PNAT Profile

Pharmacogenetics of Nicotine Addiction and Treatment

Abstract (July, 2005)

Goals

We have created an interdisciplinary multi-center research program focused on pharmacogenetic questions related to the characterization and treatment of nicotine addiction. Our Pharmacogenetics of Nicotine Addiction Treatment (PNAT) program consists of Clinical, Genetic, Bioinformatic and Statistical cores. We will conduct studies to investigate the genetic basis for individual variation in response to nicotine replacement medications and bupropion as treatments for tobacco dependence. We will also conduct exploratory studies on varenicline and rimonabant, two novel medications currently under clinical development for treating tobacco dependence. The long-term objectives of our research are to better individualize treatment for tobacco dependence, to facilitate the development of novel medications, and to reduce the impact of smoking as a major health problem.

Progress

While the PNAT Center is newly formed, many of the PNAT investigators have significant prior collaborations. Highlights of these collaborations are as follows.

• The relationship between CYP2A6 genotype variants and the disposition kinetics of nicotine and cotinine, including both plasma and urinary metabolite profiles, have been characterized.
• A novel gene-gene interaction was discovered, showing that the CYP2B6 *4 and *6 gene variants significantly increase the rate of nicotine metabolism, but only in individuals who have CYP2A6 gene variants associated with reduced metabolic activity.
• The UGT 2B7 His268Tyr polymorphism has been shown to influence the rate of glucuronidation of nicotine and cotinine. Those individuals homozygous for the 268Tyr allele have significantly slower glucuronidation of nicotine and cotinine, but not 3'-hydroxycotinine. This is the first investigation of UGT genotype and nicotine glucuronidation in vivo.
• The major proximate metabolite of nicotine is cotinine, which is in turn metabolized to 3'-hydroxycotinine (3HC). Both of these metabolic pathways are mediated primarily by CYP2A6. The ratio of the 3HC/COT, measured in plasma, urine or saliva, was shown to be a useful noninvasive marker of CYP2A6 activity and of the rate of nicotine metabolism.
• The 3HC/COT ratio was found to be a significant predictor of smoking cessation and response to nicotine patch therapy. Individuals with low metabolite ratios, representing slow metabolizers of nicotine, have a greater cessation response to nicotine patch therapy compared to individuals who have higher ratios, and who therefore are faster metabolizers of nicotine. This is the first demonstration that the rate of nicotine metabolism is an important predictor of smoking cessation treatment outcome.
• The pharmacokinetics of intravenous nicotine and cotinine were studied in 139 twin pairs. An analysis of heritability indicated that 60% of the variance in nicotine clearance is heritable. In this population, which was primarily Caucasians, the known CYP2A6 gene variants explained very little of the genetic variation, indicating that other genetic factors accounting for nicotine metabolism remain to be discovered.

Experimental Plans

The PNAT group seeks to implement a discovery process that systematically characterizes gene and gene interactions in key pathways and investigate their association with nicotine dependence and its treatment. Candidate genes will include those involved in relevant receptor and neurochemical pathways, as well as nicotine and bupropion metabolic pathways. Our approach will be as follows.

• The Clinical core will collect outcome data from various clinical trials, including quantitative traits (such as cigarettes per day), binary traits (such as abstinence after treatment), and other outcomes (such as time-to-relapse), as well as DNA samples. Our study cohorts include population-based samples, family-based samples and longitudinal samples.
• The Genetics core will perform high throughput genotyping on an extensive list of polymorphisms in candidate genes. The genetic polymorphisms to be investigated include SNPs, microsatellite markers, gene duplications, insertions and deletions.
• Statistical analysis will be conducted by generalized linear models (GLM). The analysis will also include extended Bayesian hierarchical modeling, which will incorporate biology or genetic structure to stabilize final estimates and use prior knowledge to guide model selection. Prior knowledge is determined by our understanding of underlying biological pathway typology (for example, pharmacokinetic) and bioinformatics.

PNAT Team

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