AI Identifies Drugs to Be Repurposed for Smoking Cessation
Genetic data of 1.3 million people were used by AI to identify the requirements.
Posted January 27, 2023 | Reviewed by Ekua Hagan
- A study discovered of several drugs that may be repurposed for treating nicotine addiction.
- The researchers used AI to analyze data for 1.3 million people in order to find genomic variants that are statistically associated with smoking.
- The U.S. Centers for Disease Control and Prevention (CDC) estimates that nearly 40 million American adults smoke.
This week, a new artificial intelligence (AI) study published in Nature Genetics discovered several drugs that may be repurposed for treating nicotine addiction.
“Given the tremendous public health burden that continues to be incurred by smoking, repurposing drugs for smoking cessation is extremely valuable, because it offers a potentially quicker and more cost-effective route to treatment than the development of new therapeutic targets,” wrote the study authors.
The peer-reviewed study, led by Penn State College of Medicine researchers Dajiang Liu, Ph.D., M.A., professor and vice chair for research in the Department of Public Health Sciences, and Bibo Jiang, Ph.D., M.A., an assistant professor in the Department of Public Health Sciences, and University of Minnesota Associate Professor Scott Vrieze, Ph.D., M.A., collaborated with scientists from multiple institutions to form a vast team of 90 researchers. Vrieze was awarded the Association for Psychological Science (APS) Janet Taylor Spence Award in 2019.
“Cigarette smoking is a major heritable risk factor for human diseases,” wrote the researchers.
There are 1.3 billion tobacco users globally, of which 80 percent live in low- and middle-income countries, according to the World Health Organization (WHO). Tobacco use is one of the world’s leading preventable causes of death and is responsible for over 8 million deaths each year globally, of which 1.2 million deaths are due to exposure to secondhand smoke, per WHO statistics. The WHO estimates that over 22 percent of the world’s population used tobacco in 2020.
The U.S. Centers for Disease Control and Prevention (CDC) estimates that nearly 40 million American adults smoke, and over 3 million students in middle and high school use at least one tobacco product. Almost half a million Americans die prematurely due to smoking or secondhand smoke each year, according to the CDC. The leading cause of cancer death in the U.S. is lung cancer, of which an estimated 80 percent is caused by smoking, according to the American Cancer Society.
“Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity,” the scientists wrote. “To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences, and expression quantitative trait locus (eQTL) data from diverse ancestries.”
Genome-wide association study is a research method that searches for the genomic variants that are statistically associated with a particular trait or disease risk by surveying and comparing the genomes of many people, per the National Human Genome Research Institute.
The smoking behavioral phenotypes used for the study were the age of initiation of regular smoking, cigarettes smoked per day, smoking cessation, and smoking initiation.
The researchers used AI machine learning to analyze data for 1.3 million people from two datasets, the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) and the Trans-Omics Precision Medicine (TOPMed) which contained data for 150,000 diverse ancestries.
“Leveraging shared disease pathways, we identified drugs that may be repurposed for smoking cessation treatment, including dextromethorphan and galantamine, which are already being assessed in clinical trials,” reported the researchers.
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