Synergistic COVID-19 Drug Blends
September 27, 2021
drug synergy often occurs through inhibition of biological targets (like
proteins or nucleic acids), a new artificial intelligence model
developed at MIT jointly learns drug-target interaction and drug-drug
synergy to mine new combinations.
The existential threat of Covid-19 has highlighted an
acute need to develop working therapeutics against emerging health
concerns. One of the luxuries deep learning has afforded us is the
ability to modify the landscape as it unfolds — so long as we can keep
up with the viral threat, and access the right data.
As with all new medical maladies, oftentimes the data need time to catch
up, and the virus takes no time to slow down, posing a difficult
challenge as it can quickly mutate and become resistant to existing
drugs. This led scientists from MIT’s Computer Science and Artificial
Intelligence Laboratory (CSAIL) and the Jameel Clinic for Machine
Learning in Health to ask: How can we identify the right synergistic
drug combinations for the rapidly spreading SARS-CoV-2?
Typically, data scientists use deep learning to pick out drug
combinations with large existing datasets for things like cancer and
cardiovascular disease, but, understandably, they can’t be used for new
illnesses with limited data.
Without the necessary facts and figures, the team needed a new approach:
a neural network that wears two hats. Since drug synergy often occurs
through inhibition of biological targets (like proteins or nucleic
acids), the model jointly learns drug-target interaction and drug-drug
synergy to mine new combinations. The drug-target predictor models the
interaction between a drug and a set of known biological targets that
are related to the chosen disease. The target-disease association
predictor learns to understand a drug's antiviral activity, which means
determining the virus yield in infected tissue cultures. Together, they
can predict the synergy of two drugs.
Two new drug combinations were found using this approach: remdesivir
(currently approved by the FDA to treat Covid-19) and reserpine, as well
as remdesivir and IQ-1S, which, in biological assays, proved powerful
against the virus. The study has been published in the Proceedings of
the National Academy of Sciences.
“By modeling interactions between drugs and biological targets, we can
significantly decrease the dependence on combination synergy data,” says
Wengong Jin SM '18, a postdoc at the Broad Institute of MIT and Harvard
who recently completed his doctoral work in CSAIL, and who is the lead
author on a new paper about the research. “In contrast to previous
approaches using drug-target interaction as fixed descriptors, our
method learns to predict drug-target interaction from molecular
structures. This is advantageous since a large proportion of compounds
have incomplete drug-target interaction information.”
Using multiple medications to maximize potency, while also decreasing
side effects, is practically ubiquitous for aforementioned cancer and
cardiovascular disease, including a host of others such as tuberculosis,
leprosy, and malaria. Using specialized drug cocktails can, quite
importantly, reduce the grave and sometimes public threat of resistance
(think methicillin-resistant Staphylococcus aureus known as “MRSA”),
since many drug-resistant mutations are mutually exclusive. It’s much
harder for a virus to develop two mutations at the same time and then
become resistant to two drugs in a combination therapy.
Importantly, the model isn’t limited to just one SARS-CoV-2 strain — it
could also potentially be used for the increasingly contagious Delta
variant or other variants of concern that may arise. To extend the
model's efficacy against these strains, you’d only need additional drug
combination synergy data for the relevant mutation(s). In addition, the
team applied their approach to HIV and pancreatic cancer.
To further refine their biological modeling down the line, the team
plans to incorporate additional information such as protein-protein
interaction and gene regulatory networks.
direction for future work they’re exploring is something called “active
learning.” Many drug combination models are biased toward certain
chemical spaces due to their limited size, so there's high uncertainty
in predictions. Active learning helps guide the data collection process
and improve accuracy in a wider chemical space.
Jin wrote the paper alongside Jonathan M. Stokes, Banting Fellow at The
Broad Institute of MIT and Harvard; Richard T. Eastman, a scientist at
the National Center for Advancing Translational Sciences; Zina Itkin, a
scientist at National Institutes of Health; Alexey V. Zakharo,
informatics lead at the National Center for Advancing Translational
Sciences (NCATS); James J. Collins, professor of biological engineering
at MIT; and Tommi S. Jaakkola and Regina Barzilay, MIT professors of
electrical engineering and computer science at MIT.
This project is supported by the Abdul Latif Jameel Clinic for Machine
Learning in Health; the Defense Threat Reduction Agency; Patrick J.
McGovern Foundation; the DARPA Accelerated Molecular Discovery program;
and in part by the Intramural/Extramural Research Program of the
National Center for Advancing Translational Sciences within the National
Institutes of Health.