Using a machine-learning algorithm, MIT researchers have identified a
powerful new antibiotic compound. In laboratory tests, the drug killed many of
the world’s most problematic disease-causing bacteria, including some strains
that are resistant to all known antibiotics. It also cleared infections in two
different mouse models.
The computer
model, which can screen more than a hundred million chemical compounds in a
matter of days, is designed to pick out potential antibiotics that kill
bacteria using different mechanisms than those of existing drugs.
“We wanted to
develop a platform that would allow us to harness the power of artificial
intelligence to usher in a new age of antibiotic drug discovery,” says James
Collins, the Termeer Professor of Medical Engineering and Science in MIT’s
Institute for Medical Engineering and Science (IMES) and Department of
Biological Engineering. “Our approach revealed this amazing molecule which is
arguably one of the more powerful antibiotics that has been discovered.”
In their new
study, the researchers also identified several other promising antibiotic
candidates, which they plan to test further. They believe the model could also
be used to design new drugs, based on what it has learned about chemical
structures that enable drugs to kill bacteria.
“The machine
learning model can explore, in silico, large chemical spaces that can be
prohibitively expensive for traditional experimental approaches,” says Regina
Barzilay, the Delta Electronics Professor of Electrical Engineering and
Computer Science in MIT’s Computer Science and Artificial Intelligence
Laboratory (CSAIL).
Barzilay and Collins, who are faculty co-leads for MIT’s Abdul Latif
Jameel Clinic for Machine Learning in Health, are the senior authors of the
study, which appears today in Cell. The first author of the
paper is Jonathan Stokes, a postdoc at MIT and the Broad Institute of MIT and
Harvard.
A new pipeline
Over the past
few decades, very few new antibiotics have been developed, and most of those
newly approved antibiotics are slightly different variants of existing drugs.
Current methods for screening new antibiotics are often prohibitively costly,
require a significant time investment, and are usually limited to a narrow
spectrum of chemical diversity.
“We’re facing a
growing crisis around antibiotic resistance, and this situation is being
generated by both an increasing number of pathogens becoming resistant to
existing antibiotics, and an anemic pipeline in the biotech and pharmaceutical
industries for new antibiotics,” Collins says.
To try to find
completely novel compounds, he teamed up with Barzilay, Professor Tommi
Jaakkola, and their students Kevin Yang, Kyle Swanson, and Wengong Jin, who
have previously developed machine-learning computer models that can be trained
to analyze the molecular structures of compounds and correlate them with
particular traits, such as the ability to kill bacteria.
The idea of
using predictive computer models for “in silico” screening is not new, but
until now, these models were not sufficiently accurate to transform drug
discovery. Previously, molecules were represented as vectors reflecting the
presence or absence of certain chemical groups. However, the new neural
networks can learn these representations automatically, mapping molecules into
continuous vectors which are subsequently used to predict their properties.
In this case,
the researchers designed their model to look for chemical features that make
molecules effective at killing E. coli. To do so, they trained the model on
about 2,500 molecules, including about 1,700 FDA-approved drugs and a set of
800 natural products with diverse structures and a wide range of bioactivities.
Once the model
was trained, the researchers tested it on the Broad Institute’s Drug Repurposing
Hub, a library of about 6,000 compounds. The model picked out one molecule that
was predicted to have strong antibacterial activity and had a chemical
structure different from any existing antibiotics. Using a different
machine-learning model, the researchers also showed that this molecule would
likely have low toxicity to human cells.
This molecule,
which the researchers decided to call halicin, after the fictional artificial
intelligence system from “2001: A Space Odyssey,” has been previously investigated
as possible diabetes drug. The researchers tested it against dozens of
bacterial strains isolated from patients and grown in lab dishes, and found
that it was able to kill many that are resistant to treatment, including
Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis.
The drug worked against every species that they tested, with the exception of
Pseudomonas aeruginosa, a difficult-to-treat lung pathogen.
To test
halicin’s effectiveness in living animals, the researchers used it to treat
mice infected with A. baumannii, a bacterium that has infected many U.S.
soldiers stationed in Iraq and Afghanistan. The strain of A. baumannii that
they used is resistant to all known antibiotics, but application of a
halicin-containing ointment completely cleared the infections within 24 hours.
Preliminary
studies suggest that halicin kills bacteria by disrupting their ability to
maintain an electrochemical gradient across their cell membranes. This gradient
is necessary, among other functions, to produce ATP (molecules that cells use
to store energy), so if the gradient breaks down, the cells die. This type of
killing mechanism could be difficult for bacteria to develop resistance to, the
researchers say.
“When you’re
dealing with a molecule that likely associates with membrane components, a cell
can’t necessarily acquire a single mutation or a couple of mutations to change
the chemistry of the outer membrane. Mutations like that tend to be far more
complex to acquire evolutionarily,” Stokes says.
In this study,
the researchers found that E. coli did not develop any resistance to halicin
during a 30-day treatment period. In contrast, the bacteria started to develop
resistance to the antibiotic ciprofloxacin within one to three days, and after
30 days, the bacteria were about 200 times more resistant to ciprofloxacin than
they were at the beginning of the experiment.
The researchers
plan to pursue further studies of halicin, working with a pharmaceutical
company or nonprofit organization, in hopes of developing it for use in humans.
Optimized molecules
After
identifying halicin, the researchers also used their model to screen more than
100 million molecules selected from the ZINC15 database, an online collection
of about 1.5 billion chemical compounds. This screen, which took only three
days, identified 23 candidates that were structurally dissimilar from existing
antibiotics and predicted to be nontoxic to human cells.
In laboratory
tests against five species of bacteria, the researchers found that eight of the
molecules showed antibacterial activity, and two were particularly powerful.
The researchers now plan to test these molecules further, and also to screen
more of the ZINC15 database.
The researchers
also plan to use their model to design new antibiotics and to optimize existing
molecules. For example, they could train the model to add features that would
make a particular antibiotic target only certain bacteria, preventing it from
killing beneficial bacteria in a patient’s digestive tract.
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