Machine learning identifies drugs that could potentially help smokers
quit

Date:
January 30, 2023
Source:
Penn State
Summary:
Medications like dextromethorphan, used to treat coughs caused by
cold and flu, could potentially be repurposed to help people quit
smoking cigarettes, according to a new study. Researchers developed
a novel machine learning method, where computer programs analyze
data sets for patterns and trends, to identify the drugs and said
that some of them are already being tested in clinical trials.


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FULL STORY ========================================================================== Medications like dextromethorphan, used to treat coughs caused by cold
and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine
learning method, where computer programs analyze data sets for patterns
and trends, to identify the drugs and said that some of them are already
being tested in clinical trials.


========================================================================== Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in
the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person's risk for engaging
in those behaviors.

The researchers found in a prior study that people with certain genes
are more likely to become addicted to tobacco.

Using genetic data from more than 1.3 million people, Dajiang Liu,
Ph.D., professor of public health sciences, and of biochemistry and
molecular biology and Bibo Jiang, Ph.D., assistant professor of public
health sciences, co-led a large multi-institution study that used machine learning to study these large data sets -- which include specific data
about a person's genetics and their self-reported smoking behaviors.

The researchers identified more than 400 genes that were related to
smoking behaviors. Since a person can have thousands of genes, they
had to determine why some of those genes were connected to smoking
behaviors. Genes that carry instructions for the production of nicotine receptors or are involved in signaling for the hormone dopamine,
which make people feel relaxed and happy, had easy-to-understand
connections. For the remaining genes, the research team had to determine
the role each plays in biological pathways and using that information,
figured out what medications are already approved for modifying those
existing pathways.

Most of the genetic data in the study is from people with European
ancestries, so the machine learning model had to be tailored to not only
study that data, but also a smaller data set of around 150,000 people
with Asian, African or American ancestries.

Liu and Jiang worked with more than 70 scientists on the project. They identified at least eight medications that could potentially be
repurposed for smoking cessation, such as dextromethorphan, which is
commonly used to treat coughs caused by cold and flu, and galantamine,
which is used to treat Alzheimer's disease. The study was published in
Nature Geneticstoday, Jan. 26.

"Re-purposing drugs using big biomedical data and machine learning
methods can save money, time and resources," said Liu, a Penn State
Cancer Institute and Penn State Huck Institutes of the Life Sciences researcher. "Some of the drugs we identified are already being tested
in clinical trials for their ability to help smokers quit, but there
are still other possible candidates that could be explored in future
research." While the machine learning method was able to incorporate a
small set of data from diverse ancestries, Jiang said it's still important
for researchers to build out genetic databases from individuals with
diverse ancestries.

"This will only improve the accuracy with which machine learning models
can identify individuals at risk for drug misuse and determine potential biological pathways that can be targeted for helpful treatments."
Other College of Medicine authors on the project include Fang Chen,
Xingyan Wang, Dylan Weissenkampen, Chachrit, Khunsriraksakul, Lina Yang,
Renan Sauteraud, Olivia Marx and Karine Moussa. They declare no conflicts
of interest.

This research was supported by The National Institutes of Health (grants R01HG008983, R56HG011035, R01HG011035, R56HG012358, R01GM126479,
R21AI160138 and R03OD032630) and Penn State College of Medicine's
Biomedical Informatics and Artificial Intelligence Program in the
Strategic Plan. The views of the authors do not necessarily represent
the views of the funders.

* RELATED_TOPICS
o Health_&_Medicine
# Smoking # Medical_Topics # Personalized_Medicine
# Public_Health_Education # Genes # Teen_Health #
Diseases_and_Conditions # Healthy_Aging
* RELATED_TERMS
o Pharmaceutical_company o Tobacco_smoking o
Personalized_medicine o Computational_neuroscience o Neurology
o Clinical_trial o Avian_flu o Virus

========================================================================== Story Source: Materials provided by Penn_State. Original written by
Zachary Sweger. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Fang Chen, Xingyan Wang, Seon-Kyeong Jang, Bryan C. Quach, J. Dylan
Weissenkampen, Chachrit Khunsriraksakul, Lina Yang, Renan Sauteraud,
Christine M. Albert, Nicholette D. D. Allred, Donna K. Arnett,
Allison E.

Ashley-Koch, Kathleen C. Barnes, R. Graham Barr, Diane M. Becker,
Lawrence F. Bielak, Joshua C. Bis, John Blangero, Meher Preethi
Boorgula, Daniel I. Chasman, Sameer Chavan, Yii-Der I. Chen,
Lee-Ming Chuang, Adolfo Correa, Joanne E. Curran, Sean P. David,
Lisa de las Fuentes, Ranjan Deka, Ravindranath Duggirala,
Jessica D. Faul, Melanie E. Garrett, Sina A. Gharib, Xiuqing Guo,
Michael E. Hall, Nicola L. Hawley, Jiang He, Brian D. Hobbs,
John E. Hokanson, Chao A. Hsiung, Shih-Jen Hwang, Thomas M. Hyde,
Marguerite R. Irvin, Andrew E. Jaffe, Eric O. Johnson, Robert
Kaplan, Sharon L. R. Kardia, Joel D. Kaufman, Tanika N. Kelly,
Joel E.

Kleinman, Charles Kooperberg, I-Te Lee, Daniel Levy, Sharon
M. Lutz, Ani W. Manichaikul, Lisa W. Martin, Olivia Marx, Stephen
T. McGarvey, Ryan L.

Minster, Matthew Moll, Karine A. Moussa, Take Naseri, Kari E. North,
Elizabeth C. Oelsner, Juan M. Peralta, Patricia A. Peyser, Bruce M.

Psaty, Nicholas Rafaels, Laura M. Raffield, Muagututi'a Sefuiva
Reupena, Stephen S. Rich, Jerome I. Rotter, David A. Schwartz,
Aladdin H. Shadyab, Wayne H-H. Sheu, Mario Sims, Jennifer A. Smith,
Xiao Sun, Kent D. Taylor, Marilyn J. Telen, Harold Watson, Daniel
E. Weeks, David R. Weir, Lisa R.

Yanek, Kendra A. Young, Kristin L. Young, Wei Zhao, Dana B. Hancock,
Bibo Jiang, Scott Vrieze, Dajiang J. Liu. Multi-ancestry
transcriptome-wide association analyses yield insights into
tobacco use biology and drug repurposing. Nature Genetics, 2023;
DOI: 10.1038/s41588-022-01282-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/01/230130130517.htm

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