Data scientists at the Icahn School of Medicine at Mount Sinai in New
York and colleagues have created an artificial intelligence
model that may more accurately predict which existing medicines,
not currently classified as harmful, may in fact lead to congenital
disabilities. The model, or "knowledge graph," described in the
July 17 issue of the Nature journal
Communications Medicine, also has the potential to predict the
involvement of pre-clinical compounds that may harm the developing
fetus. The study is the first known of its kind to use knowledge
graphs to integrate various data types to investigate the causes of
congenital disabilities.
NEW
YORK, July 17, 2023 /PRNewswire-PRWeb/ -- Data
scientists at the Icahn School of Medicine at Mount Sinai in New
York and colleagues have created an artificial intelligence
model that may more accurately predict which existing medicines,
not currently classified as harmful, may in fact lead to congenital
disabilities.
The model, or "knowledge graph," described in the July 17 issue of the Nature journal
Communications Medicine [DOI: 10.1038/s43856-023-00329-2], also has
the potential to predict the involvement of pre-clinical compounds
that may harm the developing fetus. The study is the first known of
its kind to use knowledge graphs to integrate various data types to
investigate the causes of congenital disabilities.
Birth defects are abnormalities that affect about 1 in 33 births
in the United States. They can be
functional or structural and are believed to result from various
factors, including genetics. However, the causes of most of these
disabilities remain unknown. Certain substances found in medicines,
cosmetics, food, and environmental pollutants can potentially lead
to birth defects if exposed during pregnancy.
"We wanted to improve our understanding of reproductive health
and fetal development, and importantly, warn about the potential of
new drugs to cause birth defects before these drugs are widely
marketed and distributed," says Avi
Ma'ayan, PhD, Professor, Pharmacological Sciences, and
Director of the Mount Sinai Center for Bioinformatics at Icahn
Mount Sinai, and senior author of the paper. "Although identifying
the underlying causes is a complicated task, we offer hope that
through complex data analysis like this that integrates evidence
from multiple sources, we will be able, in some cases, to better
predict, regulate, and protect against the significant harm that
congenital disabilities could cause."
The researchers gathered knowledge across several datasets on
birth-defect associations noted in published work, including those
produced by NIH Common Fund programs, to demonstrate how
integrating data from these resources can lead to synergistic
discoveries. Particularly, the combined data is from the known
genetics of reproductive health, classification of medicines based
on their risk during pregnancy, and how drugs and pre-clinical
compounds affect the biological mechanisms inside human cells.
Specifically, the data included studies on genetic associations,
drug- and preclinical-compound-induced gene expression changes in
cell lines, known drug targets, genetic burden scores for human
genes, and placental crossing scores for small molecule drugs.
Importantly, using ReproTox-KG, with semi-supervised learning
(SSL), the research team prioritized 30,000 preclinical small
molecule drugs for their potential to cross the placenta and induce
birth defects. SSL is a branch of machine learning that uses a
small amount of labeled data to guide predictions for much larger
unlabeled data. In addition, by analyzing the topology of the
ReproTox-KG more than 500 birth-defect/gene/drug cliques were
identified that could explain molecular mechanisms that underlie
drug-induced birth defects. In graph theory terms, cliques are
subsets of a graph where all the nodes in the clique are directly
connected to all other nodes in the clique.
Media Contact
Karin Eskenazi, Mount Sinai, 332-257-1538,
karin.eskenazi@mssm.edu
SOURCE Mount Sinai