The Canadian Institute of Health Research has awarded $929,475 to Professor Jo Knight of Lancaster Medical School, Lancaster Data Science Institute and the Department of Psychiatry at the University of Toronto.
As well as Canadian co-investigators Professor Rachel Tyndale and Dr Meghan Chenoweth, the four-year project has collaborators from across the United States.
Their aim is to understand how genetic variation can be used to optimize smoking cessation treatment choice using data from nine existing clinical trials with more than 5,000 smokers.
The research offers unparalleled precision medicine to improve the prediction of who is more likely to quit smoking based on their genetics.
Cigarette smoking is a leading cause of preventable illness and death, making treatment optimization a major public health goal.
Professor Knight said: “We propose to elucidate genetic sources of variation in the nicotine metabolite ratio (NMR), a biomarker of nicotine metabolism rate which optimizes smoking cessation treatment choice.
“Our work will improve understanding of the genetic influences on NMR and smoking cessation (and other tobacco-related behaviors and diseases), advancing our goal of genomics-guided treatment approaches; and improved treatment will in turn reduce the extensive burden of tobacco-related disease.”
The nicotine metabolite ratio (NMR) is a highly heritable biomarker of the major nicotine inactivating enzyme CYP2A6.
Research has already shown that genetic variation in CYP2A6 substantially alters nicotine clearance and resulting smoking behaviors.
Higher tobacco-related risks, including consumption, dependence, and risk for lung cancer are associated with faster CYP2A6 activity.
Smokers with lower levels of nicotine metabolite ratio (NMR) have higher quit rates on placebo and nicotine patch compared with smokers with higher levels.
The levels of NMR also affect smokers’ response to medicines such as bupropion and varenicline which are used as smoking cessation aids.
“Thus, a genomics approach to understanding and utilizing this risk factor will have major impacts on treatment optimization and disease prevention in tobacco and beyond, in line with precision medicine initiatives.”