Penn State College of Medicine scientists helped identify eight medications that may be repurposed to help individuals stop smoking cigarettes. A group of more than 70 scientists contributed to the analysis of genetic and smoking habits information from more than 1.3 million individuals. According to a study conducted by scientists at Penn State College of Medicine and the University of Minnesota, certain medications, such as dextromethorphan, which is typically utilized to treat coughs caused by colds and the influenza, may be repurposed to assist individuals in quitting cigarette smoking. The researchers recognized more than 400 genes that were related to smoking habits.
Penn State College of Medicine scientists helped recognize eight medications that may be repurposed to assist individuals stop cigarette smoking. A team of more than 70 scientists added to the analysis of hereditary and cigarette smoking habits data from more than 1.3 million individuals. Credit: Ella Maru Studio for Penn State College of Medicine
According to a study performed by researchers at Penn State College of Medicine and the University of Minnesota, specific medications, such as dextromethorphan, which is commonly used to treat coughs triggered by colds and the influenza, might be repurposed to aid people in giving up smoking. The researchers employed an innovative device learning technique, where computer system algorithms scrutinize information sets to reveal trends and patterns, to recognize the drugs that hold potential. Some of these drugs are already going through scientific trials.
Smoking is a significant contributor to cardiovascular disease, cancer, and respiratory illnesses and is accountable for almost half a million deaths annually in the United States. Although cigarette smoking practices can be established and altered, genes likewise play a part in a persons possibility of engaging in such behaviors. In a previous research study, the researchers discovered that individuals with specific hereditary traits are more vulnerable to becoming addicted to tobacco.
Using hereditary 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 teacher of public health sciences, co-led a large multi-institution research study that used machine finding out to study these large data sets– which consist of particular information about an individuals genetics and their self-reported cigarette smoking habits.
The scientists determined more than 400 genes that were related to smoking cigarettes behaviors. Because an individual can have thousands of genes, they had to figure out why some of those genes were linked to smoking habits.
The majority of the genetic information in the research study is from people with European origins, so the device learning design needed to be tailored to not only study that information, 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 researchers on the job. They identified a minimum of 8 medications that could potentially be repurposed for cigarette smoking cessation, such as dextromethorphan, which is typically used to deal with coughs triggered by cold and flu, and galantamine, which is used to treat Alzheimers illness. The study was released in Nature Genetics.
” Re-purposing substance abuse big biomedical information and artificial intelligence approaches can conserve money, time, and resources,” stated Liu, a Penn State Cancer Institute and Penn State Huck Institutes of the Life Sciences scientist. “Some of the drugs we determined are already being tested in medical trials for their capability to assist cigarette smokers quit, but there are still other possible candidates that could be checked out in future research.”
While the maker knowing technique had the ability to include a small set of information from diverse origins, Jiang stated its still essential for researchers to build out hereditary databases from individuals with varied ancestries.
” This will just enhance the precision with which artificial intelligence models can identify people at risk for drug misuse and identify prospective biological paths that can be targeted for handy treatments.”
Referral: “Multi-ancestry transcriptome-wide association analyses yield insights into tobacco usage biology and drug repurposing” by 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, Muagututia 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, and Dajiang J. Liu, 26 January 2023, Nature Genetics.DOI: 10.1038/ s41588-022-01282-x.
The study was funded by The National Institutes of Health and Penn State College of Medicines Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views of the authors do not necessarily represent the views of the funders.
