Dennis: Before I start hopping into developing of new medicines in the age
of AI and personalized medicine, just a question to the audience.
Who here has some kind of life science background? Maybe raise your hands.
Okay, maybe about 10%. So, sorry for you guys.
I will not go too much into the very sophisticated biology and pharmaceutical background.
I keep a little bit more on a higher level to show
you the concepts and the overall developments.
So, the discussion about what drugs are doing,
what's the importance of pharmaceuticals is not new.
That has been on the horizon for many decades.
It started in the mid -1850s, where you could buy
pharmacies, opium, and all other kinds of stuff.
And people started to discuss what's the meaning or what's the importance of drugs.
And a lot of things changed since then in order to understand that and
put it into perspective of how AI is implementing some of these changes.
We have to understand how drugs are currently made.
For that, let's have a look on how the current drug discovery pipeline actually looks like.
So, this is what you'll also find in all kinds of textbooks.
And I guide you through different items.
So, the very basic or the very basis of research is what you've done in academia.
So, basically, civilians of PhD students and postdocs and I guess
some professors as well contributed to a certain foundation.
That is the knowledge that we have.
On top of that, we're going to start drug discovery.
So, for instance, if you want to cure a liver disease, you figure out,
okay, there's maybe a certain protein that's associated with a disease.
I want to tackle that. There's a kidney disease. There's a certain gene involved.
I'd like to tackle this.
So, after identifying potential targets, you start the validation of the target.
Because you want to make sure that what is written
in academia or in some papers is actually true.
So, you run a bunch of experiments and test tubes and petri dishes.
You take a number of test compounds, add them on your cells and you want to
see if there's a certain effect illicit in the cell culture you're looking at.
And hopefully, this is something you want to see.
If that's going well and you have some starting
compounds to deal with, you're going to optimize them.
This is the lead optimization phase.
Chemists are going ahead and modifying the compounds,
making them more potent, more selective, essentially
increasing the power to making them as a precursor of a drug.
After that is successful, you go to the next step.
You want to test the compound in a more complex biological system, laboratory animals.
So, you want to first see, can I reproduce the effect and are there any severe side effects?
If there's a severe side effect, this molecule dies in the progression path.
If it works well, you move to the next phase. Next phase is the human.
You usually start with healthy human beings.
You want to first make sure whether the drug is toxic, if there's severe side effects.
If that's not the case, you go into patients and you want
to see whether the whole proof of concept actually works.
At the next step, you're going to a larger population and you want
to see whether there's a therapeutic effect which is beneficial.
If that all works out well, then you ask the regulatory
authorities for approval to get your drug on the market.
In the US, it's the federal drug, the Food and Drug Administration in Europe
is the European Medicines Agency and they evaluate the data that you have.
They ask for additional data, ask for additional studies, but at the end of the
day, you're successful, you get the approval to put your drug on the market.
That means, you get rich. On the other hand, the so -called phase four starts.
It's a long -term study.
The drug is on the market, it's prescribed by doctors, and then
there's a continuous surveillance how the drug actually works.
It could be that a doctor prescribes a drug to a certain patient
who has a, I don't know, heart condition and it turns out,
okay, this drug doesn't work well, there's severe side effects.
That means for future prescription, this drug is refined.
Everyone who has this condition is not getting the drug.
This happens after the drug is on the market.
So, this is essentially the whole drug discovery pipeline in a nutshell.
You can appreciate this is a pretty complicated
and complex issue and it takes a lot of time.
So, the whole drug discovery part is maybe three to five years.
Pre -clinical development, one to two years.
Clinical development, five to seven years.
The authorities take another one or two years. That are all average numbers.
For some, it takes much longer. For some, it's much faster.
But if you look at a textbook, it's just roughly the ballpark you're looking at.
The rate of success is also pretty hefty.
So, in average, you start with about 10 ,000 compounds at the
very beginning and only one of those makes it to the market.
Altogether, we look at an average drug development time of 11 to 15 years, so 13 average.
And the costs are currently more than $2 billion dollars.
So, this is a really long commitment, much of time, and a lot of money. Why?
Why is this so risky? And why is this so expensive?
So, there are several reasons to it.
One reason, or in general, the main reason is the so
-called transnational gap or also the Valley of Death.
It sits in between the body of knowledge from
academia and the application in the human being.
So, something in between goes wrong, and this is the reason
for the high price tag and also the reason why about 90 % of
all drugs will never get on the market, will fail in between.
The reason for this Valley of Death, there are several.
One of them is the problem of academic knowledge in general,
and I think we had to talk about this several years ago
here at the Congress as well, the replication crisis.
The body of knowledge we're dealing with is maybe not as robust as we hope it to be.
There are some examples in the field of cancer research.
There's the Open Science, Center for Open Science, who started a reproducibility
study looking on how reproducible is the knowledge in academia if it comes to cancer.
So, they tried to reproduce 200 experiments from about 50 papers.
A third of them, they couldn't even start it because the information wasn't there.
About half of them, they could start, but they were not able to complete it.
And the last quarter, they actually could complete, but
maybe only, by their metrics, half of them were successful.
And usually, the positive effects described in
literature were not as strong as they could reproduce.
So, this is already an issue, and if you want to spend $2
billion on this basis, that's maybe a little risky endeavor.
And the pharmaceutical industry is aware of that.
So, there are a couple of studies. The first one from Bayer is about from 2011.
They also looked in cancer studies and found that
maybe only a quarter of them they could reproduce.
So, similar ballpark numbers as the Open Science, as the Center for Open Science described.
Amgen, another company, did something similar.
They were only able to reproduce 11 % of all the academic literature.
Interestingly, what I found in this study from Amgen,
they put in this table, which you can see here.
And even if there are studies where experiments are presented and they
could not be reproduced, those are cited heavily and frequently, creating a
large trail of other papers referencing experiments you never can reproduce.
So, that is a growing number of papers on data, which is maybe
a very weak foundation for any kind of scientific endeavor.
So, if you take all the public literature, put it into
LLM and ask ChatGPT, hey, is this maybe a good drug?
You run into issue because you have all these biases here.
The LLM is not able to reproduce the experiments and is not validating
and scoring them, whether this is a good thing or a bad thing.
So, this is one issue on the scientific foundation.
The other issues are centralized around the technical
and experimental setup that we have in drug discovery.
So, this is another representation of the pipeline.
And what you do in experimental research is you want to
figure out whether the biology does what you hope it does.
So, you work with standardized cell lines.
Cell lines that have been extracted from patients decades ago, they are cancer cell lines.
How much do they represent a real human being? Maybe not so much.
We all know that, but it's one of the few models that we have.
The other issues are animal models.
They are standardized lab animals, inbred for
decades, used as a model to replicate a human disease.
Maybe there are some limitations to that.
Again, everyone knows it, but there is only so much you can do.
Drug delivery to animals works very differently.
You can inject drugs into the bloodstream of a mouse and it goes into the
brain, which does not work in humans because the brain is shielded differently.
So, there are some issues there.
You also can administer drug in the mouse in a very different way.
You can inject it into retroorbital veins, so right behind the eye.
In theory could do it with humans as well, but it's maybe not applicable.
Historically, we have a lot of data on clinical
trials, but they are usually including male Caucasians.
So, there is a lack of representation of women and minorities.
It's getting better, but historically, it's still there.
The other issue, if you look in a clinical trial, you want to see whether something works,
you focus on symptoms.
You don't necessarily understand what happens on the genetic, on the molecular mechanism.
So, you might miss something. You're flying blind to some extent.
And this is also bleeding into the selection of patients.
Let's imagine you want to put a drug on the market against a liver disease.
You have patients who have liver disease A and B and C, but all look the same.
They all have the same symptoms, and you all give them
the drug, and the drug only works against liver disease A.
People with B and C, they will not respond.
The statistics of the clinical trial look bad.
The trial fails, even though if you could only
select A, exclude B and C, it would have worked.
So, this is one issue that we have in the drug discovery world.
How can you make it better? And what is the role AI can actually have here?
So, as I said, there are a lot of issues.
AI cannot fix the issue of the knowledge base that we
have, but it maybe can help us here accelerate the process.
And without going into too much detail, there's a lot of work in
finding better in vitro models, better mouse models, having better drug
delivery systems, recruiting more minorities and women into clinical
trials, getting more data to understand what happens in a patient, and
using this to also select patients more adequately to a clinical study.
So, this is all happening, but as you can appreciate, all these come with data, data, data.
And this is the foundation for any successful AI model.
Now, if you ask, I recently was in a symposium, but that
question was asked among the attendees from pharma and
biotech and academia, where do you put your AI capabilities?
How advanced are you?
And basically, this is a little bit what the
audience said, and I think it's quite pragmatic.
Two -thirds of the audience said, yeah, we're scouting,
testing, playing around, seeing how it works.
Some of them said, yeah, it's part of our strategy.
There are some applications, and only a very small number
of attendants said, yeah, we see some beneficial usage.
So, given that, I think we have still some time before AI takes over.
Nothing to worry about.
Though there are some applications that are currently integrated
into current workflows, and I have here some examples.
One example is AlphaFold, and I guess that has been
presented, not the last one, but the Congress before, I think.
It helps to predict the protein structure.
So, imagine I want to build a drug against the
protein, which I have no idea how it looks like.
I can come up with hundreds of experiments I can do, but if I get a good estimate
how a structure could look like, I can already start with 20 experiments first, and
put some other AT on the backburners, and maybe the first 20 are already successful.
So, it helps prioritizing the experiments I can do, which is already a huge benefit.
Image analysis is another thing.
So, if I take samples from a cancer patient, what's the tumor looking like, or if
I take animal tissues to want to see whether there is an effect, I hope to observe.
I have to take the tissue, I have to prepare the tissue, I have
to stain it, I have to put it on the microscope, get the right
resolution, and then count the different patterns I see there.
This is a lot of laborious work in the past.
It has been done by someone looking at a microscope
and checking boxes and counting light dots.
Tedious work, but over the years, more and more of this has been automated.
You can automate image analysis, quality control, all of this.
This takes away a lot of tedious laborious work of someone sitting in front of a microscope.
It can be automated away. So, this is happening.
The other thing on the right -hand side is how to optimize your compounds.
There was an article from the Chemical Abstract Services, part of the American
Chemical Society, who looked into a couple of patents or drugs that have been
put out into human clinical trials who originally have been created by AI.
Their conclusion, or the conclusion of the author was so -so. It's okay.
It's not that exciting, but it's something that is happening.
So, we are already there, and already AI tools helping us to design and
optimize small molecules, making them better, turning them into potential drugs.
So, conclusion of what's happening here right now is, yes, there are applications.
They're already part of the current workflows.
They help prioritizing experiments and timelines.
They increase efficiency, and they increase productivity. That's pretty good.
So far, there has been no impact on the human workforce.
They're still there, because, especially if it's R &D, you have
thousands of questions, and if the AI helps you to answer five,
there's still so much more you can do in the other time you have.
So, that's ongoing.
That being said, there's one recent example from a company, I think it's Ins
ilico Medicine, who have first identified a target for lung disease and
designed the molecule using AI from the very beginning to the very end.
So, essentially, we have been able to start from this part and go all the
way to the first part here within 2 .5 years, about 30 months, which is, in
average, twice, three times as fast as conventional methods are happening.
And they've been even able to complete, August
last year, the first part of the clinical phase 2.
So, we're quite advanced in this development
pipeline with rather a little bit amount of time.
Now, you can argue, well, there may be hundreds of
companies working on AI, 99 fail, and just one got lucky.
They could be the lucky ones, maybe.
There could be also, I mean, the other thing is,
these are average values or average timelines.
For some drugs, it takes much longer. For some, it takes much faster.
It's hard to really put this one case into this pipeline.
Everyone is looking at it because this is kind of the prime
example of how drug discovery can be accelerated by AI.
And this is kind of a little bit of the pilot.
Everyone is looking at what's happening.
So, there might be something happening, but I
think it's premature to come to a conclusion here.
Whatever you do, and if it comes to AI, you need a good, solid basis of data.
So, good data is key, and I think it's nothing new I'm telling you.
I think you all know that.
So, good data is, and then there's a series of different adjectives you can
put on, accurate, comprehensive, curated, reliable, and so on and so on.
I think you get the gist.
If it comes to data from a biomedical context, it's a little bit tricky because
you have to obtain the data from living organisms, from patients, sometimes across
different countries, across different clinical centers, where different people work.
In order to get data that even compare among different patients and countries, you
have to make sure that they're all adhering to a detailed standardized protocol.
You need standard operating procedures. Data needs to be curated.
You have to make sure that the people handling the cells, the
samples, the biological specimen, are adhering to a technical
and experimental level, which matches a certain accuracy level.
You have to be able to trace everything that's happened.
So, this is really expensive, time -consuming, and costly.
Small academic labs or small clinical institutions are
not able to have the manpower and the resources to do so.
This falls back to larger companies and or specialized companies or, in some
cases, large national initiatives like the UK Biobank, a large nationwide cohort
study where they've actually pulled the resources together to gather that data.
Those data is costly to get, and if there's data banks,
they won't have access fee or you don't get it at all.
So, they're limited access to the available data.
What we also see is a lot of large pharmaceutical
companies have been on drug discovery markets for decades.
They have internal data sets from R &D projects 20 years ago, 30 years ago.
All this data they have usually obtained in an industry setting, so
it's higher standard, higher traceability versus an academic lab.
They have clinical data, and not everything they
had, they have submitted to the authorities.
Or you don't have to, but they are pushing to get more data
to be shared, but in the past, it hasn't been the case.
And especially clinical trials that failed.
They never submitted those, but they have the data and can learn from it.
So, they have this body of data, plus they have their own cohorts
or bought into cohorts, and now a lot of different patient groups.
So, they have a lot of data in -house, and this is kind of
the proprietary data sets, which are high -value assets.
And this is what they use, to put AI on it, to come up with new
ways to find a new drug, and try forward the drug discovery pathway.
So, if there is anything value in data, usually what happens is
some people figure out, oh, there can be some money with that.
And this is an issue in the US, but it's also an issue in
Germany and Europe, that cyber incidents happen all the time.
If it hits a big pharma company, it can be really expensive, but
if you're a big pharma company, don't worry, you're not alone.
It happens all the time.
We also saw during the corona pandemic, especially the frontrunners in the COVID
-19 vaccine race, they have been targeted, but also others who were involved.
And, as I said, clinical data are very important.
They should be protected from cyber criminals, but I
guess also from users, because that's usually a problem.
Cyber security is not a new trend.
It's becoming more important in the drug discovery and pharma world as well.
But there's one particular discovery or trend in
drug discovery I'd like to focus your attention to.
And this is the shift towards personalized or precision medicine.
The overall concept is, if there's a drug, if you have high blood pressure,
for instance, you get a certain drug, and then hopefully it works.
It could be, and maybe you know it from people you know, from families or
friends, that someone, a drug works for someone, for somebody does not.
Someone gets a headache or doesn't feel right.
Some people respond differently to drugs.
And if you would know that, you could say, okay, this group A, you maybe get this drug.
And group A, you get maybe half a dose.
And group C, yeah, probably you don't take this, take something else.
That would be great, because it would make the drug treatment more
efficient, and it would reduce the amount of side effects that you have.
If you go one step further, you end up with personalized medicine.
So, you take the consideration that we have all a unique genetic
makeup, because as you know, we are all individuals, and everyone has
to be treated in a special way to obtain the largest effect possible.
But, that comes with, well, pros and cons.
The good thing is, the promise and the idea of personalized medicine, instead of
have one size fits for all medicine that you can give to everyone, and some are
benefiting from the effect, some have nothing, and some actually have side effects,
you would have the idea that there are some drugs that work for person A, another
one for person B, another one for person C, and they all would get the best benefit.
So, this is the idea, and the promise, and the hope of personalized medicine, which
I think is quite good for the patient, because you get the best treatment possible.
The downside comes right here, if you push it even further.
Push it even further, I specifically want to refer to genetic medicine, or genetic therapy.
There are genetic therapies out there.
I put here four of the contemporary examples.
They are all targeting rare diseases, some very, very special diseases, where no
cure available, from all kinds of different companies, and it's usually one therapy.
You undergo one therapy, but this one comes with a huge price
tag, between two and four million, depending on what it is.
So, they are expensive.
For people who are suffering from this disease,
there is no treatment, maybe except that one.
They suffer, and usually die at a very young age.
So, for them, gene therapy is a big thing. It changes their life.
It actually saves their life. However, strings attached are several.
On one hand, the one on top, Lenm eldy, in the US, in Europe, it's called Libm eldy.
The company said, in 2023, they generated about $12 .7 million through that.
If you look at the numbers, that's maybe three patients.
And in 2024, it's $22 .2 million.
That's maybe six, seven patients that are super small
target populations, because it's a rare disease.
It doesn't happen often in the population.
There are only a few people who have it.
For them, this treatment makes a difference. But this treatment is not trivial.
It's really, really complicated. You need specialized centers.
You need specialized doctors. You need specialized infrastructure.
You need specialized care. That's one reason why it's so expensive.
It can't be done everywhere. You have to really go to specialized centers.
And if the cost comes to the question, who is covering the costs?
And this is a big issue. If you're in a single -payer environment, maybe.
In the private insurance, you're probably screwed.
And there comes also automatically the ethical question.
This question has been around forever since there was the idea of gene therapy.
But it's becoming more urgent, because we already see gene therapies happening.
So, what disease should we treat? Who decides that? And at the end, who gets it?
Is it a decision or an agreement that society makes?
Or is it what money allows you to do?
So, this is a question where no answer is there yet, but it should be answered
sooner or later, because we see gene therapies, and there will be more of them.
And these questions are important to answer.
There's one more development, which I'd like to focus your attention to.
And I call it the end of the power cycle, because I think
it fits nicely to the overall theme of this Congress.
We have here the drug development pipeline. This is what you've seen.
It's expensive, $2 billion on average, and it takes a long time.
For a pharmaceutical company, you need some kind of
revenue idea to fund this drug discovery pipeline.
And this is the concept of Blockbusters, which are coming in.
Blockbusters, you know, from movies, it's this one summer
movie where everyone goes there, and it earns a lot of money.
The same thing applies to drugs.
A Blockbuster drug is a drug that formally exceeds $1 billion in annual revenue.
If you adjust it for inflation, it's maybe more like $3 billion.
And here are the top 10 from last year.
They make about $10 to $30 billion for Stelara with $
10, and Keytruda for about $30 billion last year.
So, they are the essential parts for the pharmaceutical industry to
actually fund and drive forward the research and development pipeline.
Without these guys making the money, it won't work.
Why is it possible to earn that amount of money?
So, there are several reasons to it. One reason is patent protection.
And I picked the two recent examples of Ozempic and Wegovy.
Osempic, if I go back, is also number three.
It's not long ago that it came to the market, but it's already having a huge effect.
So, Ozempic is a treatment for type 2 diabetes. It's an injectable pen.
And the compound that actually does the magic is semaglutide.
In the US, you can get it for $1 ,000. In Germany, for maybe $60.
I'm not going into a discussion why we have discrepancy.
It's a whole different story, which I'm not starting here to address.
I have to stop in maybe 10 minutes or so.
The other part is Weg ovy, which is basically the treatment for obesity.
It's an injectable pen with, surprise, surprise, semaglutide.
It's slightly higher concentrated, but other than that, it's the same as Ozempic.
And it costs more because you treat now something different. Why is this?
Different discussion.
There was a study last year who they tried to figure out how much does it actually cost?
What is the production cost, essentially?
And the group, they came to a price tag between $1 and $5 for that.
So, you already see there's a pretty significant profit margin
embedded into the different prices, if I put it carefully.
This is one of the reasons for Blockbuster drugs.
You have high demand and you have a high profit margin.
That only works because we have patent protection. If that ends, we come to this.
And this is an example from a couple of years ago.
It was Pfizer's Lipitor, a drug to reduce cholesterol levels, if I'm not mistaken.
It was bringing in a lot of revenue, between $9 and $13 billion per year.
Until 2012, the patent expires, and it usually does. They do that after 20 years.
And it's a huge drop of about 80 % within a short amount of time.
It means that money is gone because now the exclusivity is gone.
Other companies can bring on the same drug on the market and maybe ask for half the price.
And you've seen the profit margins.
They can even go to a quarter of the price and still making profit.
And this is what happens and this is the reason for this huge drop in revenue.
For a company who has one or two Blockbusters, this is a
big issue because they would suddenly lose all the revenue.
Now, what's happening right now is this patent cliff, as they call it, is coming.
And it's coming big.
More than 20 Blockbuster drugs will lose the exclusivity within the next four years.
More than 20.
That means we have one of the largest, even the largest
patent cliff ever in the pharmaceutical history.
And about $230 billion in annual sales rate stake.
If you don't know what the number is, it's maybe
20 % of the entire global prescription market.
Or in Germany, it's one Doppel- Wumms.
So, this money is about to kind of vanish or move into other companies, which are
not the pharmaceutical companies, because there's no patent protection anymore.
With that development, we see some interesting
situation in the whole pharmaceutical landscape.
And if you summarize that, we have on the one hand this patent cliff.
The revenue model is in jeopardy.
We have more generic drugs, which can already treat a lot of disease.
So, if you come up with a new drug, you can't really ask
for big premium prices if there's already something else.
It has to be really, really good compared to what is on the market.
Otherwise, you can't ask for a lot of money.
So, that really puts a lot of pressure on the drug development
pipeline and the people doing the research and the companies.
Then we have precision in personal medicines.
Patient population is getting smaller. Treatments become more complicated.
It becomes decentralized.
How is this working in the whole concept of having a Blockbuster?
It doesn't really work.
On the other hand, large pharmaceutical companies, as I said, they
have this huge amount of data which they've collected over decades.
And they have it. It's usually in a pretty good shape.
They have a lot of information.
And the hope is that AI can be used on this data to identify ways to reduce
costs, to accelerate the timelines of drug discovery, and eventually also
identify new medicines with new benefits for which you can ask a higher price.
If I take all that together, we always think, oh, AI is disrupting the industry.
Here, in that particular case, I don't think it's the case.
It's more the opposite. It's a necessary development. Otherwise, it wouldn't work.
So, and I'm personally not really worried about AI in the field.
I'm more worried about the patent cliff and the shift to precision medicine.
This is very, very good for the patients because you
can address a lot of diseases you cannot address before.
You can improve care substantially. The question is, to what condition?
What defines the accessibility to a certain treatment?
Is it general societal consent? Or is it money that decides decisions?
So, that are big questions.
Risk and uncertainty is coming here close to the whole promise of precision medicine.
I think that's something we should discuss.
And I wanted to kind of summarize the current developments, give us some food for thought.
And, yeah, that's all what I had. Thank you very much.
I'm happy to take questions.
Thank you. Thank you very, very much, Dennis, for your talk.
And I think there will be a lot of questions.
Please go to the microphones in the hall. Please at the microphones in the hall.
Yeah, at one. Hey, yeah, thank you.
Super interesting, this whole pipeline. Can you maybe give some examples?
I mean, what is typical data that goes into these AI models?
And what is the output of these AI models?
So, is it like studies go in and the protein comes out?
Or, yeah, just maybe one, two examples how this actually works?
Everything and nothing. It really depends on what you're looking at.
For instance, well, I don't want to go back all the way.
But you remember some of the images where you analyze microscope data?
Then it's a very limited data set. You use a specific microscope data.
So, for that particular case, it's a narrow data set. It's very limited.
If you go for a large product discovery, where you want to figure
out on what could be a new target, then you're going more broad.
You're going to take consideration all academic literature.
You go for patent applications. You're going for grant applications.
All clinical data you can find.
Either they are publicly available, or you can find them from competitor
information, from competitor companies, internal data, all that goes.
And basically everything you can get.
The idea is the more you have, the more you can
narrow down some things and exclude some other things.
The issue is usually accessibility, because it's really
hard to get comprehensive and highly curated data.
They are really precious. But that's essentially a nutshell that asks.
That answer your question?
Yeah. Thank you.
Dennis: Perfect.
I think we have a question from the internet. Signal Angel, please.
Signal: Yes, can we also use LLMs to improve the tests that doctors
give to patients to determine what drugs to prescribe?
Um, I mean, you can use everything. Yes. With a big question mark.
I think we are not there yet.
It's maybe a little bit outside of my real expertise
what's happening on the clinical application.
If you want to understand the disease or general symptoms
of a disease, it's good to take a lot of things in context.
That's difficult for a doctor.
But I guess there's also the idea that if you get a lot of data from
the personal patient history plus some genetic information, some drug
treatments in the past and the present with all information compared
to certain data bases and it can point you in some kind of direction.
I think the idea is there. But it could be a benefit.
But I don't think we are there yet. So maybe ask me the question 10 years again.
Then we'll see.
Thanks for the question. And thanks for the answer.
The next question from the microphone 6, please.
audience: Yes.
Do you think the use of AI will enable small and mid -sized
companies to get into the pharmaceutical market more?
Yes. Yes, definitely. Now I have to go all the way back. Sorry about that.
This one example I just showed you earlier, that one.
I think Insilico Medicine is a rather small company.
And they use the AI model that they have to really come up with a
novel target and move it all the way to the human trials phase 2.
That's something that can actually happen.
And we see that for some smaller companies that it actually is happening.
The other thing is, the question is, how long do they remain small?
Because if there is a smaller company, you have something very interesting,
like a good drug or a good AI model, big companies come and usually buy them up.
That happens.
But generally, if that accelerates the timeline, if you cut down the
research, if you have a really good AI model or for something very
specialized, yes, this is really an open door for smaller companies.
And this is what we see here.
audience: Thank you.
Thanks for the answer. The next question comes from the microphone four.
So thanks for the talk. It was very interesting.
So one thing you mentioned that was that medical data sets
have a huge bias for like white, male, Caucasian people, right?
And that's like a known fact.
And then it's also known that AI sometimes even enhances this biases and not lessens it.
So how can we counteract that basically?
That's a good question. I'm not sure what the answer is.
Especially historical data has this bias.
And there's nothing you can do about that unless you
kind of fix AI, which I don't know how it should work.
But we see more modern clinical trials where they have a different recruitment scheme.
And since it's more modern clinical trials, you
have modern technology getting more data there.
We have a little bit of discrepancy between old clinical trials with a small
amount of data and larger clinical trials of modern ages with larger data.
So there is maybe the chance that over a couple of years, the
more modern clinical trials with more data will outrun the old.
And then we have kind of limited the bias.
But it's something that will take some time.
And for now, we have to be aware that a lot of
these things have the issue, as you just mentioned.
And not only for the human diversity issues, also everything in terms of biology.
We use certain cell models, which are still old.
We use old animal models, which are not really reflective.
So there's a lot of bias, not only on the human level, but also on the experimental side.
And this is something we have to address and keep
in mind whenever we use AI or something like that.
Thank you. Microphone five, please.
Thank you for the talk. What do you think about Everycure?
For example, they try to repurpose existing drugs, which are approved for other diseases.
Dennis: What's the name again of the company?
audience: Everycure.
I'm not sure if I've heard about them. But I think they are not the only one.
I think that's a general idea that many companies try.
Like the example of Ozempic on Wegovy, it goes the same direction.
It's the same compound, but it goes for two different indications or diseases.
And for a drug development, that's kind of a good way because it can shorten the
clinical trials because a lot of things you work already apply for a new one.
So it kind of reduces the development pipeline and costs.
So this is something they try to do.
If that's their business model, yeah, I think they're not the only one.
A lot of them do it. It's part of the entire business.
Another thing is, if you want to develop a drug for a rare
disease where no drug is available, you get some kind of
beneficial drug discovery credentials from the authorities.
Some things you can reduce, some smaller clinical
trials you can run because there's no competition.
And if you have then used this to get a drug on the market, you can use
this to go for a different indication and then it's already cheaper.
So that's all different strategies and it's rather standard in the industry, I would say.
Angel: We have another question from the internet.
Signal: What do you think about using AI to simulate
entire cells and protein -to -protein interaction?
Dennis: It's cool.
I think it's a little bit too early because the AI is very nice and
great but it depends on the data and the cell essentially is a black box.
We know only a little bit.
There are so many proteins which we don't even know how they look like.
There's protein modification.
There are all different kinds of lipids floating out and no one cares about lipids.
There are other parts in the cells which we don't really take into consideration.
I'm not sure how much of the real amount of unknown
we actually can include into the current procedure.
So modeling a cell on protein interaction is a very limited, narrow point of view
of what really happens on the cell and we don't only know a small portion of it.
So it's a cool idea but from the real application I would take it with a grain of salt.
Thanks. We have another question from Microphone 3.
audience: Yep, thank you.
I have a question concerning the reproducibility of studies
you mentioned earlier with problems not fixed by AI.
How can it be that so little can be reproduced? Do you have some insight on that?
Hang on. There are, I guess, a whole host of different reasons.
One reason is we work with biological materials who have this
annoying feature that there's so much happening which we don't know.
If you keep your cells growing for some time they change and you might not know it.
They change their metabolism, maybe there's a mutation
occurring and that already leads to a different outcome.
You feed the cells of certain cell media.
You hope it's the same but you don't know.
Could be some minor changes here. something is different in the, I don't
know, air system which is keeping your cells in a sterile environment.
There are a lot of so many small things you can't really control.
That's one element of contribution.
Technical feasibility of a PhD student versus a seasoned technician makes a huge difference.
And then there's, of course, in academia the pressure to produce publications so
maybe you ignore a couple of effects and push out that so you ignore a lot of
negative data and only publish a positive data so that also is some of the elements.
So there are many, many reasons that some of them just came up to my mind.
Angel: There is another question from microphone one.
Yes. Thank you a lot for your talk.
I have a small question regarding your opinion on the possible
effects of the AI studies on the clinical trials pipeline.
Well, we all know why the clinical trials are so lengthy now.
We had teledamide and other issues which made people, you know,
put some safeguards and make the trials quite long, comprehensive.
What's your opinion? Might we get more effective data treatment with AI?
Might the clinical trials be more, how do I say, compressed, more efficient?
Good question. I know there's some developments.
For some antibody developments, I think the FDA put out you don't have to
do animal trials anymore because we can reduce toxicology information, I
believe, from all the other previous studies and test tube experiments.
That's one thing that happens.
Some of that could probably bleed into human studies as well.
One of the issues is personalized medicine.
Groups of patients getting smaller and smaller.
And maybe there's a disease where only globally 1 ,000 patients.
You're not able to run a large pharmaceutical, clinical, free study with 10 ,000 people.
It's just not possible. So you have to do something smaller.
And for a smaller disease where no drug's available, you can get
away with much smaller groups because this is a specialized case
where the authorities and regulatory authorities know about that.
So we will probably see more of this if you're able to subset
your population based on precision medicine to smaller groups.
That's maybe a development that we can see if they're getting smaller.
audience: Thank you.
Thank you. Okay, thanks. Maybe the last question for microphone number four.
Yes. Hi. Very nice presentation. My question are, in fact, two questions.
If there is, you know, if there exists any effort or about develop
standardization or protocol of use AI in clinical trials to
avoid to avoid basic and how to perform a blind study using AI.
I mean, because basic results cannot come necessarily from basic data sets.
I'm sorry. I'm not sure if I got the question.
audience: question like, is there any effort how to effort to develop protocol
or standardization to avoid basic results or to perform a blind study?
Dennis: That's probably a little bit out of my comfort zone.
I'm quite sure that the authorities know about the rise of LLMs
and AI and all those tools and they take that into consideration.
I think there are also active working groups and committees to
think about how could this impact the study designs that you
have the requirements for clinical trials how we can shape this.
And if it comes to that then of course it won't have a very standardized protocol.
What kind of AI application have? What is the data set? What's the training set?
What are the different steps to apply?
We want to have the open source AI toolbox for example available.
So these things are probably under discussion.
I'm not aware that there is something out there.
I might be totally wrong here but I think it's in the
making and the process are developing if I'm not mistaken.
Thank you. Okay.
Angel: Thank you for your talk.
I know there are a lot of questions but we have no time for this.
Where can you find you at the congress?
Dennis: I'm floating around maybe somewhere outside.