How were using AI to discover new antibiotics Jim Collins
So how are we going to beat
this novel coronavirus?
By using our best tools:
our science and our technology.
In my lab, we’re using
the tools of artificial intelligence
and synthetic biology
to speed up the fight
against this pandemic.
Our work was originally designed
to tackle the antibiotic
resistance crisis.
Our project seeks to harness
the power of machine learning
to replenish our antibiotic arsenal
and avoid a globally devastating
postantibiotic era.
Importantly, the same
technology can be used
to search for antiviral compounds
that could help us fight
the current pandemic.
Machine learning is turning
the traditional model of drug discovery
on its head.
With this approach,
instead of painstakingly testing
thousands of existing molecules
one by one in a lab
for their effectiveness,
we can train a computer
to explore the exponentially larger space
of essentially all possible molecules
that could be synthesized,
and thus, instead of looking
for a needle in a haystack,
we can use the giant magnet
of computing power
to find many needles
in multiple haystacks simultaneously.
We’ve already had some early success.
Recently, we used machine learning
to discover new antibiotics
that can help us fight off
the bacterial infections
that can occur alongside
SARS-CoV-2 infections.
Two months ago, TED’s Audacious Project
approved funding for us
to massively scale up our work
with the goal of discovering
seven new classes of antibiotics
against seven of the world’s
deadly bacterial pathogens
over the next seven years.
For context:
the number of new class of antibiotics
that have been discovered
over the last three decades is zero.
While the quest for new antibiotics
is for our medium-term future,
the novel coronavirus poses
an immediate deadly threat,
and I’m excited to share that we think
we can use the same technology
to search for therapeutics
to fight this virus.
So how are we going to do it?
Well, we’re creating
a compound training library
and with collaborators applying these
molecules to SARS-CoV-2-infected cells
to see which of them exhibit
effective activity.
These data will be use to train
a machine learning model
that will be applied to an in silico
library of over a billion molecules
to search for potential
novel antiviral compounds.
We will synthesize and test
the top predictions
and advance the most promising
candidates into the clinic.
Sound too good to be true?
Well, it shouldn’t.
The Antibiotics AI Project is founded
on our proof of concept research
that led to the discovery
of a novel broad-spectrum antibiotic
called halicin.
Halicin has potent antibacterial activity
against almost all antibiotic-resistant
bacterial pathogens,
including untreatable
panresistant infections.
Importantly, in contrast
to current antibiotics,
the frequency at which bacteria
develop resistance against halicin
is remarkably low.
We tested the ability of bacteria
to evolve resistance against halicin
as well as Cipro in the lab.
In the case of Cipro,
after just one day, we saw resistance.
In the case of halicin,
after one day,
we didn’t see any resistance.
Amazingly, after even 30 days,
we didn’t see any resistance
against halicin.
In this pilot project, we first tested
roughly 2,500 compounds against E. coli.
This training set included
known antibiotics,
such as Cipro and penicillin,
as well as many drugs
that are not antibiotics.
These data we used to train a model
to learn molecular features
associated with antibacterial activity.
We then applied this model
to a drug-repurposing library
consisting of several thousand molecules
and asked the model to identify molecules
that are predicted
to have antibacterial properties
but don’t look like existing antibiotics.
Interestingly, only one molecule
in that library fit these criteria,
and that molecule
turned out to be halicin.
Given that halicin does not look
like any existing antibiotic,
it would have been impossible for a human,
including an antibiotic expert,
to identify halicin in this manner.
Imagine now what we could do
with this technology
against SARS-CoV-2.
And that’s not all.
We’re also using the tools
of synthetic biology,
tinkering with DNA
and other cellular machinery,
to serve human purposes
like combating COVID-19,
and of note, we are working
to develop a protective mask
that can also serve
as a rapid diagnostic test.
So how does that work?
Well, we recently showed
that you can take the cellular
machinery out of a living cell
and freeze-dry it along with
RNA sensors onto paper
in order to create low-cost
diagnostics for Ebola and Zika.
The sensors are activated when
they’re rehydrated by a patient sample
that could consist of blood
or saliva, for example.
It turns out, this technology
is not limited to paper
and can be applied
to other materials, including cloth.
For the COVID-19 pandemic,
we’re designing RNA sensors
to detect the virus
and freeze-drying these
along with the needed cellular machinery
into the fabric of a face mask,
where the simple act of breathing,
along with the water vapor
that comes with it,
can activate the test.
Thus, if a patient is infected
with SARS-CoV-2,
the mask will produce
a fluorescent signal
that could be detected by a simple,
inexpensive handheld device.
In one or two hours, a patient
could thus be diagnosed
safely, remotely and accurately.
We’re also using synthetic biology
to design a candidate
vaccine for COVID-19.
We are repurposing the BCG vaccine,
which had been used against TB
for almost a century.
It’s a live attenuated vaccine,
and we’re engineering it
to express SARS-CoV-2 antigens,
which should trigger the production
of protective antibodies
by the immune system.
Importantly, BCG
is massively scalable
and has a safety profile that’s among
the best of any reported vaccine.
With the tools of synthetic biology
and artificial intelligence,
we can win the fight
against this novel coronavirus.
This work is in its very early stages,
but the promise is real.
Science and technology
can give us an important advantage
in the battle of human wits
versus the genes of superbugs,
a battle we can win.
Thank you.