is not well understood.

49:21
What people say is, OK, we have pre important stem cells, it can be expanded indefinitely.

49:27
But I think what we have seen so far sort of in the lab is it's always linked to the passage number that we bank them.

49:36
It's always linked to how old the materials we have.

49:42
And I think this is less explored by the community.

49:48
Yeah, I think that's some good extra thoughts in there about ages of consumables precise number, all of which we tend not to think has an influence.

50:03
But of course, I also want to add in terms of biomaterials, for example, we tend to start or classically or all the cell cultic platform were mostly based on metro gel.

50:14
For example, they were embedded in metro gel.

50:16
So once you start to change to a sort of a automated systems with a proper mixing and control, which means this type of biomaterials are not relevant anymore.

50:29
So in that case, there are a couple of options that you can find out there, like having a hydrogel to embed the organoids inside or having sort of a micro carriers where the cells can attach or sort of some people also opt to have a metrics free systems.

50:49
And which we find sort of it really depends on the lineage that we want to mimic.

50:53
Some lineages, some organoids, you can do it without any carriers, Some you need some sort of support just to provide a good signalling for the cell biology to mimic what's in vivo.

51:06
Yeah, I think yeah, it comes back down to understanding your individual cell needs and working around them.

51:15
We'll do one last.

51:17
I have another one more question, then we'll sort of maybe do the roundup question at the end.

51:21
But Marina, just, you know, thinking about you're working on scaling patient drives PSCS.

51:31
So thinking about those cells, I'll always say they're difficult cells as someone who works because a lot with cell lines and Ms, ES, etcetera.

51:42
So what extra considerations do you need to take into account or do you take things into account and how do you control and standardise as much as you can with patient cells?

51:56
Yeah, no, absolutely RPS, CS and patient drive cells are very tricky to culture.

52:07
There are many considerations to do with them.

52:10
Apologies, I have a little bit of a cough.

52:15
And the main challenge that we usually face is absolutely batch to batch variability, especially when it comes to generating complex organised that take quite a long time to mature.

52:28
And we observe that, you know, differences in Matra gel matrixial lot that we use and growth factor lot that we use can significantly affect what the success rate of differentiation and final cell composition.

52:44
So this is something that you'll absolutely have to take into account.

52:51
And to minimise this variability, we usually batch purchase sufficient quantity of a single matrixial lot to last for the duration of the project.

53:04
And for growth factors where this approach isn't really feasible, we perform small scale validation as a for each new lot to ensure that the key components driving differentiation are performing as expected and generating the cell types that, you know, we want to generate.

53:26
Yeah, no, that makes sense.

53:28
I'm conscious of time.

53:30
I think we could go on a talk for quite a long time.

53:33
But have I got time for the last question?

53:36
Yeah, I did good.

53:37
So I think, you know, we've kind of talked a lot about, you know, the sort of the challenges and opportunities of moving to animal free systems and silico systems moving forward.

53:50
But thinking about, you know, sort of the own journeys and that you've been on yourselves.

53:55
What would be kind of your top tip or even a pledge, you know, moving forward to try to accelerate that journey as we can work together to sort of eradicate the use of animals and see the organoids.

54:11
What's I don't know who wants to go first.

54:14
Have a seat, you get ready.

54:15
Yeah, I can answer that.

54:19
I think for me, we really need to go away from the animal test model.

54:26
I really, you know, I think it's time.

54:30
We learned a lot in the past.

54:32
So as a community, I think we should be build open set database.

54:37
I think this is the only thing that can work in order for us to validate the animal clearing agent.

54:43
Because when it comes to for me, I believe it and I have, you know, use it and I know that it's working.

54:50
The thing is how you can actually make the above us that give us the money to actually have this story transformation to the other, you know, to that side.

55:01
So we really need to validate.

55:04
So we really need to put the data there in order to say, yes, we have validated, this is correct, we can actually use it and we can actually have the result.

55:13
So for me, transparency and knowledge sharing will be actually the key, if I can put it in very short words.

55:24
So this is the only way because it's nice when scientists talk with each other and share their opinions.

55:32
But keep in mind that we have bosses over bosses and over bosses, but they really need to actually give us the green light to do all these things, especially if we're working in a hospital base or in an academia and you getting grants because you're using animal model.

55:50
So you know it so transparency and validation.

55:53
I think it will be the key.

55:54
And start setting the data setting is good.

55:57
We really need all the time.

55:59
The setting is good.

56:00
I like actually, I mean, it's good.

56:02
You are not an alum to that Marina.

56:05
You agree?

56:06
Yeah, absolutely.

56:08
What I would like to add as well is that I think as a researcher, we always need to take a moment to really think through your options when we are trying to answer a particular research question.

56:20
Because it is easy indeed to kind of default to your gold standard in Preclinical Research to animal models just because that's what everyone else is doing.

56:32
But sometimes it's more of a habit and it's not the best or most up to date method.

56:39
And you know, before submitting that grant that includes cost of animal housing and experiments, it's definitely worth asking.

56:49
Could this question be answered using cell models and or computational tools instead?

56:55
And in many cases, I believe it could be a more humane and more effective approach to move away from animal experimentation.

57:06
Brilliant.

57:07
Thank you.

57:08
Anything you want to add, Andy?

57:10
Yeah, sure.

57:11
Absolutely.

57:12
I think, you know, there is no question that when talking about the organoid model, people start to jump into the science.

57:18
OK.

57:19
I want to have this type of organoid lever, organoid lung organoids.

57:24
I think the things that people always miss is setting up a proper system or infrastructure.

57:32
What it means is having a proper traceability of all the ingredients.

57:36
And this is often forgotten.

57:39
And then we start to realise in the middle of our journey on now, we cannot reproduce the data that we generated half a year ago and we don't even have a good traceability.

57:49
So we cannot even trace whether this is a sales source problem or whether this is a metric problem, for example, or whether this is other ingredients problem.

57:59
Because there is simply no tracing system or infrastructure in place.

58:03
And I think that should be the first thing to do if we want to jump into a high precision and high throughput 3D organic culture platform.

58:15
Otherwise we generate a lot amount of data, but then we're going to be puzzled by ourself why they look so different one to another.

58:24
So I think that's it from my side, not that's great.

58:28
That's really insightful.

58:29
And if we have sharing of all sorts of data and with all the intricate background of samples and ingredients, etcetera, then maybe at some point we can help overcome that challenge.

58:42
Andy, brilliant.

58:44
Thank you very much for my perspective for an excellent discussion.

58:48
I've learned a lot and I'm excited to go and get the next set of experiments going.

58:53
I'm going to hand back now back over to you.

58:56
Thank you.

58:57
Yeah, no, thank you all.

58:58
Thank you.

58:58
That was a really insightful and thought provoking discussion.

59:03
Thank you, Eileen.

59:04
Thank you, Andy.

59:04
Thank you, Vasiliki, and thank you, Marina.

59:08
This webinar will now be available on demand, so you will all be able to watch it back.

59:15
And yeah, thank you to our audience, and we hope that this session has provided valuable perspectives and, yeah, as you say, sparked new ideas for your own work.

59:23
So great.

59:25
Thank you so much.