Greetings and welcome to EHA Unplug, the official podcast
channel of the European Hematology Association EHA.
Hello, I am Isabella Rivera, a medical writer for EHA.
Today we're thrilled to have Doctor Jan Moritz Medeka, a
distinguished hematologist and AI specialist.
We're going to be talking today about AI to accelerate clinical
trials. Welcome, Doctor.
Medeka, can you please introduce yourself?
Hi, yeah, thanks for having me. I'm, I'm Moritz Malika from the
University Hospital in Dresden. I'm working there as a
hematologist and have a. Research Group.
On AI in hematology. So we want to understand how you
can accelerate clinical trials by generating data with AI.
Can you please explain first what is synthetic data?
Synthetic data is as the name says.
Data which is. Made-up and.
The idea is that we face a. Big problem with clinical trials
in general, but especially in hematology.
Where a lot of the diseases are rare diseases for.
Example acute myeloid leukemia is a rare.
Disease per seat. And we already know that.
There are several subgroups with targeted therapies, so the.
Number of patients we can include in clinical trials are
even lower and therefore we need to think about new ways how we
can. Accelerate how we.
Perform clinical trials in order to get sufficient data to get
the knowledge about novel. Therapies.
So what types of synthetic data are you generating that could be
used for clinical trials? So the idea.
Is that you have synthetic? Data which is.
Reassembling the original cohort in a A.
Most perfect way. But without being able.
To get back. To the original data.
So you're generating images. This can be used.
Used both for. Images.
But also for tabular. Data and what we did in recent
study and also other groups have shown is that you can reassemble
real clinical trial data in terms of baseline data, but also
in terms of outcome results based on cohorts of of.
Real. Patients and the synthetic data
is reassembling all the information about the original
patient which is like. Age.
Sex. But also molecular changes and
then the outcome after a given intervention.
And how do you generate the synthetic data?
So for this. We used guns, Generative
Adversarial Networks, which is a known form of AI.
Which has been. Used for image generation.
Quite successful. Already.
But can also. Be used for tabular data and in
our specific work we use two types of these, so.
Nflow and C Top Gun. So these are types of deep
learning and it's generative modelling.
You have described 4 scenarios in which synthetic data
generation can be useful and I would like to walk through this
for the audience. So first, AI generated data
could help to prevent privacy concerns about clinical trial
date data of the patients. Exactly so.
The generated synthetic data can be shared without any.
Privacy issues, so we checked that.
There was no single patient in the synthetic cohort.
Which was exactly. Matching the original patient.
So there's no way that you can go back to the original patient
which. Of course is the case if you.
Just have an anonymized data, so this makes.
It really easy. You can just upload these data
and make them available. Also for other research.
Group and see public. So how does this compare to the
de identification or anonymisation of patient data?
So anonymisation of these data isn't 100% sure, and with the
synthetic data it's 100% sure. So there's no way that you can
go back to the original patient. Because see, there's.
No original data coming from one single patient.
So it's basically 100% sure that you can't go back to the.
Characteristics of. One single patient.
So this is a big step forward for this kind of data.
Also, you would create new patients and this would lead to
either cohort augmentation, so some synthetic patients among
patients that are real or completely a substitution and
you have a complete new cohort. How robust is this process?
So for the. Generation it's.
Very robust. We showed and also other groups.
Matteo de la Porta did this for MD's.
Showed that it's. Reassembling the whole.
Crestrix of the. Patients for a given disease.
So it's not only age and sex in terms of the distribution, but
it also comes down to the molecular changes, which is very
important because we know that there are certain relationships
between certain mutations. So for example, if you have an
NPM 1 mutation, you're more likely to also have inflat 3
mutation. And these correlations are
preserved in the synthetic. Data set.
And this. Holds also true.
For correlation between age and certain cytogenetic
abnormalities like complex. Karyotypes with with.
Higher age and so on so. Biologically.
Important correlations are. Preserved.
Within this data and even more important, this also.
Holds true for the. Correlation to the outcome data,
so known factors for. Poor or good?
Prognosis are also preserved in the synthetic.
Data so if. You have a patient with some
complex karyotype. He.
Also has a worse prognosis in the.
Synthetic group of patients, and this is super important because
if we want to use him as a real control group, we have to make
sure that. This is also.
Like in the. Real world.
So there's also A use for exploratory analysis.
So synthetically enlarged data sets, what is the interest?
Of this, we know that there are several important subgroups when
we stay in the case of acute myeloid leukemia, certain
molecular subgroups which are recurrent.
But are rare. And.
So for these group. Of patients, it's of course very
important to know. How they fare with a certain.
Therapy and. With a synthetic.
Generation of data you can. Substitute or.
Enlarge. These rare subgroups.
How reliable is this process? We have all heard about
hallucinations in AI. Can this generative AI generate
misleading data? Well.
Of course, you always have to show it.
For a given group, but for the examples I mentioned.
This is very robust, so. There are some small
differences, but in general. This is.
Very robust in terms of how the baseline distribution of.
Characteristics is but also. For the outcome data, however,
especially when the outcome events get lower in the training
group. This leads to a lower.
Robustness of the model. So there is still open question
where we have to work on. And the last case you mentioned
where this kind of synthetic data can be used is in model
training or benchmarking. Can you explain this further?
So the. Idea is that.
We also have to generate. Data for rare subgroups.
And for that we can use synthetic data to enlarge the
group of. Patients, basically.
For in clinical trials this generated patients are called
now synthetic patients. Can you summarize what the
advantages to use this kind of synthetic patients?
We have to point out that. So far synthetic.
Patients have not been used within clinical trials and.
I believe that there's still a lot of work to do, also from a
regulatory. Aspect to.
Show that this is really feasible.
On the other hand, we know that the generation works and the.
Problem is so big that. We have to think of new ways how
we can accelerate clinical testing.
So for this example we used. Only intensively treated acute
myeloid leukemia patients almost with.
Our targeted therapy, so this has to.
Be shown with. Targeted therapies this has.
To be shown with. Real life data.
And with certain intervention and there's a lot of.
Work to do. Before we can really.
Say OK. So we one group of patients
within a clinical trial for in the end leading to an approval
or identification of certain efficacy of a of a given drug.
So really is work in progress, how closely does these cohorts
match real world cohorts? OK.
That's a difficult question because.
It hasn't been shown yet. So for the moment you don't know
really how well and it's being tested.
We know how well it matches. For.
A given group of patients we studied.
Or other groups. Studied and there it is working
well. However, we have to.
Yeah. Prove this for real world
cohorts. And and certain interventions.
So the idea would be to use these cohorts mostly in the
control group in a clinical trial at the beginning at least.
Yes, I, I. I think this is the most
realistic and and closest scenario that we use this as a
control group, an enlarged control group.
Yes. With a certain intervention.
Which would be interesting because then you don't have to
assign patients to a control group.
So you mentioned the regulatory considerations.
We have some tools at the moment, like GDPR or HIPAA.
Are they enough to address the potential issues that this kind
of synthetic patients would bring?
Well, I think this. Requires.
Complete new thinking about the approval process and the.
The way we look at. Clinical trials.
We need a. Way to prove that this is.
Really working and and I envision this in the 1st.
Place to go. Alongside clinical.
Trials and this is also. What we are currently doing, so
we're going back to clinical. Trials we already.
Did and we are going to see how many patients we can substitute
by synthetic patients. To get the results, we.
Already know, so for me this is an important step.
To show how. We can incorporate A synthetic
patients in real clinical trials and I think this step has to be
done in close contact. With the authorities.
That we together define criteria.
Where we say OK this is. Working and we can move on
further. So if you had a crystal ball and
could see what the future holds in five years time, do you think
it's going to be used a lot? Yeah, what 5?
Years is close in in clinical testing, but I I know.
That the way we. Did clinical trials in rare
disease like acute myeloid leukemia in the.
Past is over. So there won't be clinical.
Trials with like. 1000 patient randomized with a certain drug
because in the meanwhile other drugs are coming up we we gain
further knowledge. So this is getting harder and
harder to perform these large trials.
So we have to. Adopt new methods and I think
synthetic data is 1 very promising way.
But there are several steps. Before we can really say, OK,
that this is working and enhancing our clinical testing.
Are there other uses of this data that you are now developing
for prognostic capabilities in hematological malignancies?
So coming back to images, there's another interesting
field where we can enhance the amount of images.
For rare subgroups. Of patients, so basically doing
the same thing for small subgroups within clinical.
Trials. We can also do this for.
Images of of bone marrow smears, for example.
This is already, it's been used. Well, I wouldn't say it's been
used, but it's like we and others have shown that this is
working and that you can generate synthetic images based
on real bone marrow smears and that you can't really tell which
is a synthetic or. A real bone marrow smear
picture. And these have been used to
train. Well, it hasn't been used, but
that's. The idea.
So the idea. Is that you can generate more
data and then increase model development.
So in the future you think that you will go beyond the control
group, so you would use the synthetic data also in the test
group. The improvement of the
techniques is so fast that we can really, like simulate what
is going on within a single cancer.
Cell with a. Certain intervention and with
that I believe that in I don't know how many years, but in the
future. We will be able to.
Also simulate what will happen to a certain group of patients
when we. Give the drug X.
Yes, I'm I'm a biologist by training and I find difficult to
imagine that you will be able to reproduce this complex system in
its totality, but I look forward to it.
It would be fantastic. Yeah.
Agree on that but like. Five years back, you probably
would. Have said that in predicting
protein structure which is now working almost.
Perfectly. So yeah.
I think it's really promising and I really hope this works
because it would help enormously in accelerating clinical trials
and treating the other patients. So thank you very much, Doctor
Mideke, for sharing your insights and your experience
with us. Thank you to everybody for
listening. And if you like this episode,
don't forget to like it and share it with your colleagues.
And stay tuned for our next episode.
Thank you. Thank you.