WEBVTT

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So how are we going to beat
this novel coronavirus?

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By using our best tools:

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our science and our technology.

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In my lab, we're using
the tools of artificial intelligence

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and synthetic biology

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to speed up the fight
against this pandemic.

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Our work was originally designed

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to tackle the antibiotic
resistance crisis.

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Our project seeks to harness
the power of machine learning

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to replenish our antibiotic arsenal

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and avoid a globally devastating
postantibiotic era.

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Importantly, the same
technology can be used

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to search for antiviral compounds

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that could help us fight
the current pandemic.

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Machine learning is turning
the traditional model of drug discovery

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on its head.

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With this approach,

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instead of painstakingly testing
thousands of existing molecules

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one by one in a lab

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for their effectiveness,

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we can train a computer
to explore the exponentially larger space

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of essentially all possible molecules
that could be synthesized,

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and thus, instead of looking
for a needle in a haystack,

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we can use the giant magnet
of computing power

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to find many needles
in multiple haystacks simultaneously.

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We've already had some early success.

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Recently, we used machine learning
to discover new antibiotics

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that can help us fight off
the bacterial infections

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that can occur alongside
SARS-CoV-2 infections.

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Two months ago, TED's Audacious Project
approved funding for us

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to massively scale up our work

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with the goal of discovering
seven new classes of antibiotics

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against seven of the world's
deadly bacterial pathogens

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over the next seven years.

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For context:

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the number of new class of antibiotics

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that have been discovered
over the last three decades is zero.

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While the quest for new antibiotics
is for our medium-term future,

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the novel coronavirus poses
an immediate deadly threat,

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and I'm excited to share that we think
we can use the same technology

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to search for therapeutics
to fight this virus.

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So how are we going to do it?

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Well, we're creating
a compound training library

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and with collaborators applying these
molecules to SARS-CoV-2-infected cells

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to see which of them exhibit
effective activity.

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These data will be use to train
a machine learning model

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that will be applied to an in silico
library of over a billion molecules

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to search for potential
novel antiviral compounds.

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We will synthesize and test
the top predictions

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and advance the most promising
candidates into the clinic.

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Sound too good to be true?

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Well, it shouldn't.

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The Antibiotics AI Project is founded
on our proof of concept research

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that led to the discovery
of a novel broad-spectrum antibiotic

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called halicin.

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Halicin has potent antibacterial activity

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against almost all antibiotic-resistant
bacterial pathogens,

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including untreatable
panresistant infections.

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Importantly, in contrast
to current antibiotics,

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the frequency at which bacteria
develop resistance against halicin

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is remarkably low.

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We tested the ability of bacteria
to evolve resistance against halicin

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as well as Cipro in the lab.

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In the case of Cipro,

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after just one day, we saw resistance.

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In the case of halicin,

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after one day,
we didn't see any resistance.

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Amazingly, after even 30 days,

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we didn't see any resistance
against halicin.

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In this pilot project, we first tested
roughly 2,500 compounds against E. coli.

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This training set included
known antibiotics,

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such as Cipro and penicillin,

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as well as many drugs
that are not antibiotics.

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These data we used to train a model

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to learn molecular features
associated with antibacterial activity.

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We then applied this model
to a drug-repurposing library

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consisting of several thousand molecules

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and asked the model to identify molecules

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that are predicted
to have antibacterial properties

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but don't look like existing antibiotics.

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Interestingly, only one molecule
in that library fit these criteria,

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and that molecule
turned out to be halicin.

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Given that halicin does not look
like any existing antibiotic,

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it would have been impossible for a human,
including an antibiotic expert,

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to identify halicin in this manner.

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Imagine now what we could do
with this technology

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against SARS-CoV-2.

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And that's not all.

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We're also using the tools
of synthetic biology,

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tinkering with DNA
and other cellular machinery,

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to serve human purposes
like combating COVID-19,

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and of note, we are working
to develop a protective mask

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that can also serve
as a rapid diagnostic test.

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So how does that work?

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Well, we recently showed

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that you can take the cellular
machinery out of a living cell

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and freeze-dry it along with
RNA sensors onto paper

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in order to create low-cost
diagnostics for Ebola and Zika.

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The sensors are activated when
they're rehydrated by a patient sample

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that could consist of blood
or saliva, for example.

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It turns out, this technology
is not limited to paper

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and can be applied
to other materials, including cloth.

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For the COVID-19 pandemic,

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we're designing RNA sensors
to detect the virus

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and freeze-drying these
along with the needed cellular machinery

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into the fabric of a face mask,

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where the simple act of breathing,

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along with the water vapor
that comes with it,

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can activate the test.

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Thus, if a patient is infected
with SARS-CoV-2,

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the mask will produce
a fluorescent signal

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that could be detected by a simple,
inexpensive handheld device.

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In one or two hours, a patient
could thus be diagnosed

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safely, remotely and accurately.

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We're also using synthetic biology

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to design a candidate
vaccine for COVID-19.

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We are repurposing the BCG vaccine,

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which had been used against TB
for almost a century.

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It's a live attenuated vaccine,

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and we're engineering it
to express SARS-CoV-2 antigens,

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which should trigger the production
of protective antibodies

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by the immune system.

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Importantly, BCG
is massively scalable

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and has a safety profile that's among
the best of any reported vaccine.

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With the tools of synthetic biology
and artificial intelligence,

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we can win the fight
against this novel coronavirus.

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This work is in its very early stages,
but the promise is real.

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Science and technology
can give us an important advantage

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in the battle of human wits
versus the genes of superbugs,

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a battle we can win.

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Thank you.