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]