The Biomarker and Constituent Core (Core B) will carry out analyses of tobacco constituents in the smoke of commercial and research cigarettes, as well as in e-cigarettes, used in studies proposed by individual projects, and will analyze a panel of biomarkers in biological samples collected from users of these products. These analyses will enable the assessment of the public health impact of removing filter ventilation, as proposed in this program. The Core will employ well-established robust protocols based on standardized methodologies for the analyses of tobacco constituents and biomarkers, and will also apply some recently developed innovative methodologies for the analysis of cigarette smoke and novel biomarkers. For product analyses, smoke of commercial and research cigarettes will be analyzed for nicotine and a range of key toxicants and carcinogens (tobacco-specific N-nitrosamines, polycyclic aromatic hydrocarbons, and volatile organic compounds), filter ventilation and other cigarette physical parameters will be determined, and the differences in constituent distribution across various smoke particle sizes from ventilated and non-ventilated cigarettes will be assessed. Spent filters from cigarettes smoked by study participants will be also analyzed to correlate mouth-level constituent exposures with smoking topography measures, biomarker levels, and/or sensory perceptions. The Core will also generate cigarette smoke and e-cigarette aerosol extracts for the use in the animal behavioral studies proposed in one of the projects, and will analyze nicotine and other constituents that may contribute to abuse liability of tobacco products, such as minor alkaloids, ?-carbolines, and aldehydes. Biomarkers of specific smoke constituents to be analyzed by Core B include urinary levels of total nicotine equivalents (nicotine intake), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and its N- and O-glucuronides (exposure to tobacco-specific lung carcinogen NNK), the tobacco specific oral and esophageal carcinogen N?- nitrosonornicotine (NNN) and its N-glucuronide, phenanthrene tetraol and 3-hydroxy phenanthrene (exposure to and metabolism of the representative polycyclic aromatic hydrocarbon phenanthrene), and mercapturic acids (exposure to acrolein and crotonaldehyde), and DNA adducts N6-hydroxymethyl-deoxyadenosine and N2-ethylidene-deoxyguanosine derived from formaldehyde and acetaldehyde, respectively. Lung and systemic inflammatory processes will be assessed by analyzing inflammatory cell counts, cytokines, and gene expression profiles in bronchoscopy samples, 8-oxo-dG and 8-oxo-dA in leukocyte DNA, and PGEM and 8-iso- PGF2? in urine. In addition, untargeted metabolomics will be employed for the analysis of urine and bronchoalveolar lavage fluids to identify novel biomarker profiles distinguishing exposures from ventilated and non-ventilated cigarettes. Personnel in this Core have extensive experience in clinical trials and quantitation tobacco constituents and biomarkers, and were the first to develop many of the methodologies that will be used in this Core. Availability of the accurate, robust, and uniform measures of tobacco constituent exposures and of the effects caused by these exposures, as proposed in this Core, is critical for the conduct of studies proposed by individual projects and for the overall success of this program.
CORE B NARRATIVE Ventilation of cigarette filters is implicated as a major contributor to the historical shifts in smoke chemical profile, smoking behaviors in smokers, and the associated increase in lung cancer risk due to smoking. Services provided by this Core to all projects in this program will lead to new insights into exposures to addictive, toxic, and carcinogenic constituents from smoking cigarettes with and without filter ventilation, and the effects of such exposures on cellular and macromolecular levels. These measures will allow an evaluation of the impact of filter ventilation removal, as a potential regulatory measure by the FDA, on the behavioral and health outcomes in smokers.
| Ho, Yen-Yi; Nhu Vo, Tien; Chu, Haitao et al. (2018) A Bayesian hierarchical model for demand curve analysis. Stat Methods Med Res 27:2038-2049 |
