Pennsylvania: Researchers from Penn State College of Medicine and the University of Minnesota conducted a study to see if drugs such as dextromethorphanwhich is used to treat cold and flu related coughcan be reused to help people quit of smoking. To find the drugs, they created a revolutionary machine learning technique which uses computer programs to look for patterns and trends in data sets. They stated that some of the drugs are already being tested in clinical trials.
Smoking causes nearly 500,000 deaths a year in the United States and is a risk factor for respiratory disease, cancer, and cardiovascular disease. While smoking habits can be acquired and unlearned, heredity also affects a person’s likelihood of doing so. Researchers found in a previous 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 biochemistry and molecular biology, and Bibo Jiang, Ph.D., assistant professor of health sciences public health, co-led a large multi-institutional 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 the hormone dopamine, which makes 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, discovered which drugs are already approved to modify those existing pathways.
Most of the genetic data in the study came from people of European ancestry, so the machine learning model had to be adapted to not only study that data, but also a smaller dataset of around 150,000 people of Asian ancestry. , African or American.
Liu and Jiang worked with more than 70 scientists on the project. They identified at least eight drugs that could be repurposed for smoking cessation, including dextromethorphan, which is commonly used to treat coughs caused by colds and flu, and galantamine, which is used to treat Alzheimer’s disease. The study was published in Nature Genetics today, January 26.
“Repurposing drugs using big biomedical data and machine learning methods can save money, time and resources,” said Liu, a fellow at the Penn State Cancer Institute and Huck Institutes of Penn State from the Life Sciences investigator, adding: “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 potential candidates that could be explored in future research.”
While the machine learning method was able to incorporate a small data set from diverse ancestries, Jiang said it’s still important for researchers to create genetic databases of people with diverse ancestries.
“This will only improve the accuracy with which machine learning models can identify people at risk of drug misuse and determine potential biological pathways that may be targeted for useful treatments,” Liu said.
