Researchers from Integrated Biosciences, a biotechnology company that
combines synthetic biology and machine learning to combat aging, have
published a new study in the May issue of Nature Aging that shows the power
of artificial intelligence (AI) to find novel senolytic compounds, a class
of small molecules that are actively researched for their capacity to
inhibit age-related processes like fibrosis, inflammation, and cancer.
In a paper titled "Discovering small-molecule senolytics with deep neural
networks," researchers from MIT, the Broad Institute of MIT and Harvard, and
the Massachusetts Institute of Technology (MIT) describe how AI-guided
screening of more than 800,000 compounds led to the discovery of three drug
candidates with similar efficacy and superior medicinal chemistry properties
to those of senolytics currently being studied.
According to Felix Wong, Ph.D., co-founder of Integrated Biosciences and
first author of the study, "This research result is a significant milestone
for both longevity research and the application of artificial intelligence
to drug discovery." These results show that, in comparison to even the most
promising examples of their kind now under investigation, it is possible to
explore chemical space in silico and produce a number of potential
anti-aging drugs that are more likely to be successful in clinical
trials.
Senolytics are substances that specifically cause apoptosis, or programmed
cell death, in senescent, non-dividing cells. Senescent cells, a sign of
aging, have been linked to a variety of age-related illnesses and ailments,
including Alzheimer's disease, diabetes, cancer, and cardiovascular disease.
Despite encouraging clinical outcomes, the majority of senolytic drugs have
been discovered so far have limited bioavailability and negative side
effects. The goal of Integrated Biosciences, which was established in 2022,
is to use artificial intelligence, synthetic biology, and other
next-generation methods to overcome these challenges, focus on more
underappreciated signs of aging, and accelerate anti-aging medication
development more generally.
Finding therapeutic approaches that selectively eliminate these cells from
the body in a manner akin to how antibiotics kill bacteria without hurting
host cells is one of the most promising ways to cure age-related disorders.
Satotaka Omori, Ph.D., Head of Aging Biology at Integrated Biosciences and
joint first author of the study, stated the compounds "display high
selectivity as well as the favorable medicinal chemistry properties needed
to yield a successful drug." We think the substances found using our
technology will have better chances in clinical trials and eventually assist
restore health to aged people.
Researchers at Integrated Biosciences trained deep neural networks on data
produced via experiments to forecast the senolytic action of any chemical in
their latest study. From a chemical space of over 800,000 molecules, they
found three very selective and powerful senolytic drugs using their AI
model. In tests for hemolysis and genotoxicity, all three were shown to have
good toxicity profiles and to have chemical characteristics indicative of
excellent oral bioavailability.
According to structural and biochemical investigations, all three
substances bind Bcl-2, a protein that controls apoptosis and is also a
target for chemotherapy. One of the compounds was tested in experiments on
80-week-old mice, which are about equivalent to 80-year-old people. It was
discovered that the chemical removed senescent cells and decreased
expression of senescence-associated genes in the kidneys.
The founding chair of the Integrated Biosciences Scientific Advisory Board,
James J. Collins, Ph.D., said that this work "illustrates how AI can be used
to bring medicine a step closer to therapies that address aging, one of the
fundamental challenges in biology." In 2020, a team led by Dr. Collins,
senior author on the Nature Aging publication, found the first antibiotic
recognized by machine learning.
"Integrated Biosciences is expanding on the fundamental work that my
academic group has been doing for the last eight to ten years, which has
demonstrated that we can tune cellular stress responses utilizing systems
and synthetic biology. its study stands out in the area of drug discovery
and will significantly advance the field of longevity research thanks to its
experimental tour de force and the outstanding platform that generated
it.
