Hi.
I'm really excited to
tell you guys today
how we could use artificial
intelligence to discover drugs.
One in five Americans are
affected by nonalcoholic fatty
liver, which has no treatment.
It's usually caused
by accumulation
of fats in the liver mostly
due to obesity or diabetes.
When the disease is
progress, it leads
to devastating complications
like liver cancer
or liver failure.
But can you imagine the drug
for treating fatty liver might
already exist?
For example, there
are thousands of drugs
have been developed partially
to treat diabetes and obesity.
One of them may have therapeutic
effects for fatty liver.
But we don't know.
This is just one example.
There are thousands of
diseases with no treatment.
If you could figure out which
one of these drugs over here
could work for which
one of these patients,
we can save millions of lives.
In the last decade,
because of the availability
of genomics data
and computer power,
AI platforms have
developed to solve
this puzzle using genomic data.
But genomics alone
is not enough.
DNA and RNA is
really far from what
we observe in disease symptoms.
As you may know, DNA
will transcribe to RNA,
then RNA will
translate to proteins.
These proteins then
interact with thousands
of metabolites in human body.
Metabolites are small molecules,
like glucose, cholesterol,
all the fats that
they were accumulating
in the liver of
fatty liver patients.
The interactions between
metabolites and proteins
leads to what we observe
in disease symptoms.
And they can't tell you exactly
what's happened in a disease.
Now at ReviveMed, we are
able to use metabolomics
to solve this puzzle.
Until now, metabolomics
has been underutilized
because metabolites have
tremendous diversity
in their structures.
Each one of them is
different from the other one.
So it requires several
customized processes
to identify them in human body.
And that's why current
platform are only
focused to characterize a small
number, less than 5% of them.
Our team at MIT developed a
proprietary database and AI
algorithm to overcome
these difficulties.
I tell you a little
bit how it works.
We have created the most
comprehensive database
about metabolites, their
interactions with proteins,
the interaction of
proteins to other proteins,
the drugs that are targeting
these proteins, as well
as the association of
metabolites with diseases.
We have combine all these
data as a gigantic network.
It's a big hairball, and it
has millions of interactions.
And then we do inference
on top of that.
We then start from blood
or tissues of patients.
You think mass spectrometry, we
could measure tens of thousands
of metabolite masses.
We see, for example,
some of them
are up regulated or down
regulated in a disease.
But the problem is
that we don't know
what they are because
there could be like 10
metabolites with the same mass.
For example, if we
have this mass of 180,
it could be their glucose,
or galactose, or fructose.
They all have the same molecular
mass, but different functions
in human body.
Current platforms-- they
have to do more experiments
to figure this out.
What we are doing instead,
we are using our AI platform,
then we find an
optimal networks that
connect these metabolite
masses together.
And through these
connections, we
could figure out,
OK, this 180 is,
for example, here is glucose.
This optimal network
further provides us
with critical
therapeutic insight.
The network itself represent
disregulated disease pathways
and processes.
The proteins in
this network, they
could be therapeutic targets.
So of those metabolites, there
could be an existing drug.
Let's me show you know
a real case example.
In Huntington's disease,
it has no treatment.
We were able to identify
novel disease pathways.
We are able to identify existing
drugs with therapeutic effects
for Huntington's disease.
And we able to identify
novel therapeutic targets.
This was a work
that we did at MIT
and published it
in Nature Methods.
We then formed ReviveMed,
a start-up company,
to bring this
technology to the market
and create impact
in people's lives.
By leveraging metabolomics
and artificial intelligence,
we could discover high
clinical efficacy drugs
while saving hundreds
of millions and years
from discovery to clinic.
Currently, we are focused
to discover therapeutics
for metabolic diseases
by initially focusing
on nonalcoholic fatty liver.
Because as I mentioned
earlier, there
is significant
needs for patients.
Importantly, metabolites play
a key role in these diseases,
so our technology can
create the most impact.
In addition to our
internal programs,
we are also working with
pharmaceutical companies
to expediate their direct
discovery and development.
We have the best team
to unlock the value
of metabolomics from MIT, Broad
Institute, and Fortune 500
pharma companies.
At ReviveMed, we are
passionate about transforming
metabolomic data into
the right therapeutics
for the right patients.
Thank you.
[APPLAUSE]
